Dataset Information

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.

SUBMITTER: Jing Y

PROVIDER: S-EPMC7532211 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Jing Ying Y Liu Jin J Ye Youqiong Y Pan Lei L Deng Hui H Wang Yushu Y Yang Yang Y Diao Lixia L Lin Steven H SH Mills Gordon B GB Zhuang Guanglei G Xue Xinying X Han Leng L

Nature communications 20201002 1

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 ...[more]

PMID: 33009409

Similar Datasets

Project description:Although immune checkpoint inhibitors (ICIs) have transformed the treatment landscape for patients with many advanced malignancies, only 15-60% of patients respond, leaving a broad swath of patients who do not derive benefit. Identifying biomarkers to optimally identify patients who will benefit from ICIs is a major research focus for the oncology community. Thus far, predictive biomarker research has focused on tumor signatures such as microsatellite instability, programmed death-ligand 1 (PD-L1) expression and tumor mutational burden; clinical biomarkers have been far less studied. One potential clinical biomarker for ICI response in patients is immune-related adverse event (IRAE) onset.IRAEs are thought to represent bystander effects from activated T-cells and it is plausible that patients responding to ICIs would have greater likelihood of autoimmune toxicities (e.g. due to a more competent/treatment-responsive immune system, or cross-reactivity between tumor and host tissue). Earlier studies in melanoma patients however, suggested no association between IRAE onset and anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibody efficacy. In contrast, a growing body of literature suggests IRAE onset is predictive of anti-programmed cell death protein 1 (PD-1) and anti-PD-L1 antibody response across a variety of solid tumors. Most of these studies report that patients who experienced IRAEs demonstrate marked improvements in progression-free survival, overall survival and overall response rate compared to those lacking toxicity.Key questions regarding the association between IRAE onset and ICI efficacy remain. The most pertinent of these involve whether the association is only relevant for patients treated with anti-PD-1 and anti-PD-L1 antibodies and whether IRAE site, severity, timing of onset and management influence ICI efficacy. Herein, we discuss the seminal studies which have begun to address these questions and have shaped the narrative about the predictive value of IRAE onset for patients on ICIs, in this review.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure