Improved prediction of immune checkpoint blockade efficacy across multiple cancer types

• Published in: Nature biotechnology Vol. 40; no. 4; pp. 499 - 506
• Main Authors: Chowell, Diego, Yoo, Seong-Keun, Valero, Cristina, Pastore, Alessandro, Krishna, Chirag, Lee, Mark, Hoen, Douglas, Shi, Hongyu, Kelly, Daniel W, Patel, Neal, Makarov, Vladimir, Ma, Xiaoxiao, Vuong, Lynda, Sabio, Erich Y, Weiss, Kate, Kuo, Fengshen, Lenz, Tobias L, Samstein, Robert M, Riaz, Nadeem, Adusumilli, Prasad S, Balachandran, Vinod P, Plitas, George, Ari Hakimi, A, Abdel-Wahab, Omar, Shoushtari, Alexander N, Postow, Michael A, Motzer, Robert J, Ladanyi, Marc, Zehir, Ahmet, Berger, Michael F, Gönen, Mithat, Morris, Luc G T, Weinhold, Nils, Chan, Timothy A
• Format: Journal Article
• Language: English
• Published: United States Nature Publishing Group 01.04.2022
• Subjects: Biomarkers, Tumor - genetics; Cancer; Decision making; Genomics; Humans; Immune checkpoint; Immune Checkpoint Inhibitors - therapeutic use; Immunotherapy; Immunotherapy - methods; Machine learning; Mutation; Neoplasms - drug therapy; Neoplasms - genetics; Patients; Predictions; Retrospective Studies; Survival; Tumors
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/s41587-021-01070-8

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 purpose . 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.