Bridging the Gap in Immunotherapy Prediction: The AGAE Score as a Pan-Cancer Biomarker for Immune Checkpoint Inhibitor Response

Abstract

Background: Immune checkpoint inhibitor (ICI) therapy efficacy varies among cancer patients, necessitating precise predictive biomarkers for optimized treatment strategies. Methods: We developed the Adaptive best subset selection algorithm and Genetic algorithm Aided Ensemble learning (AGAE) score through multi-cohort transcriptomic analysis of ICI-treated patients. The AGAE score incorporated gene-pairing, Adaptive Best Subset Selection for feature optimization, and a Genetic Algorithm for optimal basic learner identification. We explored correlations between AGAE score and immune microenvironments using multi-omics data. Potential targets were screened using 17 CRISPR datasets and validated through in vitro and in vivo experiments. Results: The AGAE score demonstrated robust predictive power for ICI therapy outcomes, with lower scores correlating with enhanced treatment response. The AGAE score outperformed published signatures and conventional biomarkers. Lower AGAE scores were associated with increased immune cell infiltration, higher immunogenicity, and enhanced antitumor immune activity. The CEP55 was identified as a potential key target driving immune evasion through AGAE scoring and CRISPR screening. Experimental validation showed CEP55 downregulation attenuated tumor cell malignancy and augmented ICI therapy efficacy by modulating T cell responses. Conclusions: The AGAE score was a potent predictor of ICI therapy efficacy, facilitating refined patient stratification. CEP55's role in the tumor microenvironment's immune response highlights its potential as a therapeutic target. Targeted interventions against CEP55 may improve immunotherapy precision.