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Abstract 1023: Combining all-trans retinoic acid therapy with androgen receptor N-terminal domain inhibitors for the treatment of castration-resistant prostate cancer

2019· article· en· W2954482364 on OpenAlexaff
Jacky K. Leung, Marianne D. Sadar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSignaling Pathways in Disease
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsAndrogen receptorTransactivationProstate cancerLNCaPBicalutamideCancer researchAndrogen deprivation therapyBiologyDihydrotestosteroneSignal transductionEndocrinologyChemistryAndrogenInternal medicineCancerCell biologyMedicineTranscription factorBiochemistryGeneHormone

Abstract

fetched live from OpenAlex

Androgen receptor (AR) signaling plays an essential role in all stages of prostate cancer. Androgen-deprivation therapy is generally effective for advanced prostate cancer until progression to lethal metastatic castration-resistant prostate cancer (mCRPC). Most CRPC continues to be driven by AR signaling. AR transcriptional activity requires a functional N-terminal domain (NTD). Transactivation of AR is mediated by ligand-independent activation by cross-talk with signal transduction pathways targeting the AR NTD, gain-of-function mutations in AR ligand-binding domain (LBD), or expression of truncated AR splice variants that lack LBD (e.g., AR-V7). The intrinsically disordered AR NTD is essential for its transcriptional activity. It harbors six putative binding sites for Pin1, a proline isomerase that regulates protein conformation at specific phosphorylated-Ser/Thr-Pro motifs. All-trans retinoic acid (ATRA) is a validated and potent Pin1 inhibitor. Since conformational changes within the AR NTD are required for transactivation, perturbation of its structure may be a promising approach to block its activity. The purpose of this study was to assess a therapy that combined ATRA with antagonists of AR NTD, ralaniten (EPI-002) and its analogues. We hypothesized that targeting Pin1 activity with ATRA would disrupt the AR NTD and enhance inhibition by EPI compounds which bind to Tau-5 in the NTD. Using reporter gene assays, proliferation assays, and cell cycle analysis by flow cytometry, we tested ATRA in isolation and in combination with EPI in androgen-sensitive (LNCaP) and androgen-independent (LN95) prostate cancer cell lines. We found that treatment with ATRA decreased the transcriptional activity of AR and androgen-induced expression of PSA. ATRA also attenuated the transcriptional activity of AR-V7 and androgen-independent growth of LN95 cells expressing both full-length AR and AR-V7. Co-immunoprecipitation studies confirmed interactions between Pin1 and specific regions of AR NTD. In combination, ATRA had synergistic interaction with EPI compounds and lowered the effective inhibitory concentrations for blocking AR transcriptional activity. Furthermore, combinations decreased cell cycle progression of LN95 cells through S-phase, leading to accumulation of cells in G1 and induction of senescence. These preclinical findings showed that ATRA enhanced the potency of AR NTD inhibitors and support a novel therapeutic strategy for CRPC. Future studies will determine the in vivo antitumor effect of ATRA in combination with EPI compounds on the growth of CRPC xenografts.Citation Format: Jacky K. Leung, Marianne D. Sadar. Combining all-trans retinoic acid therapy with androgen receptor N-terminal domain inhibitors for the treatment of castration-resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1023.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.017
GPT teacher head0.272
Teacher spread0.256 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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