{"doi":"10.1101/2025.10.06.680732","title":"Transfer Learning for Survival-based Clustering of Predictors with an Application to <i>TP53</i> Mutation Annotation","abstract":"Abstract TP53 is the most frequently mutated gene in human cancers, and germline mutations in TP53 cause Li-Fraumeni syndrome (LFS), a hereditary predisposition to diverse cancers. Accurate annotation of TP53 mutations based on their survival effects is critical for informed LFS patient management. Motivated by this need, we develop a new approach for Survival-based Clustering of Predictors (SCP) by identifying homogeneous coefficients in Cox regression. We formulate this task as a fusionpenalized Cox regression problem and provide an efficient computational algorithm. A nonconvex distance-to-set penalty is adopted to facilitate parameter tuning and improve estimation accuracy. To overcome data limitations, we further develop TLSCP, a transfer learning extension that borrows coefficient ranking information from a source dataset under the assumption of similar ranking patterns between source and target. TL-SCP integrates ranking information through weighted rank averaging, allowing flexibility in accommodating cohort heterogeneity while maintaining model simplicity. Simulation studies demonstrate TL-SCP’s superior performance over SCP in clustering recovery and coefficient estimation. In the application of TP53 mutation annotation where we utilize non-LFS germline TP53 mutation carriers as a source cohort for the target LFS cohort, TL-SCP identifies biologically meaningful TP53 mutation clusters and offers improved clinical interpretability compared to experiment-based annotations.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":577025,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9622,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1485790,"name":"Hao Yan","orcid":"0000-0003-1531-3053","position":1,"is_corresponding":false},{"id":1048460,"name":"Haoming Shi","orcid":"0000-0002-0228-6116","position":2,"is_corresponding":false},{"id":1419848,"name":"Emilie Montellier","orcid":"0000-0001-5069-3536","position":3,"is_corresponding":false},{"id":1486279,"name":"Chau Eric","orcid":null,"position":4,"is_corresponding":false},{"id":54399,"name":"Pierre Hainaut","orcid":"0000-0002-1303-1610","position":5,"is_corresponding":false},{"id":106810,"name":"Wenyi Wang","orcid":"0000-0003-0617-9438","position":6,"is_corresponding":false},{"id":972735,"name":"Xiaoqian Liu","orcid":"0000-0002-3027-2491","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:58:00.620755Z","pmid":"41279906","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}