Core 2: Ecological Core

Summary – Ecological Core Recent advances in spatial ‘omics’ have led to an increased interest in studying and predicting emergent aggregate behavior and interactions between the tumor and its microenvironment. Tumors are dynamic, rapidly evolving and undergoing ecological shifts; therefore, the understanding of tumor progression and

response to therapy will benefit from studying eco-evolutionary interactions over time. Investigation of the spatial and spatiotemporal interactions within the tumor immune microenvironment requires computational approaches to identify and investigate the diverse ecological patterns that emerge by virtue of these complex

dynamics. The Ecological Analysis Core will cater to the generation, investigation, and application of the spatial ecological models and techniques to support the two non-small cell lung cancer (NSCLC)-focused Projects of the proposal. Given that ecological change over space and/or time (the Δ-Ecology) is a central theme shared

by both Projects, the Core is poised to support and tailor the tools to the requirements of each Project. The Core will have the capability to the ability to manage, visualize, store, share, and perform quantitative analyses of the high-dimensional and high-resolution clinical and experimental histology images. We will also build

important computational approaches and developmental frameworks to improve our understanding and prediction of emergent tumor behavior using the images. In summary, the Core will develop and apply spatial ecological models in concert with mathematical models from the Mathematical Modeling Core to understand

the evolutionary ecology underlying the diversity and maintenance of tumor heterogeneity, and how this can be leveraged to guide new therapeutic strategies for lung cancer.