Lau, Y. T. | Search bioRxiv

bioRxiv2025

A Computational Framework for Analysis of cfDNA Fragmentation Profiles

Circulating cell-free DNA (cfDNA) has emerged as a promising non-invasive medium for studying tumor molecular profiles. Non-random fragmentation patterns in plasma cfDNA, particularly around nucleosome-depleted regions (NDRs) near transcription start sites (TSS), have been shown to reflect epigenetic regulation and gene expression. In this study, coverage profiles of the NDR were utilized to derive an NDR score, which was subsequently used as a proxy for inferring gene expression. To reduce transcript-to-transcript variability and enhance the clarity of these expression-associated signals, we implement a method for GC-bias correction of cfDNA samples. A computational framework (NDRDiff) was then developed to enable comparative analyses of NDR score profiles across different sample groups. The GC-bias correction preserved the overall trend of the NDR signal while improving the separation of gene expression levels, as demonstrated by comparisons of healthy donor cfDNA samples with matched blood RNA-seq data. Validation on a simulated dataset showed that NDRDiff achieved an area under the precision-recall curve (AUPRC) of 0.916, outperforming a standard t-test (AUPRC of 0.777). When applied to a comparison of healthy donor cfDNA and metastatic colorectal cancer (mCRC) cfDNA, NDRDiff identified 531 differential NDR score (DNS) genes that facilitated clear separation between the two groups. These DNS genes were found to correlate with tumor fraction estimates (down-regulated DNS genes: Pearson R = 0.89, p < 0.05; up-regulated DNS genes: Pearson R = -0.88, p < 0.05) and included CLDN4, BIN2, and IRAG2, which exhibit strong associations with colorectal cancer or blood cell expression signatures. Gene set enrichment analysis further revealed enrichment of colon and other gastrointestinal tissue signatures. Collectively, these findings underscore the potential of NDR-based cfDNA analysis as a minimally invasive tool for monitoring tumor-related molecular features in cancer.

cancer biology↗

bioRxiv2025

Comprehensive benchmarking of methods for mutation calling in circulating tumor DNA

Detection of somatic mutations in cell-free DNA (cfDNA) is challenging due to low variant allele frequencies and pronounced DNA degradation. Here, we present a novel approach and resource for benchmarking of somatic variant calling algorithms in cfDNA samples from cancer patients. Using longitudinally collected cfDNA samples from colorectal and breast cancer patients, we identify patient-matched samples with high and ultra-low circulating tumor DNA (ctDNA) levels. These sample pairs, preserving patient-specific germline and somatic haematopoiesis variant backgrounds, were used to generate dilution series capturing characteristics of bona-fide cfDNA samples. To benchmark the accuracy and limit of detection of 9 somatic variant calling algorithms, we used deep Whole Genome Sequencing (WGS, 150x) and ultra-deep Whole Exome Sequencing (WES, 2,000x) to construct a reference set of [~]37,000 Single Nucleotide Variants and [~]58,000 Insertions/Deletions. We tested methods under variable ctDNA levels and depth of sequencing, generating guidelines for method choice depending on use case. Using a machine learning approach, we further evaluated the potential of fine-tuning individual variant callers, revealing features that may improve accuracy in cfDNA samples. Overall, we present a new resource for benchmarking of somatic variant calling methods in cfDNA, providing insights on method choice to realize the potential of liquid biopsies in precision oncology.

bioinformatics↗