Multi-omics prediction of immune-related adverse events during checkpoint immunotherapy

Ying Jing, Jin Liu, Youqiong Ye, Lei Pan, Hui Deng, Yushu Wang, Yang Yang, Lixia Diao, Steven H. Lin, Gordon B. Mills, Guanglei Zhuang (), Xinying Xue () and Leng Han ()
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Ying Jing: The University of Texas Health Science Center at Houston McGovern Medical School
Jin Liu: Shanghai Jiao Tong University
Youqiong Ye: The University of Texas Health Science Center at Houston McGovern Medical School
Lei Pan: Capital Medical University; Peking University Ninth School of Clinical Medicine
Hui Deng: Capital Medical University; Peking University Ninth School of Clinical Medicine
Yushu Wang: The University of Texas Health Science Center at Houston McGovern Medical School
Yang Yang: The University of Texas MD Anderson Cancer Center
Lixia Diao: The University of Texas MD Anderson Cancer Center
Steven H. Lin: The University of Texas MD Anderson Cancer Center
Gordon B. Mills: Oregon Health and Science University
Guanglei Zhuang: Shanghai Jiao Tong University
Xinying Xue: Capital Medical University; Peking University Ninth School of Clinical Medicine
Leng Han: The University of Texas Health Science Center at Houston McGovern Medical School

Nature Communications, 2020, vol. 11, issue 1, 1-7

Abstract: Abstract Immune-related adverse events (irAEs), caused by anti-PD-1/PD-L1 antibodies, can lead to fulminant and even fatal consequences and thus require early detection and aggressive management. However, a comprehensive approach to identify biomarkers of irAE is lacking. Here, we utilize a strategy that combines pharmacovigilance data and omics data, and evaluate associations between multi-omics factors and irAE reporting odds ratio across different cancer types. We identify a bivariate regression model of LCP1 and ADPGK that can accurately predict irAE. We further validate LCP1 and ADPGK as biomarkers in an independent patient-level cohort. Our approach provides a method for identifying potential biomarkers of irAE in cancer immunotherapy using both pharmacovigilance data and multi-omics data.

Date: 2020
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DOI: 10.1038/s41467-020-18742-9

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