Abstract A10: Logical modelling of tumor-immune crosstalk network to predict the mechanism of immune evasion in muscle-invasive bladder cancer (MIBC)

Abstract

Abstract Muscle-invasive bladder cancer (MIBC) is an aggressive disease with limited treatment options. Checkpoint inhibitors, although approved as second-line therapy, have limited response rate that is indicative of intrinsic resistance to immune therapy. Efforts are being made to improve the response rate by designing novel drug combinations, one such being combination of chemotherapy and checkpoint inhibitors. Chemotherapy like the platinum-based therapy perturbs the tumor immune system, which contributes to the overall response to treatment. The tumor-immune system relationship is extremely dynamic, and the tumor post treatment constantly re-equilibrates the microenvironment to support its growth. The ability to capture the dynamic changes of the immune microenvironment will allow us to design treatments that can modulate the immune cells for a sustained antitumor effect. Here we have performed transcriptomic analysis of publicly available expression data provided by TCGA (The Cancer Genome Atlas) on MIBC. Using the deconvolution algorithm Cibersort, we generated tumor immune signatures for patients of MIBC. We observed that patients with high CD8+ T-cell expression signature are positively associated with improved survival. Moreover, we observed that patients with low CD8+ T-cell infiltration signature showed nonpolarized macrophage and resting CD4 T-cell expression signature, indicating that the patients' tumors have a “noninflamed” phenotype. To model the interplay between the tumor and the immune system, we constructed a network of bladder tumor-macrophage-CD4 T-cell (BMT network) crosstalk. The tumor expression data from the TCGA were used to build the tumor network, which was linked to the macrophage polarization network and CD4 T-cell differentiation network via “extracellular” cytokines. The change in the cytokine profile modulates the regulatory network of the macrophage polarization network and CD4 T-cell differentiation network. The BMT network is made dynamic by applying logic based Boolean formalism. The network structure of the BMT network was validated by comparing the network simulations to published experimental data. Network simulation with cisplatin treatment changed the immune profile of the BMT network depending on the initial condition of the network. The initial condition is described by the cytokine profile of the “extracellular” cytokine, and the immune profile is described by the macrophage polarized state and the CD4 T-cell differentiated cell. The BMT network generates a dynamic cytokine profile under different perturbation that allows us to predict the change in the tumor immune phenotypes in response to therapy and thus generate hypotheses on designing combination treatments to different immunotherapy. Citation Format: Shruti Shah, Barbara A. Foster, Donald E. Mager. Logical modelling of tumor-immune crosstalk network to predict the mechanism of immune evasion in muscle-invasive bladder cancer (MIBC) [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr A10.