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Record W2016320036 · doi:10.1158/0008-5472.can-06-1042

Association with HSP90 Inhibits Cbl-Mediated Down-regulation of Mutant Epidermal Growth Factor Receptors

2006· article· en· W2016320036 on OpenAlexaff
Seungchan Yang, Shimian Qu, Marianela Perez-Tores, Ayana Sawai, Neal Rosen, David B. Solit, Carlos L. Arteaga

Bibliographic record

VenueCancer Research · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsImmunovaccine (Canada)
FundersNational Cancer InstituteVanderbilt University
KeywordsEpidermal growth factor receptorGeldanamycinHsp90MutantUbiquitin ligaseCell biologyTyrosine kinaseGRB2ReceptorSignal transducing adaptor proteinReceptor tyrosine kinaseBiologyEpidermal growth factorCancer researchGrowth factor receptorPhosphorylationChemistryMolecular biologySignal transductionUbiquitinBiochemistryHeat shock protein

Abstract

fetched live from OpenAlex

Activating mutations in the epidermal growth factor receptor (EGFR), localized in the activation loop within the kinase domain, have been discovered in non-small cell lung cancers (NSCLC). Most of these mutants are exquisitely sensitive to EGFR tyrosine kinase inhibitors, suggesting that they generate receptor dependence in the cancers that express them. 32D cells stably expressing EGFR-L861Q and EGFR-L858R but not wild-type EGFR exhibited ligand-independent receptor phosphorylation and viability. Ligand-induced receptor down-regulation (LIRD) was impaired in mutant-expressing cells. The EGFR mutants were constitutively associated with the E3 ubiquitin ligase Cbl but did not associate with the adaptor protein CIN85 on the addition of ligand. Inhibition of HSP90 activity with geldanamycin restored Cbl function as indicated by receptor ubiquitination and LIRD. These results suggest that EGFR mutants form defective endocytic complexes. In addition, HSP90 plays a role in maintaining the functional conformation of EGFR mutants and protecting activated receptors from LIRD.

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)0.001
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 score0.012
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.329
Teacher spread0.304 · 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

Citations79
Published2006
Admission routes1
Has abstractyes

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