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Abstract LB-77: The Met receptor tyrosine kinase drives signaling through EGFR and ErbB3 in Met-amplified non-small-cell lung cancer

2014· article· en· W1978599343 on OpenAlexaff
Yaakov E. Stern, Andrea Lai, Morag Park

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsMcGill University
Fundersnot available
KeywordsERBB3Receptor tyrosine kinaseCancer researchTyrosine kinaseErbBEpidermal growth factor receptorCyclin-dependent kinase 8Proto-oncogene tyrosine-protein kinase SrcSignal transductionBiologyERBB4ROR1AutophosphorylationKinaseCancerCell biologyPlatelet-derived growth factor receptorReceptorProtein kinase ABiochemistryNotch signaling pathwayGrowth factor

Abstract

fetched live from OpenAlex

Abstract Receptor tyrosine kinases are canonically activated by dimerization induced by the binding of a cognate ligand. This leads to autophosphorylation of the receptor molecules by their intrinsic kinase domains and the recruitment of intracellular protein complexes to initiate signaling. In many human cancers, receptor tyrosine kinases are aberrantly activated to promote proliferative signaling, thus driving tumor growth. The MET proto-oncogene is amplified in approximately 5% of non-small-cell lung cancers (NSCLC), a disease in which aberrant signaling through the epidermal growth factor receptor (EGFR) or the anaplastic lymphoma kinase is known to be oncogenic. The Met receptor tyrosine kinase is overexpressed and constitutively active in cell lines derived from MET-amplified tumors, and Met activity is required for proliferation. Furthermore, Met activity is sufficient to promote the phosphorylation of EGFR and its homologs ErbB2 and ErbB3 in the presence of inhibitors targeting EGFR, ErbB2, and Src family kinases. We show that Met utilizes the EGFR, ErbB2 and ErbB3 proteins as scaffolds to broaden downstream oncogenic signaling and promote cancer cell proliferation. The contribution of EGFR and its homologs to Met signaling in NSCLC will be presented with a focus on ErbB family kinases required for proliferation in MET-amplified NSCLC. Understanding the mechanisms by which Met drives proliferative signaling in NSCLC will become crucial as Met-targeted therapies become available in the clinic. Citation Format: Yaakov E. Stern, Andrea Z. Lai, Morag Park. The Met receptor tyrosine kinase drives signaling through EGFR and ErbB3 in Met-amplified non-small-cell lung cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-77. doi:10.1158/1538-7445.AM2014-LB-77

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 score0.004
Threshold uncertainty score0.013

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.061
GPT teacher head0.427
Teacher spread0.366 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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