  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.
