Abstract C165: Breast cancer, statins, and 3-D cell culture.
Bibliographic record
Abstract
Abstract Statins are widely used to lower serum cholesterol levels. They act by inhibiting hydroxymethylglutaryl coenzyme A reductase (HMGCR), the rate-limiting step in the mevalonate pathway. Recent work also shows that statins could be used as anticancer therapeutics, particularly in breast cancer. This study presents a preliminary rationale for the use of statins as a therapy in breast cancer. We characterized a panel of breast cancer cell lines for sensitivity to fluvastatin, using proliferation and cell-death assays. We also screened for differences in activity of the electron transport chain and glycolysis after fluvastatin treatment. We then began to expand on these results using 3D cell culture techniques to offer a more representative tumor model. The panel of breast cancer cell lines showed a range of sensitivity to fluvastatin, with MTT50 for 72 h treatment varying from 0.7 μM for MDA-MB231 cells to 162.8 μM in BT474 cells. Interestingly, triple negative cell lines were in the sensitive range. Differences in cell cycle populations were also observed, with a representative panel of sensitive cells showing an increased G1/G0 arrest and decrease in S-phase population when compared to insensitive cell lines. Delving deeper, mitochondrial respiration was decreased in the sensitive cell lines. We also observed greater changes in morphology in the sensitive cell lines than the insensitive when grown in 3D cell culture, potentially offering more relevance to our observations. These results confirm that there is a range of sensitivities to fluvastatin in breast cancer cell lines, allowing for further studies to determine the cause of these differences. The hints at metabolic differences could also lead to novel co-treatments with statins and greater therapeutic benefit. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr C165.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".