MétaCan
Menu
Back to cohort
Record W2745907548 · doi:10.1158/0008-5472.can-17-0314

Therapeutic Targeting of the CBP/p300 Bromodomain Blocks the Growth of Castration-Resistant Prostate Cancer

2017· article· en· W2745907548 on OpenAlexaff
Lingyan Jin, Jesse Garcia, Emily Chan, Cecile de la Cruz, Ehud Segal, Mark Merchant, Samir Kharbanda, Ryan Raisner, Peter M. Haverty, Zora Modrušan, Justin Q. Ly, Edna F. Choo, Susan Kaufman, Maureen H. Beresini, F. Anthony Romero, Steven Magnuson, Karen E. Gascoigne

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsDiscovery Centre
Fundersnot available
KeywordsProstate cancerBromodomainEnzalutamideCancer researchCancerAndrogen receptorMedicineCabazitaxelCoactivatorAcetylationAndrogen deprivation therapyBiologyInternal medicineGeneTranscription factor

Abstract

fetched live from OpenAlex

Abstract Resistance invariably develops to antiandrogen therapies used to treat newly diagnosed prostate cancers, but effective treatments for castration-resistant disease remain elusive. Here, we report that the transcriptional coactivator CBP/p300 is required to maintain the growth of castration-resistant prostate cancer. To exploit this vulnerability, we developed a novel small-molecule inhibitor of the CBP/p300 bromodomain that blocks prostate cancer growth in vitro and in vivo. Molecular dissection of the consequences of drug treatment revealed a critical role for CBP/p300 in histone acetylation required for the transcriptional activity of the androgen receptor and its target gene expression. Our findings offer a preclinical proof of concept for small-molecule therapies to target the CBP/p300 bromodomain as a strategy to treat castration-resistant prostate cancer. Cancer Res; 77(20); 5564–75. ©2017 AACR.

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 integrity0.0000.001
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 head0.046
GPT teacher head0.371
Teacher spread0.325 · 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

Citations152
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCancer ResearchSame topicProtein Degradation and InhibitorsFrench-language works237,207