The tumor ecosystem in cancer progression and immunotherapeutic response

项目二世。肿瘤进展和免疫治疗反应中的肿瘤生态系统

Project II. The tumor ecosystem in cancer progression and immunotherapeutic response PROJECT SUMMARY The goal of this project is to identify the ecological interactions between cancer and immune cells that govern cancer dynamics and response to therapy. We start from the idea that a tumor can be considered an ecosystem or organ, where multiple accessory cell types are interconnected and communicate with each other and with tumor cells, which serve as their clients. Through systems analysis and modeling of functional interactions in the tumor ecosystem on different scales including cellular, protein, and gene expression dynamics at a population and single cell levels this proposal will seek to identify key cellular and molecular regulatory elements in the tumor microenvironment and potential means of their manipulation for therapeutic benefit. We will explore features of, and relationships between, multiple accessory cell types and tumor cells, in experimental models of skin and lung cancer in mice and in human cancer patients (Aim 1). The accessory cells include myeloid cells, dendritic cells, innate lymphoid cells (ILC), neutrophils, eosinophils, endothelial cells, fibroblasts, and regulatory T (Treg) cells. We will also investigate features of mediators of anti-tumor immunity including NK cells, CD4 and CD8 T cells and their specialized subsets. We will use perturbation of the tumor ecology impacting its progression in mice and human patients by established and novel immunotherapeutic modalities including PD1 and CTLA4 blockade and Treg cell depletion. The impact of these perturbations will be assessed through comprehensive analysis of cellular dynamics and states in relation to biological and clinical outcomes to generate predictive models from the data (Aim 2). We will then validate key interaction components in the tumor ecosystem by modeling cell-cell interactions in vitro using tissue mimetic systems and in silico using agent-based models (Aim 3).   1