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Record W2560318971 · doi:10.1158/0008-5472.can-16-1589

RB Loss Promotes Prostate Cancer Metastasis

2016· article· en· W2560318971 on OpenAlexaff
Chellappagounder Thangavel, Ettickan Boopathi, Yi Liu, Alex Haber, Adam Ertel, Anshul Bhardwaj, Sankar Addya, Noelle L. Williams, Stephen J. Ciment, Paolo Cotzia, Jeffry L. Dean, Adam E. Snook, Christopher McNair, Matt A. Price, James R. Hernandez, Shuang G. Zhao, Ruth Birbe, James B. McCarthy, Eva A. Turley, Kenneth J. Pienta, Felix Y. Feng, Adam P. Dicker, Karen E. Knudsen, Robert B. Den

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsLondon Health Sciences CentreWestern University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer InstituteProstate Cancer FoundationNational Institutes of HealthAmerican Cancer Society
KeywordsProstate cancerMetastasisCancerMedicineProstateOncologyCancer researchCancer metastasisInternal medicine

Abstract

fetched live from OpenAlex

Abstract RB loss occurs commonly in neoplasia but its contributions to advanced cancer have not been assessed directly. Here we show that RB loss in multiple murine models of cancer produces a prometastatic phenotype. Gene expression analyses showed that regulation of the cell motility receptor RHAMM by the RB/E2F pathway was critical for epithelial–mesenchymal transition, motility, and invasion by cancer cells. Genetic modulation or pharmacologic inhibition of RHAMM activity was sufficient and necessary for metastatic phenotypes induced by RB loss in prostate cancer. Mechanistic studies in this setting established that RHAMM stabilized F-actin polymerization by controlling ROCK signaling. Collectively, our findings show how RB loss drives metastatic capacity and highlight RHAMM as a candidate therapeutic target for treating advanced prostate cancer. Cancer Res; 77(4); 982–95. ©2016 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.492
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.126
GPT teacher head0.466
Teacher spread0.341 · 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.

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

Citations96
Published2016
Admission routes1
Has abstractyes

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