Focal Adhesion Kinase Inhibitors in Combination with Erlotinib Demonstrate Enhanced Anti-Tumor Activity in Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Blockade of epidermal growth factor receptor (EGFR) activity has been a primary therapeutic target for non-small cell lung cancers (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. As a key component of downstream integrin signalling and known receptor cross-talk with EGFR, we hypothesized that targeting focal adhesion kinase (FAK) activity, which has also been shown to correlate with aggressive stage in NSCLC, would lead to enhanced activity of EGFR TKIs. As such, EGFR TKI-resistant NSCLC cells (A549, H1299, H1975) were treated with the EGFR TKI erlotinib and FAK inhibitors (PF-573,228 or PF-562,271) both as single agents and in combination. We determined cell viability, apoptosis and 3-dimensional growth in vitro and assessed tumor growth in vivo. Treatment of EGFR TKI-resistant NSCLC cells with FAK inhibitor alone effectively inhibited cell viability in all cell lines tested; however, its use in combination with the EGFR TKI erlotinib was more effective at reducing cell viability than either treatment alone when tested in both 2- and 3-dimensional assays in vitro, with enhanced benefit seen in A549 cells. This increased efficacy may be due in part to the observed inhibition of Akt phosphorylation when the drugs were used in combination, where again A549 cells demonstrated the most inhibition following treatment with the drug combination. Combining erlotinib with FAK inhibitor was also potent in vivo as evidenced by reduced tumor growth in the A549 mouse xenograft model. We further ascertained that the enhanced sensitivity was irrespective of the LKB1 mutational status. In summary, we demonstrate the effectiveness of combining erlotinib and FAK inhibitors for use in known EGFR wild-type, EGFR TKI resistant cells, with the potential that a subset of cell types, which includes A549, could be particularly sensitive to this combination treatment. As such, further evaluation of this combination therapy is warranted and could prove to be an effective therapeutic approach for patients with inherent EGFR TKI-resistant NSCLC.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 0.000 |
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.
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 teacher head, 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".