Abstract #5543: Lovastatin induces apoptosis of ovarian cancer cells and modulates the efficacy of chemotherapeutics: Implications for patient treatment
Bibliographic record
Abstract
AACR Annual Meeting-- Apr 18-22, 2009; Denver, CO The statin family of cholesterol control agents have recently been shown to have promise as anticancer therapeutics. By inhibiting HMG CoA reductase (HMGCR) and the mevalonate pathway, statins can trigger cells from certain tumor types to undergo apoptosis, in the absence of damage to normal, non-transformed cells. To maximally exploit these readily available agents for patient care, it is important to both identify statin-sensitive tumor types and delineate the mechanisms of statin-induced apoptosis. In this report we show that lovastatin can trigger ovarian cancer cells to undergo apoptosis by two complementary anticancer mechanisms. We first demonstrate that human ovarian cancer cells are sensitive to lovastatin-induced apoptosis via inhibition of HMGCR, independently of p53 mutation status. Second, we show that lovastatin synergizes with doxorubicin, a chemotherapeutic used to treat drug-resistant disease, by a mechanism that increases intracellular doxorubicin within drug-resistant cells. This activity does not depend upon HMGCR inhibition, but instead inhibits P-glycoprotein-mediated drug efflux. Elevated doxorubicin retention is accompanied by enhanced DNA damage and apoptosis, arguing for the combination of lovastatin and doxorubicin to treat drug-resistant cancers, including ovarian cancer. While previous studies exploring the effect of lovastatin on P-gp were limited, our results are made distinctly more relevant to the clinic. To achieve this, we used human cell systems selected to overexpress human P-glycoprotein as well as physiologically attainable concentrations of both doxorubicin and lovastatin in its biologically relevant acid form. This work should serve as evidence that chemotherapeutics combined with statins may have profound, synergistic effects. Whether these effects are positive or negative may depend on several variables which need to be explored. Ultimately, exploiting the ability of statins to both trigger apoptosis and block P-glycoprotein should be done in such a way to maximize anticancer activity without increasing toxicity. Citation Information: In: Proc Am Assoc Cancer Res; 2009 Apr 18-22; Denver, CO. Philadelphia (PA): AACR; 2009. Abstract nr 5543.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".