Nilotinib in KIT-driven advanced melanoma: Results from the phase II single-arm NICAM trial
Bibliographic record
Abstract
Mucosal (MM) and acral melanomas (AM) are rare melanoma subtypes of unmet clinical need; 15%-20% harbor KIT mutations potentially targeted by small-molecule inhibitors, but none yet approved in melanoma. This multicenter, single-arm Phase II trial (NICAM) investigates nilotinib safety and activity in KIT mutated metastatic MM and AM. KIT mutations are identified in 39/219 screened patients (18%); of 29/39 treated, 26 are evaluable for primary analysis. Six patients were alive and progression free at 6 months (local radiology review, 25%); 5/26 (19%) had objective response at 12 weeks; median OS was 7.7 months; ddPCR assay correctly identifies KIT alterations in circulating tumor DNA (ctDNA) in 16/17 patients. Nilotinib is active in KIT-mutant AM and MM, comparable to other KIT inhibitors, with demonstrable activity in nonhotspot KIT mutations, supporting broadening of KIT evaluation in AM and MM. Our results endorse further investigations of nilotinib for the treatment of KIT-mutated melanoma. This clinical trial was registered with ISRCTN (ISRCTN39058880) and EudraCT (2009-012945-49).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".