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; Leng Han

doi:10.1038/s41467-020-18742-9

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, Leng Han

Cell Developmental and Cancer Biology

Research output: Contribution to journal › Article › peer-review

111 Scopus citations

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.

Original language	English (US)
Article number	4946
Journal	Nature communications
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41467-020-18742-9
State	Published - Dec 1 2020

ASJC Scopus subject areas

General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy

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title = "Multi-omics prediction of immune-related adverse events during checkpoint immunotherapy",

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.",

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AU - Jing, Ying

AU - Liu, Jin

AU - Ye, Youqiong

AU - Pan, Lei

AU - Deng, Hui

AU - Wang, Yushu

AU - Yang, Yang

AU - Diao, Lixia

AU - Lin, Steven H.

AU - Mills, Gordon B.

AU - Zhuang, Guanglei

AU - Xue, Xinying

AU - Han, Leng

PY - 2020/12/1

Y1 - 2020/12/1

N2 - 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.

AB - 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.

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

Abstract

ASJC Scopus subject areas

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