Utilizing metformin to enhance the efficacy of androgen-deprivation therapy in the treatment of prostate cancer.
Bibliographic record
Abstract
22 Background: Prostate cancer (PCa) incidence varies by geographic location, with developed countries exhibiting higher levels of disease. Some attribute this to the “Westernized lifestyle” of high energy diets and limited physical activity with consequent obesity. Obesity and related diseases like diabetes, cause hyperinsulinemia, which upregulates pro-survival insulin/insulin-like growth factor signalling. Previous work shows diet-induced hyperinsulinemia enhances PCa tumor growth in vivo. Metformin, a diabetic treatment, reduces hyperinsulinemia, and also exhibits anti-neoplastic properties. We assessed the potential benefit of combining a standard PCa treatment (bicalutamide) with metformin in vitro and in vivo. Methods: The effect of bicalutamide and/or metformin on colony formation rates was assessed in LNCaP, PC3, DU145 and PC3AR2 PCa cell lines using clonogenic assay. Western blot and cell cycle analyses were used to elucidate mechanisms of interaction between the drugs. The combination treatment regimen was assessed in vivo using a murine xenograft model. Results: Micromolar bicalutamide or millimolar metformin caused significant dose-dependent reduction in colony formation rates (p<0.001). Combination treatment further significantly reduced colony formation rates (p<0.005). Differing mechanisms of interaction occurred in AR positive and negative cell lines. Following combination treatment LNCaP cells exhibited altered cell proliferation (decreased PCNA) and perturbed cell cycle kinetics (G1/S arrest). PC3 cells showed evidence of enhanced apoptosis (increased BAX, decreased caspase 3, phospho-Akt). Preliminary in vivo results show significantly diminished tumor growth following combination treatment (p<0.0001). Conclusions: Combining bicalutamide and metformin significantly reduces PCa cell colony formation rates further than either monotherapy. In AR positive cells this effect is mediated by reducing cell proliferation rates, whereas in AR negative cells combination treatment promotes apoptosis. This combination drug regimen may potentially improve prostate-cancer specific survival via the direct anti-neoplastic properties outlined. [Table: see text]
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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 | 0.001 |
| 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".