Experimental-Computational Synthesis of Altered Immune Signaling in Breast Cancer

项目摘要/摘要 这一综合的实验-计算方案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 是在系统水平上识别和表征通过免疫信号的动态信息流 健康个体和ER+BC患者的网络，并了解免疫信号缺陷是如何在 诊断反映了患者的免疫反应，导致了不同的临床结果。 这项工作及其方法是由我们在癌症免疫信号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。我们已经展示了 大约40%的ER+BC患者的免疫信号网络存在缺陷，特别是在 信号分子称为细胞因子。重要的是，我们的初步数据揭示了主要的细胞因子信号 ER+BC患者外周血免疫细胞内反映免疫活性的异常 在肿瘤内，可以预测癌症多年后复发。我们相信了解这些细胞因子是如何 相互作用和免疫信号网络的其他关键元素最终可以导致 改善癌症治疗。因为信令网络的各个组件在复杂的环境中相互作用 和难以预测的方法，我们提出了应用系统生物学计算建模的方法。首先，我们 建议实验性地捕获丰富的生物变量数据集(分子、基因和细胞数据) 使用最先进的技术从健康人(目标1)和患者的外周血中采集 ER+BC(目标2)。我们将把这些数据集成到贝叶斯网络中，贝叶斯网络是一种对数据进行建模的方式，可以 从数学上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)之间的关系，我们也将与结果相关。 影响和交付成果。这项提议将开始解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：i)在 系统生物学水平，一种预后和临床相关的免疫表型，其特征是 ER+BC中信令网络的缺陷，以及ii)开发数据驱动的计算框架 免疫信号及其缺陷在癌症患者中作为动力系统的研究。 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， 一个重要的交付内容将是一个计算系统生物学数据分析工具包， 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。

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.