Simulating the Evolution of Signaling Signatures during CART-Cell -- Tumor Cell Interactions

TL;DR

This paper introduces a computational model simulating CART-cell and tumor cell interactions to predict CART-cell functionality and efficacy in cancer immunotherapy.

Contribution

A novel coarse-grained logical model that predicts signaling signature evolution during CART-cell and tumor cell interactions.

Findings

Model demonstrates signaling signature evolution over time.

Predicts CART-cell functionality prior to experimental validation.

Provides a tool for in silico assessment of CART-cell therapies.

Abstract

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.