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PD05-12 COMBINATION OF AN ANTAGONIST TO THE ANDROGEN RECEPTOR N-TERMINAL DOMAIN WITH ENZALUTAMIDE FOR THE TREATMENT OF CASTRATION RESISTANT PROSTATE CANCER

2019· article· en· W2941511463 on OpenAlexaboutno aff
Y Hirayama, Kunzhong Jian, Raymond Anderson, Marianne D. Sadar

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsnot available
Fundersnot available
KeywordsEnzalutamideProstate cancerAndrogen receptorMedicineAndrogenInternal medicineAndrogen deprivation therapyCancerAntagonistCancer researchAbiraterone acetateEndocrinologyOncologyReceptorHormone

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyProstate Cancer: Basic Research & Pathophysiology I (PD05)1 Apr 2019PD05-12 COMBINATION OF AN ANTAGONIST TO THE ANDROGEN RECEPTOR N-TERMINAL DOMAIN WITH ENZALUTAMIDE FOR THE TREATMENT OF CASTRATION RESISTANT PROSTATE CANCER Yukiyoshi Hirayama*, Kunzhong Jian, Raymond J. Anderson, and Marianne D. Sadar Yukiyoshi Hirayama*Yukiyoshi Hirayama* More articles by this author , Kunzhong JianKunzhong Jian More articles by this author , Raymond J. AndersonRaymond J. Anderson More articles by this author , and Marianne D. SadarMarianne D. Sadar More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555072.71167.58AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Expression of androgen receptor splice variants (AR-Vs) is associated with resistance to current androgen deprivation therapies and antiandrogens. Constitutively active AR-Vs lack the ligand binding domain of androgen receptor (AR-LBD) which is the direct or indirect target of anti-androgens such as enzalutamide (ENZ) and abiraterone which blocks steroidogenesis. Recently the PLATO trial showed that the addition of abiraterone to ongoing ENZ did not improve progression-free survival in patients with castration resistant prostate cancer (CRPC). These data imply a limitation of combining AR-LBD targeting therapies. Therefore, novel therapeutic approaches are needed to improve the outcomes of CRPC patients. Both AR and truncated AR-Vs require a functional N-terminal domain (NTD) for activity and hence first-in-class antagonists of the AR NTD have been developed such as EPI-002 (ralaniten). Herein a next-generation EPI compound (EPI-7170) was evaluated as a monotherapy or in combination of ENZ in ENZ resistant prostate cancer cells that express AR-Vs. METHODS: We developed ENZ resistant VCaP cells (VCaP-MDVR) by chronic exposure to ENZ. These cells and C4-2B-MDVR cells were used as ENZ resistant prostate cancer cells to test monotherapies versus combination therapy with ENZ and EPI-7170. BrdU incorporation, clonogenic assays and flow cytometry were used to analyze effects on proliferation and cell cycle. Inhibition of expression of AR and AR-V7 target genes by ENZ, EPI-7170 or a combination was analyzed by qPCR. RESULTS: EPI-7170 had 10 times better potency than EPI-002 as measured using proliferation and PSA-reporter gene assays. VCaP-MDVR cells expressed significantly higher levels of AR-V7 protein and mRNA compared to the parental cell line. Knockdown of AR-V7 restored sensitivity of VCaP-MDVR cells to ENZ. A combination of EPI-7170 and ENZ caused synergistic inhibition of proliferation of ENZ resistant cells. Consistent results were also obtained with the clonogenic assay. While ENZ did not inhibited AR-V7 target genes, EPI-7170 inhibited both AR and AR-V7 target genes. A combination of EPI-7170 with ENZ led to a complete inhibition of DNA synthesis in S phase. CONCLUSIONS: The role of AR-Vs in the mechanism of ENZ resistance was provided by knockdown experiments and response to EPI-7170. Synergic inhibition was achieved with a combination of EPI-7170 with ENZ. These results suggest that targeting AR-NTD in addition to AR-LBD to block both FL-AR and AR-Vs could be a potential treatment option for CRPC. Source of Funding: US National Cancer Institute (R01 CA105304) awarded to Marianne D. Sadar Vancouver, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e85-e85 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Yukiyoshi Hirayama* More articles by this author Kunzhong Jian More articles by this author Raymond J. Anderson More articles by this author Marianne D. Sadar More articles by this author Expand All Advertisement PDF downloadLoading ...

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.021
GPT teacher head0.321
Teacher spread0.300 · 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 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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