Abstract

Abstract Circulating cell-free DNA (cfDNA) offers a minimally invasive lens into temporal tumor evolution. However, the accurate quantification of clonal composition from cfDNA remains challenging, particularly in low tumor fraction (TF) settings. Existing liquid biopsy deconvolution frameworks are frequently constrained by their reliance on bulk tissue references, simplified copy-number assumptions, and incomplete representations of clonal architecture, which collectively compromise sensitivity and bias evolutionary inferences. To address these limitations, we developed cfClone, a Bayesian framework that integrates single-cell whole-genome sequencing (scWGS) derived clonal structures with cfDNA whole-genome sequencing data to enable high-resolution, tissue-informed clonal tracking. Notably, while cfClone inherently leverages genomic instability, we demonstrate that cfClone achieves accurate TF estimates and circulating tumor DNA (ctDNA) detection even in malignancies with limited copy-number variant (CNV) burden. We validate cfClone in low and high CNV burden cases using simulated data derived from real patient data, establishing sensitive detection thresholds across a range of aneuploidy levels. By jointly modeling local copy-number alterations and allele-specific signals via Bayesian model selection and Markov chain Monte Carlo (MCMC) sampling, the algorithm yields uncertainty-aware estimates of clonal prevalence and TF. Applied to longitudinal clinical cohorts, cfClone reconstructs real-time evolutionary trajectories and uncovers clonal selection driving therapeutic resistance, including the de novo detection of emergent clonal populations. Github link: https://github.com/Roth-Lab/cfclone