TransSurv：面向目标队列生存预测的兼容性感知迁移学习方法
TransSurv: compatibility-aware transfer learning for target-cohort survival prediction
研究团队提出 TransSurv，一个以目标队列为中心的迁移学习框架，用于高维 Cox 生存建模，在借用辅助队列前先筛选其经验兼容性，并提供非渐近理论保证、报告哪些辅助队列有贡献。在 JAVELIN Renal 101 转移性肾细胞癌试验中，作者称这是首个以随机试验为目标队列的迁移学习生存评估，TransSurv 提升了留出集的无进展生存判别能力。在 TCGA 五个临床定义的目标队列（肾、卵巢、宫颈、肺、乳腺）中，其平均判别能力均为最高；在复旦大学附属肿瘤医院三阴性乳腺癌的跨平台、跨人群复发预测分析中，以 TCGA 和 METABRIC 为辅助队列也取得改善。
来源：Briefings in Bioinformatics · doi.org
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.