  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.
