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Record W1593299612 · doi:10.1158/1538-7445.am2014-753

Abstract 753: Enhanced anti-tumor activity of erlotinib in combination with FAK tyrosine kinase inhibitors in non-small cell lung cancer

2014· article· en· W1593299612 on OpenAlexaff
Grant A. Howe, Bin Xiao, Huijun Zhao, Glenwood Goss, Christina Addison

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsErlotinibT790MCancer researchTyrosine kinaseEpidermal growth factor receptorMedicineErlotinib HydrochlorideEGFR inhibitorsProto-oncogene tyrosine-protein kinase SrcTyrosine-kinase inhibitorGefitinibCancerInternal medicineReceptor

Abstract

fetched live from OpenAlex

Abstract The epidermal growth factor receptor (EGFR) is over-expressed in approximately 90% of non-small cell lung cancer (NSCLC) and as such, blockade of EGFR activity has been a primary therapeutic target for NSCLC. As patients with wild-type EGFR have demonstrated only modest benefit from EGFR tyrosine kinase inhibitors (TKIs) there is a need for additional therapeutic approaches in patients with wild-type EGFR. The extracellular matrix (ECM) has been shown to play an important role in tumor growth and response to therapy, and focal adhesion kinase (FAK) expression, a key component of signaling downstream of ECM binding to cell surface integrins, has been shown to correlate with aggressive stage in NSCLC. As the FAK-Src signaling axis can activate EGFR signaling independently of EGFR ligand-binding and kinase activity, the use of FAK tyrosine kinase inhibitors in combination with EGFR TKIs was assessed as a means of enhancing response to treatment in NSCLC. Treatment of EGFR TKI-resistant NSCLC cells (A549 and H1299 with wild-type EGFR, and H1975 with T790M acquired resistance mutation in EGFR) with FAK tyrosine kinase inhibitor PF-573,228 alone decreased cell viability. Treatment of these cell lines with a combination of PF-573,228 and the EGFR TKI erlotinib was more effective at reducing cell viability and cell migration than either treatment alone. Additionally, the growth of EGFR TKI-resistant NSCLC cells in 3-dimensional culture was significantly impaired with a combination of FAK inhibitor and erlotinib compared to either treatment alone. Interestingly, although erlotinib alone could inhibit the phosphorylation of Akt to an extent in NSCLC cell lines, the combination of erlotinib and FAK inhibitor was able to almost completely inhibit Akt phosphorylation. As persistent Akt activity is associated with lack of response to EGFR TKIs, the enhanced reduction in cell viability seen with the addition of a FAK inhibitor to treatment with erlotinib appears to be, at least in part, due to enhanced inhibition of Akt activity in these cells. The efficacy of the combination treatment was confirmed in vivo using a xenograft model with subcutaneously implanted A549 cells in nude mice. Significant inhibition of tumor growth was observed for A549-derived tumors when erlotinib was used in combination with FAK inhibitor, with some animals not yet developing palpable tumors by the end-point of the experiment. Thus, the combination of FAK inhibition with EGFR TKIs such as erlotinib results in decreased growth of NSCLC cells both in vitro and in vivo and could prove to be an effective therapeutic approach for patients with EGFR TKI-resistant NSCLC. Citation Format: Grant A. Howe, Bin Xiao, Huijun Zhao, Glenwood Goss, Christina L. Addison. Enhanced anti-tumor activity of erlotinib in combination with FAK tyrosine kinase inhibitors in 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 753. doi:10.1158/1538-7445.AM2014-753

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.045
GPT teacher head0.398
Teacher spread0.353 · 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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