| Literature DB >> 34725502 |
Diego Chowell1,2,3,4, Seong-Keun Yoo1,2,3, Cristina Valero1,2,5, Alessandro Pastore1, Chirag Krishna6, Mark Lee1,2, Douglas Hoen1,2,3, Hongyu Shi7,8, Daniel W Kelly9, Neal Patel1,2,5, Vladimir Makarov1,2,3, Xiaoxiao Ma1,2,3, Lynda Vuong1, Erich Y Sabio1, Kate Weiss1,2, Fengshen Kuo1,2, Tobias L Lenz10, Robert M Samstein11, Nadeem Riaz1,2,12, Prasad S Adusumilli5, Vinod P Balachandran1,5, George Plitas5, A Ari Hakimi1,2,5, Omar Abdel-Wahab1, Alexander N Shoushtari13, Michael A Postow13, Robert J Motzer13, Marc Ladanyi14, Ahmet Zehir14, Michael F Berger14, Mithat Gönen8, Luc G T Morris15,16,17, Nils Weinhold18,19, Timothy A Chan20,21,22,23,24.
Abstract
Only a fraction of patients with cancer respond to immune checkpoint blockade (ICB) treatment, but current decision-making procedures have limited accuracy. In this study, we developed a machine learning model to predict ICB response by integrating genomic, molecular, demographic and clinical data from a comprehensively curated cohort (MSK-IMPACT) with 1,479 patients treated with ICB across 16 different cancer types. In a retrospective analysis, the model achieved high sensitivity and specificity in predicting clinical response to immunotherapy and predicted both overall survival and progression-free survival in the test data across different cancer types. Our model significantly outperformed predictions based on tumor mutational burden, which was recently approved by the U.S. Food and Drug Administration for this purpose1. Additionally, the model provides quantitative assessments of the model features that are most salient for the predictions. We anticipate that this approach will substantially improve clinical decision-making in immunotherapy and inform future interventions.Entities:
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Year: 2021 PMID： 34725502 PMCID： PMC9363980 DOI： 10.1038/s41587-021-01070-8
Source DB: PubMed Journal: Nat Biotechnol ISSN： 1087-0156 Impact factor: 68.164