Fatty acid metabolism is essential to maintain functions of CD8+T cells within a hypoxic and hypoglycemic tumor microenvironment
Bibliographic record
Abstract
Abstract The efficacy of immunotherapy for solid tumors is dampened by functional declines of tumor-infiltrating T lymphocytes (TILs), which is generally viewed as the result of their exhaustion due to persistent antigenic stimulation. Disputing this notion, we observed in a mouse melanoma model that bystander CD8+TILs increase expression of co-inhibitors and lose effector functions, suggesting alternative mechanisms. Our data show that metabolic stresses within tumor microenvironment (TME) profoundly affect differentiation and functions of TILs. Hypoxia through HIF-1α drives increases of co-inhibitor LAG-3 while lack of glucose (Glu) enhances PD-1 levels on activated CD8+T cells. Both conditions impair functions of CD8+T cells. When deprived of Glu, activated CD8+T cells enhance fatty acid (FA) catabolism and this is further increased under hypoxia. Using 13C-stable isotope tracing in vivo and liquid chromatography-mass spectrometry (LC-MS) analysis, our study show that CD8+TILs in late stage tumors increasingly depend on catabolism of FAs including ketone bodies fueled by exogenous FA uptake and triacylglycerol (TG) turnover to meet their energy demand. Promoting FA catabolism of CD8+TILs increases their PD-1 expression but preserves some effector functions and improves their anti-tumor efficacy. Our findings show that lack of Glu and oxygen plays a critical role in driving the metabolic reprograming and functional exhaustion of CD8+TILs. They further indicate that metabolic interventions that promote FA catabolism of adoptively transferred or vaccine-induced CD8+T cells may improve the efficacy of cancer immunotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".