Dataset Information

High frequency of BRAF V600E mutations in ameloblastoma.

ABSTRACT: Ameloblastoma is a benign but locally infiltrative odontogenic neoplasm. Although ameloblastomas rarely metastasise, recurrences together with radical surgery often result in facial deformity and significant morbidity. Development of non-invasive therapies has been precluded by a lack of understanding of the molecular background of ameloblastoma pathogenesis. When addressing the role of ERBB receptors as potential new targets for ameloblastoma, we discovered significant EGFR over-expression in clinical samples using real-time RT-PCR, but observed variable sensitivity of novel primary ameloblastoma cells to EGFR-targeted drugs in vitro. In the quest for mutations downstream of EGFR that could explain this apparent discrepancy, Sanger sequencing revealed an oncogenic BRAF V600E mutation in the cell line resistant to EGFR inhibition. Further analysis of the clinical samples by Sanger sequencing and BRAF V600E-specific immunohistochemistry demonstrated a high frequency of BRAF V600E mutations (15 of 24 samples, 63%). These data provide novel insight into the poorly understood molecular pathogenesis of ameloblastoma and offer a rationale to test drugs targeting EGFR or mutant BRAF as novel therapies for ameloblastoma.

SUBMITTER: Kurppa KJ

PROVIDER: S-EPMC4255689 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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High frequency of BRAF V600E mutations in ameloblastoma.

Kurppa Kari J KJ Catón Javier J Morgan Peter R PR Ristimäki Ari A Ruhin Blandine B Kellokoski Jari J Elenius Klaus K Heikinheimo Kristiina K

The Journal of pathology 20140131 5

Ameloblastoma is a benign but locally infiltrative odontogenic neoplasm. Although ameloblastomas rarely metastasise, recurrences together with radical surgery often result in facial deformity and significant morbidity. Development of non-invasive therapies has been precluded by a lack of understanding of the molecular background of ameloblastoma pathogenesis. When addressing the role of ERBB receptors as potential new targets for ameloblastoma, we discovered significant EGFR over-expression in c ...[more]

PMID: 24374844

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Project description:Background Ameloblastoma is a locally invasive and aggressive epithelial odontogenic neoplasm. The BRAF-V600E gene mutation is a prevalent genetic alteration found in this tumor and is considered to have a crucial role in its pathogenesis. The objective of this study is to develop and validate a radiomics-based machine learning method for the identification of BRAF-V600E gene mutations in ameloblastoma patients. Methods In this retrospective study, data from 103 patients diagnosed with ameloblastoma who underwent BRAF-V600E mutation testing were collected. Of these patients, 72 were included in the training cohort, while 31 were included in the validation cohort. To address class imbalance, synthetic minority over-sampling technique (SMOTE) is applied in our study. Radiomics features were extracted from preprocessed CT images, and the most relevant features, including both radiomics and clinical data, were selected for analysis. Machine learning methods were utilized to construct models. The performance of these models in distinguishing between patients with and without BRAF-V600E gene mutations was evaluated using the receiver operating characteristic (ROC) curve. Results When the analysis was based on radiomics signature, Random Forest performed better than the others, with the area under the ROC curve (AUC) of 0.87 (95%CI, 0.68-1.00). The performance of XGBoost model is slightly lower than that of Random Forest, and its AUC is 0.83 (95% CI, 0.60-1.00). The nomogram evident that among younger women, the affected region primarily lies within the mandible, and patients with larger tumor diameters exhibit a heightened risk. Additionally, patients with higher radiomics signature scores are more susceptible to the BRAF-V600E gene mutations. Conclusions Our study presents a comprehensive radiomics-based machine learning model using five different methods to accurately detect BRAF-V600E gene mutations in patients diagnosed with ameloblastoma. The Random Forest model’s high predictive performance, with AUC of 0.87, demonstrates its potential for facilitating a convenient and cost-effective way of identifying patients with the mutation without the need for invasive tumor sampling for molecular testing. This non-invasive approach has the potential to guide preoperative or postoperative drug treatment for affected individuals, thereby improving outcomes.

Dataset Information

High frequency of BRAF V600E mutations in ameloblastoma.

Publications

High frequency of BRAF V600E mutations in ameloblastoma.

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