Liquid Biopsies: Methods and Protocols (Methods in Molecular Biology, 2695) 1071633457, 9781071633458

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Methods in Molecular Biology 2695

Tao Huang · Jialiang Yang Geng Tian  Editors

Liquid Biopsies Methods and Protocols

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Liquid Biopsies Methods and Protocols

Edited by

Tao Huang CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China

Jialiang Yang and Geng Tian Geneis Beijing Co. Ltd., Beijing, China

Editors Tao Huang CAS Key Laboratory of Computational Biology Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences Shanghai, China

Jialiang Yang Geneis Beijing Co. Ltd. Beijing, China

Geng Tian Geneis Beijing Co. Ltd. Beijing, China

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-3345-8 ISBN 978-1-0716-3346-5 (eBook) https://doi.org/10.1007/978-1-0716-3346-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface Cell-free DNA (cfDNA) refers to double-stranded DNA fragments of lengths 130~180bp with peak around 167bp. Deriving from the apoptosis and degradation of cells, cfDNA often appears in peripheral blood and other tissue fluids. Circulating tumor DNA (ctDNA) refers to DNA fragments of tumor origin that are constantly flowing in the human blood circulation system. These fragments carry on a lot of information about the tumor, including gene mutations, deletions, insertions, rearrangement, copy number abnormalities, methylation, and so on. The information can be used for early diagnosis of tumor, tumor progression surveillance, tumor prognosis, and personalized medication. Thus, it is critical to detect ctDNA for clinical applications. There are many experimental methods for detecting ctDNA, such as droplet digital polymerase chain reaction (ddPCR), multiplexed PCR-based barcoding of DNA, Sanger sequencing, the next-generation or third-generation sequencing, and so on. But the analysis of these data is challenging. For example, the mutation frequency may be ultra-low (demo.hisat2_summary.txt tophat2 -G GRCh37.gff3 -–no-novel-juncs -o demo GRCh37 demo_clean_R1.fq.gz demo_clean_R2.fq.gz 4. Assemble the transcripts using cufflinks. cufflinks -p 4 -g GRCh37.gff3 -I 5000 -o demo _clout demo _thout/accepted_hits.bam

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Lili Zhang et al.

5. Merged GTFs into one find . -name transcripts.gtf > assemblies.txt 6. Put the .gtf file directories together. cuffmerge -p 4 -g GRCh37.gff3 -s GRCh37.fa assemblies. txt 7. Run cuffdiff to get the expression level of each locus and transcript. In the “cuffdiff” command, to be noted, the label for each sample is defined by “-L”; the .bam files for replicates should be separated by a comma following the same order as the labels; the option “-T” allows making comparisons between successive samples rather than between all pairs of samples to relieve the computing load. cuffdiff -o diff_out -b GRCh37.fa -p 8 -T -L demo -u merged_asm/merged.gtf demo _thout/accepted_hits.bam 8. Select the transcripts with the class_codes “u,” “i,” “o,” and “x” from “merged.gtf.” cat merged.gtf | grep‘class_code “[uiox]”’> selected.gtf 9. Obtain the selected transcript sequences in the fasta format. gffread -w selected.fa -g GRCh37.fa selected.gtf 10. Use online server (http://cpc.cbi.pku.edu.cn) for assessing the coding capacities. To start a job, just submit the transcript sequences (the .fasta file), and wait to download the results. Find the noncoding transcripts longer than 200 nt from the CPC output. cat cpc.txt | awk ‘$4 < -1 && $2 > 200 {print $0}’ | cut -f1 > non-coding-transcript.txt 11. Get the noncoding transcript sequences. sed ’s/^/transcript_id "&/g’ non-coding-transcript.txt > non-coding-transcriptnew.txt cat merged.gtf | fgrep -f noncoding-transcript-new.txt > noncoding-transcript.gtf gffread -w noncoding-transcript.fa -g GRCh37.fa non-coding-transcript.gtf 12. Obtain the expressed encoding genes. cat merged.gtf | grep ‘class_code “¼”’ > coding.gtf 13. Obtain the neighbor gene pairs between lncRNAs and encoding genes. windowBed -a noncoding-transcript.gtf -b coding.gtf -w 10000 > genepair.gtf cat genepair.gtf | cut -f9 | cut -b 10-20 > lncRNA.txt cat genepair.gtf | cut -f18 | cut -b 10-20 > coding.txt paste lncRNA.txt coding.txt | sort | uniq | awk ’$1 !¼ $2 {print $0}’ > genepair.txt 14. The outputs from the “cuffdiff” run were saved in the “diff_out” folder, which preserved the data on gene expression, gene

Cell-Free RNA Sequencing from Biofluid Samples

23

annotation, and differentially expressed genes in separate files. To parse those data, the R package “cummeRbund” could be run in RStudio. > library(cummeRbund) > setwd(“/Path/to/diff_out”) > cuff cuff > lncRNA lncRNA lncRNA1 lncRNA2 write.table(lncRNA2, “Expression of noncoding genes. csv”, sep¼“\t”, quote¼FALSE) > coding coding coding1 coding2 write.table(coding2, “Expression of coding genes.csv”, sep¼“\t”, quote¼FALSE) 3.13.2 miRNA Data Analysis

1. QC. The raw sequencing data can be checked for data quality and viewed using the fastqc software. Fastqc only generates quality control reports and does not filter the data. fastqc -f fastq -d raw_data/P1-1.fq.gz 2. Data Filtering. Small RNA sequencing data is mainly focused on adapter sequences. Here, TrimGalore is used for data filtering. trim_galore --small_rna small RNA --length 18 --max_length 30 --stringency 3 --phred33 --cores 4 --dont_gzip -o clean_data raw_data/P1-1.fq.gz 3. Duplication and merging. Due to subsequent analysis software requirements, it was necessary to convert the small RNA from fq to fa format without redundancy. The id section reflects the number of occurrences of each sequence, using the mirdeep2 package program. The mapper.pl program in the mirdeep2 package was used. mapper.pl clean_data/P1-1_trimmed.fq -g P11 -h -m -s uniq_data/P11.fa 4. Alignment (bowtie). The data that can be matched to the genome will be used for subsequent taxonomic annotation, and the data that cannot be matched will be discarded. The principle of matching is to match the database of the species; no mismatch is allowed. bowtie -f -v 0 -k 1 --al P11.mapped.fa reads ../genome/ genome ../uniq_data/P11.fa > P11.genome.bwt bowtie -f -v 0 -p 1 -a --best --strata ../known_miRNA/ hairpin.ccr P11.mapped.fa > P11.miRNA.bwt

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Lili Zhang et al.

bowtie -f -v 2 -p 1 -a --best –strata ../Rfam/Rfam_rmMIR. fasta P11.mapped.fa > P11.ncRNA.bwt bowtie -f -v 0 -p 1 -a --best –strata ../ref_prepare/repeat P11.mapped.fa > P11.repeat.bwt bowtie -f -v 0 -p 1 -a --best --strata ../ref_prepare/exon P11.mapped.fa > P11.exon.bwt bowtie -f -v 0 -p 1 -a --best –strata ../ref_prepare/intron P11.mapped.fa > P11.intron.bwt 5. Small RNA taxonomy annotation. The small RNA was annotated for classification using a self-written srna_anno.pl perl program, in the order known mirna > ncrna > repeat > exon > intron. Perl rna_anno.pl -fa P11.mapped.fa –rfam ../ref_prepare/ Rfam/family.txt1 -mirna P11.miRNA.bwt -ncrna P11. ncRNA.bwt -intron P11.intron.bwt -repeat P11.repeat.bwt -exon P11.exon.bwt -outpre P11.out 6. Quantitative miRNA expression. Combine sRNA data from all samples. Use the quantifier.pl program in the mirdeep2 package for expression quantification. cat ../uniq_data/P1*.fa >all.reads.fa quantifier.pl -p all.hairpin.fa -m all.mature.fa -r all.reads.fa -g 0 7. Duplication. Since different precursor sequences may produce the same mature, it is necessary to de-duplicate based on the mature id duplication. less miRNAs_expressed_all_samples_*.csv | awk ’{ for(i=5; i miRNAs_expressed.count.txt

4

Notes 1. Equilibrate samples to room temperature, If samples are 100,000 probes

10 cycles

8 cycles

7 cycles

6 cycles

10,000–100,000 probes

12 cycles

10 cycles

9 cycles

8 cycles

500–10,000 probes

13 cycles

11 cycles

10 cycles

10 cycles

1–500 probes

14 cycles

12 cycles

11 cycles

11 cycles

2. Place the tube in a thermal cycler, and run the following program with a heated lid set at 105 °C (Table 9). The number of PCR cyclers should be optimized per panel size and the number of pooled libraries per capture. 3. We recommend starting optimization with the following (Table 10). 3.21 Purification of the Post-capture Library with AMPure XP Beads

1. Let the AMPure XP beads come to room temperature for at least 30 min and mix thoroughly on a vortex mixer. Do not freeze the beads at any time. 2. Add 75 μL of homogeneous AMPure XP beads to 50 μL of post-capture library; vortex or pipette up and down 10 times to mix. 3. Incubate at room temperature for 5 min. 4. Place the microcentrifuge tube on the magnet until the solution becomes clear and remove the supernatant carefully with a 200-μL pipette. 5. Remove the tube from the magnet, centrifuge the microcentrifuge tube slightly and place it on magnet again, and remove the supernatant with a 10-μL pipette. 6. Keep the tube on magnet, add 200 μL of freshly prepared 80% ethanol, incubate at room temperature for 30 s, and carefully remove the supernatant.

Detection of Circulating Tumor DNA in Plasma Using Targeted Sequencing

41

Fig. 4 Agilent2100 High Sensitivity Analysis for quality control of post-capture cfDNA library

7. Repeat step 6 for a second wash with freshly prepared 80% ethanol. 8. Keep the tube on the magnet and air-dry the beads for 3–5 min or until the residual ethanol completely evaporates. 9. Add 22.5 μL of nuclease-free water to each sample well. 3.22 Assess Postcapture Library Quality and Quantity

1. The concentration of cfDNA was measured using Qubit Flex Fluorometer, and the fragment distribution of cfDNA was measured using Agilent 2100 Bioanalyzer. 2. Example of Agilent2100 High Sensitivity Analysis traces (Fig. 4).

3.23 NGS Sequencing

3.24

Data Analysis

We sequence pooling post-capture libraries on MGI2000 (pairend, 150 bp). The depth of sequencing required for cfDNA depends on the application. If more detailed analysis is preferred – for example, if SNVs with a frequency of 0.01 are to be detected, the de-duplication sequencing depth needs to reach at least 1000×. The specific number of sequencing reads should be calculated according to the panel size and capture efficiency. 1. Quality filtration (Fastp) of raw reads. fastp -i demo_R1.fq.gz -I demo _R2.fq.gz -o demo_clean_R1.fq. gz -O demo_clean_R2.fq.gz -h fastp.html

2. Alignment with bwa and sorting using Samtools. bwa mem -t 8 -R "@RG\tID:$id\tSM:demo\tLB:WES\tPL:Illumina" hg19.fa cleandata/ demo_clean_R1.fq.gz cleandata/ demo_clean_R2.fq.gz | samtools sort -@ 8 -o align/demo.sort.bam –

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3. Quality control and statistics. samtools stats –reference hg19.fa align/demo.sort.bam > stats/ demo.stat plot-bamstats –p stats/demo ./stats/demo.stat qualimap bamqc –java-mem-size=10G –gff panel.bed –nr 100000 –nw 500 –nt 16 –bam align/demo.sort.bam –outdir qualimap/demo

4. Generate the panel of normal. First, run Mutect2 for each normal sample, and then use the CreateSomaticPanelOfNormals command to create a panel of normal. gatk

Mutect2

–R

hg19.fa

–I

align/demo.sort.bam

–tumor

normal1_sample_name –germline-resource af-only-gnomad.vcf. gz –O normal1_for_pon.vcf.gz gatk CreateSomaticPanelOfNormals –vcfs normal1_for_pon.vcf. gz –vcfs normal2_for_pon.vcf.gz –vcfs normal2_for_pon.vcf.gz – O pon.vcf.gz

5. Identify somatic mutations. Germline-resource specifies a germline mutation vcf file, and the gnomAD database (http://gnomad.broadinstitute.org) is selected. gatk Mutect2 –R hg19.fa –I tumor.bam –tumor tumor_sample_name – I normal.bam –normal normal_sample_name –germline-resource a f- o n l y - g n o m a d . v c f . g z

–af-of-alleles-not-in-resource

0.00003125 –panel-of-normals pon.vcf.gz –O somatic.vcf.gz

6. Filtration of vcf files. gatk-launch GetPileupSummaries –I tumor.bam –V small_exac_common_3.vcf –O pipeups.table gatk-launch CalculateContamination –I pipeups.table –O contamination.table gatk FilterMutectCalls –V somatic.vcf.gz –contamination-table contamination.table –O filtere

7. Generation of the interval file. gatk BedToIntervalList –I panel.bed –O panel.interval.list –SD hg19.dict

8. Extracting the read count information. The first step is to get the read counts of all the samples and count the reads of the bam file according to the interval file. Then, construct a CNV panel of normals and generate cnvponM.pon.hdf5 file of normal samples. For capturing sequencing data, the capturing process will introduce some noise, so noise reduction is needed later.

Detection of Circulating Tumor DNA in Plasma Using Targeted Sequencing gatk

–java-options

“-Xmx20G

43

–Djava.io.tmpdir=./”

CollectReadCounts –I demo_bam –L panel.interval.list –R hg19. fa –format HDF5 –interval-merging-rule OVERLAPPING_ONLY –output gatk/counts/demo.clean_counts.hdf5 gatk –java-options “-Xmx20G –Djava.io.tmpdir=./” CreateReadCountPanelOfNormals –minimm-interval-median-percentile 5.0 –output gatk/cnvponM.pon.hdf5 --input

gatk/counts/

demo.clean_counts.hdf5

9. Standardization and noise reduction. gatk

–java-options

“-Xmx20G

–Djava.io.tmpdir=./”

DenoiseReadCounts –I gatk/counts/demo.clean_counts.hdf5 – count-panel-of-normals gatk/cnvponM.pon.hdf5 –standardizedcopy-ratios gatk/standardizedCR/demo.clean.standardizedCR. tsv –denoised-copy-ratios gatk/standardizedCR/demo.clean.denoisedCR.tsv

10. Segment calling. gatk –java-options “-Xmx20G –Djava.io.tmpdir=./” –denoisedcopy-ratios gatk/standardizedCR/demo.clean.denoisedCR.tsv – allelic-counts gatk/allelicCounts/demo.allelicCounts.tsv – normal-allelic-counts gatk/allelicCounts/demo_germline.allelicCounts.tsv –output gatk/segments –output-prefix demo

11. Copy Ration Segments Calling. gatk –java-options “-Xmx20G –Djava.io.tmpdir=./” –I gatk/segments/demo.cr.seg –O gatk/segments/demo.clean.called.seg

3.25 Results of Reference Standard Samples

1. We used Structural Multiplex cfDNA Reference Standard (Horizon, Cat.No. HD786) to carry out the experiments (3 replications) according to the above protocol and analyzed the sequencing data. 2. Experimental data. We recorded the experimental data in the process of library construction and hybridization. Specific information is shown in Table 11, and the results matched the expectations. 3. Reference Standard and Capture Panel Information (Tables 12 and 13). 4. Sequencing performance. We analyzed the capture efficiency, coverage of the target region, read duplication ratio, and so on. Specific information is shown in Table 14, and the results matched the expectations.

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Table 11 Experimental data

Sample_ID

Number of PCR cycles Input for library amount (ng) construction

Fragment Amount of Amount of post-capture size of library (bp) library (ng) library (ng)

Fragment size of post-capture library (bp)

HD786-1A

20

8

336

1480

174

333

HD786-1B

20

8

337

1505

165

335

HD786-1C

20

8

336

1320

170

335

Table 12 Verified mutations

Chromosome

Gene

Variations

Expected allelic frequency (%)

chr.19

GNA11

Q209L

5.6

chr.14

AKT1

E17K

5

chr.3

PIK3CA

E545K

5.6

chr.7

EGFR

V769_D770insASV

5.6

chr.7

EGFR

E746–A750

5.3

chr.4 chr.6

ROS1

SLC34A2/ROS1 fusion

5.6

chr.10

RET

CCDC6/RET fusion

5

chr.7

MET

Amplification

4.5 copies

chr.2

MYC-N

Amplification

9.5 copies

Table 13 Capture panel information Probe supplier

Panel size

Target gene

IDT

171,000 bp

EGFR,BRAF,PIK3CA,KRAS,NRAS,ERBB2,MET,ALK,ROS1,RET

Table 14 Quality control of HD786

Sample ID

Capture efficiency (%)

Average depth on target

Coverage of target region (%)

Duplication rate (%)

HD786-1A

58.80

3265.35

100.00

36.47

HD786-1B

59.20

3097.82

100.00

35.70

HD786-1C

58.80

3104.52

100.00

35.75

Detection of Circulating Tumor DNA in Plasma Using Targeted Sequencing

45

Table 15 Detected mutations of HD786

Gene

Variations

Expected allelic frequency

EGFR

p.E746_A750del

5.30%

5.00%

4.45%

2.48%

EGFR

p.V769_D770insASV

5.60%

5.19%

6.72%

5.90%

PIK3CA p.E545K

5.60%

4.48%

4.06%

5.30%

MET

Amplification

4.5copies

Amplification

Amplification

Amplification

ROS1

SLC34A2/ROS1 fusion

5.60%

62 reads

65 reads

62 reads

RET

CCDC6/RET fusion

5.00%

45 reads

37 reads

42 reads

Detected allelic frequency (HD786-1A)

Detected allelic frequency (HD786-1B)

Detected allelic frequency (HD786-1C)

5. Detection of Reference Standard mutations. We analyzed the detectable sites of HD786 in this capture panel, including SNVs, Indels, CNVs, and fusions. Specific information is shown in Table 15, and the results matched the expectations.

4

Notes 1. Do not add SDS directly to the Proteinase K solution, to avoid inactivation of the Proteinase K. 2. Make sure that sufficient shaking makes the cfDNA and beads fully bind;, otherwise, the recovery efficiency of cfDNA will be reduced. 3. For cfDNA samples, we recommend that the working concentration of 1 μM for the adapter.

References 1. Sorrells RB (1974) Synovioanalysis (“liquid biopsy”). J Ark Med Soc 71(1):59 2. Siena S, Sartore-Bianchi A, Garcia-CarboneroR, Karthaus M, Smith D, Tabernero J, Van Cutsem E, Guan X, Boedigheimer M, Ang A, Twomey B, Bach BA, Jung AS, Bardelli A (2018) Dynamic molecular analysis and clinical correlates of tumor evolution within a phase II trial of panitumumab-based therapy in metastatic colorectal cancer. Ann Oncol 29(1): 119–126. https://doi.org/10.1093/annonc/ mdx504

3. Bronkhorst AJ, Ungerer V, Holdenrieder S (2019) The emerging role of cell-free DNA as a molecular marker for cancer management. Biomol Detect Quantif 17:2 4. Howell J, Atkinson SR, Pinato DJ, Knapp S, Sharma R (2019) Identification of mutations in circulating cell-free tumour DNA as a biomarker in hepatocellular carcinoma. Eur J Cancer (Oxford, England: 1990) 116:56–66 5. Wan JM, CharsMass J-C, FornMour RJ, CarosCadas SP, Rchardard NR (2017) Liquid biopsies come of age: towards implementation

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of circulating tumour DNA. Nat Rev Cancer 17:223 6. Gandara DR, Paul SM, Kowanetz M, Schleifman E, Shames DS (2018) Bloodbased tumor mutational burden as a predictor of clinical benefit in non-small-cell lung cancer patients treated with atezolizumab. Nat Med 24(9):1441 7. Willis J, Lefterova MI, Artyomenko A, Kasi PM, Nakamura Y, Mody K, Catenacci DVT, Fakih M, Barbacioru C, Zhao J, Sikora M, Fairclough SR, Lee H, Kim K-M, Kim ST, Kim J, Gavino D, Benavides M, Peled N, Nguyen T, Cusnir M, Eskander RN, Azzi G, Yoshino T, Banks KC, Raymond VM, Lanman RB, Chudova DI, Talasaz A, Kopetz S, Lee J, Odegaard JI (2019) Validation of microsatellite instability detection using a comprehensive plasma-based genotyping panel. Clin Cancer Res 25(23): 7035–7045. https://doi.org/10.1158/ 1078-0432.Ccr-19-1324 8. Machiels JP (2021) Liquid biopsy to detect minimal residual disease: methodology and impact. Cancers 13:5364 9. Newman AM, Lovejoy AF, Klass DM, Kurtz DM, Chabon JJ, Scherer F, Stehr H, Liu CL, Bratman SV, Say C (2016) Integrated digital error suppression for improved detection of circulating tumor DNA. Nat Biotechnol 34:547 10. Pel J, Broemeling D, Mai L, Poon HL, Marziali A (2009) Nonlinear electrophoretic response yields a unique parameter for separation of

biomolecules. Proc Natl Acad Sci 106(35): 14796–14801 11. Bettegowda C, Sausen M, Leary R, Kinde I, Agrawal N (2014) Detection of circulating tumor DNA in early and late stage human malignancies. Neuro Oncology 16:III7 12. Chan A, Woo J, King A (2017) Analysis of plasma Epstein-Barr virus DNA to screen for nasopharyngeal cancer. N Engl J Med 377:513 13. Sina A, Carrascosa LG, Liang Z, Grewal YS, Wardiana A, Shiddiky M, Gardiner RA, Samaratunga H, Gandhi MK, Scott RJ (2018) Epigenetically reprogrammed methylation landscape drives the DNA self-assembly and serves as a universal cancer biomarker. Nat Commun 9(1):4915 14. Guo S, Diep D, Plongthongkum N, Fung HL, Zhang K, Zhang K (2017) Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nat Genet 49(4):635 15. Chen S, Zhou Y, Chen Y, Jia G (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34(17):i884–i890 16. Li H (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv e-prints 17. Garcı´a-Alcalde F (2012) Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics 28(20):2678

Chapter 4 Exosomal RNA Sequencing from Body Fluid Samples Xiaoshuang Li, Xingyu Liu, Xiaoke Wei, Geng Tian, Lili Zhang, and Weiwei Wang Abstract Exosomes are microcapsules released by different types of cells. It contains DNA, RNA, proteins, and other substances. Exosomes spread in body fluids, and their contents can be phagocytosed by other cells and become an important mediator of intercellular communication. Previous studies show that exosomes secreted by cancer cells are involved in tumorigenesis, growth, invasion, and metastasis. With the improvement of technologies for exosome isolation and deep sequencing, we can identify exosomal RNA profiling for further detection of cancer-related biomarkers. In this chapter, we introduce the experiment procedure and bioinformatic pipeline on exosomal RNA sequencing. Key words Exosome isolation, Exosomal RNA sequencing, Exosomal RNA biomarker, Liquid biopsy, Early diagnosis

1

Introduction Exosomes are small vesicles with a lipid bilayer membrane structure, about 30–150 nm in diameter, which can be released into the extracellular matrix by the fusion of intracellular multivesicle bodies with cell membranes. Exosomes naturally exist in various body fluids, such as blood, urine, saliva, ascites, and cerebrospinal fluid. Exosomes contain cell-specific proteins, lipids, and nucleic acids. These substances can be transported to target cells through exosomes to regulate the transcription and phenotype of target cells [1]. Exosomes contain different types of RNA, including microRNA, long noncoding RNA, etc. miRNA is a class of noncoding single-stranded RNA molecules encoded by endogenous genes with a length of about 22 nucleotides, which are generated by 70–90 bases of single-stranded RNA precursors with hairpin

Authors Xiaoshuang Li and Xingyu Liu have equally contributed to this chapter. Tao Huang et al. (eds.), Liquid Biopsies: Methods and Protocols, Methods in Molecular Biology, vol. 2695, https://doi.org/10.1007/978-1-0716-3346-5_4, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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structure after processing by Dicer enzyme. miRNA can induce the degradation of target mRNA or inhibit its translation by complementing specific base pairs with target mRNA, thus regulating the post-transcriptional gene expression [2]. Previous study has shown that microRNA is selectively packaged into exosomes and involved in tumorigenesis. Exosomes were collected from glioma patient plasma before and after radiotherapy; 18 upregulated miRNA and 16 downregulated miRNA were further identified. The related target genes were mainly involved in metabolic processes, p53 signaling pathway, and cancer pathways. This study suggested that these miRNAs play an important role in glioma by regulating their target genes, further affecting the occurrence and development of the disease [3]. lncRNA, also enriched in exosomes, is another class of noncoding RNA. The length of lncRNA is more than 200 bp. LncRNA can participate in the regulation of a variety of biological processes, such as chromatin modification, transcriptional activation and inhibition, post-transcriptional mediation, etc. LncRNA can also be selectively packed into exosomes and participate in intercellular communication in the tumor microenvironment [4]. lncRNA-ATB and lncRNA-AHIF have been found to contribute to cell proliferation and invasion in cancer [5, 6]. The membrane structure of exosomes also protects RNA from degradation, so the RNA in exosomes is more stable than cell-free RNA [7]. Exosomal RNAs are reported to be involved in carcinogenesis and could be potential diagnostic or prognostic biomarkers. In this chapter, we introduce the workflow of exosomal RNA sequencing and bioinformatic pipelines.

2 2.1

Materials Reagents

1. Thrombin Plasma Prep for Exosome precipitation (500uL at 611 U/mL). 2. ExoQuick® Exosome Isolation and RNA Purification Kits. 3. Phosphate-Buffered Saline (PBS). 4. 100% Ethanol. 5. Agilent RNA 6000 Pico Kit. 6. Ovation SoLo RNA-Seq Systems. 7. SoLo AnyDeplete Probe Mix, Human. 8. Agencourt RNAClean XP Beads or AMPure XP Beads. 9. Low-EDTA TE Buffer, 1×, pH 8.0. 10. Tween 20. 11. EvaGreen, 20×. 12. Agilent High Sensitivity DNA Kit. 13. RNaseZap RNase Decontamination Solution.

Exosomal RNA Sequencing from Body Fluid Samples

2.2

Equipment

49

1. 1.5 mL centrifuge tube. 2. 0.2 mL PCR tube. 3. 0.5–2.5 μL pipette, 0.5–10 μL pipette, 2–20 μL pipette, 10–100 μL pipette, 20–200 μL pipette, 100–1000 μL pipette. 4. Nuclease-free pipette tips. 5. Standard microcentrifuge for individual 1.5 mL and 0.2 mL tubes. 6. Transmission electron microscope (TEM). 7. Particle Metrix Zeta View. 8. Electrophoresis tank. 9. Agilent 2100 Bioanalyzer. 10. Vertex. 11. Magnetic stand. 12. Thermal cycler with 0.2 mL tube heat block, heated lid, and 100 μL reaction capacity. 13. Qubit 3.0 Fluorometer.

2.3

Software

1. FastQC (http://www.bioinformatics.babraham.ac.uk/pro jects/download.html#fastqc). 2. Fastp (https://github.com/OpenGene/fastp). 3. Bowtie2 (http://sourceforge.net/projects/bowtie-bio/files/ bowtie2/2.2.6/). 4. Tophat2 (https://ccb.jhu.edu/software/tophat/index. shtml). 5. Cufflinks (http://cole-trapnell-lab.github.io/cufflinks/ install/). 6. Coding potential calculator (CPC) server (http://cpc.cbi.pku. edu.cn/). 7. Bedtools (https://github.com/arq5x/bedtools2/releases). 8. RStudio (https://www.rstudio.com/products/rstudio/down load2/). 9. R package CummeRbund, a toolkit for extracting the gene expression data, differentially expressed genes, gene annotations, etc., from the Cuffdiff output. 10. Samtools (https://sourceforge.net/projects/samtools/files/ samtools/). 11. Trim Galore (http://www.bioinformatics.babraham.ac.uk/ projects/trim_galore/). 12. miRDeep2 (https://github.com/rajewsky-lab/mirdeep2).

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Methods The workflow of the exosomal RNA sequencing is described in Fig. 1, and detailed procedures are as follows.

3.1 Plasma Defibrination

1. Thaw plasma sample on ice, and centrifuge at 3000 × g for 15 min to remove cellular debris. 2. Transfer 0.5 mL of supernatant to a new 1.5 mL centrifuge tube. 3. Add 4 μL of [611 U/mL] Thrombin per 0.5 mL of plasma to a final concentration of 5 U/mL. 4. Incubate at room temperature for 5 min while mixing (gently flicking tube).

Fig. 1 Stepwise overview of the exosomal RNA sequencing

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5. Centrifuge in a standard microfuge at 10,000 rpm, 5 min. 6. There should be a visible fibrin pellet at the bottom of the tube. 7. Transfer supernatant to a new clean 1.5 mL centrifuge tube. 3.2 Exosome Isolation

1. Add 120 μL of ExoQuick to pre-treated plasma. Mix well by inverting or flicking the tube and incubate on ice or 4 °C for 30 min. The tubes do not need to be rotated during the incubation period. 2. Centrifuge the ExoQuick/pre-treated plasma mixture at 1500 × g for 30 min. Centrifugation may be performed at either room temperature or 4 °C with similar results. After centrifugation, the exosomes may appear as a beige or white pellet at the bottom of the tube. 3. Carefully aspirate off the supernatant. Spin down any residual ExoQuick solution by centrifugation at 1500 × g for 5 min. Remove all traces of fluid by aspiration, taking great care not to disturb the precipitated exosomes in the pellet. 4. Add 100 μL of PBS to resuspend pellet. 5. Transfer 50 μL resuspension solution to a new tube, and add 1150 μL PBS to make a total of 1200 μL to identify exosomes. The remaining 50 μL resuspension solution is used to exosome RNA isolation.

3.3 Assessment of Exosomes

Before exosomal RNA extraction, it is necessary to determine the quality of the isolated exosomes. A transmission electron microscope (TEM) is used to identify the morphology of exosomes (Fig. 2a). Nanoparticle tracking analysis (NTA) technology is used to identify the size distribution of microparticles, and the size of typical exosomes is between 30 and 150 nm (Fig. 2b). Western blot is used to identify surface protein markers, including Alix, HSP90, CD81, CD63, and so on (Fig. 2c).

3.4 Extraction of Total Exosomal RNA

1. Add 350 μL of Lysis Buffer to the remaining 50uL resuspension solution from exosome isolation step and vortex for 15 s. 2. Place at room temperature for 5 min to allow complete lysis. 3. Add 200 μL of 100% ethanol to resuspended exosomes and vortex for 10 s. 4. Assemble the ExoQuick RNA column by placing the spin column into the collection tube. 5. Transfer the sample to the spin column and centrifuge at 13,000 rpm for 1 min. 6. Discard the flow-through and place the column back into the collection tube.

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Fig. 2 Quality control of isolated exosomes. (a) Morphology of exosomes using a transmission electron microscope (TEM); (b) distribution of exosome size using nanoparticle tracking analysis (NTA); (c) surface protein markers staining using Western blot to evaluate the integrity of exosome

7. To wash the column, add 400 μL Wash Buffer and centrifuge at 13,000 rpm for 1 min. Discard the flow through. 8. Repeat steps 6 and 7 one more time. 9. Discard the flow through and centrifuge at 13,000 rpm for 2 min to dry. 10. Discard the collection tube and assemble the spin column with a new, RNase-free, 1.5 mL elution tube. 11. Add 30 μL of Elution Buffer onto the membrane of the spin column and centrifuge at 2000 rpm for 2 min to load the membrane with the buffer. 12. Increase speed to 13,000 rpm and centrifuge for 1 min to elute the exoRNAs. 3.5 Quality Control of Exosomal RNA

Quality of the exosome RNA is evaluated using RNA 6000 Pico chip on an Agilent Bioanalyzer. Exosomal RNA mainly concentrated in the low molecular RNA region, usually below the size of 200 nt (Fig. 3).

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Fig. 3 Agilent 2100 Bioanalyzer electropherogram plot of exosomal RNA Table 1 DNase treatment and primer annealing master mix DNase buffer

First strand primer mix

DTT solution

Nuclease-free water

HL-dsDNase

1 μL

2.6 μL

1.7 μL

0.7 μL

2 μL

3.6 Exosomal RNA Library Construction 3.6.1 DNase Treatment and Primer Annealing

1. 10 pg–10 ng of total RNA in 10 μL of low-EDTA TE buffer or nuclease-free water is used for DNase treatment. For inputs lower than 100 pg, dilute RNA in low-EDTA TE + 0.1% Tween-20 to reduce sample loss due to binding microcentrifuge tubes. 2. Pre-thaw DNase Buffer, HL-dsDNase, First Strand Primer Mix, DTT Solution, and nuclease-free water. 3. Spin down the contents of HL-dsDNase and place on ice. 4. Thaw the other reagents at room temperature. Mix by vortexing, spin, and place on ice. 5. Prepare DNase Treatment and Primer Annealing Master Mix by combining DNase Buffer, First Strand Primer Mix, DTT Solution, nuclease-free water, and HL-dsDNase according to the volumes shown in Table 1 (see Note 1). Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 70% of the Master Mix volume. Spin down and place on ice. 6. Add 8 μL of DNase Treatment and Primer Annealing Master Mix to each sample tube containing 10 μL of RNA for a total volume of 18 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 14 μL. Spin down and place on ice.

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7. Place the tubes in a pre-warmed thermal cycler programmed to run the program: 37 °C – 10 min, 65 °C – 5 min, hold at 4 °C 8. Remove the tubes from the thermal cycler, spin to collect condensation, and place on ice. 9. Continue immediately to the First Strand cDNA synthesis protocol. 3.6.2 First Strand cDNA Synthesis

1. Pre-thaw First Strand Buffer Mix and First Strand Enzyme Mix. 2. Spin down the contents of First Strand Enzyme Mix and place on ice. 3. Thaw the First Strand Buffer Mix at room temperature, mix by vortexing, spin, and place on ice. 4. Prepare a First Strand Master Mix by combining 1 μL of First Strand Buffer Mix and 1 μL of First Strand Enzyme Mix in a 0.5 mL capped tube (see Note 1). Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 70% of the Master Mix volume. Spin down and place on ice. Use immediately. 5. Add 2 μL of First Strand Master Mix to each sample tube for a total of 20 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 16 μL. Spin down and place on ice. 6. Place the tubes in a pre-warmed thermal cycler programmed to run the program: 25 °C – 5 min, 40 °C – 30 min, 70 °C – 10 min, hold at 4 °C 7. Remove the tubes from the thermal cycler, spin to collect condensation, and place on ice. 8. Continue immediately to the cDNA Processing protocol or store at -20 °C. Optional Stopping Point.

3.6.3

cDNA Processing

1. Pre-thaw cDNA Processing Enzyme I, cDNA Processing Enzyme II, cDNA Processing Reagent I, and cDNA Processing Reagent II. 2. Spin down the contents of cDNA Processing Enzyme I and cDNA Processing Enzyme II and place on ice. 3. Thaw cDNA Processing Reagent I and cDNA Processing Reagent II at room temperature. Mix by vortexing, spin down, and place on ice. 4. Prepare a cDNA Processing Enzyme Master Mix by combining 0.5 μL of cDNA Processing Enzyme I and 0.5 μL of cDNA Processing Enzyme II in a 0.5 mL capped tube (see Note 1).

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Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 70% of the Master Mix volume. Spin down and place on ice. Use immediately. 5. Add 1 μL of the cDNA Processing Enzyme Master Mix to each sample tube for a total of 21 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 17 μL. Spin down and place on ice. 6. Place the tubes in a pre-warmed thermal cycler programmed to run the program: 37 °C – 30 min, hold at 4 °C 7. Remove the tubes from the thermal cycler, spin to collect condensation, and place on ice. 8. Add 2 μL of cDNA Processing Reagent I to each sample tube for a total of 23 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 18 μL. Spin down and place on ice. 9. Place the tubes in a pre-warmed thermal cycler programmed to run the program: 90 °C – 20 min, hold at 4 °C 10. Remove the tubes from the thermal cycler, spin to collect condensation, and place on ice. 11. Add 2 μL of cDNA Processing Reagent II to each sample tube for a total of 25 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 20 μL. Spin down and place on ice. 12. Pre-thaw cDNA Processing Reagent III and cDNA Processing Enzyme III. 13. Spin down contents of cDNA Processing Enzyme III and place on ice. 14. Thaw cDNA Processing Reagent III at room temperature. Mix by vortexing, spin down, and place on ice. 15. Prepare a cDNA Processing III Master Mix by combining 1 μL of cDNA Processing Reagent III and 1 μL of cDNA Processing Enzyme III in a 0.5-mL capped tube (see Note 1). Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 70% of the Master Mix volume. Spin down and place on ice. Use immediately. 16. Add 2 μL of the cDNA Processing III Master Mix to each sample tube for a total of 27 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 22 μL. Spin down and place on ice. 17. Place the tubes in a pre-warmed thermal cycler programmed to run the program: 37 °C – 30 min, 70 °C – 10 min, hold at 4 °C

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18. Remove the tubes from the thermal cycler, spin to collect condensation, and place on ice. 19. Pre-thaw cDNA Processing Reagent IV and cDNA Processing Enzyme IV. 20. Spin down contents of cDNA Processing Enzyme IV and place on ice. 21. Thaw cDNA Processing Reagent IV at room temperature. Mix by vortexing, spin down, and place on ice. 22. Prepare a cDNA Processing IV Master Mix by combining 1 μL of cDNA Processing Enzyme IV and 7 μL of cDNA Processing Reagent IV in a 0.5-mL capped tube (see Note 1). Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 70% of the Master Mix volume. Spin down and place on ice. Use immediately. 23. Add 8 μL of cDNA Processing IV Master Mix to the sample tube for a total of 35 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 28 μL. Spin down and place on ice. 24. Place the tubes in a pre-warmed thermal cycler programmed to run the program: 37 °C – 30 min, 75 °C – 20 min, hold at 4 °C 25. Remove the tubes from the thermal cycler, spin to collect condensation, and place on ice. 26. Continue immediately to the Second Strand Synthesis protocol. 3.6.4 Second Strand Synthesis

1. Pre-thaw Second Strand Buffer, Second Strand Primer Mix, and Second Strand Enzyme Mix. 2. Spin down the contents of Second Strand Enzyme Mix and place on ice. 3. Thaw Second Strand Primer Mix and Second Strand Buffer at room temperature. Mix by vortexing, spin, and place on ice. 4. Prepare a Second Strand Master Mix by combining 3.5 μL of Second Strand Buffer Mix, 3.5 μL of Second Strand Primer Mix, and 1 μL of Second Strand Enzyme Mix in a 0.5-mL capped tube (see Note 1). Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 70% of the Master Mix volume. Spin down and place on ice. Use immediately. 5. Add 8 μL of the Second Strand Master Mix to each sample tube for a total of 43 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 35 μL. Spin down and place on ice.

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6. Place the tubes in a pre-warmed thermal cycler programmed to run the program: 25 °C – 15 min, 37 °C – 15 min, 70 °C – 10 min, hold at 4 °C 7. Remove the tubes from the thermal cycler, spin to collect condensation, and place on ice. Continue immediately to the End Repair protocol. 3.6.5

End Repair

1. Pre-thaw End Repair Enzyme Mix and End Repair Enzyme Mix II. 2. Spin down the contents of both enzyme mixes and place on ice. 3. Prepare an End Repair Master Mix by combining 1 μL of End Repair Enzyme Mix I and 1 μL of End Repair Enzyme Mix II in a 0.5 mL capped tube (see Note 1). Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 70% of the Master Mix volume. Spin down and place on ice. Use immediately. 4. Add 2 μL of End Repair Master Mix to each sample tube for a total of 45 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 36 μL. Spin down and place on ice. 5. Place the tubes in a pre-warmed thermal cycler programmed to run the program: 25 °C – 30 min, 70 °C – 10 min, hold at 4 °C 6. Remove the tubes from the thermal cycler, spin to collect condensation, and place on ice. Continue immediately to the Adaptor Ligation protocol or store at -20 °C. Optional Stopping Point.

3.6.6

Adapter Ligation

1. Pre-thaw Ligation Buffer Mix, Ligation Enzyme Mix, the Adaptor Plate, and nuclease-free water. Also, remove the Agencourt beads from 4 °C storage and DNA Resuspension Buffer Mix from -20 °C storage. Place at room temperature for use after adaptor ligation. 2. Spin down the contents of Ligation Enzyme Mix and Adaptor plate and place them on ice. 3. Thaw Ligation Buffer Mix and nuclease-free water at room temperature. Mix by vortexing, spin, and place on ice. 4. Add 3.25 μL of the appropriate barcoded Adaptor Mix to each sample. Mix thoroughly by pipetting, spin, and place on ice. Make sure a unique barcode is used for each sample to be multiplexed together on the sequencer. 5. Prepare a Ligation Master Mix by combining 13 μL of Ligation Buffer Mix, 2 μL of Ligation Enzyme Mix, and 1.75 μL of

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nuclease-free water in a 0.5-mL capped tube (see Note 1). Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 70% of the Master Mix volume. Spin down and place on ice. Use immediately. 6. Add 16.75 μL of the Ligation Master Mix to each sample tube for a total of 65 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 52 μL. Spin down and place on ice. 7. Place the tubes in a pre-warmed thermal cycler programmed to run the program: 25 °C – 30 min, 70 °C – 10 min, hold at 4 °C 8. Remove the tubes from the thermal cycler, spin to collect condensation, and place on ice. 9. Continue immediately to the Adaptor Ligation Purification protocol or store at -20 °C. Optional Stopping Point. 3.6.7 Adapter Ligation Purification

1. Ensure the Agencourt beads and DNA Resuspension Buffer Mix have completely reached room temperature before proceeding (see Note 2). Spin down contents of DNA Resuspension Buffer Mix and leave at room temperature. 2. Resuspend the beads by vortexing. Ensure the beads are fully resuspended before adding them to samples. 3. Add 35 μL of nuclease-free water to each sample for a total of 100 μL. 4. Add 100 μL (1.0 volumes) of the bead suspension to each sample. Mix thoroughly by pipetting up and down. 5. Incubate at room temperature for 10 min. 6. Transfer the tubes to the magnet and let them stand for 5 min to completely clear the solution of beads. 7. Carefully remove 200 μL of the binding buffer and discard it (see Note 3). 8. With the tubes still on the magnet, add 200 μL of freshly prepared 70% ethanol, and allow them to stand for 30 s (see Note 4). 9. Remove the 70% ethanol wash using a pipette. 10. Repeat the 70% ethanol wash one more time for a total of two washes (see Note 5). 11. Air-dry the beads on the magnet for 10 min. Inspect each tube carefully to ensure that all of the ethanol has evaporated. It is critical that all residual ethanol be removed prior to continuing. 12. Remove the tubes from the magnet.

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13. Add 30 μL of DNA Resuspension Buffer Mix to the dried beads. Mix thoroughly to ensure all beads are resuspended. 14. Transfer the tubes to the magnet and let them stand for 3 min for the beads to clear the solution. 15. Carefully remove 30 μL of the eluate, ensuring as few beads as possible are carried over and transfer to a fresh set of PCR tubes and place on ice. 16. Continue immediately to the Library Amplification Optimization qPCR protocol or store at -20 °C. Optional Stopping Point. 3.6.8 Library Amplification Optimization qPCR

1. Pre-thaw DNA Resuspension Buffer Mix, Amplification Buffer Mix, Amplification Primer Mix I, Amplification Enzyme Mix, nuclease-free water, and 20× EvaGreen. 2. Spin down Amplification Enzyme Mix and place it on ice. 3. Thaw DNA Resuspension Buffer Mix, Amplification Buffer Mix, Amplification Primer Mix I, 20× EvaGreen, and nuclease-free water at room temperature. Mix by vortexing, spin, and place on ice. 4. Add 3 μL of DNA Resuspension Buffer Mix to each sample. Mix thoroughly by pipetting. 5. Prepare a qPCR Master Mix by combining Amplification Buffer Mix, Amplification Primer Mix I, Amplification Enzyme Mix, and EvaGreen in a 0.5-mL capped tube according to the volumes shown in Table 2 (see Note 1). Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 70% of the master mix volume. Spin down and place on ice. Use immediately. 6. Aliquot 3 μL of each sample into a fresh set of tubes. Store the remaining 30 μL of the sample on ice or at -20 °C. 7. Add 7 μL of qPCR Master Mix to 3 μL of each sample. Mix thoroughly by pipetting, spin down, and place on ice. 8. Load each 10 μL sample into qPCR tubes or plates. 9. Run qPCR on the samples using the program: 70 °C – 10 min, 35 cycles (94 °C – 30 s, 60 °C – 30 s, 72 °C – 1 min); 72 °C – 5 min, hold at 4 °C

Table 2 qPCR master mix Amplification buffer Amplification primer mix mix I

Amplification enzyme 20× mix EvaGreen

Nuclease-free water

2 μL

0.1 μL

2.5 μL

1.9 μL

0.5 μL

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10. Visualize the amplification curves as log fluorescence vs. cycle number (i.e., Log Rn vs. Cycle or Log RFU vs. Cycle). The cycle number used for subsequent library amplification should be within the exponential phase of the amplification. 11. Proceed to Library Amplification I. 3.6.9 Library Amplification I

1. Pre-thaw Amplification Buffer Mix, Amplification Primer Mix I, and Amplification Enzyme Mix. Also, remove the Agencourt RNAClean XP Beads from 4 °C storage and DNA Resuspension Buffer Mix from -20 °C storage. Place at room temperature for use after Library Amplification I. 2. Spin down Amplification Enzyme Mix and place it on ice. 3. Thaw Amplification Buffer Mix and Amplification Primer Mix I at room temperature. Mix by vortexing, spin, and place on ice. 4. Prepare a Library Amplification I Master Mix by combining 10 μL of Amplification Buffer Mix, 9.5 μL of Amplification Primer Mix I, and 0.5 μL of Amplification Enzyme Mix in a 0.5 mL capped tube (see Note 1). Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 70% of the Master Mix volume. Spin down and place on ice. Use immediately. 5. Add 20 μL of the Library Amplification I Master Mix to each sample tube for a total of 50 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 40 μL. Spin down and place on ice. 6. Place the tubes in a pre-warmed thermal cycler programmed to run the program: 70 °C – 10 min, n* cycles (94 °C – 30 s, 60 °C – 30 s, 72 °C – 1 min); 72 °C – 5 min, hold at 10 °C. *Important: The number of amplification cycles should be determined empirically by qPCR. 7. Remove the tubes from the thermal cycler, spin to collect condensation, and place on ice. 8. Proceed to the Library Amplification I Purification protocol or store at -20 °C. Optional Stopping Point.

3.6.10 Library Amplification I Purification

1. Ensure the Agencourt beads and DNA Resuspension Buffer Mix have completely reached room temperature before proceeding (see Note 2). Spin down contents of DNA Resuspension Buffer Mix and leave at room temperature. 2. Resuspend the beads by vortexing. Ensure the beads are fully resuspended before adding them to samples.

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3. Add 40 μL (0.8 volumes) of the bead suspension to the Library Amplification I reaction product. Mix thoroughly by pipetting up and down. 4. Incubate at room temperature for 10 min. 5. Transfer the tubes to the magnet and let them stand 5 min to completely clear the solution of beads. 6. Carefully remove 90 μL of the binding buffer and discard it (see Note 3). 7. With the tubes still on the magnet, add 200 μL of freshly prepared 70% ethanol and allow them to stand for 30 s (see Note 4). 8. Remove the 70% ethanol wash using a pipette. 9. Repeat the 70% ethanol wash one more time for a total of two washes (see Note 5). 10. Air-dry the beads on the magnet for 10 min. Inspect each tube carefully to ensure that all of the ethanol has evaporated. It is critical that all residual ethanol be removed prior to continuing. 11. Remove the tubes from the magnet. 12. Add 50 μL of DNA Resuspension Buffer Mix to the dried beads. Mix thoroughly to ensure all beads are resuspended. 13. Transfer the tubes to the magnet and let them stand for 3 min for the beads to clear the solution. 14. Carefully remove 50 μL of the eluate, ensuring as few beads as possible are carried over, and then transfer to a fresh set of PCR tubes. 15. At room temperature, add 40 μL (0.8 volumes) of the freshly resuspended bead suspension to the eluate. Mix thoroughly by pipetting up and down. 16. Incubate at room temperature for 10 min. 17. Transfer the tubes to the magnet and let them stand 5 min to completely clear the solution of beads. 18. Carefully remove 90 μL of the binding buffer and discard it (see Note 3). 19. With the tubes still on the magnet, add 200 μL of freshly prepared 70% ethanol and allow them to stand for 30 s (see Note 4). 20. Remove the 70% ethanol wash using a pipette. 21. Repeat the 70% ethanol wash one more time for a total of two washes (see Note 5).

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22. Air-dry the beads on the magnet for 10 min. Inspect each tube carefully to ensure that all of the ethanol has evaporated. It is critical that all residual ethanol be removed prior to continuing. 23. Remove the tubes from the magnet. 24. Add 25 μL of DNA Resuspension Buffer Mix to the dried beads. Mix thoroughly to ensure all beads are resuspended. 25. Transfer tubes to the magnet and let them stand for 3 min to clear the solution of beads. 26. Carefully remove 25 μL of the eluate, ensuring as few beads as possible are carried over, and transfer to a fresh set of PCR tubes and place on ice. 27. Continue immediately to Library Quantification or store at -20 °C. Optional Stopping Point. 3.6.11 Library Quantification

3.6.12

rRNA Depletion

Quantify the library using the Qubit dsDNA HS Assay. Alternatively, a NanoDrop or Bioanalyzer may also be used for library quantification. 1. Pre-thaw Amplification Buffer Mix, AnyDeplete Probe Mix, Strand Selection Enzyme Mix II, Amplification Enzyme Mix, and nuclease-free water. 2. Spin down the Strand Selection Enzyme Mix II and Amplification Enzyme Mix and place on ice. 3. Thaw Amplification Buffer Mix, AnyDeplete Probe Mix, and nuclease-free water at room temperature. Mix Amplification Buffer Mix and AnyDeplete Probe Mix by vortexing, spin, and place on ice. 4. Aliquot 10 ng of the library into a new 0.2-mL capped tube. Store the remaining library at -20 °C. 5. Use nuclease-free water to dilute each library to a total volume of 8.5 μL. 6. Prepare a Probe Binding Master Mix by combining Amplification Buffer Mix, AnyDeplete Probe Mix, Strand Selection Enzyme Mix II, and Amplification Enzyme Mix in a 0.5-mL capped tube according to the volumes shown in Table 3 (see Note 1). Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 70% of the Master Mix volume. Spin down and place on ice. Use immediately. 7. Add 16.5 μL of the Probe Binding Master Mix to each sample tube for a total of 25 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 20 μL. Spin down and place on ice.

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Table 3 Probe binding master mix

Amplification buffer mix

AnyDeplete probe mix

Strand selection enzyme mix II

Amplification enzyme mix

5 μL

10 μL

0.5 μL

1 μL

8. Place the tubes in a pre-warmed thermal cycler programmed to run the program: 37 °C – 10 min, 95 °C – 2 min, 50 °C – 1 min, 65 °C – 10 min, hold at 4 °C 9. Remove the tubes from the thermal cycler, spin to collect condensation, and place on ice. 10. Pre-thaw Amplification Buffer Mix, AnyDeplete Enzyme Mix II, and nuclease-free water. 11. Spin down AnyDeplete Enzyme Mix II and place on ice. 12. Thaw Amplification Buffer Mix and nuclease-free water at room temperature. Mix by vortexing, spin, and place on ice. 13. Prepare a Targeted Depletion Master Mix by combining 5 μL of Amplification Buffer Mix, 2.5 μL of AnyDeplete Enzyme Mix II, and 17.5 μL of nuclease-free water in a 0.5-mL capped tube (see Note 1). Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 70% of the Master Mix volume. Spin down and place on ice. Use immediately. 14. Add 25 μL of the Targeted Depletion Master Mix to each sample tube for a total of 50 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 40 μL. Spin down and place on ice. 15. Place the tubes in a pre-warmed thermal cycler programmed to run the program: 55 °C – 30 min, 95 °C – 5 min, hold at 4 °C 16. Remove the tubes from the thermal cycler, spin to collect condensation, and place on ice. 17. Continue immediately to the Library Amplification II protocol or store at -20 °C. Optional Stopping Point. 3.6.13 Library Amplification II

1. Pre-thaw Amplification Buffer Mix, Amplification Primer Mix II, and Amplification Enzyme Mix. Also, remove the Agencourt beads from 4 °C storage and DNA Resuspension Buffer Mix from -20 °C storage. Place at room temperature for use after Library Amplification II. 2. Spin down Amplification Enzyme Mix and place it on ice.

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3. Thaw Amplification Buffer Mix and Amplification Primer Mix II at room temperature. Mix by vortexing, spin, and place on ice. 4. Prepare a Library Amplification II Master Mix by combining 10 μL of Amplification Buffer Mix, 39.5 μL of Amplification Primer Mix II, and 0.5 μL of Amplification Enzyme Mix in a 0.5 mL capped tube (see Note 1). Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 70% of the Master Mix volume. Spin down and place on ice. Use immediately. 5. Add 50 μL of the Library Amplification II Master Mix to each sample tube for a total of 100 μL. Mix thoroughly by pipetting up and down at least 10 times with a pipettor set to 80 μL. Spin down and place on ice. 6. Mix by pipetting, spin, and place on ice. 7. Place the tubes in a pre-warmed thermal cycler programmed to run the program: 95 °C – 2 min, 2 cycles (95 °C – 30 s, 60 °C – 90 s); 6 cycles (95 °C – 30 s, 65 °C – 90 s); 65 °C – 5 min, hold at 4 °C 8. Remove the tubes from the thermal cycler, spin to collect condensation, and place on ice. 9. Continue immediately to the Library Amplification II Purification protocol or store at -20 °C. Optional Stopping Point. 3.6.14 Library Amplification II Purification

1. Ensure the Agencourt beads and DNA Resuspension Buffer Mix have completely reached room temperature before proceeding (see Note 2). Spin down contents of DNA Resuspension Buffer Mix and leave at room temperature. 2. Resuspend the beads by vortexing. Ensure the beads are fully resuspended before adding them to samples. 3. At room temperature, add 100 μL (1.0 volumes) of the bead suspension to the Library Amplification II reaction product. Mix thoroughly by pipetting up and down. 4. Incubate at room temperature for 10 min. 5. Transfer the tubes to the magnet and let them stand for 5 min to completely clear the solution of beads. 6. Carefully remove 200 μL of the binding buffer and discard it (see Note 3). 7. With tubes still on the magnet, add 200 μL of freshly prepared 70% ethanol, and allow to stand for 30 s (see Note 4). 8. Remove the 70% ethanol wash using a pipette.

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9. Repeat the 70% ethanol wash one more time for a total of two washes (see Note 5). 10. Air-dry the beads on the magnet for 10 min. Inspect each tube carefully to ensure that all of the ethanol has evaporated. It is critical that all residual ethanol be removed prior to continuing. 11. Remove the tubes from the magnet. 12. Add 50 μL of DNA Resuspension Buffer Mix to the dried beads. Mix thoroughly to ensure all beads are resuspended. 13. Transfer the tubes to the magnet and let stand for 3 min for the beads to clear the solution. 14. Carefully remove 50 μL of the eluate, ensuring as few beads as possible are carried over, and transfer to a fresh set of PCR tubes. 15. At room temperature, add 50 μL (1.0 volumes) of the freshly resuspended bead suspension to the eluate. Mix thoroughly by pipetting up and down. 16. Incubate at room temperature for 10 min. 17. Transfer the tubes to the magnet and let them stand for 5 min to completely clear the solution of beads. 18. Carefully remove 100 μL of the binding buffer and discard it (see Note 3). 19. With the tubes still on the magnet, add 200 μL of freshly prepared 70% ethanol and allow them to stand for 30 s (see Note 4). 20. Remove the 70% ethanol wash using a pipette. 21. Repeat the 70% ethanol wash one more time for a total of two washes (see Note 5). 22. Air-dry the beads on the magnet for 10 min. Inspect each tube carefully to ensure that all of the ethanol has evaporated. It is critical that all residual ethanol be removed prior to continuing. 23. Remove the tubes from the magnet. 24. Add 25 μL of DNA Resuspension Buffer Mix to the dried beads. Mix thoroughly to ensure all beads are resuspended. 25. Transfer tubes to the magnet and let them stand for 3 min to clear the solution of beads. 26. Carefully remove 25 μL of the eluate, ensuring as few beads as possible are carried over, and then transfer to a fresh set of PCR tubes and place on ice.

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Fig. 4 Agilent 2100 Bioanalyze electropherogram plot of exosomal RNA library 3.7 Quality Control of the Exosomal RNA Library

Check the quality of the exosomal RNA Library by using an Agilent High Sensitivity DNA chip on an Agilent 2100 Bioanalyzer. The size of the exosomal RNA library is approximately 150 bp (Fig. 4).

3.8 Library Sequencing

Exosomal RNA libraries are sequenced in accordance with the sequencing technology manufacturer’s protocols. For using Illumina NGS platforms, use no more than 60% of the flowcell (library pool) for exosomal RNA libraries. The remaining 40% of the flow cell should comprise non-exosomal RNA libraries and/or a control library such as phiX.

3.9

The bioinformatics pipeline is designed and modified using packages described in previous studies [8–10] to form the flow chart (Fig. 5).

Data Analysis

3.9.1 LncRNA Data Analysis

1. Build read aligner tophat index. bowtie2-build GRCh37.fa GRCh37

2. QC (Fastp) of raw reads. fastp -i demo_R1.fq.gz -I demo _R2.fq.gz -o demo_clean_R1.fq. gz -O demo_clean_R2.fq.gz -h fastp.html

3. Initialize read alignment (tophat). hisat2 –f –x GRCh37 -1 demo_clean_R1.fq.gz -2 demo_clean_R2.fq. gz -S demo.sam 2>demo.hisat2_summary.txt tophat2 -G GRCh37.gff3 -–no-novel-juncs -o demo GRCh37 demo_clean_R1.fq.gz demo_clean_R2.fq.gz

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Fig. 5 Analysis procedure of exosome RNA (lncRNA and miRNA)

4. Assemble the transcripts using cufflinks. cufflinks -p 4 -g GRCh37.gff3 -I 5000 -o demo _clout demo _thout/ accepted_hits.bam

5. Merged GTFs. find . -name transcripts.gtf > assemblies.txt

6. Put the .gtf file directories together. cuffmerge -p 4 -g GRCh37.gff3 -s GRCh37.fa assemblies.txt

7. Run cuffdiff to calculate the relative expression level of each locus and transcript. In the “cuffdiff” command, to be noted, the label for each sample is defined by “-L”; the .bam files for replicates should be separated by a comma following the same order as the labels; the option “-T” allows making comparisons between successive samples rather than between all pairs of samples to relieve the computing load. cuffdiff -o diff_out -b GRCh37.fa -p 8 -T -L demo -u merged_asm/merged.gtf demo _thout/ accepted_hits.bam

8. Select the transcripts with the class_codes “u,” “i,” “o,” and “x” from “merged.gtf”. cat merged.gtf | grep‘class_code “[uiox]”’> selected.gtf

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9. Obtain the selected transcript sequences in the fasta format. gffread -w selected.fa -g GRCh37.fa selected.gtf

10. Use the online server (http://cpc.cbi.pku.edu.cn) for assessing the coding capacities. To start a job, just submit the transcript sequences (the .fasta file), and wait to download the results. Find the noncoding transcripts longer than 200 nt from the CPC output. cat cpc.txt | awk ‘$4 < -1 && $2 > 200 {print $0}, ’ | cut -f1 > non-coding-transcript.txt

11. Get the noncoding transcript sequences. sed ’s/^/transcript_id "&/g’ non-coding-transcript.txt > non-coding-transcriptnew.txt cat merged.gtf | fgrep -f non-codingtranscript-new.txt > non-coding-transcript.gtf gffread -w non-coding-transcript.fa -g GRCh37.fa non-coding-transcript.gtf

12. Obtain the expressed encoding genes. cat merged.gtf | grep ‘class_code “¼”’ > coding.gtf

13. Obtain the neighbor gene pairs between lncRNAs and encoding genes. windowBed -a non-coding-transcript.gtf -b coding.gtf -w 10000 > genepair.gtf cat genepair.gtf | cut -f9 | cut -b 10-20 > lncRNA.txt cat genepair.gtf | cut -f18 | cut -b 10-20 > coding.txt paste lncRNA.txt coding.txt | sort | uniq | awk ’$1 !¼ $2 {p’ > genepair.txt

14. The outputs from the “cuffdiff” run were saved in the “diff_out” folder, which preserved the data on gene expression, gene annotation, and differentially expressed genes in separate files. To parse those data, the R package “cummeRbund” could be run in RStudio. > library(cummeRbund) > setwd(“/Path/to/diff_out”) > cuff cuff > lncRNA lncRNA lncRNA1 lncRNA2 write.table(lncRNA2, “Expression of noncoding genes.csv”, sep¼“\t”, quote¼FALSE) > coding coding coding1 coding2 write.table(coding2, “Expression of coding genes.csv”, sep¼“\t”, quote¼FALSE)

3.9.2 miRNA Data Analysis

1. QC. The raw sequencing data can be checked for data quality and viewed using the fastqc software. Fastqc only generates quality control reports and does not filter the data. fastqc -f fastq -d raw_data/P1-1.fq.gz

2. Data Filtering. Small RNA sequencing data is mainly focused on adapter sequences. Here, TrimGalore is used for data filtering. trim_galore --small_rna small RNA --length 18 --max_length 30 --stringency 3 --phred33 --cores 4

--dont_gzip -o clean_-

data raw_data/P1-1.fq.gz

3. Duplication and merging. Due to subsequent analysis software requirements, it was necessary to convert the small RNA from fq to fa format without redundancy. The id section reflects the number of occurrences of each sequence using the mirdeep2 package program. The mapper.pl program in the mirdeep2 package was used. mapper.pl clean_data/P1-1_trimmed.fq -g P11 -h -m -s uniq_data/P11.fa

4. Alignment (bowtie). The data that can be matched to the genome will be used for subsequent taxonomic annotation, and the data that cannot be matched will be discarded. The principle of matching is to match the database of the species; no mismatch is allowed. bowtie -f -v 0 -k 1 --al P11.mapped.fa reads ../genome/ genome ../uniq_data/P11.fa > P11.genome.bwt bowtie -f -v 0 -p 1 -a --best --strata ../known_miRNA/ hairpin.ccr P11.mapped.fa > P11.miRNA.bwt bowtie -f -v 2 -p 1 -a --best –strata .. /Rfam/Rfam_rmMIR. fasta P11.mapped.fa > P11.ncRNA.bwt bowtie -f -v 0 -p 1 -a --best –strata ../ref_prepare/repeat

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5. Small RNA taxonomy annotation. The small RNA was annotated for classification, in the order known mirna > ncrna > repeat > exon > intron. Perl srna_anno.pl -fa P11.mapped.fa –rfam ../ref_prepare/ Rfam/family.txt1 -mirna P11.miRNA.bwt -ncrna P11.ncRNA.bwt -intron P11.intron.bwt -repeat P11.repeat.bwt -exon P11.exon. bwt -outpre P11.out

6. Quantitative miRNA expression. Combine sRNA data from all samples. Use the quantifier.pl program in the mirdeep2 package for expression quantification. cat ../uniq_data/P1*.fa >all.reads.fa quantifier.pl -p all.hairpin.fa -m all.mature.fa -r all. reads.fa -g 0

7. Duplication. Since different precursor sequences may produce the same mature, it is necessary to de-duplicate based on the mature id duplication. less miRNAs_expressed_all_samples_*.csv | awk ’{ for(i=5; i miRNAs_expressed.count.tx

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Notes 1. The volumes listed in the master mix are for one reaction. 2. Remove beads from 4 °C and leave them at room temperature for at least 30 min before use to ensure that they have completely reached room temperature. Cold beads reduce recovery. 3. The beads should not disperse; instead, they will stay on the walls of the tubes. Significant loss of beads at this stage will impact the amount of DNA carried into Library Amplification I, so ensure beads are not removed with the binding buffer or the wash.

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4. Ensure that the ethanol wash is freshly prepared from fresh ethanol stocks at the indicated concentration. Lower percent ethanol mixes will reduce recovery. 5. With the second wash, it is critical to remove as much of the ethanol as possible. Remove the ethanol wash with a pipet, allow excess ethanol to collect at the bottom of the tubes, and remove any remaining ethanol with a fresh pipet tip. References 1. Hongchang B, Dinggeng et al (2018) Exosomes: isolation, analysis, and applications in cancer detection and therapy. Chembiochem A Eur J Chem Biol 20:451–461 2. Peng Y, Croce CM (2016) The role of MicroRNAs in human cancer. Signal Transduct Target Ther 1:15004 3. Li Z, Ye L, Wang L et al (2019) Identification of miRNA signatures in serum exosomes as a potential biomarker after radiotherapy treatment in glioma patients. Ann Diagn Pathol 44:151436 4. Wang M, Zhou L, Yu F et al (2019) The functional roles of exosomal long non-coding RNAs in cancer. Cell Mol Life Sci 76:2059– 2076 5. Er-Bao B, Er-Feng et al (2018) Exosomal lncRNA?ATB activates astrocytes that promote glioma cell invasion. Int J Oncol 54:713–721 6. Dai X, Liao K, Zhuang Z et al (2018) AHIF promotes glioblastoma progression and

radioresistance via exosomes. Int J Oncol 54: 261–270 7. Cheng J, Meng J, Zhu L et al (2020) Exosomal noncoding RNAs in Glioma: biological functions and potential clinical applications. Mol Cancer 19(1):66 8. Chen S, Zhou Y, Chen Y et al (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics (Oxford, England) 34(17):i884– i890 9. Kong L, Zhang Y, Ye ZQ et al (2007) CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res 35(Web Server issue):W345–W349 10. Quinlan AR, Hall et al (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics (Oxford, England) 26: 841–842

Chapter 5 Detection of Microorganisms in Body Fluid Samples Xin Ji, Shoufeng Ni, Geng Tian, Lili Zhang, and Weiwei Wang Abstract Next-generation sequencing (NGS) has been widely applied to the identification of microbiome in body fluids. The methodology of 16S rRNA amplicon sequencing is simple, fast, and cost-effective. It overcomes the problem that some microorganisms cannot be isolated or cultured. Low abundant bacteria can also be amplified and sequenced, but the resolution of classification can hardly reach species or sub-species level; moreover, this methodology is mainly used to identify bacterial populations, and other microorganisms like viruses or fungi cannot be sequenced. On the other hand, the microbiome profiling obtained by shotgun metagenomic sequencing is more comprehensive with better resolution, and more accurate classification can be expected due to higher coverage of genomic sequences from microorganisms. By combining the capture-based method with metagenomic sequencing, we can further enrich and detect low abundant microorganisms and identify the viral integration sites in host gDNA at once. Key words 16S rRNA amplicon, Shotgun metagenomics, Viral integration, Liquid biopsy, Capturebased enrichment

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Introduction The number of microorganisms in the human body is more than 10 times of human cells, which plays an extremely important role in nutrient metabolism [1], human self-development, immunity, and disease [2, 3]. Previous studies show that intestinal microorganisms are directly related to the pathogenesis of a variety of diseases, especially cancer [4]. In addition, microorganisms in blood and organs have been identified and reported to impact the microenvironment and are closely associated with some diseases and human health [5]. Some viruses can also integrate into host genomic DNA [6–8], such as HIV, HBV, and HPV. With the development of NGS technology, further improvement of probe-capturebased methods were applied to the enrichment and identification of microbiome [9, 10]. In this chapter, we introduce three protocols based on NGS for the identification of microorganisms in body fluid samples.

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Materials Reagents

1. DNeasy blood and tissue kit. 2. QIAamp® Fast DNA Stool Mini Kit. 3. Qubit dsDNA HS Assay Kit. 4. Agilent 2100 DNA High Sensitivity Kit. 5. KAPA Hyper Prep Kit. 6. AMPure XP beads. 7. Omega gel extraction kit. 8. Human Cot DNA. 9. xGen Universal Blockers. 10. Advantage® 2 PCR enzyme mix. 11. Custom panel for virus. 12. KAPA HiFi Hot Start Ready Mix.

2.2

Equipment

1. Covaris M220 focused ultrasonicator (Covaris). 2. ProFlex thermocycler (Applied Biosystems). 3. Eppendorf AG vacuum concentrator (Eppendorf). 4. Dual module heating block EL-02 (Major Science). 5. Vortex Gene 2 (Agilent). 6. Microcentrifuge (Cubee Gene Reach). 7. Qubit 3.0 (Life).

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Methods The workflow of microbiome sequencing and data analysis is described in Fig. 1, and detailed procedures are as follows. Biosafety protocols need to be carefully reviewed before the experiment (see Note 1).

3.1 Collection of Samples

1. Blood samples. We recommend PET anticoagulant blood collection vessel (CWbio, Cat. No. CW2815M). After collecting the blood sample, mix thoroughly 3–5 times. 2. Fecal samples. For fecal samples, we recommend using fecal DNA sample preservation tube (also known as FOB tube). After defecation, open the fecal DNA sample preservation tube (CWbio, Cat. No. CW2654), pay attention not to pour the preservation solution and steel column in the preservation tube, take feces

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Fig. 1 Overview of microbiome sequencing and data analysis

sample with the sampling spoon, put the spoon and fecal sample back into the tube, screw the tube cover tightly, and shake and mix thoroughly for about 30 s.

3. Saliva samples. We recommend using a salivary DNA sample preservation tube (CWbio, Cat. No. CW2667) for sample collection. Press the cheek to promote saliva secretion, and add saliva sample into the saliva collector, until the liquid level reaches the height of the scale mark. Insert the salivary DNA preservation tube into the collection tube from the upper round hole and stand for several seconds until the liquid level in the preservation tube does not rise, and then pull it out. Turn the salivary DNA sample storage tube upside down for 10 times. 4. Bronchoalveolar lavage fluid (BALF) samples. BALF samples are recommended to be collected into the blood collection tube or 50 mL sterile Corning tube.

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3.2

DNA Extraction

3.2.1 DNA Extraction from Fecal Samples

We use the QIAamp® Fast DNA Stool Mini Kit to isolate gDNA of microorganisms from fecal samples. Prepare a thermos mixer or a water bath at 70 °C and add ethanol to Buffer AW1 and Buffer AW2 concentrates. If there is precipitation or turbidity in Buffer AL and InhibitEX Buffer, heat and mix well, and mix all buffers before use. 1. Weigh 180–220 mg stool in a 2 mL microcentrifuge tube and place the tube on ice. 2. Add 1 mL InhibitEX Buffer to each stool sample. Vortex continuously for 1 min or until the stool sample is thoroughly homogenized. 3. Heat the suspension for 5 min at 95 °C. Vortex for 15 s. 4. Centrifuge the sample for 1 min to pellet stool particles. 5. Add 15 μL of Proteinase K into a new 1.5 mL microcentrifuge tube. 6. Add 200 μL of supernatant from step 4 into the 1.5-mL microcentrifuge tube containing Proteinase K (see Note 2). 7. It is essential that the sample and Buffer AL are thoroughly mixed to form a homogeneous solution. 8. Incubate at 70 °C for 10 min. 9. Add 200 μL of ethanol (96–100%) to the lysate, and mix by vortexing. 10. Carefully transfer 600 μL of lysate from step 9 to the QIAamp spin column. Close the cap and centrifuge at ≥8000× g for 1 min. Place the QIAamp spin column in a new 2 mL collection tube, and discard the tube containing the filtrate. 11. Carefully open the QIAamp spin column and add 500 μL of Buffer AW1. Centrifuge for 1 min. Place the QIAmp spin column in a new 2 mL collection tube, and discard the collection tube. 12. Carefully open the QIAamp spin column and add 500 μL of Buffer AW2. Centrifuge for 3 min. Discard the collection tube. 13. Place the QIAamp spin column in a new 2 mL collection tube and discard the old collection tube with the filtrate. Centrifuge for 3 min. 14. Transfer the QIAamp spin column into a new, labeled 1.5 mL microcentrifuge tube and pipet 200 μL of Buffer ATE directly onto the QIAamp membrane. Incubate for 1 min at room temperature, then centrifuge for 1 min to elute DNA. If the yield will be quantified by UV absorbance, clean the device using Buffer ATE to avoid false results.

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1. We use a DNeasy blood and tissue kit to isolate DNA from body fluid samples. Sample preparation (a) For saliva and BALF samples, transfer 5–10 mL of sample to Eppendorf Conical Tube, and then centrifuge for 2 min at 10000× g. Resuspend the pellet in 200 μL of PBS (see Note 3). (b) For anticoagulated blood, transfer 50–100 μL of sample to 1.5 mL microcentrifuge tube. Adjust the volume to 220 μL with PBS. 2. Add 20 μL of proteinase K. 3. Add 200 μL of Buffer AL (ethanol not added) to the sample, and then mix thoroughly by vortexing. Check the pH of the lysate and adjust it to pH 10 is an indicator of poor prognosis in patients undergoing neoadjuvant chemotherapy followed by radical cystectomy [14]. However, several studies contradicted these results. Guzzo et al. found that CTC was not a predictor of

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extravesical invasion and lymph node positivity [15]. These studies included a relatively small number of patients; therefore, the specificity and sensitivity of CTCs should be evaluated through large prospective trials.

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Circulating Tumor DNA Both normal and tumor cells emit DNA, known as cell-free DNA (cfDNA). The ctDNA is the cfDNA released by tumor cells in the circulatory system. ctDNA is a DNA fragment with a length of 90–150 base pairs [16]. The mechanism of ctDNA release into the blood is still unclear, and the primary source of ctDNA is apoptosis [17, 18]. Additionally, there are also vesicles (exosomes) released into the blood by live tumor cells, and ctDNA can also be released by macrophage phagocytic necrotic tumor cells. The ctDNA is consistent with the DNA characteristics of tumor cells, possibly reflecting the characteristic tumor changes. The ctDNA can overcome the heterogeneity of pathological biopsy and provide a comprehensive and accurate gene spectrum, reflecting the disease status. The ctDNA can be obtained directly from peripheral blood, facilitating real-time dynamic assessment of tumor progression and response to treatment. The primary challenge of ctDNA diagnosis is to identify and trace negligible amounts of altered DNA fragments from thousands of wild-type DNA. These challenges can be overcome using nextgeneration sequencing and mutation-specific digital poly synthase chain reaction (dPCR). Next-generation sequencing can quickly obtain a large amount of gene data; however, it requires a lot of time to conduct data analysis [19]. However, dPCR is targeted at only specific mutations and should be customized according to the results of next-generation sequencing. Furthermore, Gootenberg et al. combined the Cas13a enzyme to build CRISPER as the basis of the diagnosis system, named SHERLOCK (specific highsensitivity enzymatic reporter unlocking) [20]. The system can detect single-stranded ctDNA at the microrubbing level. The ctDNA can be extracted from the blood for qualitative and quantitative analysis through molecular biology techniques, which can provide important information for early diagnosis, disease detection, efficacy evaluation, and individualized treatment of tumors. Many studies have focused on the use of ctDNA in BC. Birkenkamp-Demtroder et al. retrospectively detected the serum and urine of 12 patients with NMIBC. The gene mutations were detected using next-generation sequencing, and the highly tumor-specific gene mutations were made into individual sequences suitable for dPCR to detect the plasma and urine of the remaining patients. Ten of 12 (83%) patients tested positive for ctDNA, and clinical progression was noted in 4/6 (67%) of such

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patients a few months later [21]. This is an established technique for early detection and disease prognosis prediction. The hot spot mutation sequence is faster and more economical than the method of combining individual sequences after next-generation sequencing. In another study, Christensen et al. performed ddPCR analyses and screened ctDNA for FGFR3 and PIK3CA mutations in plasma from patients with NMIBC and MIBC undergoing radical cystectomy. Thus, high levels of ctDNA in the plasma from patients undergoing radical cystectomy were associated with recurrence [22]. Increased levels of ctDNA in plasma are indicative of progression and metastasis in BC. Thus, more large-scale randomized controlled trials are needed in the future to clinically verify the current findings. With the development of detection technology, ctDNA application in BC will increasingly gain importance.

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Urinary Tumor DNA Due to the close contact between BC and urine, tumor DNA from BC can also be found in urine, which may have diagnostic value (Fig. 2). In patients with BC, the cell-free tumor DNA in urine is released from tumor cells and more concentrated than in plasma, thus making detection easier [23]. Some studies have focused on this noninvasive genetic test. Alizadeh et al. used Cancer Personalized Profiling by Deep Sequencing to detect tumor DNA in urine samples of BC and identified 83% of patients with early-stage BC [24]. Dahmcke et al. detected hot spot mutations (TERT and

Fig. 2 Fluid biopsy based on exosomes, urinary tumor DNA, urinary RNA, and tumor cell in urine

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FGFR3) and other important methylated genes (SALL3, ONE-Cut2, CCNA1, BCL2, EOMS, VIM) to verify whether they could replace cystoscopy in the case of macroscopic hematuria. Tumor-specific DNA was detected in 96/99 (97%) patients with BC [25]. Ward et al. recently studied a composite PCR and nextgeneration sequencing technique, which can detect multiple genes in a single sequence simultaneously. This technique assesses the reliability of six hot spot gene mutations (FGFR3, TERT, PIK3CA, TP53, HRAS, RXRA, KDM6A) in patients with BC. Tumor detection was detected in 86/122 (70%) patients [26]. While these leading studies require extensive analysis and validation of the results, they pave the way for urinary tumor DNA testing for BC. Urinary tumor DNA is also used to evaluate the prognosis of BC and is used as a potential marker for recurrent BC. Kinde et al. analyzed urinary tumor DNA from 76 patients with noninvasive urothelial carcinoma and showed that TERT promoter mutation was significantly associated with the recurrence of BC [27]. TERT promoter hotspots could be used to noninvasively follow up patients with NMIBC after surgery. Birken-Kamp-Demtroder et al. tested 101 urine samples based on an individualized array designed by next-generation sequencing. They detected urinary tumor DNA in 55/57 (97%) clinically progressive patients. However, only 22/44 (50%) of the recurrent patients had tumor DNA in their urine samples. Urinary tumor DNA levels were higher in the clinical progression group than in the recurrence group [21]. Moreover, Christensen et al. also found that increased FGFR3 and PIK3CA mutated DNA levels in urine were indicative of progression and metastasis in NMIBC [22]. These results suggest that urinary tumor DNA is more sensitive than plasma in BC.

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Exosome Exosomes are nanoscale vesicles ranging from 30 nm to 150 nm, involved in a variety of extracellular functions, such as immune function, regulation of metabolism, tumor metastasis, and neurodegeneration [28, 29]. Exosomes can be actively secreted by exocytosis of dendritic cells, lymphocytes, and tumor cells [30]. Exosomes contain a variety of bioactive substances, including protein, mRNA, miRNA, and lncRNA. Therefore, exosomes can be used as “messengers” to participate in the regulation of intercellular information [31]. Exosome contents are anticipated to be tumor markers, which can be used to detect mutations, splice mutations, gene fusion, and gene expression sequences. Exosomes can protect miRNA from RNA enzymes and provide abundant and stable sources of miRNA for biomarkers compared with miRNA stored in cells. Consequently, the isolation and analysis of exosomes

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may be more advantageous than ctDNA. The molecular characteristics of tumor-derived exosomes are consistent with the phenotypic characteristics of the tumor from which they are derived. Therefore, the protein, miRNA, and lncRNA carried by exosomes are promising tumor markers [32]. Tumor-derived exosomes have great potential in the early diagnosis and prognosis of BC. Moreover, exosomes are involved in the occurrence and development of BC and play an important role in drug resistance, indicating the feasibility of using exosomes in BC treatment. In urine exosomes, Long et al. confirmed that seven miRNAs, including miR-184, were significantly increased in BC-derived exosomes and were closely related to tumor grade [33]. Berrondo et al. also found that exosomes extracted from the urine of patients with high-level BC promoted the migration and angiogenesis of urothelial epithelial cells through the EDIL3 pathway, which proved the role of exosomes in tumor progression [34]. Additionally, Greco et al. found that exosomes can be used as vectors to transport polo-like kinase (PLK)-1 siRNA into BC cells and selectively silence the PLK-1 gene [35]. This study strongly suggests that exosomes serve as potential delivery vectors for BC drug therapy. Exosomes are not widely used in clinical practice as promising tumor markers owing to the complexity of biological samples, coexistence with other extracellular vesicles, and the lack of economic and accurate means of isolation and detection. Thus, more sensitive platforms for capturing exosomes should be developed to assist in the search for BC-specific protein, miRNA, and lncRNA markers.

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Circulating RNA RNA contains several types, including miRNA, mRNA, and long non-coding RNA, all of which have been proven to be potential markers of BC [36]. The miRNA is involved in regulating the cell cycle, apoptosis, and proliferation; therefore, it is a hotspot of current research. The miRNA, present in plasma and urine, may be linked to ribonucleoprotein complexes or detected in exosomes in the form of free circulating miRNA. It is worth noting that somatic mutation cannot represent the entire molecular change of tumor cells, and the change of tumor gene expression profile may be owing to the influence of miRNA on epigenetics, which cannot be detected and analyzed using ctDNA. Therefore, miRNA can provide valuable information. The miRNA expression profiles are tissue type-specific and frequently dysregulated in BC. Therefore, miRNA can be used as a marker for the diagnosis, prognosis evaluation, and even therapeutic effect evaluation of BC [37]. Several groups have identified miRNA signatures by performing miRNA profiling in the plasma or

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urine of patients with BC. Cao et al. found that the downregulation of miR-124 was closely related to the poor prognosis and tumor progression of BC, and miR-124 could be used as a potential prognostic biomarker of BC [38]. Yoshino et al. demonstrated that tumor suppressors such as miR-145, miR-143, and miR-125b were downregulated in some types of BC, while carcinogenic miR-183, miR-96, miR-175p, and miR-20a were upregulated [39]. Zhang et al. also detected that miR-155 might be used for the diagnosis and prognosis prediction of NMIBC. By comparing the changes of urinary miR-155 before and after surgery in 32 patients with NMIBC, the expression of miR-155 in urine was found to be closely related to the tumor stage and recurrence of BC [40]. Sasaki et al. confirmed that urinary miR-146a-5p was increased in patients with BC, and the concentration was related to tumor grade and invasion depth [41]. However, miRNA cannot always exist in specimens, and the miRNA structure detected each time is not nearly the same, which limits the clinical application of miRNA; therefore, the detection and application of miRNA are unstable. In the future, the application of miRNA requires a large sample and multicenter systematic study, and the separation and preservation technology should be improved consistently.

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Conclusions Although the liquid biopsy of BC has developed rapidly through CTCs, ctDNA, exosome, and circulating RNA, multi-center large sample tests are required to verify the value of its clinical application. With the persistent development of novel technologies, liquid biopsy is anticipated to be widely used in clinical practice and become a powerful tool for BC monitoring, prognosis assessment, and therapeutic efficacy evaluation.

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Liquid Biopsy in Bladder Cancer 7. Vasseur A, Kiavue N, Bidard FC, Pierga JY, Cabel L (2021) Clinical utility of circulating tumor cells: an update. Mol Oncol 15:1647– 1666 8. de Wit S, Zeune LL, Hiltermann T, Groen H, Dalum GV, Terstappen L (2018) Classification of cells in CTC-enriched samples by advanced image analysis. Cancers (Basel) 10:377 9. Msaouel P, Koutsilieris M (2011) Diagnostic value of circulating tumor cell detection in bladder and urothelial cancer: systematic review and meta-analysis. BMC Cancer 11:336 10. Engilbertsson H, Aaltonen KE, Bjo¨rnsson S et al (2015) Transurethral bladder tumor resection can cause seeding of cancer cells into the bloodstream. J Urol 193:53–57 11. Yang Z, Li C, Fan Z et al (2017) Single-cell sequencing reveals variants in ARID1A, GPRC5A and MLL2 driving self-renewal of human bladder cancer stem cells. Eur Urol 71:8–12 12. Gazzaniga P, Gradilone A, de Berardinis E et al (2012) Prognostic value of circulating tumor cells in nonmuscle invasive bladder cancer: a CellSearch analysis. Ann Oncol 23:2352–2356 13. Soave A, Riethdorf S, Dahlem R et al (2017) A nonrandomized, prospective, clinical study on the impact of circulating tumor cells on outcomes of urothelial carcinoma of the bladder patients treated with radical cystectomy with or without adjuvant chemotherapy. Int J Cancer 140:381–389 14. Alva A, Friedlander T, Clark M et al (2015) Circulating tumor cells as potential biomarkers in bladder cancer. J Urol 194:790–798 15. Guzzo TJ, McNeil BK, Bivalacqua TJ, Elliott DJ, Sokoll LJ, Schoenberg MP (2012) The presence of circulating tumor cells does not predict extravesical disease in bladder cancer patients prior to radical cystectomy. Urol Oncol 30:44–48 16. Mouliere F, Chandrananda D, Piskorz AM et al (2018) Enhanced detection of circulating tumor DNA by fragment size analysis. Sci Transl Med 10:eaat4921 17. Kustanovich A, Schwartz R, Peretz T, Grinshpun A (2019) Life and death of circulating cellfree DNA. Cancer Biol Ther 20:1057–1067 18. Heidrich I, Acˇkar L, Mossahebi Mohammadi P, Pantel K (2021) Liquid biopsies: potential and challenges. Int J Cancer 148:528–545 19. Wang O, Chin R, Cheng X et al (2019) Efficient and unique cobarcoding of secondgeneration sequencing reads from long DNA molecules enabling cost-effective and accurate sequencing, haplotyping, and de novo assembly. Genome Res 29:798–808

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20. Gootenberg JS, Abudayyeh OO, Lee JW et al (2017) Nucleic acid detection with CRISPRCas13a/C2c2. Science 356:438–442 21. Birkenkamp-Demtro¨der K, Nordentoft I, Christensen E et al (2016) Genomic alterations in liquid biopsies from patients with bladder cancer. Eur Urol 70:75–82 22. Christensen E, Birkenkamp-Demtro¨der K, Nordentoft I et al (2017) Liquid biopsy analysis of FGFR3 and PIK3CA hotspot mutations for disease surveillance in bladder cancer. Eur Urol 71:961–969 23. Satyal U, Srivastava A, Abbosh PH (2019) Urine biopsy-liquid gold for molecular detection and surveillance of bladder cancer. Front Oncol 9:1266 24. Dudley JC, Schroers-Martin J, Lazzareschi DV et al (2019) Detection and surveillance of bladder cancer using urine tumor DNA. Cancer Discov 9:500–509 25. Dahmcke CM, Steven KE, Larsen LK et al (2016) A prospective blinded evaluation of urine-DNA testing for detection of urothelial bladder carcinoma in patients with gross Hematuria. Eur Urol 70:916–919 26. Ward DG, Baxter L, Gordon NS et al (2016) Multiplex PCR and next generation sequencing for the non-invasive detection of bladder cancer. PLoS One 11:e0149756 27. Kinde I, Munari E, Faraj SF et al (2013) TERT promoter mutations occur early in urothelial neoplasia and are biomarkers of early disease and disease recurrence in urine. Cancer Res 73:7162–7167 28. Kalluri R (2016) The biology and function of exosomes in cancer. J Clin Invest 126:1208– 1215 29. Pegtel DM, Gould SJ (2019) Exosomes. Annu Rev Biochem 88:487–514 30. Vlassov AV, Magdaleno S, Setterquist R, Conrad R (2012) Exosomes: current knowledge of their composition, biological functions, and diagnostic and therapeutic potentials. Biochim Biophys Acta 1820:940–948 31. Kalluri R, LeBleu VS (2020) The biology, function, and biomedical applications of exosomes. Science 367:eaau6977 32. Zhang X, Zhou C (2019) The function of tumor-derived exosomes. J BUON 24:897– 904 33. Long JD, Sullivan TB, Humphrey J et al (2015) A non-invasive miRNA based assay to detect bladder cancer in cell-free urine. Am J Transl Res 7:2500–2509 34. Berrondo C, Flax J, Kucherov V et al (2016) Expression of the Long non-coding RNA HOTAIR correlates with disease progression

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in bladder cancer and is contained in bladder cancer patient urinary exosomes. PLoS One 11:e0147236 35. Greco KA, Franzen CA, Foreman KE, Flanigan RC, Kuo PC, Gupta GN (2016) PLK-1 silencing in bladder cancer by siRNA delivered with exosomes. Urology 91:241.e1-7 36. Kouba E, Lopez-Beltran A, Montironi R et al (2020) Liquid biopsy in the clinical management of bladder cancer: current status and future developments. Expert Rev Mol Diagn 20:255–264 37. Usuba W, Urabe F, Yamamoto Y et al (2019) Circulating miRNA panels for specific and early detection in bladder cancer. Cancer Sci 110: 408–419

38. Cao Z, Xu L, Zhao S, Zhu X (2019) The functions of microRNA-124 on bladder cancer. Onco Targets Ther 12:3429–3439 39. Yoshino H, Seki N, Itesako T, Chiyomaru T, Nakagawa M, Enokida H (2013) Aberrant expression of microRNAs in bladder cancer. Nat Rev Urol 10:396–404 40. Zhang X, Zhang Y, Liu X et al (2016) Direct quantitative detection for cell-free miR-155 in urine: a potential role in diagnosis and prognosis for non-muscle invasive bladder cancer. Oncotarget 7:3255–3266 41. Sasaki H, Yoshiike M, Nozawa S et al (2016) Expression level of urinary MicroRNA-146a5p is increased in patients with bladder cancer and decreased in those after transurethral resection. Clin Genitourin Cancer 14:e493–e499

Chapter 8 CSF Biopsy in Glioma: A Brief Review Heng Jia, Hui Zhang, Faan Miao, Dong Lu, Xingqi Wang, Liang Gong, and Yuechao Fan Abstract Glioma is the most common intracranial malignant tumor. Over the past several years, liquid biopsy in diagnosis and treatment of solid tumors have made many progressions, but there is still a gap from a large clinical application of liquid biopsy in glioma due to many limitations. However, in recent years, researchers have made many explorations into liquid biopsy in glioma. In the future, the liquid biopsy of glioma, especially cerebrospinal fluid, will have a broad prospect. In this review, we will discuss the current research progressions of CSF biopsy in glioma in recent years. Key words Liquid biopsy, Cerebrospinal fluid, Glioma

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Introduction Glioma, the most popular brain tumor, is associated with a poor prognosis and unfavorable quality of life, though treated with surgical, radiotherapy, and chemotherapy treatment [1, 2]. Although many new technologies have been applied in the treatment of glioma, such as tumor treating fields (TTFs) and immune therapy [3, 4], the overall survival of glioma has not been prolonged significantly. Liquid biopsy is an analysis of non-solid biological tissue, like patients’ blood, urine, and cerebrospinal fluid (CSF) to diagnose and monitor cancers. Circulating tumor cells (CTCs), cell-free circulating tumor DNAs (ct-DNAs), extracellular vesicles (EVs), and noncoding miRNAs are detected. In solid tumors, such as lung cancer and melanoma, the liquid biopsy has shown a lot of progression, but in glioma, due to the blood–brain barrier, the liquid biopsy has limited applications. In recent years, researchers have done a lot of research about cerebrospinal fluid (CSF) in the management of glioma. In this review, we will discuss the roles of CSF in the diagnosis and treatment of glioma.

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Overview of CSF Application in Glioma The cerebrospinal fluid is mainly formed in the choroid plexus of the ventricle, filling brain ventricles, cisterns, and the subarachnoid space in the spinal cord and circulating in the central nervous system [5]. Cerebrospinal fluid is widely used in the diagnosis and treatment of central nervous system diseases. CSF biopsy may provide diagnosis and treatment information through a lessinvasion routine. Due to the blood–brain barrier, the level of ctDNAs in glioma patients’ plasma is very low [6]; thus, the CSF has a prominent advantage over blood in the diagnosis and treatment of glioma. Common biomarkers in CSF include circulating tumor cells (CTCs), circulating tumor DNAs (ctDNAs), extracellular vesicles (EVs), and noncoding miRNAs. Biomarkers of CSF can guide clinicians in the diagnosis and treatment of glioma and help clinicians differentiate pseudoprogression from recurrence. In spite of some limitations, the prospects of CSF biopsy in glioma are still promising.

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CTCs Circulating tumor cells (CTCs) are cells that are released or positively shed by tumor tissue into human circulating fluid and other human fluid, such as cerebrospinal fluid, which have three main features: (1) originating from the primary or metastasis lesions; (2) existing in the blood vessels; and (3) involving in blood circulation [7, 8]. Accurate molecular typing of these CTCs can monitor the progress and the degree of treatment of disease and guide clinicians to judge tumor metastasis or recurrence. Methods of collecting and detecting CTCs vary among researchers, and a widely accepted method is still lacking [8]. At present, one of the main methods of detecting CTCs in blood is using magnetic particles with antibodies that target epithelial cell-adhesion molecules (EpCAM) and thus bind to EpCAM-positive cells [9]. However, this method is especially suitable for the cells expressing EpCAM, such as colorectal cancer and breast cancer, whereas gliomas tend to downregulate EpCAM. Another method for detecting CTCs of glioma is using glial fibrillary acidic protein (GFAP), which expresses on the membrane of glioblastoma cells [10]. Nevertheless, this method has some shortcomings: low sensitivity and not all glioma cells have GFAP [11]. In the previous study, Jung et al. found that GFAP can be detectable in the serum of patients with low-grade glioma but not in control patients [12]. Muller et al. found GFAP-positive cells in the peripheral blood of 29 out of 141 patients with glioma [10]. In 2016, Malara first reported two cases using blood CTCs to diagnose brain tumors by detecting the

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expression of CD20 and vimentin [13]. However, the CTCs derived from glioma are more abundant in CSF. Detection of CTCs in CSF usually has a higher sensitivity compared to peripheral blood [14]. Zhao et al. reported that the number of CTCs in CSF was higher than that in blood, and a higher number of CTCs was correlated to a poorer prognosis [15]. So far, the data on CTCs in glioma is limited. However, tracking CTCs of glioma still has a broad prospect despite these limitations.

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ctDNAs Compared to CTCs, the application of ctDNAs is broader in the CSF biopsy in glioma. Previous studies showed that the CSF ctDNAs would be more representative of brain tumors than blood ctDNA [16]. Mouliere et al. reported that the ctDNAs in cerebrospinal fluid can overcome tumor heterogeneity, reflect the spatial heterogeneity of tumors, and show more complete tumor mutations [17]. Zhao et al. found that 34.04% of the mutation sites were detected only in the ctDNA of cerebrospinal fluid, not in tissues, including key sites like BRAF and NRAS [18]. Miller et al. detected 42 ctDNAs in CSF out of 85 patients, and the genomic landscape of glioma in the CSF closely resembled the genomes of tumor biopsies [19], which could provide opportunities for clinical diagnosis and targeted treatment for patients who cannot bear operations. Other studies also reported that CSF ctDNAs could effectively monitor the progress of treatments of glioma. Miller et al. reported that patients with ctDNA-positive in cerebrospinal fluid had a fourfold higher risk of death than those without ctDNA-positive. ctDNA-positive was significantly associated with a high disease burden and shorter survival [19]. On the other hand, the CSF-ctDNA can be used to monitor drug resistance mutations in clinical targeted therapy [20]. However, due to several limitations, such as BBB, isolation procedures, tumor size, grading, and location of lesions [8], there is still much research to be done in the future.

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EVs Extracellular vesicles (EVs) are released from almost all human cells including tumor cells, existing widely in human body fluids like blood, urine, and CSF [21]. EVs not only have the function of transmitting intercellular information but also involve in the pathological process of brain diseases [22]. EVs mainly include three types: (1) exosome; (2) MV; and (3) apoptotic body. Due to the particular membrane structure of EVs, EVs can deliver therapeutic agents to the brain [23]. EVs usually contain abundant DNAs,

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miRNAs, proteins, and lipids from their parental cells, suggesting that EVs can be valuable diagnostic tools for brain tumors [24]. For instance, the level of miR-21 is ten times higher in the CSF than the control group, with a diagnostic sensitivity and specificity of 87% and 93%, respectively [25]. Due to the biological structure of exosomes, exosomes have a broad application in the therapy of glioma. Exosomes can be utilized as a targeted drug delivery tool to penetrate the blood–brain barrier. Wu et al. have reported that multifunctional exosome-mimetics (EM) combined with angiopep-2 (Ang) can enhance GBM drug delivery by manipulating protein corona (PC) [26]. Tian et al. combined EVs with a brain-tumor-targeting cyclic RGDyK peptide (RGD-EV), and EVs were loaded with siRNA against PD-L1 for immune checkpoint blockade, which could inhibit tumor growth and prolong animal survival [27]. Extracellular vesicles have shown inspiring results in tumor therapy and will have broad prospects in the future.

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miRNAs miRNAs are small noncoding RNAs with 20–24 nucleotides long. Recent studies have found that miRNAs have related to tumor genesis and proliferation and act as a potential biomarker for glioblastoma prognosis [28]. For instance, miR-301a was found to be highly expressed in glioma serum exosomes, suggesting that it might be a diagnostic and prognostic biomarker of brain tumor [29]. Akers et al. reported that miRNAs in CSF could be used as a “liquid biopsy” platform for glioblastoma diagnosis [30]. Alessandra et al. concluded that miRNAs could differentiate among different grades of gliomas [31]. Furthermore, miRNAs in CSF can reflect the therapy outcomes and monitor tumor recurrence. Teplyuk et al. reported that the level of miR-10b and miR-200 ascended during tumor relapse and the level of miR-10b and miR-200 decreased if the chemotherapeutic drug was given [32]. Also, researchers found that exosomal miR-151a could predict the chemotherapy response of TMZ-resistant GBM cells [33]. In the future, research on miRNAs in CSF and other noncoding RNAs, like circRNAs and lncRNAs, needs to be done.

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Conclusion and Prospects Liquid biopsy shows a promising prospect in the diagnosis and monitoring recurrence of malignant tumors. Many studies have been done in the blood biopsy of the management of glioma. CSF biopsy can guide clinicians to make a differential diagnosis between glioma and other CNS tumors and can help clinicians to monitor the residual tumor after operation. However, CSF biopsy

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still has many shortcomings, such as high-cost tests, lack of standard isolation procedures, limited concentration of target biomarkers, and so on. CSF biopsy still needs a long way to go to be applied broadly in clinical practice. It is believed that with the continuous progress of technology, the potential of liquid biopsy will be fully developed and applied. References 1. Komori T, Muragaki Y, Chernov MF (2018) Pathology and genetics of gliomas. Prog Neurol Surg 31:1–37 2. Molinaro AM et al (2019) Genetic and molecular epidemiology of adult diffuse glioma. Nat Rev Neurol 15(7):405–417 3. Ghiaseddin AP et al (2020) Tumor treating fields in the management of patients with malignant gliomas. Curr Treat Options in Oncol 21(9):76 4. de Groot J et al (2020) Window-of-opportunity clinical trial of pembrolizumab in patients with recurrent glioblastoma reveals predominance of immune-suppressive macrophages. Neuro Oncol 22(4):539–549 5. Sakka L, Coll G, Chazal J (2011) Anatomy and physiology of cerebrospinal fluid. Eur Ann Otorhinolaryngol Head Neck Dis 128(6): 309–316 6. Bettegowda C et al (2014) Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med 6(224): 224ra24 7. Zhang H et al (2021) Circulating tumor cells for glioma. Front Oncol 11:607150 8. Eibl RH, Schneemann M (2021) Liquid biopsy and primary brain Tumors. Cancers (Basel) 13(21):5429 9. Pan Y, Long W, Liu Q (2019) Current advances and future perspectives of cerebrospinal fluid biopsy in midline brain malignancies. Curr Treat Options in Oncol 20(12):88 10. Mu¨ller C et al (2014) Hematogenous dissemination of glioblastoma multiforme. Sci Transl Med 6(247):247ra101 11. Lim MC, Maubach G, Zhuo L (2008) Glial fibrillary acidic protein splice variants in hepatic stellate cells–expression and regulation. Mol Cells 25(3):376–384 12. Jung CS et al (2007) Serum GFAP is a diagnostic marker for glioblastoma multiforme. Brain 130(Pt 12):3336–3341 13. Malara N et al (2016) Non-invasive real-time biopsy of intracranial lesions using short time expanded circulating tumor cells on glass slide: report of two cases. BMC Neurol 16:127

14. Peng M et al (2017) Non-blood circulating tumor DNA detection in cancer. Oncotarget 8(40):69162–69173 15. Zhao Y et al (2020) Cytoplasm protein GFAP magnetic beads construction and application as cell separation target for brain tumors. J Nanobiotechnol 18(1):169 16. De Mattos-Arruda L et al (2015) Cerebrospinal fluid-derived circulating tumour DNA better represents the genomic alterations of brain tumours than plasma. Nat Commun 6:8839 17. Mouliere F et al (2018) Detection of cell-free DNA fragmentation and copy number alterations in cerebrospinal fluid from glioma patients. EMBO Mol Med 10(12):e9323 18. Zhao Z et al (2020) Applications of cerebrospinal fluid circulating tumor DNA in the diagnosis of gliomas. Jpn J Clin Oncol 50(3): 325–332 19. Miller AM et al (2019) Tracking tumour evolution in glioma through liquid biopsies of cerebrospinal fluid. Nature 565(7741): 654–658 20. Pentsova EI et al (2016) Evaluating cancer of the central nervous system through nextgeneration sequencing of cerebrospinal fluid. J Clin Oncol 34(20):2404–2415 21. Hao Y et al (2021) Promotion or inhibition of extracellular vesicle release: emerging therapeutic opportunities. J Control Release 340: 136–148 22. Kalluri R, LeBleu VS (2020) The biology, function, and biomedical applications of exosomes. Science 367(6478):eaau6977 23. Alvarez-Erviti L et al (2011) Delivery of siRNA to the mouse brain by systemic injection of targeted exosomes. Nat Biotechnol 29(4): 341–345 24. Cordonnier M et al (2017) Exosomes in cancer theranostic: diamonds in the rough. Cell Adhes Migr 11(2):151–163 25. Akers JC et al (2013) MiR-21 in the extracellular vesicles (EVs) of cerebrospinal fluid (CSF): a platform for glioblastoma biomarker development. PLoS One 8(10):e78115

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26. Wu JY et al (2021) Multifunctional exosomemimetics for targeted anti-glioblastoma therapy by manipulating protein corona. J Nanobiotechnol 19(1):405 27. Tian T et al (2022) Immune checkpoint inhibition in GBM primed with radiation by engineered extracellular vesicles. ACS Nano 16(2): 1940–1953 28. Floyd D, Purow B (2014) Micro-masters of glioblastoma biology and therapy: increasingly recognized roles for microRNAs. NeuroOncology 16(5):622–627 29. Lan F et al (2018) Serum exosomal miR-301a as a potential diagnostic and prognostic biomarker for human glioma. Cell Oncol (Dordr) 41(1):25–33

30. Akers JC et al (2017) A cerebrospinal fluid microRNA signature as biomarker for glioblastoma. Oncotarget 8(40):68769–68779 31. Drusco A et al (2015) A differentially expressed set of microRNAs in cerebro-spinal fluid (CSF) can diagnose CNS malignancies. Oncotarget 6(25):20829–20839 32. Teplyuk NM et al (2012) MicroRNAs in cerebrospinal fluid identify glioblastoma and metastatic brain cancers and reflect disease activity. Neuro Oncol 14(6):689–700 33. Zeng A et al (2018) Exosomal transfer of miR-151a enhances chemosensitivity to temozolomide in drug-resistant glioblastoma. Cancer Lett 436:10–21

Chapter 9 Diagnosis, Monitoring, and Prognosis of Liquid Biopsy in Cancer Immunotherapy Weiying Kong, Tengxiang Chen, and Yixin Li Abstract Liquid biopsy (LB), as a minimally invasive method of gleaning insight into the dynamics of diseases through a patient fluid sample, represents an interesting tool that can advise in disease monitoring, treatment selection, early diagnosis, evaluation of the response, and prognosis. Cancer immunotherapy is a breakthrough in cancer treatment, which is now recognized as the “fourth pillar” of cancer treatment, after surgery, chemotherapy, and radiotherapy. Liquid biopsy offers a different befalling for beneath invasive diagnosis, real-time accommodating monitoring, and analysis options, involving the isolation of circulating biomarkers, such as cell-free DNA (cfDNA), circulating tumor cells (CTCs), exosomes, and microRNAs (miRNAs). The biomarkers herein have great potential to allow the realization of liquid biopsy for predicting the immunotherapy response and precision medicine. Liquid biopsy offers an alternative, less invasive approach to select cancer patients who would benefit from immunotherapy and to monitor patients during their disease course. This review focuses on the use of liquid biopsy in the immunotherapy treatment of patients with cancer. In this review, we addressed the different promising liquid biopsy-based biomarkers in cancer patients that enable the selection of patients who benefit from immunotherapy and the monitoring of patients during this therapy. Key words Liquid biopsy, Immunotherapy, Circulating tumor cells, Circulating nucleic acids, Biomarkers, Cell-free DNA, Exosomes, MicroRNAs

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Introduction Cancer is the world’s second leading cause of death, with both incidence and mortality rising substantially over the last century [1]. Between 2005 and 2015, the number of cancer cases worldwide increased by 33%, correlating to increases in global population and life expectancy [2]. Intratumor heterogeneity refers to the fact that a bulk tumor can be made up of several clones of the same cancer cells (with diverse molecular and phenotypical profiles) that have different cancer biology and clonal evolution in response to treatment. Intratumor heterogeneity is a major problem in cancer treatment,

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necessitating real-time evaluation of tumor genomic data for precision medicine. Tissue biopsy often only captures samples from a tiny portion of a larger tumor, which means it may not capture the whole spatial diversity of tumor heterogeneity [3–6]. Although multi-region sequential biopsy can be performed in order to address intratumor heterogeneity [3, 4], in actual practice, it may be impractical and limited to the number of samples that the patient can endure. At the moment, imaging scans are used to monitor cancer and assess therapy effectiveness. They can only capture the tumor’s morphology as a snapshot at a certain time and location, which does not correspond to the tumor’s entire features or function. Multiple follow-up visits with imaging scans and possibly biopsies decrease patient compliance and quality of life, and they may be prohibitively expensive [7]. A noninvasive technique to monitor tumor-wide genetic information during tumor growth or treatment responses is needed to overcome the drawbacks of current imaging and tissue biopsy modalities while also capturing tumor heterogeneity [3, 4]. Liquid biopsy is a noninvasive testing method and is used to detect tumors or metastatic foci [8, 9]. Blood-based biomarkers and other humoral biomarkers, such as circulating free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), microRNAs (miRNAs), and exosomes, can all be found in liquid biopsy [8, 10–12]. Liquid biopsy technology has become a noninvasive, comprehensive, realtime, and accurate technology and has other advantages in the field of tumor monitoring [13]. Recent advances in drug-based tumor therapy, as well as its occasionally stunning results, have boosted the importance of systemic treatment for a variety of metastatic tumors [14]. Because conventional cancer treatment procedures, such as chemotherapy and radiation (RT), are often ineffective and intrusive and create major health and well-being consequences for patients, significant effort is being put into developing more tailored alternatives. Immuno-oncology, which includes checkpoint inhibitors and CAR T cell treatment, is the most recent quantum leap [15]. Immunotherapy differs from traditional chemotherapy and targeted therapy in that it modulates the immunological milieu in addition to targeting tumor cells or immune cells [16–18]. Immunotherapy, also known as immunological checkpoint blockade (ICB), is a significant step forward in the ongoing battle against cancer. Immunotherapy has drawn the interest of researchers because of its remarkable efficacy and lack of negative effects. Furthermore, multiple major breakthroughs in metastatic melanoma, kidney cancer, bladder cancer, non-small cell lung cancer, small cell lung cancer, gastric cancer, prostate cancer, breast cancer, liver cancer, and other cancers have been made as a result of an increasing number of new immunotherapies and related clinical trials [19– 23]. Immunological escape, immune tolerance, and, in particular,

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immunotherapy are continually being updated due to the rapid development of the immunology field and its connection with cancer and molecular biology. In this era of precision medicine, a thorough understanding of carcinogenesis at the molecular and genetic levels aids in the successful application of anticancer therapy. Because of its simplicity, noninvasiveness, high specificity, and ability to overcome temporal-spatial heterogeneity, liquid biopsy has become increasingly essential in the diagnosis and prognosis of cancer in clinical practice in recent years [6, 24]. The use of liquid biopsy in cancer immunotherapy treatment is the subject of this review. The following elements will be examined in this review: circulating cell-free DNA (cfDNA), circulating tumor cells (CTCs), extracellular vesicles (EVs), and microRNA (miRNA), promising liquid biopsy-based biomarkers in cancer patients that enable the selection of individuals who benefit from immunotherapy and patient monitoring during this therapy.

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Utility of CIRCULATING FREE DNA (cfDNA) for Liquid Biopsy In 1948, Mandel [25] first discovered circulating nucleic acids in the blood of healthy humans. In 1977, Leon determined that the average concentration of cfDNA in the plasma of healthy humans was 13 ± 3 g/L and the average concentration was 180 ± 38 g/L [26]. Although the results of different research methodologies later differed, all evidence revealed that the cfDNA concentration of cancer patients’ blood was much higher than that of healthy people [27]. The source and production mechanisms of cfDNA are currently unknown. The most common hypotheses are apoptosis, cell necrosis, and active secretion [10]. Because the integrity of DNA fragments in the circulation of patients with malignant tumors is significantly higher than that of healthy people, some researchers have proposed that necrolysis of tumor cells is the primary cause of the significant increase in cfDNA in cancer patients, but this still does not explain why cfDNA levels decline after radiotherapy [13, 28]. A rising number of research studies have looked into the utility of cfDNA in cancer management, with one of the most promising being the use of cfDNA for therapy monitoring and detection of resistance mechanisms [29–32]. cfDNA can be extracted from both healthy and cancerous cells. Circulating cell-free tumor DNA is the percentage that comes from tumor cells (cf tumor DNA). Apoptosis, necrosis, and active secretion from EVs and CTCs are the three main processes by which Cf tumor DNA is released into the peripheral blood. The percentage of cfDNA provided by the tumor varies substantially, ranging from 0.01% to more than 90% [33]. The

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levels of cf. tumor DNA are influenced by tumor burden and other factors such as tumor location, vascularity, and cellular turnover [34]. Encapsulated DNA (in circulating vesicles) and non-encapsulated free DNA are both referred to as cfDNA. Surprisingly, the amounts of cfDNA in the blood can also be utilized to track NSCLC patients who are undergoing immunotherapy. Eight weeks following the first injection of immunotherapy, Cabel et al. discovered a substantial association between synchronous changes in cf. tumor DNA levels and tumor size [35]. These findings were validated by other research groups. Giroux Leprieur et al. looked at advanced NSCLC patients who were being treated with nivolumab. Low cf. tumor DNA contents after 2 months were linked to nivolumab’s long-term benefit [36]. In NSCLC patients treated with immune checkpoint inhibitors, Goldberg et al. found that a decrease in cf. tumor DNA levels was an early indication of therapeutic success and a predictor of longer survival [37]. Surprisingly, the amounts of cfDNA in the blood can also be utilized to track NSCLC patients who are undergoing immunotherapy. It is worth noting that only a limited number of people (28 people) were involved in each of the three investigations. Despite the fact that these findings need to be verified in bigger study cohorts, monitoring cfDNA levels could be used to track NSCLC patients on immunotherapy [35–37]. Small amounts of cell-free DNA (cfDNA) can be detected in human plasma at very low concentrations (5–10 ng/mL) under physiological conditions [38]. Due to the release of DNA fragments in the blood stream, known as circulating tumor DNA, the amount of cfDNA in cancer patients is much higher than in healthy controls, and it is higher in patients with advanced tumors than in patients with earlier developed tumors (ctDNA) [39]. In advanced NSCLC, ctDNA and tumor cells have a high level of concordance, which means that ctDNA contains particular mutations that are identical to those seen in the main tumor and its metastases [40]. Furthermore, because ctDNA has a short half-life (15 min to 2.5 h), it can be used to track tumor mutational status and dynamic changes over time [41]. After lysis, cfDNA is a piece of DNA released into the plasma that contains genome-wide DNA information. ctDNA is a kind of cfDNA that can be found in both primary tumors and metastases. ctDNA also has a complete collection of tumor genetic information, allowing it to efficiently overcome tumor geographic heterogeneity. As a result, liquid biopsy based on ctDNA has become increasingly popular in the selection of advanced NSCLC treatment regimens, including the identification of therapeutic drug targets and the detection of immunotherapy biomarkers such as blood tumor mutational burden (bTMB) and blood microsatellite instability (bMSI) [42, 43].

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In real time, ctDNA can reveal genetic changes in the main tumor, metastatic lesions, and throughout dynamic tumor growth. Chemotherapy, targeted therapy, and radiotherapy have all been attempted using ctDNA as a guide [44–46]. However, its clinical use in immunotherapy is unknown. Studies using ctDNA-guided immunotherapy have largely focused on melanoma, according to the published literature [47], and other tumors have only sporadically been addressed. In five melanoma patients, Ashida et al. [48] studied the association between ctDNA and anti-PD-1 immunotherapy and discovered that the level of ctDNA dropped after 2–4 weeks in the three responsive patients but remained high in the two unresponsive patients. Furthermore, Goldberg SB looked at 182 serial plasma samples from 49 metastatic non-small-cell lung cancer (NSCLC) patients who were given anti-PD-1 and/or anti-PD-L1 and discovered that patients whose ctDNA levels dropped by more than 50% had longer-term benefits than those whose ctDNA levels dropped by less than 50% (205.5 vs. 69 days, P < 0.001) [37]. The discovery of efficient tumor biomarkers will aid in the development of customized immunotherapy. The detection of ctDNA, which is a common component of liquid biopsy, can be done in real time and is noninvasive, repeatable, and capable of overcoming tumor heterogeneity challenges. This method will increasingly become commonplace in clinical diagnosis. ctDNA is a unique approach for assessing immunotherapy efficacy, but it has limitations that must be considered. Improving its sensitivity is a pressing concern, and quantification and standards are further issues that must be addressed. Immune clinical experiments on ctDNA have only used small samples thus yet, and the results are not very convincing. Patients with diverse malignancies at various clinical stages and with multiple gene mutations are invited to participate in prospective immunologic clinical trials. We expect ctDNA testing to become a standard method for assessing immunotherapy efficacy in the near future. Indeed, when compared to conventional imaging and tumor markers, ctDNA delivers a more precise diagnosis and earlier indication. According to reports, ctDNA can be used to identify immunotherapy responders, assess efficacy and survival time [48], and anticipate tumor recurrence and metastasis by detecting immune checkpoint inhibitor resistance and pseudo-progression [49]. As a result, ctDNA can operate as an “Eagle Eye” in the immunotherapy process, monitoring both macro- and micro-changes. Despite the fact that ctDNA has become a popular study area, it still has limits, and improvements in sensitivity and standardization are urgently needed. This study discusses the benefits and drawbacks of ctDNA as a precise biomarker, as well as the practicality of employing ctDNA detection for routine immunotherapy monitoring.

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However, the amount of ctDNA in peripheral blood is quite low, and the amount of ctDNA and nucleic acid polymorphism in different patients varies greatly [50]. As a result, ctDNA analysis and detection pose significant technical obstacles. The release of cfDNA is linked to tissue trauma and stress reactions, both of which are linked to recurrence after surgery. Evidence showed that the level of cfDNA in tumor patients was higher than in healthy people and that it rose dramatically as nonsurgical patients’ tumors progressed [51, 52]. As a result, using dynamic changes in cfDNA in combination with ctDNA as a biomarker could give us a novel way to predict the likelihood of early cancer recurrence after surgery.

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Utility of Circulating Tumor Cells (CTCs) for Liquid Biopsy CTCs are cancer cells that have broken out from the original tumor or have spread to other parts of the body. They are a part of the process of metastasis [53]. CTCs enter the bloodstream through passive shedding and/or intravasation (with epithelialmesenchymal transition) [54]. CTCs can provide information on transcriptomic, genomic, and proteomic levels. They can be seen as a single cell or as a cluster of several CTCs when they are not grouped. When compared to single CTCs, these aggregates, which are made up of at least two CTCs, have a much higher metastatic potential. CTCs isolated from cancer patients’ peripheral blood represent a promising alternative to invasive biopsies as a source of tumor tissue for the identification, characterization, and monitoring of all nonhematologic malignancies, notwithstanding their rarity [55]. Above all, when compared to a single-site biopsy, CTC analysis provides a detailed image of the overall tumor content as well as intratumoral heterogeneity caused by branched clonal evolution [56, 57]. CTC single-cell analysis is important because it provides simultaneous information on the tumor cell’s mutational profile, copy number alteration (CNA), genomic rearrangement, and gene expression [58]. CTC detection is complicated by its rarity, which can be as low as one cell per milliliter of blood among millions of background leukocytes [59]. During treatment, CTCs’ phenotypic evolution may be tracked, revealing the appearance of stem cell characteristics and resistance markers that indicate CTC latency in the circulation and resistance to anticancer medicines [60]. Patients with NSCLC who are receiving ICIs have a high CTC count and PD-L1 positive CTCs, which are both independent negative prognostic markers [61]. CTCs are uncommon (1 out of every 100 million normal blood cells), and detecting them, even in individuals with advanced disease, can be difficult [62]. Several approaches for detecting CTCs have been developed; however, there is no consensus on which traits should be employed

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to confirm the malignant nature of CTCs at this time (e.g., molecular profile or surface antigen expression). This is a significant challenge for CTC detection, as it is dependent on the specificity of isolation techniques [63]. Importantly, the existence of PD-L1+ CTCs prior to therapy had no bearing on the clinical outcomes [64]. Another study of 24 metastatic NSCLC patients treated with nivolumab found that the existence of CTCs and the expression of PD-L1 on their surface at baseline and 3 months after therapy was linked to a poor patient outcome [65]. Another study of 24 metastatic NSCLC patients treated with nivolumab found that the existence of CTCs and the expression of PD-L1 on their surface at baseline and 3 months after therapy was linked to a poor patient outcome [66]. They found a link between CTC count and poorer response and survival during ICI treatment [67, 68]. In a recent study, Castello et al. looked at the connection between CTCs and metabolic 18F-FDG PET-based indicators in 20 NSCLC patients. CTCs offered excellent information on tumor metabolic activity in this case series, which might be used as a surrogate for tumor aggressiveness [69]. CTCs (circulating tumor cells) are a promising indicator for liquid biopsy in cancer. Primary and metastatic cancers shed circulating tumor cells (CTCs) into the bloodstream. Cancer metastasis and recurrence are aided by CTCs [70]. CTC research could lead to better illness management, such as better monitoring, treatment decisions, and risk classification [71]. CTC count appears to be a promising biomarker of effectiveness during ICI treatment, according to the data. However, technological challenges, as well as disagreements over current results, necessitate more research before CTCs may be transferred into the clinic.

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Utility of EXOSOMES for Liquid Biopsy Exosomes are 30–150 nm in diameter spherical extracellular vesicles with a lipid bilayer membrane [72]. Johnstone et al. published their findings in 1987 [73]. During the development of reticulocytes, a tiny membrane vesicle is released, which was discovered first. These vesicles were given the moniker “exosomes” because they may transport transferrin receptors between cells. Microvesicles (100–1000 nm in diameter) are shed directly from the cell membrane and are derived from intracellular multivesicular bodies, while microvesicles (100–1000 nm in diameter) are derived from intracellular multivesicular bodies [74]. Exosomes are produced by a variety of cell types, including tumor, immunological, and lymphoid cells, as well as the stroma [75]. Exosomes secreted by stromal cells have the ability to encourage neighboring tumor cells to spread. They also encourage tumor cell growth while preventing apoptosis. Tumor cells, like immune cells, produce

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immunologically active exosomes that influence other immune cells’ anticancer activity, creating a favorable environment for the tumor [76]. We will concentrate on tumor-derived exosomes in this review (TEX). TEX is important in the genesis of cancer, the production of metastasis, and disease progression while accounting for a minor percentage of total exosomes. They play a vital function in cell–cell communication, and TEX has been linked to the development of chemotherapeutic resistance. The radiation-induced bystander effect, which occurs when nontargeted cells experience the effects of radiation, is also influenced by TEX [77, 78]. TEX also has the ability to stop immunological cells from proliferating. They can also cause immune cells like CD8+ T cells to die (or be suppressed). As a result, TEX has an effect on tumor cell immunotherapy sensitivity [76, 79, 80]. In this context, a few research groups looked into the significance of TEX in determining which NSCLC patients will benefit from immunotherapy. A link was found between the amount of plasma-derived PD-L1+ exosomes and the degree of PD-L1 expression in tumor tissue in a study of 24 patients with lung cancer before surgery [81]. Li et al., on the other hand, found no association between PD-L1 status in exosomes and tumor tissue in 85 NSCLC patients [82]. According to Gunasekaran et al., PD-L1-positive TEX can be employed as a prognostic biomarker for NSCLC patients receiving immunotherapy. They noticed a decrease in PD-L1 in patients who responded to immunotherapy. This difference, however, was not statistically significant. They also found that the dynamic changes in exosomal PD-L1 (pre-treatment vs. 8 weeks of treatment) predicted the clinical outcome in terms of both PFS and OS in immunotherapy patients. Only 25 NSCLC patients were included in this trial, which was again a drawback [83]. EVs produced from tumors, on the other hand, may serve as a conduit for cancer cells to evade immune monitoring, resulting in immunotherapy failure [84]. Tumor EVs are thought to fool lymphocyte activation by exposing inhibitory ligands that trigger the immunological checkpoint response. The existence of tumor-derived EVs, as determined by blood tests, and the investigation of the vesicle-specific content could be critical for a quick and accurate diagnosis or early tumor identification [85]. During follow-up, parallel screening for antigenic EV and analysis of EV biomarkers could assist in defining the bestcustomized treatment and predicting resistance. Despite the fact that important technological and practical challenges lead to the collection of heterogeneous populations of unknown origin vesicles [86], emerging technology will most likely overcome these constraints in the not-too-distant future, restricting the use of EVs in ordinary clinical practice [85, 87].

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Utility of MicroRNAs (miRNAs) for Liquid Biopsy MicroRNAs (miRNAs) are noncoding RNAs that are 20–22 nucleotides long [88], which control the expression of tumor suppressor genes and oncogenes at the post-transcriptional level [89]. MiRNAs play a variety of roles in the cancer microenvironment and immune system modulation [90–93]. MiRNAs can also be enclosed by extracellular vesicles and used to communicate with cells in local and distant environments [94–96]. Furthermore, extracellular circulating miRNAs are highly stable in biofluids and are resistant to RNase destruction and have been used as biomarkers for early cancer detection and treatment response prediction [94, 96, 97]. Surprisingly, serum miRNA profiles have already been identified as promising diagnostic indicators in the treatment of ovarian cancer [97]. Furthermore, one of the benefits of serum miRNA analysis is the ease with which the entire profile may be obtained by sequencing a small number of samples. As a result, circulating miRNAs could be used as EOC diagnostic and prognostic biomarkers [96, 97]. Furthermore, the discovered miRNAs have a lot of promise for using liquid biopsy to predict immunotherapy response. These miRNAs are linked to interferon-related pathways, according to a functional annotation study. As a result, these biomarkers may indicate the patient’s immunological activity and may be applicable to all immune-related therapies, including immunity vaccination therapy. Previous research has found that miRNAs play a key role in cancer biology characteristics such as tumor angiogenesis, chemoresistance, and immunoregulation of T-cell activation via tumor microenvironment modulation [92, 98, 99]. As a result, miRNAs could be used as biomarkers and therapeutics in immunotherapy [100]. Another study discovered a link between the downregulation of circulating miRNA320b and - 375 expression and immunotherapy response [101]. Peng et al. looked at the use of plasma-derived exosomal miRNAs as biomarkers for ICI treatment selection [102]. Thirty patients with advanced NSCLC who were receiving immunotherapy were assessed in this study. Three miRNAs from the hsa-miR320 family (hsa-miR-320d, hsa-miR-320c, and hsa-miR-320b) have been discovered as possible predictors, and one has been identified as a potential target for anti-PD-1 therapy (hsa-miR125b-5p). Furthermore, circulating miRNAs reflect the biology of the tumor, making them good biomarkers for disease diagnosis and surveillance throughout treatment. Due to the small sample size of current studies and the various methodologies, available data on miRNAs is insufficient to draw any meaningful conclusions about their likely involvement in predicting response and survival during

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immunotherapy. Integrating miRNA quantification with other circulating biomarkers could be a way to get beyond the technique’s inherent limitations and improve the prediction power of single biomarkers.

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Discussion and Conclusion Within the last decade, targeted cancer treatment alternatives have exploded. Tissue biopsy is the most common method of detecting molecular abnormalities. However, in rare circumstances, a biopsy is not possible or the patient refuses. As a result, liquid biopsy is still the only choice. Originally, liquid biopsy only referred to circulating tumor cells (CTCs), but it now includes circulating cell-free tumor DNA (cfDNA) and exosomes as well [103]. It is worth noting that a liquid biopsy cannot replace a diagnostic tissue biopsy. Liquid biopsy may be used to get the histologic diagnosis in rare circumstances where tissue cannot be retrieved via tissue biopsy [104]. Furthermore, the development of medicines that are active against specific resistance mechanisms that arise throughout treatment makes tissue re-biopsy equally critical for characterizing tumor tissue during progression. Because tumor tissue is not always available for molecular characterization due to a lack of diagnostic specimens or difficulties carrying out invasive procedures, liquid biopsy is a viable option for overcoming these challenges. As a result, developing predictive biomarkers for cancer immunotherapy, as well as less invasive diagnostic techniques and dynamic monitoring tests, is very desirable. In gastric cancers, the informative potential of ctDNA has been proposed for patient selection or disease-course monitoring, using qualitative (e.g., molecular typing) and quantitative (e.g., ctDNA change) assessments [105]. This is a very novel notion that has just lately been utilized in clinical research. Nevertheless, routine use of circulating miRNAs in everyday clinical practice has yet to be introduced. The fundamental reason for this is the heterogeneity of existing research on miRNAs as cancer detection biomarkers, as well as their small sample sizes, absence of prospective analysis, and lack of a substantial external, impartial validation [106]. CTC analysis, on the other hand, can give an unrivaled means of probing tumor biology and metastatic progression. At the same time, when more reliable and low-cost technologies for isolating EVs and purifying their nucleic acid content emerge, EV analysis is likely to become the next frontier in cancer diagnosis. EV release is a biologically active and pliable method that tumor cells can use to impact the surrounding microenvironment and interact with distant areas, creating the metastatic niche and suppressing the immune response. Second, given the relative quantity of tumor-derived EVs in circulation compared to

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CTCs, researching EV contents and surface markers that can track back to the cell of origin of primary tumors would make diagnosis and therapy response prediction more exact and accurate. Release of cfmiRNAs, on the other hand, may account for a novel and more dynamic method of resistance acquired by cancer cells, providing unique information about disease progression. Recent breakthroughs in proteomics have opened up new avenues for including circulating proteins in the liquid biopsy diagnostic arsenal. Proteomics has supplied a wealth of information from tissue samples so far, but the lack of sensitivity and specificity required by clinics limits its potential application for blood-based testing. It is undeniably difficult because each biomarker has its own set of restrictions. Depending on the tumor stage and patient’s state, it might be the complementation of all possible methodologies of analysis to provide the most accurate prediction of therapeutic response and identify the greatest biomarker value. Furthermore, while cfDNAbased mutation analysis is the simplest and most cost-effective approach in liquid biopsy, alternative biological sources (CTC, EVs, and platelets) may perform better in detecting gene fusions and genomic rearrangements. The difficulty remains, however, that all present methodologies must be improved and standardized before they can be used in clinical practice. Nonetheless, despite its potential, liquid biopsy has several drawbacks. In most investigations, the smallest sample size is the most significant bottleneck. Biomarkers that show promise should be tested in bigger patient groups. Second, liquid biopsy has limitations due to a lack of standardization and widely agreed standard operating procedures. Finally, detection issues arising from the low abundance of most liquid biopsy chemicals must be addressed. Despite the fact that immunotherapy has changed cancer treatment, not all patients will benefit from this cutting-edge medication. Identification of possible biomarkers that can predict immunotherapy success and toxicity is critical for developing personalized treatment regimens. Liquid biopsy is a low-cost, minimally invasive technology that can provide valuable information on patient selection and therapy monitoring. Several blood biomarkers are currently being researched (circulating immune and tumor cells, soluble immunological mediators, peripheral blood cells). To validate their usage in clinical practice, prospective clinical trials are required. Liquid biopsies have arisen as promising new tools for doctors to guide treatment regimens in clinical practice, despite the fact that tissue samples remain the gold standard for tumor genotyping. However, we anticipate that 1 day liquid biopsy will be able to replace present approaches, thereby improving the comfort and quality of life of patients.

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Precision oncology will be able to execute adaptive, real-time treatment adjustments by combining wide tumor genetic profiling with dynamic monitoring of responses using liquid biopsy techniques. Novel liquid biopsy procedures must also be clinically validated and standardized before being widely employed in everyday practice. Liquid biopsy has the potential to help doctors better identify individuals who will benefit from immunotherapy. Despite the evaluation of various blood biomarkers, we are still distant from finding a viable predictive biomarker. Due to the varied nature of cancer and the complicated interaction between the tumor and TME, a single biomarker may not be able to predict immunotherapy response. Liquid biopsy applications for selecting and monitoring immunotherapy patients appear promising for detecting recognized and novel qualitative–quantitative indicators in the “circulome” and prompting treatment changes in primary and secondary-resistant malignancies. It seems that if the liquid biopsy and the immunotherapy revolution in cancer treatment eventually meet, they can facilitate patient compliance and improve overall outcomes. This review discusses the use of liquid biopsy in cancer therapeutic management, with a focus on immunotherapy. Liquid biopsy has the potential to aid in the detection of ICI resistance biomarkers during treatment, avoiding the problem of tumor heterogeneity. The most major advantage of this method over imaging is that it is less intrusive, allowing for more frequent sampling. A liquid biopsy sample can be obtained at almost any moment (regardless of, e.g., the location of the tumor or the health status of the patient). Finally, a number of research studies have suggested that liquid biopsy can yield useful biomarkers [107–110] for early detection of cancer in the nonmetastatic situation [107, 108], calculate the size of the tumor [109–111], and evaluate the extent of the tumor removal as well as the prognosis [112, 113]. Liquid biopsy technology has been extensively researched and utilized in a variety of tumor forms, and it can provide more precise treatment measures for tumor patients while also allowing them to achieve the greatest clinical outcomes. Despite this, liquid biopsy has numerous hurdles and is still in its early stages of development. For additional verification, large-scale prospective clinical trials are required. Liquid biopsy is thought to provide patients hope in terms of a more precise diagnosis and prognosis. Liquid biopsy is anticipated to become standard in everyday practice in the future, replacing tissue biopsy in large part, thanks to the development of ever more precise and cost-effective techniques. As a result, patient complication rates might be reduced, and continuous disease monitoring would be much easier.

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Acknowledgments Not applicable. Availability of Data and Materials Not applicable. Authors’ Contributions All authors were involved in the conception of the study and revised and approved the final manuscript. All authors take responsibility for publishing this review paper. WY. K performed the literature search, wrote the manuscript, and critically analyzed the existing knowledge; WY. K, TX. C, and YX. L have ideated; TX.C and YX. L significantly contributed to editing the manuscript; WY. K, TX. C, and YX. L were significantly involved in the drafting of the manuscript. Ethics Approval and Consent to Participate Not applicable. Patient Consent for Publication Not applicable. Conflicts of Interest The authors declare no potential conflict of interest.

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Chapter 10 The Implication of Liquid Biopsy in the Non-small Cell Lung Cancer: Potential and Expectation Jianghao Ren and Ruijun Liu Abstract Nowadays, lung cancer has remained the most lethal cancer, despite great advances in diagnosis and treatment. However, a large proportion of patients were diagnosed with locally advanced or metastatic disease and have poor prognosis. Immunotherapy and targeted drugs have greatly improved the survival and prognosis of patients with advanced lung cancer. However, how to identify the optimal patients to accept those therapies and how to monitor therapeutic efficacy are still in dispute. In the past few decades, tissue biopsy, including percutaneous fine needle biopsy and surgical excision, has still been the gold standard for examining the gene mutation such as EGFR, ALK, ROS, and PD-1/PD/L1, which can indicate the follow-up treatment. Nevertheless, the biopsy techniques mentioned above were invasive and unrepeatable, which were not suitable for advanced patients. Liquid biopsy, accounting for heterogeneity compared with tissue biopsy, is an alternative technique for monitoring the mutation, and a large quantity of research has demonstrated its feasibility to detect the circulating tumor cell, cell-free DNA, circulating tumor DNA, and extracellular vesicles from peripheral venous blood. The proposal of the concept of precision medicine brings a novel medical model developed with the rapid progress of genome sequencing technology and the cross-application of bioinformation, which was based on personalized medicine. The emerging method of liquid biopsy might contribute to promoting the development of precision medicine. In this review, we intend to describe the liquid biopsy in non-small cell lung cancer in detail in the aspect of screening, diagnosis, monitoring, treatment, and drug resistance. Key words Liquid biopsy, Non-small cell lung cancer, PD-1, PD-L1, Immunotherapy, Epidermal growth factor receptor (EGFR), Circulating tumor cell (CTC), Cell-free DNA (cfDNA), Circulating tumor DNA (ctDNA), Extracellular vesicle

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Background Cancer is usually a molecular disease caused by specific mutation of genetic code on key circuits at the nuclear level, such as substitution, translocation, deletion, and insertion, resulting in the uncontrolled growth of mutated cells and destroying the homeostasis of the internal environment [1]. According to Global Cancer Statistics of 2020, approximately 19.3 million new cases and 10 million cancer-related deaths occurred in 2020 alone. Lung cancer,

Tao Huang et al. (eds.), Liquid Biopsies: Methods and Protocols, Methods in Molecular Biology, vol. 2695, https://doi.org/10.1007/978-1-0716-3346-5_10, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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accounting for an estimated 1.8 million deaths, has still been classified as the most lethal cancer, followed by breast cancer [2]. As the histological classification of cancer issued by the World Health Organization in 2015 indicates, lung cancer is characterized by small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). The latter accounts for almost 80% of lung cancer cases and most of them suffer from locally advanced or metastatic disease [3, 4]. With the advent of the first EGFR-targeted tyrosine kinase inhibitor (EGFR-TKI), Iressa/Gefitinib (AstraZeneca), in 2009 in Europe, targeted therapy has raised a tremendous revolution of drug investigation. EGFR-TKI has already been adopted into clinical guidelines like National Comprehensive Cancer Network (NCCN) as first- and second-line treatment options currently. Epidermal growth factor receptor (EGFR), an expression product of proto-oncogene C-erbb-1, is the most deeply studied target, which was essentially a transmembrane protein and located on the short arm of chromosome 7. The mutations of EGFR-TKI are frequently present on exons 19–21 among its whole 28 exons, especially 19 exon deletion (accounting for nearly 47%) and point mutation on 21 exon (about 32%) [5]. Besides, EGFR exon 20 insertion (Exon20ins) in non-small cell lung cancer (NSCLC) is insensitive to EGFR-TKI and remains a challenge to drug design and therapy selection for decades. Recently, the research of Amivantamab (JNJ-61186372) has demonstrated its in vivo efficacy and safety on the target of Exon20ins, which proves to have satisfying tumor responses and be superior to cetuximab or poziotinib [6]. The rise of tumor immunotherapy is setting off a new revolution in tumor therapy. Compared to traditional tumor therapy, it has durable efficacy and patients have considerable toleration. At present, there are total of five immunotherapeutic drugs of PD-1/ PD-L1 listed globally, of which three types of immunotherapeutic drugs are most concerned for lung cancer that are drug K (keytruda), drug o (opdivo) and drug I (imfinzi). The common immunotherapeutic targets are mainly checkpoint inhibitors (CTLA-4, PD-1/PD-L1) and anti-tumor monoclonal antibodies (complement-dependent cytotoxicity, CDC; antibody-dependent cellular cytotoxicity, ADCC; antibody-dependent phagocytosis, ADCP). Cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) was first found by James Allison, and the initial inhibitor of CTLA4, ipilimumab, was approved to cure metastatic melanoma in 2011 [7]. After that, Ishida et al. found programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) in 1992 [8]. It is recommended to accept PD-1 therapy on patients with PD-L1 expression positive ≥1% and on patients negative for actionable molecular markers in the current guidelines. Recently, a series of research has confirmed the feasibility of immune therapy, such as a series of

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Impower clinical trials [9], a series of Checkmate trials [10], PACIFIC research [11], and ADVANCE study. Although the drug treatments mentioned above have achieved great progress in the clinic, those patients should be precisely selected before adopting the drugs. In the past, we tended to use tissue biopsy to detect the expression level of PD-1/PD-L1 and the positive driver gene. The tissue biopsy has still been the golden standard for discriminating the targeted mutation for decades, such as surgical biopsy and aspiration biopsy [12]. However, sometimes tissue biopsy is not that perfect. Most cases of non-small cell lung cancer are locally advanced and metastatic. Those patients may be in poor physical condition and always companied with severe complications, who cannot sustain extra trauma. In addition, the existence of intra-tumor heterogeneity limited its accuracy to reflect the real mutational status of the tumor and long-term tendency of progress [13]. Moreover, those advanced patients need to accept frequent monitoring or several biopsies to detect the emergence of new mutation or genetic modifications; thus, the treatment can be adjusted correspondently. Such limitations make the tissue biopsy not that suitable for every case. Under this circumstance, with the investigation of liquid biopsy, frequent monitoring gradually becomes possible. Liquid biopsy, a novel method of biopsy to detect potential mutation by evaluating the circulating tumor cell (CTC), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), and extracellular vesicles (ev) in peripheral venous blood, is a promising alternative technique. Unlike most tissue biopsies, liquid biopsies are less onerous and have a much lower risk of complications. Besides, during the treatment, the liquid biopsy can contribute to screening some gene mutations relevant to drug resistance such as EGFRT790M and further guide the treatment management accordingly. Due to its immature characteristic at present, current guidelines recommend having liquid biopsy as an alternative only when the sources of tissue are limited [14, 15]. Altogether, the liquid biopsy is a noninvasive, repeatable, quicker, less expensive technique and helps to promote precision medicine.

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Definition and Introduction During the progress of cancer, there are a series of tumor components released into the blood. Those potential biomarkers can be found in various kinds of body fluids like blood, urine, tissue, bronchoalveolar lavage fluid, and saliva [16–18]. The biopsy is a novel technique aimed at analyzing any tumor-derived substance circulating in blood or body fluid [19], with which we can realize early screening, genetic profiling, monitoring recurrence, and treatment guiding. As prof. Wu stated in the Chinese Society of Clinical

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Oncology in 2016 that liquid biopsy has played an important role in exploring the field of precision medicine and the major components of detection mainly contain three aspects: circulating tumor cell (CTC), cell-free DNA in plasma (cfDNA) or circulating tumor DNA (ctDNA), and exosomes.

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Supplementary Examination on Screening and Diagnosis Currently In the past few decades, we have been accustomed to using imaging techniques and histopathology methods to perform early screening, monitor real-time efficacy, and identify the targets of treatment as well as the mechanism of drug resistance. In some aspects, imaging techniques like CT, PET-CT, and ultrasonic examination are necessary to initially determine the characteristic of the lesion and clinical stage, which will guide the following treatment. A large number of patients are diagnosed when in the advanced stage and lose the opportunity to accept radical resection. So it is urgent to explore and enhance the ability of early diagnosis. Nowadays, low-dose computed tomography (LDCT) is recommended to have early screening in some countries to decrease radiation exposure. National Lung Screening Trial also defined the criterion of a positive screen by LDCT, resulting in reduced mortality [20]. However, this guideline still recurs a high rate of false-positive findings [21]. Besides, imaging examination cannot describe the real status of the lesion and cannot reflect potential invasion sometimes. As for histopathology methods, we usually adopt endobronchial ultrasound-guided transbronchial needle aspiration (EBUSTBNA), percutaneous lung biopsy (PTLB), and even tissue from surgical resection. Tissue biopsy of tumors has still been the gold standard for distinguishing between primary and metastatic lung tumors and determining some relevant gene mutations. However, sometimes the sources of tissue are limited to have such examination above because nearly 25% of patients with NSCLC are advanced and cannot tolerate frequent trauma [12]. Though needle aspiration is an effective operation with less invasion, its accuracy is not so ideal. Almost all lung cancer patients die of distant metastasis, but traditional histopathology methods can only describe the resected tumor, which neglect the unique genomic characteristics of metastatic tumor [22, 23].

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Comparison Between Traditional Tissue Biopsy and Liquid Biopsy It is widely recognized that traditional tissue biopsy like endobronchial ultrasound-guided transbronchial needle aspiration (EBUSTBNA) and percutaneous lung biopsy (PTLB) is the gold standard for evaluating gene mutations and treatment efficacy. However, as

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an invasive operation, sometimes the traditional biopsy is not that suitable for every patients such as advanced patients who need to accept frequent operations to detect mutation changes and drugresistant mechanisms. The traditional biopsy is also companied with a high incidence of complications [24] and cannot reflect the real situation of the lesion due to the intratumoral heterogeneity. Therefore, small biopsy and cytology samples may not represent the real status of the overall tumor [25]. Sometimes the tissue acquired from traditional biopsy in 10%–20% of cases is not enough to have gene analysis [13, 26], and its accuracy in acquiring the lesion tissue depends on individual experiences. Recently, targeted therapy and immunotherapy revolute the treatment of non-small cell lung cancer. During the progress of the disease and the treatment, we need to further assess the new mutations and the tendency of the expression level of gene targets, which demand a swift, noninvasive, and repeatable examination to track and evaluate the treatment. Usually, rebiopsy is rarely performed in most centers due to complaints of patients and risk of tumor spread [27], which lose the dynamic tracking of cancer development. Liquid is a noninvasive and novel technique to detect the components in the blood (also body fluid) correlated to tumors frequently, such as circulating tumor cells. The liquid biopsy can monitor the progress of cancer in real-time and have the potential to overcome the tumor heterogeneity of space as well as time [28, 29]. The metastatic tumors are always diagnosed occasionally, which have drastic and partially organ-dependent heterogeneity [30]. Liquid biopsy represents the whole genomic condition of the tumor and can describe the abnormal genetic information both in primary tumor and metastatic lesions. Nowadays, liquid biopsy is also explored in the area of monitoring recurrence, evaluating the influence of minimal residual disease (MRD) and early diagnosis. With the rapid development of liquid biopsy, although a few researchers have proved the feasibility and safety of liquid biopsy, the traditional biopsy is still irreplaceable because it can indicate the genetic status correctly [16]. In the study of Prof. Wang, specimens from surgical resection had a significantly higher overall mutation rate over cytology specimens [31]. Some guidelines suggest that the liquid biopsy is only recommended when the tissue specimens are limited or unavailable at the time of diagnosis.

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Circulating Tumor Cell (CTC) Circulating tumor cells (CTCs), first reported in 1869 by Ashworth in the peripheral blood of a patient with breast cancer in Australia, are derived from tumor tissue in the development of cancer [32]. During the progression of cancer, the micrangium and

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capillaries will be invaded gradually. Since then, some tumor cells detached from the lump, which are robust and have high potential metastasis, will potentially destroy the basilar membrane and eventually spread into distant organs through blood. As those tumors have undergone epithelial-mesenchymal transition (EMT), a biological process by which epithelial cells are transformed into mesenchymal phenotypes by a specific procedure, they are endowed with metastatic ability and remain partial features of the primary lesion. According to this characteristic, we can evaluate the cancer progression by analyzing the gene expression level of CTC [33]. Nowadays, some relevant research studies have proved that the circulating tumor cell can be isolated from those who do not have any lung nodes in the chest CT through which a high-risk population of cancer can be identified [34]. The imaging techniques are not perfect in most cases, although it is recommended to have low-dose CT to have early screening in guidelines [35]. It is inevitable to produce false positive results, and the accuracy of discriminating between benign and malignant tumors mainly depends on individual experiences. The imaging tools cannot figure out the node clearly smaller than 3 mm with low density while the circulating tumor cells are correlating to lesion 9% at 2 months had a long-term benefit of nivolumab [80]. In another study, Angela Alama et al. found that patients receiving nivolumab who have cfDNA and CTCs below the median value have better prognoses than those who have circulating biomarkers above the median value [81]. Notably, blood TMB was more correlated with TMB in metastatic tissue than in primary tumors [82]. However, this biomarker of TMB has not yet been routinely used in clinical practice due to some controversial results from multiple studies [83, 84]. Minimal residual disease (MRD), a medical term derived from therapies in the blood system, is usually applied to describe a small number of cancer cells that remain in the body after cancer treatment. It has been adopted in the NCCN guidelines for lung cancer. The positive detection of MRD indicates a compromised prognosis

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that the remaining cancer cells in the circulating system will actively reproduce in a special time, resulting in local recurrence and distant progression. Recently, with the development of laboratory technology, various researchers have assured the practicability of ctDNA in MRD mornitoring [85]. Technique of cancer personalized profiling by deep sequencing (CAPP-seq) has been shown to be effective in MRD detection following adjuvant therapies for localized lung cancer. It is reported by Aadel A Chaudhuri et al. that nearly 94% of patients of recurrence in the study are ctDNA available [86]. The adjuvant treatment plays a nonnegligible role in non-small cell lung cancer, with which the long-term outcomes have improved a lot. In updated guidelines, it is suggested that patients of stages II–III and high-risk stage IB should receive adjuvant therapy in NCCN. However, whether stage IB should take adjuvant treatment is on debate. Under this circumstance, the MRD is a corresponding index that can help determine the treatment on stage IB [85]. More and more studies have shown that ctDNA is the ideal substance to assess MRD in solid tumors. The sensitivities vary from 78.57% to 93.75% [87–89], and similar phenomenon can be found in breast cancer. Some scholars compared the ctDNA value before surgery and after surgery, and the differences between them also reveal the implication of ctDNA on detecting MRD [90]. Jeanne Tie et al. used ctDNA detection on patients after resection of stage II colon cancer to identify patients at the highest risk of recurrence. In their study, patients with detected ctDNA have a high risk of recurrence after stage II colon cancer resection, which gives evidence of MRD directly [88]. Moreover, the detection of minimal residual disease (MRD) after radiotherapy and chemotherapy (CRT) has high sensitivity and specificity for the progression of NSCLC [86]. Besides, Luo et al. found that methylation analysis of ctDNA have potent potential to identify patients with colorectal cancer (CRC) [91]. The methylation of ctDNA may be a new biomarker to indicate minimal residual disease.

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Extracellular Vesicles and Exosomes Extracellular vesicles (EVs) are vesicular bodies with double-layer membrane structures that fall off from the cell membrane or are secreted by cells. The extracellular vesicles mainly include microvesicles (MVs), exosomes (EXs), and apoptotic body, with diameter varying from 40 nm to 1000 nm. Micro-vesicles are small vesicles that are produced by serosal budding after cell activation, injury, or apoptosis, and apoptotic bodies are formed by the rupture of recently dead cells with nuclear fragments. As for exosomes, they are released to the outside of the cell in the form of exocrine secretion after the fusion of intracellular multivesicular bodies and

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cell membrane [92]. Exosomes were initially described by Pan et al. in 1985 [93] and first mentioned as a type of extracellular vesicle by Johnstone et al. in 1987 [94]. Since then, a series of studies have been performed to explore the potential of exosomes. According to the existing research results, exosomes widely exist in various body fluids like blood, lymph, saliva, urine, semen, and even milk. Almost all kinds of in-vivo cells have abilities to produce exosomes, even tumor cells. They carry a variety of proteins, lipids, DNA, mRNA, and miRNA, with which the extracellular vesicles participate in the procedure of carcinogenesis, intercellular signal transduction, drug resistance mechanism, and intercellular communication through fusion to the receptor cell membrane [95]. As is roughly estimated, there are nearly 2000 trillion exosomes in blood of normal body and about 4000 trillion in cancer patients [96]. It was reported that the mean exosome concentration of patients with lung adenocarcinoma is almost 4 times that of normal people [97]. Due to the small size of exosomes, they cannot be observed under an optical microscope. After the separation of exosomes, it needs a series of identification to the isolated exosomes. In the past few years, enzyme-linked immunosorbent assay (ELISA) and flow cytometry are common methods to identify its physical characteristics and surface molecular markers. However, those techniques have some potential limitations that ELISA is easily disturbed by outside soluble antigens, and hard to fetch the size as well as the number of vesicles [98]. Western blot test is another common technique to identify exosome-specific proteins such as tetraspanins of CD9, proteins in the cytoplasm, and some hot shock proteins. High throughput detection on exosomes can analyze proteins and nucleic acids related to cell sources. Some novel methods are also demonstrated to be effective and feasible, like scanning electron microscopy (SEM), nanoparticle tracking analysis (NTA), atomic force microscopy (AFM), dynamic light scattering, and transmission electron microscopy (TEM) [99]. TEM is the preferred method to observe exosomes. The isolated exosomes were directly installed on the metal form grid and observed after negative staining with uranyl acetate [100]. In cell physiology, the exosomes are endowed with transport substances from originating cells to recipient cells and will not be destroyed by the immune system; thus, the exosomes are deemed as an ideal and natural therapeutic carrier to transport the specific drug to targeted lesion. Existing drug transportation systems are mainly limited as they cannot cross the blood–brain barrier (BBB). However, exosomes derived from brain endothelial cells may be used to deliver anticancer drugs to the brain because these exosomes can easily cross the blood–brain barrier [101]. Though targeted drugs have reformed the treatments for lung cancer nowadays, some patients still encounter the dilemma that lesions are not that sensitive to drugs and they will resist those targeted

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drugs in a short time. The natural ability of exosomes to carry functional biomolecules in their cavities, such as microRNA, DNA, and small proteins, makes them to be the hotspot in the exploration of new treatments [102]. Farrukh Aqil et al. discovered that exosomes loaded with celastrol (CEL), an inhibitor of Hsp90 and NF-κB signal pathway, presented drastic anti-tumor efficacy against lung cancer in 2016 [103]. In the same year, Akhil Srivastava et al. designed a novel Exo-GNP-based (exosome-gold nanoparticles) therapeutic delivery system for lung cancer therapy where the doxorubicin (DOX) was conjugated to GNPs [104]. Radha Munagala et al. showed that milk-derived exosomes are also biocompatible to act as a carrier for chemotherapeutic agents [105]. There are two key factors of exosomes as a therapeutic: the high encapsulation efficiency of loaded molecules and the stability of biomolecules and exosomes [100]. Hypoxia signaling is essential for the initiation of angiogenesis and proteins secreted by tumor exosomes. Those proteins are captured by normal endothelial cells in the host and eventually reproduce new blood vessels [106]. Hypoxia condition provides a suitable internal environment for most drug-resistant cells in tumors. It has been demonstrated that hypoxia stimulates the secretion of exosomes and can induce hypoxia-related factors (HIFs), resulting in angiogenesis [107]. VEGF is one of the relevant factors that are found higher in the hypoxia lung cancer cells, and it is considered to be an important promoter of angiogenesis [108]. In addition, Hiroko Tadokoro et al. found that miRNA derived from exosomes secreted by cancer cells under hypoxic conditions can influence the angiogenic process in endothelial cells to some extent [109]. Except for the factors mentioned above, Mei Xue et al. demonstrated that exosomal lncRNAUCA1 was facilitated to secrete in the hypoxic bladder cancer cells and had the potential to be a diagnostic biomarker for bladder cancer [110]. Exosomal miRNA, a non-coding single standard RNA molecule with a length of about 22 nucleotides encoded by endogenous genes, was deemed as a source of biomarkers. The miRNA modulates various physiological processes like cell differentiation, cell division, apoptosis, and morphogenesis of various organs. In some research, the miRNA content of circulating exosomes is similar to that in cancer cells of origin, which highlights the possibility of miRNA for cancer diagnosis [111]. With the help of exosomes, specific miRNA can be transported from stromal cells to epithelial cells, promoting the progression of cancer. The implications of miRNA reported in existing studies are listed in Table 1. Together, the exosomes have numerous advantages in liquid biopsy due to their wide distribution in body fluid, low toxicity, poor immunogenicity, no mutagenicity compared to other drug delivery systems, and abundant ligands to target specific cells [128].

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Table 1 The clinical significance of exosomal miRNA in the lung cancer miRNA

Reference

Year Explanation and indication

miR-17-3p, miR-21, miR-146, miR-155, miR-191, miR-203, miR-205, miR-214

Majid Rezayi et al. (reviewed) [95]

2019 Important for diagnosis of lung cancer

let-7a-2, let-7a-3, miR-15b, miR-21, mir-155, miR-200b, miR-21

Naiara C Cinegaglia et al. (discovered) [112]

2016 Higher levels of expression were associated with lower patient survival

hsa-miR-155, hsa-let-7a-2

Nozomu Yanaihara et al. (discovered) [113]

2006 High hsa-mir-155 and low hsa-let-7a2 expression correlated with poor survival

miR-29a-3p, miR-150-5p

Tru-Khang T Dinh et al. (discovered) [114]

2016 miR-29a-3p and miR-150-5p were correlated with the delivered RT dose and may eventually help predict toxicity in radiation therapy

miR-21, miR-4257

Hitoshi Dejima et al. (discovered) [115]

2017 Predictive biomarker for recurrence in NSCLC patients

miR-30b, miR-30c, miR-103, Marco Giallombardo miR-122, miR-195, et al. (discovered) miR-203, miR-221, [116] miR-222

2016 Correlating to NSCLC

miR-96

2017 The miR-96-LMO7 axis may be a therapeutic target for lung cancer

Hao Wu et al. (discovered) [117]

miR-197-5p, miR-4443, Xiaobing Qin [118] miR-642a-3p, miR-27b-3p, et al. (discovered) and miR-100-5p [118]

2017 Involved in the drug resistance of lung cancer cells to cisplatin

miR-21 and miR-155

Radha Munagala et al. (discovered) [119]

2016 miR-21 and miR-155 were found to be significantly upregulated in recurrent tumors

miR-21, miR-29a

Muller Fabbri et al. (discovered) [120]

2012 Important in tumor growth

miR-193a-3p, miR-210-3p, miR-5100

Xina Zhang et al. (discovered) [121]

2019 Promote invasion of lung cancer cells by activating STAT3 signalinginduced EMT

miR-9, miR-16, miR-205, miR-486

Maria Sromek et al. (discovered) [122]

2017 Serve as NSCLC biomarkers to diagnose

miR-378a, miR-379, Riccardo Cazzoli et al. miR-139-5p, miR-200b-5p (discovered) [123]

2013 Discriminate between lung adenocarcinoma and granuloma

miR-425-3p

Daolu Yuwen et al. (discovered) [124]

2019 Predicting the clinical response to platinum-based chemotherapy

miR-181-5p, miR-30a-3p, miR-30e-3p, miR-361-5p

Xiance Jin et al. (discovered) [125]

2017 Adenocarcinoma-specific miRNA (continued)

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Table 1 (continued) miRNA

Reference

Year Explanation and indication

miR-10b-5p, miR-15b-5p, miR-320b

Xiance Jin et al. (discovered) [125]

2017 Squamous cell carcinoma-specific miRNA

miR-18a, miR-20a, miR-92a

Xiaoxiao Xu et al. (discovered) [126]

2018 Indicating poor prognosis

miR-30b-5p, miR 30c-5p, miR 221-3p, miR 222-3p

Marco Giallombardo et al. (discovered) [127]

2016 Correlated to a good clinical outcome during osimertinib treatment

8

Conclusion and Expectation Lung cancer remains the main cause of cancer death all over the world for a long time. With the development of precision medicine, real-time monitoring and individual treatments are required to evaluate the therapeutic efficacy and make relevant adjustments. Compared to traditional tissue biopsy, liquid biopsy can frequently obtain detailed information about the lesion tissue and regularly track the gene mutation during the treatment process so as to identify the mutation sites related to drug resistance. In addition, liquid biopsy is a noninvasive procedure that reduces unnecessary trauma and pain. A large number of studies have confirmed that liquid biopsy has similar sensitivity and specificity to tissue biopsy. Although tissue biopsy is still the gold standard, liquid biopsy may be a good alternative for some advanced patients and patients treated with drugs.

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Chapter 11 Cell-Free DNA, MicroRNAs, Proteins, and Peptides as Liquid Biopsy Biomarkers in Prostate Cancer and Bladder Cancer Haoran Chen, Chenyang Xu, Zujun Fang, and Shanhua Mao Abstract Liquid biopsy, as a novel noninvasive tool for biomarker discovery, has gained a lot of attention and represents a significant innovation in precision medicine. Due to its minimally invasive nature, liquid biopsy has fewer complications and can be scheduled more frequently to provide individualized snapshots of the disease at successive time points. This is particularly valuable in providing simultaneous measurements of tumor burden during treatment and early detection of tumor recurrence or drug resistance. Blood-based liquid biopsy is an attractive, minimally invasive alternative, which has shown promise in diagnosis, risk stratification, disease monitoring, and more. Urine has gained popularity due to its less invasive sampling, the ability to easily repeat samples, and the ability to track tumor evolution in real time, making it a powerful tool for diagnosis and treatment monitoring, especially in urologic cancers. In this review, we provide a detailed discussion on the potential clinical applications of prostate cancer (PCa) and bladder cancer (BCa), with cell-free DNA (cfDNA), microRNAs (miRNAs), proteins, and peptides as liquid biopsy biomarkers. Key words Liquid biopsy, Prostate cancer, Bladder cancer, Cell-free DNA, MicroRNAs, Proteins, Peptides

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Introduction Although tissue biopsy remained the gold standard for clinical molecular diagnosis, its main disadvantages involved difficulties in obtaining tissue samples, invasive procedures, and complicated techniques. In light of these limitations on the use of tissue biopsy, new methods of observing tumor genetics and tumor dynamics have been developed over the past few decades. In 1948, one publication that described cfDNA and miRNAs in human blood has unknowingly taken the first steps toward the “liquid biopsy” [1]. Nowadays, the detection of tumor-derived cfDNA has been

Tao Huang et al. (eds.), Liquid Biopsies: Methods and Protocols, Methods in Molecular Biology, vol. 2695, https://doi.org/10.1007/978-1-0716-3346-5_11, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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shown to correlate with changes in response to treatment and indications of tumor cell subsets that can proliferate in response to treatment [2]. Clinically, most of the BCa patients do not undergo diagnostic procedures until clinical manifestations appear. For these patients, traditional radiological examinations confront the limitation in distinguishing tumor body due to the small size of the tumor. Endoscopy is not suitable for large-scale screening in the high-risk population because of its invasiveness. Exfoliative urinary cytology is noninvasive and still has poor sensitivity for the diagnosis of primary BCa. In addition, these methods have strict requirements of sampling and long period of the process, which are not suitable for large-scale and routine applications. Therefore, a novel noninvasive detection method that can provide high sensitivity and specificity is very necessary. Clinically, screening for PCa relies on digital rectal examination (DRE) and prostate-specific antigen (PSA). A limitation of PSA testing is relatively low diagnostic sensitivity and specificity, especially with the range of 4–10 ng/mL. Besides, it is unable to distinguish between low-grade and high-grade lesions by PSA. The introduction of biopsy techniques and improved ultrasound equipment have led to a well-known stage migration toward earlier detection and pathological localization of disease. Despite efforts to improve the specificity of PSA, it is still insufficient. Better serum/ plasma biomarkers and urine biomarkers are needed to complement the application of PSA in the diagnosis and treatment of diseases with diverse manifestations and clinical outcomes.

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Cell-Free DNA Genomic instability leads to copy number variation, which serves as a marker of malignant transformation that can be identified by massive parallel sequencing. Accordingly, cfDNA present in body fluids can serve as a real-time, readily available marker for detection. In healthy population, the concentrations of cfDNA are low because most nonviable cells are removed efficiently from circulation by phagocytes [3]. Apoptosis has been confirmed to be one of the major sources of cfDNA in plasma or serum [4], and the minor sources include tumor cell necrosis [5, 6]. So the concentrations of cfDNA in serum or plasma of tumor patients are often higher than in healthy people. Other sample sources for cancer diagnosis, including urine, synovial fluid, saliva, and sputum, have also been assessed for the presence of cfDNA [7]. The idea that cfDNA in plasma or serum could be used for clinical detection of human malignancies has been studied for many years [8, 9]. Many studies focused on the ability to distinguish cancer-specific cfDNA markers from extracellular fluids and benign

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tissue [10, 11] and found that the constant amount of cfDNA produced by apoptosis can be distinguished from tumor cell necrosis [12, 13]. These findings were confirmed by Jahr et al. By quantitative polymerase chain reaction (PCR) of the CDKN2A tumor suppressor gene in the plasma, they found that the elevated levels of cfDNA appear to be a feature of carcinoma diseases and pointed out that necrosis of tumor cells mainly causes this phenomenon [14]. The detection of cfDNA is currently in a stage of rapid development. The next-generation sequencing (NGS) platforms with massive sequence capacity provide the ability to advance the clinical application of cfDNA [15]. As a “liquid biopsy” method, quantitative detection of cfDNA has been confirmed to accurately reflect the changing genomic instability observed in cancer [16, 17]. Felix K.-H. Chun et al. identified plasma cfDNA as a potential clinical marker for early detection of PCa, by using the concentration of cfDNA in plasma to distinguish malignant disease from benign hyperplasia. The prospective study included patients with suspected PCa by abnormal PSA levels and/or DRE. The isolated cfDNA from plasma were spectrophotometrically determined at 260 and 280 nm. The results showed that the predictive accuracy of plasma cfDNA was 77.4%, which was second only to free/total (f/t) PSA of all predictors, and plasma cfDNA level was independent of total PSA, f/t PSA, and age. Thus, the plasma cfDNA can serve as a potential new marker. In univariate analysis, plasma cfDNA level was an independent predictor (P = 0.032) [18]. CfDNA has the potential as a biomarker for distinguishing PCa from benign prostate lesions. Different from Felix K.-H. Chun et al. to measure the content of cfDNA at the specific wavelength, Ekkehard Schutz et al. used whole-genome amplification (WGA) to analyze cfDNA. They extracted cfDNA from the serum of patients with PCa, patients with benign hyperplasia, and healthy people. By WGA, they found a difference in the number of sequence reads of cfDNA in 100-kbp intervals between patients with PCa and healthy people, providing an area under the curve (AUC) of 0.81 (95%CI 0.7–0.9, P < 0.001) by using receiver operating characteristic curve (ROC) to asses diagnostic performance [19]. Ekkehard Schutz et al. explored the utility of assessing the entire PCa genome from cfDNA fragments as a potential clinically useful biomarker. DNA is so stable that it can be amplified by using PCR-based techniques widely available in clinical diagnostics. The technique makes it possible to extract small amounts of DNA from patients with early tumor lesions or small cancers and enables sensitive detection of cancer cells. On this basis, many detections are possible such as point mutations, microsatellite changes, hypermethylation of promoter sequences, and chromosomal changes [20].

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DNA Methylation

Cancer is commonly believed to be the result of the interaction among the genetic factor, the epigenetic factor, and the environment. While genetic mutations are often the subject of research, they account for only a small fraction of most cancers. Epigenetics represents the most frequent DNA changes that can contribute to the development and progression of cancer. DNA methylation is the most widely investigated epigenetic mechanism. DNA methylation typically occurs more frequently than gene mutations and can be detected before tumorigenesis [21–23]. Key suppressor genes are so rarely methylated in normal cells that this epigenetic event transmits heritable silencing of target genes from malignant parental cells to daughter cells. DNA methylation in CpG islands as the epigenetic event happens nearly at the beginning of cancer development, and so it has the potential of being a biomarker for early diagnosis [24–26]. Aberrant methylation of these genes is now considered a hallmark of tumor development. Molecular detection that determines the methylation of specific genes can readily be used in clinical diagnosis of cancer, such as liquid biopsy [27]. Methylation-specific PCR (MSPCR) is currently the most commonly used method to detect the methylation of critical genes and has been widely used to detect hypermethylated genes derived from cancer cells in body fluids (such as serum, urine, and saliva). Many researches have shown the advantages of this approach, including higher sensitivity, specificity, and reproducibility than the traditional cytology assays [28]. Particularly, the hypermethylation of glutathione-S-transferase P1 (GSTP1) is the most common epigenetic change in PCa, and its detection discriminates between normal and neoplastic status in body fluids with high sensitivity and specificity by MSPCR. In a study carried out by Annalisa Altimari et al., cfDNA was quantified by real-time PCR assessment of the human telomerase reverse transcriptase (hTERT) gene in blood from patients with PCa and healthy people. The results showed that the levels of cfDNA were significantly higher in the PCa patients (15.4 ± 10.9 ng/mL) than in healthy people (5.5 ± 3.5 ng/mL). Besides, methylation of the GSTP1 gene was used to confirm the neoplastic origin of cfDNA in selected cases. Methylation of the GSTP1 gene was found in 4 of 16 cfDNA samples (25%), and none of the cfDNA from healthy people showed methylation of the GSTP1 [29]. Methylation of critical genes is so rare in normal cells that it can serve as a biomarker to monitor tumorigenesis. Previous studies have shown that 28–40% of PCa patients develop biochemical recurrence (BCR) with elevated serum PSA within 10–15 years after radical prostatectomy (RP) [30]. After androgen deprivation therapy, a part of these patients became resistant to the treatment. Currently, clinicians rely on the Gleason score (GS) from tissue biopsy and serum PSA to predict the likelihood of tumor delay and recurrence. While GS is valuable as a

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predictive marker for BCR, it is involved in an aggressive prostate biopsy procedure, which may not be representative of the entire prostate tumor area due to the multifocal nature of prostate tumor. Quantified characteristics of cfDNA can serve as a less-invasive and more-reliable biomarker for prognostic value, particularly methylation of DNA. Many previous studies have attempted to predict BCR by measuring DNA methylation in blood. In a study carried out by Aaron M. Horning et al., researchers used methyl-binding domain capture sequencing (MBDCap-seq) to screen for aberrant DNA methylation in primary tumors. The results showed that androgen biosynthesis pathway-related genes displayed different methylation in promoter regions. In particular, they reported increased methylation levels of SRD5A2 and CYP11A1 in primary tumors with BCR development. The CYP11A1 methylation level in BCR was 0.41 times compared to the level in no BCR group (P = 0.011). However, the CYP11A1 methylation levels were correlated to the corresponding SRD5A2 methylation levels in the BCR (P = 0.000001) [31]. Moreover, they found that the hypermethylation status of these DNA methylation levels was present in the plasma of patients after RP. The results suggest that increased DNA methylation levels of SRD5A2 and CYP11A1 detectable in plasma have prognostic value for noninvasive monitoring of BCR in patients after RP. Urine specimens are in direct contact with bladder tissue, and the application of quantitative MSPCR can preliminarily find that DNA methylation in urine samples is an early event of BCa. Until now, there are many studies concerned about urinary methylation biomarkers. A study carried out by Isabel Renard et al. showed that methylation of TWIST1 and NID2 genes is associated with BCa. TWIST1 is an anti-apoptotic and metastasis-promoting basic helical transcription factor. NID2 is a ubiquitous multidomain basement membrane protein that serves the important role of establishing and maintaining basement membrane and tissue architecture [32]. In the diagnosis of BCa, the sensitivity of this combination of TWIST1 and NID2 (90%) was significantly better than that of cytology (48%), with comparable specificity (93% and 96%, respectively). The positive and negative predictive values of the two-gene combination were 86% and 95%, respectively [33]. This study is the first to find TWIST1 hypermethylation in urine from BCa patients as compared with healthy people. The detection of TWIST1 and NID2 gene methylation in urine samples by MSPCR provides a highly sensitive (>90%), specific, and noninvasive method for the detection of primary BCa. Previous studies have investigated bladder cancer-specific methylation biomarkers and found that MSPCR and quantitative MSPCR on urine samples can be used to differentiate patients with BCa from healthy people. In a study by Thomas Reinert et al., the

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levels of DNA methylation were quantified in urine samples from patients with BCa and patients with benign prostatic hyperplasia (BPH) or bladder stones. The methylation difference between urine from BCa patients and healthy people was significant in ZNF154 (P < 0.0001), HOXA9 (P < 0.0001), POU4F2 (P < 0.0001), and EOMES (P = 0.0005). These methylations are promising cancer markers for the early detection of BCa [34]. CfDNA present in urine is susceptible to degradation without proper storage and transportation from the clinic to molecular biology laboratory. So, urine biopsy requires new technologies to maintain the integrity and fidelity of the sample. If the urine sample is not properly stored or transported, false positives or false negatives can affect the diagnosis, prognosis, and monitoring of urothelial carcinoma. In the future development of this field, researchers must face the problem. 2.2

Gene Mutation

In the field of genetic mutations, many promising biomarkers have been identified, including genomic alterations and transcriptional subtypes. Multiple studies have focused on using urine samples to monitor hotspot mutations, with frequent activating mutations found in telomerase reverse transcriptase (TERT) promoter as well as in fibroblast growth factor receptor 3 (FGFR3) and phosphatidylinositol-3-kinase catalytic subunit alpha (PI3KCA) [35, 36], mainly from nonmuscle invasive bladder cancer (NMIBC) patients [37]. Activating mutations in the TERT gene promoter are found in most muscle-invasive urothelial carcinomas. To investigate their role in the development of BCa and to evaluate their role as urinary markers for early detection, Isaac Kinde et al. sequenced TERT promoters in 76 papillary and flat noninvasive urothelial carcinomas. TERT promoter mutations were founded in 56/76 (74%) of these patients and were not detected in adjacent normal urothelium. Isaac Kinde et al. showed that TERT promoter mutations were somatic and limited to the neoplastic urothelium in the bladder [38, 39]. The mutations of the TERT promoter appear in both papillary and flat lesions, are the most common genetic alterations found in noninvasive precursor lesions of the bladder, are detectable in urine, and appear to be associated with the recurrence of BCa. These results suggest that TERT promoter mutations may provide a useful urinary biomarker for the early detection and monitoring of BCa. Further studies of cfDNA mutations in advanced urothelial carcinoma using a large cfDNA platform have also uncovered mutational lesions in several potential actionable targets. In a retrospective study, Grivas et al. analyzed the cfDNA of 124 patients with advanced urothelial carcinoma and found relative genetic alternations, especially the DNA repair genes BRCA1 and RAF1. BRCA1 and RAF1 alterations were associated with worse overall

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survival (OS) (hazard ratio (HR) 2.48, p = 0.07; HR 4.87, p = 0.007) and failure-free-survival (FFS) (HR 2.35, p = 0.016; HR 2.40, p = 0.047) [40]. These genes are negatively associated with the prognosis of disease and have potential value in suggesting a negative disease prognosis. In a recent study, Emile Christensen et al. investigated FGFR3, TERT promoter, and stromal antigen (STAG2) gene mutations as markers for the diagnosis and follow-up of NMIBC. The mutations of cfDNA in urine were detected by digital PCR. The sensitivity and specificity of these markers in relation to clinical outcomes can be used as a criterion for detection efficiency. The single marker (TERT) assay had a sensitivity of 87% and a specificity of 77%. The combined detection of the two biomarkers (TERT+FGFR3) increased the specificity to 100%, with a sensitivity of 72%. The results showed that tumor-specific structural variants were detected in urine samples, even in NMIBC patients, and in patients with high levels detected prior to progression to muscle invasive bladder cancer (MIBC) [41]. In this proof-of-concept study, Emil Christensen et al. showed that plasma- or urine-based surveillance of tumor aggressiveness can be viable in patients with FGFR3 mutations. Based on the results, applying the combination of markers with simpler detection procedures will lead to developing diagnosis procedures for BCa with high specificity and sensitivity. Clinically, some detection methods based on liquid biopsy have been included in the National Comprehensive Cancer Network (NCCN) Guidelines for Prostate Cancer Early Detection since 2020. Among them, the Mi-Prostate scores (MiPS) include measurements of PCA3 and TMPRSS2: ERG gene expression in urine samples. According to its quality and accuracy, the detection of PCA3 in urine samples has been approved by the FDA as a novel diagnostic tool for PCa [42]. PCA3 levels in urine samples have also been associated with tumor volume burden and extracapsular extension and provide prognostic information after RP [43]. Thanks to recent advances in molecular genetics techniques, monitoring genetic changes in cfDNA is not very difficult. Several studies of “Catalog of Somatic Mutations in Cancer” (COSMIC) confirmed that circulating cfDNA analysis can be an excellent prognostic and diagnostic tool [44–46]. One of the most promising applications of cfDNA is treatment response monitoring [47–49]. For real-time tracking, genetic changes in tumor cfDNA can be used to personalize medicine to select the best treatment [50]. While diagnostic accuracy for specific cfDNA mutations is high, detecting rare variants can be challenging due to tumorspecific mutations account for only 0.01% of total cfDNA. Recently, one trend, seeking to assess holistic chromosomal structure instability rather than individual changes, has emerged. This may prove to be useful for diagnosis, especially in patients with rare variants.

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MicroRNA MiRNAs are small single-stranded RNA molecules, with 19 to 23 nucleotides in length. Some miRNAs only found in cells and tissues were also found in extracellular fluids, such as serum, plasma, and urine [51]. The levels and composition of these extracellular miRNAs show changes that are strongly associated with normal and neoplastic status [52]. These findings show that cell-free miRNAs can serve as informative biomarkers to assess and monitor the pathophysiologic status of the human body. Since the discovery of miRNAs involved in tumor tissue, miRNAs have been shown to exist in various body fluids with remarkable stability [53, 54]. The advantages of using miRNAs in cancer diagnosis include low cost and ease of the assays used for miRNAs analysis, as well as rapid results by using PCR-based methods. Serum miR-21 has been reported to be a useful biomarker during disease progression in PCa patients. In a study carried out by Hailiang Zhang et al., serum samples were collected from 56 patients, including localized PCa, androgen-dependent prostate cancer (ADPC), hormone-refractory prostate cancer (HRPC), and BPH. The HRPC patients received docetaxel-based chemotherapy. The serum miR-21 level of HRPC was higher than that of ADPC and localized PCa, and the serum miR-21 level of ADPC with low serum PSA level was similar to that of localized PCa and BPH. Serum miR-21 levels were positively correlated with serum PSA levels in ADPC and HRPC (P = 0.012 and 0.049, respectively). Levels of serum miR-21 were higher in the HRPC patients than in the ADPC patients (P = 0.032) [55]. Comparing serum miR-21 with PSA, high serum PSA levels are considered a marker of a large tumor burden in PCa, elevated PSA levels are almost a harbinger of disease progression, and decreased PSA levels usually indicate disease remission. Serum miR-21 levels have similar changes to serum PSA levels. MiR-21 has potential as a marker of hormone-refractory disease transformation and a predictor of response to docetaxelbased chemotherapy. Alterations in plasma miRNA levels may also be useful predictors for identifying PCa patients with different risks of disease aggressiveness. Jing Shen et al. reported that miRNA levels in plasma could differentiate patients by aggressiveness in 82 PCa patients. The results showed that the expression of miR-20a and miR-21 in serum was significantly increased in the patients with high-risk (PSA level > 20 ng/mL, or a GS of ≥8). The expression of miR-21 and miR-145 in serum was significantly increased in the patients with intermediate- (PSA level >10 ng/mL but 0.3 and P < 0.05 were adopted to identify differential metabolites between S-DFS and L-DFS groups. The KEGG (homo sapiens) pathway library was referred for pathway analysis and enrichment analysis based on differential metabolites.

3.7 Statistical Analyses

Differences between groups were compared with ANOVA, Pearson’s chi-square test, Mann–Whitney U test, or Kruskal–Wallis test based on data characteristics. Survival analyses were performed using univariable and multivariable Cox proportional hazards regression models. A two-tailed P-value of 0.3 and P < 0.05, we identified 29 differential aqueous metabolites between S-DFS and L-DFS groups (Table 2) but no differential lipid metabolites. These

Table 1 Patient characteristics Plasma metabolomics Short-survival (n = 26)

Long-survival (n = 37)

P

Age, years

61.7 ± 8.3

62.9 ± 8.3

0.55

Gender (female)

15 (57.7)

16 (43.2)

0.26

Body mass index

24.8 ± 2.6

24.9 ± 3.6

0.90

Smoking history

11 (42.3)

14 (37.8)

0.46

Sublobar resection

1 (3.8)

11 (29.7)

0.024

Lobectomy

25 (96.2)

26 (70.3)

2.2 ± 0.5

2.0 ± 0.7

0.21

22/2/2 (84.6/7.7/7.7)

35/2/0 (94.6/5.4/0)

0.21

I /II/III

13/3/10 (50.0/11.5/38.5)

36/1/0 (97.3/2.7/0)

0.3, P < 0.001

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Fig. 1 The unsupervised clustering heatmap based on differential metabolites in ESI+ (a) and ESI- (b) modes

differential metabolites included 16 metabolites detected in ESI+ mode, 10 metabolites detected in ESI- mode, and 3 metabolites (inosine, thelephoric acid, and bilirubin) detected in both ionization modes. Amino acids and organic acids constitute the majority of these metabolites. The unsupervised clustering heatmap based on differential metabolites in ESI+ mode (Fig. 1a) demonstrated the clustering of thelephoric acid, L-cystine, L-histidine, dictamnine, and skimmianine in the S-DFS group and the clustering of

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Table 3 Pathway analysis based on the identified differentiated metabolites Metabolic pathway

-Log10(P)

Impact

Cysteine and methionine metabolism

1.8819

0.13818

Arginine biosynthesis

1.7288

0.07614

Aminoacyl-tRNA biosynthesis

1.4469

0

Purine metabolism

1.1191

0.03102

Glycine, serine, and threonine metabolism

1.0425

0.05034

Valine, leucine, and isoleucine biosynthesis

0.93832

0

Arginine and proline metabolism

0.92954

0.05786

Histidine metabolism

0.65383

0.22131

Pantothenate and CoA biosynthesis

0.58858

0

Citrate cycle (TCA cycle)

0.56942

0.04634

Beta-alanine metabolism

0.55134

0

Pyruvate metabolism

0.53423

0.20684

Glycolysis / gluconeogenesis

0.47401

0.10044

Alanine, aspartate and glutamate metabolism

0.44795

0

Porphyrin and chlorophyll metabolism

0.42408

0.05288

Glyoxylate and dicarboxylate metabolism

0.40212

0

Glycerophospholipid metabolism

0.36301

0.00937

Valine, leucine and isoleucine degradation

0.32919

0

Tryptophan metabolism

0.32143

0.05157

Tyrosine metabolism

0.31393

0

Fatty acid biosynthesis

0.27977

0

Pathway analyses were based on the KEGG (Homo sapiens) pathway library

inosine and hypoxanthine in the L-DFS group. The unsupervised clustering heatmap based on differential metabolites in ESI- mode (Fig. 1b) only demonstrated the clustering of thelephoric acid in the S-DFS group. Using the KEGG (Homo sapiens) pathway library, the metabolic pathway analysis of 29 differential metabolites indicated mostly amino acid metabolism pathways, especially the cysteine and methionine metabolism and arginine biosynthesis (Table 3 and Fig. 2a). Other metabolic pathways were aminoacyltRNA, purine, glucose, and lipid metabolism pathways. The enrichment analysis using KEGG (Homo sapiens) pathway library also highlights the cysteine and methionine metabolism and arginine biosynthesis (Fig. 2b).

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Fig. 2 Metabolic pathway analysis (a) and enrichment analysis (b) of differential metabolites based on the KEGG (Homo sapiens) pathway library 4.3 Prognostic Values of Metabolites

Considering the nonnormal distribution of peak intensities of metabolites, we classified the peak intensities based on median and tertile values to observe prognostic values. The univariable Cox proportional hazards regression models (Table 4) confirmed the predictive values of 18 metabolites for DFS (P < 0.05). Another 9 metabolites also showed potential predictive values for DFS (P < 0.10). The elevated intensities of thelephoric acid, phosphocholine, dictamnine, L-cystine, creatinine, N-acetylornithine, L-arginine, hydroxyphenyllactic acid, and pyruvic acid were associated with shorter DFS (hazard ratio (HR) > 1.00, P < 0.05). The elevated intensities of inosine, 3-hydroxyanthranilic acid, betaine, hypoxanthine, xanthine, D-erythrose, bilirubin, heptadecanoic acid, and 4-hydroxybenzoic acid were associated with longer DFS (HR < 1.00, P < 0.05). After adjusting for the cancer stage, the most important prognostic factor, only the phosphocholine and xanthine were still predictive of DFS (HR 3.40, P = 0.011; HR 0.41, P = 0.037).

4.4 Metabolites and Cancer Progression

Based on the mentioned prognosis analysis, the prognostic values of most metabolites were attenuated by the cancer stage, indicating that the changes of these metabolites in plasma may be associated with cancer progression. To elucidate these issues, we then investigated the association between metabolite intensity and tumor diameter, lymph node metastasis, and TNM stage. The intensity of thelephoric acid was positively associated with tumor diameter (P = 0.034), while the intensities of homocysteine (P = 0.077) and 3-hydroxyanthranilic acid (P = 0.082) showed a negative association. However, none of these metabolites were significantly associated with lymph node metastasis. Regarding the TNM stage

Table 4 Predictive value of metabolites for disease-free survival based on Cox proportional hazard regression Univariable analysis

Multivariable analysis

Metabolites

Cut-offs

HR (95% CI)

P

HR (95% CI)

P

Inosine

Median

0.43 (0.19–0.97)

0.041

0.50 (0.22–1.13)

0.096

Homocysteine

Tertiles

0.46 (0.19–1.15)

0.097

3-Hydroxyanthranilic acid

Tertiles

0.29 (0.087–0.97)

0.045

Thelephoric acid

Median

2.95 (1.28–6.79)

0.011

Betaine

Median

0.44 (0.20–0.99)

0.046

Hypoxanthine

Tertiles

0.33 (0.13–0.91)

0.032

0.40 (0.15–1.06)

0.066

Phosphocholine

Median

4.98 (1.99–12.5)

0.001

3.40 (1.32–8.74)

0.011

Bilirubin

Tertiles

0.39 (0.15–1.03)

0.057

Xanthine

Median

0.33 (0.15–0.77)

0.010

0.41 (0.18–0.95)

0.037

N-Acetylleucine

Median

0.47 (0.21–1.06)

0.069

Dictamnine

Tertiles

2.40 (1.11–5.18)

0.026

L-Cystine

Tertiles

2.87 (1.33–6.22)

0.008

Skimmianine

Tertiles

1.90 (0.88–4.12)

0.10

Creatinine

Median

2.53 (1.13–5.69)

0.025

2.08 (0.92–4.69)

0.080

L-histidine

Median

2.00 (0.91–4.42)

0.085

D-Erythrose

Median

0.43 (0.19–0.96)

0.039

L-Theanine

Tertiles

1.95 (0.90–4.22)

0.091

Triethanolamine

Tertiles

0.49 (0.23–1.05)

0.068

L-valine

Median

0.49 (0.22–1.10)

0.083

Bilirubin [ESI-]

Median

0.33 (0.14–0.79)

0.013

Inosine [ESI-]

Tertiles

0.34 (0.13–0.91)

0.032

Thelephoric acid [ESI-]

Median

3.19 (1.45–7.06)

0.004

Gallic acid

Median

2.05 (0.93–4.53)

0.075

N-Acetylornithine

Median

3.11 (1.34–7.16)

0.008

Galacturonic acid

Tertiles

2.03 (0.81–5.05)

0.13

Heptadecanoic acid

Tertiles

0.44 (0.20–0.96)

0.038

16-methyl Heptadecanoic acid

Tertiles

0.73 (0.33–1.58)

0.42

L-arginine

Median

2.93 (1.27–6.74)

0.012

Hydroxyphenyllactic acid

Median

2.46 (1.10–5.54)

0.029

Pyruvic acid

Median

2.51 (1.12–5.63)

0.026

2.18 (0.97–4.91)

0.061

Myristic acid

Tertiles

0.60 (0.27–1.30)

0.20

4-Hydroxybenzoic acid

Tertiles

0.35 (0.16–0.75)

0.007

0.51 (0.23–1.13)

0.096

The peak intensity of metabolites was grouped according to the median or tertile values Multivariate Cox proportional hazards regression models with the backward conditional method were used to adjust for age, gender, and cancer stage HR hazard ratio, CI confidence interval

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classified as IA1, IA2, IA3-IB, and II-III, the intensities of thelephoric acid (P = 0.001), phosphocholine (P = 0.027), L-theanine (P = 0.096), and N-acetylornithine (P = 0.064) were positively associated with advanced stage while the intensities of inosine (P = 0.039), homocysteine (P = 0.008), 3-hydroxyanthranilic acid (P = 0.007), hypoxanthine (P = 0.049), xanthine (P = 0.003), 16-methyl heptadecanoic acid (P = 0.049), myristic acid (P = 0.047), and 4-hydroxybenzoic acid (P = 0.044) showed negative association. In conclusion, plasma metabolomics is capable of identifying prognostic metabolites and abnormal metabolic pathways in lung cancer patients. It is promisingly highlighted for mechanism research, drug development, and precise treatments.

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Chapter 13 The Detection of Exosomal PD-L1 in Peripheral Blood Rui Wang, Yanjia Yang, Jiajun Huang, and Yandan Yao Abstract Peripheral blood is a source for liquid biopsy, which can meet the requirements of pretreatment disease typing to determine precise targeted therapy and monitoring of posttreatment minimal residual disease monitoring. Compared with ctDNA and CTC, exosomes have a higher concentration, good biostability, biocompatibility, low immunogenicity, and low toxicity in peripheral blood. Tumors generally secrete a large amounts of exosomes, which have potential pathophysiological roles in tumor progression. With the continuous improvement of liquid biopsy technology, many researchers have found that exosomes are the key for tumor PD-L1 to exert its role, which may be the mechanism that leads to PD-L1 and/or PD-1 inhibitor therapy resistance. Namely, tumor-derived exosomes may mediate systemic immunosuppression against PD-1 or PD-L1 inhibitor therapy, endogenous tumor cell–derived exosomal PD-L1, and tumor microenvironment–derived exosomes. Induction of PD-L1 by exosomes may be a crucial mechanisms of exosome-mediated antitumor immune tolerance. This article reviews the relationship between the detection of peripheral blood exosomal PD-L1 and tumor progression and the mechanism of exosomal PD-L1 in tumor immunotherapy. Key words Exosomes, Liquid biopsy, PD-L1, Immunotherapy, Tumor

1 Introduction In recent years, with the application of liquid biopsy in tumor diagnosis and treatment, including the application of cancer diagnosis, screening, treatment, and prognosis monitoring or risk assessment, liquid biopsy has become a new method in the field of cancer management [1]. Although tumor tissue biopsy remains the gold standard prescribed in the field of disease diagnosis, it helps to provide evaluation and diagnosis of cancer type, tumor differentiation, aggressiveness, tumor mutation status, and

Authors’ Contributions: Rui Wang conceived the study hypothesis and drafted the manuscript. Yanjia Yang reviewed the literature and drafted the manuscript. Jiajun Huang contributed to drawing the figures and drafting the manuscript. Yandan Yao conceived the study hypothesis, revised it critically for important intellectual content, and supervised the writing of the manuscript. All the authors read and approved the final manuscript. Tao Huang et al. (eds.), Liquid Biopsies: Methods and Protocols, Methods in Molecular Biology, vol. 2695, https://doi.org/10.1007/978-1-0716-3346-5_13, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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prognosis, which is beneficial to personalized diagnosis and treatment. However, tumor tissue biopsy is invasive, one-time use, and only reflects the current condition of the tumor tissue. With the evolution of tumor clones, tissue biopsy cannot monitor its characteristics in real time, hence recyclable test specimens are needed. Liquid biopsy uses patient body fluids, such as peripheral blood, saliva, urine, and cerebrospinal fluid. It is a noninvasive diagnostic method with little or no trauma, and by detecting specific indicators, the patient’s physical condition can be monitored in real time. It is more comprehensive for cancer chemotherapy and monitoring of disease treatment response, where the utilization of peripheral blood exosome as a type of liquid biopsy has attracted wide attention. Since exosomes are lipid bilayer vesicles, the substances they carry are not degraded or affected by surrounding substances. The contents of tumor-derived exosomes are partially derived from the parental tumor cell surface membrane and cellular endosomes, hence tumor-derived exosomes can be distinguished from normal cell–derived exosomes [2]. Exosomes were extracted from the serum of cancer patients, and the experiment was performed to verify the expression of various immunosuppressive molecules on the surface of exosomes, including checkpoint receptor ligands PD-L1, FasL, TRAIL, or inhibitory cytokines including IL-10, TGF-β1, etc. [2]. This review discusses the fluid detection of exosomal PD-L1 in peripheral blood in recent years and its relationship with tumor staging and progression, in addition to outlining the mechanism of tumor-derived exosomal PD-L1 in tumor immunotherapy.

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Exosomes as Tumor Biomarkers Liquid biopsy includes ctDNA, CTCs, exosomes, and extracellular free nucleic acids (DNA and RNA). Exosomes are released into the extracellular nanoscale extracellular lipid bilayer by exocytosis, with secreted organelles of approximately 30 to 200 nm in diameter, which have the same topology as the cells, in addition to being enriched in selected sugars, proteins, lipids, and nucleic acids [3]. This method of information exchange through intercellular vesicle transport affects various aspects of human health and disease progression, including human organ development, cancer development, immune diseases, neurodegenerative diseases, etc. Exosomes carry a variety of information about the parental cells, hence exosomes as biomarkers for various types of tumors have become a hot spot in tumor research and translation. Exosomes are produced more frequently in tumor cells than in normally proliferating cells [4, 5]. The contents of exosomes, which include proteins and nucleic acids (DNA, miRNA, lncRNA, circRNAs, mRNA), etc. (Fig. 1), they play a very important role in

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Fig. 1 Biogenesis of exosomes and its contents. Exosomes originate from multivesicular endosome (MVE), which carry a variety of proteins and nucleic acids, such as DNA, mRNA, and noncoding RNA (miRNA, lncRNA, circRNA, etc.). These substances play a key role in tumor invasion and metastasis

cancer progression and can be used as the basis for the prognosis, treatment, and staging of tumor patients [6]. Some studies on exosomes, including nucleic acids and protein, will be described as follows. 2.1 Exosomal Noncoding RNAs

miRNAs carried by exosomes are closely related to various cancer processes. A large number of literatures have proven their application that they are used as biomarkers in the early screening, investigation, diagnosis, and prognosis of various cancers including breast cancer, lung cancer, liver cancer, etc. [7, 8]. Peripheral blood exosomal miR-5684 and miR-125b-5p in non-small-cell lung cancer (NSCLC) patients were significantly lower than those in healthy donors [9]. Exosomal miRNA-196a and miRNA-1246 have been reported to be significantly increased in pancreatic cancer–derived exosomes. Exosomal miRNA-191, miRNA-21, miR-451a, and miRNA-483-3p show significant increase in liquid biopsies of pancreatic cancer patients [10–12]. Combinatorial detection of many miRNAs contained in exosomes can be used as a noninvasive liquid biopsy marker for different hematological malignancies such as leukemia, multiple myeloma, and lymphoma [13]. Exosomal miRNAs that can be used as noninvasive assays in patients with primary colorectal cancer include let-7a, miR-1229, miR-1246, etc. The expression of these miRNAs was significantly elevated in peripheral blood circulating exosomes of patients with early-stage colorectal cancer. The secretion levels of these markers were significantly

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increased in the peripheral blood of patients with colon cancer compared with healthy controls and were downregulated after surgical tumor resection [14]. It has been reported in the literature that the levels of exosomal miRNA-301a and miRNA-21 in the peripheral blood of patients can reflect the state of gliomas and the pathological changes of breast cancer bone metastases, respectively, and the expression of exosome-transported miRNA can be used as a new-generation indicator to diagnose early-stage cancers such as gliomas, in addition to the prognosis of advanced cancers, which is also a potential target for clinical diagnosis and treatment [15, 16]. All of the above mentioned evidence suggest that tumor cells–derived exosomal miRNAs can serve as noninvasive novel biomarkers for tumor screening, prognosis, or precision therapy. Recent evidence in the field suggests that miRNAs could be responsible for tumor progression and metastasis, in addition to modulating immune responses and the ability of tumor cells to be sensitive to chemotherapeutic drugs. Similarly to miRNAs, long noncoding RNAs (lncRNA) are also stable in the environment, making it easy to be extracted from exosomes. Yiran Liang et al. found that lncRNA BCRT1 may serve as a potential biomarker and therapeutic target for breast cancer [17]. Yu Rim Lee et al. showed that the higher the circulating levels of exosomal miRNA-21and lncRNA-ATB in patients with hepatocellular carcinoma (HCC), the lower the overall survival and progression-free survival. They demonstrated that circulating exosomal noncoding RNAs (miRNA-21 and lncRNA-ATB) could serve as novel therapeutic biomarkers and targets for HCC [18]. Amro Baassiri et al. summarized exosomal lncRNAs in serum including UCA1, CCAT2, RPPH1, CRNDE-h, GAS5, HOTTIP, etc., which could serve as diagnostic and prognostic biomarkers of colorectal cancer [19]. Although the current research on exosomal lncRNAs as tumor biomarkers is still in its infancy, they demonstrate promise as new prognostic biomarkers and new therapeutic targets for breast cancer [20]. Circular RNAs (circRNAs) are a class of endogenous noncoding RNAs with high conservation and stability. It has become the potential biomarkers for tumor diagnosis. Yezhao Wang et al. reported that several circRNAs and exosomal circRNAs have been used as diagnostic tool for gastrointestinal cancers screening including hsa_circ_0000190, hsa_circ_0000140, hsa_circ_0130810 (circ-KIAA1244), hsa_circ_0000745, circ-PSMC3, etc. [21]. Since the development of liquid biopsy technology and the emergence of various detection technologies, tumor-derived exosomal circRNAs have also become a hot spot in the research of new tumor biomarkers. Compared with traditional monitoring methods, exosomes are used as the carrier of detection substances, which is expected to more comprehensively reflect the tumor mutation status and tumor specificity, and can be used as a biomarker to overcome the tumor heterogeneity faced by tissue biopsy [22].

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Exosomal mRNA

2.3 Exosomal Proteins

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The mRNA in exosomes secreted by tumor cells contains many genetic materials similar to the source of tumor cells. Some of these substances are involved in cell cycle regulation, and some are related to important cell activities such as chromosome segregation, proliferation, and migration. Therefore, the detection of exosomesspecific mRNAs in vivo can be used to diagnose tumors, assess tumor progression, and monitor treatment response. Jin Ji et al. developed and optimized a strategy for detection with circulating exosomal mRNA and identified some novel exosomal mRNAs that could be used to detect prostate cancer [23]. In other studies of prostate cancer and exosomal mRNA, there is also abundant evidence that mRNAs are involved in cancer progression or remodeling of the tumor microenvironment, revealing that tumor-derived exosomal mRNAs play a very important role in tumor progression. An article revealed that exosomal mRNA expression of PD-L1 and IFN-γ was associated with immunotherapy response to PD-1 inhibitors in NSCLC. It showed that higher levels of exosomal IFN-γ mRNA were associated with shorter median PFS. Furthermore, exosomal PD-L1 mRNA levels in patients with tumor progression increased at 3 months versus Baseline [24]. The findings mentioned above suggest that tumor-derived circulating exosomal mRNA can effectively diagnose tumors and evaluate tumor prognosis. Tumor-derived exosomes are rich in proteins that are novel biomarkers for cancer diagnosis, therapeutic efficacy, cancer prognosis assessment, and monitoring of cancer. Tumor-derived exosomal proteins are expressed differently in different tumors. The detection of exosomal proteins in blood has high sensitivity, which is beneficial to the early diagnosis of cancer. The exosomal proteins reported in the literature include transmembrane proteins, membrane transport, membrane fusion–related proteins, MVE-related proteins, and other proteins, such as cell adhesion–related proteins [6]. Li et al. summarized the relationship between tumor-derived exosomal proteins as novel diagnostic and prognostic markers and various cancers including pancreatic cancer (GPC1, MIF), ovarian cancer, breast cancer, lung cancer etc. [25]. Researchers have isolated exosomes which carry PD-L1 and suppress T cell function from the peripheral blood patients with head and neck squamous cell carcinoma (HNSCC) patients to assess the potential contribution of PD-L1+ exosomes for immunosuppression and disease activity [26]. The findings suggest that it is not the level of sPD-L1, but the level of PD-L1 on exosomes that correlates with disease progression in HNSCC patients. Peripheral blood PD-L1+ exosomes become a useful indicator for monitoring of disease progression and immune activity in HNSCC patients [26]. Chen et al. has proven that the detection of circulating exosomal PD-L1 could distinguish clinical responders from non-responders and could be used as an indicator of the adaptive response of patient tumor cells

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to regenerative T cells [27]. Cordonnier’s team discovered through enzyme-linked immunosorbent assay kinetics that melanoma cells from 100 melanoma patients secreted exosome-PD-L1 via exosomes, confirming the relationship between exosome PD-L1 and tumors [28]. The above mentioned findings all indicate that exosomes circulating in the body fluids of tumor patients can selectively carry and enrich the characteristic proteins of tumor lesions. Detection of PD-L1 expression in exosomes appears to be more reliable than detection of PD-L1 expression in tumor tissue. Monitoring circulating exosomal PD-L1 may be useful in assessing the response to therapy in clinical cancer patients.

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Detection of Exosome PD-L1 in Peripheral Blood Circulating exosomal PD-L1 is a special tumor biomarker. There needs to be a method to detect exosomes in peripheral blood. Although there is no standard detection method at this time, with the development of experimental technology and the exploration of researchers, both traditional detection methods and new detection methods have been applied in experiments (Table 1 and Fig. 2).

Table 1 Different detection methods of exosome PD-L1 and their advantages and disadvantages Detection method

Advantage

Disadvantage

ELISA

High stability and repeatability

Exosomes need to be separated from peripheral blood, low sensitivity

Droplet digital PCR

High sensitivity; absolute quantification

There are some differences between the actual content of protein and the translation of mRNA, and the accuracy needs to be verified

FCM

High efficiency

It is necessary to bind exosomes to beads that are large enough to be individually resolved on the flow cytometer available

SERS

There is no need to separate exosomes from plasma; high sensitivity; low minimum detection limit.

There are few studies using this technology in the field; Small application scope; Inadequate technical maturity

HOLMESHigh sensitivity; low detection limit; less Not for clinical detection Exo-PDsample size; can distinguish free PD-L1 from L1 method exosomal PD-L1

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Fig. 2 Different detection methods of exosomal PD-L1 currently used in research. The exosomes were separated from the peripheral blood of cancer patients by ultracentrifugation or immunoaffinity, and then the content of exosomal PD-L1 was detected. In the study of exosomes, these five detection methods are more mature. Among them, ELISA, droplet digital PCR, and flow cytometry, as traditional detection technologies, are mostly used in clinical research. The other two are only used in basic research because of their immature technology 3.1

ELISA

Enzyme-linked immunosorbent assay (ELISA), as a classical immunological detection method commonly used for the detection of proteins, can be used for exosome detection and has good stability and repeatability. In a previous study, in non-small-cell lung cancer, researchers used the ELISA kit of exon PD-L1 to detect the contents of exosomal PD-L1 and soluble PD-L1 (sPD-L1), study their effects on tumor development, and explore whether the expression levels of exosomal PD-L1 and PD-L1 were the same in the cancer tissues of patients with non-small-cell lung cancer [29]. Guo et al.

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constructed a new detection system based on ELISA to detect exosomal PD-L1 in plasma. It is known that there are both exosomal PD-L1 and free PD-L1 in body fluid [27]. If these two kinds of PD-L1 cannot be separated, it will affect the detection results. 3.1.1

Methods

1. ELISA plates (96-well) (Biolegend) were coated with 0.25 μg per well (100 μL) of monoclonal antibody against PD-L1 (clone 5H1-A3) overnight at 4 °C. 2. Free binding sites were blocked with 200 μL of blocking buffer (Pierce) for 1 h at room temperature. 3. Then 100 μL of plasma samples with or without EV removal, or EV samples purified from plasma or cell culture supernatants, was added to each well. 4. The exosome samples purified from cell culture supernatants were prepared by serial dilution in respect of the total protein level to analyze the enrichment of PD-L1 on exosomes. 5. The exosome samples derived from the plasma samples of healthy donors or melanoma patients were prepared using the same volume of PBS as the plasma as they were originally derived from. 6. The plasma samples with (exosome-excluded) or without (total) exosome removal were diluted with PBS in a 1:0.75 volume ratio. 7. After overnight incubation at 4 °C, biotinylated monoclonal PD-L1 antibody (clone MIH1, eBioscience) was added to each well and incubated for 1 h at room temperature. 8. After overnight incubation at 4 °C, biotinylated monoclonal PD-L1 antibody (clone MIH1, eBioscience) was added to each well and incubated for 1 h at room temperature. 9. Plates were developed with tetramethylbenzidine (Pierce) and stopped with 0.5 N H2SO4. The plates were read at 450 nm with a BioTek plate reader. 10. Recombinant human PD-L1 protein (R & D Systems, Cat# 156-B7) was used to make the standard curve. Recombinant P-selectin protein (R & D Systems, Cat# 137-PS) was used as the negative control to verify the detection specificity.

3.2 Droplet Digital PCR

Droplet digital PCR is a type of nucleic acid detection method with high sensitivity and absolute quantification. In early tumor diagnosis, droplet digital PCR is often used to detect ctDNA to assess the growth of tumors. Tumor exosomes contain not only protein markers but also mRNA and other genetic substances related to expression regulation. Therefore, droplet digital PCR can also be used to detect PD-L1 exosome in body fluid. Danesi et al. used droplet digital PCR to quantitatively analyze the mRNA copy number of PD-L1 in plasma exosomes of patients with melanoma

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and lung cancer and found that exosomal PD-L1 was correlated with tumor treatment effect [30]. However, the translation of mRNA is different from the actual content of protein, so the accuracy of this method needs further verification. 3.2.1

Methods

1. A blood sample of 6 mL was collected in EDTA tubes and centrifuged for 10 min at 1900 g within 2 h. 2. Using the exoRNeasy kit (Qiagen, Valencia, CA) to isolate exosomes and extract RNA. 3. The analysis of PD-L1 mRNA was performed by the QX100 ddPCR (Bio-Rad, Hercules, CA, USA) using the One-Step RT-ddPCR kit. 4. The PrimePCR ddPCR Expression Probe Assay for CD274 (human) was used to assess PD-L1 expression, and the human ß-actin ddPCR assay was used as internal control. 5. Fluorescence signal quantification was performed by the droplet reader and the QuantaSoft software (Bio-Rad). 6. The ratio of positive versus negative droplets was used to determine the number of mRNA copies per mL of the target molecule in the input reaction. 7. Droplets with a fluorescence intensity threshold higher than 4000 were considered positive. 8. Each plasma sample was extracted once, and triplicate ddPCR analyses were performed per sample.

3.3

Flow Cytometry

Flow cytometry (FCM) can be used to detect single particles, such as single cell suspension and biological particles. Now it can also be used in the detection of exosomes. Whiteside’s team used beads carrying antibodies to bind to exosomes containing PD-L1 and analyzed the number of exosomes PD-L1 exosomes by flow cytometry. It was proven that the progression of patients with tumors was related to the expression level of exosomal PD-L1 [26]. In the meanwhile, the effectiveness of this method was proven by comparison with the immunofluorescence method. At present, the flow analyzer specially used for exosome detection has also been listed, such as the Apogee A50 Micro flow cytometer (MFC) [31], which is more suitable for the detection of exosome proteins compared with the traditional flow analyzer. Methods 1. In preliminary titration experiments, different concentrations (0.625–5.0 μg) of the fluorochrome-conjugated detection Abs and isotype controls were coincubated with 10 μg of exosome protein and 10 μL of beads to determine the optimal conditions for staining and detection of PD-1 and PD-L1 carried by captured exosomes.

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2. The isotype control was used according to the manufacturer’s instructions and had the same concentration as the test Abs. 3. The optimal Abs concentration (0.5 μg) that gave the highest separation index between the detection Abs and isotype control (MFI1-MFI2/[√(SD1 + SD2)/2]) upon flow cytometry was selected for all experiments. 4. Detection was performed immediately after staining using the Gallios flow cytometer equipped with Kaluza 1.0 software (Beckman Coulter). 5. Samples were run for 2 min, and around 10,000 events were acquired. 6. Gates were set on the bead fraction visible in the forward/ sideward light scatter. 3.4

SERS

Surface-Enhanced Raman Scattering (SERS) is a common Raman Spectra analysis method to determine the samples adsorbed on the surface of colloidal metal particles. At present, it has been applied in the field of biomedicine. In the study of Xiao’s team, firstly, TiO2 was used to adsorb the exosomes in plasma, and then SERS probe carrying PD-L1 antibody was used to modify gold nanoparticles and incubate them in exosomes, and then PD-L1 positive exosomes were detected by instrument. This method has high sensitivity and low detection limit, and there is no need to separate exosomes from plasma in advance [32]. However, there are few studies on exosomal PD-L1 using this technology, and the practical clinical application needs to be further explored. Methods 1. Exosomal PD-L1 quantification by the SERS immunoassay, the model exosome solution from the plasma samples was diluted to the appropriate concentration, and then the model exosome solution was incubated with 0.8 mg Fe3O4@TiO2 nanospheres and incubated for 5 min at room temperature under mild shaking to allow sufficient attachment. 2. After enrichment, the Fe3O4@TiO2/exosome complex was magnetic separated. Then, 10 μL of SERS tags (50 mg/mL) was dropped to incubate with the complex for 30 min at 37 °C. 3. Finally, the Fe3O4@TiO2/exosome/SERS tags were magnetic separated and then washed twice with PBST. 4. Raman signals of 1074 cm-1 were collected by using a portable Raman system B & W Tek, i-Raman Plus BWS465–785H spectrometer. 5. Samples were excited by a 785 nm laser with a power of 25 mW and a total acquisition time of 20 s for each SERS spectrum.

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HOLMES-Exo PD-L1 method is a new method for the detection of exosomal PD-L1 protein. This method designed a new MJ5C aptamer against PD-L1, which can efficiently bind PD-L1 protein on exosome membrane without interference from PD-L1 protein glycosylation. In this method, the fluorescent-labeled MJ5C aptamer was co-incubated with exosomes in a fine capillary tube, and the infrared laser was gathered to the fine capillary tube to form a micron-level temperature gradient. The thermophoresis rate of free MJ5C aptamer is different from that of exosome MJ5C aptamer complex. Compared with exosome aptamer complex, the thermophoresis depletion of free aptamer is fast, and the free aptamer is separated from the reaction system. Therefore, under the action of excitation, the content of exosomal PD-L1 protein can be determined by detecting the fluorescence intensity of fluorescent labeled aptamer [33]. This method has the advantages of high sensitivity, low detection limit, small sample size, and short detection time. It has enlightening value for the development of new exosome PD-L1 protein detection method, but right now it is not suitable for clinical detection.

Relationship Between Exosomal PD-L1 and Tumor Progression Cargoes carried by exosomes play a pivotal role in regulating tumor clonality, invasion, invasiveness, angiogenesis, and tumor metastasis in the process of tumorigenesis and development. Some research studies were found that the increase of exosomal PD-L1 expression was positively correlated with the increase of size of breast cancer, activity of disease, and the increase of overall clinical stage. Meanwhile, with the increase of tumor grade, the number of exosomal PD-L1 co-localization increased [34]. The expression of exosomal PD-L1 has also been shown to be related to the tumor volume of glioblastoma and the clinical stage and level of lymph node involvement level of head and neck squamous cell carcinoma [26]. Exosome PD-L1 exerts immunosuppressive function and promotes tumor development [35]. Exosomal PD-L1 can bind with anti-PD-L1 antibody, which makes the antibody unable to reach the tumor site and produce immunotherapeutic resistance. Recent studies have shown that, in the TRAMP-C2 prostate cancer model, PD-L1 treatment cannot influence tumors in mice due to the presence of exosomal PD-L1. On the contrary, by knocking out the Rab27 and nSMase2 genes in mice, the exosomes of tumors in mice could not be produced, and the growth of prostate cancer cells was limited [27]. In the 4 T1 breast cancer model, the researchers found that exocrine PD-L1 was aggregated in the tumor microenvironment, inhibiting the function of T cells by inhibiting the

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production of GzmB in T cells, resulting in immunotherapy tolerance [34]. In the study of prostate cancer by Poggio et al [36], at the animal level, by blocking the release of exosomal PD-L1, it was found that it was unable to only inhibit the growth of local tumor cells but also inhibited the growth of distal tumor cells at the same time or after a few months. Through the combined treatment of anti-PD-L1 antibody and reducing the secretion of exosomal PD-L1, tumor mice have a longer survival time. Moreover, in this study, it was found that after transplanting tumors without exosomal PD-L1 expression and then generating antitumor memory immunity, immune cells are no longer immunosuppressed by exosomal PD-L1, so they have the ability to attack tumors. Existing studies show that inhibiting exosomal PD-L1 seems to have a positive effect on tumor progression, but further research and more and more accurate basis are needed to truly clarify the relationship between exosome PD-L1 and tumor progression.

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The Role of Exosomal PD-L1 in Tumor Immune Escape and Immunotherapy Exosomes have lipid bilayers, which can provide shelter for a large number of functional molecules they contain. Exosomes act as a communication bridge between cells in the body by shuttling between tissues and cells. Tumor cells escape the immune surveillance of the body by upregulating the expression of PD-L1 on the surface of their derived exosomes, and PD-L1 interacts with the receptor PD-1 on immune cells to trigger immunosuppression [37]. Although the research and application of anti-PD-1 and anti-PD-L1 antibodies in tumor immunotherapy are overwhelming and promising, they are still tolerated by many patients, so there is an urgent need for better understanding of how exosomal PD-L1 mediats the mechanism of immune escape and for accurate prediction of the corresponding patient’s treatment. The amount and type of PD-L1 packaged in exosomes are different in different cancer types and even different cell lines, which is the premise of its use as a detection indicator [38]: (i) The release of exosomal PD-L1 is influenced by the expression of biogenic proteins, such as transport (ESCRT)-associated protein, ALIX, Rab27a, and nSMase2 [36, 39]; (ii) the metastatic ability of tumor cells to release exosomal PD-L1, for example, more aggressive tumor cells can secrete higher levels of PD-L1+ exosomes [27]. Data from multiple studies confirm that exosomal PD-L1 secreted by tumors can reflect tumor expansion and invasive metastasis to a certain extent. Meanwhile, PD-L1 transported by exosomes in cancer patients is involved in the pathogenesis of various cancers, including melanoma, breast cancer, glioblastoma, pancreatic cancer, gastric cancer, head and neck squamous cell

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carcinoma, etc. [26, 27, 34, 36, 40]. Further studies have shown that coculture of PD-L1+ or PD-L1 high-expressing exosomes with T cells in vitro can reduce the expression of CD69 on the membrane of CD8+ T cells, which is a marker of early T cell activation, resulting in the inhibition of T cell killing activity [26, 40]. In addition, exosomal PD-L1 secreted by tumors can also act on other types of immune cells in the body; what’s more, it can not only exert local immunosuppressive abilities in the microenvironment of its primary tumor but also through peripherally via the circulation of blood or other body fluids acting on the whole body [38]. As previously mentioned, in a variety of cancer types, tumorderived exosomes can bind to PD-1 on the target cell membrane through the PD-L1 they carry, inducing the tumor microenvironment to produce an immunosuppressive phenotype, to make CD8+ T cells dysfunctional and help tumors expand and metastasize. Samantha M Morrissey summarizes exosomal PD-L1 functions in promoting tumor immune escape in multiple mouse and human cancer models [38]. Recent research on the immunoregulatory properties of tumor-derived exosomes has shown that exosomes can act on multiple signaling pathways in target cells to induce immune tolerance through intercellular signaling. Tumor-derived exosomes PD-L1+ immunosuppression may be achieved by inhibiting T cell activation in draining lymph nodes and inhibiting antigen presentation by dendritic cells in the tumor microenvironment or in lymph nodes [2, 36, 41] (Fig. 3). A more in-depth study showed that the interaction between exosome-derived PD-L1 and PD-1 on target cell membranes was dose-dependent. Furthermore, the researchers took a closer look and noticed that tumor cell–derived PD-L1 was upregulated with increased IFN-γ release from activated T cells [27]. Tumor cells produce PD-L1-carrying exosomes, and the interaction of PD-1 on CD8+ T cells can render T cells into a state of exhaustion and dysfunction, and then, the activated PD-1 on the T cell membrane inhibits PI3K/Akt and Ras through the MEK/Erk signaling pathway to attenuate receptor (TCR) activity on T cell membranes involved in activation functions [42–45]. In addition, after PD-1 is activated by exosomal PD-L1, it can activate the phosphatase of PTEN to inhibit the PI3K/Akt signaling pathway, thereby inhibiting T cell activation, which is achieved by inhibiting casein kinase 2 (CK2) [46]. It was also discovered that activated PD-1 could also inhibit the expression of downstream cytokines such as IL2, GzmB, and TNF, which is mediated by downregulating PKC; in addition, activated PD-1 can downregulated TCR/ZAP70/CD3ζ signaling, thereby inhibiting the function of immune cells to kill tumors [47] (Fig. 4). Exosomes can transmit regulatory biological information through processes such

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Fig. 3 Mechanism of tumor-derived exosomal PD-L1 in immune escape. Exosomal PD-L1 promotes tumor growth and metastasis

as recognition and fusion with target cells, thereby affecting the functional activity of target cells. Tumor-derived exosomal PD-L1 contributes to the progression of malignant tumors by acting on the tumor microenvironment and through humoral circulation. The pivotal roles of PD-L1+ exosomes that can be extracted and detected from peripheral blood in tumor immunotherapy, immunosuppression, and disease progression have been elucidated. As mentioned above, the expression level of exosomal PD-L1 is closely related to disease activity, stage, or lymph node status, which is a reliable indicator for diagnosis and detection [26].

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Fig. 4 Exosomes secreted by tumors can inhibit the killing function of T cells. Exosomal PD-L1 combines with PD-1 to inhibit the expression of downstream cytokines such as IL2, GzmB, and TNF to inhibit the killing function of T cells. The combination of exosomal MHC-II and TCR can promote the synthesis of RAS, PI3K, and other proteins and inhibit the killing function of T cells

6

Conclusion Occasionally, false-negative results may occur due to the hyper mutational nature of tumors [48]. Compared with tumor tissue– level detection, liquid biopsy is a noninvasive detection method. More and more research has proven the unique advantages of peripheral blood exosomal PD-L1 detection. The demand exists to establish a detection method of circulating exosomal PD-L1, which has the advantages of simple separation method or no separation, less sample requirement, high sensitivity, good repeatability, and the detection signal is not easily disturbed. The secretion level of PD-L1 in tumor-derived exosomes is highly correlated with cancer diagnosis, staging, metastasis, and immune tolerance. Therefore, further study is needed to explore the implementation of the detection, diagnosis, immunotherapy, and prognosis monitoring of exosomal PD-L1 derived from tumor cells and their microenvironment in various cancer types, so as to lay a reliable foundation for exosomal PD-L1 to become a new biomarker of cancer management [49].

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Acknowledgments This work was supported by grants from the Fundamental Research Funds for the Central Universities (20ykjc03), the National Science Foundation of China (82071859), and Guangdong Innovation and Entrepreneurship Team Projects (2019BT02Y198). References 1. Saini A, Pershad Y, Albadawi H, Kuo M, Alzubaidi S, Naidu S, Knuttinen MG, Oklu R (2018) Liquid biopsy in gastrointestinal cancers. Diagnostics (Basel, Switzerland) 8(4):75 2. Whiteside TL (2016) Exosomes and tumormediated immune suppression. J Clin Invest 126(4):1216–1223 3. Pegtel DM, Gould SJ (2019) Exosomes. Annu Rev Biochem 88:487–514 4. Szczepanski MJ, Szajnik M, Welsh A, Whiteside TL, Boyiadzis M (2011) Blast-derived microvesicles in sera from patients with acute myeloid leukemia suppress natural killer cell function via membrane-associated transforming growth factor-beta1. Haematologica 96(9):1302–1309 5. Dabitao D, Margolick JB, Lopez J, Bream JH (2011) Multiplex measurement of proinflammatory cytokines in human serum: comparison of the Meso Scale Discovery electrochemiluminescence assay and the Cytometric Bead Array. J Immunol Methods 372(1–2):71–77 6. Dai J, Su Y, Zhong S, Cong L, Liu B, Yang J, Tao Y, He Z, Chen C, Jiang Y (2020) Exosomes: key players in cancer and potential therapeutic strategy. Signal Transduct Target Ther 5(1):145 7. Ariston Gabriel AN, Wang F, Jiao Q, Yvette U, Yang X, Al-Ameri SA, Du L, Wang YS, Wang C (2020) The involvement of exosomes in the diagnosis and treatment of pancreatic cancer. Mol Cancer 19(1):132 8. Wang J, Ni J, Beretov J, Thompson J, Graham P, Li Y (2020) Exosomal microRNAs as liquid biopsy biomarkers in prostate cancer. Crit Rev Oncol Hematol 145:102860 9. Zhang Z, Tang Y, Song X, Xie L, Zhao S, Song X (2020) Tumor-derived exosomal miRNAs as diagnostic biomarkers in non-small cell lung cancer. Front Oncol 10:560025 10. Xu YF, Hannafon BN, Zhao YD, Postier RG, Ding WQ (2017) Plasma exosome miR-196a and miR-1246 are potential indicators of localized pancreatic cancer. Oncotarget 8(44): 77028–77040

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Chapter 14 Liquid Biopsy in Hepatocellular Carcinoma Zheyu Zhou, Xiaoliang Xu, Yang Liu, Qiaoyu Liu, Wenjie Zhang, Kun Wang, Jincheng Wang, and Yin Yin Abstract Hepatocellular carcinoma (HCC) is one of the most deadly neoplasms with a poor prognosis. Due to the significant tumor heterogeneity of HCC, alpha-fetoprotein (AFP) or liver biopsy has not yet met the clinical needs in terms of early diagnosis or determining prognosis. In recent years, liquid biopsy techniques that analyze tumor by-products released into the circulation have shown great potential. Its ability to monitor tumors in real time and respond to their global characteristics is expected to improve the management of HCC patients clinically. This review discusses some of the findings of a liquid biopsy in terms of diagnosis and prognosis of HCC. Key words Hepatocellular carcinoma, Liquid biopsy, Circulating tumor cells, Cell-free DNA, Extracellular vesicles, Diagnosis, Prognosis, Biomarkers

1

Introduction Cancer has become the top-three cause of disease-related death in China. According to the newest data posted by the National Cancer Center, liver cancer is the fifth most prevalent of all malignancies. Despite the decreasing incidence and death of liver cancer, its prognosis remains poor [1]. Hepatocellular carcinoma (HCC) accounts for over 70% liver cancer and frequently occurs in advanced liver fibrosis or cirrhosis [2]. Clinically, early detection and monitoring of HCC depend on imaging (e.g., liver ultrasound), serum alpha-fetoprotein (AFP), and liver biopsy. However, imaging has low sensitivity and specificity for lesions smaller than 2 cm. Poor sensitivity limits AFP as a biomarker for early detection [3]. Other serum markers (e.g., AFP-L3, DCP, GPC3) have not been fully validated for clinical use. Meanwhile, liver biopsy is not routinely performed as an invasive test with bleeding and tumor dissemination risk. Due to

Tao Huang et al. (eds.), Liquid Biopsies: Methods and Protocols, Methods in Molecular Biology, vol. 2695, https://doi.org/10.1007/978-1-0716-3346-5_14, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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Fig. 1 The concept of circulating tumor by-products in liquid biopsy

the significant inter- or intra-tumor heterogeneity in HCC, a single biopsy sample may also not represent the entire HCC tumor [4]. Therefore, there is an urgent need to find a reliable for tumor surveillance. Liquid biopsy is a novel diagnostic concept that has emerged in recent years by collecting human non-solid biological tissues (e.g., blood, cerebrospinal fluid, urine) for different analyses. Cerebrospinal fluid can detect tumors in the central nervous system; saliva can detect tumors in the head and neck; and ascites can detect tumors in the abdomen or metastases. In HCC, circulating tumor cells (CTCs), cell-free DNA (cfDNA), and extracellular vesicles (EVs) have become as primary forms of liquid biopsy (Fig. 1). They offer the possibility to demonstrate tumor characteristics fully and respond to tumor variation on time. In this review, we focus on the application of the above three in the diagnosis and prognosis of HCC, hoping to provide some evidence for the clinical management of HCC patients.

2

CTCs in HCC When malignant tumors proliferate and invade adjacent tissues, tumor cells enter further into the circulation by disrupting the basement membrane through the secretion of matrix metalloproteinases [5]. These tumor cells that enter the circulation from primary or metastatic tumor lesions are called CTCs, leading to lethal new metastases. Currently, there are more definitions of CTCs, and the CellSearch definition is recognized as the reference: CTC is a circulating nucleated cell, negative for the leukocyte-specific antigen CD45, and expressing both epithelial cell adhesion molecule (EpCAM) and cytokeratins 8, 18, and 19 [6].

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It is important to note that CTCs can alter their phenotypic and molecular characteristics in response to local microenvironments and therapeutic stress. They are not identical clonal populations but heterogeneous cell populations in different tumor foci [7]. Furthermore, CTC clusters (formed by CTCs and platelets, endothelial cells, leukocytes, or fibroblasts) have a stronger survival and metastatic capacity than individual CTC [8]. Therefore, CTC-based liquid biopsy should be considered as a way to comprehensively understand heterogeneous tumor lesions (Table 1). 2.1 CTCs Detection and Isolation

Different techniques for CTCs isolation have been developed. There are two main categories: biological and physical methods. The former can capture CTCs by antibodies directed against specific tumor-associated antigens on the CTCs surface (positive enrichment) or deplete other cells in the background by antibodies directed against CD45 (negative enrichment) [9]. The CellSearch system is the CTCs diagnostic technology, only approved by FDA, which uses a ferromagnetic microsphere immunoseparation system encapsulated with EpCAM antibodies [10]. In addition, microcolumns encapsulated by antibodies and geometrically arranged form a microfluidic device, which optimizes cell attachment [11]. CTC-iChip, which combines microfluidic and immunomagnetic technologies, has higher detection sensitivity [12]. The latter is due to the physical characteristics of CTCs (e.g., size and density). Filtration, centrifugation, or electrophoresis can separate CTCs [13].

2.2 CTCs as a Biomarker in HCC

CTCs enrichment at the CellSearch system has been used to capture EpCAM+ CTCs in HCC patients. Sun et al. found 67% of patients undergoing tumor resection had EpCAM+ CTCs detected prior to surgery, and a preoperative CTC count greater than or equal to 2 was proved predictive value of postoperative tumor recurrence [14]. Other studies have shown that EpCAM+ CTCs in HCC are related to faster disease progression [15], higher relapse rates [16], and poorer overall survival [17]. In addition, Zhou et al. reported that higher levels of EpCAM+ CTC and regulatory T cells (Treg) contributed to earlier recurrence and poorer clinical outcomes in HCC [18]. However, tumor cells undergo epithelial–mesenchymal transition (EMT), which is characterized by decreased epithelial markers and increased mesenchymal markers. These cells are highly metastatic [19]. Meanwhile, about 35% of HCC patients express EpCAM [20]. Therefore, several alternative tumor markers have been investigated. Asialoglycoprotein receptor (ASGPR) is a transmembrane glycoprotein, which can be found only on the surface of hepatocytes and has been used to detect CTCs in HCC.

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Table 1 Studies on CTCs as biomarkers in HCC patients CTC Marker

Patients

EpCAM

Findings

References

HCC 123 CellSearch BLD 5 HV 10

Identified in 67% of pre-op patients; 28% 1 month after resection; ≥2 CTCs/7.5 mL predicted recurrence

Sun et al. [14]

EpCAM

HCC 89 (after TACE)

CellSearch

Detected in 56% of patients; Shen et al. higher number associated with [15] mortality and progression

EpCAM

HCC 57 (after resection)

CellSearch

Detected in 15% of patients; von Felden positivity associated with et al. [16] higher recurrence and shorter median RFS

EpCAM

HCC 59 BLD 19

CellSearch

Identified in 31% of patients; Schulze et al. associated with advance stage, [17] vascular invasion, and shorter OS

HCC 49 EpCAM CD4+CD25+Foxp3+ (after curative resection) HV 50

Method

Quantitative Higher risk of postoperative recurrence and 1-year RT-PCR recurrence RosetteSep

Zhou et al. [18]

Xu et al. [21]

ASGPR, HER2, TP53

HCC 85 AutoMACS BLD 37 OT 14 HV 20

Identified in 81% of patients; positivity and number associated with tumor size, PVTT, differentiation status, and disease extent

ASGPR, P-CK, CPS1

HCC 36 BLD 14

Detected in 100% of patients; Zhang et al. captured CTCs could be [22] expanded to form a spheroidlike structure

ASGPR, CPS1

HCC 32 Ficoll-Paque CD45 depletion of leukocytes Liu et al. [23] BLD 12 OT 17 RosetteSep recovered more LC 15 CH 10 CTCs vs. ASGPR+ selection; AH 3 HV 20 combining ASGPR and CPS1 improved CTCs detection vs. either antibody alone, detected in 91% of patients

EpCAM, CK8, CK18, CK19 Vimentin, twist

HCC 62

CTC-chip

CanPatrol

Recurrence associated with mesenchymal CTCs: HR = 4.74 (2.04–11.01) Mixed CTCs: HR = 2.95 (1.18–76.35)

Wang et al. [24]

Abbreviations: AH acute hepatitis, BLD benign liver disease, CH chronic hepatitis, CTCs, circulating tumor cells, HR hazard ratio; HV, healthy volunteers, LC liver cirrhosis, OS overall survival, OT other malignant tumors, PVTT portal vein tumor thrombosis, RFS recurrence-free survival, TACE hepatic arterial chemoembolization

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Xu et al. in 2011 used an ASGPR-based ligand (biotinylated asialofetuin) for immunomagnetic separation and could detect CTCs in 81% of HCC patients. The number of CTCs was significantly related to tumor size, differentiation status, and portal vein tumor thrombosis [21]. In other studies, microarrays with antibodies against CPS1, P-CK, and ASGPR isolated CTCs from all (n = 36) HCC patients [22]. CTCs were detected in 91% of HCC patients using antibody complexes against CPS1 and ASGPR, combined with negative enrichment [23]. For monitoring recurrence, Wang et al. found that CTCs were significantly higher in the recurrence group with mesenchymal and mixed phenotypes than in the non-recurrence group. Moreover, positive mesenchymal CTCs were identified as a predictor for early recurrence [24].

3

cfDNA in HCC cfDNA is double-stranded DNA present in serum or plasma and is released by dying host cells or actively secreted by lymphocytes [25]. If cfDNA is derived from necrotic or apoptotic tumor cells, they can be considered ctDNA. Primary or metastatic tumors are capable of releasing ctDNA [26]. ctDNA is usually shorter than normal cfDNA, and many tumors have ctDNA less than 167 bp in length (DNA wrapped around chromosomes) [27]. In principle, ctDNA contains the same genetic alterations as tumor cells from which they originate, including single-nucleotide mutations, copy number variants, and epigenetic changes [28, 29]. Current techniques for analyzing ctDNA are divided into targeted, which means detecting mutations in a predetermined set of genes (digital PCR techniques have been widely used), and untargeted, which refers to whole-genome sequencing (WGS) in order to discover new genomic aberrations [30, 31]. The ctDNA assay allows us to monitor tumors dynamically, such as detecting genetic mutations that drive treatment resistance (Table 2).

3.1

Amount of cfDNA

Since high levels of serum cfDNA indicate tumor growth and tumor load, assessing its total amount is the easiest way to do so. Piciocchi et al. showed that total cfDNA achieved 91% sensitivity and 43% specificity in discriminating HCC from CLD/cirrhosis. The AUC was 0.69 [32]. In HCC, cfDNA is not specific. Therefore, some studies have combined it with AFP to diagnose HCC. Results have shown that this can improve the accuracy of diagnosis [33, 34]. In addition, the total amount of cfDNA can be used as a biomarker of prognosis. Tokuhisa et al. showed that higher levels of post-hepatectomy cfDNA indicated an increased risk of

None

HCC 72 HV 41

HCC 24 CLD 62

HCC 87

HCC 46

HCC 151

HCC 72

HCC 65 CLD 70

Total amount

Total amount

Total amount

Total amount

Total amount

Methylation (APC, GSTP1, RASSF1A, SFRP1)

Mutations (CTNNB1, TP53, TERT promoter, AXIN1), AFP, DCP, sex, age

HCC 172

Methylation (LINE-1)

NR

LR

Variable treatment

Sorafenib

LR or LT

LR

AFP (80.5 ng/mL)

AFP (NR)

LINE-1 hypomethylation associated with: Shorter OS: Adjusted HR = 1.77 (1.12–2.79)

Yeh et al. [50]

Lu et al. [49]

Oversoe et al. [44]

Higher mortality: Adjusted HR = 2.16 (1.20–3.88) HCC vs. controls: 84%/83%, 0.87 Sensitivity: 75% of patients with low AFP

Qu et al. [41]

Huang et al. [40]

Oh et al. [37]

Ono et al. [36]

Tokuhisa et al. [35]

Yan et al. [34]

Huang et al., [33]

Piciocchi et al. [32]

References

AFP or US positive suspected: 85%/93%, 0.928

cfDNA: 92.7%/81.9%

Higher cfDNA associated with: Shorter OS: HR = 3.50 (2.36–5.20) Shorter TTP: HR = 1.71 (1.20–2.44)

Presence of cfDNA associated with: Increased recurrence (P = 0.01) Incerased extrahepatic metastases (P = 0.04) Increased risk of microscopic vascular invasion: HR = 6.10 (1.11–33.33)

High cfDNA associated with: Poorer OS: HR = 3.4 (1.5–7.6) Higher recurrence in distant organs: HR = 4.5 (1.3–11.9)

AFP: 47.8%/93.2%, 0.67 cfDNA: 62.5%/93.6%, 0.82 cfDNA + AFP + age: 87%/100%, 0.98

AFP: 90.3%/90.2%, 0.949 cfDNA + AFP: 95.1%/94.4%, 0.974

AFP: 45%/83%, 0.64 cfDNA: 91%/43%, 0.69

Findings (Sensitivity/Specificity, AUC, etc.)

Abbreviations: AFP alpha-fetoprotein, CLD chronic liver disease, HR hazard ratio, HV healthy volunteers, LC liver cirrhosis, LR liver resection, LT liver transplantation, NR not reported, OS overall survival, TTP time to progress

HCC 203 CLD 104 HV 50

Methylation (APC, COX2, RASSF1A), miR-203

Mutation (TERT promoter) HCC 95

None

HCC 66 LC 35 CLD 41

Total amount

AFP (14 ng/mL)

Patients

cfDNA Property

Comparator or Treatment

Table 2 Studies on cell-free DNA (cfDNA) as biomarkers in HCC patients 218 Zheyu Zhou et al.

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metastasis and worse overall survival [35]. In other studies, patients treated with liver transplantation or sorafenib with higher levels of cfDNA had a poorer prognosis [36, 37]. 3.2 Mutations of cfDNA

Studies on cfDNA have focused on mutational and epigenetic analysis. HCC owns a low mutational burden compared with other malignant tumors [38]. DNA mutations in HCC mainly affect the WNT signaling pathway (CTNNB1), cell cycle (TP53), and telomere integrity (TERT promoter) [39]. Huang et al. showed that at least one of these three mutations was recorded in 56.3% of 48 HCC patients. While only 22.2% detected the corresponding mutation in paired liver tissues [40]. In addition, combining several cfDNA mutations with serum biomarkers (AFP and DCP), gender and age allowed for effective identification of early HCC in a high-risk group of HBsAg seropositive individuals. This showed 85% sensitivity and 93% specificity in a training cohort [41]. TP53 is considered the most common mutation in HCC cfDNA, and its R249S mutation also appears to be highly specific. In a large study, TP53 R249S positive mutations were shown to act as prognostic markers and were predictors of poorer OS and progression-free survival [42]. The human telomerase reverse transcriptase gene (TERT) plays a vital role in maintaining telomere homeostasis and chromosomal integrity. Mutations in its promoter can cause cellular immortalization [43]. Oversoe et al. showed that this indicates poor prognosis of patients after diverse treatments [44].

3.3 Methylation of cfDNA

DNA methylation is essential in the development of HCC. Methylation patterns are unique for specific cell types with high stability under physiological and pathological conditions [45]. Also, since methylation of cfDNA occurs early in tumorigenesis, methylation detection may offer hope for early detection of tumors. Wong et al. first identified both p15 and p16 methylation positivity in serum or plasma of HCC patients in 2000 [46]. Since then, the Ras association domain family 1A (RASSF1A) promoter was also found to be hypermethylated in the cfDNA in HCC, which could distinguish HCC patients from chronic HCV infection and healthy controls separately [47]. Several studies have shown that combining the methylation patterns of different genes can lead to high diagnostic accuracy. In a study by Huang et al., combining four abnormally methylated genes in cfDNA (SFRP1, RASSF1A, GSTP1, and APC) was able to distinguish HCC from healthy patients with a sensitivity of 92.7% and a specificity of 81.9% [48]. In another study, HCC was identified in close to 75% of patients (not diagnosed at 20 ng/mL AFP) after miR-203, and three abnormally methylated genes (APC, COX2, and RASSF1A) were combined [49].

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In terms of prognostic prediction, LINE-1 hypomethylation indicated poor prognosis in HCC patients [50].

4

EVs in HCC Apoptotic bodies, microvesicles, and exosomes are different types of EVs. EVs are nanoparticles encapsulated by lipid bilayers [51] and are heterogeneous in biogenesis and composition. They are released by all cells as part of their normal physiological function [52]. Various bioactive molecules, including proteins, lipids, mRNAs, lncRNAs, and miRNAs, are transported by EVs [53]. Studies have shown that extracellular RNAs (exRNAs) from EVs significantly contribute to regulating intercellular communication, either in the local microenvironment where they are released or at a distant site [51, 54]. Importantly, EVs are very stable in the body circulation, which offers the possibility of their quantification and assessment in the blood (Table 3). In the initial study, circulating levels of EVs were higher in HCC patients than controls [38]. Also, the measurement of total EVs had slightly higher sensitivity and specificity than AFP in diagnosing HCC [55]. Subsequently, researchers have also focused on analyzing the substances contained in EVs. A study by Lu et al. showed that the AUC of the three lncRNAs in exosomes to identify HCC patients reached 0.96 and 0.53 in the training and validation sets, respectively. Combining them with AFP resulted in AUC of 0.97 and 0.87, respectively [56]. In other studies, AFP and exosomal miR-122, miR-148a were integrated for differentiating between HCC and cirrhotic patients with an AUC of 0.947 and 0.665 when isolated AFP was used [57]. In terms of prognostic studies, exosomal miRNAs have received more attention. Several studies have found that high levels of miR-21 are related to increased risk of progression and poorer survival in HCC patients [58, 59]. While low levels of miR-125b and miR-638 were obviously related to faster recurrence and shorter overall survival for HCC patients [60, 61].

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Challenges and Prospects Although liquid biopsies have shown great potential, their widespread clinical application is limited. CTCs are subject to apoptosis caused by hemodynamic shear, loss of adhesion to the extracellular matrix, and attack attributed to the immune system, when entering the circulatory system [62]. Only about 0.01% of CTCs survive after being released into the blood [63]. Also, they have a short half-life in the circulation of

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Table 3 Studies on EVs as biomarkers in HCC patients Comparator Findings (Sensitivity/Specificity References or treatment or AUC, etc.)

EVs property

Patients

Total amount

HCC 12 NR CLD 11 HV 6

Total amount

AFP (20 ng/ AFP: 85.7%/40.0% HCC 55 mL) EVs: 88.9%/62.6% LC 40 HV 21

AFP lncRNAs: HCC 200 ENSG00000248932.1 CLD 200 HV ENST00000440688.1 200 ENST00000457302.2

EVs concentration higher in HCC patients vs. healthy controls or cirrhosis

lncRNAs: 0.96/0.53 in training/validation cohorts lncRNAs + AFP: 0.97/0.87 in training/validation cohorts

Cheng et al. [38] Wang et al. [55] Lu et al. [56]

miR-148a, miR-1246

HCC 68 AFP (20 ng/ Early stage HCC vs. cirrhosis: CLD 103 HV mL) AFP: 0.665 64 miRNAs + AFP: 0.947

Wang et al. [57]

miR-21, miR-10b

HCC 124

Tian et al. [58]

LR

Poorer DFS associated with: High miR-21: Adjusted HR = 2.45 (1.25–4.78) High miR-10b: Adjusted HR = 2.55 (1.30–4.99)

miR-21, lncRNA-ATB HCC 79

Variable treatment

High miR-21 and lncRNA-ATB Lee et al. [59] associated with: Mortality: HR = 2.87 and 2.17, respectively Disease progression: HR = 2.53 and 2.55, respectively

miR-125b

HCC 128

LR

Low miR-125b associated with: Lower time-to-recurrence: HR = 0.14 (0.08–0.27) Poorer OS: HR = 0.33 (0.18–0.62)

miR-638

HCC 126

LR

Shi et al. Low miR-638 associated with: [61] Poorer OS: Adjusted HR = 2.80 (1.24–4.31)

Liu et al. [60]

Abbreviations: AFP alpha-fetoprotein, CLD chronic liver disease, DFS disease-free survival, EVs extracellular vesicles, HR hazard ratio, HV healthy volunteers, LC liver cirrhosis, LR liver resection, NR not reported, OS overall survival

1–2.4 h [64]. More importantly, the fewer CTCs in circulation indicate the earlier tumor stage [62]. The use of CTCs is limited due to these characteristics in early tumor diagnosis. For ctDNA, they also have a very short half-life [65]. Also, they represent only a tiny proportion of the all cfDNA and are always diluted by large amounts of DNA from non-tumor sources. There are also no approaches for isolating ctDNA from cfDNA specifically [66]. The presence of ctDNA is only indicated by the inspection of tumor-specific mutations on cfDNA. However, some genes (e.g.,

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TP53, BRAF) are detected in various other tumors and often lack tissue specificity. This requires the use of epigenetic biomarkers of cfDNA, such as DNA methylation, as methylation profiles are highly tissue-specific [67, 68]. For EVs, they are usually divided into small type (exosomes) and large type (apoptotic bodies, microvesicles). Although the two can be distinguished by several markers (CD63, HSP70, CD9, CD81, and integrins) [69], obvious differences are not very clear [70]. At present, not many studies have combined the analysis of different assays (e.g., simultaneous evaluation of CTCs and ctDNA). Meanwhile, most current studies use different assay techniques, resulting in different sensitivities and specificities. In other words, there is a lack of relatively uniform assay standards. Therefore, we need more multicenter, large-scale, long-term studies (including clinical trials) to validate the use of liquid biopsy in HCC. In any case, liquid biopsies that are noninvasive, easily reproducible, and able to overcome the shortcomings of current testing techniques deserve more attention.

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Chapter 15 Role of Circulating Tumor DNA in Colorectal Cancer Haotian Li, Sheng Lu, Zidong Zhou, Xiaocheng Zhu, and Yong Shao Abstract Colorectal cancer (CRC) is a very common gastrointestinal tumor, ranking second in the global cause of cancer death. Because of the invasive nature of biopsy and cannot reflect the heterogeneity of tumor or monitor the dynamic progress of tumor, it is necessary to induce a novel noninvasive method to improve the current treatment strategies of colorectal cancer. Among all the components of liquid biopsy, circulating tumor DNA (ctDNA) may have the best future. CtDNA maintains the same genomic characteristics as those in matched tumor tissues, so it allows quantitative evaluation and analysis of mutation load in body fluid. Furthermore, because the half-life of ctDNA is from 16 min to several hours in circulation, the circulating ctDNA can be measured repeatedly within a certain period to monitor the response of CRC to treatment, the occurrence of drug resistance, and the diagnosis of recurrence. Key words Circulating tumor DNA, Colorectal cancer, Noninvasive, Body fluid, Drug resistance, Carcinoembryonic antigen (CEA), Digital PCR, Machine learning, Cancer screening, Early diagnosis

1

Introduction Colorectal cancer is a common digestive tract tumor, with the incidence rate third in the world, and the number of deaths is second in the world [1, 2]. The total related deaths in rectal and colon cancer is predicted to up to 60% and 71.5% respectively by the year 2035 [3]. In the United States, there was a survey that showed for patients with stage I, II, and IV colorectal cancers, the 5-year relative survival rates were 91%, 82%, and 12%, respectively [4]. Therefore, the diagnosis and treatment of colorectal cancer in early stage can improve the early prognosis, the living standard, and cure rate of patients apparently. The traditional clinical diagnosis methods of colorectal cancer include certain markers of tumor, imaging examination, colonoscopy, and tissue biopsy. Carcinoembryonic antigen (CEA) and carbohydrate antigen 19–9 (CA19–9) are usually regarded as serum markers for colorectal cancer, and they have the characteristics of relative low sensitivity and weak specificity. Therefore, only these two markers cannot fully meet

Tao Huang et al. (eds.), Liquid Biopsies: Methods and Protocols, Methods in Molecular Biology, vol. 2695, https://doi.org/10.1007/978-1-0716-3346-5_15, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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the clinical needs [5]. Colonoscopy is usually considered as the best method for early visual detection of colorectal cancer, but it has the disadvantages of discomfort, invasive, time-consuming, and expensive, which may increase the incidence of complications. Tumor biopsy is the gold standard diagnosis for colorectal cancer, but due to severe trauma and poor patient compliance, it is almost impossible to get repeated biopsy to catch the progress of the disease. Thus, in order to better describe tumor characteristics, monitor disease progression and response to treatment, standardized and noninvasive detection technology needs to be developed. In recent years, liquid biopsy has attracted more and more attention from scientists and enterprises [6]. Liquid biopsy is a minimally invasive method to predict or diagnose tumors by detecting a variety of biological substances in body fluid, such as circulating cells, cell-free DNA (cfDNA), platelets, extracellular vesicles, mRNA, miRNA, and protein. From the blood of cancer patients, tumor cells release part of cfDNA through apoptosis, necrosis, or active release [7], so this special cfDNA is called circulating tumor DNA (ctDNA). CtDNA can be regarded as a new tumor biomarker because of its specific tumor mutation in its sequence. Because circulating tumor DNA (ctDNA) has the advantages of small trauma, easy to obtain samples, and repeated collection, it has been used in cancer screening, early diagnosis, change monitoring during treatment, or recurrence prediction.

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Biological Characteristics of cfDNA and ctDNA Cell-free DNA (cfDNA) refers to extracellular DNA (doublestranded DNA and mitochondrial DNA) derived from different kinds of cell types in autologous fluid. In 1948, Mandel and Metis made the first report on cell-free DNA (cfDNA) and detected non-cell-bound nucleic acids in the blood patients with cancer [8]. Leon and Shapiro firstly reported the high quantitation of cfDNA in the serum of various cancers in 1977, which proved the diagnostic characteristics of cfDNA in cancer. At the same time, they reported that the level of cfDNA in patients with metastatic cancer expressed higher than patients with non-metastatic cancer. They also observed that there is a decrease of cfDNA in serum after radiotherapy and point out that cfDNA could be a promising and predicted biomarker to evaluate treatment response [9]. Stroun explained that part of the cfDNA in plasma of cancer patients originates from tumor cells [10]. Tumor-specific aberrations found in cfDNA, such as mutations in oncogenes and tumor suppressor genes [11], microsatellite instability (MSI) [12], and DNA methylation [13], were confirmed, which indicate that tumors release cfDNA into circulation. Therefore, increasing number of studies focused on cfDNA releasing from cancer cells. Besides, it

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should be noted that urine, cerebrospinal fluid, pleural fluid, and saliva are also the types of body fluids that can be analyzed by liquid biopsy. In some physiological or clinical conditions, such as acute trauma [14], cerebral infarction [15], exercise [16], transplantation [17], and infection [18], cfDNA concentration will also escalate. Most cfDNA fragments released from apoptotic cells are between 180 and 200 base pairs in size, after cfDNA is released into circulation, it is rapidly cleared by the spleen, liver, and kidney. The halflife of circulating cfDNA is very short, ranging from 16 min to several hours [19–21], which makes cfDNA level could be regarded as a “real-time” snapshot for the burden of disease. CtDNA refers to cfDNA derived from tumor cells, a study of colorectal cancer revealed an apparently increased range of ctDNA in colorectal cancer patients (22–3922 ng/mL of blood) when compared with healthy people (5–16 ng/mL of blood) [22]. Although the total amount of cfDNA in cancer patients is higher than that in healthy people, according to some studies, ctDNA only accounts for a small part (