The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: A retrospective study Journal Article
| Authors: | Zauderer, M. G.; Martin, A.; Egger, J.; Rizvi, H.; Offin, M.; Rimner, A.; Adusumilli, P. S.; Rusch, V. W.; Kris, M. G.; Sauter, J. L.; Ladanyi, M.; Shen, R. |
| Article Title: | The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: A retrospective study |
| 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. METHODS: 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). FINDINGS: 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). INTERPRETATION: 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. FUNDING: US National Institutes of Health/National Cancer Institute. Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved. |
| Keywords: | aged; middle aged; survival analysis; retrospective studies; mortality; cancer staging; neoplasm staging; cohort studies; classification; lung neoplasms; cohort analysis; risk factors; retrospective study; risk factor; risk; lung tumor; mesothelioma; pleura tumor; epidemiology; new york; pleural neoplasms; machine learning; high throughput sequencing; high-throughput nucleotide sequencing; humans; prognosis; human; male; female; mesothelioma, malignant |
| Journal Title: | The Lancet Digital Health |
| Volume: | 3 |
| Issue: | 9 |
| ISSN: | 2589-7500 |
| Publisher: | Elsevier Inc. |
| Date Published: | 2021-09-01 |
| Start Page: | e565 |
| End Page: | e576 |
| Language: | English |
| DOI: | 10.1016/s2589-7500(21)00104-7 |
| PUBMED: | 34332931 |
| PROVIDER: | scopus |
| PMCID: | PMC8459747 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 2 November 2021 -- Source: Scopus |
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
Related MSK Work