An immunogenomic classification of solid tumours reveals subtype-specific therapeutic vulnerabilities for immunotherapy.

Abstract

The efficacy of immune checkpoint blockade (ICB) is heterogeneous across patients. Tumour immune phenotype classification (immune-inflamed, -excluded, and -desert) represents a foundational but inadequate framework for predicting ICB efficacy. Here we aimed to develop an integrated immunogenomic classification to improve ICB response prediction and identify subtype-specific therapeutic vulnerabilities. We analysed 13 public ICB cohorts and an in-house cohort. Using RNA-seq data, we developed ImmPred, a seven-gene classifier trained on IHC-defined immune phenotypes, and integrated it with TMB to define immunogenomic subtypes. Subtype-specific resistance mechanisms were investigated via pathway analysis and validated in syngeneic mouse models. Patients with cancers can be stratified into five immunogenomic subtypes with divergent responses to ICB, which are TMB-High (H) inflamed, TMB-Low (L) inflamed, TMB-H excluded, TMB-L excluded, and desert phenotypes. In immune-excluded tumours, MTAP deficiency contributes to ICB resistance in TMB-H excluded subtype and PRMT5 inhibitors enhances ICB efficacy in MTAP-KO B16-F10 and CT26 syngeneic mouse model, whereas TGF-β hyperactivation drives intrinsic resistance of TMB-L excluded subtype and TGF-β blockade potentiates anti-tumour immunity in MB49 and EMT6 mouse model. In TMB-H inflamed tumours, IFN-γ is a critical determinant of ICB efficacy, and TLR7 agonist, via enhancing IFN-γ signalling, improves anti-PD-L1 efficacy in MC38 mouse model. In TMB-L inflamed tumours, targeting COX-2-PGE2 axis with celecoxib sensitises ICB in LLC1 mouse model. Leveraging clinical feasible RNA-seq and TMB analysis, our model exhibits robust predictive efficacy of ICB response in multiple cancers, enabling subtype-tailored therapeutic combinations to improve immunotherapy response. This work was supported by grants from National Key Research and Development Program of China (2021YFA1300602), National Natural Science Foundation of China (82025026, 82230091, 82472775), Guang Dong Basic and Applied Basic Research Foundation (2023A1515012412 and 2023A1515011214), Guangdong Science and Technology Department (2023B1212060013, 2023B1111030006), Key R&D Program of Zhejiang (2024C03160), Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang Province (2024R01005 and 2025R01009).