Structure-informed dissection of cancer-specific intracellular and paracrine networks

了解癌细胞的自主行为和促癌亚群对肿瘤的募集 微环境(TME)在很大程度上依赖于准确而全面的细胞和 蜂窝间网络。项目1的目标是开发一种新的、集成的、经过广泛验证的 对肿瘤微环境中的细胞-细胞信号进行建模、操作和分析的框架，包括 耦合到细胞内信号网络的细胞外配体-受体相互作用。项目1将建立在 为应对多重挑战而在上一个CSBC供资期间产生的方法和成果 通过(A)利用新技术扩展蛋白质-蛋白质相互作用(PPI)网络的结构信息预测 深度学习方法，(B)基于时间相关性分析的改进信号转导网络 药物扰动分析，以及(C)阐明配体/受体介导的旁分泌相互作用网络 调节健康细胞的募集--可能还有重新编程--到TME，以创建一个亲恶性的 环境。为了实现这些目标，重点将放在两种高度侵袭性的肿瘤上--结肠 腺癌(CoAD)和胰腺导管腺癌(PDAC)-其数据、模型、试剂 分析工具是在上一个供资周期中产生的。 项目1基于三个具体目标。通过整合蛋白质-蛋白质的深度学习方法 针对癌症特征的相互作用和基于结构的网络的创建，AIM 1将提供3D- 整个项目1中拟议工作的结构背景。目标2将定义基于磷酸蛋白质组学 细胞内信号网络，并描述它们对药物扰动的反应。目标3将定义旁分泌- 基于细胞-细胞信号网络，并用一种新型的芯片上器官平台进行验证。 项目1的影响将很大程度上来自其创新办法，其中包括使用结构-- 基于分析的蛋白质相互作用网络建模；基于结构的建模与深度分析的结合 学习算法，包括蛋白质语言模型，以提供基本上所有相互作用的模型 将在提案中预测和观察；基于磷蛋白质组学的磷蛋白的推断 活动，以提供关键的时间依赖和扰动敏感的细胞信号成分； 整合旁分泌信号；以及新的实验验证技术，包括Matted 磷蛋白组学和转录图谱，以及肿瘤和正常细胞的生物工程 相互连接的微腔，以更好地概括体内的组织生理学。 项目1的主要成果是细胞内和细胞间耦合的可询问的整体模型 通过对机制的剖析，将作为整个中心的基础的信号 有助于肿瘤相关细胞状态的稳定，它们的配体/受体介导的与 TME中的亚群及其在药理上可操作的分子依赖。

Understanding cancer cell-autonomous behavior and recruitment of pro-malignant subpopulations to the tumor microenvironment (TME) is critically dependent on the generation of accurate and comprehensive cellular and intercellular networks. The goal of Project 1 is to develop a novel, integrated, and extensively validated framework to model, manipulate, and dissect cell-cell signaling in the tumor microenvironment involving extracellular ligand-receptor interactions coupled to intracellular signaling networks. Project 1 will build on the methodologies and results generated during the previous CSBC funding period to address multiple challenges by (a) expanding structure-informed prediction of protein-protein interactions (PPI) networks by leveraging novel deep learning approaches, (b) improving signal transduction networks based on the analysis of time-dependent drug perturbation assays, and (c) elucidating ligand/receptor-mediated paracrine interaction networks that mediate recruitment—and possibly reprogramming—of healthy cells to the TME to create a pro-malignant environment. To accomplish these goals, the focus will be on two highly aggressive tumors—colon adenocarcinoma (COAD) and pancreatic ductal adenocarcinoma (PDAC)—for which data, models, reagents, and analytical tools were generated during the prior funding cycle. Project 1 is based on three specific aims. Through the integration of deep learning approaches to protein-protein interactions and the creation of structure-based networks for the Hallmarks of Cancer, Aim 1 will provide a 3D- structural context for the proposed work throughout Project 1. Aim 2 will define phosphoproteomics-based intracellular signaling networks and describe their response to drug perturbations. Aim 3 will define paracrine- based cell-cell signaling networks and validate them with a novel organs-on-a-chip platform. The impact of Project 1 will derive largely from its innovative approaches, which include the use of structure- based analyses to model protein interaction networks; the integration of structure-based modeling with deep learning algorithms, including Protein Language Models, to provide models for essentially all interactions that will be predicted and observed in the proposal; the inference of phosphoproteomics-based phosphoprotein activity to provide critical time-dependent and perturbation-sensitive components of cellular signaling; the incorporation of paracrine signaling; and novel experimental validation technologies including matched phosphoproteomic and transcriptional profiles, and the bioengineering of tumors and normal cells within interconnected micro-chambers to better recapitulate tissue physiology in vivo. The major deliverable for Project 1 is an interrogable and holistic model for coupled intra- and inter-cellular signaling which will serve as the foundation for the entire center by enabling the dissection of the mechanisms contributing to the stability of tumor-related cell states, their ligand/receptor-mediated interaction with other subpopulations in the TME, and their pharmacologically actionable molecular dependencies.