Phase II Study of Erlotinib in Recurrent or Metastatic Endometrial Cancer: NCIC IND-148
Bibliographic record
Abstract
PURPOSE: Epidermal growth factor receptor (EGFR) overexpression is common in endometrial cancers and may have a major role in tumor growth and progression. Erlotinib is an orally active, selective inhibitor of EGFR tyrosine kinase activity. PATIENTS AND METHODS: A multinomial design two-stage phase II study was performed to evaluate single-agent activity of erlotinib in women with advanced endometrial cancer with recurrent or metastatic disease who were chemotherapy naïve and had received up to one line of prior hormonal therapy. Erlotinib was administered at daily dose of 150 mg. Archival tumor tissue was analyzed for EGFR expression by immunohistochemistry (IHC) and gene amplification by fluorescent in situ hybridization (FISH). Mutational status of EGFR was determined in responders. RESULTS: Thirty-two of 34 entered patients are assessable for response. Treatment was well tolerated and severe toxicity infrequent, with the only grade 4 toxicity being an elevation of transaminases (AST). There were four confirmed partial responses (PRs; 12.5%; 95% CI, 3.5% to 29%) lasting 2 to 36 months. Fifteen patients had stable disease (SD), with median duration of 3.7 months (range, 2 to 12 months). EGFR expression was analyzed in thirty patients; 19 were positive, nine were negative, and two were not assessable. Of the 19 patients who were EGFR positive, three had PR (16%), seven SD, and eight progressive disease, and one was not assessable. No mutations were identified in responders. FISH showed no correlation of response with gene amplification. CONCLUSION: Erlotinib is well tolerated with an overall objective response rate of 12.5%. Molecular analysis did not identify EGFR mutations in responders or correlation of response with gene amplification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".