<i>RAS</i> Mutations in Cutaneous Squamous-Cell Carcinomas in Patients Treated with BRAF Inhibitors
Bibliographic record
Abstract
BACKGROUND: Cutaneous squamous-cell carcinomas and keratoacanthomas are common findings in patients treated with BRAF inhibitors. METHODS: We performed a molecular analysis to identify oncogenic mutations (HRAS, KRAS, NRAS, CDKN2A, and TP53) in the lesions from patients treated with the BRAF inhibitor vemurafenib. An analysis of an independent validation set and functional studies with BRAF inhibitors in the presence of the prevalent RAS mutation was also performed. RESULTS: Among 21 tumor samples, 13 had RAS mutations (12 in HRAS). In a validation set of 14 samples, 8 had RAS mutations (4 in HRAS). Thus, 60% (21 of 35) of the specimens harbored RAS mutations, the most prevalent being HRAS Q61L. Increased proliferation of HRAS Q61L-mutant cell lines exposed to vemurafenib was associated with mitogen-activated protein kinase (MAPK)-pathway signaling and activation of ERK-mediated transcription. In a mouse model of HRAS Q61L-mediated skin carcinogenesis, the vemurafenib analogue PLX4720 was not an initiator or a promoter of carcinogenesis but accelerated growth of the lesions harboring HRAS mutations, and this growth was blocked by concomitant treatment with a MEK inhibitor. CONCLUSIONS: Mutations in RAS, particularly HRAS, are frequent in cutaneous squamous-cell carcinomas and keratoacanthomas that develop in patients treated with vemurafenib. The molecular mechanism is consistent with the paradoxical activation of MAPK signaling and leads to accelerated growth of these lesions. (Funded by Hoffmann-La Roche and others; ClinicalTrials.gov numbers, NCT00405587, NCT00949702, NCT01001299, and NCT01006980.).
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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 | 0.001 | 0.001 |
| 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".