Synapse - Improved prediction of immune checkpoint blockade efficacy across multiple cancer types

Improved prediction of immune checkpoint blockade efficacy across multiple cancer types Journal Article

Authors:	Chowell, D.; Yoo, S. K.; Valero, C.; Pastore, A.; Krishna, C.; Lee, M.; Hoen, D.; Shi, H.; Kelly, D. W.; Patel, N.; Makarov, V.; Ma, X.; Vuong, L.; Sabio, E. Y.; Weiss, K.; Kuo, F.; Lenz, T. L.; Samstein, R. M.; Riaz, N.; Adusumilli, P. S.; Balachandran, V. P.; Plitas, G.; Hakimi, A. A.; Abdel-Wahab, O.; Shoushtari, A. N.; Postow, M. A.; Motzer, R. J.; Ladanyi, M.; Zehir, A.; Berger, M. F.; Gönen, M.; Morris, L. G. T.; Weinhold, N.; Chan, T. A.
Article Title:	Improved prediction of immune checkpoint blockade efficacy across multiple cancer types
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. © 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.
Keywords:	retrospective studies; genetics; mutation; neoplasm; neoplasms; patient monitoring; retrospective study; tumor marker; immunotherapy; forecasting; patient treatment; decision making; high sensitivity; diseases; retrospective analysis; procedures; clinical data; genomic data; demographic data; humans; human; immune checkpoint inhibitors; biomarkers, tumor; molecular data; machine learning models; 'current; decision making procedure; high specificity
Journal Title:	Nature Biotechnology
Volume:	40
Issue:	4
ISSN:	1087-0156
Publisher:	Nature Publishing Group
Date Published:	2022-04-01
Start Page:	499
End Page:	506
Language:	English
DOI:	10.1038/s41587-021-01070-8
PUBMED:	34725502
PROVIDER:	scopus
PMCID:	PMC9363980
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118408256&doi=10.1038%2fs41587-021-01070-8&partnerID=40&md5=f3725b030393cd5c76077d143af451e3
Notes:	Article -- Export Date: 25 April 2022 -- Source: Scopus