Latent Effector Capacity Governs Reversible T Cell Exhaustion: A Mathematical Model for Mechanistically Predictive AI in PD-1 Blockade

Abstract

T cell exhaustion is commonly viewed as a terminal differentiation state marked by irreversible loss of effector function during chronic infection and cancer. However, the rapid restoration of cytotoxic activity following PD-1 checkpoint blockade challenges this view, revealing a central paradox: T cells that appear functionally inert can regain effector function on timescales incompatible with de novo differentiation or extensive epigenetic reprogramming. To resolve this contradiction, we present a mathematical framework that explicitly decouples latent effector capacity from active effector output. We define latent effector capacity as a slow, history-dependent state variable representing preserved epigenetic accessibility and regulatory readiness at effector loci, distinct from instantaneous transcriptional activity. Within this framework, PD-1 signaling functions as a reversible, graded masking mechanism that suppresses effector realization without erasing latent capacity, thereby explaining the coexistence of preserved chromatin accessibility, rapid functional rebound, and heterogeneous responses to checkpoint blockade. Incorporating nonlinear self-maintenance of epigenetic programs together with checkpoint-dependent erosion of latent capacity reveals a bistable regime and a history-dependent point of no return, beyond which exhaustion becomes irreversible. Critically, the model demonstrates that PD-1 checkpoint blockade unmasks pre-existing effector potential but cannot recreate lost capacity, because therapeutic reversibility is governed by the prior dynamical stability of a latent epigenetic state rather than by instantaneous transcriptional output. This framework establishes a mathematical foundation for mechanistically predictive AI in PD-1 blockade therapy by identifying latent, history-dependent variables that can be inferred from epigenetic and transcriptional data to predict therapeutic responsiveness and irreversibility.