ArticleThe Lancet. Digital health2021
The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: a retrospective study.
Article in The Lancet. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed.
- CDKPredict: a clinicogenomic model for risk-adapted therapeutic intervention in breast cancer.Research square · 2026Article
- BAP1 Loss Might Be a Predictive Biomarker for Immunotherapy Response in Pleural Mesothelioma.Thoracic cancer · 2026Article
- Molecular profiling and personalized medicine within EURACAN in the SPECTA Arcagen study in comparison to the France Genomic Medicine 2025 Plan in the AURAGEN platform in peritoneal mesotheliomas.Translational oncology · 2026Article
- Pleural Mesothelioma: Current Therapeutic Strategies and Future Directions.Current oncology reports · 2026Review
- The genetics of cancer heterogeneity and mesothelioma.Frontiers in oncology · 2026Review
- Clinicopathologic Characteristics and Clinical Outcomes of Patients with Testicular Mesothelioma.Annals of surgical oncology · 2025Article
- Article
- Developing Novel Genomic Risk Stratification Models in Soft Tissue and Uterine Leiomyosarcoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Article
- The genetic susceptibility in the development of malignant pleural mesothelioma: somatic and germline variants, clinicopathological features and implication in practical medical/surgical care: a narrative review.Journal of thoracic disease · 2024Review
- Clinical Next Generation Sequencing Application in Mesothelioma: Finding a Golden Needle in the Haystack.Cancers · 2023Review
- Genomic Landscape of Pleural Mesothelioma and Therapeutic Aftermaths.Current oncology reports · 2023Review
- Novel Genomic Risk Stratification Model for Primary Gastrointestinal Stromal Tumors (GIST) in the Adjuvant Therapy Era.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
- Machine Learning-Assisted Recurrence Prediction for Patients With Early-Stage Non-Small-Cell Lung Cancer.JCO clinical cancer informatics · 2023Article
- The past, present, and future of targeted therapeutic approaches in patients with diffuse pleural mesotheliomas.Journal of cancer metastasis and treatment · 2023Article
- Heterogeneous RNA editing and influence of ADAR2 on mesothelioma chemoresistance and the tumor microenvironment.Molecular oncology · 2022Article
- Multimodality Therapy in Patients With Primary Pericardial Mesothelioma.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2022Article
- IsolatedCancers · 2022Article
- Genomic and transcriptomic analysis of a diffuse pleural mesothelioma patient-derived xenograft library.Genome medicine · 2022Article
- Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine.Briefings in bioinformatics · 2022Review
- Evolving Landscape of Initial Treatments for Patients with Malignant Pleural Mesotheliomas: Clinical Trials to Clinical Practice.The oncologist · 2022Article
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12 authors.
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Abstract
backgroundCurrent 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.
methodsWe 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).
findingsBetween 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).
interpretationOncoCast-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.
fundingUS National Institutes of Health/National Cancer Institute.
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