Experimental-Computational Synthesis of Altered Immune Signaling in Breast Cancer

乳腺癌免疫信号改变的实验计算综合

• 批准号: 10682540
• 负责人: Peter Poon-Hang Lee
• 金额: $ 18.39万
• 依托单位: BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10682540
• 关键词: Bayesian Network; Biological; Biological Assay; Blood; Blood Cells; Blood Tests; Breast Cancer Patient; Cancer Center; Cancer Patient; Cancer Relapse; Cities; Clinical; Collaborations; Color; Computational Biology; Computer Analysis; Computer Models; Control Groups; Cytokine Signaling; Data; Data Analyses; Data Set; Defect; Development; Diagnosis; Disease; Elements; Estrogen receptor positive; Flow Cytometry; Gene Expression; Genetic; Goals; Helper-Inducer T-Lymphocyte; Histology; Hormonal; Human; Image Analysis; Immune; Immune System Diseases; Immune response; Immune signaling; Immune system; Individual; Knowledge; Malignant Neoplasms; Mammary Neoplasms; Mathematics; Measures; Methods; Modeling; Molecular; Network-based; Oncologist; Outcome; Patients; Pattern; Peripheral Blood Mononuclear Cell; Persons; Phenotype; Post-Translational Protein Processing; Process; Regulatory T-Lymphocyte; Role; Signal Pathway; Signal Transduction; Signaling Molecule; Structure; System; Systems Biology; Technology; Testing; Time; Tissues; Training; Tumor Tissue; Tumor-infiltrating immune cells; Work; archived data; cancer therapy; cancer type; clinically relevant; computer framework; computerized tools; cytokine; data integration; data modeling; data-driven model; dynamic system; experience; high dimensionality; immunological diversity; immunoregulation; improved; improved outcome; interest; malignant breast neoplasm; mathematical methods; mathematical model; monocyte; multidisciplinary; multimodality; neoplastic cell; novel; novel strategies; peripheral blood; predict clinical outcome; predictive modeling; prognostic; prospective; single-cell RNA sequencing; tool; tumor

PROJECT SUMMARY/ABSTRACT This integrated experimental–computational proposal seeks to unravel the complexity of immune signaling networks in healthy individuals and patients with estrogen receptor-positive breast cancer (ER+ BC). Our goal is to identify and characterize, at a systems level, the dynamical flow of information through immune signaling networks in healthy individuals and ER+ BC patients and to understand how immune signaling defects at diagnosis reflect patients' immune responses, leading to different clinical outcomes. This work and its approach are motivated by our experience in immune signaling in cancer and our existing collaborations with oncologists and computational biologists at the City of Hope Cancer Center. We have shown that approximately 40% of ER+ BC patients harbor defects in their immune signaling network, specifically in signaling molecules called cytokines. Importantly, our preliminary data revealed major cytokine signaling abnormalities within immune cells from the peripheral blood of ER+ BC patients, which reflect immune activity within tumors and can predict cancer relapse years later. We believe that understanding how these cytokines interact with each other and other critical elements of the immune signaling network can ultimately lead to improved cancer treatments. Because the individual components of signaling networks interact in complicated and difficult-to-predict ways, we propose to apply a systems biology computational modeling approach. First, we propose to experimentally capture a rich data set of biological variables (molecular, genetic, and cellular data) using state-of-the-art technologies in peripheral blood collected from healthy people (Aim 1) and patients with ER+ BC (Aim 2). We will integrate these data into Bayesian networks, a way of modeling the data that will allow us to mathematically and statistically describe the relationships between cancer and measured variables. We will also perform high-dimensional histology and spatial image analysis of human ER+ breast tumors (Aim 3), then apply Bayesian networks and dynamical mathematical models to identify common immune features between tumor tissue (Aim 3) and peripheral blood (Aims 1 and 2), which we will also correlate with outcome. Impact and deliverables. This proposal will begin unraveling the complexity of the immune signaling network from a systems biology perspective. Significant outcomes of the proposed studies will include i) identification, at the systems biology level, of a prognostic and clinically relevant immune phenotype that is characterized by defects in signaling networks in ER+ BC, and ii) development of a data-driven, computational framework for the study of immune signaling and its defects as a dynamical system in cancer patients with such signaling defects. Our approach should be broadly applicable to other types of cancers and immunological diseases. Therefore, an important deliverable will be a computational systems biology data analysis toolkit to construct, interrogate, and dissect immune signaling networks that can be shared with other groups and applied to other diseases.

项目总结/摘要 这个综合实验-计算的建议试图解开免疫信号的复杂性 在健康个体和雌激素受体阳性乳腺癌（ER+ BC）患者中的网络。我们的目标 是在系统层面上识别和表征通过免疫信号传递的动态信息流 在健康个体和ER+ BC患者中的免疫信号传导网络，并了解 诊断反映了患者的免疫反应，导致不同的临床结果。 这项工作及其方法的动机是我们在癌症免疫信号传导方面的经验和我们现有的 与希望之城癌症中心的肿瘤学家和计算生物学家合作。我们已经表明 大约40%的ER+ BC患者的免疫信号网络存在缺陷，特别是在 称为细胞因子的信号分子。重要的是，我们的初步数据揭示了主要的细胞因子信号传导 ER+ BC患者外周血免疫细胞内的异常，反映了免疫活性 并能预测几年后的癌症复发。我们相信，了解这些细胞因子如何 相互作用，免疫信号网络的其他关键要素最终可能导致 改善癌症治疗。由于信令网络的各个组件以复杂的方式相互作用， 和难以预测的方式，我们建议应用系统生物学计算建模方法。一是 建议通过实验捕获丰富的生物变量数据集（分子、遗传和细胞数据） 使用最先进的技术，从健康人（目标1）和患有 ER+ BC（目标2）。我们将把这些数据整合到贝叶斯网络中，这是一种建模数据的方法， 我们可以从数学和统计学上描述癌症和测量变量之间的关系。我们 还将对人类ER+乳腺肿瘤进行高维组织学和空间图像分析（目标3）， 然后应用贝叶斯网络和动态数学模型来识别共同的免疫特征 肿瘤组织（目标3）和外周血（目标1和2）之间的差异，我们也将其与结果相关。 影响和交付成果。这项提议将开始揭开免疫信号网络的复杂性 从系统生物学的角度来看。拟议研究的重要成果将包括： 系统生物学水平，预后和临床相关免疫表型，其特征在于 ER+ BC中信号网络的缺陷，以及ii）开发数据驱动的计算框架， 研究免疫信号及其缺陷作为一个动态系统在癌症患者与这种信号缺陷。 我们的方法应该广泛适用于其他类型的癌症和免疫性疾病。因此，我们认为， 一个重要的可交付成果将是一个计算系统生物学数据分析工具包，用于构建，询问， 并剖析免疫信号网络，这些网络可以与其他群体共享并应用于其他疾病。