Combining mutation detection with fragmentomics features leads to improved tumor-informed ctDNA detection

Abstract

Liquid biopsy through circulating tumor DNA (ctDNA) analysis enables non-invasive detection of minimal residual disease (MRD) and early identification of cancer relapse, facilitating timely clinical intervention. However, detecting ctDNA in plasma samples with low tumor burden remains challenging due to the scarcity of mutant molecules, the background noise of sequencing errors and somatic mutations in normal cell-free DNA (cfDNA). Here, we present a mutation-informed fragmentomic framework and evaluate it on 90 stage III colorectal cancer patients with three years of follow-up. Using 712 serial whole-genome sequenced cfDNA samples (30x) with matched whole-genome sequencing of tumor tissue and germline DNA from buffycoat for each patient, we collected cfDNA fragments spanning tumor-derived somatic mutation positions and compared fragmentomic characteristics of mutation-bearing and non-mutated cfDNA fragments within the same sample. By leveraging fragment length and fragment end-motif patterns, our approach can distinguish cancer-positive from cancer-negative plasma samples without requiring model training or panel-of-normals calibration. The method achieved AUCs of 0.863 and 0.74 using fragment length and end motif features, respectively, and 0.871 when combined, outperforming tumor fraction estimates based on the frequency of mutated fragments (AUC=0.832). Integrating fragmentomic features with tumor fraction further improved performance, yielding an AUC of 0.873, indicating complementary signals between fragmentomic patterns and mutation burden. Aggregated analyses revealed ctDNA-specific patterns, including fragment shortening, motif enrichment of A/T ends, and depletion of C/G ends, directly linking fragmentomic features to tumor-derived cfDNA. Overall, mutation-informed fragmentomic profiling improves ctDNA detection beyond counting mutant reads and provides a scalable, training-free strategy for MRD assessment and early relapse detection while offering mechanistic insights into tumor-specific cfDNA biology.