Improved prediction of immune checkpoint blockade efficacy across multiple cancer types

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 f...

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Bibliographic Details
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:	New York Nature Publishing Group US 01.04.2022 Nature Publishing Group
Subjects:	631/114/2413 631/67/69 692/53/2421 692/699/67 Agriculture Bioinformatics Biomarkers, Tumor - genetics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Cancer Clinical decision making Decision making Genomics Humans Immune checkpoint Immune Checkpoint Inhibitors - therapeutic use Immunotherapy Immunotherapy - methods Life Sciences Machine learning Mutation Neoplasms - drug therapy Neoplasms - genetics Patients Predictions Retrospective Studies Survival Tumors
ISSN:	1087-0156, 1546-1696, 1546-1696
Online Access:	Get full text
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