Speaker
Description
By coupling omics‑derived cell states to ABMs of PDAC ecosystems, this work develops a new in silico framework that can systematically investigate the implication of candidate antigen presentation and T cell activation mechanisms in the microenvironment from PDAC spatial multi-omics data, allowing us to better understand the interplay of pro-activation and pro-tolerance signals experienced by T cells in the PDAC microenvironment. This framework supports in silico identification of microenvironmental features that favor control over progression, and allows us to forecast putative outcomes of rational combination therapy strategies in PDAC on a per-tissue basis. In the context of PDAC, this will pave the way for prevention studies in future work adapting this framework to pancreatic precancer and to understanding the sequential microenvironment transformations that enable lesion progression. Moreover, the modeling framework and TME cell types selected here are generic to be used for cell behavior and therapy modeling across tumor atlases, providing a pipeline for modeling antigen presentation and immune recognition in any solid tumor microenvironment.
