A phase II study of lapatinib in recurrent or metastatic EGFR and/or ErbB2 expressing adenoid cystic (ACC) and non-ACC malignant tumors of the salivary glands (MSGT)
Bibliographic record
Abstract
5566 Background: The limited therapeutic index of chemotherapy in recurrent or metastatic MSGT provides a strong rationale for the evaluation of molecularly targeted agents in this disease. Lapatinib is a dual inhibitor of EGFR and ErbB2 tyrosine kinase activity. Expression of ErbB2 and EGFR has been associated with biological aggressiveness and poor prognosis in MSGT, respectively. We conducted a phase II study to determine the antitumor activity of lapatinib in MSGT. Methods: The main study has a two-stage design in which patients (pts) with progressive, recurrent or metastatic ACC, and immunohistochemically expressing at least 1+ EGFR and/or 2+ ErbB2, were treated with lapatinib 1500 mg PO daily. Each cycle consists of 4 weeks of continuous dosing. Pts with non-ACC MSGT of other histologies, meeting identical eligibility criteria, were treated in this trial as a single-stage, separate cohort. Results: Of 57 pts screened for this study, 29/33 (88%) ACC and 22/24 (92%) non-ACC pts expressed EGFR and/or ErbB2. Thirty-eight pts have been accrued to the study to date (20 ACC/18 non-ACC). The remaining 13 pts who were screened positive either declined entry or were ineligible for other reasons. Baseline data on 34 pts are: M:F = 25:9, median age 56 (range 38–80), PS 0:1:2 = 16:17:1, prior radiation:chemotherapy = 30:18. After 92 cycles of therapy, the most frequent adverse events experienced (as % of cycles) were diarrhea (54%), pain (52%), fatigue (52%), lymphopenia (39%), anemia (38%), hyperglycemia (38%) and dyspnea (34%). No grade 4 adverse events have occurred and only 8 pts experienced a grade 3 adverse event, primarily pain and dyspnea. No significant cardiac toxicity has been observed. Among 14 ACC pts evaluable for response so far: 9 have SD (range 2–9 cycles), 3 PD, and 2 died prior to cycle 2. For 12 evaluable non-ACC pts: 8 have SD (range 2–9 cycles), and 4 PD. No pts have had an objective response. Conclusions: Although there are no objective responses to date, lapatinib is well tolerated, with tumor stabilization achieved by 64% of pts and 24/38 pts remain on treatment at present. Trial accrual of ACC pts into the first stage has been completed, the second stage will open if an objective response is seen. No significant financial relationships to disclose.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".