Protein Biomarkers in Malignant Melanoma: An Image Analysis-Based Study on Melanoma Markers of Potential Clinical Relevance
(English)Manuscript (preprint) (Other academic)
The thickness of a primary malignant melanoma tumor is the most important prognostic indicator for a patient with primary cutaneous malignant melanoma. To optimize the management and treatment of melanoma patients there is an unmet need to identify characteristics that can further stratify melanoma patients into high or low risk for progressive disease. Despite numerous studies no single marker has yet been shown to add significant prognostic information. An algorithmic approach, combining data from several markers provides an attractive model to identify patients of increased risk of dying from malignant melanoma. The primary aim of the present study was to analyze the correlation between clinical outcome and protein expression patterns of multiple proteins in malignant melanoma tumors using immunohistochemistry and tissue microarrays. Candidate proteins were identified based on a selective and differential expression pattern in melanoma tumors and tested in a cohort of 143 melanoma patients. Protein expression was analyzed using both manual scoring and automated image analysis-based algorithms. We found no single marker of prognosis that was independent of tumor thickness. When combining potential prognostic markers we could define a prognostic index, based on RBM3, MITF, SOX10 and Ki-67, that was independent of tumor thickness in multivariate analysis. Our findings suggest that a good prognosis signature can be identified in melanoma patients with tumors showing a low fraction of Ki-67 positive tumor cells and a high fraction of RBM3 positive tumor cells combined with low intensity levels of SOX10 and MITF.
malignant melanoma, immunohistochemistry, tissue microarray, protein expression, automated analysis, RBM3, SOX10, MITF, Ki-67
Cell and Molecular Biology
Research subject Pathology; Bioinformatics
IdentifiersURN: urn:nbn:se:uu:diva-144108OAI: oai:DiVA.org:uu-144108DiVA: diva2:398173