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Record W2605764624 · doi:10.18632/oncotarget.17124

Discovery and characterization of small molecules targeting the DNA-binding ETS domain of ERG in prostate cancer

2017· article· en· W2605764624 on OpenAlexafffundabout
Miriam Butler, Mani Roshan‐Moniri, Michael Hsing, Desmond Lau, Ari Kim, Paul Yen, Marta Mroczek, Mannan Nouri, Scott Lien, Peter Axerio-Cilies, Kush Dalal, Clement Yau, Fariba Ghaidi, Yubin Guo, Takeshi Yamazaki, Sam Lawn, Martin Gleave, Cheryl Y. Gregory‐Evans, Lawrence P. McIntosh, Michael Cox, Paul S. Rennie, Artem Cherkasov

Bibliographic record

VenueOncotarget · 2017
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersCongressionally Directed Medical Research ProgramsBritish Columbia Knowledge Development FundCanadian Institutes of Health ResearchNational Cancer InstituteProstate Cancer FoundationNational Institutes of HealthProstate Cancer CanadaU.S. Department of Defense
KeywordsProstate cancerErgMedicineCancer researchDNA damageComputational biologyDNAOncologyCancerInternal medicineGeneticsBiologyOphthalmology

Abstract

fetched live from OpenAlex

// Miriam S. Butler 1, *, # , Mani Roshan-Moniri 1, *, # , Michael Hsing 1, *, # , Desmond Lau 2, *, # , Ari Kim 1 , Paul Yen 1 , Marta Mroczek 1 , Mannan Nouri 1 , Scott Lien 1 , Peter Axerio-Cilies 1 , Kush Dalal 1 , Clement Yau 1 , Fariba Ghaidi 1 , Yubin Guo 1 , Takeshi Yamazaki 1 , Sam Lawn 1 , Martin E. Gleave 1 , Cheryl Y. Gregory-Evans 3 , Lawrence P. McIntosh 2, * , Michael E. Cox 1, * , Paul S. Rennie 1, * and Artem Cherkasov 1, * 1 Vancouver Prostate Centre and the Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6H 3Z6, Canada 2 Department of Biochemistry and Molecular Biology, Department of Chemistry, Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z3, Canada 3 Department of Ophthalmology and Visual Sciences, Eye Care Centre, University of British Columbia, Vancouver, BC V5Z 3N9, Canada * These authors contributed equally to this work # Co-first authors Correspondence to: Michael E. Cox, email: mcox@prostatecentre.com Artem Cherkasov, email: acherkasov@prostatecentre.com Keywords: prostate cancer, ERG, rational drug design, small molecule inhibitor, TMPRSS2-ERG Received: July 29, 2016      Accepted: April 04, 2017      Published: April 15, 2017 ABSTRACT Genomic alterations involving translocations of the ETS-related gene ERG occur in approximately half of prostate cancer cases. These alterations result in aberrant, androgen-regulated production of ERG protein variants that directly contribute to disease development and progression. This study describes the discovery and characterization of a new class of small molecule ERG antagonists identified through rational in silico methods. These antagonists are designed to sterically block DNA binding by the ETS domain of ERG and thereby disrupt transcriptional activity. We confirmed the direct binding of a lead compound, VPC-18005, with the ERG-ETS domain using biophysical approaches. We then demonstrated VPC-18005 reduced migration and invasion rates of ERG expressing prostate cancer cells, and reduced metastasis in a zebrafish xenograft model. These results demonstrate proof-of-principal that small molecule targeting of the ERG-ETS domain can suppress transcriptional activity and reverse transformed characteristics of prostate cancers aberrantly expressing ERG. Clinical advancement of the developed small molecule inhibitors may provide new therapeutic agents for use as alternatives to, or in combination with, current therapies for men with ERG-expressing metastatic castration-resistant prostate cancer.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.218

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 head0.021
GPT teacher head0.309
Teacher spread0.288 · 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 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

Citations45
Published2017
Admission routes3
Has abstractyes

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