A multi-marker assay to distinguish malignant melanomas from benign nevi

Abstract

The histopathological diagnosis of melanoma can be challenging. No currently used molecular markers accurately distinguish between nevus and melanoma. Recent transcriptome analyses have shown the differential expression of several genes in melanoma progression. Here, we describe a multi-marker diagnostic assay using 5 markers (ARPC2, FN1, RGS1, SPP1, and WNT2) overexpressed in melanomas. Immunohistochemical marker expression was analyzed in 693 melanocytic neoplasms comprising a training set (tissue microarray of 534 melanomas and nevi), and 4 independent validation sets: tissue sections of melanoma arising in a nevus; dysplastic nevi; Spitz nevi; and misdiagnosed melanocytic neoplasms. Both intensity and pattern of expression were scored for each marker. Based on the differential expression of these 5 markers between nevi and melanomas in the training set, a diagnostic algorithm was obtained. Using this algorithm, the lesions in the validation sets were diagnosed as nevus or melanoma, and the results were compared with the known histological diagnoses. Both the intensity and pattern of expression of each marker were significantly different in melanomas compared to nevi. The diagnostic algorithm exploiting these differences achieved a specificity of 95% and a sensitivity of 91% in the training set. In the validation sets, the multi-marker assay correctly diagnosed a high percentage of melanomas arising in a nevus, Spitz nevi, dysplastic nevi, and misdiagnosed lesions. The multi-marker assay described here can aid in the diagnosis of melanoma.

Fig. 1.

Photomicrographs of WNT2 immunostaining in a benign nevus (A), melanoma (B), and melanoma arising in association with a nevus (C), where M represents the melanoma and N represents the nevus.

Fig. 2.

ROC plots in the diagnosis of melanoma using marker intensity scores for 1 marker alone (FN1, curve 1), 3 markers (FN1, ARPC2, SPP1, curve 2), and all 5 markers (curve 3).

Fig. 3.

Diagnostic algorithm obtained from the training data and applied to the validation sets based on marker intensity scores as well as top-to-bottom differences. For each diagnostic statement in the algorithm the number of correct and incorrect diagnoses achieved in the training set are indicated.