Abstract
BackgroundThe cancer-immunity cycle (CI cycle) provides a theoretical framework to illustrate the process of the anticancer immune response. Recently, the update of the CI cycle theory emphasizes the importance of tumor’s immunological phenotype. However, there is lack of immunological phenotype of pan-cancer based on CI cycle theory.MethodsHere, we applied a visualizing method termed ‘cancer immunogram’ to visualize the state of CI cycle of 8460 solid tumors from TCGA cohort. Unsupervised clustering of the cancer immunogram was performed using the nonnegative matrix factorization (NMF) analysis. We applied an evolutionary genomics approach (dN/dS ratio) to evaluate the clonal selection patterns of tumors with distinct immunogram subtypes.ResultsWe defined four major CI cycle patterns across 32 cancer types using a cancer immunogram approach. Immunogram-I was characterized by ‘hot’ and ‘exhausted’ features, indicating a favorable prognosis. Strikingly, immunogram-II, immunogram-III, and immunogram-IV represented distinct immunosuppressive patterns of ‘cold’ tumor. Immunogram-II was characterized by ‘cold’ and ‘radical’ features, which represented increased expression of immune inhibitor molecules and high levels of positive selection, indicating the worst prognosis. Immunogram-III was characterized by ‘cold’ and ‘recognizable’ features and upregulated expression of MHC I molecules. Immunogram-IV was characterized by ‘cold’ and ‘inert’ features, which represented overall immunosuppression, lower levels of immunoediting and positive selection, and accumulation of more tumor neoantigens. In particular, favorable overall survival was observed in metastatic urothelial cancer patients with immunogram-I and immunogram-IV after immune checkpoint inhibitor (ICI) therapy. Meanwhile, a higher response rate to ICI therapy was observed in metastatic gastric cancer patients with immunogram-I phenotype.ConclusionsOur findings provide new insight into the interaction between immunity and cancer evolution, which may contribute to optimizing immunotherapy strategies.
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