Tumor-Associated Macrophages Enhance Tumor Hypoxia and Aerobic Glycolysis
Bibliographic record
Abstract
Abstract Tumor hypoxia and aerobic glycolysis are well-known resistance factors for anticancer therapies. Here, we demonstrate that tumor-associated macrophages (TAM) enhance tumor hypoxia and aerobic glycolysis in mice subcutaneous tumors and in patients with non–small cell lung cancer (NSCLC). We found a strong correlation between CD68 TAM immunostaining and PET 18fluoro-deoxyglucose (FDG) uptake in 98 matched tumors of patients with NSCLC. We also observed a significant correlation between CD68 and glycolytic gene signatures in 513 patients with NSCLC from The Cancer Genome Atlas database. TAM secreted TNFα to promote tumor cell glycolysis, whereas increased AMP-activated protein kinase and peroxisome proliferator-activated receptor gamma coactivator 1-alpha in TAM facilitated tumor hypoxia. Depletion of TAM by clodronate was sufficient to abrogate aerobic glycolysis and tumor hypoxia, thereby improving tumor response to anticancer therapies. TAM depletion led to a significant increase in programmed death-ligand 1 (PD-L1) expression in aerobic cancer cells as well as T-cell infiltration in tumors, resulting in antitumor efficacy by PD-L1 antibodies, which were otherwise completely ineffective. These data suggest that TAM can significantly alter tumor metabolism, further complicating tumor response to anticancer therapies, including immunotherapy. Significance: These findings show that tumor-associated macrophages can significantly modulate tumor metabolism, hindering the efficacy of anticancer therapies, including anti-PD-L1 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".