Multiscale analysis of metabolic inflammation as a driver of breast cancer

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Women with breast cancer and co-morbid Type 2 diabetes (T2D) have up to 40% worse overall survival compared to non-diabetic women; this co-morbidity burden is disproportionately high among vulnerable cohorts, such as patients at safety net hospitals in the U.S., where it can affect half of the patient population. Yet, current models of breast tumor progression and immunotherapy are based on data from metabolically healthy cancer patients, ignoring metabolic /inflammatory components of T2D. Preliminary and published data support an overall hypothesis: specific metabolic and immune exhaustion networks in breast cancer patients with co-morbid T2D promote tumor aggressiveness. We propose an innovative multiscale modelling framework to identify these networks by integrating metabolic, inflammatory and immune signatures in multi-omics cancer models encompassing RNA-seq and phosphoproteomics data. We take a systems biology approach to combine innovative computational, clinical and patient-derived tumor organoid experiments to investigate interactions among putative driver genes, T2D and immune exhaustion, with tumor progression/aggressiveness as the primary outcome variable in estrogen receptor-negative (ER-) breast cancer, which has poor prognosis and is highly prevalent among safety net hospital patients. We will model how T2D rewires signaling hubs, nodes and edges in newly diagnosed breast cancer patients, then test these networks in breast organoid models. We will develop a unified model through three Aims: Aim 1: Determine how T2D reprograms immune exhaustion and metabolism in the tumor microenvironment of ER negative (ER-) breast cancer. We will apply RNAseq and scRNAseq to primary ER- breast cancer cells and tumor immune infiltrates to compare three groups of patients (T2D, T2D+ metformin-medicated (T2D+M), non-diabetic (ND) controls) to construct a preliminary network supplemented with TCGA data. Differential gene and pathway analyses will elucidate regulatory relationships and key hubs. We hypothesize that the connectivity of the ER- cluster in T2D will be altered and denser than in ND or T2D+M. Aim 2: We will generate patient-derived organoids, including organoid-primed T cells (OpT), to test the computational model for metabolism and immune checkpoints. We will evaluate mechanistic hypotheses that T2D medications, immune checkpoint-blocking antibodies and chemical inhibitors of BET bromodomain proteins (which regulate checkpoint expression) overcome immune exhaustion to improve OpT cell metabolism and tumor cell killing. TCR sequencing will reveal emergent OpT oligoclonality; deep immunophenotyping will reveal T2D-driven signaling networks. Aim 3: Determine abnormal signaling networks impacting cancer immunity in organoid and OpT models. We will perform deep phosphoproteomic profiling of primary tumors, organoids, circulating T cells and OpT cells, from the three metabolic groups, then use pathway projection and network analyses to refine our integrated model. Together, our unique systems biology approach will capture the complex interactions among tumor, immune infiltrates and metabolic genes to address the cancer burden of T2D.