Who’s Driving? Switch of Drivers in Immunotherapy-Treated Progressing Sinonasal Melanoma
Bibliographic record
Abstract
Mucosal melanoma can be driven by various driver mutations in genes such as NRAS, KIT, or KRAS. However, some cases present with only weak drivers, or lacking known oncogenic drivers, suggesting immunotherapy over targeted therapy. While resistance mechanisms to immunotherapy in cutaneous melanoma have been uncovered, including alterations in JAK1/2, B2M, or STK11, a switch of oncogenic drivers under immunotherapy has not yet been observed. We report three cases of metastatic sinonasal melanoma that switched oncogenic drivers from KRAS, KIT, or no driver to NRAS during or after immunotherapy, thereby showing progressive disease. One of the cases presented with three spatially separate driver mutations in the primary tumor, whereas the NRAS clone persisted under immunotherapy. In comparison, three different control cases receiving radiotherapy only did not show a change of the detectable molecular drivers in their respective recurrences or metastases. In summary, these data provide an important rationale for longitudinal molecular testing, based on evidence for an unforeseen recurrent event of molecular driver switch to NRAS in progressing sinonasal melanoma. These findings provide the basis for further studies on a potential causal relation of emerging NRAS mutant clones and immunotherapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".