Baseline Gene Expression Predicts Sensitivity to Gefitinib in Non–Small Cell Lung Cancer Cell Lines
Bibliographic record
Abstract
Tyrosine kinase inhibitors (TKI) of the epidermal growth factor receptor (EGFR) produce objective responses in a minority of patients with advanced-stage non-small cell lung cancer (NSCLC), and about half of all treated patients progress within 6 weeks of instituting therapy. Because the target of these agents is known, it should be possible to develop biological predictors of response, but EGFR protein levels have not been proven useful as a predictor of TKI response in patients and the mechanism of primary resistance is unclear. We used microarray gene expression profiling to uncover a pattern of gene expression associated with sensitivity to EGFR-TKIs by comparing NSCLC cell lines that were either highly sensitive or highly resistant to gefitinib. This sensitivity-associated expression profile was used to predict gefitinib sensitivity in a panel of NSCLC cell lines with known gene expression profiles but unknown gefitinib sensitivity. Gefitinib sensitivity was then determined for members of this test panel, and the microarray-based sensitivity prediction was correct in eight of nine NSCLC cell lines. Gene and protein expression differences were confirmed with a combination of quantitative reverse transcription-PCR, flow cytometry, and immunohistochemistry. This gene expression pattern related to gefitinib sensitivity was independent from sensitivity associated with EGFR mutations. Several genes associated with sensitivity encode proteins involved in HER pathway signaling or pathways that interrelate to the HER signaling pathway. Some of these genes could be targets of pharmacologic interventions to overcome primary resistance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".