Although considerable efforts have been dedicated to identifying predictive signatures for immune checkpoint inhibitor (ICI) treatment response, current biomarkers suffer from poor generalizability and reproducibility across different studies and cancer types. The integration of large-scale multiomics studies holds great promise for discovering robust biomarkers and shedding light on the mechanisms of immune resistance. In this study, we conducted the most extensive meta-analysis involving 3,037 ICI-treated patients with genetic and/or transcriptomics profiles across 14 types of solid tumor. The comprehensive analysis uncovered both known and novel reliable signatures associated with ICI treatment outcomes. The signatures included tumor mutational burden (TMB), IFNG and PDCD1 expression, and notably, interactions between macrophages and T cells driving their activation and recruitment. Independent data from single-cell RNA sequencing and dynamic transcriptomic profiles during the ICI treatment provided further evidence that enhanced cross-talk between macrophages and T cells contributes to ICI response. A multivariable model based on eight nonredundant signatures significantly outperformed existing models in five independent validation datasets representing various cancer types. Collectively, this study discovered biomarkers predicting ICI response that highlight the contribution of immune cell networks to immunotherapy efficacy and could help guide patient treatment.
Significance: Identification of robust immunogenomic connections, particularly macrophage T-cell interactions, in a large-scale pan-cancer meta-analysis and development of a predictive model for immunotherapy response that outperformed existing models could facilitate clinical decision-making.
©2023 The Authors; Published by the American Association for Cancer Research.