Multiscale analysis of metabolic inflammation as a driver of breast cancer

代谢炎症作为乳腺癌驱动因素的多尺度分析

• 批准号: 10473886
• 负责人: Gerald V Denis
• 金额: $ 66.26万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-09 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10473886
• 关键词: Acute; Address; Adipose tissue; Affect; Algorithms; American; Apoptotic; Automobile Driving; Base Sequence; Biological Assay; Blocking Antibodies; Breast; Breast Cancer Cell; Breast Cancer Model; Breast Cancer Patient; Bromodomain; CD8B1 gene; Cancer Burden; Cancer Model; Cancer Patient; Cell model; Cells; Cellular Metabolic Process; Chemicals; Chronic; Cities; Clinical; Coculture Techniques; Complex; Computer Models; Dangerousness; Data; Development; Diabetes Mellitus; Disease; Endocrinology; Estrogen receptor negative; Genes; Goals; Hospitals; Hypertension; Immune; Immunology; Immunooncology; Immunophenotyping; Immunotherapy; Inflammation; Inflammatory; Knowledge; Malignant Neoplasms; Mammary Neoplasms; Mass Spectrum Analysis; Medical Oncology; Metabolic; Metabolic Diseases; Metabolism; Metformin; Micrometastasis; Modeling; Molecular; Neoplasm Metastasis; Newly Diagnosed; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Organoids; Outcome; Pathway Analysis; Pathway interactions; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Population Sciences; Prediabetes syndrome; Prevalence; Primary Neoplasm; Process; Prognosis; Prospective Studies; Proteins; Proteome; Public Health; Publishing; Research Personnel; Resolution; Risk; Signal Transduction; Suggestion; Surgical Oncology; System; Systems Biology; T cell receptor repertoire sequencing; T-Lymphocyte; T-Lymphocyte Subsets; Talents; Testing; The Cancer Genome Atlas; Tumor Immunity; Tumor-Derived; Tumor-infiltrating immune cells; Woman; base; breast cancer progression; cancer subtypes; cell killing; checkpoint receptors; chronic inflammatory disease; clinically relevant; cohort; comorbidity; epidemiologic data; exhaust; exhaustion; experience; experimental study; immune checkpoint; improved; inhibitor; innovation; malignant breast neoplasm; mathematical model; metabolic profile; mortality; multi-scale modeling; multidisciplinary; multiple omics; neoplasm immunotherapy; neoplastic cell; network models; non-diabetic; novel; patient population; phosphoproteomics; polarized cell; primary outcome; profiles in patients; response; safety net; transcriptome; transcriptome sequencing; treatment response; triple-negative invasive breast carcinoma; tumor; tumor microenvironment; tumor progression

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.

患有乳腺癌并合并2型糖尿病（T2D）的女性总生存率差40%