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

基本信息

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
项目类别：
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

项目摘要

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.

患有乳腺癌和共病 2 型糖尿病 (T2D) 的女性的总体生存率较差 40% 与非糖尿病女性相比；这种共病负担在弱势群体中尤为严重，例如美国安全网络医院的患者，它可能影响到一半的患者群体。然而，目前乳腺肿瘤进展和免疫治疗模型基于代谢健康癌症的数据患者，忽略 T2D 的代谢/炎症成分。初步和已发布的数据支持总体假设：合并 T2D 的乳腺癌患者存在特定的代谢和免疫衰竭网络促进肿瘤的侵袭性。我们提出了一个创新的多尺度建模框架来识别这些通过在多组学癌症模型中整合代谢、炎症和免疫特征来构建网络涵盖 RNA-seq 和磷酸蛋白质组学数据。我们采用系统生物学方法来结合创新的计算、临床和患者来源的肿瘤类器官实验来研究相互作用在假定的驱动基因、T2D 和免疫衰竭中，肿瘤进展/攻击性是雌激素受体阴性 (ER-) 乳腺癌的主要结局变量，其预后较差，且在安全网医院患者中非常普遍。我们将模拟 T2D 如何重新连接信号集线器、节点和新诊断的乳腺癌患者的边缘，然后在乳腺类器官模型中测试这些网络。我们将通过三个目标开发统一模型：目标 1：确定 T2D 如何重新编程免疫衰竭和 ER阴性（ER-）乳腺癌肿瘤微环境中的代谢。我们将应用 RNAseq 和 scRNAseq 对原发性 ER-乳腺癌细胞和肿瘤免疫浸润进行比较三组患者（T2D、T2D+ 二甲双胍治疗 (T2D+M)、非糖尿病 (ND) 对照）构建初步网络补充了 TCGA 数据。差异基因和通路分析将阐明调控关系和关键枢纽。我们假设 T2D 中 ER 簇的连接性将发生改变，并且比 T2D 中的更密集。 ND 或 T2D+M。目标 2：我们将生成源自患者的类器官，包括类器官引发的 T 细胞 (OpT)，以测试代谢和免疫检查点的计算模型。我们将评估机制假设 T2D 药物、免疫检查点阻断抗体和 BET 溴结构域化学抑制剂蛋白质（调节检查点表达）克服免疫衰竭，改善 OpT 细胞代谢和肿瘤细胞杀伤作用。 TCR 测序将揭示新出现的 OpT 寡克隆性；深度免疫表型分析将揭示 T2D 驱动的信号网络。目标 3：确定影响癌症免疫的异常信号网络在类器官和 OpT 模型中。我们将对原发性肿瘤、类器官、来自三个代谢组的循环 T 细胞和 OpT 细胞，然后使用通路投影和网络分析以完善我们的集成模型。我们独特的系统生物学方法将共同捕获复杂的肿瘤、免疫浸润和代谢基因之间的相互作用，以解决 T2D 的癌症负担。