Cell-free DNA Fragmentation Profiling at Transcription Start Sites Improves upon Cancer-Type-Specific Region Selection for Cancer Detection
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Motivation
Accurate discrimination between healthy individuals and patients with cancer using minimally invasive liquid biopsies could improve cancer diagnosis and monitoring. Circulating cell-free DNA (cfDNA) is a promising biomarker, since fragmentation patterns reflect chromatin organization and have been used to interrogate regulatory regions such as transcription start sites (TSSs). Classification approaches typically rely on hypothesis-driven selection of genomic regions based on literature or external tissue data. Therefore, they assume that tumor-derived cfDNA constitutes the dominant diagnostic signal, potentially overlooking a systemic, genome-wide shift in the cfDNA pool.
Results
We present a data-driven framework that identifies discriminative genomic loci directly from cfDNA whole-genome sequencing data. Using fragmentomic features captured at TSSs within a nested cross-validation framework, the model outperforms ichorCNA and hypothesis-driven baselines in distinguishing healthy from colorectal and breast cancer samples (AUROC 0.95 ± 0.039). Performance was maintained in a pan-cancer setting across seven malignancies (AUROC 0.946 ± 0.032) and generalized to previously unseen cancer types within the same cohorts (AUROC 0.934 ± 0.006). While validation in an independent external cohort showed a performance gap (AUROC 0.694), the data-driven model was consistently competitive with baseline methods. These results indicate that robust cancer detection is enabled by integrating distributed genome-wide fragmentation patterns rather than restricting analysis to predefined regions.
Availability and implementation
Scripts to reproduce the results are available at https://github.com/brmprnk/comp/
Contact
i.b.pronk@tudelft.nl
Supplementary information
available at NAR Online.
