Abstract B073: Inhibiting prostate cancer by targeting the metabolic mevalonate pathway
Bibliographic record
Abstract
Abstract Background: A major challenge in the clinical management of prostate cancer (PCa) is inhibiting progression to lethal castrate-resistant PC (CRPC). Deregulated activity of mevalonate (MVA), cholesterol biosynthetic pathway is a recognized hallmark of PCa cells. Statins are potent inhibitors of this metabolic pathway that have been used for decades in the control of hypercholesterolemia. Statins have recently been shown to have anti-PCa activity, however statin treatment triggers a feedback response that restores the MVA pathway, which reduces statin efficacy and contributes to resistance. This restorative feedback loop is controlled by the transcriptional activity of sterol regulatory-element binding protein (SREBP), which primarily induces fatty acid biosynthesis and MVA pathway genes. We have recently identified the anti-platelet agent dipyridamole (DP) as an inhibitor of the statin-induced SREBP-mediated feedback response. However, DP is not SREBP-specific and given its anti-platelet activity, may not be suitable for every cancer patient. Thus, our goal was to identify additional drugs that potentiate the pro-apoptotic activity of statins, which can be used to treat PCa patients. Methods: Two independent, yet complimentary strategies were used. The first focused on performing an in silico analysis to identify drugs that had similar properties to DP at the level of drug structure, molecular perturbations and cell line sensitivity. The second strategy involved a high-content imaging analysis of 1508 FDA approved drugs, which was performed in LNCaP (relatively statin insensitive and feedback competent) and PC3 cells (statin sensitive and feedback incompetent). Cells were treated with a sub-lethal dose of fluvastatin, the drugs or the fluvastatin-drug combination, then treated with apoptotic stains (TMRE, Annexin, Draq 5). Captured images were analyzed using a machine learning approach. Results: Validation of hits from the in silico MVA-DNF approach and high-content screening has identified several drugs that fulfill our criteria of potentiating statin-induced cell death in a feedback-dependent or feedback-independent manner. Interestingly, many show higher Z-scores compared to DP indicating their superior statin potentiation activity to drive PCa cell death. Moreover, a sub-set of these drug significantly inhibit statin-triggered expression of MVA pathway genes HMGCS1 and INSIG1 (p < 0.001) more potently than DP. Conclusions: We have detailed two successful strategies to identify drugs that inhibit SREBP activation in response to statin treatment. These statin-drug combinations represent an effective ‘one-two punch’ to inhibit CRPC progression. These novel inhibitors of SREBP activation potentiate statin at clinically relevant concentrations more potently than DP and have no effect on the platelet activity. Excitingly, many of these agents are FDA-approved and can be immediately used in combination with statins for the treatment of PCa. Overall, our research will lead to novel therapies to improve patient outcome. Citation Format: Diandra Zipinotti dos Santos, Mohamad Elbaz, Emily Branchard, Wiebke Schormann, David W. Andrews, Linda Z. Penn. Inhibiting prostate cancer by targeting the metabolic mevalonate pathway [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr B073.
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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) | 0.003 | 0.001 |
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".