A cfDNA fragmentomics-based machine learning model for predicting treatment response and recurrence in locally advanced rectal cancer after neoadjuvant chemoradiotherapy

Neoadjuvant chemoradiotherapy (nCRT) is standard treatment for locally advanced rectal cancer (LARC), but responses vary substantially. We developed Fragmentia-AI Response, a machine learning model that uses cell-free DNA (cfDNA) fragment features to predict pathological response and recurrence risk without relying on somatic mutations. We analyzed 102 patients with LARC treated with nCRT followed by surgery, using 510 longitudinal plasma samples collected at five perioperative time points. The model was trained on post-nCRT (T4) cfDNA from 62 patients to predict pathological complete response (pCR), then evaluated in an internal validation cohort (N = 40) and an independent external cohort (N = 96). Associations with tumor regression grade and recurrence-free survival (RFS) were assessed. The model achieved AUCs of 0.909 (95% confidence interval [CI]: 0.812-1.000) in the training cohort, 0.903 (95% CI: 0.785-1.000) in the internal validation cohort, and 0.835 (95% CI: 0.754-0.916) in the external cohort. Model scores were higher in patients with pCR than in those with non-pCR (Wilcoxon p < 0.001) and were strongly associated with tumor regression grade (Jonckheere-Terpstra test p < 0.001). Higher post-nCRT and post-surgery scores were associated with improved RFS (log-rank p = 0.012 and p = 0.024, respectively). Fragment-level analyses revealed a selective enrichment of 50 – 100 bp cfDNA fragments and a higher proportion of subnucleosomal particles in pCR patients, suggesting a biologically grounded signal underlying model prediction. Fragmentia-AI Response is a mutation-independent cfDNA fragmentomics model that showed reproducible discrimination of pathological response and associations with recurrence risk in LARC, supporting further evaluation as a minimally invasive biomarker for treatment stratification and surveillance.