Beyond Mutation Detection: Cell-Free DNA for Functional Inference and Adaptive Oncology

Liquid biopsy has evolved beyond its original role as a minimally invasive approach for mutation detection and is now being developed as a broader analytical framework for cancer detection, stratification, and longitudinal monitoring. Improvements in next-generation sequencing, assay chemistry, and computational analysis have increased analytical sensitivity, including in settings with low tumor fraction and very low variant allele abundance. These advances have expanded the utility of cfDNA analysis in measurable residual disease assessment and in the detection of low-abundance tumor-derived signals across multiple clinical contexts. At the same time, the field has shifted toward interpreting cfDNA as a carrier of higher-order biological information rather than solely a substrate for mutation calling. Fragmentation profiles, nucleosome positioning, and chromatin accessibility patterns derived from plasma DNA have been used to infer transcriptional and regulatory states, raising the possibility that cfDNA may capture functional tumor states not readily accessible through genotype-focused assays alone. These developments have prompted growing interest in chromatin-informed cfDNA analysis as a means of identifying pathway activity, enhancer usage, transcription factor occupancy, and potentially actionable biological dependencies. However, the translational relevance of many such inferences remains incompletely established, and preanalytical variability, limited cross-cohort generalizability, and the gap between analytical performance and clinical utility continue to constrain clinical translation. This review examines the role of cfDNA in adaptive oncology, highlighting recent analytical advances, assessing the current evidence supporting their biological and clinical utility, and considering the extent to which cfDNA-derived regulatory inference may contribute to adaptive oncology and therapeutic decision-making.