Abstract 1652: Therapeutic targeting of ETS factor ERG for the treatment of prostate cancer
Bibliographic record
Abstract
Abstract Prostate cancer (PCa) is one of the leading causes of cancer-related death in men worldwide. The common treatment option for recurring and advanced PCa focuses on inhibiting the androgen receptor (AR). Unfortunately, despite an initial response to this treatment, drug resistance occurs, and the cancer relapses to an incurable, castration-resistant form; thus, there is a pressing need for new therapeutics. Previous research has shown that in up to 50% of all prostate cancer cases, the cause of the disease may be attributed to a common genomic rearrangement, the fusion between TMPRSS2 and ERG (ETS-related gene). ERG is a transcription factor mainly involved in hematopoiesis regulation during embryonic development, and it is not normally expressed in prostate cells in adults. However, its fusion with the TMPRSS2 promoter puts ERG under the regulation of AR, and as a consequence, ERG is one of the most commonly overexpressed genes in PCa. ERG overexpression in prostate epithelium has been shown to induce transformation and promote epithelial-mesenchymal transition (EMT) that gives cancer cells enhanced migratory and invasive characteristics. Currently, there is no approved therapeutic targeting ERG or any other member of the ETS family. While targeting transcription factors has been challenging, our integrated research team is specialized in targeting protein-DNA interaction sites. Using our established computer-aided drug discovery pipeline, we have identified several small molecules that can bind to and inhibit ERG. A total of 133 candidate compounds, pre-selected from the in silico screening of millions of chemical structures, were tested using a luciferase-based transcriptional reporter assay across two cell lines: the TMPRSS2-ERG fusion positive VCaP cell line, and the human prostate epithelial cell line (PNT1B) engineered to constitutively express ERG. In addition to transcriptional inhibition, the most potent compounds inhibited migration of PNT1B-ERG cells as demonstrated by a Real-Time Cell Analyzer. Significantly, both compounds shifted the binding spectra in protein NMR assays, indicating their direct interactions with residues located in the DNA binding domain of the ERG protein. We anticipate that results from this project will lead to the development of new drugs that can be used alternatively or synergistically with current anti-AR therapy to benefit patients with the most deadly forms of prostate cancer. (Supported by a grant from Prostate Cancer Canada) Citation Format: Mani Roshan-Moniri, Michael Hsing, Miriam S. Butler, Desmond Lau, Peter Axerio-Cilies, Paul Yen, Ari Kim, Scott Lien, Marta Mroczek, Dennis Ma, Huifang Li, Yubin Guo, Fuqiang Ban, Fariba Ghaidi, Eric LeBlanc, Lawrence McIntosh, Michael Cox, Artem Cherkasov, Paul S. Rennie. Therapeutic targeting of ETS factor ERG for the treatment of 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 1652. doi:10.1158/1538-7445.AM2015-1652
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.006 | 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".