Actin Cytoskeleton in Cancer Progression and Metastasis, Part C [1 ed.] 0128241381, 9780128241387

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VOLUME THREE HUNDRED AND SIXTY

INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY Actin Cytoskeleton in Cancer Progression and Metastasis – Part C

INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY Series Editors GEOFFREY H. BOURNE JAMES F. DANIELLI KWANG W. JEON MARTIN FRIEDLANDER JONATHAN JARVIK LORENZO GALLUZZI Editorial Advisory Board AARON CIECHANOVER SANDRA DEMARIA SILVIA FINNEMANN KWANG JEON CARLOS LOPEZ-OTIN

1949–1988 1949–1984 1967–2016 1984–1992 1993–1995 2016–

WALLACE MARSHALL SHIGEKAZU NAGATA MOSHE OREN ANNE SIMONSEN

VOLUME THREE HUNDRED AND SIXTY

INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY Actin Cytoskeleton in Cancer Progression and Metastasis – Part C Edited by

 CLEMENT THOMAS Luxembourg Institute of Health, Luxembourg City, Luxembourg

LORENZO GALLUZZI Weill Cornell Medical College, New York, NY, United States

Academic Press is an imprint of Elsevier 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States 525 B Street, Suite 1650, San Diego, CA 92101, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 125 London Wall, London, EC2Y 5AS, United Kingdom First edition 2021 Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher's permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-12-824138-7 ISSN: 1937-6448 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Zoe Kruze Acquisitions Editor: Ashlie M. Jackman Developmental Editor: Tara Nadera Production Project Manager: Denny Mansingh Cover Designer: Alan Studholme Typeset by SPi Global, India

Contents Contributors

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1. Force balancing ACT-IN the tumor microenvironment: Cytoskeletal modifications in cancer and stromal cells to promote malignancy

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Michelle R. Dawson, Botai Xuan, Jeffrey Hsu, and Deepraj Ghosh 1. 2. 3. 4.

Overview Actin cytoskeleton Measuring intracellular and extracellular forces Utilizing force measurements to distinguish non-invasive and invasive cancer cells 5. Utilizing force measurements to study tumor and stromal cell crosstalk 6. Conclusions 7. Experimental challenges and future research efforts References

2. Novel facets of glioma invasion

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Carina Fabian, Mingzhi Han, Rolf Bjerkvig, and Simone P. Niclou 1. 2. 3. 4. 5. 6.

Introduction Routes of glioma cell invasion Modes of glioma cell invasion The impact of tumor metabolism on cell invasion The extracellular matrix of the glioma microenvironment Involvement of proteases and the tumor microenvironment in glioma cell invasion 7. The actin cytoskeleton and its related membrane protrusions in glioma cell invasion 8. Can glioma cell invasion and the actin cytoskeleton be targeted? 9. Conclusions and future prospects Acknowledgments References

3. Actin dynamics during tumor cell dissemination

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Chandrani Mondal, Julie S. Di Martino, and Jose Javier Bravo-Cordero 1. Introduction 2. Tumor dissemination and metastasis

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3. Cancer cell migration 4. Actin structures in cancer cell migration 5. The cell cycle and cancer cell invasion 6. Imaging advances and future directions in studying tumor cell invasion 7. Conclusion Acknowledgments References

4. The multiple roles of actin-binding proteins at invadopodia

71 77 83 83 86 87 87

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Takouhie Mgrditchian, Gabriele Sakalauskaite, Tanja M€ uller, Celine Hoffmann, and Clement Thomas 1. Introduction 2. Invadopodial actin assemblies 3. Actin machineries at the cell leading edge 4. Actin-binding proteins in invadopodia morphogenesis 5. Actin polymerization-based protrusion at invadopodia 6. Concluding remarks Acknowledgments References

5. Cancer type-specific alterations in actin genes: Worth a closer look?

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Christophe Ampe, Laura Witjes, and Marleen Van Troys 1. Introduction 2. The human actin gene/protein family: A tale of functional redundancy and distinction 3. Actin: Basic structure-function relationships as a context for interpreting actin mutations in cancer 4. Actin gene alterations and actin mutants: Do they occur in cancer? 5. Alterations in actin genes in patient cancer genomes: An untapped resource 6. Summary, conclusions, and perspectives References

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Contributors Christophe Ampe Department of Biomolecular Medicine, Ghent University, Gent, Belgium Rolf Bjerkvig NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg; Department of Biomedicine, University of Bergen, Bergen, Norway Jose Javier Bravo-Cordero Department of Medicine, Division of Hematology and Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States Michelle R. Dawson Department of Molecular Pharmacology, Physiology, and Biotechnology; Department of Molecular Biology, Cell Biology and Biochemistry, Brown University; Brown University, Center for Biomedical Engineering, Providence, RI, United States Carina Fabian NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg; Department of Biomedicine, University of Bergen, Bergen, Norway Deepraj Ghosh Department of Molecular Pharmacology, Physiology, and Biotechnology, Brown University, Providence, RI, United States Mingzhi Han Department of Biomedicine, University of Bergen, Bergen, Norway; Department of Neurosurgery, Qilu Hospital of Shandong University and Institute of Brain and Brain-Inspired Science, Shandong University; Shandong Key Laboratory of Brain Function Remodeling, Jinan, China C
eline Hoffmann Cytoskeleton and Cancer Progression, Department of Oncology, Luxembourg Institute of Health, Luxembourg City, Luxembourg Jeffrey Hsu Department of Molecular Pharmacology, Physiology, and Biotechnology, Brown University, Providence, RI, United States Takouhie Mgrditchian Cytoskeleton and Cancer Progression, Department of Oncology, Luxembourg Institute of Health, Luxembourg City, Luxembourg Chandrani Mondal Department of Medicine, Division of Hematology and Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States

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Contributors

Tanja M€ uller Department of Oncology, Luxembourg Centre of Neuropathology, Luxembourg Institute of Health, Luxembourg City, Luxembourg Simone P. Niclou NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg; Department of Biomedicine, University of Bergen, Bergen, Norway Julie S. Di Martino Department of Medicine, Division of Hematology and Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States Gabriele Sakalauskaite Cytoskeleton and Cancer Progression, Department of Oncology, Luxembourg Institute of Health, Luxembourg City, Luxembourg Cl
ement Thomas Cytoskeleton and Cancer Progression, Department of Oncology, Luxembourg Institute of Health, Luxembourg City, Luxembourg Marleen Van Troys Department of Biomolecular Medicine, Ghent University, Gent, Belgium Laura Witjes Department of Biomolecular Medicine, Ghent University, Gent, Belgium Botai Xuan Department of Molecular Pharmacology, Physiology, and Biotechnology, Brown University, Providence, RI, United States

CHAPTER ONE

Force balancing ACT-IN the tumor microenvironment: Cytoskeletal modifications in cancer and stromal cells to promote malignancy Michelle R. Dawsona,b,c,∗, Botai Xuana, Jeffrey Hsua, and Deepraj Ghosha a

Department of Molecular Pharmacology, Physiology, and Biotechnology, Brown University, Providence, RI, United States Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, United States c Brown University, Center for Biomedical Engineering, Providence, RI, United States ∗ Corresponding author: e-mail address: [email protected] b

Contents 1. Overview 2. Actin cytoskeleton 3. Measuring intracellular and extracellular forces 3.1 Intracellular particle tracking microrheology 3.2 Traction force microscopy 4. Utilizing force measurements to distinguish non-invasive and invasive cancer cells 4.1 Genetically induced EMT makes cancer cells more deformable 4.2 Invasive cancer cells exert increased and polarized traction forces in a context dependent manner 4.3 Rho-ROCK signaling regulates distinct mechanical response of differing cancer cell types 4.4 Utilizing force profiles to characterize chemoresistant subpopulations 5. Utilizing force measurements to study tumor and stromal cell crosstalk 5.1 Cancer cell invasiveness determine direct intercellular interaction with stromal cells 5.2 MSCs and cancer cells undergo dramatic changes in cell mechanics in response to SF crosstalk 6. Conclusions 7. Experimental challenges and future research efforts References

International Review of Cell and Molecular Biology, Volume 360 ISSN 1937-6448 https://doi.org/10.1016/bs.ircmb.2020.09.005

Copyright

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2021 Elsevier Inc. All rights reserved.

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Abstract The tumor microenvironment is a complex milieu that dictates the growth, invasion, and metastasis of cancer cells. Both cancer and stromal cells in the tumor tissue encounter and adapt to a variety of extracellular factors, and subsequently contribute and drive the progression of the disease to more advanced stages. As the disease progresses, a small population of cancer cells becomes more invasive through a complex process known as epithelial-mesenchymal transition, and nearby stromal cells assume a carcinoma associated fibroblast phenotype characterized by enhanced migration, cell contractility, and matrix secretion with the ability to reorganize extracellular matrices. As cells transition into more malignant phenotypes their biophysical properties, controlled by the organization of cytoskeletal proteins, are altered. Actin and its associated proteins are essential modulators and facilitators of these changes. As the cells respond to the cues in the microenvironment, actin driven mechanical forces inside and outside the cells also evolve. Recent advances in biophysical techniques have enabled us to probe these actin driven changes in cancer and stromal cells and demarcate their role in driving changes in the microenvironment. Understanding the underlying biophysical mechanisms that drive cancer progression could provide critical insight on novel therapeutic approaches in the fight against cancer.

1. Overview The dynamic progression of the tumor microenvironment (TME) requires the participation of a wide variety of cell types, facilitating a complex network of chemical and physical crosstalk (Balkwill et al., 2012; Fukumura and Jain, 2007; Quail and Joyce, 2013; Stroka and Konstantopoulos, 2014; Whiteside, 2008). A multitude of cell types are recruited to the tumor under the influence of tumor secreted growth factors and chemokines; this includes immune cells, endothelial cells, mesenchymal stem cells (MSCs), and fibroblasts that play important roles in tumor growth by modulating the immune response, promoting angiogenesis, and forming the stroma. These cells crosstalk with cancer cells through direct cell contacts and paracrine signaling, in order to restructure the TME to one that is permissible for tumor growth and metastasis. Increased understanding of the molecules in the TME and their interactions with cancer cells may be critical in identifying novel targets for therapeutic intervention. Along with selective influences within the TME, such as extracellular matrix (ECM) remodeling and abnormal vascularization, cancer cells within the tumor develop transcriptionally and phenotypically heterogeneous subclones with different levels of malignancy (Marusyk et al., 2012; Meacham and Morrison, 2013). Although recent technological advancements in

Probing actin driven force balance in tumor microenvironment

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deep-sequencing at the single-cell level have allowed researchers to obtain preliminary insights into cancer heterogeneity (Patel et al., 2014; Tirosh et al., 2016), more studies are needed to probe how this intratumor heterogeneity affects the development of chemoresistant subpopulations, cancer recurrence after therapy, and cancer metastasis (Meacham and Morrison, 2013; Sharma et al., 2010). Heterogeneity in cancer cell phenotypes is thought to rely on the inherent variation in the rate of stochastic mutations (Lawson et al., 2018). As genomic instability increases, the cell cycle becomes more abnormal and cancer cells with diverse malignant characteristics begin to form, including cells with high metastatic potential (Burrell et al., 2013; Cifone and Fidler, 1981; Joung et al., 2017). However, cancer therapy also contributes to intratumor heterogeneity. Single-cell sequencing analysis demonstrated that there were distinct subpopulations of cancer cells with different genomic and transcriptomic profiles in Paclitaxel treated cells/ tumors compared to control (D’Alterio et al., 2020; Lee et al., 2014). TME itself could also confer selective pressures to cancer cells. For instance, stromal cell-secreted growth factors and cytokines have been found to profoundly influence phenotypic developments in cancer cells by promoting epithelial to mesenchymal transition (EMT) (Polyak and Weinberg, 2009). In conjunction with ECM remodeling (changes in matrix rigidity), environmental pressures could largely alter transcriptomic and proteomic properties, and subsequently, the phenotypic and biophysical properties of cancer cells (Emon et al., 2018; Hanahan et al., 2011; Lu et al., 2012; Spill et al., 2016). Lastly, histology studies have consistently reported spatial heterogeneity in the tumor architecture, which could have profound implications on cancer biophysics (Ramo´n y Cajal et al., 2020). This heterogeneity includes regional differences in the collagen architecture, level of vascularization, stromal cell incorporation, and cancer cell phenotype (Malandrino et al., 2018; Yamauchi et al., 2020). For instance, some regions of the tumor may have collagen-rich basement membrane; whereas, other regions have very little collagen (Case et al., 2017; Conklin et al., 2011). Also, some regions of the tumor are highly vascularized; whereas, other regions are hypoxic (Fukumura et al., 2010; Petrova et al., 2018). Cells near the collagen-rich basement membrane may be more migratory; whereas, cells at the tumor core may be under solid stress, which drives the collective migration of surrounding cells (Tse et al., 2012). Taken together, cancer is a highly heterogeneous disease that requires additional characterization, particularly through the lens of cancer biophysics, to better elucidate drivers of phenotypic heterogeneity in the tumor.

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This review will highlight recent work from our lab and other labs demonstrating the importance of biophysical properties in identifying aggressive cell populations. Additionally, we will show how chemical and physical cues from the TME alter cell shape and cytoskeletal organization to dynamically affect cell function and cell-cell interactions. The complexity of the TME is a major barrier in understanding the molecular and mechanical interactions of cells in the tumor. Quantitative biophysical analysis allows us to probe the biomechanical properties of cells with an unprecedented level of detail to enhance our understanding of cancer.

2. Actin cytoskeleton Cytoskeletal proteins mechanically support the cell structure and spatially organize the contents of the cell (Fletcher and Mullins, 2010). This group of filamentous proteins is categorized into three main families: actin microfilaments, microtubules, and intermediate filaments. While microtubules and intermediate filaments contribute significantly to the organizational integrity of cells, actin and its associated proteins enable cells to respond and adapt to dynamic changes in the microenvironment. The hierarchical structure of the actin network is controlled by small Rho GTPases, myosin motor proteins, and a large group of cytoplasmic mediators known as actin binding proteins (ABPs) (illustrated in Fig. 1) (Hall, 1998; Parsons et al., 2010; Winder and Ayscough, 2005). Dynamic changes in the organization of the cytoskeleton transform cell shape and generate mechanical forces required for numerous cellular processes, including adhesion, migration, division, molecular transport, and differentiation (DuFort et al., 2011; Eyckmans et al., 2011; Humphrey et al., 2014; Iskratsch et al., 2014). The cytoskeletal network responds dynamically to soluble or mechanical cues from the tumor ECM and is connected directly to canonical signal transduction pathways important in cancer (Chin et al., 2016; Huang and Ingber, 2005; Shieh, 2011; Stroka and Konstantopoulos, 2014). The family of RhoGTPases and ABPs have been strongly implicated in multiple stages of cancer progression to metastasis (Sahai and Marshall, 2002; Stevenson et al., 2012; SUN et al., 2015; Vega and Ridley, 2008). For example, Arp2/3—a protein facilitating actin branch formation, is overexpressed in malignant tumors, such as breast carcinomas (Molinie and Gautreau, 2018). Another actin binding protein Filamin that crosslinks actin bundles and provide mechanical strength has been detected in the blood from metastatic breast cancer patients (Yue et al., 2013).

Probing actin driven force balance in tumor microenvironment

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Fig. 1 (A) Globular actin (G-actin) subunits polymerize to form filamentous actin structure (F-Actin), which can be organized into multitude of networks to support location specific function in cells. Actin binding proteins (ABPs) facilitate the assembly of F-Actin into various forms including bundling, crosslinking, and branching examples shown here. Additionally, myosin motor proteins can bind between two adjacent bundles of crosslinked actin structures to generate contractile actin stress fibers. (B) The family of small Rho-GTPases such as RhoA is converted from a GDP-bound inactive form to GTP-bound active form by guanine nucleotide exchange factors (GEFs) and the reverse process of inactivation is mediated by GTPase-activating proteins (GAPs). Activation of Rho GTPases leads to activation of ROCK which can trigger multitude of downstream cytoskeletal reorganization processes including blocking myosin light chain (MLC) phosphatase activity and facilitate MLC phosphorylation leading to increased actomyosin contractility.

3. Measuring intracellular and extracellular forces A growing body of evidence has emerged highlighting the importance of mechanical cues in both normal tissue development and cancer (DuFort et al., 2011; Kumar and Weaver, 2009). Despite highly divergent chemical signaling cascades, a highly conserved feature of mechanical signaling is that it requires transmission of force from the ECM to the internal cytoskeleton, which forms the structure of the cell. Forces from the external environment activate Rho/Rho associated protein kinases (ROCK) signaling pathways that regulate the actin cytoskeleton and cytoskeletal tension. Upregulation of ROCK, which increases actomyosin contractility, results in tissue stiffening

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and malignant transformation. Actin bundling (e.g., α-actinin, fascin) and crosslinking (e.g., filamin) proteins give rise to actin stress fibers that link the cytoskeleton to focal adhesions and actin networks that modulate intracellular stiffness (Hall, 1998; Winder and Ayscough, 2005). Since cytoskeletal alterations depend on the mechanical environments and vice versa, it is necessary to use biophysical tools to probe essential forces at both the intracellular and extracellular levels.

3.1 Intracellular particle tracking microrheology Cytoskeletal actin forms a mesh-like structure in the cell cytoplasm that regulates the intracellular tension (Fletcher and Mullins, 2010; Hale et al., 2009). Parallel actin bundles provide tensile strength and strong contractile activity, whereas crosslinked bundles of actin filaments increase intracellular elasticity. Depending on the location in the cell, actin architecture can vary drastically and manifest heterogeneous local mechanical properties (Tseng et al., 2002). To measure cell mechanical properties, multiple techniques have been developed over the years, including, atomic force microscopy (AFM), magnetic bead twist, optical tweezers, micropipette aspiration, hydrodynamic stretching and particle-tracking microrheology (Kollmannsberger and Fabry, 2011; Moeendarbary and Harris, 2014). Intracellular particle tracking microrheology (IPTM) allows direct and rapid measurement of the local microrheological properties throughout the cell (Crocker and Hoffman, 2007; Dawson et al., 2014; Li et al., 2009; Wirtz, 2009). Briefly, fluorescent particles are ballistically injected into the cell and their thermal energy driven movements captured at a high magnification with a high-speed camera to obtain information about the local polymeric network. The 2D Brownian motion of these submicron probe particles is then used to calculate particle mean square displacements (MSDs). MSDs of particles moving in a viscous liquid vary linearly (slope
1) with time scale. However, for viscoelastic fluids, the motion of the embedded particles becomes more restricted due to the presence of mesh-like structures. Because of the sub-diffusive restricted motion of particles, the time-dependent MSD curves flatten (slope ≪ 1). In a viscous liquid, diffusivity due to thermal energy driven motion can be described using the Stokes-Einstein Eq. (1), where D is the diffusion coefficient, kB is Boltzmann’s constant, T is temperature, a is particle radius, and η is the fluid viscosity.

Probing actin driven force balance in tumor microenvironment

D¼

hΔr 2 ðτÞi k T ¼ B 6πaη 4τ

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(1)

To describe viscoelastic properties of complex fluids, Mason et al. derived complex shear modulus of the viscoelastic fluid using a modified StokesEinstein equation in the frequency domain (Eq. 2), where G∗ is the frequency-dependent complex shear modulus and Γ is the gamma function. The in-phase component of the complex shear modulus (G*) is known as the elastic modulus (G0 ), and out-of-phase component is known as the viscous modulus (G00 ) (Eq. 3). 2 kB T 3 π a < Δr 2 ð1=ωÞ > Γ ½1 + αðωÞ    
∗

∗
παðωÞ παðωÞ 0 00



G ðωÞ ¼ G ðωÞ cos ; G ðωÞ ¼ G ðωÞ sin 2 2 G∗ ðωÞ ¼

(2) (3)

The IPTM approach is illustrated for analyzing MDA-MB-231 breast cancer cells in Fig. 2. At lower time scales, particle transport in the cytosol remains restricted and MSD varies almost independent of time scale (α ≪ 1); whereas at longer time scales, as the structures around the particles begin to relax, particles are able to move longer distances, and MSDs vary more linearly (α
1). For the more restricted transport regime of embedded particles, cells typically resemble a viscoelastic fluid with comparable magnitudes of both viscous and elastic moduli. At linear regime of particle motion, cells properties are very similar to a viscous liquid with a highly dominant viscous modulus. Individual location specific particle MSDs can be used to calculate local viscoelastic properties; whereas, all MSDs from a cell can be ensemble-averaged to evaluate overall viscoelastic behavior. Particle tracking microrheology (PTM) has been successfully adapted to characterize a great range of complex biological fluids, including mucus, reconstituted actin solutions, and the cell cytoplasm (Dawson et al., 2014; Mason et al., 1997; Wirtz, 2009). Although IPTM is primarily conducted on 2-D cultures, the application of this method in 3D and in vivo has been investigated (Baker et al., 2010; Daniels et al., 2006; Panorchan et al., 2006; Zhou et al., 2008). IPTM does not require an external probe unlike other techniques like AFM, thus provides an advantage in tracking cell mechanics in 3D. Panorchan et al. embedded human umbilical vein endothelial cells ballistically injected with 100 nm fluorescent particles in 3D peptide hydrogels and monitored changes

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Fig. 2 Illustration of intracellular particle tracking microrheology (IPTM). (A) Fluorescent submicron (200 nm) probe particles were injected into MDA-MB-231 breast cancer cells using PDS-100 ballistic particle injection system. The representative trace of a single particle undergoing Brownian motion in the cell cytoplasm is shown in the inset. (B) The x-y displacements of particles are used to calculate ensemble average MSDs. (C) Frequencydependent viscous (G0 ) and elastic (G00 ) moduli are then calculated from the MSDs as described by Mason et al. (1997). Adapted from Dawson, M.R., Tseng, Y., Lee, J.S.H., McAndrews, K.M., 2014. Intracellular particle tracking microrheology, In: Handbook of Imaging in Biological Mechanics. CRC Press, pp. 381–388. https://doi.org/10.1201/ b17566-40.

in cell mechanics after stimulation with vascular endothelial growth factor (VEGF). Exposure to VEGF led to softening of the cytoplasm highlighted by significant reduction in the elastic modulus (Panorchan et al., 2006). Other studies in 3D have used microbeads and nanotubes or endogenous organelles to track changes in cell mechanics in more physiologically relevant microenvironment. Wu et al. recently demonstrated the combination of IPTM and intravital imaging to measure biophysical parameters of live cells in mice (Wu et al., 2020). In short, 200 nm fluorescent particles were ballistically injected into EGFP-labeled MDA-MB-231 breast cancer cells.

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The cells were then implanted in mice using the dorsal skinfold chamber window and examined using intravital fluorescent microscopy. Similar to 2-D IPTM, the thermal motion of particles that were embedded in the GFP-labeled cells were captured and used to determine cell microrheology. Using this method, in vivo cell biophysical properties can be more accurately captured along with effects of the surrounding tissue microenvironment. However, there were several limitations to this method. First, a stringent correction was needed for animal movement, as the rhythmic breathing motion was several magnitudes higher than the tracked particle motion. Secondly, in vivo imaging requires increased working distances to see deep into the tissue, limiting spatial and temporal resolution, which are critical in IPTM. Similar resolution limits may apply when using IPTM to determine cell microrheology in 3-D gels.

3.2 Traction force microscopy Physical interactions between cells and the surrounding ECM regulate the reciprocal forces via adhesion molecules linking the cell cytoskeleton to the ECM. The magnitude of traction forces generated at these adhesion sites, along with the strength of adhesions, are critical in regulating cell processes. Traction forces have been quantified in both 2D and 3D environments (Koch et al., 2012; Kraning-Rush et al., 2011; McGrail et al., 2015b; Munevar et al., 2001; Sabass et al., 2008). Studies on 2D elastic substrates formed from synthetic materials, including silicon, polyacrylamide, and polydimethylsiloxane allow for cell-generated force measurements on a wide range of stiffnesses. Traction force microscopy (TFM) has been combined with other techniques for simultaneous characterization of cell adhesion machinery using total internal reflection microscopy (TIRF) or intracellular rheology using IPTM (Gutierrez et al., 2011; McAndrews et al., 2014). To mimic more physiologically relevant microenvironments, TFM has been performed in 3D hydrogels and collagen matrices (Legant et al., 2010, 2013; Steinwachs et al., 2016). Due to non-linear elastic properties of collagen quantitative analysis of traction forces is limited/not possible, but particle displacements are still useful in understanding collagen matrix deformations. In elastic hydrogels, the algorithm to derive cell-generated forces is extremely complex, limiting the usefulness of this approach (Legant et al., 2013). A more detailed review on the current 2D and 3D TFM techniques and their limitations can be found here (Cho et al., 2016; Hur et al., 2020).

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2D traction forces are characterized for cells cultured on polyacrylamide substrates with rigidities tuned to mimic specific biological tissues (Kim et al., 2009; Nerger et al., 2017; Plotnikov et al., 2014). Fluorescent nanoparticles embedded in the substrates are displaced under cell-exerted stress. When cells are removed, the particles revert to their unstressed locations. Thus, cell-induced displacements from stressed and unstressed particle images can be used in traction force calculations (illustrated in * Fig. 3). In Boussinesq theory, the displacement field u of an elastic *

substrate is correlated to the traction field ðT Þ (Eq. 4), where G is the Green’s function (Munevar et al., 2001; Sabass et al., 2008). * *

* *  * *  u x ¼G x  x 0 T x 0

(4)

The estimation of the Green’s function is critical for inverse calculation of the traction field (Eq. 5). The Green’s function includes displacement vector * * r ¼ x  x 0 components (rx, ry), the Young’s modulus E, and the Poisson ratio υ

Fig. 3 Traction force microscopy (TFM). (A–B) Fluorescently labeled cells (SKOV-3 epithelial ovarian cancer cells shown in green) were cultured on collagen-coated polyacrylamide substrates embedded with fluorescent red particles (200 nm). Images of the embedded nanoparticles were taken before (A) and after (B) detaching the cells. Red arrows point to the zones with high displacements. (C–D) Previous images were used to calculate displacement vectors for each particle and followed by the estimation of traction force field and polarization. (D) Heatmap of traction forces is overlaid with symbols indicating the cell’s center of mass () and force-weighted center of mass (Δ). Adapted from Mcgrail, D.J., 2015. Mechanics & Malignancy: Physical Cues And Changes That Drive Tumor Progression.

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  ð1 + υÞ * G r ¼ πEr 3

"

ð1  υÞr 2 + υr 2x

υr x r y

υr x r y

ð1  υÞr 2 + υr 2y

# (5)

Furthermore, polarization is calculated as the difference in un-weighted center of mass of the cell and the traction force-weighted center of mass of the cell (Eq. 6), where Munwt is the un-weighted center of mass and i Mwt is the weighted center of mass. i rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2  unwt 2  unwt wt + M  M (6) Polarization ðdÞ ¼ M x  M wt x y y The aforementioned techniques are two of the most commonly used methods to measure forces inside and outside of living cells. However, other techniques exist and could be useful in collecting similar measurements. Our chapter focuses on how our lab has combined the two methods we previously described with cell fate analysis to understand cell behavior in tumor and tissue microenvironments.

4. Utilizing force measurements to distinguish non-invasive and invasive cancer cells The transformation of cancer cells to highly invasive phenotypes allows cells to distort their shape and generate forces to navigate through dense stroma. Epithelial cancer cells undergoing EMT lose some epithelial characteristics, including reduced expression of cadherins responsible for cell-cell junctions and increased expression of ECM binding integrins important in cell-ECM adhesion (Kalluri and Weinberg, 2009). Invasive and migratory properties acquired through EMT are prerequisites for metastasis; thus, it is imperative to identify or even predict which cancer cells undergo EMT. Using biophysical approaches to interrogate actin cytoskeletal modifications in cancer cells, we previously examined the phenotype of cancer cells undergoing EMT.

4.1 Genetically induced EMT makes cancer cells more deformable The breast cancer cell line MCF7 was modified to constitutively express Snail, a zinc-finger transcription factor that triggers EMT by suppressing E-Cadherin expression. While cells transformed with an empty vector (MCF7-NEO)

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Fig. 4 SNAIL-induced EMT alters cytoskeletal and mechanical properties of MCF-7 cells. (A–C) Immunostaining for actin, showed change in cytoskeletal organization of MCF7SNAIL cells compared control (Scale bar ¼ 50 μm). Analysis of actin intensity revealed that overexpression of SNAIL reduced actin polymerization (B). Gray intensity distribution along the line (A) across the images were quantified in image J to look at differences in actin (C). (D–E) MSDs of 200 nm particles injected into the cytoplasm were increased for MCF7-SNAIL cells across all time lags suggesting relatively softer cytosol. Elastic modulus of MCF7-SNAIL cells was reduced significantly at ω ¼ 1 Hz. To calculate statistical significance student’s t-test was used and P-values of less than 0.05 was considered significant (*P < 0.05, **P < 0.01, ***P < 0.001) (McGrail et al., 2015b).

were more epithelial with mostly round morphology, cells transformed with Snail (MCF7-SNAIL) exhibited a more mesenchymal phenotype. MCF7SNAIL cells displayed an elongated morphology, downregulation of E-Cadherin, and upregulation of N-Cadherin and Vimentin, characteristics of a mesenchymal phenotype (McGrail et al., 2015b). To understand the underlying changes in the cytoskeletal organization, we analyzed the actin structure using immunostaining (Fig. 4A). The integrated fluorescence intensity of actin in MCF7-SNAIL was 3-fold lower in comparison to MCF7-NEO cells (Fig. 4B). Analysis of actin distribution across individual MCF7-NEO cells showed high intensity in cortical regions indicating presence of polymerized actin stress fibers. In contrast, MCF7-SNAIL cells displayed significantly lower actin intensity suggesting dissolution of polymerized actin structure (Fig. 4C). Using IPTM, we confirmed that the intracellular mechanical properties were markedly altered. Embedded

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nanoparticles in MCF7-SNAIL cell cytosol displayed higher MSDs at all timescales and subsequently revealed a significant reduction in elastic modulus (Fig. 4D–E). Together, these results confirmed that MCF7-SNAIL cells possess a more deformable cytosol in comparison to MCF7-NEO. Results from our study corroborated with the results reported by Craene et al. in colon cancer cells (De Craene et al., 2005). Expression of Snail in these cells led to significant loss in cytoskeletal proteins, including ABPs. Additionally, highly invasive cancer cells from different tissues including breast and ovarian cancer have been shown to display less actin stress fibers compared to the normal cells (Alibert et al., 2017). However, other EMT studies with cancer and normal epithelial cells treated with transforming growth factor β (TGFβ)—a known inducer of EMT, have shown increased actin stress fiber formation (Haynes et al., 2011; Nalluri et al., 2015; Sousa-Squiavinato et al., 2019; Zhitnyak et al., 2020). This EMT response may vary in different cell types, perhaps due to intrinsic differences in the cells undergoing EMT or differences in the surrounding environment. Similarly, while most studies have reported that more invasive cancer cells are often softer than less invasive cells, a few studies have reported stiffening of invasive cancer cells (Alibert et al., 2017). Different probing techniques can contribute to the reported differences in cancer cell mechanics. Measurement with techniques that use external probe at local regions of the cell can be influenced by cortical actin structure, which is more polymerized in invasive cells that exhibit high traction forces. For example, studies using AFM can be measuring a specific region of the cell, not the intracellular mechanics (Alibert et al., 2017). Together these results highlight the need for a more comprehensive biophysical analysis of cancer cells undergoing malignant transformation. In addition to the intracellular changes, MCF7-SNAIL also displayed significantly different traction force profile on a polyacrylamide substrate (Fig. 5A–B). As the cells assumed a more elongated phenotype, it generated higher traction forces localized at cell periphery. MCF7-SNAIL-generated peak traction forces were threefold higher than those exerted by NEO cells. Consequently, MCF7-SNAIL demonstrated significantly higher migratory behavior with more than 2-fold increase in cell velocity (Fig. 5C). Coefficient of variation, calculated by dividing the standard deviation with the mean, provides a measure of heterogeneity in the population and was significantly increased in SNAIL cells for both traction force and migration (Fig. 5D).

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Fig. 5 (A) Traction heat maps of MCF7-NEO and MCF7-SNAIL cells are overlaid with matrix displacements with force range is specified in Pascals (Scale bars ¼ 10 μm). (B) Peak traction stresses in SNAIL cells were significantly higher than NEO cells. (C) Mean velocity of MCF7-SNAIL cells was significantly increased. (D–E) Coefficient of variation calculated for both traction force (D) and cell velocity (E) was significantly higher in MCF7-SNAIL cells. To calculate statistical significance student’s t-test was used and P-values of less than 0.05 was considered significant (*P < 0.05, **P < 0.01, ***P < 0.001) (McGrail et al., 2015b).

4.2 Invasive cancer cells exert increased and polarized traction forces in a context dependent manner In addition to the biochemical signals, the biomechanical properties of the ECM can also dictate the traction force profile of the cancer cells. Solid tumors are generally stiffer compared to their surrounding tissues (Chang et al., 2011; Egeblad et al., 2010; Youk et al., 2014). This increased rigidity has been shown to promote an invasive behavior in cancer cells from multiple tissues, including breast, liver, and prostate (Acerbi et al., 2015; Kostic et al., 2009; Leight et al., 2017; Pickup et al., 2014; Tilghman et al., 2010; Ulrich et al., 2009). Inversely, invasive tumor cells often exhibit some form of durotaxis or response to increased substrate rigidity (Acerbi et al., 2015; Lachowski et al., 2017; Samuel et al., 2011). This observation is certainly

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true in highly metastatic MDA-MB-231 breast cancer cells. We have shown that when MDA-MB-231 cells were cultured on hard polyacrylamidecollagen-coated substrates (35 kPa), they exhibited characteristic of malignancy including, significantly increased proliferation rate and resistance to the chemotherapeutic, compared to those of soft (3 kPa) substrates (Fig. 6A–B) (McGrail et al., 2015a). Other characteristic properties of invasiveness also followed the same trend. MDA-MB-231 cells showed a significantly higher migration velocity (Fig. 6C) and increased cell spreading when cultured on hard substrates relative to soft substrates. However, metastatic SKOV-3 ovarian cancer cells displayed an opposite mechanical response to substrate stiffness. When SKOV-3 cells were cultured on soft

Fig. 6 Context dependent response of cancer cells. MDA-MB-231 and SKOV-3 cells were cultured on soft (3 kPa) and hard (35 kPa) collagen-coated polyacrylamide substrates or collagen-coated glass. (A) Percent proliferation was determined by BrdU incorporation. (B) Viability was determined by MTT assay after 2 μM (MDA-MB-231) or 0.1 μM (SKOV3) Doxorubicin—treatment. (C) The average cell velocity was determined by tracking cell nuclei at 5-min intervals over an 8-h period. (D) Average traction force was quantified from displacement of fluorescent nanoparticles embedded in substrates showed Increased traction forces were correlated with increased migration velocity, increased proliferation, and treatment resistance for SKOV-3 cells on soft substrates and MDA-MB-231 cells cultured on hard substrates or glass. To calculate statistical significance student’s t-test was used and P-values of less than 0.05 was considered significant (*P < 0.05, **P < 0.01, ***P < 0.001) (McGrail et al., 2015a).

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matrices, they exhibited greater proliferation rates, migration velocity, and cell spreading compared to those cultured on hard matrices (Fig. 6A–C). This discrepant behavior between MDA-MB-231 and SKOV-3 cells demonstrated that mechanical responses to substrate stiffness are cell specific, most likely due to differences in their physiological environment in the primary tumor or metastatic niche (Kostic et al., 2009; Kraning-Rush et al., 2012). Moreover, MDA-MB-231 and SKOV-3 cells became highly polarized and exerted greater traction force on hard and soft substrates, respectively (Fig. 6). The mechanism of this progression has been well-studied in breast cancer, where increased ECM stiffness leads to integrin activation followed by focal adhesion formation and increased actomyosin contractility (Levental et al., 2009). However, the increased forces exerted by SKOV-3 on soft matrices had not been characterized before our studies. Rigidity dependent behavior of cancer cells has been correlated to their ability to metastasize to specific locations in vivo (Kostic et al., 2009). Indeed, studies in murine models found that MDA-MB-231 cells yielded significantly more bone (stiff ) metastases compared to lung (soft) metastases (Kang et al., 2003; Kostic et al., 2009). Though the idea of different cancer cell types invading sites with contrasting mechanical properties may seem counterintuitive, our studies demonstrated that cells adapted their response using Rho-ROCK mediated actomyosin contractility and intracellular cytoskeletal tension (McGrail et al., 2014, 2015a,b).

4.3 Rho-ROCK signaling regulates distinct mechanical response of differing cancer cell types Mechanotransduction signaling pathways can create a mechanicallyinduced positive feedback loop, whereby increased ECM deposition and rigidity enhances malignant properties in cancer cells (Chin et al., 2016; Lu et al., 2012). For cancer cells to find balance between intracellular cytoskeletal tension and extracellular adhesion, optimal levels of Rho-ROCK pathway activation must be maintained (McGrail et al., 2015a). We demonstrated the salience of optimal Rho-ROCK signaling activation in MDAMB-231 and SKOV-3 cells cultured on matrices of different rigidities by measuring traction forces before and after introducing Rho-ROCK pathway inhibitor (Y27632). Using TFM, we first found that both MDAMB-231 and SKOV-3 cells exerted significantly larger (23-fold increase) cell-substrate traction forces when cultured on their respective preferential substrates, hard and soft (Fig. 7). In this paper preferred substrate referred to the

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Fig. 7 Rho-ROCK pathway controls cancer cell response. (A–B) SKOV-3 and MDA-MB231 cells cultured on soft (3 kPa) and hard (35 kPa) polyacrylamide substrates were treated with ROCK inhibitor (Y27632). Quantification of traction forces for MDA-MB-231 (A) and SKOV-3(B), respectively. To calculate statistical significance student’s t-test was used and P-values of less than 0.05 was considered significant (*P < 0.05, **P < 0.01, ***P < 0.001) (McGrail et al., 2015a).

substrate rigidity that elicited more malignant properties in mechanosensitive cancer cells. SKOV-3 ovarian cancer cells were more proliferative, migrated more rapidly, and exhibited reduced sensitivity to chemotherapeutic drugs on soft substrates, which were considered their preferred rigidity. Generally, increased Rho-ROCK signaling correlates with increased traction force, so we sought to further understand how this pathway was linked to contractility in the context of ECM rigidity. When the cells were treated with ROCK inhibitor Y27632 on their preferred substrates, we saw no increase in traction force for both MDA-MB-231 and SKOV-3 lines. Interestingly, when MDA-MB-231 tumor cells were cultured on their nonpreferred soft substrates, we observed a gain-of-function (slightly increased traction force), compared to those without Y27632 treatment. This phenomenon can be largely explained by the idea that cancer cells’ inherent contractility needs to be matched with substrates of optimal rigidity to generate maximum traction forces — larger cell-intrinsic contractility matched with a stiffer ECM or lower cell-intrinsic contractility matched with a softer ECM results in optimal cell-ECM traction. Since our past studies have shown that MDA-MB-231 cells are inherently more contractile than SKOV-3 cells, a reduction in MDA-MB-231 cells’ inherent contractility with Y27632 rescued its function on its nonpreferred, soft substrates. Therefore, it is evident that optimal Rho-ROCK signaling inherent in individual cancer cell types governs their actomyosin contractility, which in effect defines

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their predisposed matrix compliances. Taken together, these findings highlight the complexity of cancer progression, and drive the need to take a nuanced approach in examining the biophysical landscapes of different tumors.

4.4 Utilizing force profiles to characterize chemoresistant subpopulations Often, a small subpopulation of cells can survive initial treatment, through efficient drug efflux or quiescence. Polyploidal giant cancer cells (PGCCs) are thought to be able to survive chemotherapy via quiescence (Zhang et al., 2014). Despite their apparent dormancy and morphological similarities, PGCCs are distinct from senescent cells, as they can give rise to daughter cells trough amitotic budding (Lv et al., 2014; White-Gilbertson et al., 2020). These morphologically enlarged and often multinucleated cells are often seen in tumors that have undergone treatment, or in late stage and aggressive disease (Fei et al., 2015; Lopez-Sa´nchez et al., 2014; Zhang et al., 2014). Furthermore, injection of PGCCs into mouse xenograft models have led to the growth of new tumors, highlighting the tumorigenic potential of this unique subpopulation (Niu et al., 2017). Previous studies conducted in our lab have shown that MDA-MB-231 PGCCs have increased migratory persistence and migrate more readily into the scratch wound (Xuan et al., 2018). In order to understand exactly how PGCCs maintain their enlarged morphology and sustain high migratory persistence despite their increased size, we performed single cell IPTM and TFM (Fig. 8). We found that PGCCs on average had increased cytoplasmic stiffness, and a stiffer but more deformable nuclei. This is evidenced in the MSD plots of particle motion embedded within the cytoplasm of the cell and heterochromatic foci within the nucleus, where PGCCs had a lower MSD, indicating higher levels of constraint. Furthermore, we examined the MSD traces of individual cells and noticed a higher spread in PGCC populations. This indicates that PGCCs are more heterogeneous than non-PGCCs; this increased heterogeneity has been observed in chemoresistant and highly invasive cancer cells. When we stained for and quantified the actin cytoskeleton of our PGCCs, we found that PGCCs expressed both thicker and longer actin stress bundles (Fig. 8A–B), which is associated with higher traction forces and increased migration. Indeed, when we performed TFM on our MDA-MB-231 cells, we found that PGCCs on average had over twice the exerted traction force compared to our non-PGCCs (Fig. 8C). In order to see if the unique organization of their actin structure was responsible for PGCCs cytoplasmic stiffness,

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Fig. 8 (A) Fluorescent images of non-PGCCs and PGCCs stained for microtubule (green), actin (red), and DNA (blue) (Scale bar ¼ 50μm). (B) Quantification of average stress fiber width and length. (C) Mean traction forces exerted by non-PGCC and PGCC cancer cells on a 10kPa stiffness polyacrylamide substrate. (D–E) Ensemble averaged MSDs of tracked particles of Non-PGCCs (D) and PGCCs (E) in control and inhibitor treated conditions. To calculate statistical significance student’s t-test was used and P-values of less than 0.05 was considered significant (*P < 0.05, **P < 0.01, ***P < 0.001) (Xuan et al., 2018).

we inhibited parts of the RhoA/ROCK pathway, which is responsible for controlling actin cytoskeletal organization. Using inhibitors ML7 (MLCK inhibitor), H1152 (ROCK inhibitor), and latrunculin A (actin polymerization inhibitor), we observed consistent reductions in cytoplasmic stiffnesses in our polyploid cells (Fig. 8D–E). Taken together, these results demonstrate the biophysical characterization of a unique and highly chemoresistant subpopulation, which is more invasive as well as highly tumorigenic.

5. Utilizing force measurements to study tumor and stromal cell crosstalk Interactions between cancer and stromal cells and the surrounding TME, with its diversity in cell types and matrix mechanics, play a critical role in directing cancer progression. Carcinoma associated fibroblasts (CAFs) are

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one major stromal contributor to TME malignancy (Karagiannis et al., 2012; Luo et al., 2015; Tao et al., 2017). They mediate hallmark cancer cell behaviors by secreting paracrine factors that alter tumor growth and cell survival, ECM proteins for matrix stiffening, and pro-inflammatory signals important in cancer progression. A CAF-like phenotype is characterized by changes in cytoskeletal architecture, motility, and adhesion, along with increased expression of α-smooth muscle actin (αSMA) and fibroblast activated protein (FAP) (Mishra et al., 2008). These myofibroblast-like cells arise from normal fibroblasts and MSCs that have been activated by tumor-secreted factors to form CAFs. MSCs that spontaneously home to tumors from the bone marrow may persist as stem cells in the tumor or differentiate into stromal cells (Bergfeld and DeClerck, 2010; Spaeth et al., 2009; Torsvik and Bjerkvig, 2013). Thus, MSCs serve as important tools in the study of the stroma-cancer crosstalk and are utilized extensively in our studies.

5.1 Cancer cell invasiveness determine direct intercellular interaction with stromal cells Recruitment and engraftment of stromal cells, including fibroblasts and MSCs in TME are critical for cancer progression to malignancy and are associated with poor prognosis (Oudin and Weaver, 2016). As the disease become more invasive, the cell adhesion molecules on tumor cell surface are significantly altered and these altered interactions can subsequently modify both initial engraftment and long-term fate of stromal cells ( Janiszewska et al., 2020). We elucidated the role of altered adhesion molecule repertoire on stromal cell engraftment with monolayers of cancer cells with varying levels of aggressiveness (McAndrews et al., 2015). Stromal cells were more adherent and spread more readily on more aggressive breast, ovarian, prostate, and taxol resistant cell lines. The aggressive cell lines expressed EMT associated cell-cell adhesion markers cadherin 2 (N-cadherin) and/or cadherin 11 (OB-cadherin) to a different degree. Both of these proteins and especially the OB-cadherin were also expressed by stromal cells. Subsequently blocking cadherin 11 on stromal cells reversed the enhanced adhesion to invasive cancer cells even with the cancer cells with low level of OB-cadherin expression. This suggests that OB-cadherin on stromal cells enabled them to bind to the cancer cells via homotypic (OB-cadherin) or heterotypic (N-cadherin) interaction and can be used as a therapeutic target to abrogate cancer cell-stromal cell interaction. Through the extensive bidirectional crosstalk between cancer cells and stromal cells, there is a feedback loop wherein stromal cell recruitment increases cancer cell invasion and malignancy, which in turn increases stromal cell recruitment.

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When stromal cells are initially recruited to the TME, they undergo a multitude of changes due to the close interaction with tumor cells. These cells serve to prime the TME and create a supportive environment for cancer cells, which in turn enhances their invasive and metastatic potential. To understand how tumor cells can modulate MSC behavior in order to induce a CAF phenotype, we must first examine the multitude of factors that they are exposed to upon initial exposure to the TME. MSCs recruited to the tumor are exposed to a wide variety of soluble factors (SFs), including platelet derived growth factor (PDGF), TGF-β1, and the cocktail of pro-migratory molecules released by tumor cells (Fig. 9). To study the

Fig. 9 Tumor-secreted soluble factors (SFs) Alter MSC Mechanics. (A) Time-dependent ensemble average MSDs for MSCs in control media (CM), tumor conditioned media (TCM), or media supplemented with PDGF and/or 5 ng/mL TGF-β1. MSCs stiffen in response to TCM, TGFB1, and the combination of PDGF and TGF-β1, but not PDGF. (B) SF treatment was then combined with small molecule inhibitors of PDGF (JNJ10198409) and TGF-β1 (SB-505124). Inhibition of these signaling pathways reversed this stiffening response. (C) The mean traction stresses were determined for MSCs pretreated with CM or CM supplemented with 5 ng/mL TGF-β1 (Scale bar ¼ 10 μm). Treatment with TGF-β1 resulted in higher traction stresses. To calculate statistical significance student’s t-test was used and P-values of less than 0.05 was considered significant (*P < 0.05, **P < 0.01, ***P < 0.001) (Dawson et al., 2014; Ghosh et al., 2014; McGrail et al., 2012).

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biophysical changes that occur when MSCs are exposed to the tumorsecreted factors, we exposed MSCs to tumor conditioned media in order to simulate the tumor stromal cell paracrine signaling occurring in vivo. We combined biophysical characterization to elucidate the changes in actin structure and mechanical response of MSCs (Ghosh et al., 2014; McGrail et al., 2012).

5.2 MSCs and cancer cells undergo dramatic changes in cell mechanics in response to SF crosstalk IPTM revealed that MSC treatment with soluble factors in tumor conditioned media (TCM) results in sudden cytoskeletal stiffening (characterized by a change in the slope of the MSD), which completely changed the intracellular mechanical phenotype of MSCs. TCM treatment resulted in increased expression of all Rho GTPases, with dramatic effects on the expression of Cdc42, indicating that this molecule was largely responsible for the altered mechanical response. Based on previous studies, TGF-β1 has been shown to be an important pleiotropic factor that contributes to cytoskeletal stiffening (Nalluri et al., 2015). In addition, it is also an important part of the paracrine signaling molecules within TCM. Indeed, when treating MSCs, TGF-β1 alone was sufficient to induce the biophysical changes observed with TCM. Furthermore, when treated in conjunction with PDGF, TGF-β1 can enhance cell stiffening in MSCs. Like TCM, TGF-β1 alone and in combination with PDGF profoundly increased condensed and elongated microtubules and actin filaments. Although PDGF alone did not result in any significant biophysical changes, the addition of PDGF to TGF-β1 amplified this cellular response, indicating possible interaction between these two signaling pathways. Subsequently, blocking PDGF signaling in TGF-β1 treated cells was enough to abrogate the stiffening, similar to the expected effect of TGF-B receptor inhibitor. This result demonstrates the integral role of PDGF signaling in regulating TGF-β1mediated cell stiffening and further highlights the complexity of SF interactions in mediating cell mechanics responses. This suggested that TGF-β1 was working in conjunction with various other factors in order to induce the changes. TGF-β1 treated MSCs were also able to generate significantly larger traction force but were unable to polarize them. Overall, as cancer cells become more aggressive, they become more deformable, while paracrine factors from these aggressive cancer cells make

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Fig. 10 Actin binding protein profile of genetically induced EMT in cancer cells and soluble factor (TGF-β1) treated MSCs (Ghosh et al., 2014; McGrail et al., 2015b).

MSCs less deformable. Whole genome microarrays used to probe for transcriptional differences in for genetically induced EMT in cancer cells and SF treated MSCs showed that the number of differentially expressed ABP genes were significantly altered (Fig. 10). For cancer cells undergoing EMT, actin cross-linking and stabilizing protein genes were down-regulated corroborating with our observation of depolymerized actin and softening of the cytoplasm. Conversely, SF treated MSCs that underwent cytosolic stiffening displayed significant upregulation in crosslinking and stabilizing proteins, along with downregulation of capping and severing proteins. This highlights the incredibly complex crosstalk that occurs within the TME, that collectively enhance the CAF phenotype and promote a microenvironment that favors cancer invasion and metastasis.

6. Conclusions We have combined quantitative analysis of intracellular mechanics and surface traction forces with analysis of cell fate processes to study the malignant transformation of cancer cells and their interaction with stromal cells. Multivariable analysis is critical in determining the role of mechanical

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forces in cancer progression and in analyzing heterogeneity in cancer cell populations. This heterogeneity makes it difficult to target and kill all cancer cells. In 3D microenvironments that are non-uniform in structure, such as human tumors and 3D tissue culture models, the ability for invasive cancer cells to respond to gradients in soluble factor and matrix mechanics may further contribute to the heterogeneity in cancer cells. Metastasis is a highly selective process with less than 0.1% of tumor cells capable of forming metastatic tumors. Thus, it is critical to understand how heterogeneity in the primary tumor gives rise to metastatic disease.

7. Experimental challenges and future research efforts Despite advances in the field of cancer biophysics, such as single-cell biophysical characterization in 3D models and patient samples, additional developments are needed to reproducibly characterize cancer cells in these more complex conditions. More importantly, novel studies and methodologies are needed to characterize and isolate malignant subpopulations of cancer cells. This will allow for a better understanding of cancer cell heterogeneity from biophysical measurements. Finally, developing high-throughput ways of quantifying cancer biophysical properties, while maintaining high spatial and temporal resolution, would increase the accuracy of these measurements.

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CHAPTER TWO

Novel facets of glioma invasion Carina Fabiana,b, Mingzhi Hanb,c, Rolf Bjerkviga,b,∗, and Simone P. Nicloua,b,∗ a

NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Department of Biomedicine, University of Bergen, Bergen, Norway c Department of Neurosurgery, Qilu Hospital of Shandong University and Institute of Brain and Brain-Inspired Science, Shandong University; Shandong Key Laboratory of Brain Function Remodeling, Jinan, China ∗ Corresponding authors: e-mail address: [email protected]; [email protected] b

Contents 1. Introduction 2. Routes of glioma cell invasion 2.1 Invasion within the perivascular space 2.2 Invasion along white matter tracts 2.3 Invasion within the subarachnoid space 2.4 Invasion into the brain parenchyma 3. Modes of glioma cell invasion 3.1 Single cell invasion 3.2 Collective cell invasion 4. The impact of tumor metabolism on cell invasion 5. The extracellular matrix of the glioma microenvironment 6. Involvement of proteases and the tumor microenvironment in glioma cell invasion 7. The actin cytoskeleton and its related membrane protrusions in glioma cell invasion 7.1 Actin regulation 7.2 Lamellipodia 7.3 Filopodia 7.4 Invadopodia 8. Can glioma cell invasion and the actin cytoskeleton be targeted? 9. Conclusions and future prospects Acknowledgments References

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Abstract Malignant gliomas including Glioblastoma (GBM) are characterized by extensive diffuse tumor cell infiltration throughout the brain, which represents a major challenge in clinical disease management. While surgical resection is beneficial for patient outcome, it is well recognized that tumor cells at the invasive front or beyond stay behind and

International Review of Cell and Molecular Biology, Volume 360 ISSN 1937-6448 https://doi.org/10.1016/bs.ircmb.2020.08.001

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constitute a major source of tumor recurrence. Invasive glioma cells also represent a difficult therapeutic target since they are localized within normal functional brain areas with an intact blood brain barrier (BBB), thereby excluding most systemic drug treatments. Cell movement is mediated via the actin cytoskeleton where corresponding membrane protrusions play essential roles. This review provides an overview of the various paths of glioma cell invasion and underlines the specific aspects of the brain microenvironment. We highlight recent insight into tumor microtubes, neuro-glioma synapses and tumor metabolism which can regulate collective invasion processes. We also focus on the deregulation of actin cytoskeleton-related components in the context of glioma invasion, a deregulation that may be controlled by genomic alterations in tumor cells as well as by various external factors, including extracellular matrix (ECM) components and non-malignant stromal cells. Finally we critically assess the challenges and opportunities for therapeutically targeting glioma cell invasion.

1. Introduction During brain development, cell motility is a tightly regulated process leading to organized structural and functional networks between neurons and/or glial cells. Defects in cell migration can cause various pathological conditions leading to neurological defects such as mental retardation (Rahimi-Balaei et al., 2018). For example mutations in the Reelin pathway, which regulates neuronal migration, are linked to cerebellar hypoplasia, ataxia and cognitive aberrations (Rahimi-Balaei et al., 2018). Similarly, mutations in Doublecortin (DCX) interfere with neuronal migration, in addition DCX may play a role in cancer cell invasion (Ayanlaja et al., 2017). Beside neuronal migration, astrocyte motility is also important in brain development for the control of neuronal activity, establishment of synapses and the blood-brain-barrier (BBB). Oligodendrocyte precursors migrate along blood vessels to reach target axons for myelinization. In the adult brain controlled movement of neural stem cells continues and is supported by glial cells. Active cell migration is also observed by microglia, the endogenous immune cells of the brain, which are activated and chemoattracted by inflammation, brain injuries or neurodegenerative processes (Hefendehl et al., 2014). Similarly, neural stem cells and resident glial progenitors in the adult brain, can migrate under pathological conditions including infections, inflammation, ischemia, trauma, neurodegenerative diseases and cancer. Gliomas represent a vast group of primary brain tumors that may be regarded as local central nervous system (CNS) malignancies since metastatic spread out of the CNS is rarely observed. This is in contrast to secondary

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brain tumors from, e.g., melanomas, breast and lung cancer that metastasize to the brain. Compared to secondary brain tumors (brain metastases) which usually form highly circumscribed lesions within the brain, glial tumors display extensive CNS infiltration. Gliomas can be divided into two groups based on their growth pattern and histology. On the one hand, diffuse gliomas represent largely incurable glial tumors defined by their highly infiltrative growth within the CNS parenchyma. On the other hand, non-diffuse gliomas like pilocytic astrocytomas and ependymal tumors, show generally a slow and more circumscribed growth pattern (Perry and Wesseling, 2016). Diffuse gliomas represent the vast majority of glial tumors and are further subdivided into oligodendrogliomas, astrocytomas and glioblastomas (GBM), the latter representing the most aggressive subtype (Louis et al., 2016). GBMs are characterized by high mitotic activity, nuclear atypia, microvascular proliferation, areas of necrosis and high tumor heterogeneity at the cellular and molecular level. Molecular markers like the mutation status of the metabolic enzyme isocitrate dehydrogenase (IDH), co-deletion of chromosomal arms 1p/19q, as well as the presence or absence of the chromatin remodeling protein ATRX or mutations in the tumor-suppressor gene p53 represent important factors in glioma classification as defined by the World Health Organization (WHO) (Louis et al., 2016). GBMs are characterized by wild-type IDH and represent de novo or primary GBMs (90%), occurring without prior lesion and usually in patients older than 55 years. The remaining 10% represent secondary GBMs, normally derived from a lower grade astrocytoma carrying an IDH mutation. Even though cellular and molecular information on diffuse gliomas has advanced significantly over the last decades, this knowledge has not translated into new effective treatments. The current standard of care for GBM still involves maximal safe resection followed by radio- and chemotherapy using the DNA-alkylating agent temozolomide. Patient survival is still around a dismal 12–14 months (Stupp et al., 2005). In particular, extensive tumor cell invasion represents a challenge in clinical management as it precludes complete surgical resection. Moreover an intact blood brain barrier (BBB) in the areas invaded by tumor cells limits the concentration of effective drugs to reach the infiltrative cells. This highlights the need to better understand tumor cell-intrinsic mechanisms in order to identify potential novel drug targets towards the GBM invasive compartment. The actin cytoskeleton plays a central role in cell movement and is consequently also of importance in GBM cell invasion.

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This review will focus on the specificities of glioma cell invasion in the brain microenvironment with special emphasis on the actin cytoskeleton. We will generally refer to glioma cell invasion, encompassing the various subtypes of diffuse glioma. In cases where differences between subtypes are known, these will be highlighted. The different routes and modes of glioma cell invasion as well as the contribution of non-malignant stromal cells in the context of invasion, will to some extent be addressed. Finally past and ongoing attempts to interfere with GBM cell invasion as a therapeutic strategy will be discussed.

2. Routes of glioma cell invasion As early as in 1938, the German neuropathologist Hans-Joachim Scherer carried out a comprehensive study where he serially sectioned the brains of 100 GBM patients and carefully examined the tumor and the surrounding brain. He found that the tumors preferentially migrate along pre-existing brain structures of least resistance such as blood vessels, white matter tracts and the leptomeningeal space (also called subarachnoid space) (Scherer, 1938). Glioma cells are also capable to diffusely infiltrate the brain parenchyma leading to tissue remodeling and destruction. These routes have become known as Scherer’s structures and are still used today to describe the different routes of GBM cell invasion (Cuddapah et al., 2014).

2.1 Invasion within the perivascular space Clinically GBM cells show extensive invasion along vascular structures. In particular, they invade the perivascular space (Virchow Robin Space), which is filled with cerebrospinal fluid. Here, the tumor cells have access to extracellular matrix components of the basal lamina of endothelial cells, as well as the glia limitans, the protective physical barrier of the BBB formed by astrocytic end feet. Experimental studies in vivo, including the implantation of GBM cells into the rodent brain, have shown that the majority of cells outside the tumor bulk locate in close proximity to blood vessels, indicating that perivascular invasion represents a frequent GBM invasion route (Fig. 1A). Preclinical studies have found that this is at least partially due to bradykinin release from endothelial cells, leading to activation of the bradykinin receptor and intracellular calcium (Ca2+) changes in GBM cells (Cuddapah et al., 2013). Similar to migrating neurons in the developing brain, Ca2+ oscillations are a key signaling effector of glioma cell motility (Cuddapah et al., 2014). The perivascular space represents also an invasion

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Fig. 1 Routes of glioma cell invasion recapitulated in a patient-derived rat xenograft model. A human GBM transplanted into the brain of immunodeficient rats (Sakariassen et al., 2006; Wang et al., 2009). Tumor cells are visualized by human nestin immunohistochemistry. Arrows point at invading tumor cells within perivascular spaces (A). (B) Tumor cell invasion along white matter tracts. Note that the GBM cells align themselves along the fiber tracts. (C) Single cell invasion within the gray matter. The invasive cells are spindle shaped, indicating the presence of numerous filopodia and invadopodia.

path for secondary brain tumors, indicating that this mode of invasion is not unique for gliomas. This was found to be particularly prominent for brain metastases of melanoma, but was also seen with non-small cell lung cancer (NSCLC) (Berghoff et al., 2013). It may be that these sites also represent preferential entry points of brain metastasis.

2.2 Invasion along white matter tracts In 1928, the neurosurgeon Walter Dandy performed brain hemispherectomies in order to cure GBM patients. These efforts failed since contralateral tumor recurrences were observed in the opposite hemispheres. From these radical efforts, it was realized that GBM cells migrate over long distances along white matter tracts from the primary tumor mass to the contralateral brain hemisphere (Dandy, 1928). The white matter is composed of

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myelinated axons connecting various brain compartments, such as the corpus callosum between the two hemispheres. These structures represent ideal ‘highways’ for cell movement along parallel fiber spaces and are also observed in pre-clinical models (Fig. 1B). Within white matter tracts, individual tumor cells can be observed migrating along myelinated fibers (Fig. 1B).

2.3 Invasion within the subarachnoid space The subarachnoid space also known as the leptomeningeal space represents the cerebrospinal fluid-filled area between the arachnoid layer and the pia mater. It is part of the meningeal linings that separate the brain from the skull. Since the subarachnoid space is continuous with the perivascular space, it also represents an, albeit less common, route for tumor cell invasion. Glioma cells which invade the perivascular space may reach the surface of the brain and continue to invade the subarachnoid space. This route most likely involves a dissemination of single cells mediated by the shear flow of the cerebrospinal fluid as well as potentially by active cell movement (Marienhagen et al., 1994).

2.4 Invasion into the brain parenchyma A major characteristic of diffuse gliomas is their capacity to diffusely infiltrate the brain parenchyma. Here glioma cells invade into extracellular spaces of the neuropil representing the gray matter of the CNS composed of neuronal cell bodies and glial cells (Fig. 1C). Compared to the nerve fibers and blood vessels, the parenchyma is of a softer matrix but with a high cellular density providing more physical hindrance for cell movement. The patterns of invasion in this context may be subdivided into (i) single cell invasion and (ii) collective cell invasion (see below).

3. Modes of glioma cell invasion 3.1 Single cell invasion Single cell invasion can occur in the form of mesenchymal or amoeboid invasion. Mesenchymal invasion is characterized by cells adopting an elongated, spindle-like shape. It represents a slow and complex process, which is associated with integrin-mediated adhesion and proteolytic extracellular matrix (ECM) degradation (Friedl and Wolf, 2010). Amoeboid invasion, in contrast, is a rather fast process that involves a weak adhesion with limited

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proteolytic ECM degradation. This process is characterized by rapid changes in cell shape and the development of filopodia allowing cells to scan their environment and move through small gaps in the matrix (Pankova et al., 2010). It was shown in invading glioma cells that this process is accompanied by a significant reduction in cell volume (30–35%) which relies on K+ and Cl channels expelling unbound water from the cytoplasm. Inhibitors of these K+ and Cl channels were found to significantly reduce transwell migration in vitro (Watkins and Sontheimer, 2011). In general, it has become evident that tumor cells can switch between mesenchymal and amoeboid invasion, indicating that these processes are highly dynamic involving a migratory plasticity in response to changes in the brain microenvironment. For example, it appears that changes in ECM density and stiffness can influence the transition between these two modes of single cell invasion (Taddei et al., 2013).

3.2 Collective cell invasion In contrast to single cell invasion, collective cell invasion is characterized by the coherent movement of groups of cells maintaining contact with each other (Friedl et al., 2012). The presence of cell-cell junctional processes between tumor cells is accompanied by a reorganization of the actin cytoskeleton. A number of leading cells, which sense and process potential guidance signals from the microenvironment mediate invasive traction (Haeger et al., 2015). The concept of collective invasion has gained renewed momentum following the discovery of tumor microtubes (TMs) in GBMs, which may increase in number during tumor progression (Osswald et al., 2015). Generally larger than nanotubes, these actin-rich TMs represent very long (>500 μm) and thin (1-2 μm) filamentous membrane protrusions. It is postulated that TMs represent tracks for invasion by establishing large networks of glioma cells. The TM network may vary according to the tumor type, the ECM microenvironment and its anatomical location. For instance, TMs were described in astrocytomas of various grades, including GBM, but not in oligodendrogliomas (Osswald et al., 2015). TM networks functionally connect and coordinate communication between astrocytoma cells. This connection is facilitated by gap junctions, which form channels that allow molecular exchange between cells including Ca2+. Inhibition of intracellular Ca2+ waves through the cellular networks reduced their invasion capacity (Osswald et al., 2015). As a component of gap junctions connexin

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43 (Cx43) plays a major role in TM-linked connections between astrocytoma cells. Also the armadillo repeat-containing protein p120-catenin is required for TM-mediated cell-cell interactions as it induces adherens junctions during collective migration (Gritsenko et al., 2020). Knockdown of either Cx43 or p120-catenin impaired TM formation and glioma cell invasion (Gritsenko et al., 2020; Osswald et al., 2015). Interestingly, such multicellular connections may also render GBM cells resistant to chemo- and radiotherapy while single tumor cells could be eradicated more effectively by these treatments (Osswald et al., 2015; Weil et al., 2017).

4. The impact of tumor metabolism on cell invasion It is well known that glutamate is released by GBM cells and acts in an autocrine fashion through Ca2+-permeable AMPA receptors to stimulate cell motility via intracellular Ca2+ signaling (Lefranc et al., 2018). In line with this, recent work suggests that TM networks also underlie electrochemical communication through neuron-glioma synapses formed between presynaptic neurons and postsynaptic GBM cells (Venkataramani et al., 2019; Venkatesh et al., 2019). Such neuro-gliomal synapses were found to elicit spontaneous excitatory postsynaptic currents (sEPSCs), including Ca2+permeable AMPA receptor associated currents. Pharmacological inhibition of AMPA receptors may lead to decreased glioma growth and to reduced Ca2+-mediated invasion of TM-connected GBM cells (Venkataramani et al., 2019). Thus, GBM cells can be activated by glutamate released in the microenvironment and through neuro-gliomal synapses to modulate GBM growth and invasion. It remains to be seen to what extent this can be generalized to other glioma subtypes. We have previously shown that glutamate levels are reduced in clinical samples of lower grade IDH mutant gliomas compared to GBM (Fack et al., 2017). This might be explained by the fact that in IDH mutant gliomas, glutamate is needed to fulfill metabolic demands, such as TCA cycle replenishment in the absence of α-ketoglutarate (αKG). It may be that xCT transporters that export glutamate are less active in IDH mutant gliomas and instead glutamate may be taken up from the surroundings (Van Lith et al., 2014). Other metabolic pathways have been shown to affect glioma cell invasion. The serine dependent one-carbon metabolism was recently shown to drive cancer cell invasion including GBM (Meiser et al., 2018). One-carbon metabolism is essential for nucleotide synthesis and thus cell proliferation and

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is highly upregulated in many cancers. We recently found that this pathway is also involved in catabolic processes leading to formate release which in turn increases cancer cell invasion (Meiser et al., 2018). Interestingly, serine to formate conversion is accompanied by ATP production and is localized to the mitochondria. It is thus tempting to speculate that this pathway may locally provide ATP to the actin cytoskeleton while at the same time releases formate to stimulate the invasive process. Such activities may be exquisitely localized to actin-rich membrane protrusions including tumor microtubes, shown to contain mitochondria.

5. The extracellular matrix of the glioma microenvironment The ECM represents an important substrate for glioma cell invasion, comprising around 20% of the adult brain volume (De Gooijer et al., 2018). The ECM composition varies depending on the invasion location. For instance, in the perivascular space the ECM is rather rigid based on the presence of laminins, collagens, fibronectin, heparan sulfate, entactin and vitronectin, several of which are associated with the basal lamina and the glia limitans (Cuddapah et al., 2014). In contrast, the normal brain parenchyma contains a softer ECM with tenascin-C, thrombospondin 1 (THBS1), hyaluronan, glycosaminoglycans and various proteoglycans as the main components (De Gooijer et al., 2018). Glioma cells have the ability to adapt to these different ECMs at the transcriptional and metabolic level (Giese et al., 2003). An upregulation of THBS1, tenascin-C and Secreted Protein Acidic and Rich in Cysteine (SPARC) has been observed in the perivascular space, whereas in the brain parenchyma, a downregulation of versican and THBS1 has been reported, accompanied by an upregulation of hyaluronan, vitronectin, osteopontin, collagens and tenascin-C (Zimmermann and Dours-Zimmermann, 2008). THBS1 is known to be involved in angiogenesis, yet recently we have shown that transforming growth factor β1 (TGFβ1) can induce THBS1 expression thereby contributing to the invasive behavior of GBM cells. In addition, tumor cell-bound CD47 is implicated in this process (Daubon et al., 2019). Tenascin-C, which is not only produced by resident glia but also by glioma cells, was found to be overexpressed in the GBM infiltrative areas where it supports invasion while reducing proliferation (Xia et al., 2016). In addition, integrin transmembrane receptors are important for cell-cell and cell-matrix interactions and consequently can influence GBM cell invasion. For instance, integrin ß1,

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involved in tenascin-C signaling, is upregulated at mRNA level in GBM compared to normal brain tissue (GlioVis portal: http://gliovis.bioinfo. cnio.es/ (Bowman et al., 2017)).

6. Involvement of proteases and the tumor microenvironment in glioma cell invasion Glioma cell invasion involves the degradation of the brain ECM, which can be mediated by several proteases, including matrix metalloproteinases (MMPs), urokinase-type plasminogen activator (uPA), cathepsins, a Disintegrin and Metalloproteinases (ADAMs) and ADAMs with Thrombospondin motifs (ADAMTSs). Numerous reviews have described the involvement of these proteases in GBM cell invasion (see e.g., Hagemann et al., 2012; Hatoum et al., 2019; Mentlein et al., 2012), a brief overview will be provided below. One important group of proteases are MMPs, which are endopeptidases involved in tissue remodeling through the proteolytic degradation of various ECM proteins. Although non-malignant cells like endothelial cells, microglia and macrophages can also secrete MMPs, glioma cells are the main source of these proteases. Especially the secreted MMP-2 and MMP-9 as well as MT1-MMP (membrane type 1-matrix metalloproteinase, also known as MMP-14) play important roles in GBM invasion and their expression is correlated with tumor grade (Hagemann et al., 2012; Hatoum et al., 2019). This is confirmed by public databases showing an upregulation of these MMPs at the mRNA level compared to normal brain. All three MMPs also show the highest expression in GBM compared to other glioma types like astrocytoma and oligodendroglioma (http://gliovis.bioinfo.cnio.es/ (Bowman et al., 2017)). GBM cell expression of MT1-MMP can be induced, at least in vitro, by interleukin-6 (IL-6) released by astrocytes (Chen et al., 2016). In this context, it has also been shown that tumor-associated microglia and macrophages (TAMs) express MT1-MMP, which is involved in ECM remodeling and invasion. MT1-MMP supports glioma cell invasion by the proteolytic cleavage of glioma cell-derived pro-MMP-2 into its active form (Markovic et al., 2009). uPA is a serine protease involved in ECM degradation. The secretion of uPA occurs as soluble pro-uPA and requires activation via plasmin-mediated cleavage. Another important ECM-degrading molecule in GBM is the lysosomal cysteine protease cathepsin B, which is involved in direct and indirect pro-uPA and pro-MMP activation, including MMP-2 and MMP-9

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(Hatoum et al., 2019). According to the Gliovis data portal, cathepsin B and uPA mRNA are highly upregulated in GBM compared to normal brain and lower grade gliomas (Bowman et al., 2017). Moreover, uPA has been reported to be preferentially expressed at the invasive front of GBM (Colin et al., 2009). GBM cell invasion is also promoted through the cross-talk between GBM cells and reactive tumor-associated astrocytes. This crosstalk can induce the interaction between uPA and its corresponding receptor uPAR on the astrocyte surface resulting in the activation of plasmin, which can in turn activate MMP-2 and thereby promote GBM cell invasion (Le et al., 2003). Other ECM-degrading proteases, influencing invasion, are the metalloproteinases ADAMs and ADAMTSs that affect cell adhesion through integrin interactions via their disintegrin domain (Mentlein et al., 2012). They can also act as ‘sheddases’ by cleaving the extracellular fragment of transmembrane proteins to release soluble ectodomains. Particularly ADAMTS-4/5 are upregulated in GBM surgical samples and show confined expression in astroglial and GBM cells. In vitro, expression can be increased by cytokines like IL-1β and TGF-β, resulting in enhanced GBM cell invasion (Hatoum et al., 2019; Held-Feindt et al., 2006). ADAMTS-5 may also promote invasion through cleavage of the brain-specific ECM proteoglycan brevican. ADAM-17 may affect GBM invasion through its function as sheddase for activation of EGFR ligands such as tumor necrosis factor α (TNF-α) and TGF-α (Mentlein et al., 2012).

7. The actin cytoskeleton and its related membrane protrusions in glioma cell invasion The intracellular machinery that drives glioma cell invasion is the cytoskeleton. Considerable knowledge has been gained regarding the reorganization of the actin cytoskeleton during cellular processes such as adhesion, polarization, cell migration and invasion. Monomeric G-actin subunits assemble into actin filaments (F-actin), a process that is dynamically controlled by a high number of actin regulatory molecules. In the context of glioma invasion into the CNS, the actin filaments are responsible for the formation of membrane protrusions (lamellipodia and filopodia) involved in cell movement. Similarly, the tumor microtubes (TMs) fostering collective cell invasion represent elongated membrane protrusions rich in actin, microtubules and myosin.

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7.1 Actin regulation Actin turnover is exceptionally high during cell movement and invasion, where actin filament polymerization and depolymerization happens very fast. This process is termed actin treadmilling involving a large number of actin-binding proteins (ABPs) involved in actin nucleation (F-actin initiation) and actin disassembly (Fig. 2A) (see also: Alexandrova et al., 2020 and Biber et al., 2020 in this issue). The treadmilling process is characterized by dynamic transitions between monomeric G-actin and filamentous F-actin forms. Actin, in an ATP bound state plays a crucial role in F-actin formation by the assembly of actin-ATP at the barbed (+) end of the growing filament. Through ATP hydrolysis, filament disassembly happens at the pointed ( ) end with a release of ADP G-actin monomers. Besides hydrolysis, actin-depolymerizing factors including cofilins are involved in the formation of G-actin monomers. The depolymerizing events can also be controlled by factors such as tropomodulin (Fig. 2A). The treadmilling process also involves the actin-binding protein profilin, which catalyzes the exchange of actin-bound ADP to readily polymerizing ATP actin monomers. Barbed end elongation by actin/profilin complexes is also facilitated by the actin-nucleating proteins WASP (WAVE), VASP and formin (Svitkina, 2018). Most of the ABPs have many modular functions involved in actin-binding processes, protein-protein interactions, membrane binding through focal adhesions and cell signaling (see Table 1 for details). In particular, invasive cells through cell surface receptors, respond to ECM components by upregulating Rho GTPases that control several ABPs involved in actin filament nucleation and assembly. In the context of GBM, we queried through the Ivy Glioblastoma Atlas Project, mRNA sequencing data from the GBM infiltrative zone (https://glioblastoma.alleninstitute.org (Puchalski et al., 2018)). Moreover, we obtained RNA-sequencing data from invasive cells in our brainorganoid co-culture system (unpublished data). Collectively, these data indicate a significant (p < 0.05) upregulation of profilin, WASP, formin, the actin-crosslinking protein α-actinin found in filopodia (see below), the actin bundling protein fascin 1, the actin-severing protein gelsolin, the actinstabilizing protein tropomyosin and the capping protein tropomodulin. Moreover, the motor protein myosin X, associated with filopodia, was upregulated (Figs. 2 and 3). A previous transcriptomic study reported that the cofilin pathway, involved in the disassembly of actin filaments, is dysregulated in GBM. Cofilin as well as the LIM domain kinases 1 and 2

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Fig. 2 Overview of the actin treadmilling process and ABPs upregulated in GBM. (A) Dynamic transitions occur between monomeric G-actin and filamentous F-actin forms. F-actin formation occurs through the assembly of actin-ATP at the barbed (+) end of the growing filament. Through ATP hydrolysis, filament disassembly happens at the pointed end ( ) with a release of ADP G-actin monomers. A number of actin depolymerizing factors are also involved in this process. Profilin plays a major role in the formation of ATP-actin for F-actin elongation. The depolymerizing process is controlled by factors such as tropomodulin. Actin binding proteins (ABPs) that are upregulated in GBMs are outlined in red. (B) Gene expression analysis of ABPs upregulated in GBM compared to non-tumor brain tissue (from TCGA data).

Table 1 Actin polymerization and depolymerization factors: Their function, chromosomal location and putative signaling pathways. Gene name and chromosomal Pathways associated with cell Actin Polymerization localization migration References Factors Function

Coactosin

Profilin (3 isoforms)

Formins (15 formins have been identified based on their architectures)

Capping proteins regulate the rate of COTL1, Chr: 16, actin polymerization at the barbed 16q24.1 end. Coactosin counteracts capping protein activities, indicating that coactosin indirectly promotes actin polymerization

Coactosin functions downstream of Rac signaling to mediate lamellipodia and filopodia formation

Hou et al. (2013)

At low concentrations, profilin binds PFN1, Chr: 17, to G-actin in order to exchange ADP 17p13.2 for ATP. This promotes the monomeric addition to the barbed end of F-actin filaments. At high concentrations profilin inhibits actin polymerization

Induced through the activation of the Slit-Robo receptor that downstream influences the actin dynamics through several mechanisms

McConnell et al. (2016)

Actin polymerization from free actin FMN1, Chr:15, monomers is mediated by formins. 15q13.3 FMN2, Formins remain associated with the Chr:1, 1q43 growing barbed end

Formins are recognized as effectors K€ uhn and Geyer of Rho GTPase-mediated (2014) signaling

R€ ohrig et al. (1995)

Blockus and Chedotal (2016)

Actin depolymerizing factors Actin Depolymerizing Enhances actin turnover rate. Binds Factor (ADF), also to G-actin monomers known as Destrin (DSTN)

DSTN, Chr:20, 20p12.1

Destrin activation is dependent on Bernard (2007) serine 3 phosphorylation of LIM Toshima et al. (2001) kinase (LIMK), which is regulated by the Rho small GTPase Kobayashi et al. (2006) signaling pathway

CFL1, Chr:11, 11q13.1 CFL2, Chr: 14, 14q13.1

Cofilin 1&2

Cofilin 1 (non muscle), Cofilin 2 (muscular). Accelerates treadmilling of actin filaments by removing actin monomers from the pointed end

Twinfilin

Twinfilin-1 inhibits nucleotide TWF1, Chr: 12, exchange on actin monomers. 12q12 TWF2, Chr: 3, Prevents the assembly of the 3p21.2 monomer into filaments. May depolymerize both barbed and pointed ends of actin filaments. Pointed end depolymerization seems to be species specific

The subcellular localzation of twinfilin-1 appears to be regulated by Rac1 and Cdc42. These two small GTPases are central regulators of polarized growth and motility in non-muscle cells

Hilton et al. (2018)

Ca+-dependent multi-functional GSN, Chr: 9, 9q33.2 actin-modulating protein. Gelsolin can bind and end-block the barbed ends of actin filaments, preventing monomer exchange. It can promote filament polymerization and sever existing filaments. Gelsolin binding to F-actin is inhibited by tropomyosin

Associated with the Phosphatidyl 4,5-biphosphate (PIP2) pathway. Through G-protein activation PIP2 is activated leading to downstream signals that open Ca channels in the endoplasmic reticulum

Sun et al. (1999)

Gelsolin

Known to be involved in SlitYang et al. (1998) Robo signaling during Bamburg et al. (1999) development by G-protein RAC1 signaling

Palmgren et al. (2002) Vartiainen et al. (2003) Johnston et al. (2018)

Continued

Table 1 Actin polymerization and depolymerization factors: Their function, chromosomal location and putative signaling pathways.—cont’d Gene name and chromosomal Pathways associated with cell Actin Polymerization localization migration References Factors Function

Actin modulating factors Drebrin (two isoforms, A and E)

Binds to F-actin but not to G-actin. It DBN1, Chr: 9, changes the helical pitch of actin 5q35.3 filaments. Drebrin-decorated F-actin slows treadmilling and decreases the rate of depolymerization by inhibiting the binding of cofilin to F-actin

Wang et al. (2010) Drebrin has been identified as a direct transcriptional target of the Weigle et al. (2005) SOX11 protein which is upregulated in gliomas

Thymosin-β4 (Tβ4)

A sequestering protein that maintains TMSB4X, Chr:X, the pool of monomeric actin (GXp22.2 actin). Tβ4 prevents G-actin from joining a filament

TGFß and its downstream Pollard and Cooper activators Smad and TCF/LEF (2009) have been shown to activate Tβ4 Morita and Hayashi expression (2018)

Glia Maturation Factor-γ (GMFG)

Binds to the Arp2/3 complex with GMFG, Chr: 19, high affinity, but not to actin. The 19q13.2 GMF-Arp2/3 complex catalyzes actin debranching filament networks and inhibits actin Arp2/3 nucleation.

Rac and Cdc42 can modulate the Ikeda et al. (2006) function of GMFG. The phosphorylation of the N-terminal serine in GMFG can also modify the association of GMFG to F-actin

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Fig. 3 Comparison of actin distribution within lamellipodia and filopodia. In lamellipodia, actin bundles are crosslinked through the Arp2/3 complex. Expression of WASP activates the Arp2/3 complex that nucleates new actin fibers at a 70 degree angle from preexisting actin filaments. Rho GTPases function as activators of actin stress fiber formation. This effect is elicited through direct interaction with members of WASP protein family (Lane et al., 2014). Formin plays a major role in actin polymerization. In contrast to lamellipodia, the actin filaments in filopodia consist of parallel fascin 1 connected bundles. Myosin X, the motor protein, transports actin monomers to the tip of filopodia where VASP and Dia 2 cause actin polymerization and filopodia tip-elongation. α-actinin can also cross-link actin filaments and further plays a role in focal adhesion complexes by anchoring integrins. The IRSp53 protein couples Rho-GTPase signaling to the cytoskeleton. The proteins marked in red are upregulated in GBMs. Arp2/3 (actin-related proteins 2/3), WASP/WAVE (Wiskott-Aldrich-Syndrom protein/WASP-family verprolinhomologous protein), IRSp53 (Insulin receptor substrate protein of 53 kDa), VASP (vasodilator-stimulated phosphoprotein), Dia2 (Diaphanous Related Formin 2).

(LIMK1/2), which are important for cofilin inactivation via its phosphorylation, were strongly upregulated in GBM compared to normal brain tissue (Park et al., 2014). We verified expression of several of the actin binding proteins mentioned above in The Cancer Genome Atlas (TCGA) and highlight the ones that are upregulated in GBM compared to normal brain (Fig. 2B). It should however be emphasized that the TCGA data represent analyses of bulk tissue taken from the main tumor mass and not from infiltrative/invasive areas.

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7.2 Lamellipodia Lamellipodia are characterized as sheet-like, broad membrane protrusions that drive cell movement through attachment and pull of the cell body (Yamaguchi and Condeelis, 2007). An important function of lamellipodia is to regulate the direction of cell movement through pushing forces generated by the actin cytoskeleton (Bryce et al., 2005). The WASP complex is overexpressed in lamellipodia where it activates the Arp2/3 complex that nucleates new actin fibers at a 700 angle from pre-existing actin filaments (Fig. 3). Reorganization of the actin cytoskeleton and further tumor cell invasion has been shown to be affected by the guanine nucleotide exchange factor (GEF) switch-associated protein 70 (SWAP-70). A positive correlation was shown between SWAP-70 and CD44 expression, supporting the idea that SWAP-70-mediated promotion of GBM cell migration can be controlled by CD44 expression. GBM patients with high SWAP-70 were shown to have a poor prognosis and SWAP-70 knockdown leads to a reduced GBM migration and invasion in vitro. These processes can be rescued by CD44 overexpression in vitro (Hilpel€a et al., 2003; Shi et al., 2019). Also the transmembrane receptor protein and tumor-associated antigen CD99 has been found to be involved in GBM cell invasion by affecting membrane protrusions. From the two existing alternative splice variants of CD99, isoform 1 appeared to inhibit invasion, while isoform 2 promoted it (Zucchini et al., 2014). Only isoform 1 was reported to be expressed in astrocytomas of all grades (I-IV), where the expression level was higher compared to that in normal brain tissue (Cardoso et al., 2019). Interestingly, based on large scale gene expression analysis of TCGA data we observed a higher expression in GBM (grade IV astrocytoma) compared to lower grade astrocytomas (http://gliovis.bioinfo.cnio.es/ (Bowman et al., 2017)).

7.3 Filopodia Filopodia are thin, long membrane protrusions that extend beyond the lamellipodium. Like lamellipodia these structures are present at the leading edge of moving cells and play a role in directional cell movement. Filopodia express growth factor and integrin receptors, which detect ligands in brain tissue, likely contributing to affecting a path for invasion. Chemoattractants detected by these receptors cause filopodia to generate tension upon the substrate which results in cellular movement (Mattila and Lappalainen, 2008). The distribution of actin filaments within filopodia is different from that seen in lamellipodia (Fig. 3). Actin filaments in filopodia are characterized by

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parallel fascin 1 connected bundles. Fascin is thus a bundling protein, mainly found in filopodia and invadopodia. High fascin-1 expression in GBM patients has been reported to positively correlate with migration, invasion, tumor grade and prognosis (Gunal et al., 2008; Hwang et al., 2008). Stable knockdown of fascin-1 in GBM cells in vitro led to a loss of filopodia and cell polarity, which reduced their migratory response to chemoattractants. Moreover the cells were more prone to killing by cytotoxic lymphocytes, presumably due to the loss of defensive microspikes and microvilli at the surface (Hoa et al., 2015). This is in line with recent work highlighting the importance of the actin cytoskeleton in cancer cells to mount the defense against immune cells at the immunological synapse (Al Absi et al., 2018; Biolato et al., 2020). Myosin X, which is upregulated in GBM cells (Mischel et al., 2003) is a motor protein with the main function to transport actin monomers to the tip of filopodia where VASP and Dia 2 (which belong to the formin family of actin polymerization proteins) cause actin polymerization and filopodia tipelongation. Another component that we found upregulated in invasive GBM cells at the mRNA expression level is the actin cross-linking protein α-actinin. Besides cross-linking actin filaments, α-actinin has a function in focal adhesion complexes by anchoring integrins (Fig. 3). Focal adhesions (also cell-matrix adhesions) are subcellular structures that connect a cell to the ECM and relay mechanical and biochemical signaling events. The transmembrane receptors integrins provide the mechanical link between intracellular actin bundles and the extracellular substrate. The Rho GTPase cell division control protein 42 homolog (Cdc42) has been reported to be a key regulator of actin dynamics where its function is mainly to connect multiple signals involved in actin polymerization through the WASP effector proteins. In vitro knockdown and overexpression experiments support a functional role of Cdc42 in GBM cell invasion (Okura et al., 2016). Activated Cdc42 interacts with the scaffold multidomain protein IQ motif containing Ras GTPase-activating-like protein 1 (IQGAP1) and phosphorylated focal adhesion kinase (FAK), which leads to a more aggressive and invasive phenotype. Thus, IQGAP1 may stimulate actin cytoskeleton reorganization associated with GBM cell migration and invasion (Okura et al., 2016). In GBMs IQGAP1 has been shown to be enriched in invadopodia (McDonald et al., 2007; Rotoli et al., 2017). In filopodia, the IRSp53 protein functions under the control of Cdc42 to couple Rho-GTPase signaling to the cytoskeleton (Disanza et al., 2006; Krugmann et al., 2001).

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7.4 Invadopodia There are many similarities between filopodia and invadopodia, yet filopodia seem to be more transient whereas invadopodia are thought to be more stable and have a function in ECM substrate degradation ( Jacquemet et al., 2015). As mentioned above, filopodia are associated with, among others, proteins such as fascin, myosin X, and α-actinin whereas invadopodia contain cortactin-mediated expression of MMP-2 and MMP-9. Cortactin is a ubiquitously expressed monomeric cytoplasmic protein involved in actin cytoskeletal polymerization and reorganization. The invadopodium is also associated with MT1-MMP shuttling to the leading edge. Therefore these structures may be directly associated with cancer cell invasion. In this context it has been shown that blocking cortactin or MMPs significantly impairs cancer cell migration and invasion (Clark et al., 2007). Several actin cytoskeleton regulating components are involved in the formation of invadopodia and consequently affect GBM cell invasion. One of them is the scaffolding protein spinophilin which is highly expressed in the brain and regulates actin cytoskeletal dynamics partly through interacting with F-actin as well as α-actinin (Baucum 2nd et al., 2010). In mouse models spinophilin was found to be involved in the negative regulation of perivascular invasion of GBM cells partly through regulating Rac1 activity and invadopodia disassembly (Cheerathodi et al., 2016). Perivascular GBM invasion can also be influenced by the ubiquitously expressed CRN2, an actin-binding protein of the coronin protein family. It was shown that CRN2 binds tissue inhibitor of metalloproteinases 4 (TIMP4) as well as MT1-MMP thereby promoting perivascular GBM invasion (Solga et al., 2019). Furthermore CRN2 overexpression increased invadopodia-like protrusions whereas loss of CRN2 showed the opposite effect (Ziemann et al., 2013). Similar to lamellipodia, invadopodia formation is also affected by Rho-GTPases. In GBM tissue RhoG-GTPase has been found to be overexpressed and its inhibition appears to block Rac1 activation, lamellipodia and invadopodia formation and consequently GBM cell invasion (Kwiatkowska et al., 2012). Furthermore calmodulin, a calcium-binding messenger protein, is upregulated in high-grade gliomas, where its expression positively correlates with invadopodia formation and invasion. In experimental systems it was shown that calmodulin silencing leads to a reduction in invadopodia and invasion (Li et al., 2018).

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8. Can glioma cell invasion and the actin cytoskeleton be targeted? To target the diffuse infiltration of gliomas including GBM poses significant challenges. A major challenge is that the invasive cells are situated within CNS compartments where the BBB represents a major pharmacokinetic barrier for drug delivery. Another challenge is that, usually, GBM cells have already invaded the normal brain far beyond the tumor core when clinical disease manifestations occur. The notion of a pure anti-invasive strategy may therefore be futile. Nevertheless, reducing invasion would ideally complement current strategies of targeting the tumor center with surgery and radio-chemotherapy. Benefits of such combination treatments may lead to the formation of a more compact tumor that is potentially more drugpenetrable with a switch to a proliferative state rendering tumor cells more sensitive to anti-proliferative treatment (Osuka and Van Meir, 2017). Unfortunately to date no successful treatment has been developed that targets invasive glioma cells. Below we will briefly address available targets to specifically interfere with glioma cell invasion and their limitations. New insights into cellular networks and tumor microtubes may also provide new opportunities. As mentioned above, MMPs are heavily involved in GBM cell invasion and were long proposed as promising drug targets. The use of broadspectrum MMP inhibitors like marimastat did not show beneficial effects throughout various cancer types and also a Phase II study of marimastat on GBM patients failed to improve patient survival (Levin et al., 2006). These broad-spectrum MMP inhibitors result in musculoskeletal pain as major adverse effect and as their name indicates inhibit various MMPs, some of which may also have anti-tumoural functions (Mentlein et al., 2012). To overcome these aspects a more specific inhibitor (prinomastat) was developed to specifically target MMP-2 and MMP-9. Unfortunately, also this drug failed to increase survival of GBM patients in a phase II study in combination with temozolomide (Levin et al., 2002). Similarly, integrins have been extensively studied in the context of GBM as they can influence invasion through their function in cell adhesion. The latest efforts to target integrins in GBM were based on the αvβ5 integrin antagonist cilengitide. Unfortunately the promising effect on GBM patient

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survival shown in a phase I/II study (Nabors et al., 2012) was not confirmed in a phase III trial (Stupp et al., 2014). Inhibition of TGF-ß receptor 1 has long been considered a valuable target for GBM, based on its pleiotropic regulation of various cellular processes, including differentiation, proliferation, invasion and apoptosis. Several therapeutic approaches have been unsuccessful so far, including the late galunisertib, a small molecule inhibitor, which did not show beneficial effects in a phase I/II trial (NCT01220271) (Wick et al., 2020). Currently a phase I trial is ongoing using the monoclonal antibody KB004 against EphA3 to target GBM cell invasion (NCT03374943) (De Gooijer et al., 2018). EGFR and its mutant variant EGFRvIII are heavily involved in the promotion of GBM invasion as well as angiogenesis, yet several attempts to target EGFR by monoclonal antibodies have failed in the clinic (Keller and Schmidt, 2017). Therefore improved approaches are being implemented. This involves, e.g., Sym-004, a combination of futuximab and modotuximab that target nonoverlapping epitopes on EGFR. In preclinical in vivo studies it has been shown that Sym-004, via the internalization and degradation of EGFR WT and EGFRvIII, is able to inhibit tumor growth. Compared to cetuximab, Sym-004 seems to be more effective, suggesting that this combination might be able to overcome cetuximab resistance in GBM (Keir et al., 2018). Sym 004 is currently tested in an ongoing phase II trial for recurrent GBM patients (NCT02540161). Based on the difficulty of antibodies to enter the brain, it is however questionable whether antibody-mediated therapy can be effective against invasive GBM cells. Ongoing and future trials also aim at metabolic targets. Attempts have been made to inhibit glutamate release and/or target glutamate receptors, which have recently also been shown to be involved in neuro-gliomal synapse regulation ( Jung et al., 2019; Lefranc et al., 2018). However, since glutamate is the main excitatory neurotransmitter in the brain, the therapeutic impact on normal brain function needs to be carefully addressed. This also holds true for ion channels such as K+ and Cl channels as well as for the ion transporter NKCC, for which an FDA-approved inhibitor (Bumetanide) is available with potential for GBM therapy (Schiapparelli et al., 2017). In addition to K+ and Cl channels, targeting calcium channels may be considered as a treatment option based on the important role of calcium signaling in GBM invasion. Mibefradil, a selective T-type calcium channel blocker that was developed but withdrawn for hypertension, has been tested against GBM. A Phase I study in recurrent GBM reported that the combination with TMZ was well tolerated and showed response in some patients, highlighting the need for further studies (Holdhoff et al., 2017).

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In general cancer cell invasion can be successfully impaired by targeting cytoskeletal elements directed towards actin (latrunculin A, cytochalasin D and jasplakinolide) and microtubules (vincristine, taxol and taxotere). With regard to GBM, a tubulin-binding peptide was found to be specifically taken up by GBM cell lines causing a reduction in GBM cell proliferation and motility in vitro, accompanied by a disruption of the microtubule network (Berges et al., 2012). This drug appears to be still in pre-clinical development (Karim et al., 2018). Another therapeutic modality that interferes with microtubules is depatuxizumab mafodotin (also known as Depatux-M or ABT-414), an antibody-drug conjugate linking the antimicrotubule agent monomethyl auristatin F (MMAF) to an antibody directed against EGFR. Depatuxizumab binds to EGFR and as soon as the complex is internalized MMAF is released, resulting in apoptosis. Unfortunately a Phase III trial in newly diagnosed EGFR-amplified GBM patients (INTELLANCE-1), did not meet primary endpoint of overall survival at the interim analysis and was halted in May 2019. Nevertheless results of a randomized Phase II study in recurrent GBM patients (INTELLANCE-2/EORTC 1410) suggest a potential clinical benefit of Depatux-M in combination with temozolomide (Van Den Bent et al., 2020). In the context of microtubule targeting, tumor treating field (TTF) therapy has emerged as a promising therapeutic alternative. TTF therapy is based on alternating electric fields of low intensity and intermediate frequency (200 kHz) thought to impact the mitotic spindle during cytokinesis (Kirson et al., 2007). Multiple in vitro studies have shown that TTFs specifically interfere with cell division as well as cell migration and invasion (Giladi et al., 2015; Kim et al., 2016; Silginer et al., 2017). Of note, a Phase III randomized trial of TTF therapy given to newly diagnosed GBM patients showed an improvement of progression free survival and median overall survival by 3.1 months and 4.9 months respectively (Stupp et al., 2015). Although the exact mechanism on how TTF work in a therapeutic context is still elusive, it is clear that numerous charged biological molecules, i.e., proteins that have both positive and negative charges, will be spatially oriented and thus affected by the alternating TTF field. This implies that protein-protein interactions that depend on a spatial alignment within the cell are potentially impaired, supporting the notion that TTFs may affect numerous cellular processes (Kissling and Di Santo, 2020). Yet, an unanswered question in this context is how the TTF therapy creates a therapeutic window, i.e., why are cancer cells affected and no other cells within the brain, in particular electrically active neurons? The TTF therapy has thus

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created a considerable debate within the neuro-oncology community based on its extensive costs, major practical implications and an unclear mode of action (Cloughesy and Lassman, 2017; Taillibert et al., 2015).

9. Conclusions and future prospects Extensive glioma cell invasion into normal brain structures represents a major clinical challenge since these cells evade local treatments such as surgery, chemo- or radiotherapy. A main problem is that invasion has already occurred before diagnosis, which to a large extent explains the high recurrence rates seen in GBM patients. Even though considerable advances in glioma research have been made over the past decades, there is still no possibility to effectively interfere with glioma cell invasion. This highlights the need to get a better mechanistic insight into the invasive process. As mentioned in this review, the cytoskeleton and its upstream and downstream regulators play an essential role in cell motility and invasion. In particular microtubules are an attractive target, because of a potential dual effect on invasion and proliferation. Nevertheless the challenge of targeting the cytoskeleton in GBM is considerable, based on the fact that these structures are also vital for normal cells and brain function, therefore the focus should be directed towards identifying glioma-specific deregulations to avoid adverse effects. In this context, the development of combinatorial treatment strategies may be of high value.

Acknowledgments This work was supported by the CANBIO DTU (PRIDE15/10675146) of the National Research Fund (FNR) of Luxembourg, by the Fondation Cancer of Luxembourg (INVGBM Project), by the Norwegian Cancer Society, the Norwegian Research Council (ES563961), Haukeland University Hospital, Helse-Vest and the University of Bergen.

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CHAPTER THREE

Actin dynamics during tumor cell dissemination Chandrani Mondal, Julie S. Di Martino, and Jose Javier Bravo-Cordero* Department of Medicine, Division of Hematology and Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States *Corresponding author: e-mail address: [email protected]

Contents 1. Introduction 2. Tumor dissemination and metastasis 2.1 Epithelial-to-mesenchymal transition (EMT) 2.2 Intravasation 2.3 Extravasation 3. Cancer cell migration 3.1 Single-cell migration and the cell motility cycle 3.2 Multicellular movement 4. Actin structures in cancer cell migration 4.1 Lamellipodia 4.2 Filopodia 4.3 Invadopodia 4.4 Focal adhesions 5. The cell cycle and cancer cell invasion 6. Imaging advances and future directions in studying tumor cell invasion 6.1 Single-molecule superresolution imaging 6.2 FRET-based imaging 6.3 Intravital imaging and the tumor microenvironment 7. Conclusion Acknowledgments References

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Abstract The actin cytoskeleton is a dynamic network that regulates cellular behavior from development to disease. By rearranging the actin cytoskeleton, cells are capable of migrating and invading during developmental processes; however, many of these cellular properties are hijacked by cancer cells to escape primary tumors and disseminate to distant organs in the body. In this review article, we highlight recent work describing how cancer cells regulate the actin cytoskeleton to achieve efficient invasion and

International Review of Cell and Molecular Biology, Volume 360 ISSN 1937-6448 https://doi.org/10.1016/bs.ircmb.2020.09.004

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metastatic colonization. We also review new imaging technologies that are capable of revealing the complex architecture and regulation of the actin cytoskeleton during motility and invasion of tumor cells.

1. Introduction In metazoans, cell motility is required for key developmental processes, including gastrulation (Keller, 2005), neurulation (Theveneau and Mayor, 2012), the maintenance of tissue integrity and wound repair (Friedl and Gilmour, 2009), and immune cell trafficking (Friedl and Weigelin, 2008). Cell invasion is also conserved during embryonic development and homeostasis, as well as immune cell function (Medwig and Matus, 2017; Stuelten et al., 2018). The ability of a cell to move and invade is primarily dependent on the reorganization of the actin cytoskeleton, which requires spatiotemporal coordination of signaling pathways with actin regulatory and binding proteins (Blanchoin et al., 2014; Lauffenburger and Horwitz, 1996; Pollard and Cooper, 2009). Multiple human diseases and pathologies, including cancer cell invasion and metastasis, are associated with aberrant and deregulated cell motility and invasion (Hanahan and Weinberg, 2011). The acquisition of motile and invasive phenotypes is a characteristic of aggressive tumors. Migration and invasion are needed for local invasion, intravasation into the vasculature, and extravasation at distant sites (Chaffer and Weinberg, 2011). The formation of actin-rich protrusions is a key feature of cancer cells that allows them to disseminate and colonize other organs (Bravo-Cordero et al., 2012). Thus, tumor cell motility and invasion are key rate-limiting steps during cancer progression and metastasis formation. Tumor cells are capable of activating different molecular mechanisms to remodel the actin cytoskeleton in order to leave primary tumors and travel to other organs (Bravo-Cordero et al., 2013a; Yamaguchi and Condeelis, 2007). From pseudopodia to filopodia and invadopodia protrusions, cancer cells display a repertoire of subcellular actin-rich structures that facilitate overcoming different barriers during tumor dissemination. In this review, we address key modes of cancer cell motility and underlying signaling pathways, effects of the tumor microenvironment on cancer invasion, and recent technological advances that have been developed to visualize the invasion-metastasis cascade (Fig. 1).

Fig. 1 Invasion-metastasis cascade. Primary tumors have a complex tumor microenvironment (e.g., immune cells, cancer-associated fibroblasts, the extracellular matrix) that plays a dynamic role in affecting tumor cell dissemination and how tumor cells intravasate into the vasculature (1), disseminate through the circulatory system (2), extravasate out of the vasculature (3), and colonize distant organs (4).

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2. Tumor dissemination and metastasis In most epithelial cancers, tumor cells must acquire an invasive phenotype in order to escape the primary tumor and have the potential to invade locally, intravasate into the vasculature, survive circulation, extravasate into distant organs and colonize (Chaffer and Weinberg, 2011). The tumor microenvironment (TME) is a complex milieu of tumor cells, a dynamic extracellular matrix (ECM), and many stromal cells, including cancerassociated fibroblasts and immune cells (Fig. 1); in an orchestrated effort, components of the TME may act in concert to drive tumor cell invasion and metastasis formation (Clark and Vignjevic, 2015; Di Martino et al., 2019).

2.1 Epithelial-to-mesenchymal transition (EMT) One mechanism by which epithelial tumor cells can become more invasive is by co-opting an epithelial-to-mesenchymal transition (EMT) developmental program (Dongre and Weinberg, 2019). An EMT induction results in the expression of core EMT transcription factors (e.g., Twist, Slug, Snail, Zeb1, Zeb2) which upregulate genes that promote a mesenchymal-like cell migratory phenotype. An EMT or partial-EMT can be induced through TGF-β, Notch, and Wnt signaling pathways, and is often dependent on the secretion of specific chemokines and cytokines by the TME (Dongre and Weinberg, 2019). Signaling through TGF-β1, which can be produced by tumor cells, cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), myeloid-derived suppressor cells, and regulatory T cells (Tregs), as well as STAT3 signaling downstream of Il-6 and Il-23 secreted by leukocytes (Smith and Kang, 2013) has been shown to induce EMT. Tumor-associated macrophages, which are often found at the invasive front of tumors (Condeelis and Pollard, 2006) secrete pro-inflammatory cytokines such as TNF-α, which regulate the NF-κB pathway (Chen et al., 2018b). Multiple studies have demonstrated that EMT can be activated by TNF-α; TNF-α secreted by TAMs activated NF-κB signaling and stabilized Snail, which resulted in increased cancer cell invasion in vitro (Wu et al., 2009). TNF-α has also been demonstrated to upregulate Twist1 through NF-κB signaling in breast cancer cells (Li et al., 2012), induce EMT in renal cell carcinoma in a GSK3β-dependent manner (Ho et al., 2012), and

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stabilize Slug through NF-κB signaling in head and neck squamous cell carcinoma (HNSCC) (Liu et al., 2018a). Induction of an EMT program can upregulate matrix metalloproteinases that are capable of degrading the basement membrane (Olmeda et al., 2007), which consists mainly of laminin and type IV collagen (Bosman et al., 1985). The Snail1 transcription factor is capable of inducing an invasion program dependent on MMPs; Snail1 induction increases both MT1-MMP and MT2-MMP expression in breast carcinoma cells (Ota et al., 2009). In addition, a subset of invasive cancer cells can form invadopodia (see Section 4.3) structures, which are capable of recruiting MT1-MMP, MMPs, and ADAMs, which allow them to degrade the extracellular matrix (Eddy et al., 2017). EMT has been demonstrated to drive invadopodia formation in a Twist1-dependent manner (Eckert et al., 2011). Recently, the concept of an EMT has been challenged in that it may not be necessary for metastasis formation (Aiello and Kang, 2019). Using an EMT lineage tracing system in the PyMT model of spontaneous breast cancer, lung metastases were shown to form when EMT was inhibited (Fischer et al., 2015), and the tracing system was validated using single-cell RNAseq (Lourenco et al., 2020). In a study on invasive ductal carcinomas, expression of E-cadherin promoted tumor cell survival and the establishment of metastases (Padmanaban et al., 2019); loss of E-cadherin is one of the hallmarks of an epithelial-to-mesenchymal transition (Dongre and Weinberg, 2019). In the KPC model of pancreatic ductal adenocarcinoma, loss of Snail or Twist did not have a significant reduction in metastases formation (Zheng et al., 2015), yet Zeb1 depletion in the same background did (Krebs et al., 2017), suggesting the requirement for EMT may vary based on the context (Aiello and Kang, 2019). Additional factors in the tumor microenvironment can directly impact tumor cell dissemination; an important aspect of tumor biology is the mechanical properties of the surrounding ECM (Mohammadi and Sahai, 2018). It has been shown that in vivo, tumor cells move along highly aligned collagen fibers (Condeelis and Segall, 2003) to facilitate local invasion (Provenzano et al., 2006). Also, increased matrix stiffness can induce EMT through Twist1 activation (Wei et al., 2015).

2.2 Intravasation After cancer cells invade locally, they can intravasate into the vasculature and travel through the hematogenous system or more rarely, through the

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lymphatic vasculature (Chiang et al., 2016; Olmeda et al., 2017) (Fig. 1). Capturing intravasation events in vivo is rare and challenging (Wyckoff et al., 2007); work using intravital microscopy combined with a mammary imaging window and photoconvertible Dendra2 to mark and track breast tumor cells demonstrated that vascularized regions had a greater number of photoconverted tumor cells lining up around the blood vessels, presumably intravasating into the vasculature, as characterized by more lung metastases (Kedrin et al., 2008). A proxy for intravasation events in vivo is to quantify circulating tumor cells in the blood. This type of analysis in a breast cancer model demonstrated that ERBB2 has a greater effect on intravasation than ERBB1; both are highly altered and aberrantly expressed receptors in aggressive breast cancers (Kedrin et al., 2009). Recent work in a PyMT model of breast cancer showed that TIE2hi macrophages were able to promote intravasation of tumor cells through the secretion of VEGFA, which resulted in transient permeability of blood vessels (Harney et al., 2015). The intravasation events characterized in this study were restricted to tripartite structures known as TMEMs (tumor microenvironment of metastasis), where a tumor cell, macrophage, and an endothelial cell are in direct contact with each other (Harney et al., 2015). Additional studies on intravasation have used in vitro assays and microfluidic devices to characterize signaling pathways promoting intravasation. One mechanism by which tumor cells have access to the vasculature is through endothelial barrier impairment during tumor progression, which was demonstrated through macrophage-secreted TNF-α, which increased vasculature permeability and the rate of tumor cell intravasation (Zervantonakis et al., 2012). Invadopodia formation has also been linked with intravasation events; macrophages in direct heterotypic contact with breast tumor cells are able to induce global activation of RhoA signaling in tumor cells, resulting in tumor cells forming an increased number of invadopodia which are necessary for transendothelial migration (RohJohnson et al., 2014). A Notch1/MenaINV signaling program has been demonstrated to regulate macrophage-induced invadopodium formation and transendothelial migration of breast cancer cells (Pignatelli et al., 2016).

2.3 Extravasation Extravasation out of the vasculature has been visualized using ex ovo chicken embryos (Leong et al., 2014), zebrafish embryos (Berens et al., 2016), and tail vein injections in the mouse (Mohanty and Xu, 2010). One of the early

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studies in optically transparent zebrafish embryos showed that overexpression of Twist or VEGFA in highly invasive breast tumor cells increased the percentage of cells that were able to extravasate (Stoletov et al., 2010), and Twist expression induced a change in the mode of extravasation to β1-integrin independent (Stoletov et al., 2010). To examine the effects of inflamed neutrophils on tumor cell dissemination, LPS-stimulated neutrophils were co-injected with melanoma cells in zebrafish, resulting in increased extravasation (Chen et al., 2018a). Using an ex ovo chicken embryo model, human epidermoid cancer cells, as well as a series of other cancer cell lines were demonstrated to extravasate at endothelial junctions. Extravasation was dependent on invadopodia formation, as determined by localization of cortactin, Tks4, and Tks5, which are invadopodia components, using intravital imaging (Leong et al., 2014). Tropism, or the homing of cancer cells to specific organs is often dependent on the tumor of origin; for example, the vast majority of patients with metastatic breast or prostate cancer have metastases in the bone (Weilbaecher et al., 2011). Cells in the bone secrete factors that attract cancer cells to the bone marrow, including RANKL, CXCL12, OPN, and BMPs ( Jones et al., 2006; Obenauf and Massagu
e, 2015). Many breast cancer cells express EREG, MMP1, MMP2 and COX2 (Gupta et al., 2007), which allow them to selectively metastasize to the lung, as well as ANGPTL4 and SPARC (Padua et al., 2008; Tichet et al., 2015). Interestingly, COX2 and MMP1 signaling also allows breast cancer cells to overcome the blood brain barrier and form brain metastases (Wu et al., 2015), suggesting that some factors for tropism are organ-specific, and others are not as restrictive.

3. Cancer cell migration 3.1 Single-cell migration and the cell motility cycle 3.1.1 Mesenchymal motility Within tumors, cancer cells can move as single, distinct entities or collectively as multicellular sheets or multicellular streams (Di Martino et al., 2019; Friedl and Wolf, 2010; Lintz et al., 2017; Roussos et al., 2011b). The tumor microenvironment is a key component that regulates the different modes of tumor cell migration; for example, the ECM stiffness, density, and orientation that a tumor cell encounters is one parameter that determines whether cells move in a mesenchymal or amoeboid manner (Friedl and Wolf, 2010; Talkenberger et al., 2017). When cancer cells are in contact with stiff substrata, they are capable of adopting an elongated, mesenchymal-based mode of motility.

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Fig. 2 Cell motility cycle. Upon integrating signaling cues, a resting cell can form lamellipodia, or protrusive actin-rich structures, and adhere to the substrata with nascent adhesions. In order for the cell body to translocate, the cell experiences contractile tension and rear detachment through de-adhesion.

The extension of a leading edge protrusion (lamellipodium in 2D or pseudopodia in 3D) is the first step of the cell motility cycle (Fig. 2). In vitro, flat, veillike lamellipodia form as a result of membrane deformation due to force generated by dynamic actin polymerization (Bravo-Cordero et al., 2013a; Yamaguchi and Condeelis, 2007). As the lamellipodia extends forward and the actin network moves backward through retrograde actin flow, the cell body is able to make new attachments to the substratum through coupling actin stress fibers to adhesion receptors (e.g., integrins). The cell body initially makes transient focal contacts with the substratum that can mature into focal adhesions, which are active signaling platforms that regulate mechanotransduction (Huttenlocher, 1995; Huttenlocher and Horwitz, 2011). As the cell is adhering in the front, it begins to disassemble focal adhesions in the rear and detach from the substratum. The cell body is able to translocate through the retraction force generated in the rear in a myosin II-dependent manner, which is regulated by Rho GTPase signaling (Friedl and Alexander, 2011). In vivo, cancer cells can polarize (resulting in cellular asymmetry) and extend pseudopodial protrusions (Fig. 3) (Bravo-Cordero et al., 2012). Cancer cell polarization can be achieved through sensing external cues or gradients, including chemotactic (Roussos et al., 2011b), haptotactic (King et al., 2016), durotactic (DuChez et al., 2019), and galvanotactic (Huang et al., 2016), as well as mechanical and topographical constraints (Northcott et al., 2018). In response to these stimuli, polarized cancer cells are capable of activating signaling pathways that primarily converge on the Rho GTPase family to initiate actin polymerization and generate protrusions that extend outward from the leading edge, or front of the cell to facilitate cell movement (Bravo-Cordero et al., 2013b; Haga and Ridley, 2016; Lawson and Ridley, 2018; Yamaguchi and Condeelis, 2007). In order for cancer cells to invade, they must remodel the extracellular matrix through expression of MMPs and other proteases (either cancer

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Fig. 3 Tumor cell in a 3D context. In the tumor microenvironment, cancer cells encounter a complex extracellular matrix, and are capable of forming different types of protrusive structures, including pseudopodia.

cell-intrinsic, or through stromal cells (Egeblad and Werb, 2002)) and in some cases, may recruit MT1-MMP, MMP2 and MMP9 through the formation of invadopodia structures, which are specialized F-actin-rich protrusions that degrade the extracellular matrix (Clark and Weaver, 2008; Clark et al., 2007; Eddy et al., 2017; Murphy and Courtneidge, 2011) (see Section 4.3). Recent work has shown that MMP activity can be regulated by cell density through an IL-6,8 paracrine loop ( Jayatilaka et al., 2018), suggesting that MMP activity can be regulated locally through the homotypic interactions between tumor cells. 3.1.2 Amoeboid motility Another mode of single-cell movement is amoeboid motility, which is characterized by cells with a rounded morphology, and can present in multiple forms, including bleb-based (Petrie and Yamada, 2012). Bleb-based motility is movement that requires high levels of cell contractility, and resembles the motility of the single-cell organism Dictyostelium discoideum (Pinner and Sahai, 2008). Unlike mesenchymal-like migration, blebbing motility is defined as protease-independent, and relies on the cell’s capability to deform rapidly through dynamic alterations of the cortical actin cytoskeleton in the

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rear in a Rho-ROCK dependent manner (Sahai and Marshall, 2003). The high levels of contractility necessary for the cell to propel forward is dependent on the phosphorylation of myosin II-light chain (MLC2) via ROCK kinases, which are effectors of the Rho GTPases (Wilkinson et al., 2005). In addition, amoeboid motility usually occurs in areas with soft matrix, and amoeboid cells often have a decrease in integrin signaling and form weak adhesions (Bra´bek et al., 2010; Talkenberger et al., 2017). Interestingly, the use of protease inhibitors in HT-1080 fibrosarcoma cells and MDA-MB-231 breast carcinoma cells resulted in a transition from mesenchymal-to-amoeboid motility, rather than an abrogation of cellular invasion, demonstrating the plasticity of cancer cell movement (Wolf et al., 2003). Cancer cells are able to interconvert between different motility patterns (Wolf et al., 2003). In triple-negative breast cancer cells, loss of the NEDD9 scaffolding protein resulted in more bleb-driven motility in vitro, including a loss of pFAK/pPaxillin mature focal adhesions, an increase in pMLC2, and a concurrent decrease in active Rac1 and increase in active RhoA ( Jones et al., 2017). Similarly, in melanoma cells, bleb-based movement was driven through an active Rac GAP, ARHGAP22, which inactivated Rac signaling; conversely, mesenchymal-like motility was regulated by the NEDD9/ DOCK3 complex which activated Rac signaling (Sanz-Moreno et al., 2008). In mammary adenocarcinoma tumors in vivo, single-cell motility is characterized by the rapid movement of cancer cells that display an amoeboid morphology with the presence of F-actin-rich protrusions named pseudopodia at the leading front (Bravo-Cordero et al., 2012; Condeelis and Segall, 2003). These pseudopodia protrusions are characteristic of fast moving amoeboid cancer cells and are involved in chemotaxis toward blood vessels prior to intravasation (Condeelis and Segall, 2003).

3.2 Multicellular movement In a 3D environment or in vivo, cancer cells encounter a complex microenvironment and are highly plastic in their motile behavior. Individual cancer cells are capable of moving in multicellular streams (Friedl and Alexander, 2011; Roussos et al., 2011a,b). In human orthotopic breast xenografts, cancer cells have been visualized to move in a multicellular stream (where a minimum of two cells follow each other in a directed manner) (Patsialou et al., 2013), or in a stream with host cells, such as tumor-associated macrophages (TAMs) (Patsialou et al., 2013). Co-migration of TAMs and breast cancer cells has also been demonstrated in a rat mammary adenocarcinoma

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model, and the transgenic PyMT spontaneous model of breast cancer. The interaction between these two cell types is dependent on an EGF/CSF-1 paracrine signaling loop (Wyckoff et al., 2004). MenaINV, a splice isoform of Mena, an actin regulatory protein, is spontaneously upregulated in invasive carcinoma cells; expression of MenaINV in a rat mammary adenocarcinoma model promotes multicellular streaming between tumor cells, as well as co-migration between tumor cells and TAMs in an EGF/CSF-1 dependent manner in vivo (Roussos et al., 2011a). Intravital imaging of B16 F2 tumors shows tumor cells following each other on the same tracks, in a multicellular stream. The streaming cells have increased SRF reporter activity (when compared to non-motile cells) (Manning et al., 2015); SRF is a master regulator of the actin cytoskeleton, and regulates the transcription of 200 + actin-related genes (Olson and Nordheim, 2010). Multicellular streaming has also been observed in a human orthotopic glioblastoma xenograft model, where tumor cells at the “invasive” margin between the tumor and brain parenchyma are capable of moving in succession, and overall, migrate with a lower velocity and increased persistence when compared to other motile tumor cells (Alieva et al., 2019). Intravital imaging of melanoma xenografts demonstrates that melanoma cells are capable of both single cell motility (Fig. 4A), as well as streaming, multicellular motility (Fig. 4B). 3.2.1 Collective cancer cell migration Collective cancer cell invasion is another mode of tumor cell movement found in many cancers, including breast (Cheung et al., 2013), squamous cell carcinoma (Hidalgo-Carcedo et al., 2011), liver cancer (Han et al., 2019), melanoma (Hegerfeldt et al., 2002), colorectal cancer (Chung et al., 2016), and lung cancer (Kuriyama et al., 2016). Collective cancer cell migration is sheet or strand-like multicellular movement that requires cancer cells to maintain cell:cell cohesion mechanisms. In some instances, cells at the front of the strand polarize and become “leader” cells, followed by a stream of “follower” cells (Friedl et al., 2012). Invasive fibrosarcoma and breast cancer cells have been demonstrated to move collectively upon large-scale MT1-MMP mediated proteolysis in a spheroid invasion model; tracks were initially created by “leader” cells, followed by larger tracks created by multicellular invasion strands (Wolf et al., 2007). Using 3D organoid and in vivo model systems of luminal breast cancer, multicellular invasion strands were characterized by “leader” cells that were K14+, and preferentially turned on basal epithelial markers (Cheung et al., 2013). Cancer cells are highly plastic; breast cancer cells in vivo have been demonstrated

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Fig. 4 Intravital imaging of a melanoma xenograft. Two-photon imaging of tumor cells (in green) and second harmonic generation of fibrillar collagen (in blue). (A) Yellow arrow points to a single cell moving over time (indicated above each panel). (B) Example of streaming motility; yellow arrows point to cancer cells following each other, and red arrow points to another cell type in the tumor microenvironment moving within the multicellular stream. Scale Bar: 10 μm. SK-Mel-147 GFP-labeled melanoma cells were injected subcutaneously in 6-week old female nude mice and tumors were allowed to grow up to 1 cm3. Intravital imaging of the primary tumor was performed as in Patsialou et al. (2013) and collagen fibers were visualized by second harmonic generation. Images were acquired every 2 min for 30 min, with a step-size of 5 μm.

to switch from collective to single cell migration through increased local TGF-β signaling (Giampieri et al., 2009). The mechanisms underlying collective cancer cell migration are not well understood; there is some evidence that the Wnt/PCP (planar cell polarity) non-canonical Wnt pathway can be co-opted from developmental processes to promote collective cell migration in gastric, ovarian and melanoma cancers (VanderVorst et al., 2019). The DDR1 receptor has been demonstrated to be required for collective cell migration of A431 squamous cell carcinoma

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through regulation of the actomyosin network and interaction with the Par3/Par6 cell polarity complexes (Hidalgo-Carcedo et al., 2011). The tumor microenvironment has also contributed to collective cell migration signaling. Cancer associated fibroblasts (CAFs) are able to transmit force to human A431 squamous carcinoma cells and mediate collective cancer cell invasion in 3D through the formation of heterophilic N-cadherin/ E-cadherin adhesions (Labernadie et al., 2017).

4. Actin structures in cancer cell migration 4.1 Lamellipodia Lamellipodia (as well as pseudopodia in a 3D context or in vivo) are protrusive structures formed at the leading edge of cells that can drive cancer cell migration. The generation of lamellipodia requires nascent branched actin polymerization in order to generate sufficient force to push the cell membrane forward; this occurs through multiple mechanisms, including de novo nucleation via activation of the Arp2/3 complex through nucleationpromoting factors (i.e., WASP and WAVE proteins) and the generation of free barbed ends (polymerization competent-ends of F-actin) through cofilin severing of pre-existing filaments (Bravo-Cordero et al., 2013a; Yamaguchi and Condeelis, 2007). Upstream regulation of the actin machinery that form lamellipodia include activation of migratory signaling pathways through extracellular stimuli, which converge on the Rho GTPase signaling node (Ridley, 2015). For example, Rac1 can promote membrane ruffling through interaction with WAVE complexes via IRSp53 (Miki et al., 2000), resulting in Arp2/3-mediated actin polymerization (Ridley, 2015). Lamellipodium-driven migration is regulated directly by intricate coordination of the Rho GTPases RhoA, Rac1 and Cdc42, as shown by FRET biosensor imaging studies (Machacek et al., 2009) (see Section 6.2). In the context of cancer, the use of an optogenetic system with a photoactivatable Rac1 biosensor in prostate cancer cells demonstrated that Rac1-dependent lamellipodium extension functioned downstream of active PI3K signaling (Kato et al., 2014). RhoA has also been shown to play an important role during lamellipodium protrusion formation. By using a RhoA biosensor in breast cancer, studies have shown that the activity of this GTPase is highly confined to the first micron of the leading edge where it mediates lamellar extension (Bravo-Cordero et al., 2013b). Work with Rho GTPase FRET biosensors also revealed that another isoform from the Rho subfamily, RhoC, displays a particular spatial activation during protrusion formation.

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RhoC is activated in areas behind the leading edge where it regulates cofilin phosphorylation to confine cofilin activity (Bravo-Cordero et al., 2011). These studies showed that RhoA and RhoC GTPases have a unique spatiotemporal activation pattern that is necessary in order to achieve efficient lamellipodium extension. GTPase signaling and activation is dependent on cycling between GDP- and GTP- bound states. This process is regulated by guanine nucleotide exchange factors (GEFs), GTPase activating proteins (GAPs), and guanine nucleotide dissociation inhibitors (GDIs) (Haga and Ridley, 2016), many of which are mutated and aberrantly expressed in different cancer types (Porter et al., 2016). For example, P-rex1, a Rac1-specific GEF which can be regulated through PI3K-PI(3,4,5)P3 and GPCR signaling, has been demonstrated to promote invasion in melanoma in a Rac1-dependent manner (Lindsay et al., 2011), and is required for ErbB2-driven breast cancer cell migration (Sosa et al., 2010). At the level of actin binding and regulatory proteins, there are changes in expression of many key actin regulators in multiple cancers, including the WASP/WAVE family (Iwaya et al., 2007; Kulkarni et al., 2012) and Mena proteins, amongst others (Gertler and Condeelis, 2011; Olson and Sahai, 2008; Yamaguchi and Condeelis, 2007). For example, in invasive breast cancer cells derived from rat mammary adenocarcinomas and the PyMT model of breast cancer, signaling through chemotactic factors, such as EGF, can directly affect lamellipodia formation by regulating the gene expression of actin nucleators, including several Arp2/3 subunits, as well as actin regulatory proteins that antagonize capping, including Mena (Wang et al., 2007). Mena, an Ena/VASP protein that binds the barbed ends of actin filaments and delays termination by capping proteins, has splice isoforms with distinct functions in breast cancer cells (Gertler and Condeelis, 2011); the Mena11a isoform dampens growth-factor elicited lamellipodial protrusions (Balsamo et al., 2016), whereas the MenaINV isoform promotes lamellipodial protrusions (Hughes et al., 2015; Philippar et al., 2008). In invasive breast cancer cells, Lamellipodin, a binding partner of Ena/VASP proteins, is required for EGF-dependent lamellipodial protrusion and can promote 3D cancer cell invasion through specific interactions with Scar/WAVE and Ena/VASP complexes (Carmona et al., 2016).

4.2 Filopodia Filopodia are thin projections that require the elongation of bundled, parallel actin filaments; they arise primarily from de novo actin nucleation by formins,

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or through an Arp2/3-mediated convergent elongation model (Gupton and Gertler, 2007; Jacquemet et al., 2015). In migrating cells, filopodia have been found at the leading edge, where they are able to emerge out of the lamellipodium meshwork downstream of Rho GTPase signaling, which results in the regulation of proteins including Ena/VASP (which are enriched at filopodia tips) (Lebrand et al., 2004) and IRSp53 (an effector of Cdc42 that can induce membrane curvature) (Disanza et al., 2013). Filopodia are bundled by actin bundling proteins, such as fascin or alphaactinin, and are capable of extracellular sensing and cargo transport ( Jacquemet et al., 2015). Filopodia-like protrusions (FLPs) have been observed in mouse mammary carcinoma cells that have extravasated into the lung parenchyma. FLPs are regulated by Rif and mDia2 and are decorated with β1 integrin. FLP contact with the ECM initiates adhesion-dependent signaling and results in increased tumor cell proliferation (Shibue et al., 2012). By using quantitative microscopy, recent work showed that filopodia density increases as breast cancer progresses ( Jacquemet et al., 2017). In addition, filopodia stabilization through the L-type calcium channel is required for directed migration and invasion ( Jacquemet et al., 2016). In a separate study, it was also identified that upregulation of Myosin-X in p53-driven cancers is needed for invasion through the formation of filopodia (Arjonen et al., 2014).

4.3 Invadopodia Invadopodia are F-actin-rich protrusive structures that are formed by invasive cancer cells in contact with the extracellular matrix. Invadopodia have proteolytic function and can focalize the secretion and accumulation of metalloproteinases, such as MMP2, MMP9 and MT1-MMP (Eddy et al., 2017; Jacob and Prekeris, 2015). The ability of invadopodia to degrade the ECM promotes local invasion of tumor cells, intravasation, and extravasation events (Bravo-Cordero et al., 2012; Gligorijevic et al., 2012, 2014; Leong et al., 2014; Roh-Johnson et al., 2014). Invadopodia are induced by a variety of stimuli. Growth factors such as EGF (DesMarais et al., 2009) and TGF-β1 (Mandal et al., 2008) stimulate invadopodia formation in breast tumor cells. GABA and EGFR, which are chemotaxis receptors, are involved in invadopodia dynamics in vivo and can guide cancer cell extravasation and promote brain tropism in breast cancer metastasis (Williams et al., 2019). Recently, IKKε has been described

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as a novel regulator of invadopodia formation and can promote metastasis in colorectal cancer (Liu et al., 2020). The tumor microenvironment can also regulate invadopodia; extracellular fibrillar collagen I was demonstrated as stimuli for cancer cells to form invadopodia in both 2D and 3D ( Juin et al., 2012). Interestingly, collagen I induces invadopodia formation through the DDR1 collagen receptor in a kinase independent manner. The DDR1 receptor aligns along collagen I fibers, establishing linear invadosomes that recruit Cdc42 via the Tuba RhoGEF resulting in increased proteolytic activity ( Juin et al., 2014). Other components of the extracellular matrix, including SERPINB5 and CSTB, can increase invadopodia formation and in vivo extravasation in pancreatic ductal adenocarcinoma (PDAC) (Tian et al., 2020). Adipocyte-derived lipid uptake by FATP proteins overexpressed in melanoma cells was able to induce invadopodia formation and drive melanoma progression (Zhang et al., 2018). Mechanosensing of the extracellular matrix can also form invadopodia, as invadopodia can contain integrin receptors (Mueller et al., 1999; Pela´ez et al., 2019) and CD44 (Petropoulos et al., 2018). The formation of invadopodia occurs in steps, and is regulated temporally; briefly, invadopodium precursor structures assemble downstream of signaling cues (Beaty and Condeelis, 2014). The invadopodium precursor core is composed of cortactin, N-WASP, cofilin, and actin; invadopodium precursors are incapable of matrix degradation. Within seconds, Tks5 is recruited to the early invadopodium precursor where it stabilizes the structure. Subsequently, cortactin is phosphorylated and promotes the maturation of invadopodia, which endows them with the capability to polymerize new actin filaments and degrade the ECM (Eddy et al., 2017). Rho GTPases, including Rac1, RhoA and Cdc42, have functional consequences during invadopodia formation (Beaty and Condeelis, 2014). Overexpression of the active form of Cdc42 and Rac1 induces invadopodia formation in cancer cells (Dutartre et al., 1996; Nakahara et al., 2003). RhoA drives invadopodium maturation (Bravo-Cordero et al., 2011; Sakurai-Yageta et al., 2008). Cdc42 is also a critical regulator of invadopodia dynamics, and affects invadopodium precursor assembly and maturation (DesMarais et al., 2009; Sakurai-Yageta et al., 2008; Yamaguchi et al., 2005; Eddy et al., 2017). A minimal signature to define invadopodia was proposed in 2014; actin structures that colocalize with Tks5 and the active form of Ccd42 are considered invadopodia (Di Martino et al., 2014). Rho GTPases play an important role in regulating invadopodium dynamics. Work using Rho FRET biosensors showed that RhoC activation

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regulates cofilin activity at invadopodia (Bravo-Cordero et al., 2011). Use of FRET biosensor technology also demonstrated that Rac1 is required for invadopodium disassembly (Moshfegh et al., 2014) and Rac3 regulates integrin signaling at invadopodia and adhesion to the extracellular matrix (Donnelly et al., 2017). In relation to invadopodia formation, there is a small body of work describing GEF and GAP activity: one study has described how RhoC, which is important for metastasis formation (Clark et al., 2000), is spatially regulated at invadopodia by p190RhoGEF and p190RhoGAP (Bravo-Cordero et al., 2011). p190RhoGAP inactivates RhoC within the invadopodium core, and p190RhoGEF activates RhoC in areas surrounding the invadopodia (Bravo-Cordero et al., 2011). A few GEFs have been shown to be important for invadopodia function, including Vav1 (Razidlo et al., 2014), β-PIX (Donnelly et al., 2017; Md Hashim et al., 2013), Fgd1 (Ayala et al., 2009), Frabin (Nakahara et al., 2003), Trio (Moshfegh et al., 2014) and SGEF (Goicoechea et al., 2017), as well as some GAPs, including p190RhoGAP (Bravo-Cordero et al., 2011) and ArhGAP12 in melanoma and breast cancer cells (Diring et al., 2019).

4.4 Focal adhesions Focal adhesions are dynamic signaling nodes that connect the actin cytoskeleton directly to the extracellular matrix (Huttenlocher, 1995). The main adhesion receptors that link the ECM to actin stress fibers are integrins, which are bidirectional signaling molecules that can be activated in an “outside-in” or “inside-out” manner (Hynes, 1992). Integrins can be activated by binding to their respective ligands (e.g., collagens, fibronectin), and recruit adaptor proteins, such as talin, kindlin (Sun et al., 2019) and paxillin (Turner, 2000), F-actin binding proteins (e.g., vinculin, alpha-actinin), receptor tyrosine kinases such as FAK (Hanks et al., 1992) and Src (Schaller et al., 1999), as well as the many proteins that make up the “adhesome” (Horton et al., 2016; Zaidel-Bar et al., 2007). Cells are capable of forming multiple types of ECM-adhesions, including focal complexes, classic focal adhesions, and fibrillar adhesions in a 2D setting (Geiger and Yamada, 2011), as well as cell-matrix adhesions in 3D (Geiger et al., 2009). At the leading edge of a motile cell in 2D (within 1–2 μm), nascent adhesions, or focal complexes (FCs) can form underneath the lamellipodium (Geiger et al., 2001; Nobes and Hall, 1995; Geiger and Yamada, 2011). Although not fully characterized, the molecular composition of focal complexes contains a few hundred proteins, including integrins, actin

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binding proteins (e.g., talin), and signaling molecules (i.e., FAK) (Zaidel-Bar et al., 2003; Geiger and Yamada, 2011). Focal complexes have short lifetimes, and can either disassemble rapidly or mature into focal adhesions. Focal adhesions, which are more elongated than focal complexes, remain primarily under the lamella (>2 μm from lamellipodial tips) at the ends of stress fibers (Geiger et al., 2009; Geiger and Yamada, 2011). The maturation of focal complexes into focal adhesions requires tension and force, through actomyosin contractility (Choi et al., 2008; Giannone et al., 2007), tyrosine phosphorylation of certain proteins (i.e., paxillin), alterations in protein composition through recruitment of scaffolding proteins and increased adhesion-based signaling (Geiger et al., 2009; Geiger and Yamada, 2011). Rho GTPase proteins have a role in the formation of ECM-adhesions; Rac1 can control focal complex formation, and RhoA has a role in the maturation of focal adhesions (Parsons et al., 2010). Force generation can convert focal adhesions into fibrillar adhesions, which are mainly composed of α5β1 integrin and tensin, and regulate processes such as fibronectin fibrillogenesis (Danen et al., 2002; Zamir et al., 1999; Geiger and Yamada, 2011). Focal adhesion turnover, which can regulate cell migration, requires Src activity and the phosphorylation of FAK (Wozniak et al., 2004). Cell-matrix adhesions have also been observed in 3D, which behave differently than adhesions in 2D. In cell-derived matrix, fibroblasts were able to make adhesions that were α5 integrin and paxillin positive (Cukierman et al., 2001). Breast epithelial cells in an attached 3D collagen gel are able to form small 3D adhesions with phosphorylated FAK Y397, whereas the same epithelial cells in a floating 3D matrix have adhesions that are absent of FAK phosphorylation (Wozniak et al., 2003); thus, 3D adhesions are mechanosensors that behave differently based on the ECM rigidity. Cancer associated fibroblasts (CAFs) are key regulators of ECM deposition and remodeling during tumor progression, and are involved in paracrine signaling with tumor cells (Attieh and Vignjevic, 2016). In CAFs, Hic-5 was shown to promote the formation of fibrillar adhesions through interaction with tensin1, which are conserved in 3D cell-derived matrices (Goreczny et al., 2018). Interestingly, during breast cancer cell invasion, FAK, a key kinase at focal adhesions, differentially regulates tyrosine phosphorylation at focal adhesion and invadopodia components (Chan et al., 2009), suggesting there may be crosstalk between the signaling pathways that regulate the formation of actin-rich structures during cancer cell invasion.

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5. The cell cycle and cancer cell invasion In the hallmarks of cancer, a deregulated cell cycle state and cancer cell invasion have been regarded as distinct programs (Hanahan and Weinberg, 2011); however, recent evidence suggests that there is a closer link between regulators of proliferation and invasion during cancer progression than previously thought (Kohrman and Matus, 2017). In breast carcinoma cells, it has been elucidated that invadopodia preferentially form in the G1 phase of the cell cycle (Bayarmagnai et al., 2019). p27, a cell cycle inhibitor that binds to Cdk-cyclin complexes in the nucleus, is able to regulate tumor cell invasion when it mislocalizes to the cytoplasm (Chu et al., 2008). In addition, p27 has been demonstrated to localize to invadopodia (Bayarmagnai et al., 2019; Jeannot et al., 2017) and regulates its activity through a Rac1-PAK1cortactin signaling axis ( Jeannot et al., 2017). In melanoma, tumor cells are able to switch between a highproliferative/low invasive state to a low-proliferative/high invasive state, known as phenotype switching, as the disease progresses (Arozarena and Wellbrock, 2019). Tumor cells expressing high levels of the MITF transcription factor, low levels of the Axl receptor and the associated transcriptional program remain in the high-proliferative/low invasive state; a switch to an Axl high and MITF low state shifts cells into a high-invasive/low-proliferative program (Rambow et al., 2019). The reduction of transcription factor MITF has been shown to induce a G1 cell cycle arrest through p27, and concurrently leads to the downregulation of Dia1 and promotes ROCK-mediated invasion (Carreira et al., 2006). In the PyMT model of breast cancer, loss of p21CIP1 suppressed invasion and increased cell proliferation (Qian et al., 2013), suggesting that p21CIP1 may mediate switching between proliferation and invasion.

6. Imaging advances and future directions in studying tumor cell invasion 6.1 Single-molecule superresolution imaging The advances in the study of the actin cytoskeleton have been driven by the implementation of different high-resolution imaging techniques. Actin-rich structures are complex subcellular entities that contain several actin regulatory molecules. Recent proteomics studies (Attanasio et al., 2011; Ezzoukhry

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et al., 2018) have elucidated the composition of invadopodia and invadosomes; however, these techniques are limited in that the spatial distribution of the components are unable to be characterized. Recently, the development of superresolution microscopy has revealed the organization of structures such as focal adhesions and podosomes (matrix-degrading protrusions similar to invadopodia formed in cells with a monocytic lineage; Linder and Wiesner, 2015). A seminal study from the Waterman lab revealed the supramolecular organization of focal adhesions using superresolution microscopy, specifically PALM (Kanchanawong et al., 2010). This study showed that focal adhesions are multilaminar structures formed by three layers: an integrin layer, a force transduction layer, and an actin layer. PALM microscopy (Stubb et al., 2019) has been utilized to show the architecture of focal adhesions of stem cells, demonstrating that the organization of cornerstone adhesions and central adhesions are different at the nanoscale level; similar superresolution techniques have been used to image podosomes (Cox and Jones, 2013). PALM, STORM, and other single molecule superresolution techniques can be applied to study the spatial organization of lamellipodium and invadopodium structures in cancer cells. MDA-MB-231 triple-negative breast cancer cells plated on a gelatin matrix were imaged with both widefield fluorescent microscopy and direct STORM (Fig. 5, left and

Fig. 5 dSTORM image of a breast cancer cell. (Left panel): Widefield image of a cancer cell on gelatin matrix. F-actin in blue, cortactin in red. (Middle panel): dSTORM image of the same cancer cell. Top and bottom inset #1 (yellow box): Widefield and dSTORM of a lamellipodia structure. Top and bottom inset #2 (green box): Widefield and dSTORM showing spatial distribution of F-actin and cortactin in an invadopodia structure. Left and middle panel, scale bar: 10 μm. Insets, scale bar: 1 μm. Data acquisition for dSTORM was carried out on the Nanoimager S (Oxford NanoImaging, ONI, Oxford, UK). Signals from Alexa 647 and Alexa 488 were recorded sequentially for 10,000 frames each. Localization and image rendering were performed in the NimOS v1.4 software, and the final reconstruction displayed in a precision mode.

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middle panel); the use of dSTORM greatly increases the image resolution of subcellular structures and provides more detailed structural information. As shown in Fig. 5, the lamellipodium of cancer cells show a distinct localization of cortactin and F-actin at the leading edge. Interestingly, dSTORM imaging of invadopodia reveals complex spatial distribution of cortactin and F-actin, where F-actin is strongly enriched in the invadopodia core and cortactin molecules are scattered throughout the invadopodia structure (Fig. 5).

6.2 FRET-based imaging To analyze signaling pathways of tumor cell motility, FRET-based imaging of Rho GTPases has been utilized both in vitro and in vivo and provides spatial information on Rho GTPase signaling and activation. Single-chain FRET biosensors, a more recent development in the field, consist of an N-terminal GTPase effector fragment, two fluorophores (an acceptor and donor FRET pair) separated by a linker, and a C-terminal GTPase (e.g., RhoA) (Donnelly et al., 2014; Mondal et al., 2020). When the GTPase is active (GTP-bound), it binds to the GTPase effector, resulting in the FRET pair coming into close proximity of each other and increasing the FRET signal (the donor fluorophore emission overlaps with the acceptor fluorophore excitation). Additional modifications of this technology include a near-IR FRET pair that allows for compatible imaging with other FRET-based biosensors (including CFP-YFP FRET pairs), as well as potential usage in vivo due to the optimal properties of near-IR fluorophores for deep imaging (Shcherbakova et al., 2018). In this particular study, the use of a near-IR Rac1 biosensor with a RhoA CFP-YFP FRET biosensor revealed that antagonistic RhoA and Rac1 activity in motile cells is dependent on ROCK signaling (Shcherbakova et al., 2018). FRET-based imaging allows for spatial localization of Rho GTPase activity at a subcellular level and links it directly to tumor cell motility and invasion. For example, FRET biosensor technology has demonstrated that macrophage-tumor cell contact increases RhoA activity in tumor cells to promote invadopodia formation and intravasation (Roh-Johnson et al., 2014), whereas Rac1 activity can increase as invadopodia structures disassemble (Moshfegh et al., 2014). FRET biosensors are now being utilized in vivo to analyze RhoA activity in invasive breast and pancreatic cancer (Nobis et al., 2017); further exploration with FRET biosensors in vivo will be essential to elucidate signaling mechanisms activated during invasion and metastasis formation.

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6.3 Intravital imaging and the tumor microenvironment Techniques used to image in vivo, such as two photon intravital imaging and lattice light-sheet microscopy, are revealing the dynamics of tumor cells at the single-cell level during the metastatic cascade. Two-photon microscopy has visualized cancer cell motility in tumors (reviewed in Mondal et al., 2020), and more recently, behaviors of the surrounding microenvironment (reviewed in Di Martino et al., 2019). For example, the extracellular matrix has a key effect on modes of tumor cell migration; a combination of intravital two-photon imaging and computational modeling was used to delineate how tumor cells move based on what extracellular matrix structures they encounter (Tozluoglu et al., 2013). Immune cells within the TME have also been characterized with two-photon imaging. Longitudinal studies using two-photon microscopy of tumor-associated macrophages in glioblastoma (GBM) has clearly defined two distinct types of TAMs, brain-resident microglia and bone marrow-derived macrophages, that are demonstrated to have distinct migratory behaviors (Chen et al., 2019). Recently, the development of lattice light-sheet microscopy has allowed for imaging of tumor cell extravasation events. Lattice-light sheet imaging of zebrafish xenografts with labeled vasculature can capture the dynamics of cancer cells during extravasation with high temporal resolution in 3D (Liu et al., 2018b). Further studies using new tools of high-resolution imaging will help to illuminate the interplay between the TME and cancer cells, and how the TME affects tumor cell motility and invasion.

7. Conclusion Understanding the dynamics of the actin cytoskeleton will help to develop targeted therapeutics that may prevent the dissemination of cancer cells and metastasis formation. The application of superresolution microscopy to investigate the macromolecular organization of invasive structures will provide valuable information about the spatial and temporal formation of invadopodia and pseudopodia and how the different components organize. We can envision that drugs that perturb the spatial organization of these molecules may interfere with the function of these actin-rich structures and may prevent the invasion and migration of cancer cells. As more work in the imaging field is developed, our understanding of actin dynamics at actin-rich structures will reveal possible candidates and additional signaling pathways to target during tumor cell dissemination.

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Acknowledgments We would like to thank Jill Gregory for her illustrations (Figs. 1–3), Linnea Olofsson for the acquisition and analysis of the widefield and STORM images (Fig. 5), and the Microscopy CoRE facility at Mount Sinai. This work was supported by a Susan G. Komen Career Catalyst Research Grant (CCR18547848), an NCI Career Transition Award (K22CA196750), an NCI R01 (CA244780), the Schneider-Lesser Foundation Award, a Stony Brook-Mount Sinai pilot award, an ACCRF award (to J.J.B.C); the Tisch Cancer Institute NIH Cancer Center grant (P30 CA196521). Chandrani Mondal received support from an NIH T32 CA078207 Training Program in Cancer Biology.

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Wu, Y., et al., 2009. Stabilization of snail by NF-kappaB is required for inflammationinduced cell migration and invasion. Cancer Cell 15 (5), 416–428. https://doi.org/10. 1016/j.ccr.2009.03.016. Wu, K., et al., 2015. Roles of the cyclooxygenase 2 matrix metalloproteinase 1 pathway in brain metastasis of breast cancer. J. Biol. Chem. 290 (15), 9842–9854. https://doi.org/10. 1074/jbc.M114.602185. American Society for Biochemistry and Molecular Biology. Wyckoff, J., et al., 2004. A paracrine loop between tumor cells and macrophages is required for tumor cell migration in mammary tumors. Cancer Res. 64 (19), 7022–7029. https:// doi.org/10.1158/0008-5472.CAN-04-1449. United States. Wyckoff, J.B., et al., 2007. Direct visualization of macrophage-assisted tumor cell intravasation in mammary tumors. Cancer Res. 67 (6), 2649–2656. https://doi.org/10. 1158/0008-5472.CAN-06-1823. United Statefs. Yamaguchi, H., Condeelis, J., 2007. Regulation of the actin cytoskeleton in cancer cell migration and invasion. Biochim. Biophys. Acta (BBA)—Mol. Cell Res. 1773 (5), 642–652. https://doi.org/10.1016/j.bbamcr.2006.07.001. Yamaguchi, H., et al., 2005. Molecular mechanisms of invadopodium formation. J. Cell Biol. 168 (3), 441 LP–452. Available at: http://jcb.rupress.org/content/168/3/441.abstract. Zaidel-Bar, R., et al., 2003. Early molecular events in the assembly of matrix adhesions at the leading edge of migrating cells. J. Cell Sci. 116 (22), 4605 LP–4613. https://doi.org/10. 1242/jcs.00792. Zaidel-Bar, R., et al., 2007. Functional atlas of the integrin adhesome. Nat. Cell Biol. 9 (8), 858–867. Nature Publishing Group. Available at: https://doi.org/10.1038/ncb0807-858. Zamir, E., et al., 1999. Molecular diversity of cell-matrix adhesions. J. Cell Sci. 112 (11), 1655 LP–1669. Available at: http://jcs.biologists.org/content/112/11/1655.abstract. Zervantonakis, I.K., et al., 2012. Three-dimensional microfluidic model for tumor cell intravasation and endothelial barrier function. Proc. Natl. Acad. Sci. U. S. A. 109 (34), 13515 LP–13520. https://doi.org/10.1073/pnas.1210182109. Zhang, M., et al., 2018. Adipocyte-derived lipids mediate melanoma progression via FATP proteins. Cancer Discov. 8 (8), 1006–1025. https://doi.org/10.1158/2159-8290.CD17-1371. United States. Zheng, X., et al., 2015. Epithelial-to-mesenchymal transition is dispensable for metastasis but induces chemoresistance in pancreatic cancer. Nature 527 (7579), 525–530. https://doi.org/10.1038/nature16064.

CHAPTER FOUR

The multiple roles of actin-binding proteins at invadopodia € llerb, Takouhie Mgrditchiana,†, Gabriele Sakalauskaitea,†, Tanja Mu ment Thomasa,* line Hoffmanna, and Cle Ce a

Cytoskeleton and Cancer Progression, Department of Oncology, Luxembourg Institute of Health, Luxembourg City, Luxembourg b Department of Oncology, Luxembourg Centre of Neuropathology, Luxembourg Institute of Health, Luxembourg City, Luxembourg *Corresponding author: e-mail address: [email protected]

Contents 1. Introduction 2. Invadopodial actin assemblies 3. Actin machineries at the cell leading edge 3.1 Actin dynamics in tumor cell motility—Generalities 3.2 The lamellipodial actin machinery 3.3 The filopodial actin machinery 4. Actin-binding proteins in invadopodia morphogenesis 4.1 Invadopodium initiation and stabilization of precursors 4.2 Invadopodium maturation 5. Actin polymerization-based protrusion at invadopodia 6. Concluding remarks Acknowledgments References

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Abstract Invadopodia are actin-rich membrane protrusions that facilitate cancer cell dissemination by focusing on proteolytic activity and clearing paths for migration through physical barriers, such as basement membranes, dense extracellular matrices, and endothelial cell junctions. Invadopodium formation and activity require spatially and temporally regulated changes in actin filament organization and dynamics. About three decades of research have led to a remarkable understanding of how these changes are orchestrated by sequential recruitment and coordinated activity of different sets of actin-binding proteins. In this chapter, we provide an update on the roles of the actin cytoskeleton during the main stages of invadopodium development with a particular focus on actin polymerization machineries and production of pushing forces driving extracellular matrix remodeling. †

Co-first authors.

International Review of Cell and Molecular Biology, Volume 360 ISSN 1937-6448 https://doi.org/10.1016/bs.ircmb.2021.03.004

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2021 Elsevier Inc. All rights reserved.

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1. Introduction Metastasis, i.e., the spread of cancer cells from the primary tumor to distant organs, is the most life-threatening aspect of cancer, being responsible for at least two-third of cancer deaths (Dillekas et al., 2019; Siegel et al., 2020). Although “activating invasion and metastasis” has been recognized as a hallmark of cancer (Hanahan and Weinberg, 2011), our understanding of the biological complexity of this process is still incomplete and there is no effective anti-metastatic therapy currently available (Meirson et al., 2020). Development of new strategies to inhibit tumor cell dissemination requires identification and molecular characterization of the fundamental mechanisms underlying metastasis as well as a shift in the clinical development paradigm allowing an evaluation of anti-metastatic agents based on more relevant endpoints than tumor shrinkage (Anderson et al., 2019; Meirson and Gil-Henn, 2018; Roussos et al., 2011; Steeg, 2016). A critical process leading to cancer cell metastasis is the acquisition of an invasive behavior which facilitates several key steps of the metastatic cascade. This includes migration through the basement membrane and the extracellular matrix (ECM) surrounding primary tumors, as well as movement into and out of blood and lymphatic vessels (intravasation and extravasation, respectively). The intrinsic invasive capacity of tumor cells can be attributed to the formation of specific F-actin-cortactin-rich membrane protrusions known as invadopodia. In 2D cell cultures, invadopodia form on the ventral surface of invasive tumor cells and promote focal degradation of the underlying ECM. Depending on the cell model and the biochemical and physical properties of the ECM, invadopodia can adopt different morphologies and relative distribution (Artym et al., 2015; Parekh et al., 2011). On a thin layer of denatured collagen (or gelatin), an artificial matrix commonly used to evaluate tumor cell degradative activity, invadopodia typically develop as discrete dot-like structures predominantly distributed underneath the nucleus. If the experimental settings allow them to elongate without restriction (Schoumacher et al., 2010), these invadopodia can protrude over several microns (Hoffmann et al., 2016; Schoumacher et al., 2010). In contrast, when plated on fibrillar collagen I, a matrix that better mimics the fibrous collagen-rich microenvironment of tumors, invasive cells assemble linear (or curvilinear) invadopodia that align along collagen fibrils and extend in the plasma membrane plane (Castagnino et al., 2018; Ferrari et al., 2019; Infante et al., 2018; Juin et al., 2012, 2014; Monteiro et al., 2013).

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Finally, in 3D environments, invadopodia were reported to develop at the cell leading edge in the form of several thin protrusions extending from a thick protruding base (Hoffmann et al., 2016; Tolde et al., 2010; Wisdom et al., 2018) or in a region anterior to the nucleus (relative to the cell movement) at plasma membrane-ECM contact sites (Ferrari et al., 2019; Infante et al., 2018). Such invadopodial plasticity is assumed to confer on tumor cells the capacity to invade through the complex and changing microenvironments encountered during the metastatic cascade. Invadopodium morphogenesis, molecular composition and activity have mainly been investigated in vitro. However, there is substantial evidence that invadopodia facilitate invasion and metastasis in vivo. Intravital imaging studies have shown invadopodium formation in conjunction with matrix remodeling and/or tumor cell invasion, in metastasis mouse models and other relevant experimental models, such as the chick embryo chorioallantoic membrane extravasation model (Bayarmagnai et al., 2018, 2019; Gligorijevic et al., 2014, 2012; Kim et al., 2016; Leong et al., 2014; Stoletov and Lewis, 2015; Williams et al., 2019). In addition, various studies based on genetic or pharmacological ablation of invadopodium components or upstream regulatory pathways have confirmed the therapeutic potential of targeting invadopodium to inhibit metastasis (Meirson and Gil-Henn, 2018). Besides their pro-invasive and -metastatic functions, invadopodia have been suggested to facilitate 3D proliferation (Blouw et al., 2015, 2008; Iizuka et al., 2016). Although the exact underlying mechanisms remain unclear, invadopodiummediated proteolysis was suggested to generate increased space for tumor development, promote the release of latent forms of growth factors and cytokines present in the tumor microenvironment, and stimulate angiogenesis (Saini and Courtneidge, 2018). The proteolytic activity of invadopodia originates from the recruitment and targeted secretion of membrane-anchored and soluble secreted proteases, of which matrix metalloproteinases (MMPs) have emerged as one of the most critical families. Membrane-type 1 (MT1)-MMP (or MMP-14) has been repetitively recognized as the dominant MMP in driving invasion and metastasis in various types of malignancies (Castro-Castro et al., 2016). Functional investigations support a prominent role of MT1-MMP in all the key steps of the metastatic cascade, including basement membrane transmigration, intravasation, and extravasation (Hotary et al., 2006; Leong et al., 2014; Lodillinsky et al., 2016; Monteiro et al., 2013; Perentes et al., 2011; Rowe and Weiss, 2008). On a biochemical level, MT1-MMP potently hydrolyzes collagen fibers and other ECM components, and mediates activation of other

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pro-invasive MMPs, such as MMP-2 and MMP-13 (Deryugina et al., 2001; Hotary et al., 2006; Knauper et al., 1996; Sato et al., 1994). Other MMP family members that are frequently upregulated in cancer cells and closely associated with metastatic progression and poor prognosis include the gelatinases MMP-2 and MMP-9 (Li et al., 2017; Mehner et al., 2014). Importantly, invadopodiamediated ECM degradation has proven to be critical for invasion ( Jacob and Prekeris, 2015; Nakahara et al., 1997; Steffen et al., 2008), and intracellular trafficking of MMPs and their polarization to invadopodia has emerged as an important field of research (Castagnino et al., 2018; Jacob and Prekeris, 2015; Marchesin et al., 2015; Monteiro et al., 2013; Poincloux et al., 2009). Besides hydrolyzing ECM components, MMPs carry out various additional functions in cancer progression by catalyzing proteolysis of non-ECM extracellular and intracellular molecules, activating intracellular signaling pathways in response to specific ligands, and acting as transcription factors (Bauvois, 2012; Cauwe and Opdenakker, 2010; Chang and Werb, 2001; D’ortho et al., 1997; Knapinska and Fields, 2019; Ma et al., 2014; Nguyen et al., 2016). Hence, MMPs have been considered as promising therapeutic targets and MMP inhibitors have been actively pursued. First-generation inhibitors failed in clinical trials for several reasons, such as lack of specificity, low bioavailability, and high toxicity. The reader is referred to the following reviews for further information on these aspects (DuFour and Overall, 2013; Fields, 2019; Overall and Kleifeld, 2006; Vandenbroucke and Libert, 2014). The growing knowledge of MMP biology in both cancer and healthy cells has stimulated the development of new classes of MMP inhibitors and strategies to deliver MMP inhibitors more effectively and more specifically to the tumor microenvironment (Lyu et al., 2019; Tauro et al., 2014). Various growth factors present in the tumor microenvironment promote invadopodium formation and protease-dependent invasion, such as epidermal growth factor (EGF), platelet-derived growth factor (PDGF), hepatocyte growth factor (HGF), and transforming growth factor β (TGF-β) (Hoshino et al., 2013; Mandal et al., 2008; Masi et al., 2020; Pignatelli et al., 2012). Reduction in oxygen availability (hypoxia) and modifications in the composition and density of the ECM, such as the increase in collagen-I fibers during the desmoplastic reaction associated with various malignancies, are additional potent inducers of invadopodia (Artym, 2016; Artym et al., 2015; Gould and Courtneidge, 2014; Masi et al., 2020). These extracellular stimuli activate signaling pathways converging on the assembly of an actin core from which invadopodia further develop. The actin cytoskeleton and its direct

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regulators, the actin-binding proteins (ABPs), are the most downstream effectors regulating invadopodium morphogenesis and activity. More than three decades of research have provided a relatively detailed picture of the multiple roles of invadopodial actin filaments (AFs) and ABPs. In this chapter, we review the functions of the actin cytoskeleton and ABPs during invadopodium development. We provide an overview of the invadopodial actin cytoskeleton architectures and actin machineries that generate the pushing forces driving invadopodium protrusion and ECM remodeling.

2. Invadopodial actin assemblies Invadopodia were originally described in the 1980s as actin-, vinculin-, and alpha-actinin-rich rosette-shaped patches that developed underneath chicken embryo fibroblasts transformed by the Rous sarcoma virus (David-Pfeuty and Singer, 1980) and that differed from other focal contacts by their potent proteolytic activity (Chen, 1989). Since their discovery, invadopodia have been extensively characterized at the structural and molecular levels, and they turned out to be specific to cancer cells. Although invadopodia share various features with podosomes that form in noncancer invasive cells, such as macrophages, dendritic cells, and osteoclasts (Calle et al., 2006; Davies and Stossel, 1977; Destaing et al., 2003), they uniquely show the ability to protrude into the ECM over long distances, usually exhibiting a longer life span and stronger proteolytic activity than podosomes (Artym et al., 2011; Linder et al., 2011; Takkunen et al., 2010). When invasive cancer cells are plated on gelatin, invadopodia develop as discrete protrusions exhibiting a prototypical domain organization consisting of a central “invasive” domain (or actin core) and an adhesion domain or “ring” surrounding the actin core at its base, i.e., the interface between the actin core and the ventral cell membrane (Hoshino et al., 2013; Revach and Geiger, 2014; Revach et al., 2020). The actin core contains the actin-nucleation machinery and accessory ABPs responsible for the protrusive activity (see Section 4), while the adhesion ring is enriched in integrins and adhesion proteins, such as β-1 and β-3 integrins, vinculin, talin, paxillin, and Hic-5 (Branch et al., 2012; Pignatelli et al., 2012; Revach and Geiger, 2014; Revach et al., 2020, 2015; Sharma et al., 2013). Adhesion rings are assembled shortly after invadopodia initiation and actin core formation but are barely visible in mature invadopodia (Branch et al., 2012; Revach et al., 2015). Whether the disappearance of adhesion rings is due to the proteolytic degradation of the ECM or the oscillatory nature of these

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structures that makes them difficult to capture remains unclear (Branch et al., 2012; Revach and Geiger, 2014; Sharma et al., 2013). Importantly, adhesion rings have exclusively been observed when cells are plated on 2D flat surfaces, such as a monolayer layer of gelatin, and invadopodia develop as small, dot-shaped protrusions. This strikingly contrasts with the linear/curvilinear invadopodia which form on fibrillar collagen I and lacks proper adhesion rings as well as typical adhesion components such as integrins, paxillin, and vinculin ( Juin et al., 2012). Thus the morphology, domain organization, and molecular composition of invadopodia can significantly differ depending on the microenvironmental conditions. A complete view of actin assemblies in long invadopodia protruding into the ECM is missing. However, ultrastructural transmission electron microscopy analyses have identified two types of actin arrays (Schoumacher et al., 2010). At the base and sides of invadopodia, AFs take the appearance of dendritic networks reminiscent of the lamellipodial branched actin network, while at the tip, AFs are tightly packed into parallel arrays or bundles resembling those found in filopodia (see Section 3.3). The presence of bundled AFs in “higher” sections of elongated invadopodia can be assumed due to the presence of actin bundling proteins, such as fascin and cysteine-rich protein 2 (CSRP2), along the entire protrusion (Hoffmann et al., 2016; Schoumacher et al., 2010). Interestingly, the actin core protrudes not only toward the ECM but also toward the cell interior where it physically connects to the nuclear envelope (Revach et al., 2015). Using correlated light and transmission electron microscopy as well as TIRFM microscopy, the apical tips of core actin bundles have been shown to push against the nuclear envelope, creating indentations. Such physical interaction between the invadopodial actin cytoskeleton and the nucleus has been suggested to have important mechanistic implications, which will be discussed in the following sections. It is worth noting that from the ventral cell membrane to the nucleus, the core actin bundle is surrounded by a dense array of microtubules (Revach et al., 2015). In addition, both microtubules and vimentin intermediate filaments penetrate inside the protrusive domain of mature invadopodia, suggesting that they are required for invadopodium extension and/or stabilization ( Jacob et al., 2016; Kikuchi and Takahashi, 2008; Schoumacher et al., 2010). A recent study using platinum replica electron microscopy has provided a remarkably clear picture of AF ultrastructure in collagenolytic linear/curvilinear invadopodia (Ferrari et al., 2019). Networks of branched AFs were found to closely associate with curved collagen fibers, more specifically along their concave side.

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Immunogold staining revealed the presence of ARPC5, a subunit of the ARP2/3 complex (see below), supporting that the branched network originates from ARP2/3 complex activity.

3. Actin machineries at the cell leading edge 3.1 Actin dynamics in tumor cell motility—Generalities The actin cytoskeleton is a highly dynamic structure found in all eukaryotic cells with pivotal roles in multiple processes, such as cell morphogenesis, division, contraction and migration/invasion, as well as intracellular transport and membrane trafficking (Svitkina, 2018; Thomas and Staiger, 2014). Its building block, monomeric actin (or G actin), has a molecular weight of 42kDa and is one of the most abundant and evolutionarily conserved proteins. Actin monomers reversibly polymerize into AFs with preferential incorporation of ATP-actin-profilin complexes at the barbed (or “plus”) ends. Following incorporation of an actin subunit into the filament, profilin dissociates and ATP is hydrolyzed as the filament ages (Blanchoin et al., 2000). The nucleotide state of actin subunits is a critical determinant of actin dynamics as it is “used” by ABPs to sense the local age of AFs and discriminate between newly formed (ATP-rich) AFs and older (ADP-rich) AFs that should be recycled (Blanchoin and Pollard, 1999; Cai et al., 2007a,b; Suarez et al., 2011). Actin filaments further assemble into higher-order networks whose mechanical properties and biological functions are determined by the size, density, dynamics, and relative orientation of AFs, as well as their positioning in the cell (Oda et al., 2009; Rottner et al., 2017). Actin networks maintain a high degree of plasticity and can reorganize very quickly (Letort et al., 2015). The organization and dynamics of the actin cytoskeleton are finely regulated by a large array of ABPs controlling basic reactions, such as actin filament nucleation, elongation, stabilization, capping, cross-linking, severing, disassembly, and association with the cell membrane (Merino et al., 2020; Pollard, 2016). A fundamental function of the actin cytoskeleton is to produce the forces required to drive cell movements. Two main mechanistic principles underlie actin-based force generation. The coordinated polymerization of AFs arranged in specific arrays produces pushing forces while the mutual sliding of AFs and myosin motors is responsible for contractile and pulling forces, both mechanisms being critical to cell migration (Kovar and Pollard, 2004; Pollard and Borisy, 2003; Rottner et al., 2017; Svitkina, 2018). At the leading edge of cells migrating on two-dimensional surfaces, actin polymerizationbased pushing forces promote the formation of two major types of protrusions,

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namely the lamellipodium and filopodia. As previously mentioned, invadopodia are made of both the orthogonal/branched and longitudinal/ bundled AF arrays typically found in lamellipodia and filopodia (Schoumacher et al., 2010). It is therefore not surprising that the main components of the respective actin machineries are also found in invadopodia (Schoumacher et al., 2010; Yamaguchi et al., 2005). In the following subsections we briefly introduce the main principles of actin polymerization-based force generation with emphasis on the lamellipodial and filopodial actin machineries as they constitute the “tool kit” for invadopodium formation.

3.2 The lamellipodial actin machinery Lamellipodium formation and maintenance are driven by extension of a large and branched network of AFs assembled by the actin-related protein (Arp)2/3 complex (Goley and Welch, 2006; Svitkina and Borisy, 1999; Wu et al., 2012). This seven subunit nucleator binds to the side of preexisting (mother) AFs and subsequently initiates the formation of new (daughter) AFs at a fixed angle of approximately 70° (Amann and Pollard, 2001; Mullins et al., 1998). Mechanistically, ARP2 and ARP3 subunits can adopt an actin dimer-like conformation and contribute to the assembly of a “nucleus” from which develop new AFs (Machesky et al., 1994; Robinson et al., 2001). The latter elongate from their free barbed (or “plus”) end while remaining anchored by the ARP2/3 complex to the mother filament through their pointed (or “minus”) end. The resulting fast-growing dendritic network generates large pushing forces that can drive lamellipodial protrusion (Svitkina, 2018; Svitkina and Borisy, 1999). The ARP2/3 complex is intrinsically inactive as ARP2 and ARP3 are held apart by the five scaffolding subunits ARPC1-C5 (Robinson et al., 2001). Activation is triggered by nucleation-promoting factors (NPFs) that fall in two main classes (Welch and Mullins, 2002). Class I NPFs, such as the Wiskott-Aldrich syndrome protein (WASP) family proteins, bind to both the ARP2/3 complex and actin monomers (Alekhina et al., 2017; Burianek and Soderling, 2013; Goley et al., 2004). They abolish ARP2/3 complex autoinhibition and stimulate a conformational change in the complex (Rodal et al., 2005; Rodnick-Smith et al., 2016; Rouiller et al., 2008) as well as side binding to the mother filament (Rouiller et al., 2008; Smith et al., 2013). Although the exact underlying mechanism remains a matter of debate, it is commonly accepted that two molecules of class I NPFs synergistically activate the ARP2/3 complex and recruit actin subunits, which,

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together with ARP2 and ARP3, form a stable nucleation seed (Boczkowska et al., 2014; Padrick et al., 2011; Ti et al., 2011; Zimmet et al., 2020). Class II NPFs differ from class I NPFs in that they bind to AFs instead of actin monomers and usually exhibit weaker nucleation-promoting activity. A prototypical class II NPF that is critical to both lamellipodium and invadopodium formation is the SRC kinase substrate cortactin. Cortactin binds to the ARP2/3 complex and facilitates its recruitment to AFs (Uruno et al., 2001; Weaver et al., 2001; Weed et al., 2000). In addition, it promotes persistence of ARP2/3 branched actin networks by remaining associated with branch points and antagonizing the activity of turnover-promoting factors (Bryce et al., 2005; Cai et al., 2008; Weaver et al., 2001). In this regard, actin severing proteins, such as actin depolymerizing factors (ADFs) and cofilin, facilitate dissociation of the aged, ADP-enriched, actin branches at the rear of the actin network for which they have a higher affinity (Blanchoin and Pollard, 1999; Cai et al., 2007a,b; Suarez et al., 2011). Severing ABPs do not only induce actin network disassembly, but also critically assist the ARP2/3 complex in initiating new lamellipodia. By severing AFs, ADFs and cofilin increase the number of free barbed ends available for polymerization (Bravo-Cordero et al., 2013; Condeelis, 2001; Ghosh et al., 2004; Ichetovkin et al., 2002). Moreover, the resulting newly polymerized filaments provide additional branching sites for the ARP2/3 complex, which give rise to significantly more stable branches as compared to those established from older filaments (Mahaffy and Pollard, 2006). Other essential ABPs of the molecular machinery controlling the formation of dendritic actin networks are barbed-end capping proteins, of which the heterodimeric capping protein (CP) is the most ubiquitous and abundant representative (Mejillano et al., 2004). Shortly after the formation of a new branch, CP binds to AF barbed ends and inhibits further elongation. This restricts the length of the individual branches, which prevents their buckling and makes the pushing force more efficient, and increases the nucleation rate by the ARP2/3 complex (Akin and Mullins, 2008; Pollard and Borisy, 2003). Remarkably, depletion of CP results in loss of lamellipodia and explosive formation of filopodia, positioning CP as a key determinant of the ARP2/3-mediated branched network and negative regulator of filopodium formation (Mejillano et al., 2004). The 2D orthogonal architecture of the lamellipodial actin network amplifies the force generated by polymerization at the expense of displacement, allowing the protrusion of a large section of the membrane at a relatively reduced speed (Dmitrieff and Nedelec, 2016). Comparatively, the

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1D longitudinal organization of filopodial AFs (see below) is optimized to generate thin and fast-growing protrusions extending forward the leading edge. For further details on the regulation of the ARP2/3 branching activity, in particular in the context of cancer, the reader is referred to the following recent review article (Molinie and Gautreau, 2018).

3.3 The filopodial actin machinery Filopodia are dynamic, micrometer-long, fingerlike protrusions that extend beyond the lamellipodium leading edge and explore the microenvironment for the presence of molecules, such as chemoattractants and ligands for cell adhesion and motility (Galbraith et al., 2007; Gupton and Gertler, 2007), and tissue mechanics (Wong et al., 2014). They play particularly important roles during direct cell migration and escape of tumor cells from the primary tumors ( Jacquemet et al., 2015). Filopodia are embedded in the lamellipodial branched actin network at their base and protrude beyond the cell leading edge over several micrometers (Svitkina et al., 2003; Vignjevic et al., 2006). How filopodia are initiated and whether they exclusively originate from the ARP2/3-mediated actin network remain a matter of discussion (Rottner et al., 2017). Their protrusive section mainly contains nonbranched AFs tightly cross-linked into a parallel and rigid bundle by the cross-linking protein fascin (Vignjevic et al., 2006). Actin bundles provide mechanical support to cellular structures and contribute to establishing and maintaining cell polarity (Bartles, 2000; Thomas et al., 2009) (see below). They have been frequently implicated in cancer progression and metastasis (Stevenson et al., 2012). Filopodial bundles are laterally attached to the plasma membrane by ezrin-radixin-moesin (ERM) family proteins and anchored to the tip by formin and Enabled/vasodilator-stimulated phosphorylation (Ena/VASP) family proteins (see below). Filopodial actin bundles are unipolar, i.e., the AFs they contain are uniformly oriented with their fast-growing barbed end toward the filopodium tip (Small et al., 1978). This conformation allows actin polymerization to generate sufficient force for filopodial protrusion and maximizes growth speed (Dmitrieff and Nedelec, 2016; Mogilner and Rubinstein, 2005). Filopodia grow by a treadmilling mechanism where actin monomers are incorporated at the tip and released at the rear. The balance between the rate of actin polymerization and retrograde flow dictates extension and retraction of the protrusion (Mallavarapu and Mitchison, 1999). Polymerization at the tip is facilitated by formins and Ena/VASP proteins that increase the rate and

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duration of actin polymerization by associating with AF barbed ends, delivering profilin-actin complexes and preventing actin polymerization termination (Bear et al., 2002; Breitsprecher et al., 2008; Hansen and Mullins, 2010; Pollard, 2016; Pruyne et al., 2002; Romero et al., 2004; Schirenbeck et al., 2005; Svitkina et al., 2003). They also anchor the growing barbed ends to the membrane which is required to converge and enhance the pushing force at the invadopodial tip. Their respective modes of action are not fully understood and several aspects are still under investigation. For instance, while formins and capping proteins have been proposed to bind the barbed end of AFs in a mutually exclusive manner, a recent study has established that mDia1 and FMNL2 can simultaneously bind AF barbed ends and rapidly displace each other, allowing very fine-tuning of filament elongation (Shekhar et al., 2015). It is worth noting that formins and Ena/VASP proteins also facilitate elongation of AFs nucleated by the ARP2/3 complex, protect them against premature capping, and thereby enhance the pushing force generated by the lamellipodial actin network (Block et al., 2012; Damiano-Guercio et al., 2020; Kage et al., 2017; Yang et al., 2007). Integrin receptors and adhesion components located at the tip enable filipodia to function as mechanosensors (Alieva et al., 2019; Lagarrigue et al., 2015; Wong et al., 2014). At the base of filopodia, myosin II generates pulling forces that are transmitted to the filopodia tip through the actin core bundle and tip-located formins, as well as those that are critically required for filopodia adhesion and growth (Alieva et al., 2019). Actin bundle dissociation at the base of filopodia is promoted by the severing and depolymerizing activity of ADFs and cofilin, which is possibly facilitated by fascin and myosin II (Breitsprecher et al., 2011; Medeiros et al., 2006). Recycled actin monomers and ABPs, as well as other mechanosensing components, such as integrins, are suggested to be transported to the invadopodial tip by myosin X, an unconventional myosin X that accumulates at the tips of filopodia and presumably also contributes to AF organization directly (Berg and Cheney, 2002; Kerber and Cheney, 2011; Tokuo and Ikebe, 2004; Tokuo et al., 2007; Zhang et al., 2004). Recently, myosin X has been shown to be structurally optimized for movement on actin bundles, with long and flexible lever arms allowing particularly large step sizes and increased velocities on actin bundles (Ropars et al., 2016). For a more comprehensive and detailed coverage of actin dynamics in cell migration, the reader is referred to the following excellent review articles (Alexandrova et al., 2020; Blanchoin et al., 2014; Buracco et al., 2019; Schaks et al., 2019; Svitkina, 2018).

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4. Actin-binding proteins in invadopodia morphogenesis The formation of functional invadopodia is a sequential process that can be divided into three main stages: initiation, stabilization, and maturation (Artym et al., 2006; Beaty and Condeelis, 2014). Each stage is described below with particular emphasis on the actin polymerization machineries and most critical ABPs involved (Fig. 1). Note that the multiple stimuli and signaling pathways triggering invadopodium initiation are not covered here (Beaty and Condeelis, 2014; Masi et al., 2020; Revach et al., 2020; Rivier et al., 2021).

4.1 Invadopodium initiation and stabilization of precursors Invadopodium formation initiates with the assembly of a “minimal” actin polymerization machinery or “actin core” including, but maybe not limited to, the actin nucleating ARP2/3 complex and its cofactors N-WASP, cortactin, and cofilin (Beaty and Condeelis, 2014; Yamaguchi et al., 2005). Based on the requirement of its ARP2/3 complex- and N-WASP- binding domain for precursor assembly, cortactin has been suggested to act as a scaffold to bring together the actin core components (Oser et al., 2009). Stabilization of short-lived actin cores at the cell membrane is a fundamental event (Seals et al., 2005; Yamaguchi et al., 2005) that is promoted by tyrosine kinase substrate 5 (TKS5; also known as FISH or SH3PXD2A) (Sharma et al., 2013). As compared to the other invadopodial proteins, which are also present in lamellipodia, filopodia, stress fibers, or focal adhesions, TKS5 exhibits a more restricted subcellular distribution and is recognized as the most reliable marker and defining element of invadopodia (Saini and Courtneidge, 2018). Importantly, TKS5 has recently been validated as an indispensable component of collagenolytic linear invadopodia (Zagryazhskaya-Masson et al., 2020), supporting that its function is not restricted to small dot-like invadopodia but common to all types of invadopodia. The N-terminal phox homology (PX) domain of TKS5 has affinity for membrane inositol phospholipids (Abram et al., 2003; Seals et al., 2005) and anchors the precursor to phosphatidylinositol 3,4-bisphosphate (Abram et al., 2003; Sharma et al., 2013). TKS5 has no intrinsic ability to regulate AF organization or dynamics but its five SH3 domains mediate interaction with various partners (Abram et al., 2003; Moodley et al., 2015; Rufer et al., 2009; Thompson et al., 2008; Thuault et al., 2020), including, but not

Fig. 1 See figure legend on next page.

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limited to, several ABPs and important upstream actin regulators, in particular N-WASP (Oikawa et al., 2008), CDC42 (Bagci et al., 2020), and cortactin (Crimaldi et al., 2009; Stylli et al., 2009). In addition, SRCphosphorylated TKS5 interacts with the adaptor protein NCK (Stylli et al., 2009), an activator of the N-WASP-ARP2/3 pathway (Okrut et al., 2015; Rivera et al., 2004; Rohatgi et al., 2001). Recently, faciogenital dysplasia protein 1 (FGD1), a guanine exchange factor (GEF) specific for CDC42 (Zheng et al., 1996) previously reported to regulate actin core formation (Ayala et al., 2009), was identified as a novel TKS5 interacting protein (Zagryazhskaya-Masson et al., 2020). Interaction of FGD1 with TKS5 is necessary for the assembly and function of collagenolytic linear invadopodia in MDA-MB-231 breast cancer cells. Mechanistically, the TKS5-GFD1-CDC42 axis regulates the polarization of MT1-MMP storage compartments ahead of the nucleus (Zagryazhskaya-Masson et al., 2020), a process required for nucleus translocation and persistent invasion through the dense fibrillar collagen network (Infante et al., 2018). Interestingly, Hs578T breast cancer cells solely express short isoforms of TKS5 which lack the N-terminal PX domain (Zagryazhskaya-Masson et al., 2020) and are accordingly unable to anchor invadopodium precursors at the cell membrane (Saini and Courtneidge, 2018). Instead, Hs578T cells express high levels of Fig. 1 Main actin-binding proteins and their direct regulators during invadopodium formation on gelatin (upper part) and fibrillar collagen I (lower part). Formation of both types of invadopodia is initiated with the assembly of an unstable precursor core consisting in the ARP2/3 complex, cortactin, N-WASP, and cofilin (COF) (1). At this stage the actin machinery is inactive. Anchoring and stabilization of the precursor to PI(3,4)P2-rich regions of the plasma membrane is achieved by TKS5 (2). The latter also facilitates recruitment of additional regulators and proteases to the complex (3). Collagen fibrils are recognized by surface-exposed MT1-MMP, which activates a signaling cascade leading to TKS5 recruitment and the assembly of linear/curvilinear invadopodia (20 ). During the maturation stage (3), phosphorylation of cortactin by SRC and ABL tyrosine kinases releases cofilin severing activity and promotes recruitment of the ARP2/3 complex activator NCK1. Integrin-associated Talin triggers moesin-dependent recruitment of NHE-1, leading to a local increase of pH and further stimulation of cofilin severing activity. In cells plated on gelatin, actin bundling proteins, such as fascin, CRP2, and L-plastin cooperate to reorganize and stabilize invadopodial AFs in long bundles (4). The apical tip of elongating actin bundles physically interacts with the nucleus, creating indentations. The resulting reaction force (large arrow) is transmitted to the invadopodial tip and promotes protrusion. On fibrillar collagen (40 ), the pushing force directed toward confining collagen fibers is powered by reaction forces resulting from the growth of AFs against each other (large arrow). The upstream regulatory pathways and the proteolytic component of the invadopodial machinery are not depicted.

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TKS4, a close homolog of TKS5 (Zagryazhskaya-Masson et al., 2020), which presumably functionally replaces TKS5. Yet TKS4 does not interact with GFD1, and CDC42 silencing does not inhibit invadopodium formation and activity in Hs578T cells, indicating that alternative actin regulatory pathways control the assembly of invadopodia in this cell model. Other important noncytoskeletal partners of TKS5 include several metalloproteinases of the disintegrin and metalloproteinase (ADAM) family (Abram et al., 2003), of which ADAM12 is a potent facilitator of cancer cell invasion (Diaz et al., 2013; Eckert et al., 2017) due to its capacity to shed cell surface heparin-binding EGF-like growth factor (HB-EGF) and activate EGFR-dependent invadopodium formation (Diaz et al., 2013). TKS5 was also reported to interact with RAB40B (through its PX domain), a Ras-like GTPase regulating MMP-2 and MMP-9 targeting and secretion ( Jacob et al., 2013), and function as a tethering factor to recruit Rab40b transport vesicles to invadopodia ( Jacob et al., 2016). Together these studies place TKS5 at the center of the regulation and coordination of actin dynamics and proteolytic activity at invadopodia. For more in-depth reviews on the functions of TKS5 and TKS4 in healthy and diseased cells, the reader is referred to the following recent review articles (Kudlik et al., 2020; Saini and Courtneidge, 2018).

4.2 Invadopodium maturation The maturation stage consists in actin polymerization-driven invadopodia growth and stabilization, which is coupled to the activation of the secretory machinery responsible for the proteolytic activity, and protease recruitment. The cortactin-cofilin axis has emerged as a key point of regulation for invadopodium maturation ( Jeannot and Besson, 2020). A pioneering study established that cofilin knockdown significantly reduces the lifetime and degradative capacity of invadopodia while it only marginally reduces precursor formation, raising the idea that cofilin is required for maturation of invadopodia rather than for their initiation (Yamaguchi et al., 2005). Cofilin is recruited during invadopodium initiation through its interaction with cortactin, but this interaction maintains cofilin in an inactive state. Tyrosine phosphorylation of cortactin by SRC and ABL kinases releases cofilin severing activity, leading to an increase in the free actin barbed ends available for polymerization (see also below) (Mader et al., 2011; Oser et al., 2009, 2010). Although both formins and Ena/VASP proteins have been suggested to facilitate actin polymerization at the elongating tip of

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invadopodia and contribute to invadopodium maturation (Lizarraga et al., 2009; Nurnberg et al., 2011; Philippar et al., 2008), their exact roles remain poorly defined. Recently, FMNL2, a member of the diaphanous-related formin family, was shown to directly interact with cortactin and stimulate both invadopodium formation and activity, possibly by promoting actin polymerization and MT1-MMP containing endosome motility (Ren et al., 2018). Adhesion rings have been proposed to play an important role in stimulating polymerization of the actin core (Beaty et al., 2013; Branch et al., 2012; Revach et al., 2015). A key ABP of adhesion rings is talin, a large 18 domain-containing, dimeric F-actin-binding protein that physically links integrins to the actin cytoskeleton (Calderwood et al., 2013). Talin is well known for its role in focal adhesions where it triggers conformational changes of integrin extracellular domains leading to increased affinity for ligands (Tadokoro et al., 2003; Tanentzapf and Brown, 2006; Wegener et al., 2007). In addition to integrin activation, talin critically determines the nanoscale architecture of focal adhesions (Liu et al., 2015) and functions as a scaffold for various other cytoskeletal and signaling proteins involved in mechanotransduction (Goult et al., 2018; Kanchanawong et al., 2010). In invadopodia, talin is recruited directly after precursor formation and SRC and ARG kinase activation (Beaty et al., 2013, 2014), and its knockdown results in strongly reduced invadopodium lifetime and proteolytic activity (Beaty et al., 2014). Mechanistically, talin promotes moesin-dependent recruitment of sodium/hydrogen exchanger-1 (NHE-1), resulting in a local increase in pH and regulation of proximal pH-sensitive ABPs. Among those, cofilin is released from its inhibitory interaction with cortactin (Oser et al., 2009) and subsequently severs AFs to generate free barbed ends that synergize with the ARP2/3 complex to amplify actin polymerization (Desmarais et al., 2009; Ichetovkin et al., 2002) and promote invadopodium growth (Beaty et al., 2014; Magalhaes et al., 2011). In addition to the regulation of cofilin activity, cortactin phosphorylation further supports actin polymerization by recruiting both NCK1 (Oser et al., 2009), an activator of N-WASP (Tehrani et al., 2007), and the Rho-family guanine nucleotide exchange factor VAV2 triggering RAC3 activation (Rosenberg et al., 2017). The two isoforms of talin (talin1 and talin2) localize to invadopodia (Qi et al., 2016). Although ablation of either talin 1 or 2 significantly inhibits invadopodium-mediated matrix degradation, talin1 cannot rescue invadopodium activity in talin2-depleted cells, indicating that

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they carry out distinct roles. Notably, talin2 has a much higher affinity toward β-integrin tails as compared to talin1, and its strong interaction with β-integrin mediates traction forces that are required for invadopodium proteolytic activity and tumor cell invasion. An additional role for talin2 in invadopodium maturation has been recently identified with its requirement for the trafficking of MMP9- and possibly MMP2-containing vesicles to the ventral plasma membrane (Baster et al., 2020). Although the underlying mechanism remains to be established, the talin2-β-integrin complex has been shown to act as a docking site for MMP9 vesicles in invadopodia. In conclusion, talin is an important driver of invadopodium maturation and mediates several processes, including actin polymerization, traction force production, and MMP vesicle recruitment. Another class of ABPs required for invadopodium maturation includes actin bundling proteins, of which fascin has been the most extensively studied (Li et al., 2010; Schoumacher et al., 2010). It is noteworthy that fascin upregulation closely correlates with disease progression, metastasis, and poor clinical outcome in carcinoma (Alam et al., 2012; Bu et al., 2019; Papaspyrou et al., 2014; Tan et al., 2013; Vignjevic et al., 2007; Zhao et al., 2015) and osteosarcoma (Arlt et al., 2019). Fascin inhibitors are actively pursued as anti-metastatic agents (Alburquerque-Gonza´lez et al., 2021; Chen et al., 2010; Han et al., 2016; Huang et al., 2015; Wang et al., 2020). By analogy with filopodia, and since fascin exclusively cross- links AFs into unipolar bundles in vitro (Hoffmann et al., 2014; Jansen et al., 2011), invadopodial actin bundles are presumably oriented with their AF barbed ends facing the invadopodium tip to generate the pushing force responsible for protrusion. Other invadopodial actin bundling proteins have been identified and found to be as critical as fascin for the maturation of proteolytic active invadopodia, including L-plastin and cysteine-rich protein 2 (CRP2) (Hoffmann et al., 2016; Van Audenhove et al., 2016). Consistent with their different domain composition and organization, fascin, L-plastin, and CRP2 assemble actin bundles of different properties (Breitsprecher et al., 2011; Hoffmann et al., 2016; Van Audenhove et al., 2016). In mature invadopodia, they similarly distribute along the entire length of the elongated actin core (Hoffmann et al., 2016; Schoumacher et al., 2010; Van Audenhove et al., 2016), suggesting that they cooperate to generate bundles with optimal architecture and mechanical characteristics. In support of this view, an elegant study based on nanobodies inhibiting the bundling activity of fascin or L-plastin established that the two bundling proteins do not compensate for each other but exhibit complementary roles

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in mediating invadopodium development, with fascin providing the rigidity necessary for protrusive activity and L-plastin conferring sufficient flexibility for elongation (Van Audenhove et al., 2016). Interestingly, fascin and CRP2 are upregulated by hypoxia (Bu et al., 2019; Hoffmann et al., 2018; Zhao et al., 2014), one of the most common microenvironmental alterations in solid tumors that is closely associated with tumor invasion and metastasis (Gilkes et al., 2014; Hockel et al., 1996; Semenza, 2016). Both are direct targets of the transcription factor hypoxiainducible factor-1 (HIF-1) (Bu et al., 2019, Hoffmann et al., 2018, Zhao et al., 2014). In breast cancer cells, HIF-1-induced upregulation of CRP2 is indispensable for stimulation of invadopodium formation by hypoxia. In addition, forced expression of CRP2 is sufficient to enhance the invasive capacity of HIF-1α-depleted cells under hypoxic conditions. Thus, invadopodial ABPs can serve as direct and key mediators of pro-invasive microenvironmental conditions. As in filopodia, myosin X concentrates at the tips of mature invadopodia (Schoumacher et al., 2010). In parallel to its function in filopodia, and since its depletion reduces invadopodium length and impairs ECM degradation (Cao et al., 2014; Schoumacher et al., 2010) while having no effect on invadopodium lifetime (Schoumacher et al., 2010), myosin X has been proposed to function as a molecular motor transporting cargo proteins involved in actin polymerization or proteolysis along actin bundles to the invadopodium tip (Schoumacher et al., 2010). However, the exact mechanisms by which myosin X contributes to invadopodium maturation and promotes metastasis in breast cancer mouse models (Cao et al., 2014) remain to be elucidated.

5. Actin polymerization-based protrusion at invadopodia Although the activity of the two main types of in vitro invadopodia, i.e., the small dot-like actin protrusions that elongate perpendicularly to the ECM and plasma membrane and collagenolytic linear/curvilinear invadopodia that develop along collagen fibrils, critically relies on actin polymerization-mediated pushing forces, the underlying mechanisms differ. Plating cells on a thin layer of substrate, usually gelatin, leads to invadopodia development in close proximity to the nucleus (Buccione et al., 2009; Revach et al., 2015; Tolde et al., 2010). Their apical tip projects into the cell interior and interacts with the nucleus, creating deep nuclear

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indentations visible by electron or TIRF microscopy (Revach et al., 2015, 2020). It is proposed that the elongating actin core pushes against the nucleus, which creates a mechanical force transmitted to the protruding tip and applied to the ECM (Fig. 1, 4). Based on the elastic properties of the nucleus and size of indentations, the compressive force is estimated to be sufficient to promote invasion in mesenchymal connective tissue (Revach et al., 2015). Recently, direct measurements of invadopodial forces revealed that the pushing forces produced by mature invadopodia are significantly higher as well as more dynamic and persistent than those produced by immature invadopodia (Dalaka et al., 2020). These mechanical differences likely account for the capacity of mature invadopodia to promote ECM remodeling and tumor cell invasion. In the second model, proteolytically active linear/curvilinear invadopodia can form at distant sites from the nucleus along confining collagen fibers, excluding a direct role of the nucleus in amplifying the actin polymerizationbased pushing force (Ferrari et al., 2019). In the absence of a solid substrate to provide reaction forces, the authors propose that ARP2/3 complex-mediated actin polymerization within each individual invadopodium generates a pushing force directed toward the underlying matrix fiber (Fig. 1, 40 ). Such directional pushing force is powered by reaction forces generated by the growth of AFs against each other and requires the curved geometry of the invadopodia/collagen fiber ensemble. The assembly of linear invadopodia is initiated by recognition of collagen fibers by surface-exposed MT1-MMP, which activates a signaling cascade leading to TKS5 recruitment and ARP2/ 3-mediated actin polymerization (Ferrari et al., 2019). Remarkably, this novel function of MT1-MMP was shown to be independent of its catalytic activity. For information on MT1-MMP trafficking to and recycling at the cell surface, the reader is referred to the following key articles (Castagnino et al., 2018; Infante et al., 2018; Marchesin et al., 2015; Monteiro et al., 2013). In both models of invadopodia, actin-based pushing forces work in concert with MMP-mediated proteolysis to remodel the ECM and generate pores large enough for nucleus translocation (Eddy et al., 2017; Ferrari et al., 2019; Infante et al., 2018; Revach and Geiger, 2014). However, recent data suggest that, in matrices exhibiting substantial mechanical plasticity, such viscous breast tumor tissues (Sinkus et al., 2007; Wisdom et al., 2018), cancer cells can adopt highly protrusive morphologies and use invadopodia to mechanically initiate and open up permanent channels through which they invade independently of proteases (Wisdom et al., 2018). Future studies should evaluate the in vivo relevance of this novel mode

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of invasion which could in principle contribute to the clinical failure of MMP inhibitors and calls for the development of therapeutic strategies targeting the invadopodial force-producing actin machinery.

6. Concluding remarks The molecular components of invadopodia, in particular ABPs and their upstream regulatory pathways, have proven to be reliable predictors of metastatic dissemination and promising therapeutic targets for blocking metastasis (Meirson and Gil-Henn, 2018). Recently, invadopodia have been shown to contain chemotaxis receptors that guide cancer cell extravasation in response to microenvironment signals favorable to metastasis outgrowth (Williams et al., 2019). The underlying mechanism involves the RAC1 effector p21-activated kinase 1 (PAK1) which, in the absence of a signal that permits cancer cell growth, promotes invadopodium disassembly and retraction by promoting cofilin and myosin light chain phosphorylation. Remarkably, ablation of PAK1 potently reduces the tumor burden and size of brain metastases in a brain-topic basal-like breast cancer mouse model, suggesting that invadopodium disassembly represents an additional therapeutic opportunity. However, several potential hurdles should be considered when designing strategies aimed at targeting invadopodia. First, migration plasticity allows cancer cells to switch from invadopodium- and MMP-dependent mesenchymal migration to MMP-independent amoeboid migration, thereby quickly adapting to changing microenvironments (Alexandrova et al., 2020). Inhibition of pericellular proteolytic activity has been shown to induce cancer cell conversion toward an amoeboid phenotype and proteaseindependent dissemination (Wolf et al., 2003), suggesting that migration plasticity underlies resistance to anti-MMP therapies. Second, a recent study established an intriguing relationship between cell cycle progression and invadopodium function, and identified TKS5 and cortactin as important effectors of this relationship (Bayarmagnai et al., 2019). In the G1 phase of the cell cycle, breast carcinoma cell lines show a significantly increased ability to form proteolytically active invadopodia and invade through high-density collagen due to increased expression of cortactin (and MT1-MMP) and recruitment of TKS5 to invadopodia. In turn, TKS5 ablation alters cell cycle dynamics and increases the fraction of cells in the early S phase, suggesting that TKS5 mediates a reciprocal effect of invadopodia on the cell cycle. Future studies are warranted to evaluate the clinical implications of coordination between invadopodia and the cell cycle. While antiproliferative drugs

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arresting cancer cells in G1 could conceivably promote invadopodium function and thereby stimulate metastasis, blocking invadopodium formation holds the risk of stimulating tumor cell proliferation (Bayarmagnai et al., 2019). Thus combination approaches targeting both tumor cell proliferative and invasive capabilities will likely be required to achieve satisfactory clinical results.

Acknowledgments Thomas’s lab is supported by grants from the National Research Fund (FNR, ACTIVATION C19/BM/13579644), La Fondation Cancer Luxembourg (ACTIMMUNE, FC/2019/02), and Think Pink Lux (Marian Aldred Award). T. Mgrditchian is the recipient of a postdoctoral fellowship from Fonds De La Recherche Scientifique (CRP2-TME, FNRS, TLV/7.4537.19). T. M€ uller. is supported by the National Research Fund (FNR PEARL P16/BM/11192868). Fig. 1 has been created with BioRender.

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CHAPTER FIVE

Cancer type-specific alterations in actin genes: Worth a closer look? Christophe Ampe*, Laura Witjes, and Marleen Van Troys Department of Biomolecular Medicine, Ghent University, Gent, Belgium *Corresponding author: e-mail address: [email protected]

Contents 1. Introduction 2. The human actin gene/protein family: A tale of functional redundancy and distinction 3. Actin: Basic structure-function relationships as a context for interpreting actin mutations in cancer 3.1 Actin monomer structure and the regions involved in filament formation 3.2 ABP binding sites on actin and post-translational modifications of actins 4. Actin gene alterations and actin mutants: Do they occur in cancer? 5. Alterations in actin genes in patient cancer genomes: An untapped resource 5.1 From patient actin mutations to 3D-structure: A primer based on hematological cancers 5.2 Mutations and copy number alterations in actin genes in patient samples: Selected case studies 5.3 Fusions with actin genes in cancers 6. Summary, conclusions, and perspectives References

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Abstract Actins form a strongly conserved family of proteins that are central to the functioning of the actin cytoskeleton partaking in natural processes such as cell division, adhesion, contraction and migration. These processes, however, also occur during the various phases of cancer progression. Yet, surprisingly, alterations in the six human actin genes in cancer studies have received little attention and the focus was mostly on deregulated expression levels of actins and even more so of actin-binding or regulatory proteins. Starting from the early mutation work in the 1980s, we propose based on reviewing literature and data from patient cancer genomes that alterations in actin genes are different in distinct cancer subtypes, suggesting some specificity. These actin gene alterations include (missense) mutations, gene fusions and copy number alterations (deletions and amplifications) and we illustrate their occurrence for a limited number

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of examples including actin mutations in lymphoid cancers and nonmelanoma skin cancer and actin gene copy number alterations for breast, prostate and liver cancers. A challenge in the future will be to further sort out the specificity per actin gene, alteration type and cancer subtype. Even more challenging is (experimentally) distinguishing between cause and consequence: which alterations are passengers and which are involved in tumor progression of particular cancer subtypes?

1. Introduction The actin cytoskeleton is complex and can be considered as a three-layered subcellular system. The first layer is formed by actin as an executer, either in monomeric or in filamentous form (and in the contractile apparatus together with myosins). The second one consists of an evergrowing number of actin binding proteins (ABPs) acting as modulators of this activity (Merino et al., 2020; Pollard, 2016). A third layer is formed by associated proteins that regulate ABP activity in response to signal transduction events or environmental cues (see, e.g., Lambrechts et al., 2004; Romero et al., 2020; Rottner and Schaks, 2019). An additional level of complexity exists due to the presence of paralogues or of alternatively spliced mRNA products within each of the layers described above. In many cases these related proteins are co-expressed in one cell and display variations in activity or in regulation. This is exemplified by the large family of tropomyosin ABPs that copolymerize with actins (Hardeman et al., 2020; Janco et al., 2016), by the actin modulating proteins of the β-thymosin, cofilin or profilin families in mammals (Dhaese et al., 2007; Polet et al., 2007; Van Troys et al., 2008a), and by the much studied upstream regulators Rho kinase 1 and 2 ( Julian and Olson, 2014). Interestingly, the occurrence of paralogues is also the case for the actin molecules themselves for which in vertebrates eight orthologous groups exist of which in humans six distinct actins are present (see below) (Ampe and Van Troys, 2017; Witjes et al., 2019). A fourth layer of functional complexity emerges based on the accumulating evidence that not only in the cytosol an actin skeleton exists but that actin monomers and filaments (and ABPs) actively contribute to nuclear function and organization, and that pools of actin in both compartments communicate with each other (Kyher€ oinen and Vartiainen, 2020). This complex layering of the actin system is key to numerous cell functions that are as diverse as gene expression or DNA repair (Kelpsch and Tootle, 2018) and actual cell translocation (Blanchoin et al., 2014). As such, the actin system is essential in building, regulating or tightly associating with

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diverse dynamic cellular structures that support these functions in a cell type-dependent manner with as examples: immunological synapses, cytokinetic rings, cell-cell and cell-matrix adhesion structures, cellular contractile stress fibers and protrusive structures (e.g., Boiero Sanders et al., 2020; Horton et al., 2016; Lehtimaki et al., 2017; Svitkina, 2018, 2020). The integrated role of the actin system in healthy cells implies that also cancer cells heavily rely on its function. Especially to acquire the capability to invade and metastasize to distant sites (the hallmark of cancer that is most associated with mortality), primary tumor cells use the propulsive force of actin polymerization and/or the contractility of the actomyosin system (Biber et al., 2020; Meirson et al., 2020). In addition, epithelialmesenchymal transition (EMT) or mesenchymal-to-amoeboid transition (MAT) are tightly linked to actin remodeling (Alexandrova et al., 2020; Izdebska et al., 2020; Shankar and Nabi, 2015). These two processes are examples of the context-dependent plasticity that cancer cells display during the metastatic process. In addition, evidence is accumulating that remodeling of the actin cytoskeleton may enable tumor cells in acquiring additional cancer hallmark features such as evading normal apoptotic signaling (Desouza et al., 2012), which can be exploited for drug-induced apoptosis in cancer cells. Moreover, the actin system has also been associated with cancer drug resistance via the effects of actin reorganization on membrane transporters and anion channels. This mechanism was for instance recently suggested for cisplatin resistance in breast cancer cells (Shimizu et al., 2020). The deregulation of the actin cytoskeleton is frequently based on deregulated signaling cascades downstream of (non)receptor tyrosine kinase and/or based on improper functioning of small RhoGTPases (Kazanietz et al., 2018; Lee et al., 2019; Xiang et al., 2017). The proteins of the RhoGTPase family are key regulators of organization and dynamics of the actin cytoskeleton and are deregulated in tumors via a variety of mechanisms. These include: overexpression of some of the Rho family members with oncogenic activity or downregulation of other members with tumorsuppressive activity; changes in upstream regulators and effector proteins; altered posttranslational modifications such as lipid modification or the occurrence of activating mutations ( Jung et al., 2020; Porter et al., 2016). However, in cancer not only the upper layer that regulates and signals to the actin system is deregulated. Indeed, also the expression level or activity level of specific ABPs or groups of ABPs involved in cell motility or cell adhesion are frequently altered in invasive cancers (Coumans et al., 2018; Gross, 2013; Van Troys et al., 2008b; Yamaguchi and Condeelis, 2007).

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This has recently been extensively reviewed for many ABPs including those that nucleate actin filament formation (Aseervatham, 2020; Biber et al., 2020; Izdebska et al., 2020). Finally, also changes in the expression of actin itself have been reported in cancer. This is well documented for one of the two nonmuscle actins (β-cytoplasmic actin, see below) by Guo et al. (2013) and evidences that the expression of this actin is increased in, among other, liver, melanoma, renal, gastric, pancreatic, esophageal, lung, breast, prostate and ovarian cancers and in leukemia and lymphoma. Frequently - but not always - this increased expression level is associated with increased invasion and metastasis. In addition, another actin: α-smooth muscle actin (often abbreviated as SMA), is often associated with tumor progression. Indeed, together with the intermediate filament protein vimentin, SMA is a biomarker of activated myofibroblasts or of cancer associated fibroblast (CAFs). The presence of the latter cells in the tumor microenvironment is reported to promote local dissemination and invasion in different cancer types (De Wever et al., 2008; Dzobo and Dandara, 2020; Nomura, 2019; Nurmik et al., 2020). The level of this same actin can, however, also be upregulated in the tumor cells themselves and is for example overexpressed in tamoxifen-resistant breast cancer cells (Kim et al., 2019b). In the present chapter we zoom in on cancer related changes that can be identified in actins. However, we do not (extensively) report on deregulation in expression and its consequences but rather on identified changes in the actin genes and the actin proteins themselves. Unlike for other diseases, reports on actin mutations in cancer are still very limited and in our opinion have received too little attention. We here present a more extensive view on alterations in actin genes including missense mutations, copy number alterations (CNAs) or gene fusion events in cancer. This is among other based on exploring and reviewing available genome data from patients from specific cancer types. Before we discuss these actin alterations it is important to provide a background on the actin gene family and on relevant structure-function relations of actin.

2. The human actin gene/protein family: A tale of functional redundancy and distinction As mentioned above, the term actin covers a family of highly similar actin paralogues. In humans six actin genes are present: ACTA1, ACTA2, ACTB, ACTC1, ACTG1 and ACTG2. For none of these, splice variants

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have been reported. Strictly speaking, the encoded proteins should therefore not be considered as isoforms (i.e., resulting from differential expression of a single gene (In˜iguez and Herna´ndez, 2017)) although this term is traditionally often used to describe the members of the actin (gene) family. We will use throughout this chapter the term “actin paralogues” since this is the term to be used for products of genes that arose from a duplication event. Expression of the six actin genes is typically associated with tissue specificity and their names have been given accordingly (Vandekerckhove and Weber, 1978). ACTA1 encodes α-striated muscle actin, ACTC1 cardiac muscle actin, ACTA2 α-(vascular) smooth muscle actin, ACTG2 γ-(enteric) smooth muscle actin and ACTB and ACTG1 encode cytoplasmic β-actin and γ-cytoplasmic actin, respectively. The first four are muscle actins and the last two cytoplasmic actins are also referred to as nonmuscle actins. In reality this needs to be nuanced because most healthy tissues express two or more actins (reviewed in Tondeleir et al., 2009). Indeed, α-skeletal muscle actin is for example also expressed in heart. Most smooth muscle tissues express combinations of α-smooth and γ-enteric actin and all nonmuscle tissues contain both cytoplasmic β-actin and γ-cytoplasmic actin (albeit in different ratios). Moreover, the nonmuscle actins have also been shown to be expressed and important in muscle tissue. For instance, it was reported that muscle specific deletion of ACTB results in myopathy (Prins et al., 2011). What is described above is the situation in healthy tissues but in the context of cancer, deregulated expression of actin paralogues is extensively reported in literature (e.g., reviewed for ACTB in Guo et al., 2013). Recently, it was reported that ACTG1 shows a high frequency of amplification and associated overexpression in each of the four subtypes of uterine cancers (Richter et al., 2020). This actin paralogue ACTG1 has also been proposed as marker in alcohol-associated hepatocellular carcinoma (Gao et al., 2019). In addition, ectopic expression of actin paralogues, i.e. expression outside their “normal” tissue, is also frequently observed in cancer. This is illustrated at mRNA level for the skeletal muscle ACTA1 in triple negative breast cancer which remains the most difficult-to-treat type of this cancer (Fig. 1; Eswaran et al., 2012). Deregulation of an actin paralogue may also affect the actual cellular transcriptome or proteome in cancer cells. For instance, a β-cytoplasmic actin dependent activity on transcription of specific genes was suggested during PMA-induced differentiation of acute myeloid leukemia HL-60 cells (Xu et al., 2010). Nuclear actin functions and a regulatory role in transcription have often been associated with this β-cytoplasmic actin (Tondeleir et al., 2012; Visa and Percipalle, 2010) but

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Fig. 1 Actin expression in cancer: example study. Altered and ectopic expression of different actins in triple negative breast cancer (TNBC) or in non-TNBC versus normal breast tissue. (Based on (Eswaran et al., 2012) with 8–9 samples per condition). ACTC1 is not shown since its log2 fold change in expression was below 1. Data are extracted from the open-source database Expression Atlas release 36 at https://www. ebi.ac.uk/gxa/home.

the paralogue dependence of this aspect of actin’s activity is still far from understood both in normal and cancer cells and a potential role of other actins herein is also explored (see, e.g., Lechuga et al., 2014). The observation that tissues or cells often express more than one actin complicates their study in both healthy tissue and in cancer because silencing or overexpression of a particular actin always occurs in the context of the presence of another actin (because of co-expression). This is aggravated by the observation that modulating expression of one actin gene influences expression of other actin genes (Ampe and Van Troys, 2017; Malek et al., 2020). Many studies in mouse and cells have shown that ablation or silencing of for instance β-cytoplasmic actin results in upregulation of γ-cytoplasmic actin and, in particular cases, also of α-smooth muscle actin (Bunnell et al., 2011; Lechuga et al., 2020; Patrinostro et al., 2017; Tondeleir et al., 2012; Vanslembrouck et al., 2020; and refs herein). Similarly, genetic ablation of ACTA2 in airway smooth muscle cells resulted in robust expression of both α-striated muscle actin and γ-smooth muscle actin proteins (Schildmeyer et al., 2000; Takeji et al., 2006). This indicates that transcription and/or expression of actin paralogues are linked to one another, a mechanism termed genetic compensation, and that cells thus somehow sense and regulate the total amount of actin.

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In the context of cancer this implies that a loss or gain of one specific actin through gene mutation, deletion, amplification, or deregulation of transcription or translation is likely to induce expression level changes in other actins so that the observed phenotype is the resultant of multiple “actin changes.” This would not form a major difficulty if both the specific and the redundant functions of each actin paralogue were firmly established but unfortunately this is not the case. Especially for the two nonmuscle actins that only differ in four N-terminally located residues (out of 375, see below), that are generally considered the main actins that steer cell migration in normal and cancer cells, the debate on functional redundancy and diversity is far from finished. Several studies aimed to clarify the roles of nonmuscle actins by overexpression, silencing, or knockout of the coding sequences for β- or γ-actin in (cancer) cells and model organisms and we refer to Ampe and Van Troys (2017), Dugina et al. (2015), Simiczyjew et al. (2017), Vanslembrouck et al. (2020), and Vedula and Kashina (2018) for the experimental evidence and current views. Important recent results adding to this discussion are found in the study of Malek et al. (2020) that managed for the first time to study each of the nonmuscle actins separately by using CRISPR/Cas9 (D10A) gene editing in a melanoma cancer cell line. Their results firmly indicate that the isoforms are far from redundant at the cellular level. Indeed, even when present alone in a cell they distribute to different subcellular regions and engage in different actin-rich structures. Moreover, their absence differently affects the formation of cell-ECM adhesion sites as well as the efficiency of cell migration and invasion. Strikingly the γ-cytoplasmic actin knockout has more severe consequences than the β-cytoplasmic actin knockout in these cellular properties (Malek et al., 2020).

3. Actin: Basic structure-function relationships as a context for interpreting actin mutations in cancer 3.1 Actin monomer structure and the regions involved in filament formation We here provide a concise description of the structure of the actin monomer (or G(lobular)-actin) and filament (F-actin) as a foundation for evaluating or forming hypotheses on the impact of mutations discussed below. The primary structure of actins is 375–377 amino acids long (depending on the paralogue). When all six human actin proteins are compared, less than 8% of their residues show substitutions and often these changes are conservative

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(Tondeleir et al., 2011; Vandekerckhove and Weber, 1978). In line with their recently updated evolutionary origins (Witjes et al., 2019), the pairs of actin genes with the most similar tissue-specific expression, i.e. ACTA1–ACTC1, ACTA2–ACTG2 and ACTB–ACTG1, differ the least in amino acid sequence at the protein level. Given this conservation it is expected that the 3D-structure of actins and their properties are similar. Obviously, we cannot exclude that subtle differences in the structures of each paralogue renders slightly different functionalities with respect to polymerization kinetics (this section) or interaction with actin binding proteins (Section 3.2). This can have impact on the interpretation of effects of point mutations for β- and γ-cytoplasmic actin discussed in Section 5.1 or on future understanding of mutations in other actin paralogues not discussed here. The 3D-structure of the actin monomer, which was first unraveled to atomic resolution in 1990, shows a protein with four subdomains (SD) in which SD1–2 and SD3–4 line two clefts at the top and bottom of the structure as shown in Fig. 2 (Kabsch et al., 1990; Kabsch and Vandekerckhove, 1992). The highly acidic N-terminus of actin enables distinguishing all six actins (Ampe and Van Troys, 2017; Vandekerckhove and Weber, 1978) and is located together with the C-terminus in SD1. Two spatially proximal polypeptide stretches (hinges in Fig. 2) connect SD1 and SD3 and act as a pivot point for intramolecular subdomain movement. This movement is, in part, stabilized by the essential actin ligands: ATP/ADP and a divalent cation. Three central loops and a proline-rich region (P1-loop, P2-loop, sensor loop, Pro-rich region in Fig. 2) either position the phosphates of the nucleotide, sense the nucleotide state or partake in Pi-release (Chou and Pollard, 2019; Kabsch et al., 1990; Murakami et al., 2010). In the context of this chapter it is relevant that an alanine scan of β-cytoplasmic actin revealed that at many positions an insertion of five consecutive alanines still resulted in a stably folded protein, indicating the plasticity of this structure (Rommelaere et al., 2003). All actins form polymers by noncovalent association of the monomers in a head-to-tail fashion (Pollard, 2016 and references therein). This, together with hydrolysis of the bound nucleotide, results in polarized actin filaments (F-actin) with different structural and kinetic properties at the two ends. It has been long known that nucleotide hydrolysis and Pi-release does not occur directly upon actin monomer incorporation at the barbed end, resulting in filaments with a different nucleotide status along their length (Carlier and Pantaloni, 1988). Filaments consequently have an age marker

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Fig. 2 Structure of the actin monomer. The actin monomer is shown in two orientations in ribbon representation. The four subdomains SD1–4 with comprised amino acid residue numbers are indicated in the left panel. The ATP-analogue β,γ-imidoadenosine 50 -triphosphate (AMPPNP) is in orange stick and the magnesium ion is in dark green. Regions in black are important for filament formation, i.e. intersubunit actin-actin contacts in F-actin. Regions in other colors are involved in nucleotide binding or hydrolysis or for interdomain movement (see main text). Indicated are the DNase I-binding loop (D-loop; residues 40–50), sensor loop (residues 71–77), proline-rich region (Pro-rich region; residues 108–112), hinge region (hinge helix: residues 137–145 and hinge loop: residues 335–337), WH2-domain binding loop (W-loop; residues 165–172), hydrophilic plug (H-plug; residues 263–273) and Pi-binding loops (P-loop 1; residues 13–16 and P-loop 2; residues 156–159). Additional residues in black (actin-actin contact): P38, R38, R62, I64, K113, T148, H173, E195, T203, D244, D286, R290, C374. Amino (N)- and carboxy (C)-termini are indicated; residue G245 (G244 in β-cytoplasmic actin) and R28 are indicated using ball and stick, red/blue: O/N-atoms with residue numbering based on N-terminally processed α-striated muscle actin. This figure was prepared using the 6DJM obtained from www.rscb.org/pdb (Chou and Pollard, 2019) and processed using Viewerlite 5.0 (Accelrys, USA). The figure is adapted from Witjes, L. 2020 (PhD dissertation, UGent)

with a “young” ATP/ADP-Pi end and an “old” ADP end. These ends are indicated as “plus or barbed or fast-growing end” and “minus or pointed or slow-growing end” (indicated on monomer structure in Fig. 2). At least in vitro co-polymers containing different actin paralogues are possible (Bergeron et al., 2010; M€ uller et al., 2013). The actin polymer is twisted since each monomer is rotated over approximately
166.7° in a left-hand fashion allowing to describe the filament as a helical structure (Chou and Pollard, 2019; Holmes et al., 1990). Since the publication of the first

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F-actin model in 1990 (Holmes et al., 1990), the insight into the actin-actin contacts in F-actin (both along or across the helical axis) and insight into the conformational changes in the actin subunit upon incorporation, nucleotide hydrolysis and Pi-release has been expanded and is now at near atomic resolution (selected refs: Chou and Pollard, 2019; Galkin et al., 2010; Merino et al., 2018, 2020). For the present chapter, the relevant regions of actin that are involved in establishing the actin-actin interactions are indicated in Fig. 2. It consists of regions in SD2, 3 and 4 that are referred to as the D-loop, W-loop and H-plug and an additional loop at the top of SD4.

3.2 ABP binding sites on actin and post-translational modifications of actins Actin filaments are involved in a plethora of functions in nonmuscle cells. To manage these complex tasks cells have evolved to use actin-binding proteins, an extensive set of proteins that all directly contact and modulate the activity of actin monomers or filaments. In general terms, the role of actin filaments in these cells constitutes a combination of providing structure and generating force. At the same time the aspect of actin filament dynamics is essential: filaments need to form, elongate and depolymerize in a manner that is controlled in time and subcellular space (Pollard, 2016). To these purposes, cellular actin filaments are present in diverse higher-order structures of complex architecture each with specific functions. These include: dense networks of branched filaments (as in protruding lamellipodia, at sites of endocytosis, cell-cell adhesion, or in neuronal growth cones and dendritic spines), parallel bundles of varying length and density (as in microvilli, filopodia, …) or contractile bundles of actin filaments in a bi-polar arrangement (as in stress fibers). We refer to a number of reviews for further details: (Gallop, 2020; Rottner and Schaks, 2019; Svitkina, 2018; Tojkander et al., 2012). The main role of these architectures is indeed functional with, as the best-known example, the actual advancement of the cell edge in migrating cells that is driven by a branched actin filament network, which pushes the plasma membrane forward through coordinated elongation of actin filaments against the membrane. The many ABPs simultaneously present in these actin-rich higher-order structures have varied functions. Some ABPs affect the (rates of ) filament elongation or depolymerization in a positive or negative manner. These include actin monomer sequestering proteins (e.g., β-thymosins and profilins) but also actin filament nucleators (e.g., specific WH2-proteins, formins), actin

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polymerases (formins), barbed end capping proteins (e.g., capping protein), filament severing proteins (such as cofilin and gelsolin). ABPs also include F-actin decorating proteins such as tropomyosins or cofilins which regulate both filament stability and accessibility. Other ABPs have an important organizing role and act as tunable filament cross-linkers (e.g., α-actinins) or bundlers (e.g., filamins, fascin) or act to initiate or modulate the stability of branches on actin filaments (Arp2/3 complex, cortactin, cofilin). A special subset of ABPs are the myosins that either use actin filaments to support intracellular trafficking or upon their organization in myosin filaments function in actomyosin-based contractility. Of interest, some ABPs have been described to be specific for an actin paralogue (e.g., β- or γ-cytoplasmic), bind actin (or actin subunits in a filament) depending on the nucleotide status (ATP, ADP or ADP-Pi, e.g., cofilin) or even promote nucleotide hydrolysis or exchange (e.g., profilin). The details on the functions of ABPs or specific subsets of ABPs have recently been reviewed by Manstein and Mulvihill (2016), Merino et al. (2020), Pollard (2016), Rottner and Schaks (2019), SitonMendelson and Bernheim-Groswasser (2017), Titus (2018), and Zaidel-Bar (2013). Given that ABPs all have actin as a binding partner this necessarily implies that for many their binding sites on actin are overlapping. The binding sites of many ABPs (profilin, cofilin, domains of gelsolin, thymosin-β4 and the related WH2-proteins, formins, capping protein, tropomyosin and myosins) are known either from studies using X-ray crystallography or NMR (for complexes with G-actin) or cryo-electron microscopy (for complexes with F-actin) (overviews in: Dominguez, 2016; Janco et al., 2016; Merino et al., 2020; Pollard, 2016). Intriguingly, the hydrophobic cleft (also called target cleft) between actin SD1 and SD3 at the barbed end is a hot-spot for ABP-interaction (Dominguez, 2007; Merino et al., 2020) (see Fig. 2). For instance, cofilin interferes with the actin-actin contact between SD2 and SD3 by binding the target cleft and the D-loop on different actin subunits along the F-actin helix (see Fig. 2) and thereby interferes with the actin-actin contacts between SD2 and SD3 (Huehn et al., 2020; Tanaka et al., 2018). SD1 appears to be the main binding site for myosin motor heads (Behrmann et al., 2012; Von Der Ecken et al., 2016). Taken together, this overlap in binding sites on (F-)actin points to the complexity of the system. Therefore, it is not only difficult to predict whether an interaction with a particular actin-binding protein will be positively or negatively affected by a mutation in actin but also what the outcome on, e.g., filament formation or organization will be.

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Not only the interaction with ABPs but also the growing knowledge on post-translational modification (PTM) of actin paralogues (Varland et al., 2019) needs to be taken into account to interpret the possible impact of cancer associated actin alterations. Actins have been known for a long time to be processed and acetylated at the N-terminus and methylated at His73 with the latter affecting polymerization kinetics. Only recently the enzymes responsible for these PTMs have been identified (Drazic et al., 2018; Goris et al., 2018; Kim et al., 2019a; Rebowski et al., 2020; Varland et al., 2019; Wiame et al., 2018; Wilkinson et al., 2019). Of interest, methylation of the His73 of actin by one of these enzymes, METTL18, was shown to influence the metastatic responses of breast tumor cells through modulation of the actin cytoskeleton and Src phosphorylation (Kim et al., 2019a). Similarly, cells with knock out of the NAA80 enzyme that acetylates processed actin N-termini display defective cell motility (Drazic et al., 2018). Intriguingly, the ABP profilin has been shown to facilitate this enzymatic reaction (Rebowski et al., 2020). Varland and co-authors have documented an impressive list of 140 PTMs on no less than 94 residues of actin with some occurring with high stoichiometry and others only affecting a subset of the cellular actin pool (see Table 1 in Varland et al., 2019). Next to acetylation and methylation also arginylation, oxidation, phosphorylation, ubiquitination, and SUMOylation of actins occur. It is clear that the functional roles of all these PTMs on actin are far from understood. However, it is to be expected that altered capacity to undergo PTMs will play a role in cancer cells, not in the least because many of the enzymes implicated in these PTMs are reported to be aberrantly expressed or mutated in cancer (e.g., NAA80 or members of the actin-oxidizing MICAL enzyme family that induce rapid filament depolymerization) (refs in Varland et al., 2019). In addition, when an actin is mutated in a cancer context at a site that is the target residue of a PTM modification or that is in the neighborhood of such a residue, this can alter the normal balance in PTM-dependent actin functions.

4. Actin gene alterations and actin mutants: Do they occur in cancer? In the eighties of the 20th century, prior to the determination of the G- and F-actin structure, two actin mutations in β-cytoplasmic actin were described in cancer cell lines. Today, selected studies report on different actin gene alterations in cancer, e.g., in childhood leukemia

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(Verrills et al., 2006), in blood cancer subtypes (Witjes et al., 2020), in uterine cancer subtypes (Richter et al., 2020) and in soft tissue cancers (Koh et al., 2019; Xu et al., 2020). However, a broader answer to the question how widespread actin gene alterations occur in cancer may lie within the steeply growing big data of cancer genomics and is, for many researchers, possibly still obscured by inherent hurdles to analyze these data. The first actin mutation associated with oncogenic transformation was indeed reported in 1980: a mutation of glycine 244 (residue 245 in unprocessed β-actin) to aspartic acid was identified in β-cytoplasmic actin in carcinogentreated HuT-14 cells (Vandekerckhove et al., 1980). These cells express the mutant form in addition to wild type β- and γ-cytoplasmic actin. Gly244 is located in a loop in actin SD4. Based on the filament model described above, this loop in SD4 interacts with loops in SD3 in an adjacent actin and thus contributes to longitudinal actin-actin contacts within an actin filament (Fig. 2). The mutant actin G244D was indeed shown to have a lower filament nucleation and polymerization capacity in vitro (Blache et al., 2013). Even in the presence of wild type cytoplasmic actins, the mutant actin displayed reduced incorporation in filaments in vitro (Taniguchi et al., 1988), suggesting a dominant effect. Originally the HuT cells were thought to be derived from human (Hu) transformed (T) fibroblasts but later evidence suggested they originate from the human sarcoma cell line 8387 (Mccormick et al., 1988). Regardless of their origin, the clonal variant cell lines expressing the mutant G244D actin induce tumors in athymic mice and the potential to do so correlates with the expression level of this particular mutant (Leavitt et al., 1987b). The tumorigenicity induced by expressing the actin mutant was also confirmed in other cell types (Blache et al., 2013). At the cellular level this mutant is stable and in a HuT cell line incorporates into stress fibers but shows reduced presence in perinuclear actin structures (Leavitt et al., 1987a). Subsequently it was discovered that B16 melanoma cells express a mutant β-cytoplasmic actin (named Ax or betam) in which R28 is substituted by Leu (Sadano et al., 1988). In wild type actin, the positively charged R28 is present in actin SD1 at the start of an amino acid strand that connects SD1 and 2 (Fig. 2). This mutant actin incorporates into actin networks to the same extent as wild-type β-cytoplasmic actin in vitro and in cells, although the mutation can impair depolymerization (Shimokawa-Kuroki et al., 1994). Curiously, the expression of the R28L mutant resulted in increased levels of actin stress fibers and its expression negatively correlated with invasiveness and metastatic potential in a series of B16 sublines (Shimokawa-Kuroki et al., 1994; Taniguchi et al., 1989).

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The two cases described above date from the pre-genomic era. However, what does scientific literature tell us today? A search within PubMed (on November 19, 2020) with keywords “actin mutations cancer” (article type review) yields 201 reviews. However, hardly any of these deal with actin mutations themselves, rather the actin cytoskeleton is mentioned in the context of mutations in upstream regulators (e.g., growth factor receptors, proto-oncogenes (Ras-members, p53, Src), E-cadherins and β-catenin or small GTPases of the Rho family) or together with genetic alteration of ABPs (e.g., profilin, testin family members, Ena, WASP, paxilin). Although this exemplifies the link of the actin cytoskeleton with cancer phenomena in general as introduced above, it also demonstrates that mutations in actins in cancers are rarely reported in scientific papers. The result of this search is in sharp contrast with the situation in congenital diseases. Indeed, a similar PubMed search on “actin mutation congenital disease” (publication type review) yields 179 reviews that readily and recurrently identify studies on specific mutations in different actin paralogues in such diseases. Likewise, using the Human Gene Mutation Database (HGMD) which collects published germline mutations causing human genetic disease (http:// www.hgmd.cf.ac.uk), Parker and coworkers extracted over 400 mutations summed over the six actins (Parker et al., 2020). These are mainly (>90%) missense mutations and are identified in patients with severe disease phenotypes. The mutations appear most numerous in ACTA1 and these cause the nonprogressive skeletal muscle disease, nemaline myopathy (NM), that usually has an early onset, sometimes with severe symptoms already at birth. Most of the mutations in ACTA1 genes in this disease are heterozygous dominant and they induce muscle cell myofibril disorganization and the occurrence of actin-containing rod structures (Sewry et al., 2019). The tight connection of these phenomena and mutations in a limited set of muscle proteins including ACTA1 has formed the basis for the identification of the many ACTA1 mutations in NM (Parker et al., 2020; Rubenstein and Wen, 2014). Likewise, mutations in ACTC1, ACTG2 and ACTA2 have been identified in other muscle diseases that range in severity form mild to lethal (see full list at https://www.mdpi.com/1422-0067/21/9/3371/s1 (Parker et al., 2020)). In line with their less restricted tissue-specific expression, congenital mutations in ACTB and ACTG1 are associated with diseases with multiple defects in the patients: ACTB and ACTG1 mutations are causative for the Baraitser-Winter syndrome whereas only ACTG1 mutations are associated with deafness (Parker et al., 2020; Rubenstein and Wen, 2014; refs in https://omim.org/ entry/604717). Given the expected role in cancer of actin mutations, the

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question has been raised whether these actinopathies or congenital diseases characterized by actin mutations predispose patients towards cancer development. Although not yet demonstrated in general, this has recently been suggested based on three Baraitser-Winter patients that either developed acute myeloid leukemia (AML), acute lymphocytic leukemia (ALL) and cutaneous lymphoma (Cianci et al., 2017; Verloes et al., 2015). Nonetheless, this analysis overall demonstrates that even though actin mutations are extensively linked to disease they are, at least in the published record, rarely reported in cancer today. However, a completely different picture emerges when the vastly growing data emanating from large-scale cancer genome projects are considered. The cBioPortal database for instance is one of the current databases that collects and allows the mining of genome data of cancer patient samples. In cBioPortal no less than 47,534 cancer patient samples are present from 181 studies (numbers from November 2020 and only considering the curated nonredundant studies) (Cerami et al., 2012; Gao et al., 2013). Mining of these publicly available data indeed indicates that alterations, including somatic mutations, do occur in all six actin genes in cancer and can be associated to particular cancer subtypes. Not surprisingly, this occurs in an actin gene-specific manner but also in an alteration type-specific manner as we recently showed for ACTB missense mutations in blood cancer subtypes (see below) (Witjes et al., 2020). cBioPortal distinguishes between (missense) mutations and copy number alterations (CNAs), the latter splitting up in (homozygous) deletions and amplifications. In contrast to the conclusion above based on curated published data in for example the Human Gene Mutation Database (HGMD), cancer patient genomes available at cBioPortal allowed identifying no less than 278 missense mutations in ACTB, 162 in ACTG1, 154 in ACTA1, 226 in ACTC1, 115 in ACTA2 and 143 in ACTG2 in November 2020, albeit overall mutational frequencies in actin remain relatively low. It is impossible to provide a comprehensive overview of alterations in actin genes in all publically available cancer patient sample data. However, this information is readily available for data mining and can subsequently be used to formulate hypotheses and steer preclinical investigations on actin paralogues in the context of cancer progression and metastasis for specific cancer subtypes. Using a few examples, we illustrate that alterations in actin genes in the genomes of cancer patients are worth a closer look. As such our aim is to possibly bring these alterations in actins, that are key proteins in the cellular machinery, more to the foreground in cancer research.

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5. Alterations in actin genes in patient cancer genomes: An untapped resource We first discuss our recent analysis showing enrichment of somatic mutations in the cytoplasmic actins in specific subtypes of hematological cancers (Witjes et al., 2020). We subsequently expand to mutations and copy number alterations (CNAs) in actin genes across four cancer types: breast, liver, skin (melanoma and nonmelanoma) and prostate cancers. This sets a base to focus on putative similarities and differences in the actin missense mutations in specific subtypes of skin cancers. For CNAs in actin genes of cancer patient samples we further zoom in on breast, liver and prostate cancer. All data are from the database cBioPortal (Cerami et al., 2012; Gao et al., 2013) at www.cbioportal.org unless otherwise stated. Important to realize is that, as is the case for any database, the cBioPortal data is continuously expanding and evolving. Therefore, the percentages indicated below are not definitive. They can however be used to compare the actins within a particular cancer type or to discern differential mutational patterns across cancers. In our discussion we will most frequently compare the actin genes that, from an evolutionary perspective, are closest together and in addition are often coexpressed in the same tissue (Witjes et al., 2019).

5.1 From patient actin mutations to 3D-structure: A primer based on hematological cancers According to the WHO classification depending on cell lineage, hematological cancers contain lymphoid cancers and myeloid neoplasms, both including different types of leukemia (Bruneau and Molina, 2020). Diffuse large B-cell lymphoma (DLBCL) and multiple myeloma (MM) are subtypes of mature B-cell neoplasms that belong to the lymphoid cancers. Using the cancer genome data in cBioPortaI (January 2020 version) we recently uncovered in these two lymphoid cancer subtypes a differential mutation frequency for the ACTB gene versus the ACTG1 gene (Witjes et al. 2020). Indeed, ACTB is more frequently mutated in DLBCL whereas ACTG1 is more frequently mutated in MM. Of interest, this analysis in Witjes et al. (2020) additionally demonstrated that the enriched presence of cytoplasmic actin mutations in lymphoid cancers is contrasted by their striking absence in myeloid cancers. These findings resulted from a multistep route of mining the cBioPortal database (see Witjes et al., 2020 for details).

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Missense mutations in a particular actin gene as identified in DLBCL for ACTB and in MM for ACTG1 can affect the actin protein structure or stability or its ability to polymerize. In addition, the mutations can also disrupt the interactions with ABPs. Such aberrantly functioning mutant actin proteins may indeed affect the working of their specific “cellular protein network” and thus potentially promote steps in tumor progression. However, an identification of a mutation in patient genomes only indicates a correlation and the mutations may just as well be passenger mutations. To approach this for the actin mutations one may evaluate the frequencies of the mutations in a cancer subtype-specific manner and compare these to the frequencies of known cancer driver genes and/or one may link the mutations to the known structure-function relations. This is addressed in more general terms in Martı´nez-Jimenez et al. (2020) and was applied to the actin mutations found in DLBCL and MM (Witjes et al., 2020). RHOA is reported as a driver gene and candidate cancer gene of DLBCL (Chapuy et al., 2018; Reddy et al., 2017a). In Witjes et al. (2020), we reported that the frequency of somatic mutations in the ACTB gene in the DLBCL cBioPortal studies is comparable to that of the RHOA gene. Indeed, RHOA is mutated in 39 of 1250 samples (3.1%) and ACTB is mutated in 34 of 1250 samples (2.7%). Interestingly, the RHOA and ACTB mutants do not occur simultaneously in the patient samples suggesting a possible functional redundancy (within the same pathway) but this of course needs further study. As an additional confirmation of these findings based on cancer genomes, studies more focused on identifying driver mutations in DLBCL and MM have also identified ACTB and ACTG1 respectively as potential driver genes or at least meeting set criteria for driver genes (Reddy et al., 2017b; Walker et al., 2018). Furthermore, for DLBCL, the enrichment of mutations in ACTB is in line with an extensive independent analysis of one of the studies that was partly included in Witjes et al. (2020) and Chapuy et al. (2018). Indeed, based on a statistical analysis using MutSig2CV (Lawrence et al., 2013), ACTB was identified by Chapuy and coworkers as a candidate cancer gene (rank 42/98) (Chapuy et al., 2018). For comparison RHOA, BRAF and KRAS were respectively ranked at positions 29, 36 and 49 in this analysis. In addition, RHOA and ACTB are present together in one of the five clusters of tumors with coordinated genetic signatures identified in DLBCL; this cluster also includes: histone genes and genes involved in immune evasion and in BCR/PI3K, NFκB and RAS/JAK/STAT signaling (Chapuy et al., 2018). However, in these papers the role of the actins was not further elaborated upon possibly because

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β-cytoplasmic actin is considered as a housekeeping gene whereas several lines of evidence provide ample evidence one should not do this in the context of cancer research (Guo et al., 2013). Lollipop views visualizing the relative locations and frequency of identified mutations based on the primary structure are rendered in many genome analysis tools. Whereas this is informative for comparing paralogues, it is difficult to relate this to functionality especially when the proteins under study are discontinuous multidomain proteins such as actins (Kabsch and Vandekerckhove, 1992; Fig. 2). In such cases it is useful to plot the mutated sites on the 3D-structure. Fig. 3A and B shows the point mutations in β- and γ-cytoplasmic actin found in the two lymphoid cancer subtypes DLBCL and MM on the 3D-structure of actin. As discussed extensively in Witjes et al. (2020), the mutations in β-cytoplasmic actin in DLBCL (magenta in Fig. 3A) appear to mainly localize in regions that are implicated in filament formation, nucleotide-binding and Pi-release (compare to the indicated functional regions in Fig. 2). Mutations in β-cytoplasmic actin in DLBCL are present, among other, in SD2 including the D-loop, in P2-loop and in the W-loop. Indeed, seven ACTB mutations in DLBCL are residues directly implicated as actin-actin contacts, and multiple other mutations are in neighboring residues. In contrast, for γ-cytoplasmic actin (both in MM and the few mutations in DLBLC, green in Fig. 3) the mutations rather involve the amino acids ranging from 3 to 25 and include the acidic residues in the extreme N-terminus. The latter is a known contact site for myosins (Miller et al., 1996; Von Der Ecken et al., 2016). For instance, the loss of charge or charge reversal mutations at positions 3 and 4 in γ-cytoplasmic actin in MM (while charge in maintained in DLBCL) are expected to impact myosin interaction. Despite that these observations remain correlative for now, the observed actin gene and cancer subtype differences raise interest into a further investigation of the putative role of the identified mutations in these specific diseases. The potential effects of the observed mutations on actin properties (polymerization, myosin-binding) imply that mutations in cytoplasmic actins merit dedicated research in these two hematological cancer subtypes.

5.2 Mutations and copy number alterations in actin genes in patient samples: Selected case studies To first provide an overall picture, we present the frequency of different types of alterations that occur for the six human actin genes in selected

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Fig. 3 Mutation in specific cancer subtypes on the G-actin structure. (A, B) Mutations in the lymphoid cancers DLBCL (magenta) and multiple myeloma (MM) (green) in β-cytoplasmic actin (A) and γ-cytoplasmic actin (B) as reported in (Witjes et al., 2020). (C, D) Mutations in skin cancers in β-cytoplasmic actin in either melanoma (C, blue) or nonmelanoma (D, red) cancers. These mutations are based on the nonredundant curated hematological or skin cancers studies present in cBioPortal. Actin subdomains SD1–4 are indicated; ATP is shown in orange and Mg2+ in black (A–D). The data in (A)–(D) are plotted on the crystal structure of the monomeric form of β-cytoplasmic actin (2BTF); no analogous structure of the γ-cytoplasmic actin monomer is available but it is expected that the 3D-structures of β-cytoplasmic actin and γ-cytoplasmic actin are very similar. The boxed mutations in C, D are common between melanoma and nonmelanoma. (A, B) are adapted from https://encyclopedia.pub/1347. Structures are processed using Viewerlite 5.0 (Accelrys, USA).

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studies of breast, liver, skin (melanoma and nonmelanoma) and prostate cancers (extracted from cBioPortal November 2020). Fig. 4 shows that the % alteration frequencies for each of the alteration types (red amplification, blue deletion, green mutation) are quite different for each actin gene in the selected cancers. All actin genes have in common a relatively high mutation frequency (4% to >12%) in nonmelanoma patient samples compared to the other cancer samples, including these of melanoma patients. We further discuss this below. The profiles for the genes ACTB and ACTG1 (encoding nonmuscle actins) are rather similar except that ACTG1 amplifications are more strongly present in liver cancer samples and to a lesser extent also in breast cancer samples. By contrast ACTA2 and ACTG2 (encoding smooth muscle actins) are quite dissimilar with ACTG2 showing the least alterations in these cancers. Likewise, the genes encoding the skeletal muscle actins ACTA1 and ACTC1 are distinct with CNAs rarely occurring for ACTC1 whereas these prevail for ACTA1 in the selected cancers (except in nonmelanoma but this cancer type was not profiled for CNAs). Furthermore, it is very apparent that ACTA2 is the actin gene showing the most homodeletions and ACTA1 the most amplifications across the chosen cancers. The % deletion frequency in ACTA2 is highest in prostate cancers whereas for ACTA1 the % amplification frequency is highest in breast cancer (see also below). Although this obviously at best relates to actin paralogue-dependent correlations, the overall picture already suggests that actin genes display alterations across cancer types in a nonrandom manner. 5.2.1 Mutations in actin genes in nonmelanoma skin cancer subtypes Recently ACTG1 was identified as a new skin cancer pathogenic gene by a combination of bioinformatics and experimental validation approaches (Dong et al., 2018). Based on this paper and upon a paper by Nindl et al. (2006), also ACTB expression was associated with skin cancer but apart from increased expression in patient tumor samples this was not further verified experimentally. Note that the latter study did not incorporate data available at cBioPortal. Whereas these two papers focused on expression levels of both genes we here argue that mutations in ACTB and ACTG1 genes in skin cancer also deserve attention. In cBioPortal skin cancers fall into three main classes: basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (SCC) and melanoma, which in this order increase in level of tumor cell dissemination and thus aggressive nature. We already reported in Witjes et al. (2020) that in two genomic skin

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Fig. 4 Alteration frequencies (%) in actin genes. Data are shown based on patient genomes present in breast, liver, prostate and skin studies in cBioPortal (nonredundant, curated studies, query: cancer type, November 2020). Per actin gene the % alterations (Continued)

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cancer studies the % mutation frequency of the cytoplasmic actin genes ACTB and ACTG1 was highest relative to all the studies present in the curated cBioPortal database. It concerned studies on the two nonmelanoma cancer subtypes: BCC (Bonilla et al., 2016) and cSCC (Pickering et al., 2014). This is also shown in Fig. 4A and B and Table 1 based on the November 2020 cBioPortal data. Fig. 4 in addition shows that in (cutaneous) melanoma ACTB and ACTG1 genes have a much lower mutational frequency but do also display CNAs, which are mainly amplifications. Unfortunately, CNA data are not available in cBioPortal for the studies on the nonmelanoma cancer subtypes (BCC) and (cSCC) (Bonilla et al., 2016; Pickering et al., 2014) and thus cannot be used in the present comparison between nonmelanoma and melanoma skin cancer. As stated above, the percentage mutation frequency in nonmelanoma patients (BCC, cSCC) compared to melanoma is higher not only for the cytoplasmic actins but for all actin genes (Fig. 4; Table 1; Fig. 5A and B: note different range of y-axes in A versus B). Upon comparing the frequencies of actin mutations across subtypes of nonmelanoma skin cancer a further interesting distinction can be made. ACTB mutations (and to a lesser extent ACTA2 mutations) are mostly present in the patient cancer genomes of the BCC study whereas mutations in ACTG2 and ACTC1, as well as in ACTA1, prevail in the patient cancer genomes of the cSCC study (see % in Fig. 5B). This is also apparent from Table 1 (frequencies above 15% for ACTB in BCC and for ACTG2 and ACTC1 in cSCC). It forms a clear example of a cancer subtype-dependent distinction but the relevance of this needs further support by validation studies. Although in melanoma the mutation % are lower compared to nonmelanoma, Fig. 5A suggests that also here an actin paralogue-specific pattern occurs in subtypes. Melanoma is traditionally divided in subtypes based on the site of origin. Cutaneous melanoma is one of four subtypes originating Fig. 4—Cont’d are given: (missense) mutations in green, copy number alterations (CNA): homozygous deletions in blue and amplifications in red (fusions with actin genes are rare and not included here). The alteration frequency is defined as the % altered over profiled patient samples in each of the indicated classification of cancer types in cBioPortal: BC, breast cancer; IBC, invasive breast cancer; HC, hepatocellular carcinoma; PA, prostate adenocarcinoma; PC, prostate carcinoma; M, melanoma; NM, nonmelanoma. ACTB and ACTG1: cytoplasmic nonmuscle actin genes; ACTA2 and ACTG2, smooth muscle actin genes; ACTA1 and ACTC1, striated muscle actin genes. Note that the NM samples were not profiled for CNA and thus only mutations are shown.

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Table 1 Percentage of mutation frequency in skin cancers. Melanoma

Nonmelanoma

Curated set of nonredundant Cutaneous studiesa melanomab Melanomab

Skin cancer nonmelanoma (basal cell carcinoma, BCC)b

ACTB

1.1c

1.9

3.2

15.1

5.1

ACTG1

0.6

2.0

2.4

7.9

7.7

ACTA2

0.5

1.7

3.2

6.3

2.6

ACTG2

0.6

3.0

4.0

7.9

15.5

ACTA1

0.6

1.3

2.4

4.0

7.7

ACTC1

0.8

4.8

3.2

7.1

20.5

d

Cutaneous squamous cell carcinoma (cSCC)b

MYCN

0.9

2.9

2.4

30.0

PPP6C

0.8

7.1

6.5

14.7

2.6

STK19

0.6

3.2

7.3

14.7

20.5

LATS1

1.4

3.6

4.0

19.8

17.9

ERBB2

2.4

4.5

5.6

13.0

17.6

PIK3CA

11

4.1

8.9

10.6

11.8

CDKN2A 2.7

11.9

4.8

3.8

44.1

10.3

HRAS

0.9

1.5

1.4

2.0

13.2

KRAS

7

1.6

0

1.7

4.4

NRAS

2.3

23.7c

14.5

1.0

2.9

BRAF

5

50.4

67.6

10.6

14.7

RAC1

0.5

5.2

6.5

2.7

2.6

MITF

0.7

1.4

2.4

3.8

2.9

PTEN

5.0

8.8

15.3

4.1

2.9

PREX2

4.0

21.4

17.7

37.5

32.4c

TERT

2.3

3.9

4.8

10.9

13.2

a

Without MSK-IMPACT Clinical Sequencing Cohort (Zehir et al., 2017) and Pediatric Pan-cancer (Oberg et al., 2016). Data derived from 32,374 patients/33,767 samples in 176 studies. b % of mutations (without CNAs) for the genes for the indicated skin cancers from the cancer detailed analysis option in cBioPortal (Bonilla et al., 2016; Pickering et al., 2014; Reddy et al., 2017b). c Values for actin genes are in regular bold. d Values for driver genes for cutaneous melanoma, BCC, or cSCC are in bold italics.

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Fig. 5 Mutation frequency (%) in actin genes in skin cancers subtypes. cBioPortal data on % mutations in actin genes in samples of patients with skin cancers (curated, nonredundant studies, November 2020, using query: cancer type detailed). The mutation frequency is defined as % mutated actin gene in the number of patient samples per subtype. Only data on Melanoma (Mel), Cutaneous Melanoma (CutMel) (A), Cutaneous Squamous Cell Carcinoma (cSCC) and Basal Cell Carcinoma (BCC) (Skin Cancer NonMelanoma in cBioPortal) (B) are taken up. Data on Desmoplastic Melanoma (four mutations in three genes) or Acral Melanoma (no mutations) are not used. Actin gene symbols (see text or legend Fig. 4). Note the different range of the Y-axis in (A) and (B) indicating that the mutation frequencies strongly differ for the different actin genes for melanoma versus nonmelanoma. Within nonmelanoma (B) the mutation frequencies of individual actin genes between the subtypes cSCC and BCC is also different except for ACTG1.

from nonglabrous skin (i.e. not devoid of hair follicles) and it has a specific UV-damage related mutational landscape (Rabbie et al., 2019). The data in Fig. 5A are generated using the functionality “cancer type” in cBioPortal which does not readily couple to a specific study and therefore is not easily interpretable in relation to subtypes. Nevertheless, the data in Fig. 5A suggests that, similar to cSCC, the mutations in cutaneous melanoma are slightly more frequent in ACTC1. It has to be noted that skin cancers, including melanoma, BCC, and cSCC display a high tumor mutational burden (Chalmers et al., 2017). Also individual skin cells (e.g., melanocytes) from healthy donors already contain multiple mutations and this is correlated with levels of sun exposure and thus to UV-radiation damage (Tang et al., 2020). The high mutational burden of skin cancers could therefore suggest that the mutations in actin genes described above are likely passenger mutations. This may especially be the case for the muscle actins that are not expressed in healthy skin cells

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but also appear substantially mutated in the nonmelanoma cancer genomes (Figs. 4 and 5; Table 1). To put the observed high mutation frequencies for actins in the nonmelanoma cancers in perspective we again provide data in Table 1 on the mutation frequencies of known driver genes. For comparison, Table 1 shows that the percentage of mutations for actin genes across all nonredundant studies available at cBioPortal is low compared to these of genes such as PIK3CA, PTEN or the proto-oncogenes from the RAS-family that are considered as drivers for various cancers (Martı´nez-Jimenez et al., 2020). This indicates that actins in general are not acting as widespread tumor promotors. Similarly, the percentage of mutations in actins in melanoma samples is low although there is a noticeable increase compared to these in patient samples of all nonredundant studies. This view changes quite strongly when only focusing on the nonmelanoma skin cancers BCC and cutaneous cSCC (Table 1). For ACTB in BCC the percentage of mutation is indeed dramatically increased and is approximately similar to that of proposed driver genes of BCC such as PPP6C, STK19 and ERBB2 (Bonilla et al., 2016). In addition, the mutation frequency of ACTG1 in BCC is higher than these of CDKN2 and the various RAS paralogues which in the before mentioned study were also proposed as drivers. In cSCC the mutation frequency of ACTB and ACTG1 is comparable but significantly lower than the driver PREX2. Expression of either of these two genes is significant in skin cells and it is to be expected that their mutation influences cell homeostasis or behavior. However, in cSCC the smooth and skeletal muscle actins ACTG2 and ACTC1 are the actin genes with the higher mutation frequency as stated above (Fig. 5B; Table 1) and for these the mutation frequencies are indeed comparable to some known protooncogenes (Table 1). We note however, that the number of cSCC mutations identified is currently small, so confirmation in larger cohorts is warranted. In addition, given the low expression levels of the muscle actins in normal skin cells, it is at present unclear whether in cSCC these mutant smooth and skeletal muscle actins exert a role. Of note, some studies relate skin cancer to the mechanical forces acting upon skin cells. It is a fact that skin tissue needs to respond to a wide range of mechanical signals during homeostasis and during malignancy. Recent studies have revealed a number of proteins (including many ABPs) that are aberrantly expressed in for example cSCC. These link among other to cell migratory signals, reorganization of the actin cytoskeleton and actomyosin signaling (Boyle and Kopecki, 2020; Yamaguchi and Condeelis, 2007). As such, one could speculate that, as a result of aberrant mechanotransduction, muscle actins are induced in

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cSCC and/or their mutations are positively selected to some degree. This would be reminiscent of the processes such as, e.g., smooth muscle actin expression during the activation of fibroblasts to myofibroblasts (Zent and Guo, 2018). In line with this, Sasaki et al. correlated increased expression of α-smooth muscle actin in CAFs with the histological subtype cSCC but not with BCC or melanoma (Sasaki et al., 2018). Finally, we show in Fig. 3C and D the identified mutations in the selected cBioPortal studies for both melanoma (20 mutations) and nonmelanoma (19 mutations in BCC plus 2 in cSCC) for β-cytoplasmic actin on the actin 3D-structure. Note that despite the similar absolute values in melanoma and nonmelanoma, the mutations for the β-cytoplasmic actin paralogue still occur at lower frequencies in melanoma (see Fig. 5). Fig. 3C/D demonstrates that one nonmelanoma and three melanoma mutations are in residues that are directly involved in the polymerization cycle (including ATP-hydrolysis and phosphate release) based on Chou and Pollard (2019). In addition, we note that 8 out of the 20 mutations in melanoma and 7 out of 16 in nonmelanoma skin cancer (among other in the D-loop in SD2, compare Fig. 3C/D to Fig. 2) are adjacent to such residues. It can therefore not be excluded that these mutations do affect the local conformations and thereby influence (de)polymerization or ABPinteractions in a positive or negative manner. Most notably, the S338F mutation occurring in four nonmelanoma cancer patients is adjacent to the hinge loop between subdomain 3 and 1. The mutation at residue 334 (one in a BCC sample and one in cSCC) precedes this loop that also contains K336 and contacts the adenine base. In addition, residue 265 is part of the H-plug that participates in actin-actin subunit contacts across the helical axis of an actin filament (see Fig. 2). S265 is not a direct contact residue herein (Chou and Pollard, 2019). It is mutated in melanoma in a possibly neutral way: S265C, but in contrast, replacement by the bulky phenylalanine in nonmelanoma putatively has a larger impact. Taken together, an effect on actin filament stability and formation is a hypothetical consequence of the mutations present in β-cytoplasmic actin in BCC and other skin cancers. Unlike γ-cytoplasmic actin mutations in the hematological cancer subtype multiple myeloma (MM) (Witjes et al., 2020; Fig. 3A and B) there are no melanoma or nonmelanoma mutations in the extreme N-terminus of β-cytoplasmic actin. In conclusion, we demonstrate that the enrichment of mutation in skin cancer are actin-specific and subtype-specific with interesting enrichments mainly for the nonmelanoma subtypes BCC and cSCC. The mutations that

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have been identified may affect actin filament properties but this, and whether their occurrence in these cancer subtypes goes beyond correlation, needs to be further investigated. 5.2.2 Mutations in actin genes in other cancer (sub)types: A moving target We further report on a recent study that focused on actin gene mutations in a rare cancer type. The paralogue ACTG1 was found frequently mutated in Malignant Pleural Mesothelioma (MPM) in a phase II Ramucirumab Mesothelioma clinical trial study (Pagano et al., 2020). This study is not available at cBioPortal (November 2020) and this is not unrelated to the fact that MPM is a rare cancer. Because of this, genomic studies are limited and often involve a small number of samples. It is however a highly aggressive malignancy that is mainly associated with exposure to asbestos fibers. Pagano et al. report on a targeted study of 110 sample focusing on 34 genes (selected based on among other (Hylebos et al., 2016)). Three actin genes were profiled for mutations: ACTG1, ACTB and ACTG2. Few mutations were detected in the last two actins. By contrast, ACTG1 mutations belonged to the top five of mutated genes (approx. 10%) in their experimental set-up. The occurrence of mutations in ACTG1 significantly correlated with the sarcomatoid and biphasic subtype of MPM and not with the epithelioid subtype. 80% of the cases mutated in ACTG1 clustered together within a group of samples with RDX (radixin) mutations (Pagano et al., 2020). RDX is the most frequently mutated gene in MPM. Interestingly, RDX encodes the actin cytoskeletal protein radixin (one of the three ERM proteins) that, among other, links actin structures to the plasma membrane, thus these two proteins (γ-cytoplasmic actin and radixin) are also functionally linked. RDX itself has been associated with tumor progression in different cancer types in several studies (Clucas and Valderrama, 2014). Obviously, this forms an avenue for further research in this highly aggressive cancer. This example in the rare cancer MPM, but also the examples discussed above on DLBCL, BCC, and cSCC, demonstrate that to reveal enrichment of actin paralogue mutations a sufficiently detailed mining of the genomic data is required. Indeed, only when comparing cancer subtypes and scrutinizing the data to this level, putatively interesting correlations for actin mutations in specific subtypes are revealed. In line with this, overall frequencies of actin gene mutation are low when all redundant cancer studies in cBioPortal are considered (Table 1). Likewise, actin genes are currently

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not part of the 568-gene compendium of mutated cancer driver genes identified across all cancers (Martı´nez-Jimenez et al., 2020). This compendium, which is accessible via www.intogen.org, now mainly lists genes that were to a significant extent already reported as candidate driver genes (with above 10% mutations across malignancies) (Martı´nez-Jimenez et al., 2020). However, databases on patient cancer genomes continue to grow and will become more refined with respect to cancer subtypes and more extensive with regard to cohort sizes per subtype. It can therefore be anticipated that the extractable correlative data on mutations in actin genes will evolve. In the meantime, tools such as cBioPortal, www.intogen.org or other cancer genomics databases are particularly suitable to keep track of this as well as of the position-based frequencies of mutations in actin paralogues across cancers and cancer subtype. 5.2.3 Copy number alterations identified in patient genomes for specific cancer types As can be appreciated from Fig. 4, the observations for the different actin genes are globally different with respect to the two types of CNAs across cancers. Also, within one cancer type the situation can be different with respect to CNAs depending on the actin. For ease of comparing the actin pairs that are normally expressed together, we present in Fig. 6 only the CNAs for actin genes in cancers associated with the cBioPortal classifications: breast cancer, invasive breast carcinoma, hepatocellular carcinoma, prostate adenocarcinoma and prostate cancer. 5.2.3.1 CNAs in cytoplasmic actins

ACTB and ACTG1 show, in general, more amplifications than deletions in the selected cancers (Fig. 6). Strikingly, ACTG1 displays, relative to ACTB, a higher frequency of amplifications both in (invasive) breast cancer and hepatocellular carcinoma (HCC) and possibly also prostate cancer. ACTG1 is present in the chromosomal region 17q. This region is often amplified in basal-like breast carcinoma subgroups (Milioli et al., 2017) and also in HCC (Mei et al., 2020). With respect to breast cancer we note that the gene encoding breast cancer antigen 1 (BRCA1) is in this same chromosomal region as ACTG1. In triple negative breast cancers, a breast cancer with poor outcome, BRCA1 mutations are associated with amplification in chromosomal region 17q25.3 (Toffoli et al., 2014). It remains to be seen whether ACTG1 amplifications have an active role in these cancers but expression data presented below can point to this.

Fig. 6 Copy number alterations in actin genes in breast, liver and prostate cancers types. cBioPortal data on % CNA in actin genes in samples of profiled patients of the indicated selected cancers (curated, nonredundant studies, query cancer type, November 2020): BC, breast cancer; IBC, invasive breast cancer; HC, hepatocellular carcinoma; PA, prostate adenocarcinoma; PC, prostate carcinoma. Cytoplasmic actin genes are shown in upper panels, smooth muscle actin genes in middle and striated muscle actins in lower panels. Note that the scale is different for the pairs of different panels (ACTB-ACTG1 versus ACTA2-ACTG2) and a 10-fold difference within the pair ACTA1ACTC1. Blue: deletions; red: amplifications.

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5.2.3.2 CNAs in smooth muscle actins

In prostate cancer (combined prostate cancer and prostate adenoma) deletions of ACTA2 are very prominent which contrasts with the predominant amplifications of ACTB and ACTG1 in these cancers (Fig. 6, note different Y-axis for nonmuscle and smooth muscle actins in Fig. 6). ACTA2 expression is, however, in scientific literature frequently reported to be increased in prostate cancer for instance in (Gerashchenko et al., 2018; Kmetova´ Sivonˇova´ et al., 2020) and this is at first glance not easy to reconcile with the data in Fig. 6. Increased expression of ACTA2 is in prostate cancer also observed in cancer associated fibroblasts (CAFs) from the activated tumor environment (e.g., in Untergasser et al., 2005) and these CAFs may be present in tumor biopsies. Indeed, it has been reported that the effects of CNAs on gene expression levels in tumor cells are often masked by the effects of nontumor cells associated with the tumor environment (Bhattacharya et al., 2020). It remains unclear whether this plays a role in the contrasting observations at DNA-level (deletions) (Fig. 6) versus the higher transcript level of ACTA2 mRNA or protein level for α-smooth muscle actin reported in different prostate cancer samples. Also peculiar is the difference in CNA between ACTA2 and ACTG2 with the latter (nearly) completely devoid of CNAs not only in prostate cancer genomes but also in the other cancer genomes selected from cBioPortal (Fig. 6). Nevertheless, overexpression of ACTG2 has been reported as a promoter of HCC by functional studies in cell lines and a mouse model (Wu et al., 2017). 5.2.3.3 CNAs in striated muscle actins

For the striated muscle actin genes the % of CNAs for ACTA1 and ACTC1 are very different in the selected cancer types (Fig. 6, note the very different Y-axis for ACTA1 and ACTC1). Especially amplifications for ACTA1 in breast cancer stand out in Figs. 4 and 6; amplifications are also present for HCC. In the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study that sampled 2433 breast cancer samples the amplification frequency of ACTA1 even amounts to 23% (Pereira et al., 2016). Amplification of chromosomal region 1q containing ACTA1 is quite common in many types of breast cancer including basal breast carcinoma (Milioli et al., 2017; Muthuswami et al., 2013). The data presented in Fig. 1 on a small scale study also indicate that ACTA1 is overexpressed in triple negative breast cancer (TNBC) (Eswaran et al., 2012). Interestingly, ACTA1 was recently proposed together with eight other genes as a biomarker for drug resistance in basal breast carcinoma and this

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nine-gene signature predicted with high accuracy the relapse in a cohort of TNBC cancer patients (Wu et al., 2015). 5.2.3.4 CNAs and expression data

The occurrence of an amplification or deletion does not necessarily correlate with increased or reduced expression, respectively, although a recent pancancer study indicates this is mostly the case (Shao et al., 2019). The data at cBioPortal suggest a weak correlation between CNAs and expression for ACTG1 and ACTA1, the two most frequently amplified actin genes in breast cancers (Fig. 6) in the Breast Invasive Carcinoma study (TCGA, PanCancer Atlas) (see references in Nawy, 2018). This is shown in Fig. 7A where for these two genes the transcript level (based on RNASeq) is plotted versus the degree of CNAs and the color code indicates the (level of ) amplification or deletion. For ACTA1, but not for ACTG1, this also occurs in a HCC study (Fig. 8). This type of paired data on CNAs and expression levels is required to fully understand the impact of reported CNAs. Interestingly, the expression data from the Breast Invasive Carcinoma study (TCGA, PanCancer Atlas, see references in Nawy, 2018) indicate that there is a correlation between (i) gene deletion of ACTB and (ii) increased expression of ACTG1 and vice versa. Fig. 7B shows in the two panels the same data, i.e. ACTG1 expression level versus ACTB expression level, but overlaid with either the CNA-information of ACTG1 (for comparison) or the CNA-information of ACTB. The correlation between ACTB gene deletion and ACTG1 overexpression also holds for HCC, albeit less homogenously across the data set (Fig. 8). This observation suggests that not only the level of one actin is altered in these breast/HCC cancers but also the ratio of β- versus γ-cytoplasmic actin. For HCC this fits with ACTG1 upregulation in Hepatitis virus X-induced hepato-carcinogenesis in a mouse model (Sun et al., 2007) and with the report that γ-cytoplasmic actin is a marker for alcohol associated HCC (Gao et al., 2019). In the latter report, expression of ACTG1 was enhanced by the action of miR-6819-3p and miR-6877-3P. Interestingly an ACTB mRNA with a mutated 30 UTR also promotes HCC by increased binding to miRNAs (Li et al., 2020). It has been proposed that the ACTB 30 UTR portion of the mRNA is a sink for miRNAs due to its abundance and may thereby regulate expression of other genes (Vedula and Kashina, 2018). Moreover, this ACTB 30 UTR can in particular have a role in the compensating expression of other actin genes as suggested by one of the β-cytoplasmic actin knock-out mouse studies (Tondeleir et al., 2012). Indeed, the correlation demonstrated in

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Fig. 7 Expression data coupled to copy number alteration in cytoplasmic actin genes in invasive breast cancer patient samples. (A) Correlation of mRNA expression for ACTG1 (left) and ACTA1 (right) versus log2 copy number value for breast invasive carcinoma patient samples (TCGA, PanCancer Atlas, 994 samples). mRNA expression is expressed as a z-score relative to normal samples (batch normalization). For the X-axes: Log2 for diploid ¼ 0. Each data point is colored based on the type of CNA in the ACTG1 gene (left) and ACTA1 gene (right) using the color code shown in the legend: deletions (blue) and gains (red). (B) Both panels show the correlation between ACTG1 log2 mRNA expression (y-axis) (same data as in left panel in A) and ACTB log2 mRNA (x-axis). In the left panel this is overlaid with the type of CNAs (gains or deletions) of the ACTG1 gene in these samples whereas in the right panel the overlay is done with the type of CNAs for the ACTB gene. Note the correlation between the ACTG1 expression with gain/amplifications in the ACTG1 gene (left panel) but with deletions in the ACTB gene (right panel). The same is true for ACTB expression with respect to own type of CNAs versus ACTG1 CNAs.

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Fig. 8 Expression data coupled to copy number alteration in cytoplasmic actin genes in hepatocellular carcinoma patient samples. (A) Correlation of mRNA expression for ACTG1 (left) and ACTA1 (right) versus log2 copy number value for hepatocellular carcinoma patient samples (TCGA, PanCancer Atlas, 353 samples). mRNA expression is expressed as a z-score relative to normal samples (batch normalization). For the X-axes: Log2 for diploid ¼ 0. Each data point is colored based on the type of CNA in the ACTG1 gene (left) and ACTA1 gene (right) using the color code shown in the legend: deletions (blue) and gains (red). (B) Both panels show the correlation between ACTG1 log2 mRNA expression (y-axis) (same data as in left panel in A) and ACTB log2 mRNA (x-axis). In the left panel this is overlaid with the type of CNA (gains or deletions) of the ACTG1 gene in these samples whereas in the right panel the overlay is done with the type of CNA for the ACTB gene. Note the correlation between the ACTG1 expression with gain/amplifications in the ACTG1 gene (left panel) but with deletions in the ACTB gene (right panel).

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Figs. 7 and 8 should also be viewed in light of the genetic compensating expression of ACTB and ACTG1 that has been repeatedly observed in gene ablation mouse models as mentioned in Section 2 (see Patrinostro et al., 2017; Tondeleir et al., 2012; Vanslembrouck et al., 2020; and references herein). Although it remains to be proven whether this is relevant for what is observed in the patient samples, it forms an attractive hypothesis for future studies, especially in relation to the observation of actin paralogue-specific effects on cell migration, adhesion and invasion in cancer cell lines (Dugina et al., 2015; Malek et al., 2020). Richter et al. recently reported on an extended version of the in silico approach described here to come to the conclusion that ACTG1 is a potential biomarker in uterine cancer and they provide evidence that allows to suggest that precision strategies against ACTG1 could yield benefits for a subset of uterine cancer patients with high-grade tumors (Richter et al., 2020). These authors focused on a small set of nine genes coding for sarcomeric proteins including ACTG1, ACTA1 and ACTC1 and initially analyzed two large pan-cancer studies in cBioPortal from which ACTG1 emanated as being most strongly deregulated via amplifications and overexpression in uterine cancers. For four subtypes of uterine cancers: mixed endometrial carcinomas, serous carcinomas, endometrioid carcinomas, and carcinosarcomas they observed frequencies of 5%–20% of amplification or overexpression of the ACTG1 gene. Next they used the available clinical evidence to show that uterine cancer patients with an ACTG1 gain had poor prognosis and that their transcriptome reflected activation of specific oncogenic pathways and repression of innate immunity raising possibilities for immune therapy (Richter et al., 2020). In conclusion, this overview of CNAs in selected cancer genomes indicates that amplifications and deletions of actin genes can be frequent in cancers and occur both in an actin paralogue and cancer type specific fashion. The CNAs appear to correlate with associated expression data on actins in the same genomes and with expression data (albeit not consistently) from literature. Of interest, the interpretation of an actin CNA in a cancer patient genome should also consider co-altered compensating expression of other actins because this can impact transcriptional control and/or cytoplasmic actin functions contributing to cancer progression.

5.3 Fusions with actin genes in cancers In this section we briefly discuss what is in cBioPortal or has been reported for actin genes in cancer with respect to fusion with other genes. In general,

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fusion genes with actin genes appear to be rare and are even not found for ACTA1 and ACTC1 in the patient studies currently in cBioPortal (November 2020). For the other actin genes fusions do occur in various cancers (see Table 2) but the consequences of expressing the fusion proteins remain to be explored in most cases. Fusions of the actin genes usually occur with genes on different chromosomes. Exceptions are ACTB-RNF216 (chromosome 7) and ACTA2-ANK3 (chromosome 10) as well as most of the fusions with ACTG2 (chromosome 2): TET3, PELI1 or ALK. In addition, the fusions listed for the actin genes in cBioPortal do not appear to be specific for a particular cancer. For instance, the fusion of ACTG2 with TET3, coding for an enzyme that catalyzes one of the steps in cytosine demethylation and thus functions in epigenetic chromatin reprogramming, occurs four times but in three different cancers. For ACTB cBioPortal lists among others an interchromosomal fusion (chromosome 3 and 4) with the proto-oncogene MYC in one DLBCL patient that results in a transcript fusion (Chapuy et al., 2018). The data in cBioPortal are biased towards cancers with large cohorts of patients. Data on actin fusions from rare cancers are however available from dedicated investigational studies. An interesting example is the ACTG1-MITF fusion identified in renal cell carcinoma and involved in cellular transformation. This fusion protein is functional as it was capable of inducing the expression of downstream target genes (Durinck et al., 2015). Curiously, the transcription factor MITF is usually connected to melanoma (Hartman and Czyz, 2015). In addition, ACTB has been repeatedly found as a fusion with the gene of transcription factor GLI1 (Glioma-associated oncogene I) in soft tissue cancers such as pericytoma (Panagopoulos et al., 2020; Koh et al., 2019; Miettinen et al., 2019) and head and neck cancer (four in seven investigated cases) (Xu et al., 2020). Fusions of ACTB also occur with TFEB (coding for transcription Factor EB) in subtypes of renal cell carcinoma (Xia et al. 2020) and recurrently with FOSB (coding for a subunit of the AP1 transcription factor) in pseudomyogenic hemangioendothelioma (PHE) (Agaram et al., 2018; Zhu et al., 2019). The ACTB-FOSB gene fusion appears as prevalent in PHE as the SERPINE1/PAI1-FOSB gene fusion that was already established as the hallmark genetic abnormality in this vascular cancer (7 ACTB-FOSB versus 8 SERPINE1-FOSB fusions in 15 cases) and similar clinicopathologic features are associated with the presence of either fusion (Agaram et al., 2018). Zhu and coworkers showed that in two PHE-cases the fusion is between ACTB exon 3 and FOSB exon 2 at a known breakpoint in FOSB that at least

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Table 2 Information on fusions with actin genes listed in cBioPortal or retrieved from PubMed. Fusion Product of gene fused to Reference gene Cancer actin (UniProt ID) (cBP 5 cBioPortal)

ACTBMYC

Diffuse large B-cell lymphoma

Myc proto-oncogen (transcription factor) (P01106)

cBP (Chapuy et al., 2018)

ALDH2ACTB

Adrenocortical carcinoma

Aldehyde dehydrogenase, mitochondrial (P05091)

cBP

ACTBRNF216

Breast invasive ductal carcinoma

E3 ubiquitin ligase (Q9NWF9)

cBP

ACTBHead and neck COL17A1 squamous cell cancer

Collagen alpha-1(XVII) chain (Q9UMD9)

cBP

GAPDHACTB

Lung squamous cell carcinoma

cBP Glyceraldehyde3-Phosphate Dehydrogenase (P04406)

ACTBGLI1

Ovarian pericytoma

ACTBGLI1

Pericytoma various tissues

Glioma-Associated Oncogene Homolog 1 (Zinc Finger Protein) (P08151)

ACTBGLI1

Head and neck mesenchymal neoplasm

ACTBTFEB

Renal cell carcinoma

ACTBFOSB

Pseudomyogenic FosB Proto-Oncogene, Agaram et al. hemangioendothelioma AP-1 Transcription (2018); Zhu et al. Factor Subunit (P53539) (2019)

ACTG1MITF

Renal nonclear cell carcinoma

Melanoma inducting transcription factor (O75030)

cBP (Durinck et al., 2015)

ACTG1SPRR1B

Head and neck squamous cell Carcinoma

Small proline-rich protein 1 (Cornifin B) (P22528)

cBP

ACTG1CCNC

Lung squamous cell carcinoma

Cyclin C (P24863)

cBP

Koh et al. (2019) Panagopoulos et al. (2020) and references herein in Table 1; Miettinen et al. (2019) Xu et al. (2020)

Transcription Factor EB Xia et al. (2020) (P19484)

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Table 2 Information on fusions with actin genes listed in cBioPortal or retrieved from PubMed.—cont’d Fusion Product of gene fused to Reference gene Cancer actin (UniProt ID) (cBP 5 cBioPortal)

CO9A3ACTG1

Papillary thyroid cancer Collagen Type IX Alpha cBP 3 Chain (Q14050)

ACTA2BCR

DLBCL germinal center B-cell type

ANK3ACTA2

Colon adenocarcinoma Ankyrin 3 (Q12955)

cBP

TET3ACTG2

Breast invasive carcinoma (NOS)

cBP

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Breast invasive ductal carcinoma

TET3ACTG2

Esophageal adenocarcinoma

cBP

TET3ACTG2

Serous ovarian cancer

cBP

FLNAACTG2

Prostate adenocarcinoma

Filamin-A (P21333)

cBP

ACTG2ALK

Leiomyosarcoma

Anaplastic lymphoma kinase (Q9UM73)

cBP Davis et al. (2019)

PELI1ACTG2

Breast invasive ductal carcinoma

cBP E3 ubiquitin-protein ligase pellino homolog 1 (Q96FA3)

Breakpoint cluster region cBP protein (activator of RhoGEF and GTPase) (P11274)

Tet methylcytosine dioxygenase 3 (O43151)

cBP

in one case results in an in-frame transcript (Zhu et al., 2019). In the ACTG2ALK fusion identified in leiomyosarcoma, the part of the smooth muscle actin gene containing exon 1 and 2 is the “in frame 5” partner’ gene of the receptor tyrosine kinase ALK. ALK rearrangements are established oncogenic drivers in different cancers (Davis et al., 2019). Notably, smooth muscle actin is expected to be highly expressed since leiomyosarcoma is a malignant neoplasm with smooth muscle differentiation, suggesting that the expression level of the actin paralogue is important in generating the ALK-dependent oncogenic properties (Davis et al., 2019; Guo et al., 2015). We note that in prostate cancer, FLNA-ACTG2 is a fusion to a partner gene encoding

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the actin crosslinking protein filamin-A. The latter has been already implicated in several ways in tumor progression, also for prostate cancer (Izdebska et al., 2020).

6. Summary, conclusions, and perspectives Actins as building blocks for the actin cytoskeleton play pivotal roles in cell behavior including cellular properties like cell proliferation, invasion, and metastasis that are considered hallmarks of cancer (Biber et al., 2020; Hanahan and Weinberg, 2011). The notion that actin mutations are associated with tumor progression dates back to the 1980s and since then a wealth of evidence has demonstrated that the actin system is indeed deregulated in cancer cells at different levels (Izdebska et al., 2018; Yamaguchi and Condeelis, 2007). Meanwhile actin mutations were identified in (congenital) disease contexts, especially muscle diseases (Peckham et al., 2001; Rubenstein and Wen, 2014) and detailed insight into the structure of actin monomers and actin filaments as well as that of their contacts with many ABPs has been acquired to (near) atomic resolution (Chou and Pollard, 2019; Merino et al., 2020). However, the research into actin gene alterations in cancer stagnated until recently. This current change is made possible based on the opportunities created by the massive efforts in the cancer field to not only document cancer patient genomes but also generate public portals such as cBioPortal (used here) or COSMIC (Catalogue Of Somatic Mutations In Cancer at https://cancer.sanger.ac.uk/cosmic) that allow mining in detail data of hundreds of studies on different cancer types. Prior to the availability of these organized big data sets, the problem was one of the proverbial “finding the appropriate needle in the haystack.” This was especially the case for genes like actin that do not stand out as genes that are deregulated to a high frequency in all cancers. The current cancer genomic resources, however, allow to pinpoint those cancer lineages that show tendencies to dysregulate the function of actin (Richter et al., 2020) via deregulation of an actin gene and consequently identify cancer subtypes for which actins and their gene alterations are worth a closer look as potential biomarkers or as putative targets. With the selected examples presented here, we support this notion and moreover substantiate that alterations of specific actin paralogues need to be considered within specific cancer (sub)types. We indeed present correlative data on enrichment of ACTB mutations in the hematological cancer subtype DLBCL (Witjes et al., 2020) and

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present data for a high mutational rate of ACTB and the ACTG1/ACTC1 pair in the nonmelanoma skin cancer subtypes BCC and cSCC, respectively (this study based on cBioPortal). Moreover, we report on the high frequency of ACTG1 mutations in Malignant Pleural Mesothelioma (MPM) (Pagano et al., 2020). In uterine cancer subtypes, ACTG1 displays high frequency of amplification and/or increased expression and this appears correlated with poor prognosis in some uterine subtypes (Richter et al., 2020). For breast cancer for example, we demonstrate based on cBioPortal data that the actin genes ACTA1 and ACTG1 are amplified at high frequency and we moreover document that for ACTG1 this is correlated with increased expression (on mRNA level, Fig. 7A). In general terms, the actin gene alterations such as mutations may lead to either a defect or a gain in function whereas CNAs can result in higher or lower expression levels. With respect to CNAs the situation is likely even more complex because expression levels of actin paralogues are coupled to each other by little understood genetic compensations or possibly other mechanisms. This has been documented in normal cells and in model systems (see Section 2). Of interest, exploration of genomic data that was complemented with expression data allowed us to document that this interplay may also occur in cancer patients. Indeed, in breast cancer and HCC for example, deletion of one cytoplasmic actin gene coincides with increased expression of the other (Figs. 7B and 8B). Together this demonstrates that cancer patient genome data are definitely worth further exploring in terms of actin and, more general, in the actin cytoskeleton. However, the derived data are correlative or circumstantial evidence and far from conclusive on cause and consequence in tumor progression. The correlative data is strengthened by comparing, e.g., mutational frequency of an actin paralogue of interest in a specific cancer subtype to mutational frequencies of established cancer driver genes in the same cancer subtype as was illustrated for the hematological cancers (Witjes et al., 2020) and here for skin cancers. In addition, for actin mutations, the in-depth insight in structure-function relationships for actin as well as the insights emanating from parallel research on actin mutations in congenital diseases form an additional vantage point. Mapping mutations identified in a specific cancer subtype on the 3D actin structure as illustrated here for the actin mutations from genomes of DLBCL, MM, BCC and cSCC patients (Fig. 3) may indeed give additional clues on which actin functions are mostly likely affected. Although such an analysis is, because of the multifunctionality of actin, not straightforward, it does deliver leads for future functional oriented research.

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So where do we go from here? The cBioPortal data are derived from clinical samples so the obvious follow-up question is how to take the available correlative data on actin gene alterations to bench and eventually further to bedside. Coupling the derived data to clinical patient data is an obvious route (as done by Richter et al., 2020) as well as a coupling to omics-based expression data. Whereas this is preferably done on patients that have the gene alteration of interest, a wealth of information is also available from transcriptome (GEO, gene expression omnibus: https://www.ncbi.nlm. nih.gov/geo/), proteome (PRIDE, proteomics identifications database: https://www.ebi.ac.uk/pride/) or interactome data (e.g., BioGrid, https:// thebiogrid.org/). Yet, such systems biology datamining approaches on their own will not be sufficient to understand the role of actin gene alterations in the context of particular cancers although they serve as a powerful prequel to subsequent functional or (pre)clinical validation studies. Appropriate cellular models and dedicated assays will need to be employed to either study effects on actin dynamics or on cancer cell behavior, including altered invasiveness. These can be combined with actin perturbation studies for assessing their impact on the cancer cell transcriptome/ proteome. We previously showed in a fibroblast model that ablation of ACTB alters the genetic program of these cells leading to an arrest of migration that can be alleviated by inhibiting the Rho-ROCK pathway (Tondeleir et al., 2012). Such studies on actin genes within a cancer disease context are generally lacking. The study by Malek et al. (2020) using the CRISPR/Cas9 technology on melanoma A375 cells forms a nice example that this is possible. In this respect, we recommend that in a cancer context, where (as we show also here) ectopic actin paralogue expression is a frequent phenomenon, any study that perturbs expression of one actin as a standard monitors compensatory changes in expression of all paralogues. A system to do this has been developed recently for mouse actins (Patrinostro et al., 2017). Genome editing approaches also offer possibilities when it comes to studying actin mutations. It will be increasingly possible to study the impact of a specific mutation in one actin gene on cellular cancer properties either by mimicking it in a relevant context or, starting from patient material, by restoring it. In vitro functional assays of cancer cell properties in validation experiments, most notably invasion assays, are instrumental and can provide important information on the function of the actin paralogue under study. However, these assays are heterogeneous, complex, and difficult to standardize across laboratories. Recently, awareness was aroused that these

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aspects need improvement at least in providing the appropriate metadata and processing of data (Masuzzo et al., 2013, 2015; Gonzalez-Beltran et al. 2020). This is perhaps not necessary for small intralab comparative experiments but will in future be needed to overcome interlab variability upon comparing the behavior of ACTB mutant X generated and analyzed in one lab with that of ACTB mutant Y in another lab using the above-mentioned genome editing approaches. Such preclinical functional studies on specific actin mutants, using a variety of standardized assays leading to multi-parametric data, will be necessary to understand their potential contribution to cancer progression, or to assess whether their alterations are more than passenger phenomena. In conclusion, mining cancer genomes is forming a new basis to focus clinically relevant research of actin-based deregulations in cancer. There are still limitations to using these big data portals. For instance, in cBioPortal there is a bias to prevalent cancers in number of studies and patients. Studies on rare cancers usually have small datasets and are in need of additional samples to get a more global view on deregulations within these tumor types. An example is the ACTB-GLI1 fusion which is absent in cBioPortal because no pericytoma studies are currently available. The same holds for cancer subtypes which are sometimes only represented by small cohorts. However, continuing global efforts in cancer genomics are likely to gradually fill these gaps and provide promise for further tapping these cancer genome resources from an actin-based perspective.

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