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T-Cell Exhaustion and Senescence in Cancer: Regulatory Circuits and Therapeutic State Engineering.

Sep 2026 · Cancer Letters · pp. 218841 · 0 citations · 142 references
Medicine

TL;DR

This mechanistic view shifts the therapeutic question from how broadly to activate T cells to which states should be generated, preserved, or rescued and provides a testable basis for state-informed biomarker development and therapeutic design.

Abstract

In cancer, T-cell state strongly influences antitumor immunity and response to immunotherapy. High-parameter cytometry, single-cell transcriptomics, and multiomic profiling now resolve lineage identity, differentiation history, clonality, metabolic fitness, and tissue adaptation in parallel. These studies place naïve, activated, effector, memory, exhausted, and senescent T cells along continuous, context-dependent trajectories rather than within rigid subset boundaries. Their transitions are shaped by interacting transcriptional, epigenetic, signaling, and metabolic programs that integrate antigen strength, co-stimulation, and cytokines with cues from the tumor microenvironment. Within that microenvironment, suppressive myeloid, erythroid, and stromal populations, extracellular matrix (ECM) remodeling and spatial exclusion, nutrient competition, and tissue-specific conditioning impose a chronic stress that, together with sustained immune-checkpoint signaling, drives exhaustion and senescence and underlies immune evasion. This mechanistic view shifts the therapeutic question from how broadly to activate T cells to which states should be generated, preserved, or rescued. We organize dysfunctional T-cell states along five complementary dimensions: reversibility, antigen dependence, proliferative history, epigenetic fixation, and metabolic collapse. We then map precision cytokines, spatially restricted co-stimulation, metabolic interventions, epigenetic modulation, immune-checkpoint blockade, and genome engineering onto the state transitions they are intended to influence. This framework separates clinically established approaches from exploratory strategies and provides a testable basis for state-informed biomarker development and therapeutic design.

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