Combined metformin-salicylate treatment provides improved anti-tumor activity and enhanced radiotherapy response in prostate cancer; drug synergy at clinically relevant doses
Bibliographic record
Abstract
There is need for well-tolerated therapies for prostate cancer (PrCa) secondary prevention and to improve response to radiotherapy (RT). The anti-diabetic agent metformin (MET) and the aspirin metabolite salicylate (SAL) are shown to activate AMP-activated protein kinase (AMPK), suppress de novo lipogenesis (DNL), the mammalian target of rapamycin (mTOR) pathway and reduce PrCa proliferation in-vitro. The purpose of this study was to examine whether combined MET+SAL treatment could provide enhanced PrCa tumor suppression and improve response to RT. Androgen-sensitive (22RV1) and resistant (PC3, DU-145) PrCa cells and PC3 xenografts were used to examine whether combined treatment with MET+SAL can provide improved anti-tumor activity compared to each agent alone in non-irradiated and irradiated PrCa cells and tumors. Mechanisms of action were investigated with analysis of signaling events, mitochondria respiration and DNL activity assays. We observed that PrCa cells are resistant to clinically relevant doses of MET. Combined MET + SAL treatment provides synergistic anti-proliferative activity at clinically relevant doses and enhances the anti-proliferative effects of RT. This was associated with suppression of oxygen consumption rate (OCR), activation of AMPK, suppression of acetyl-CoA carboxylase (ACC)-DNL and mTOR-p70s6k/4EBP1 and HIF1α pathways. MET + SAL reduced tumor growth in non-irradiated tumors and enhanced the effects of RT. MET+SAL treatment suppresses PrCa cell proliferation and tumor growth and enhances responses to RT at clinically relevant doses. Since MET and SAL are safe, widely-used and inexpensive agents, these data support the investigation of MET+SAL in PrCa clinical trials alone and in combination with RT.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".