Abstract PR12: A genome-wide shRNA screen reveals that inhibiting kinases potentiates the anti-breast cancer activity of fluvastatin
Bibliographic record
Abstract
Abstract Background: Statins are widely used to manage hypercholesterolemia, and have also been shown to possess anti-tumor effects. Breast cancer clinical trials have demonstrated that statins are effective in some but not all patients. We aim to identify combination treatments that can expand the anti-tumor benefit of statins to a larger subset of breast cancer patients. We hypothesize that a genome-wide shRNA screen will identify novel genomic targets to inhibit in co-therapies with fluvastatin. Methods: We used a pooled shRNA dropout screen to determine if knocking down specific genomic targets increases the anti-proliferative effects of fluvastatin. Cells transduced with the 80K TRC1 shRNA library were treated with either sublethal doses of fluvastatin or vehicle control over 12 days. Genomic DNA was collected from these cells every three days for hybridization to custom Affymetrix Gene Modulation Array Platform (GMAP) arrays. Candidate shRNA dropout hits were validated using shRNAs, siRNAs, and pharmacological inhibitors. Results: Our shRNA screen identified several kinase targets as dropouts, suggesting that knocking down specific kinases can potentiate the anti-proliferative effects of fluvastatin. We validated two candidate hits, PI4KB (phosphatidylinositol 4-kinase beta) and CSNK2B (casein kinase 2, beta polypeptide) in two breast cancer cell lines: MDA-MB-231 cells, which are highly sensitive to fluvastatin, and MCF-7 cells, which are less sensitive to fluvastatin. We used shRNAs and siRNAs targeting PI4KB or CSNK2B to confirm that knockdown of these kinases potentiates the anti-proliferative effects of fluvastatin. We also used pharmacological inhibitors of PI4KB and CSNK2B, which also increased the anti-proliferative activity of fluvastatin. Conclusions: Statins show promising anti-tumor effects, but co-treatments will be required to increase both their efficacy and the number of patients who will respond. We show here that kinases are a class of targets that can potentiate fluvastatin efficacy in breast cancer cell lines. We are now performing a small-molecule kinase inhibitor library screen that is designed to identify FDA-approved kinase inhibitors to combine with fluvastatin. The screen readout involves high-content confocal imaging and is currently underway. This work may lead to the discovery of effective and novel co-treatments for breast cancer that will better impact patient care. This abstract is also presented as Poster A16. Citation Format: Janice Pong, Aleksandra Pandyra, Carolyn Goard, Elke Ericson, Kevin Brown, Jarkko Ylanko, David Andrews, Corey Nislow, Jason Moffat, Linda Penn. A genome-wide shRNA screen reveals that inhibiting kinases potentiates the anti-breast cancer activity of fluvastatin. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr PR12.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".