Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs.

Immunotherapies, including checkpoint blockade and chimeric antigen receptor T cell (CAR-T) therapy, have revolutionized cancer treatment; however, many patients remain unresponsive to these treatments or relapse following treatment. CRISPR screenings have been used to identify novel single gene targets that can enhance immunotherapy effectiveness, but the identification of combinational targets remains a challenge. Here, we introduce a computational approach that uses sgRNA set enrichment analysis to identify cancer-intrinsic paralog pairs for enhancing immunotherapy using genome-wide screens. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features to predict paralog gene pairs that influence immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using CRISPR double knockout (DKO). These data and analyses collectively provide a sensitive approach to identifying previously undetected paralog gene pairs that can significantly affect cancer immunotherapy response, even when individual genes within the pair have limited effect.