Phase II Study of Lapatinib in Recurrent or Metastatic Epidermal Growth Factor Receptor and/or erbB2 Expressing Adenoid Cystic Carcinoma and Non–Adenoid Cystic Carcinoma Malignant Tumors of the Salivary Glands
Bibliographic record
Abstract
PURPOSE: Expression of erbB2 and/or epidermal growth factor receptor (EGFR) is associated with biologic aggressiveness and poor prognosis in malignant salivary gland tumors (MSGTs). This phase II study was conducted to determine the antitumor activity of lapatinib, a dual inhibitor of EGFR and erbB2 tyrosine kinase activity, in MSGTs. PATIENTS AND METHODS: Patients with progressive, recurrent, or metastatic adenoid cystic carcinoma (ACC) immunohistochemically expressing at least 1+ EGFR and/or 2+ erbB2 were treated with lapatinib 1,500 mg daily, in a two-stage cohort. Patients with non-ACC MSGTs were treated as a separate single-stage cohort. RESULTS: Of 62 patients screened, 29 of 33 (88%) ACC and 28 of 29 (97%) non-ACC patients expressed EGFR and/or erbB2. Forty patients with progressive disease were enrolled onto the study. Among 19 assessable ACC patients, there were no objective responses, 15 patients (79%) had stable disease (SD), nine patients (47%) had SD > or = 6 months, and four patients (21%) had progressive disease (PD). For 17 assessable non-ACC patients, there were no objective responses, eight patients (47%) had SD, four patients (24%) had SD > or = 6 months, and nine patients (53%) had PD. The most frequent adverse events were grade 1 to 2 diarrhea, fatigue, and rash. Eight paired tumor biopsies for correlative studies were procured; results did not correlate with clinical outcome. CONCLUSION: Although no responses were observed, lapatinib was well tolerated, with prolonged tumor stabilization of > or = 6 months in 36% (95% CI, 21% to 54%) of assessable patients. The antitumor effects of lapatinib in MGSTs appear mainly cytostatic, hence evaluation of other molecular targeted agents, or combinations with lapatinib, may be considered. Continued efforts should be made to gain better understanding into the biology of this heterogeneous group of malignancies.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".