T cell equation as a conceptual model of T cell responses for maximizing the efficacy of cancer immunotherapy

TL;DR

This paper introduces a conceptual model that integrates positive and negative signals to predict and optimize T cell responses, aiming to improve cancer immunotherapy efficacy.

Contribution

A novel digital model combining co-stimulatory and inhibitory signals to predict T cell responses and guide immunotherapy strategies.

Findings

Model predicts T cell responses based on integrated signals.

Strategy for enhancing antitumor T cell responses.

Potential to optimize combination immunotherapies.

Abstract

Following antigen stimulation, the net outcomes of a T cell response are shaped by integrated signals from both positive co-stimulatory and negative regulatory molecules. Recently, the blockade of negative regulatory molecules (i.e. immune checkpoint signals) demonstrates therapeutic effects in treatment of human cancer, but only in a fraction of cancer patients. Since this therapy is aimed to enhance T cell responses to cancers, here we devised a conceptual model by integrating both positive and negative signals in addition to antigen stimulation. A digital range of adjustment of each signal is formulated in our model for prediction of a final T cell response. This model allows us to evaluate strategies in order to enhance antitumor T cell responses. Our model provides a rational combination strategy for maximizing the therapeutic effects of cancer immunotherapy.