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.
Jia-Shu Han, Xiaohong Lyu, Mengwei Wu et al.· Cancer Letters· 0 citations
BACKGROUND
Previous studies on folate and cancer-related outcomes have reported inconsistent findings. This study examined the association between serum folate levels and mortality among cancer survivors using National Health and Nutrition Examination Survey (NHANES) 1999-2018 data.
METHODS
We included 3524 cancer survivors aged ≥20 years with available serum folate and mortality data. Because of its right-skewed distribution, serum folate was log2-transformed and analyzed as both a continuous variable and quartiles. Quartiles were generated based on the distribution of log2-transformed serum folate in the final analytic sample. Cox proportional hazards models were used to estimate associations with all-cause, cancer-specific, and cardiovascular disease (CVD) mortality, adjusting for demographic, socioeconomic, lifestyle, and clinical covariates.
RESULTS
In the fully adjusted model, log2-transformed serum folate was inversely associated with all-cause mortality during follow-up (HR = 0.89, 95% CI: 0.83-0.96, P < 0.01). Compared with Q3, participants in the lowest quartile had higher risks of all-cause mortality (HR = 1.52, 95% CI: 1.26-1.83, P < 0.001), cancer-specific mortality (HR = 2.03, 95% CI: 1.44-2.87, P < 0.001), and CVD mortality (HR = 1.60, 95% CI: 1.04-2.44, P = 0.03). The highest quartile showed a higher crude mortality proportion than Q3 and was not associated with lower mortality after full adjustment.
CONCLUSIONS
Lower serum folate levels were associated with increased mortality among cancer survivors. The findings suggest a non-linear pattern, with more favorable outcomes at intermediate folate levels and no additional benefit at higher levels. Further prospective studies are needed to confirm these associations.
Yang Qu, Juan Sun, Lu Gao et al.· Cancer Treatment and Researc...· 0 citations
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