Tivantinib (ARQ 197), a selective inhibitor of MET, in patients with microphthalmia transcription factor–associated tumors
Bibliographic record
Abstract
BACKGROUND: Microphthalmia transcription factor (MITF)-associated (MiT) tumors are a family of rare malignancies, including alveolar soft part sarcoma (ASPS), clear cell sarcoma (CCS), and translocation-associated renal cell carcinoma (tRCC) that have dysregulated expression of oncogenic MITF family proteins. The MET receptor tyrosine kinase gene is transcriptionally activated by MITF family proteins, making MET a potential therapeutic target for MiT tumors. This study assessed the activity of tivantinib (ARQ 197), a selective MET inhibitor, in patients with MiT-associated tumors. METHODS: This multicenter, single-arm, phase 2 trial enrolled patients with advanced MiT tumors. Patients initially received tivantinib 120 mg orally twice daily, then 360 mg twice daily per protocol amendment. The primary endpoint was overall response rate. Secondary endpoints included safety, progression-free survival, pharmacokinetics, and correlative studies. RESULTS: A total of 47 patients (median age, 25 years; range, 11-73 years) with ASPS (n = 27), CCS (n = 11), tRCC (n = 6), or other tumor types (n = 3) were enrolled. Common grade 3/4 drug-related adverse events included anemia (4%) and neutropenia (4%). Three patients (6.4%) experienced 4 treatment-related serious adverse events (grade 3 febrile neutropenia, thrombocytopenia, and deep vein thrombosis, and grade 4 thrombocytopenia). Best response was partial response in 1 CCS patient (2%) and stable disease in 28 patients (60%). Median progression-free survival was 3.6 months (overall), 5.5 months (ASPS), and 1.9 months (CCS and tRCC). Baseline MET expression was strongly or focally positive in tumor samples from 14 of 19 patients (74%). CONCLUSIONS: Tivantinib was safe and tolerable in patients with MiT tumors, but antitumor activity was modest.
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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 | 0.000 |
| 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 | 0.001 |
| 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".