Identification of biomarkers for immune-related adverse events

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Immune-related adverse events (irAEs) during anti-programmed death 1 (PD-1) or anti-programmed death ligand 1 (PD-L1) antibody therapy, resulting from immune activation combined with disturbed immunologic homoeostasis, can affect any organ systems and in some cases can be lethal (Postow et al., 2018). Therefore, predictive biomarkers of irAEs are required to determine the benefit/risk ratio for patients receiving anti-PD-1/PD-L1 therapy. A comprehensive approach to identify biomarkers of irAE is lacking. We aimed to identify predictive biomarkers of irAEs to balance the benefit/risk ratio of immunotherapy patients.

Inspired by a recent study (Lee and Ruppin, 2019), we utilized an alternative strategy to combine the power of real-world pharmacovigilance data and omics data (Jing et al, 2020). We calculated the irAE reporting odds ratio (ROR) based on safety reports from the US FDA Adverse Event Reporting System (FAERS) across 26 different cancer types and evaluated the associations between 36 known immune related factors. Cytolytic activity, IFN γ signature, PD-1 expression, TCR diversity, macrophages M1, CD8+ T-cell abundance, and naive B cells showed positive association with irAE risks. Further analysis revealed that the combination of CD8+ T cells and TCR diversity achieved the best predictive accuracy. We further performed similar analysis to identify potential irAE predictors among mRNA/miRNA/lncRNA/protein expression, and nonsynonymous mutations across 26 cancer types. The top irAE risk associated factors were enriched in T-cell activation pathways. The bivariate model comprised of LCP1 and ADPGK achieved the maximum irAE predictive efficacy. We then collaborated with physician scientists and cancer biologists, and collected pretreatment tumor tissues of a cohort of 28 anti-PD-1/PD-L1 cancer patients. It is also really challenging to collect samples and conduct experiments through the COVID-19 pandemic, but our collaborators successfully made it. Indeed, the AUC of LCP1 +ADPGK in predicting irAEs was 0.80 in our validation cohort.

Our proof-of-concept study set up an analytic framework to explore irAE biomarkers and propose possible underpinning key factors, which might have important implications for management of patients with immunotherapy. Our analysis enables the study of promising signals of immune related toxicities in unprecedented large sample cohorts, while collecting both molecular data and irAE information of thousands of patients with immunotherapy requires the collaboration of multicenter with several years’ efforts. Future work is necessary to further study predictive performance of LCP1 and ADPGK in larger anti-PD-1/PD-L1 patient cohorts. We envision that LCP1 and ADPGK might enable a pre-risk-check of patients before receiving anti-PD-1/PD-L1 agents with further study. More importantly, our work suggests that the strategy that to combine the power of real-world data and omics data is robust and powerful, especially in the absence of a large number of patient samples.

 

Leng Han

Assistant Professor, University of Texas Health Science Center at Houston

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