Abstract #3559: A quantitative survey of tyrosine phosphorylation changes with erlotinib treatment in EGFR mutant and wildtype NSCLC cell lines
Bibliographic record
Abstract
The Epidermal Growth Factor Receptor (EGFR) is a transmembrane receptor that is frequently expressed in carcinoma. Erlotinib is a small molecule inhibitor of EGFR preventing the receptor9s autophosphorylation and downstream signaling. Erlotinib has been approved for second line treatment of advanced stage non-small-cell lung cancer (NSCLC) patients. NSCLC patients with mutations in the kinase domain of EGFR (L858R and exon 19 deletion) and/or EGFR gene amplifications demonstrate greater response rates and improved survival when treated by erlotinib. However, not all molecular factors that influence the clinical response to this drug are as yet identified. Greater understanding on how erlotinib influences EGFR induced cellular signaling in wild type or EGFR mutant/amplified NSCLC cells lines may provide insights on potential novel biomarkers for predicting response to erlotinib treatment, and/or novel downstream targets that may be developed against lung cancer that are resistant to erlotinib. A general quantitative survey of tyrosine phosphorylation comparing EGFR mutant, EGFR amplified wild type and EGFR low copy number wild-type NSCLC cell lines using tandem mass spectrometry (LTQ Orbitrap) was generated following erlotinib treatment, to probe differences in the impact of erlotinib on cellular phosphotyrosine targets in erlotinib-sensitive and erlotinib-resistant cell lines. Preliminary downstream effectors of erlotinib have been identified and quantified by analysis of high resolution extracted ion currents. A subset of these will be subject to validation and quantification in xenograft tumor models by using Selected Reaction Monitoring (SRM) with a triple quadrupole instrument (TSQ Quantum Ultra). (Supported by the Ontario Institute of Cancer Research Grant 07NOV-78) Citation Information: In: Proc Am Assoc Cancer Res; 2009 Apr 18-22; Denver, CO. Philadelphia (PA): AACR; 2009. Abstract nr 3559.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".