The dysplastic nevus is a discreet histologic entity that exhibits some clinical and histologic features overlapping with common nevi and melanoma. These overlapping features present a therapeutic challenge, and with a lack of accepted guidelines, the management of dysplastic nevi remains a controversial subject. Although some differences between dysplastic and common nevi can be detected at the molecular level, there are currently no established markers to predict biologic behavior. In part II of this continuing medical education article, we will review the molecular aspects of dysplastic nevi and their therapeutic implications. Our goal is to provide the clinician with an up-to-date understanding of this entity to facilitate clinical management of patients with nevi that have histologic dysplasia.
[Show abstract][Hide abstract] ABSTRACT: Pigmented lesions and melanocytic nevi are commonly evaluated lesions in pediatric dermatology clinics and the rising incidence of melanoma has increased public awareness of malignant potential. Diagnosis and management of pigmented lesions, especially dysplastic nevi, medium and large congenital melanocytic nevi (CMN), and Spitz nevi, can be particularly challenging. We summarize the recent literature for melanocytic nevi and pigmented lesions as relevant to the practice of pediatric dermatology with particular attention to dermatoscopic techniques, histopathologic interpretation, molecular biology, and management recommendations for CMN and Spitz nevi.
[Show abstract][Hide abstract] ABSTRACT: Distinguishing melanoma from dysplastic nevi can be challenging.
To assess which putative molecular biomarkers can be optimally combined to aid in the clinical diagnosis of melanoma from dysplastic nevi.
Immunohistochemical expressions of 12 promising biomarkers (pAkt, Bim, BRG1, BRMS1, CTHRC1, Cul1, ING4, MCL1, NQO1, SKP2, SNF5 and SOX4) were studied in 122 melanomas and 33 dysplastic nevi on tissue microarrays. The expression difference between melanoma and dysplastic nevi was performed by univariate and multiple logistic regression analysis, diagnostic accuracy of single marker and optimal combinations were performed by receiver operating characteristic (ROC) curve and artificial neural network (ANN) analysis. Classification and regression tree (CART) was used to examine markers simultaneous optimizing the accuracy of melanoma. Ten-fold cross-validation was analyzed for estimating generalization error for classification.
Four (Bim, BRG1, Cul1 and ING4) of 12 markers were significantly differentially expressed in melanoma compared with dysplastic nevi by both univariate and multiple logistic regression analysis (p < 0.01). These four combined markers achieved 94.3% sensitivity, 81.8% specificity and attained 84.3% area under the ROC curve (AUC) and the ANN classified accuracy with training of 83.2% and testing of 81.2% for distinguishing melanoma from dysplastic nevi. The classification trees identified ING4, Cul1 and BRG1 were the most important classification parameters in ranking top-performing biomarkers with cross-validation error of 0.03.
The multiple biomarkers ING4, Cul1, BRG1 and Bim described here can aid in the discrimination of melanoma from dysplastic nevi and provide a new insight to help clinicians recognize melanoma.
PLoS ONE 09/2012; 7(9):e45037. DOI:10.1371/journal.pone.0045037 · 3.23 Impact Factor
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