Molecular Predictors of Outcome With Gefitinib and Docetaxel in Previously Treated Non–Small-Cell Lung Cancer: Data From the Randomized Phase III INTEREST Trial
Bibliographic record
Abstract
PURPOSE In the phase III INTEREST trial, 1,466 pretreated patients with advanced non-small cell lung cancer (NSCLC) were randomly assigned to receive gefitinib or docetaxel. As a preplanned analysis, we prospectively analyzed available tumor biopsies to investigate the relationship between biomarkers and clinical outcomes. METHODS Biomarkers included epidermal growth factor receptor (EGFR) copy number by fluorescent in situ hybridization (374 assessable samples), EGFR protein expression by immunohistochemistry (n = 380), and EGFR (n = 297) and KRAS (n = 275) mutations. Results For all biomarker subgroups analyzed, survival was similar for gefitinib and docetaxel, with no statistically significant differences between treatments and no significant treatment by biomarker status interaction tests. EGFR mutation-positive patients had longer progression-free survival (PFS; hazard ratio [HR], 0.16; 95% CI, 0.05 to 0.49; P = .001) and higher objective response rate (ORR; 42.1% v 21.1%; P = .04), and patients with high EGFR copy number had higher ORR (13.0% v 7.4%; P = .04) with gefitinib versus docetaxel. CONCLUSION These biomarkers do not appear to be predictive factors for differential survival between gefitinib and docetaxel in this setting of previously treated patients; however, subsequent treatments may have influenced the survival results. For secondary end points of PFS and ORR, some advantages for gefitinib over docetaxel were seen in EGFR mutation-positive and high EGFR copy number patients. There was no statistically significant difference between gefitinib and docetaxel in biomarker-negative patients. This suggests gefitinib can provide similar overall survival to docetaxel in patients across a broad range of clinical subgroups and that EGFR biomarkers such as mutation status may additionally identify which patients are likely to gain greatest PFS and ORR benefit from gefitinib.
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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".