2023/02/08 by Viren Shah, Justin Womack, Shah, Viren +7
Biochemistry, Genetics and Molecular Biology · Engineering · Medicine · #Advancements in Semiconductor Devices and Circuit Design #CAR-T cell therapy research #Cell Image Analysis Techniques #FOS: Biological sciences #Molecular Networks (q-bio.MN)
paper · pdf · doi:10.48550/arxiv.2302.04338
openalex publication_date 2023/02/08 · openalex created_date 2023/02/13 · openalex updated_date 2026/07/28
Immunotherapies have been proven to have significant therapeutic efficacy in the treatment of cancer. The last decade has seen adoptive cell therapies, such as chimeric antigen receptor T-cell (CART-cell) therapy, gain FDA approval against specific cancers. Additionally, there are numerous clinical trials ongoing investigating additional designs and targets. Nevertheless, despite the excitement and promising potential of CART-cell therapy, response rates to therapy vary greatly between studies, patients, and cancers. There remains an unmet need to develop computational frameworks that more accurately predict CART-cell function and clinical efficacy. Here we present a coarse-grained model simulated with logical rules that demonstrates the evolution of signaling signatures following the inter-action between CART-cells and tumor cells and allows for in silico based prediction of CART-cell functionality prior to experimentation.