Abstract 3543: Targeting the metabolic mevalonate pathway with statins as anti-breast cancer agents
Bibliographic record
Abstract
Abstract The statin family of drugs target the mevalonate pathway and have been used for decades in the control of hypercholesterolemia, however recent evidence suggests these approved agents may also be useful as anti-cancer therapeutics (see our recent Nature Review Cancer article1). For example, statins can trigger tumor-specific apoptosis and two independent pre-op clinical trials in breast cancer, evaluating cholesterol-lowering doses of fluvastatin and atorvastatin, resulted in breast tumor shrinkage due to decreased growth and increased apoptosis. Our hypothesis is that statins have utility as anti-breast cancer agents. To maximize efficacy and speak to personalized medicine, our objectives are to develop biomarkers to distinguish which patients will benefit from the addition of statins to their treatment regimen and how best to use statins in combination with other agents to augment anti-tumor efficacy. To identify biomarkers of statin sensitivity we evaluated fluvastatin activity across a panel of BCa cell lines and showed that the basal, estrogen receptor-negative subtype were significantly sensitive to statin-induced apoptosis. As this included the difficult-to-treat triple negative BCa (TNBCa) we have extended this work and further evaluated a panel of TNBCa cell lines for statin sensitivity. From these results we are identifying features associated with robust apoptosis in response to statin exposure. To identify how best to use statins, we conducted two unbiased screens and have shown that blocking the restorative feedback response to statin exposure potentiates statin-induced apoptosis. This is reversible with exogenous mevalonate, reinforcing that this is an on-target effect. We also identified another approved agent, dipyridamole, as able to potentiate the anti-cancer activity of statins. Mechanistically we have shown that dipyridamole blocks the feedback response to statin exposure. We have extended these studies and shown that the combination of statins and dipyridamole is effective against TNBCa both in vitro and in vivo. Thus, we provide essential pre-clinical data to support the further evaluation of statins and dipyridamole in BCa. Reference: Mullen, P.J. et al. The interplay between cell signalling and the mevalonate pathway in cancer. Nat Rev Cancer. 16, 718-731 (2016). Citation Format: Jenna van Leeuwen, Aleksandra Pandyra, Carolyn Goard, Peter J. Mullen, Rosemary Yu, Linda Z. Penn. Targeting the metabolic mevalonate pathway with statins as anti-breast cancer agents [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3543. doi:10.1158/1538-7445.AM2017-3543
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".