# Multiscale analysis of metabolic inflammation as a driver of breast cancer

> **NIH NIH U01** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2020 · $593,459

## Abstract

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 ou...

## Key facts

- **NIH application ID:** 10063646
- **Project number:** 1U01CA243004-01A1
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** Gerald V Denis
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $593,459
- **Award type:** 1
- **Project period:** 2020-09-09 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10063646

## Citation

> US National Institutes of Health, RePORTER application 10063646, Multiscale analysis of metabolic inflammation as a driver of breast cancer (1U01CA243004-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10063646. Licensed CC0.

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