Data from Reprogramming Metabolism with Metformin Improves Tumor Oxygenation and Radiotherapy Response
Bibliographic record
Abstract
Abstract Purpose: Tumor hypoxia is a negative prognostic factor in multiple cancers, due in part to its role in causing resistance to radiotherapy. Hypoxia arises in tumor regions distal to blood vessels as oxygen is consumed by more proximal tumor cells. Reducing the rate of oxygen consumption is therefore a potential strategy to reduce tumor hypoxia. We hypothesized that the anti-diabetic drug metformin, which reduces oxygen consumption through inhibition of mitochondrial complex I, would improve radiation response by increasing tumor oxygenation. Experimental Design: Tumor hypoxia was measured in xenografts before and after metformin treatment using 2-nitroimidazole hypoxia markers quantified by immunohistochemistry (IHC), flow cytometry, and positron emission tomography (PET) imaging. Radiation response was determined by tumor growth delay and clonogenic survival in xenografts with and without administration of metformin. The impact of metformin use on outcome was assessed in 504 patients with localized prostate cancer treated with curative-intent, image-guided radiotherapy (IGRT) from 1996 to 2012. Three-year biochemical relapse-free rates were assessed using the Kaplan–Meier method. Results: Metformin treatment significantly improved tumor oxygenation in two xenograft models as measured by IHC, flow cytometry, and PET imaging. Metformin also led to improved radiotherapy responses when mice were administered metformin immediately before irradiation. Clinically, metformin use was associated with an independent and significant decrease in early biochemical relapse rates (P = 0.0106). Conclusion: Our data demonstrate that metformin can improve tumor oxygenation and response to radiotherapy. Our study suggests that metformin may represent an effective and inexpensive means to improve radiotherapy outcome with an optimal therapeutic ratio. Clin Cancer Res; 19(24); 6741–50. ©2013 AACR.
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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) | 0.003 | 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".