  Immune checkpoint therapy is one of the most promising immunotherapeutic
methods that are likely able to give rise to durable treatment response for
various cancer types. Despite much progress in the past decade, there are still
critical open questions with particular regards to quantifying and predicting
the efficacy of treatment and potential optimal regimens for combining
different immune-checkpoint blockades. To shed light on this issue, here we
develop clinically-relevant, dynamical systems models of cancer immunotherapy
with a focus on the immune checkpoint PD-1/PD-L1 blockades. Our model allows
the acquisition of adaptive immune resistance in the absence of treatment,
whereas immune checkpoint blockades can reverse such resistance and boost
anti-tumor activities of effector cells. Our numerical analysis predicts that
anti-PD-1 agents are commonly less effective than anti-PD-L1 agents for a wide
range of model parameters. We also observe that combination treatment of
anti-PD-1 and anti-PD-L1 blockades leads to a desirable synergistic effect. Our
modeling framework lays the ground for future data-driven analysis on
combination therapeutics of immune-checkpoint treatment regimes and thorough
investigation of optimized treatment on a patient-by-patient basis.
