Skin Tumors Induced by Sorafenib; Paradoxic RAS–RAF Pathway Activation and Oncogenic Mutations of <i>HRAS</i> , <i>TP53</i> , and <i>TGFBR1</i>
Bibliographic record
Abstract
PURPOSE: The emergence of skin tumors in patients treated with sorafenib or with more recent BRAF inhibitors is an intriguing and potentially serious event. We carried out a clinical, pathologic, and molecular study of skin lesions occurring in patients receiving sorafenib. EXPERIMENTAL DESIGN: Thirty-one skin lesions from patients receiving sorafenib were characterized clinically and pathologically. DNA extracted from the lesions was screened for mutation hot spots of HRAS, NRAS, KiRAS, TP53, EGFR, BRAF, AKT1, PI3KCA, TGFBR1, and PTEN. Biological effect of sorafenib was studied in vivo in normal skin specimen and in vitro on cultured keratinocytes. RESULTS: We observed a continuous spectrum of lesions: from benign to more inflammatory and proliferative lesions, all seemingly initiated in the hair follicles. Eight oncogenic HRAS, TGFBR1, and TP53 mutations were found in 2 benign lesions, 3 keratoacanthomas (KA) and 3 KA-like squamous cell carcinoma (SCC). Six of them correspond to the typical UV signature. Treatment with sorafenib led to an increased keratinocyte proliferation and a tendency toward increased mitogen-activated protein kinase (MAPK) pathway activation in normal skin. Sorafenib induced BRAF-CRAF dimerization in cultured keratinocytes and activated CRAF with a dose-dependent effect on MAP-kinase pathway activation and on keratinocyte proliferation. CONCLUSION: Sorafenib induces keratinocyte proliferation in vivo and a time- and dose-dependent activation of the MAP kinase pathway in vitro. It is associated with a spectrum of lesions ranging from benign follicular cystic lesions to KA-like SCC. Additional and potentially preexisting somatic genetic events, like UV-induced mutations, might influence the evolution of benign lesions to more proliferative and malignant tumors.
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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 | 0.000 |
| 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".