The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: a retrospective study.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34332931", "kind": "science-record", "revision": 3, "content_hash": "sha256:2bb2d2be16665ed4976beb8c35c252cefbd098c1a6ee54170f84146c54222dd2", "created_at": "2026-11-01T17:35:49Z", "updated_at": "2026-11-01T21:25:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: a retrospective study.", "doi": "10.1016/S2589-7500(21)00104-7", "pmid": "34332931", "authors": ["Marjorie G Zauderer", "Axel Martin", "Jacklynn Egger", "Hira Rizvi", "Michael Offin", "Andreas Rimner", "Prasad S Adusumilli", "Valerie W Rusch", "Mark G Kris", "Jennifer L Sauter", "Marc Ladanyi", "Ronglai Shen"], "journal": "The Lancet. Digital health", "year": 2021, "abstract": "BACKGROUND: Current risk stratification for patients with malignant pleural mesothelioma based on disease stage and histology is inadequate. For some individuals with early-stage epithelioid tumours, a good prognosis by current guidelines can progress rapidly; for others with advanced sarcomatoid cancers, a poor prognosis can progress slowly. Therefore, we aimed to develop and validate a machine-learning tool-known as OncoCast-MPM-that could create a model for patient prognosis.\nMETHODS: We did a retrospective study looking at malignant pleural mesothelioma tumours using next-generation sequencing from the Memorial Sloan Kettering Cancer Center-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT). We collected clinical, pathological, and routine next-generation sequencing data from consecutive patients with malignant pleural mesothelioma treated at the Memorial Sloan Kettering Cancer Center (New York, NY, USA), as well as the MSK-IMPACT data. Together, these data comprised the MSK-IMPACT cohort. Using OncoCast-MPM, an open-source, web-accessible, machine-learning risk-prediction model, we integrated available data to create risk scores that stratified patients into low-risk and high-risk groups. Risk stratification of the MSK-IMPACT cohort was then validated using publicly available malignant pleural mesothelioma data from The Cancer Genome Atlas (ie, the TCGA cohort).\nFINDINGS: Between Feb 15, 2014, and Jan 28, 2019, we collected MSK-IMPACT data from the tumour tissue of 194 patients in the MSK-IMPACT cohort. The median overall survival was higher in the low-risk group than in the high-risk group as determined by OncoCast-MPM (30·8 months [95% CI 22·7-36·2] vs 13·9 months [10·7-18·0]; hazard ratio [HR] 3·0 [95% CI 2·0-4·5]; p<0·0001). No single factor or gene alteration drove risk differentiation. OncoCast-MPM was validated against the TCGA cohort, which consisted of 74 patients. The median overall survival was higher in the low-risk group than in the high-risk group (23·6 months [95% CI 15·1-28·4] vs 13·6 months [9·8-17·9]; HR 2·3 [95% CI 1·3-3·8]; p=0·0019). Although stage-based risk stratification was unable to differentiate survival among risk groups at 3 years in the MSK-IMPACT cohort (31% for early-stage disease vs 30% for advanced-stage disease; p=0·90), the OncoCast-MPM-derived 3-year survival was significantly higher in the low-risk group than in the high-risk group (40% vs 7%; p=0·0052).\nINTERPRETATION: OncoCast-MPM generated accurate, individual patient-level risk assessment scores. After prospective validation with the TCGA cohort, OncoCast-MPM might offer new opportunities for enhanced risk stratification of patients with malignant pleural mesothelioma in clinical trials and drug development.\nFUNDING: US National Institutes of Health/National Cancer Institute.", "claims": [{"id": "claim-method", "statement": "OncoCast is an ensemble learning approach for survival stratification and feature selection based on elastic-net penalised Cox proportional hazard models.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34332931", "locator": "sections[4] \"Model development\" paragraph 2", "quote": "OncoCast is an ensemble learning approach for survival stratification and feature selection based on elastic-net penalised Cox proportional hazard models."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Individual participant data that underlie the results reported in this Article, after de-identification will be made available with publication upon request to the corresponding author. 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