Abstract B073: Inhibiting prostate cancer by targeting the metabolic mevalonate pathway
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».