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

基本信息

批准号：
10474417
负责人：
Peter Poon-Hang Lee
金额：
$ 61.23万
依托单位：
BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
依托单位国家：
美国
项目类别：
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/10474417
关键词：
Archives Bayesian Network Biological Biological Assay Blood Blood Cells Blood Tests Breast Cancer Patient Cancer Center Cancer Model 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 Goals Helper-Inducer T-Lymphocyte Histology Hormonal Human Image Analysis Immune Immune System Diseases Immune response Immune signaling Immune system Individual Knowledge Lead Malignant Neoplasms Mammary Neoplasms Mathematics Measures Methods Modeling Molecular Molecular Genetics 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 Study models System Systems Biology Technology Testing Time Training Tumor Tissue Tumor-infiltrating immune cells Work cancer therapy cancer type clinically relevant computer framework computerized tools cytokine data archive data modeling data-driven model dynamic system experience high dimensionality immunological diversity immunoregulation improved improved outcome individual patient 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 tissue archive tool tumor

项目摘要

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.

项目概要/摘要这一综合实验-计算方案旨在揭示免疫信号传导的复杂性我们的目标是在健康个体和雌激素受体阳性乳腺癌 (ER+ BC) 患者中建立网络。是在系统水平上识别和表征通过免疫信号传导的动态信息流健康个体和 ER+ BC 患者的网络，并了解免疫信号缺陷如何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以及我们现有的我们已经展示了与希望之城癌症中心的肿瘤学家和计算生物学家的合作。大约 40% 的 ER+ BC 患者的免疫信号网络存在缺陷，特别是重要的是，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异常，反映免疫活动我们相信，了解这些细胞因子如何预测肿瘤内的肿瘤并可以预测癌症的复发。免疫信号网络的其他关键要素相互作用，最终可以导致因为信号网络的各个组成部分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。提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）和患有疾病的患者身上采集外周血 ER+ BC（目标 2）。我们将把这些数据集成到贝叶斯网络中，这是一种允许数据建模的方法。我们以数学和财务方式描述癌症与测量变量之间的关系。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常见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联。该提案将开始揭示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 i) 识别，系统生物学水平，具有预后和临床相关的免疫表型，其特征是 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具有此类信号缺陷的癌症患者的动态系统。我们的方法应该广泛适用于其他类型的癌症和免疫疾病。一个重要的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、并剖析可以与其他群体共享并应用于其他疾病的免疫信号网络。