Abstract 3516: A genomics model to predict immune-related adverse events in cancer patients treated with checkpoint inhibitors

Abstract

Abstract The overall objective of this study is to use next-generation sequencing technology and bioinformatics to better inform the safety of immunotherapy treatment for cancer. Cancer patients commonly develop immune-related adverse events (irAEs) during and after treatment with checkpoint inhibitors. These irAEs can be serious or even fatal. Therefore, a biomarker for prediction of irAE development could have utility for heightened surveillance, personalized therapy decisions, and regulatory evaluation of drugs. Due to the similarity between irAEs and autoimmune diseases and the high heritability of autoimmune diseases, we hypothesized that certain patients could have a genetic predisposition for developing irAEs. To test this hypothesis, we conducted whole exome sequencing on an Illumina NextSeq to interrogate germline genomes of solid tumor patients (n=50) treated with an anti-PD-L1 antibody (NCT01772004). Relevant clinical data, such as adverse events and irAE classification, were provided for this retrospective analysis; twenty percent of patients (10/50) had irAEs. A preliminary germline genetic model of irAEs was constructed using short variant calls from this initial training set. This was generated using a proprietary algorithm that implements a Monte-Carlo simulation expansion of Fisher’s regularized linear discriminant analysis (RLDA) in a multidimensional measurement system to create a model that maximizes separation between two groups. This model consists of 131 genes, each of which make a relatively small contribution to the overall signature. The ten genes with the highest contribution coefficients together account for 21% of the signature. Ingenuity Pathway Analysis (IPA) identified a network associated with infectious diseases, antimicrobial response, and inflammatory response that contains 21 interconnected genes from the signature. IPA also revealed that genes in the signature have a variety of molecular and cellular functions, the most significant of which are cell death and survival, cellular movement, and cell-to-cell signaling and interaction. This model has 100% sensitivity, specificity, and accuracy on the training set. Future directions will test the performance of this putative genomic model on a new dataset to assess the validity and utility of the model as a predictive biomarker to identify patients at risk for developing irAEs in response to checkpoint inhibition. Citation Format: Emma C. Scott, Dickran Kazandjian, Luis Santana-Quintero, Tigran Ghazanchyan, Svetlana Petrovskaya, Yong Zhang, Amy Rosenberg, V. Ashutosh Rao, Jennifer L. Marte, Gideon M. Blumenthal, Marc R. Theoret, Richard Pazdur, James L. Gulley, Julia A. Beaver. A genomics model to predict immune-related adverse events in cancer patients treated with checkpoint inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3516.