Abstract A015: Prediction of immunotherapy response using mutations to cancer protein assemblies

Abstract

While immune checkpoint inhibitors (ICI) have substantially advanced cancer treatment, favorable responses are limited to a subset of patients and are not strongly predicted by histology or overall mutation burden. Here, we factor mutational burden across a landscape of known and putative multimeric protein assemblies, yielding Assembly Mutation Burden (AMB) scores for each patient. Predictive modeling identifies 14 assemblies in which mutation is associated with ICI response in 555 bladder cancer (BLCA) and non-small-cell lung cancer (NSCLC) patients, outperforming predictions based on genome-wide mutation burden alone. The assemblies capture immunogenic hallmarks and accurately predict treatment outcomes in a validation cohort of 68 immunotherapy-treated NSCLC patients. We explore a predictive assembly of 50 immunoregulatory complex proteins, in which we show CRISPR/Cas9 genetic disruptions influence ICI-treatment outcomes in mice. This study provides a roadmap for using knowledge of tumor cell biology to interpret the immunogenome.

Citation Format: JungHo Kong, Robin Bachelder, Xiaoyu Zhao, Sungjoon Park, Akshat Singhal, Hannah Carter, Trey Ideker. Prediction of immunotherapy response using mutations to cancer protein assemblies [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A015.