Abstract 1648: Targeting ETS factor ETV4 as a novel therapeutic for the management of breast and prostate cancer
Bibliographic record
Abstract
Abstract Despite the development of several successful targeted therapies, drug resistance and metastasis remain a significant challenge in the treatment of both breast (BCa) and prostate (PCa) cancers. For these reasons, there is an immediate need to identify a novel class of therapeutics targeting alternative factors, such as those that promote the metastatic capacity of tumour cells. ETS translocation variant 4 (ETV4) is a member of the ETS transcription factor family and is a significant mediator of tumorigenesis through its activation of several downstream pathways that are associated with migration and invasion. ETV4 is overexpressed in breast tumours and is associated with distant metastasis and poor prognosis particularly in triple negative BCa, which still lacks an approved targeted therapy. Similarly in PCa, overexpression of ETV4 is associated with the deregulation of the PI3K and Ras signalling pathways that are commonly implicated in metastatic disease. Like other ETS factors such as ERG and ETV1, fusion of the ETV4 gene can be found in a subtype of PCa cases and is associated with the disease progression. Thus, ETV4 is an important therapeutic candidate with potential applications in both advanced BCa and PCa. Drug development against ETV4 is made even more significant due to the lack of any approved therapy that directly targets it or any other members of the ETS family. Using a combination of in silico screening and in vitro assays, we have identified several small molecules with strong binding affinities and selectivity toward the DNA binding domain of ETV4. The prostate cell line, PC3, and the triple negative breast cancer cell line, MDA-MB-231, were used for in vitro studies as they endogenously express moderate to high levels of ETV4. Over 100 candidate compounds, which were selected from virtual screening against millions of small-molecular structures, were tested across the two cell lines using a luciferase-based transcriptional reporter assay. From this assay approximately 30 compounds were identified with significant inhibition on the transcriptional activity of ETV4 without a cytotoxic effect. The most potent of these compounds was shown by protein nuclear magnetic resonance (NMR) to directly interact with specific residues within the DNA binding domain. These compounds were also able to inhibit the migratory capacity of cancer cells. This data provides evidence for the direct targeting of ETV4 by small molecules, and future work will aim to further characterize their mechanism of action and effects on downstream targets in an effort to create a novel therapeutic strategy to treat metastatic breast and prostate cancers. (Supported by grants from the Canadian Cancer Society Research Initiative and Prostate Cancer Canada) Citation Format: Miriam S. Butler, Michael Hsing, Mani Roshan Moniri, Desmond Lau, Paul Yen, Ari Kim, Scott Lien, Marta Mroczek, Fariba Ghaidi, Eric LeBlanc, Lawrence McIntosh, Michael Cox, Artem Cherkasov, Paul S. Rennie. Targeting ETS factor ETV4 as a novel therapeutic for the management of breast and prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1648. doi:10.1158/1538-7445.AM2015-1648
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".