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

癌症特异性细胞内和旁分泌网络的结构知情解剖

• 批准号: 10729385
• 负责人: BARRY H HONIG
• 金额: $ 58.09万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10729385
• 关键词: 3-Dimensional; Address; Algorithms; Automobile Driving; Behavior; Binding; Biological Assay; Biomedical Engineering; Cancer Center; Cell Line; Cells; Colon Adenocarcinoma; Colonic Neoplasms; Communities; Complex; Coupled; Data; Dependence; Development; Dissection; Drug resistance; Environment; Foundations; Funding; Gene Expression Profile; Generations; Genetic Transcription; Goals; Human; Individual; Investigation; Knowledge; Language; Ligands; Malignant - descriptor; Malignant Neoplasms; Maps; Mediating; Methodology; Modeling; Molecular; Natural Language Processing; Nature; Network-based; Normal Cell; Oncoproteins; Organ; Pancreatic Ductal Adenocarcinoma; Paracrine Communication; Peptides; Pharmaceutical Preparations; Phenotype; Phosphoproteins; Physiology; Printing; Process; Proteins; Proteome; Publications; Reagent; Research; Research Personnel; Reverse engineering; Scientist; Signal Transduction; Stromal Cells; Structure; System; Techniques; Technology; Therapeutic; Time; Tissues; Validation; Work; analytical tool; cancer cell; clinically relevant; deep learning; deep learning algorithm; drug sensitivity; extracellular; improved; in vivo; individual variation; innovation; intercellular communication; multiple omics; neoplastic cell; network models; novel; organ on a chip; paracrine; pharmacologic; phosphoproteomics; protein protein interaction; protein structure; receptor; recruit; response; small molecule; small molecule inhibitor; technology validation; therapeutic target; three dimensional structure; tumor; tumor microenvironment; tumorigenic

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.

了解癌细胞自主行为和促癌亚群向肿瘤的募集 微环境（TME）是关键依赖于准确和全面的细胞和 细胞间网络项目1的目标是开发一种新颖的，集成的，广泛验证的 一个框架来建模，操纵和剖析肿瘤微环境中的细胞-细胞信号传导， 细胞外配体-受体相互作用耦合到细胞内信号网络。项目1将建立在 在上一个CSBC资助期间产生的方法和结果，以应对多种挑战 通过（a）利用新的方法扩展蛋白质-蛋白质相互作用（PPI）网络的结构预测 深度学习方法，（B）基于时间依赖性的分析来改进信号转导网络 药物扰动分析，和（c）阐明配体/受体介导的旁分泌相互作用网络， 介导健康细胞向TME的募集--并可能重新编程--以产生促恶性肿瘤细胞。 环境为了实现这些目标，重点将放在两个高度侵袭性的肿瘤-结肠癌 腺癌（COAD）和胰腺导管腺癌（PDAC）-对于这些数据、模型、试剂， 分析工具是在上一个供资周期生成的。 项目1基于三个具体目标。通过将深度学习方法整合到蛋白质-蛋白质 相互作用和创建基于结构的网络的癌症的标志，目标1将提供一个3D- 整个项目1中拟议工作的结构背景。目标2将定义基于磷酸化蛋白质组学的 细胞内信号网络，并描述了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，并使用新型器官芯片平台对其进行验证。 项目1的影响将主要来自其创新办法，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 学习算法，包括蛋白质语言模型，为基本上所有的相互作用提供模型， 将预测和观察的建议;推断磷酸蛋白质组学为基础的磷蛋白 提供细胞信号传导的关键时间依赖性和扰动敏感性组分的活性; 并纳入旁分泌信号;和新的实验验证技术，包括匹配 磷酸蛋白质组学和转录谱，以及肿瘤和正常细胞内的生物工程 互连的微腔室，以更好地概括体内组织生理学。 项目1的主要可交付成果是一个用于耦合细胞内和细胞间的可询问和整体模型。 信号传递，它将作为整个中心的基础， 有助于肿瘤相关细胞状态的稳定性，它们的配体/受体介导的与其他细胞的相互作用 TME中的亚群，以及它们可重复的分子依赖性。