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Record W2741925895 · doi:10.1158/1538-7445.am2017-1516

Abstract 1516: Androgen-repressed and androgen-induced genes: challenging the traditional dogma of prostate cancer therapy

2017· article· en· W2741925895 on OpenAlexaff
Daniel P. Caley, Nasrin R. Mawji, Marianne D. Sadar

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsAntiandrogensEnzalutamideAndrogen receptorProstate cancerLNCaPBicalutamideAndrogenCancer researchAntiandrogenInternal medicineEndocrinologyMedicineBiologyCancerHormone

Abstract

fetched live from OpenAlex

Abstract Background: Benign and malignant prostate tissues are dependent upon the activity of androgen receptor (AR). The primary function of full-length AR is as a ligand activated transcription factor to increase or repress gene expression. Many androgen-repressed genes regulate the cell cycle and proliferation. With castration, the main therapeutic approach for advanced prostate cancer (PC), these genes are believed to play a role in the initial clinical response. Current approved therapies for advanced PC and castration-resistant PC (CRPC), target the AR C-terminal ligand-binding domain (LBD), such as antiandrogens. Recent antagonists of the AR N-terminal domain (NTD) have been described with EPI-506, the prodrug of EPI-002, now in Phase 1 clinical trials. EPI-002 binds tau-5 in activation function-1 (AF-1) of the NTD that is essential for AR transcriptional activity. As expected with an AF-1 antagonist, EPI-002 is an excellent inhibitor of androgen-induced gene expression and at blocking the transcriptional activities of truncated AR splice variants lacking LBD, such as AR-V7. EPI-002 blocks expression of genes regulated by truncated AR-V7 such as UBE2C while antiandrogens have no effect. Here we reveal that the major difference in gene expression regulated by full-length AR between EPI-002 and antiandrogens is their abilities to de-repress genes that are turned off by androgen. Methods: The androgen sensitive human prostate cancer cell line LNCaP, which expresses full-length AR, was treated with antiandrogens (bicalutamide [BIC] and enzalutamide [ENZA]), EPI-002 and a control vehicle, with and without androgen. Gene expression was analysed using Affymetrix microarrays. Bioinformatical analysis was completed and a selection of androgen-repressed genes that were de-repressed with antiandrogens and/or EPI-002, were selected for validation using qRT-PCR. Results: EPI-002 de-repressed known androgen-repressed genes including SPLTLC3, ST7, PSAT1, TMEM140 and TNFRSF21. EPI-002 was as effective or better than BIC or ENZA in de-repressing a subset of androgen-repressed genes. Importantly, EPI-002 failed to de-repress expression of many androgen-repressed genes that antiandrogens de-repressed, such as SLITRK3, GPR63 and DAB1. Conclusions: EPI binds AR NTD which blocks the transcriptional activities of full-length AR and truncated AR splice variants. EPI-002 was excellent at inhibiting androgen-induced genes. However, EPI did not broadly de-repress expression of genes turned off by androgen when compared to antiandrogens. Such differences between EPI-002, a tau-5/NTD antagonist, and C-terminal LBD antiandrogens probably reflect the complexities of the mechanisms of repression of gene expression that may involve different domains of AR. Citation Format: Daniel P. Caley, Nasrin R. Mawji, Marianne Sadar. Androgen-repressed and androgen-induced genes: challenging the traditional dogma of prostate cancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1516. doi:10.1158/1538-7445.AM2017-1516

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.253
GPT teacher head0.446
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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