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

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          Summary

          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.

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          Most cited references47

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          Regularization Paths for Generalized Linear Models via Coordinate Descent

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            OncoKB: A Precision Oncology Knowledge Base

            Purpose With prospective clinical sequencing of tumors emerging as a mainstay in cancer care, an urgent need exists for a clinical support tool that distills the clinical implications associated with specific mutation events into a standardized and easily interpretable format. To this end, we developed OncoKB, an expert-guided precision oncology knowledge base. Methods OncoKB annotates the biologic and oncogenic effects and prognostic and predictive significance of somatic molecular alterations. Potential treatment implications are stratified by the level of evidence that a specific molecular alteration is predictive of drug response on the basis of US Food and Drug Administration labeling, National Comprehensive Cancer Network guidelines, disease-focused expert group recommendations, and scientific literature. Results To date, > 3,000 unique mutations, fusions, and copy number alterations in 418 cancer-associated genes have been annotated. To test the utility of OncoKB, we annotated all genomic events in 5,983 primary tumor samples in 19 cancer types. Forty-one percent of samples harbored at least one potentially actionable alteration, of which 7.5% were predictive of clinical benefit from a standard treatment. OncoKB annotations are available through a public Web resource ( http://oncokb.org ) and are incorporated into the cBioPortal for Cancer Genomics to facilitate the interpretation of genomic alterations by physicians and researchers. Conclusion OncoKB, a comprehensive and curated precision oncology knowledge base, offers oncologists detailed, evidence-based information about individual somatic mutations and structural alterations present in patient tumors with the goal of supporting optimal treatment decisions.
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              Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): A Hybridization Capture-Based Next-Generation Sequencing Clinical Assay for Solid Tumor Molecular Oncology.

              The identification of specific genetic alterations as key oncogenic drivers and the development of targeted therapies are together transforming clinical oncology and creating a pressing need for increased breadth and throughput of clinical genotyping. Next-generation sequencing assays allow the efficient and unbiased detection of clinically actionable mutations. To enable precision oncology in patients with solid tumors, we developed Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT), a hybridization capture-based next-generation sequencing assay for targeted deep sequencing of all exons and selected introns of 341 key cancer genes in formalin-fixed, paraffin-embedded tumors. Barcoded libraries from patient-matched tumor and normal samples were captured, sequenced, and subjected to a custom analysis pipeline to identify somatic mutations. Sensitivity, specificity, reproducibility of MSK-IMPACT were assessed through extensive analytical validation. We tested 284 tumor samples with previously known point mutations and insertions/deletions in 47 exons of 19 cancer genes. All known variants were accurately detected, and there was high reproducibility of inter- and intrarun replicates. The detection limit for low-frequency variants was approximately 2% for hotspot mutations and 5% for nonhotspot mutations. Copy number alterations and structural rearrangements were also reliably detected. MSK-IMPACT profiles oncogenic DNA alterations in clinical solid tumor samples with high accuracy and sensitivity. Paired analysis of tumors and patient-matched normal samples enables unambiguous detection of somatic mutations to guide treatment decisions.
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                Author and article information

                Contributors
                Journal
                101751302
                48799
                Lancet Digit Health
                Lancet Digit Health
                The Lancet. Digital health
                2589-7500
                14 September 2021
                September 2021
                28 July 2021
                23 September 2021
                : 3
                : 9
                : e565-e576
                Affiliations
                Thoracic Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                Department of Medicine, Weill Cornell Medical College, New York, NY, USA
                Biostatistics Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                Thoracic Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                Department of Medicine, Weill Cornell Medical College, New York, NY, USA
                Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                Thoracic Surgery, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                Thoracic Surgery, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                Thoracic Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                Department of Medicine, Weill Cornell Medical College, New York, NY, USA
                Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                Biostatistics Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                Author notes

                Contributors

                MGZ, AM, MGK, and RS were responsible for the conception and design of the study. MGZ, AM, JE, HR, and RS provided administrative support. MGZ, AR, VWR, PSA, JLS, and ML provided study materials or assisted with patient recruitment. MGZ, AM, JE, HR, and RS were responsible for data collection and data assembly. MGZ, AM, JE, HR, MO, MGK, JLS, ML, and RS were responsible for data analysis and data interpretation. All authors had access to all the raw datasets, and were responsible for manuscript writing, final approval of the manuscript, and all aspects of the work. MGZ and RS verified the data. MGZ, RS, and AM had access to all the data. MGZ was responsible for the decision to submit the manuscript.

                Correspondence to: Dr Marjorie G Zauderer, Thoracic Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA zauderem@ 123456mskcc.org
                Article
                NIHMS1735789
                10.1016/S2589-7500(21)00104-7
                8459747
                34332931
                47dba721-c5aa-4eaf-ae2c-465f3d4faba7

                This is an Open Access article under the CC BY-NC-ND 4.0 license.

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