Data from Fibroblast growth factor receptor-mediated signals contribute to the malignant phenotype of non-small cell lung cancer cells: therapeutic implications and synergism with epidermal growth factor receptor inhibition
Bibliographic record
Abstract
<div>Abstract<p>Fibroblast growth factors (FGF) and their high-affinity receptors (FGFR) represent an extensive cellular growth and survival system. Aim of this study was to evaluate the contribution of FGF/FGFR-mediated signals to the malignant growth of non-small cell lung cancer (NSCLC) and to assess their potential as targets for therapeutic interventions. Multiple FGFR mRNA splice variants were coexpressed in NSCLC cells (<i>n</i> = 16) with predominance of FGFR1. Accordingly, both expression of a dominant-negative FGFR1 (dnFGFR1) IIIc-green fluorescent protein fusion protein and application of FGFR small-molecule inhibitors (SU5402 and PD166866) significantly reduced growth, survival, clonogenicity, and migratory potential of the majority of NSCLC cell lines. Moreover, dnFGFR1 expression completely blocked or at least significantly attenuated s.c. tumor formation of NSCLC cells in severe combined immunodeficient mice. Xenograft tumors expressing dnFGFR1 exhibited significantly reduced size and mitosis rate, enhanced cell death, and decreased tissue invasion. When FGFR inhibitors were combined with chemotherapy, antagonistic to synergistic <i>in vitro</i> anticancer activities were obtained depending on the application schedule. In contrast, simultaneous blockage of FGFR- and epidermal growth factor receptor-mediated signals exerted synergistic effects. In summary, FGFR-mediated signals in cooperation with those transmitted by epidermal growth factor receptor are involved in growth and survival of human NSCLC cells and should be considered as targets for combined therapeutic approaches. [Mol Cancer Ther 2008;7(10):3408–19]</p></div>
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".