Tipifarnib in recurrent, metastatic HRAS‐mutant salivary gland cancer
Bibliographic record
Abstract
BACKGROUND: To the authors' knowledge, there are no approved therapies for recurrent, metastatic (R/M) salivary gland carcinoma (SGC), but molecularly targeted therapies warrant ongoing investigation. In the current study, the authors have reported on the efficacy of tipifarnib in patients with aggressive HRAS-mutant, R/M SGC. METHODS: The current prospective, nonrandomized, multicenter, international cohort study involved 8 centers and was conducted from May 2015 to June 2019. The median follow-up was 22 months (range, 6-55 months). Subjects with HRAS-mutant R/M SGC (any histology) and disease progression within the last 6 months were enrolled. Tipifarnib was dosed orally twice daily. The authors determined the objective response rate using Response Evaluation Criteria in Solid Tumors (version 1.1), duration of response, and molecular predictors of response. RESULTS: A total of 13 patients with R/M SGC were enrolled; all had received prior systemic therapy (1-3 regimens). One objective response was observed; an additional 7 of 12 evaluable patients (58%) had stable disease as their best response with a median duration of 9 months (range, 3-14 months). Five of 7 patients had >10% tumor regression and 6 of 7 had stable disease lasting >6 months. Q61R was the most frequent activating HRAS mutation noted (7 of 13 patients; 54%), but gene variant and allele frequency did not correlate with outcomes. The median progression-free survival was 7 months (95% confidence interval, 5.9-10.1 months), and the median overall survival was 18 months (95% confidence interval, 9.6-22.4 months) with approximately 58.6% of patients alive at 1 year. Survival was similar regardless of HRAS mutant variant or co-occurring PIK3CA alterations. No participant discontinued treatment because of toxicity. CONCLUSIONS: Tipifarnib resulted in modest clinical activity with a promising disease control rate among patients with HRAS-mutant, R/M SGC who developed disease progression within the last 6 months.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".