Plasma cell-free DNA methylomes for hepatocellular carcinoma (HCC) detection and monitoring of recurrence after liver resection or transplantation

Introduction

The detection of hepatocellular carcinoma (HCC) via mutational profiling of cell-free DNA (cfDNA) in blood plasma is hampered by mutational diversity and the challenge of acquiring tumor tissue for targeted gene panels. Alternatively, DNA methylation patterns can illuminate biological processes without requiring prior mutation-specific knowledge. Our investigation evaluated the effectiveness of cell-free methylated DNA immunoprecipitation and sequencing (cfMeDIP-Seq) in identifying HCC and monitoring its recurrence after surgery. This research represents the first application of cfMeDIP-Seq to HCC for both its identification and the tracking of residual disease post-operatively. We collected baseline plasma samples from HCC patients undergoing curative interventions or liver transplants, as well as from healthy individuals, including living liver donors. Utilizing cfMeDIP-Seq and machine learning, we identified HCC-specific differentially methylated regions (DMRs) and developed a HCC methylation score (HMS). This score accurately distinguished HCC with high specificity and sensitivity, and importantly, predicted the likelihood of postoperative recurrence. Analysis of post-surgery follow-up samples using cfMeDIP-Seq revealed that changes in HMS were indicative of future recurrence, demonstrating cfMeDIP-Seq’s utility for both precise HCC detection and recurrence prediction without the need for tumor-specific mutations.
Our findings demonstrate the potential of cfMeDIP-Seq for sensitive and specific HCC detection irrespective of tumor type, as well as for predicting recurrent disease post-surgery. The comprehensive analysis of tumor-agnostic cfDNA methylomes accurately detected HCC and forecasted recurrence following liver resection or transplantation. This innovative approach may address current challenges in minimally invasive HCC diagnosis using liquid biopsy, offering significant implications for screening, early detection, treatment decisions, and disease progression monitoring and therapy response assessment.

Contents

The repository contains bash scripts, data processing scripts, and R scripts designed to reproduce figures for the manuscript "Plasma cell-free DNA methylomes for hepatocellular carcinoma detection and monitoring after liver resection or transplantation"

HCC cfMeDIP Study Workflow

HCC cfMeDIP Data Analysis Scheme

Setup

R scripts were executed in either R version 3.5.0 or 4.0.1, depending on the specific package dependencies (listeded in Table 1), utilizing RStudio versions (2022.12.0+353 ~ 2023.12.0+369).

The R packages required are detailed within each script.

Dependencies

Tool	Function	Version	Running_Platform	Language	Alternative_tool	Link
FastQC	FASTQ QC	0.11.5	H4H_shell	Java		http://www.bioinformatics.babraham.ac.uk/projects/fastqc
MultiQC	❶ FASTQ QC ❷ count reads	1.7	H4H_shell	Python		https://multiqc.info
fastp	❶ ISD QC3 ❷ trim	0.23.1	H4H_shell	C++	❶ Trim Galore ❷ Trimmomatic ❸ Cutadapt	https://github.com/OpenGene/fastp
bowtie2	align	2.4.5	H4H_shell	C++	BWA-mem	https://bowtie-bio.sourceforge.net/bowtie2/index.shtml
qualimap	BAM QC	2.2	H4H_shell	Java		http://qualimap.conesalab.org
SAMtools	sort - index	1.14	H4H_shell	C	Picard	http://www.htslib.org
sambamba	deduplicate	0.7.0	H4H_shell	D	❶ SAMtools ❷ Picard	https://lomereiter.github.io/sambamba
MEDIPS	❶ QC2 ❷ export wig ❸ DMRs/ROIs(edgeR)	1.50.0	H4H_R/3.5.0	R	DESeq2	https://doi.org/doi:10.18129/B9.bioc.MEDIPS
MeDEStrand	absolute_methylation_level	0.0.0.9000	H4H_R/3.5.0	R		https://github.com/jxu1234/MeDEStrand
sva/ComBat_seq	reduce batch effect	3.46.0	H4H_R/4.0.1	R		https://github.com/zhangyuqing/ComBat-seq
edgeR	❶ TMM normalization ❷ CPM counts	3.28.0	H4H_R/4.0.1	R		https://bioconductor.org/packages/release/bioc/html/edgeR.html
limma	❶ vCounts ❷ DMRs(CPM-trend)	3.42.0	❶ H4H_R/4.0.1 ❷ RStudio	R		https://bioconductor.org/packages/release/bioc/html/limma.html
FactoMineR/factoextra	PCA	2.8/1.0.7	❶ H4H_R/4.0.1 ❷ RStudio	R		https://rpkgs.datanovia.com/factoextra/index.html
randomForest	❶ build & train models ❷ classification prediction	4.7.1.1	❶ H4H_R/3.5.0 ❷ RStudio	R		https://www.stat.berkeley.edu/users/breiman/RandomForests
glmnet	regularized linear modeling	4.1.7	❶ H4H_R/3.5.0 ❷ RStudio	R		https://glmnet.stanford.edu/index.html
caret	model training configuration	6.0	❶ H4H_R/3.5.0 ❷ RStudio	R		https://topepo.github.io/caret
pROC	AUROC	1.18.0	RStudio	R		https://xrobin.github.io/pROC
ggplot2	data visualization	3.4.2	RStudio	R		https://ggplot2.tidyverse.org

Scripts List

Step	Script	Analysis	Panels Generated
1	step_1_fastq_to_bam.sh
1s	step_1s_ISD.sh	QC	Figure S2B/2C
2	step_2_bam_to_wig.R
3	step_3_wig_to_txt.sh
4	step_4_matrices_processing.R
5	step_5_medips_qc.R	QC	Figure S2A
6	step_6_hcc_classifiy_hms.R	kFold modeling AUROC HMS PCA Confusion matrix Simply moving average Postoperative HMS trajectory	Figure 2 Figure 3 Figure 4 Figure 5 Figure S3C Figure S4
7	step_7_hcc_subtyping.R	HCC subgrouping	Figure S3A/B
7s	step_7s_one_vs_each_hcc.R	OnevsEach.hcc() required by hcc_subgroup.R

Supporting Files List

Name	Size	Script using this file	Description
hg19_chr1_22_m_coord.rds	90 MB	step_4_matrices_processing.R	genomic coordinates for 4801145 300bp bins (hg19, chr:1-22 and M)
hg19_chr1_22_coord.rds	94.4 MB	-	optional if only chr:1-22 needed
hg19_chr1_22_x_y_coord.rds	101.4 MB	-	optional if only chr:X&Y needed
hg19_chr1_22_m_x_y_coord.rds	101.4 MB	-	optional if only chr:M needed
black_bin_v2.RData	7.8 MB	step_6_hcc_classifiy_hms.R	109120 bins falling in ENCODE blacklist regions
n236_cpm.rds	1.31 GB	-	cfMeDIP signals (summed CPM) analysis
n236_lcpm.rds	1.33 GB	step_6_hcc_classifiy_hms.R	matrix for kFold cross-validation limma-trend
vCount_n236.RData	1.33 GB	-	option for limma-voom
sample.rds	1KB	step_6_hcc_classifiy_hms.R	Clinical metadata
sample_sq.rds	1KB	step_6_hcc_classifiy_hms.R	Clinical metadata
sample_124.rds	1KB	step_7_hcc_subtyping.R	Clinical metadata
kFold_ML_res.RData	1.5 MB	step_6_hcc_classifiy_hms.R	machine learning result of discovery cohort
Val.prob.list.rds	11.1 MB	step_6_hcc_classifiy_hms.R	machine learning result of validation cohort

FastQ to BAM Processing Summary

File Type	Storage Path	Operation/Function
Merged FastQ Files	../bam	cat for merging L001 and L002
FastQC Pre-Processing	../fastqc_pre	fastqc for initial QC
Fastp Reports	../fastqc_pre/fastp_report	fastp for quality and adapter trimming
FastQC Post-Processing	../fastqc_post	fastqc for QC after fastp
Aligned SAM Files	Temporary storage	bowtie2 for alignment
Unsorted BAM Files	Temporary storage	samtools view for SAM to BAM conversion
Sorted BAM Files	../bam	samtools sort for sorting BAM
BAM Index Files	../bam	samtools index for indexing BAM
Pre-QC Reports	../fastq_bam_QC	samtools flagstat and qualimap
Deduplicated BAM Files	../bam	sambamba markdup for deduplication
Post-QC Reports	../fastq_bam_QC	samtools flagstat and qualimap

Please note that temporary files, such as unsorted BAM and aligned SAM files, are removed after they are no longer needed to save storage space.

MEDIPS_QC Results List

This table clearly delineates the output files from the MEDIPS QC analysis, where to find them, and which specific function in the MEDIPS package or custom script is responsible for generating each file. This comprehensive overview aids in understanding the workflow and accessing specific results for further analysis or reporting.

Result File Description	File Pattern/Type	Storage Path	Generating Function
Saturation Plots	_SaturationPlot.pdf	outdir/pdf/	MEDIPS.plotSaturation()
Sequence Coverage Pie Charts	_SeqCoveragePlot_Pie.pdf	outdir/pdf/	MEDIPS.plotSeqCoverage() (pie)
Sequence Coverage Histograms	_SeqCoveragePlot_Hist.pdf	outdir/pdf/	MEDIPS.plotSeqCoverage() (hist)
Sequence Coverage Pie Charts (Including Non-Unique Reads)	_SeqCoveragePlot_Pie_uniqF.pdf	outdir/pdf/	MEDIPS.plotSeqCoverage() (pie)
Sequence Coverage Histograms (Including Non-Unique Reads)	_SeqCoveragePlotHist_uniqF.pdf	outdir/pdf/	MEDIPS.plotSeqCoverage() (hist)
Saturation Metrics (FigureS2A, BAM QC)	_saturation.txt	outdir/QCstats/	MEDIPS.saturation()
Coverage Metrics	_coverage.txt	outdir/QCstats/	MEDIPS.seqCoverage()
CpG Enrichment Analysis (FigureS2A, BAM QC)	_enrichment.txt	outdir/QCstats/	MEDIPS.CpGenrich()
Comprehensive QC Statistics	_QCstats.txt	outdir/QCstats/	Custom aggregation of MEDIPS data
Window Coordinates per Chromosome	_window_per_chr.csv	outdir/ or specified subdirectory	Custom script using MEDIPS data
PNG versions of Saturation, Coverage, and Calibration Plots	Various PNG files	outdir/png/	MEDIPS.plotSaturation() MEDIPS.plotSeqCoverage() MEDIPS.plotCalibrationPlot()

H4H - R Compatibility Chart

Package	Version	H4H/R3.5	H4H/R4.0	H4H/R4.2	--mem	Running Time
MEDIPS	1.50.0	✅	✅	❌	≥ 180G	~1 day
MeDEStrand	0.0.0.9000	✅	❌	❌	≥ 180G	1~2 days
ChAMP	2.28.0	❌	✅	NA	≥ 300G	1~2 days
DESeq2	1.38.2	✅	❌	✅	≥ 180G	~1 day
limma	3.42.0	✅	✅	✅		1~2 days
edgeR	3.28.0	✅	✅	❌		1~2 days
FactoMineR	2.8	❌	✅	✅
factoextra	1.0.7	❌	✅	✅
dplyr	1.1.1	✅	❌	✅
tidyr	1.3.0	✅	❌	✅
sva	3.46.0	❌	✅	❌	≥ 500G	~1 day
caret	6.0	✅	❌	❌	≥ 80G	~2 days
randomForest	4.7.1.1	✅	✅	❌	≥ 80G	~2 days
glmnet	4.1.7	✅	❌	❌	≥ 80G	~2 days

Key Arguments

Tools	Arguments	Support Data	Dependencies
fastp	-w 10 -g -f 5 -F 5 --adapor_F== --adapor_R== --correction --html=$.html	Adaptor Sequences Table
Bowtie2	-p 10 --minins 50 --maxins 500 -x hg19	hg19.1.bt2 hg19.2.bt2 hg19.3.bt2 hg19.4.bt2 hg19.rev.1.bt2 hg19.rev.2.bt2
samtools view	-@ 10 -bS -f 2 -F 4
qualimap	-c --java-mem-size=30G -gff hg19.gtf -outformat pdf -nt 10	hg19.gtf
sambamba markdup	-r -p -t 10 --overflow-list-size 600000
MEDIPS/QC	- BSgenome="BSgenome.Hsapiens.UCSC.hg19" - uniq <- 1 - extend <- 300 - shift <- 0 -ws <- 300 - paired <- TRUE - chr.select <- paste0("chr",c(1:22,"X","Y","M"))	NA	BSgenome.Hsapiens.UCSC.hg19
MEDIPS/wig_to_counts	- BSgenome="BSgenome.Hsapiens.UCSC.hg19" - uniq <- 1e-3 - extend <- 300 - shift <- 0 - ws <- 300 - paired <- TRUE - chr.select <- paste0("chr",c(1:22,"M"))	hg19_chr1_22_m_coord.rds hg19_all_chr_coord.rds hg19_chr1_22_x_coord.rds hg19_chr1_22_x_y_coord.rds	BSgenome.Hsapiens.UCSC.hg19 edgeR
MeDEStrand	- BSgenome="BSgenome.Hsapiens.UCSC.hg19" - uniq <- 1 - extend <- 300 - shift <- 0 - ws <- 300 - paired <- TRUE - chr.select <- paste0("chr",c(1:22,"M"))		BSgenome.Hsapiens.UCSC.hg19 BSgenome GenomicRanges MEDIPSData

Data availability

Clinical metadata, raw fastq files and processed cfMeDIP-seq data used to replicate the study are available to qualified researchers at the European Genome-Phenome Archive under accession number

Contact

Kui Chen, kui.chen@uhn.ca