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Record W2561948781 · doi:10.1158/1538-7445.am2015-732

Abstract 732: Using functional and chemical genomics to identify mechanisms of Enzalutamide resistance in prostate cancer

2015· article· en· W2561948781 on OpenAlexaff
Sujeeve Jeganathan, Amina Zoubeidi, Martin Gleave, Bradly G. Wouters, Anthony M. Joshua

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsEnzalutamideProstate cancerAndrogen receptorCancer researchPharmacologyIn vivoKinomeCancerCancer cellSignal transductionMedicineBiologyInternal medicineCell biologyGenetics

Abstract

fetched live from OpenAlex

Abstract Introduction: Enzalutamide is a recently approved potent androgen receptor antagonist for prostate cancer both in the pre- and post-docetaxel settings. Its action relies upon 3 defined mechanisms of action: 1) blocking testosterone binding to the androgen receptor (AR), 2) preventing nuclear translocation of AR, and 3) inhibiting AR-DNA binding. However, despite its utility in prolonged overall survival in both these settings, disease relapse is inevitable, suggesting that resistance to the drug is somehow being acquired and is of vital concern. Methods: Using a combination of high-throughput functional genomic (kinome-shRNA) and chemical genomic (pharmacological inhibitor) screens, along with various in vitro / in vivo assays (proliferation, apoptosis, necrosis, soft agar growth, etc.) we have identified the IkB kinase (IKK) / IkB / NFkB signaling axis as important for acquisition of, and continued resistance to, Enzalutamide. Results: Using a panel of Enzalutamide-resistant and -sensitive cell lines, we show that targeting this signaling axis will selectively kill Enzalutamide-resistant cells while not affecting the proliferation of sensitive cells. Moreover, our data shows that the pharmacological inhibitors and shRNAs are effective even without Enzalutamide administration, negating any potential drug-drug interaction issues. Finally, we have found that our cells are resistant to Enzalutamide in an AR-V7-independent manner, suggesting that NFkB signaling is acting in a novel manner to bring about the resistance phenotype. Conclusions: Through in vitro and in vivo experiments, we provide evidence that targeting activators of NFκB signalling can be a strategy to help patients with Enzalutamide-resistant prostate cancer. Note: This abstract was not presented at the meeting. Citation Format: Sujeeve Jeganathan, Amina Zoubeidi, Martin Gleave, Brad G. Wouters, Anthony M. Joshua. Using functional and chemical genomics to identify mechanisms of Enzalutamide resistance in 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 732. doi:10.1158/1538-7445.AM2015-732

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.187
GPT teacher head0.461
Teacher spread0.274 · 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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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