TransSurv: compatibility-aware transfer learning for target-cohort survival prediction

Transcriptomic prognostic models are most useful for narrowly specified, event-limited target populations such as clinical-trial cohorts or stage-defined subtypes. Candidate auxiliary cohorts may carry related prognostic information but differ in disease context, assay platform, and population structure, so naive pooling-and transfer learning without compatibility screening-can erode target-cohort discrimination. We present TransSurv, a target-centered transfer-learning framework for high-dimensional Cox modeling that screens candidate auxiliary cohorts for empirical compatibility before borrowing, comes with non-asymptotic guarantees, and reports which auxiliary cohorts contributed. In the JAVELIN Renal 101 metastatic renal cell carcinoma trial-to our knowledge, the first transfer-learning survival evaluation with a randomized trial as the target-TransSurv raised held-out progression-free survival discrimination. It also achieved the highest mean discrimination in all five clinically defined target cohorts (kidney, ovarian, cervical, lung, breast) from The Cancer Genome Atlas (TCGA) and improved recurrence prediction in a cross-platform, cross-population Fudan University Shanghai Cancer Center triple-negative breast cancer analysis using TCGA and METABRIC auxiliaries.