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

Authors

Jiashu Han, Xiaohong Lyu, Mengwei Wu, Chen Lin, Yidong Zhou, Weibin Wang

Published in

Cancer letters. Pages 218841. Sep 19, 2026. Epub Sep 19, 2026.

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.

PMID:
42762964
Bibliographic data and abstract were imported from PubMed on 20 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.