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Record W2150701033 · doi:10.1073/pnas.1101263108

HER2 overcomes PTEN (loss)-induced senescence to cause aggressive prostate cancer

2011· article· en· W2150701033 on OpenAlexaff
Imran Ahmad, Rachana Patel, Lukram Babloo Singh, Colin Nixon, Morag Seywright, Robert J. Barnetson, Valerie G. Brunton, William J. Muller, Joanne Edwards, Owen J. Sansom, Hing Y. Leung

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2011
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsMcGill University
FundersCancer Research UK
KeywordsPTENProstate cancerCancer researchSenescenceLoss functionMAPK/ERK pathwayCancerProstateBiologyMedicinePI3K/AKT/mTOR pathwayInternal medicineSignal transductionPhenotypeCell biologyGeneticsGene

Abstract

fetched live from OpenAlex

Prostate cancer (CaP) is the most common cancer among adult men in the Western world. Better insight into its tumor-activating pathways may facilitate the development of targeted therapies. In this study, we show that patients who develop prostate tumors with low levels of PTEN and high levels of HER2/3 have a poor prognosis. This is functionally relevant, as targeting Her2 activation to the murine prostate cooperates with Pten loss and drives CaP progression. Mechanistically, this is associated with activation of the MAPK pathway and abrogation of the Pten loss-induced cellular senescence program. Importantly, inhibition of MEK function strongly suppressed proliferation within these tumors by restoring the Pten loss-induced cellular senescence program. Taken together, these data suggest that stratification of CaP patients for HER2/3 and PTEN status could identify patients with aggressive CaP who may respond favorably to MEK inhibition.

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 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 score0.067
Threshold uncertainty score0.249

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)0.0000.000

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 head0.114
GPT teacher head0.384
Teacher spread0.270 · 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 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

Citations62
Published2011
Admission routes1
Has abstractyes

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