Biomarker Discovery in Cutaneous Malignant Melanoma : A Study Based on Tissue Microarrays and Immunohistochemistry

Abstract: The incidence of cutaneous malignant melanoma has increased dramatically in Caucasians the last few decades, an increase that is partly explained by altered sun exposure habits. For the individual patient, with a localized disease, the tumor thickness of the excised lesion is the most important prognostic factor. However, there is a need to identify characteristics that can place patients in certain risk groups. In this study, the protein expression of multiple proteins in malignant melanoma tumors was studied, with the aim of identifying potential new candidate biomarkers. Representative samples from melanoma tissues were assembled in a tissue microarray format and protein expression was detected using immunohistochemistry. Multiple cohorts were used and for a subset of proteins the expression was also analyzed in melanocytes in normal skin and in benign nevi. The immunohistochemical staining was evaluated manually and for part of the proteins also with an automated algorithm. The protein expression of STX7 was described for the first time in tumors of the melanocytic lineage. Stronger expression of STX7 and SOX10 was seen in superficial spreading melanomas compared with nodular malignant melanomas. An inverse relationship between STX7 expression and T-stage was seen and between SOX10 expression and T-stage and Ki-67, respectively. In a population-based cohort the expression of MITF was analyzed and found to be associated with prognosis. Twenty-one potential biomarkers were analyzed using bioinformatics tools and a protein signature was identified which had a prognostic value independent of T-stage. The protein driving this signature was RBM3, a protein not previously described in malignant melanoma. Other markers included in the signature were MITF, SOX10 and Ki-67. In conclusion, the protein expression of numerous potential biomarkers was extensively studied and a new prognostic protein panel was identified which can be of value for risk stratification.