It is challenging to identify the tumor-immune system interactions that modulate immune states and immunotherapy responses due to the prohibitively complex space of possible interactions. Our statistical analysis framework, ImogiMap quantifies tumor-immune interactions based on their synergistic co-associations with immune-associated phenotypes. ImogiMap-based analyses recapitulated known interactions modulating immunotherapy responses and nominated the CD86/CD70 axis as an immunotherapy target that overlaps with IFNG overexpression and patient survival in endometrial carcinoma.

The interactions between tumor intrinsic processes and immune checkpoints can mediate immune evasion by cancer cells and responses to immunotherapy. It is, however, challenging to identify functional interactions due to the prohibitively complex molecular landscape of the tumor-immune interfaces. We address this challenge with a statistical analysis framework, immuno-oncology gene interaction maps (ImogiMap). ImogiMap quantifies and statistically validates tumor-immune checkpoint interactions based on their co-associations with immune-associated phenotypes. The outcome is a catalog of tumor-immune checkpoint interaction maps for diverse immune-associated phenotypes. Applications of ImogiMap recapitulate the interaction of SERPINB9 and immune checkpoints with interferon gamma (IFNγ) expression. Our analyses suggest that CD86-CD70 and CD274-CD70 immunoregulatory interactions are significantly associated with IFNγ expression in uterine corpus endometrial carcinoma and basal-like breast cancer, respectively. The open-source ImogiMap software and user-friendly web application will enable future applications of ImogiMap. Such applications may guide the discovery of previously unknown tumor-immune interactions and immunotherapy targets.

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The interactions between tumor intrinsic processes and immune checkpoints can mediate the immune evasion by tumors and responses to immunotherapy. It is, however, challenging to identify functional interactions due to the prohibitively complex molecular landscape of the tumor-immune interfaces. We address this challenge with a statistical analysis framework, termed immuno-oncology gene interaction maps (ImogiMap). ImogiMap quantifies tumor-immune checkpoint interactions based on their co-associations with immune-associated phenotypes. The interactions are statistically validated for significance, robustness, and specificity. The outcome is a catalog of maps that quantify the interactions at the tumor-immune interface for diverse immune phenotypes and checkpoints. We demonstrate that applications of ImogiMap on immune checkpoints and T-cell dysfunction signatures genes recapitulates the previously reported interaction of SERPINB9 and immune checkpoints in the tumor microenvironment. Our analyses also suggest other immune checkpoint and T-cell dysfunction signatures interactions that are significantly associated with IFNγ expression in uterine corpus endometrial carcinoma and basal-like breast cancer. We also provide open-source software and a user-friendly web application to enable future applications and rapid adoption of ImogiMap. Such applications may guide the discovery of previously unknown tumor-immune interactions and help the identification of immunotherapy targets. Citation Format: Behnaz Bozorgui, Elisabeth K. Kong, Augustin Luna, Anil Korkut. Mapping the functional interactions at the tumor-immune checkpoint interface [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3144.