Improved prediction of immune checkpoint blockade efficacy across multiple cancer types

Diego Chowell, Seong Keun Yoo, Cristina Valero, Alessandro Pastore, Chirag Krishna, Mark Lee, Douglas Hoen, Hongyu Shi, Daniel W. Kelly, Neal Patel, Vladimir Makarov, Xiaoxiao Ma, Lynda Vuong, Erich Y. Sabio, Kate Weiss, Fengshen Kuo, Tobias L. Lenz, Robert M. Samstein, Nadeem Riaz, Prasad S. AdusumilliVinod P. Balachandran, George Plitas, A. Ari Hakimi, Omar Abdel-Wahab, Alexander N. Shoushtari, Michael A. Postow, Robert J. Motzer, Marc Ladanyi, Ahmet Zehir, Michael F. Berger, Mithat Gönen, Luc G.T. Morris, Nils Weinhold, Timothy A. Chan

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


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.

Original languageEnglish
Pages (from-to)499-506
Number of pages8
JournalNature Biotechnology
Issue number4
StatePublished - Apr 2022


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