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.

患有乳腺癌和共病的2型糖尿病(T2D)的女性总体存活率低40% 与非糖尿病妇女相比，这种共同发病负担在易患糖尿病的队列中高得不成比例， 例如美国安全网医院的患者，在那里它可以影响到一半的患者。然而，目前 乳腺肿瘤进展和免疫治疗的模型是基于代谢健康的癌症的数据 患者，忽略T2D的代谢/炎症成分。初步和公布的数据支持总体 假说：伴有T2D的乳腺癌患者的特定代谢和免疫衰竭网络 促进肿瘤侵袭性。我们提出了一个创新的多尺度建模框架来识别这些 在多组学癌症模型中整合代谢、炎症和免疫信号的网络 包括rna-seq和磷酸蛋白质组学数据。我们采用了系统生物学的方法来结合 创新的计算、临床和患者来源的肿瘤器官实验以研究相互作用 在可能的驱动基因中，T2D和免疫衰竭，肿瘤进展/侵袭性是 雌激素受体阴性(ER-)乳腺癌的主要结局变量，预后差， 在安全网医院的患者中高度流行。我们将对T2D如何重新布线信号集线器、节点和 在新诊断的乳腺癌患者的边缘，然后在乳腺器官模型中测试这些网络。我们会 通过三个目标建立统一模型：目标1：确定T2D如何重新编程免疫衰竭和 ER阴性(ER-)乳腺癌肿瘤微环境的代谢我们将应用RNAseq和 ScRNAseq对原代ER-乳腺癌细胞和肿瘤免疫浸润物的比较 (T2D、T2D+二甲双胍+药物(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星团的连接性将发生改变，并且密度比 ND或T2D+M目标2：我们将产生患者衍生的器官，包括器官免疫的T细胞(OPT)，以 测试新陈代谢和免疫检查点的计算模型。我们将评估机械论假说 BET溴域的T2D药物、免疫检查点阻断抗体和化学抑制剂 调节检查点表达的蛋白质克服免疫衰竭改善OPT细胞新陈代谢 和肿瘤细胞的杀伤力。TCR测序将揭示紧急OPT寡克隆；深度免疫表型将 揭示T2D驱动的信令网络。目的3：确定影响癌症免疫的异常信号网络 在有机体和OPT模型中。我们将对原发肿瘤、器官组织、 来自三个代谢组的循环T细胞和OPT细胞，然后使用路径投影和网络 分析以完善我们的集成模型。总之，我们独特的系统生物学方法将捕捉到复杂的 肿瘤、免疫浸润物和代谢基因之间的相互作用以解决T2D的癌症负担。