bioRxivCancer BiologyPreprint
Saturating hepatic clearance drives elevated cfDNA and fragment shortening in cancer
Thomas W. RachmaniD, William A. LaFramboiseiD, Phillip H. Gallo, Patricia Petrosko, Daisong Liu, Rahul Kumar, Marija BalićiD, Steffi OesterreichiD, Julia FoldiiD, Adrian V. LeeiD, Patrick L WagneriD, David L. BartlettiD, Russell S. SchwartziD, Oana CarjaiD
Abstract
Liquid biopsy studies consistently report both elevated circulating cell-free DNA (cfDNA) concentrations and shortened fragment lengths in cancer. These features are often attributed to tumor-specific processes, despite tumor-derived cfDNA frequently constituting less than 1% of the total. Here, we consider an alternative explanation: Saturation of cfDNA clearance, which prolongs cfDNA circulation time, increases exposure to plasma nucleases and is expected to produce similar fragmentomic signatures independent of tumor burden. By combining a mechanistic model of cfDNA fragmentation with analyses of two independent cancer patient cohorts, and publicly available clearance-perturbation experiments, we demonstrate that elevated cfDNA levels are accompanied by a characteristic leftward shift in fragment length distributions consistent with impaired hepatic clearance. This fragmentation signature becomes more pronounced at higher cfDNA concentrations, is independent of circulating tumor DNA (ctDNA) fraction, is reproducible under experimentally reduced clearance, and is independently prognostic of patient survival. Together, these results identify saturating clearance as a central determinant of cfDNA abundance and fragment length, re-framing cancer-associated fragmentomic patterns as systemic consequences of clearance dynamics rather than tumor burden alone. More broadly, they highlight the value of mechanistic modeling of clearance processes in extracting clinically meaningful signals from cfDNA fragmentation data.
- organism: human
- assay: DNA methylation
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: mechanistic / dynamical model
- Citations
- 0
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