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

点击翻译按钮获取中文摘要

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.