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Record W2087764235 · doi:10.1186/1471-2407-7-128

Correlations of EGFR mutations and increases in EGFR and HER2 copy number to gefitinib response in a retrospective analysis of lung cancer patients

2007· article· en· W2087764235 on OpenAlexafffund
Trevor J. Pugh, Gwyn Bebb, Lorena Barclay, Margaret Sutcliffe, John Fee, Chris Salski, Robert O’Connor, Cheryl Ho, Nevin Murray, Barbara Melosky, John C. English, J. Vielkind, Doug Horsman, Janessa Laskin, Marco A. Marra

Bibliographic record

VenueBMC Cancer · 2007
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsVancouver General HospitalBaker Hughes (Canada)Canada's Michael Smith Genome Sciences CentreBC Cancer Agency
FundersBC Cancer AgencyUniversity of British ColumbiaMichael Smith Health Research BCGenome British ColumbiaCanada's Michael Smith Genome Sciences CentreGenome Canada
KeywordsGefitinibExonEpidermal growth factor receptorLung cancerCancer researchGene duplicationPoint mutationTyrosine kinaseTyrosine-kinase inhibitorAdenocarcinomaBiologyCopy-number variationMedicineOncologyMolecular biologyMutationCancerGeneInternal medicineGeneticsReceptorGenome

Abstract

fetched live from OpenAlex

BACKGROUND: Gefitinib, a small molecule tyrosine kinase inhibitor of the Epidermal Growth Factor Receptor (EGFR), has shown limited efficacy in the treatment of lung cancer. Recognized clinical predictors of response to this drug, specifically female, non-smoker, Asian descent, and adenocarcinoma, together suggest a genetic basis for drug response. Recent studies have addressed the relationship between response and either sequence mutations or increased copy number of specific receptor tyrosine kinases. We set out to examine the relationship between response and the molecular status of two such kinases, EGFR and HER2, in 39 patients treated with gefitinib at the BC Cancer Agency. METHODS: Archival patient material was reviewed by a pathologist and malignant cells were selectively isolated by laser microdissection or manual recovery of cells from microscope slides. Genomic DNA was extracted from 37 such patient samples and exons 18-24, coding for the tyrosine kinase domain of EGFR, were amplified by PCR and sequenced. EGFR and HER2 copy number status were also assessed using FISH in 26 samples. Correlations between molecular features and drug response were assessed using the two-sided Fisher's exact test. RESULTS: Mutations previously correlated with response were detected in five tumours, four with exon 19 deletions and one with an exon 21 missense L858R point mutation. Increased gene copy number was observed in thirteen tumours, seven with EGFR amplification, three with HER2 amplification, and three with amplification of both genes. In our study cohort, a correlation was not observed between response and EGFR mutations (exon 19 deletion p = 0.0889, we observed a single exon 21 mutation in a non-responder) or increases in EGFR or HER2 copy number (p = 0.552 and 0.437, respectively). CONCLUSION: Neither mutation of EGFR nor increased copy number of EGFR or HER2 was diagnostic of response to gefitinib in this cohort. However, validation of these features in a larger sample set is appropriate. Identification of additional predictive biomarkers beyond EGFR status may be necessary to accurately predict treatment outcome.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.369
Teacher spread0.359 · 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 designObservational
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

Citations42
Published2007
Admission routes2
Has abstractyes

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