Abstract 3661: Development of novel small-molecule inhibitors of ETS oncoproteins as anti-prostate cancer agents
Bibliographic record
Abstract
Abstract Currently advanced prostate cancer is treated by suppressing androgen receptor (AR) signaling. Since AR could be activated via multiple mechanisms, inhibition of one or two of these mechanisms could be initially effective, but drug resistance can rapidly develop due to kick-in of other AR-activating mechanisms, resulting in lethal castration-resistant prostate cancer (CRPC). The therapeutic effects of all of the currently available AR-targeting agents are short-lived. To achieve long lasting therapeutic effect for prostate cancer, it appears we need to get out of the box of AR. Here, we propose to develop chemical inhibitors of oncogenic protein ERG, which is an ETS transcriptional factor. The rationale for inhibiting ERG as a possible treatment for prostate cancer has two folds: i) ERG plays a critical role in prostate cancer initiation and progression, and ii) Half of all prostate cancer patients are TMPRSS2-ERG fusion positive, which result in high level of ERG oncoprotein. ERG is a transcriptional factor that directly regulates EZH2, oncogene c-Myc and tumor suppressor Nkx3.1 and many other targets in prostate cancer. ERG cooperates with PI3K/AKT and AR in tumor initiation. ERG knockdown inhibits tumor growth in xenograft of VCaP cells. To date, accumulated studies in patients and experimental models have indicated that ERG overexpression as a result of TMPRSS2-ERG fusion plays a critical role in prostate cancer initiation and progression. However, ERG protein has no obvious functional sites or enzymatic activity to be inhibited and is considered ‘undruggable’. In contrast to such view, our laboratory has recently discovered a novel chemical inhibitor of ERG by combining virtual screening and reporter assay. We have built a structural model of ERG DBD and identified a putative binding-site for virtual screening study. We discovered a novel compound, which substantially inhibits ERG-dependent reporter activity in HEK293 cells and inhibits expression of ERG-regulated genes, such as EZH2 and c-Myc, in VCaP cells. We further showed that our compound inhibits proliferation of VCaP cells, and disrupts association of ERG with its essential coactivator PARP1 by co-IP assay. Surface Plasmon Resonance (SPR) analysis has confirmed that our compound has direct binding with recombinant ERG protein. As recent studies indicated that advanced prostate cancer with TMPRSS2-ERG fusion have substantial higher tendency to metastasize to lymph nodes, ERG inhibitors could be an effective therapeutics to prevent this progression. Importantly, ERG inhibitors could be effective therapeutics for advanced prostate cancer. Citation Format: Xiaohong Tian. Development of novel small-molecule inhibitors of ETS oncoproteins as anti-prostate cancer agents. [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 3661. doi:10.1158/1538-7445.AM2015-3661
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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) | 0.000 | 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".