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并募集肿瘤的促主体亚群 微环境（TME）严重取决于准确和全面的细胞的产生 细胞间网络。项目1的目标是开发一种小说，整合和广泛验证 在肿瘤微环境中建模，操纵和解剖细胞细胞信号的框架，涉及 细胞外配体 - 受体相互作用与细胞内信号网络相连。项目1将建立在 在上一个CSBC资金期间生成的方法和结果解决了多种挑战 通过（a）通过利用新颖的蛋白质 - 蛋白质相互作用（PPI）网络扩展结构信息预测 深度学习方法，（b）基于时间依赖性的分析改善信号传输网络 药物扰动测定，（c）阐明配体/受体介导的旁分泌相互作用网络 调解健康细胞的招聘以及可能的重新编程，以创建促恶性 环境。为了实现这些目标，重点将放在两个高度侵略性的肿瘤上 - 颜色 腺癌（COAD）和胰腺导管腺癌（PDAC） - 对于哪些数据，模型，试剂，， 并在先前的资金周期中生成了分析工具。 项目1基于三个特定目标。通过整合深度学习方法的蛋白质蛋白质 AIM 1的相互作用和建立基于结构的网络，AIM 1将提供3D- 整个项目1的拟议工作的结构背景。AIM2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。 AIM 3将定义旁分线 - 基于细胞 - 细胞信号网络，并使用新型的芯片机器人平台对其进行验证。 项目1的影响将主要来自其创新方法，其中包括使用结构 - 基于建模蛋白质相互作用网络的分析；将基于结构的建模与深度集成 学习算法，包括蛋白质语言模型，为所有互动提供模型 该提案将被预测和观察；基于磷酸蛋白质组学的推断 提供关键的细胞信号传导时间依赖性和扰动敏感的成分；这 旁分泌信号传导的掺入；以及包括匹配的新型实验验证技术 磷酸化蛋白质组学和转录谱，以及肿瘤和正常细胞内的生物工程 相互连接的微室在体内更好地概括了组织生理。 项目1的主要可交付方式是耦合细胞内和细胞间的可询问和整体模型 通过启用机制的解剖来作为整个中心的信号传导 有助于肿瘤相关细胞态的稳定性，其配体/受体介导的相互作用与其他 TME中的亚群及其药物可操作的分子依赖性。