Molecular classification and prediction of metastatic potential in early malignant melanoma
This is an Open Access Thesis
The incidence of cutaneous malignant melanoma continues to increase every year, and remains the leading cause of skin cancer death in industrialized countries. In spite of the aggressive nature of advanced melanoma, there are no standard biological assays in clinical usage that can predict metastasis. This may be due, in part, to the inadequacy of reproducible assessment of protein expression using traditional immunohistochemistry. This dissertation will discuss the use of tissue microarrays combined with quantitative in situ molecular analysis of protein expression to allow prediction of melanoma metastasis. Through the identification and validation of novel prognostic biomarkers, we seek to identify subsets of patients that are at high or low risk for melanoma recurrence or melanoma-related death. Some of these biomarkers may also serve as potential targets for future biologic therapy in melanoma, a disease for which no effective medical treatment is currently available. We demonstrate that quantitative assessment of a small number of markers is predictive of metastasis and outcome, augmenting the current system of prognosis.The dissertation begins with a brief introduction on the current state of melanoma diagnosis, staging, and treatment, as well as a review of current efforts to understand the biology of melanoma progression and metastasis. The fundamentals of tissue microarray technology are then described. Critical aspects of quantitative immunohistochemistry, including a description of the Automated Quantitative Analysis (AQUA) system developed in our laboratory, are also addressed. The second chapter demonstrates the use of tissue microarray technology to examine melanoma specimens by the current field standard, with a study of activating transcription factor 2 (ATF2); an example of semi-quantitative immunohistochemical analysis of protein expression. The third chapter provides validation of the AQUA technology on melanoma tissue by evaluation of the human homologue of murine double minute 2 protein (HDM2). Chapter four demonstrates an example of the critical--and beneficial--aspect of subcellular compartmentalization that the AQUA system provides, demonstrating that the ratio of cytoplasmic-to-nuclear expression of activator protein 2 (AP-2) predicts outcome in melanoma patients. The last chapter draws these concepts together and presents results from the analysis of 50 protein biomarkers in melanoma. It also introduces the use of a number of statistical methods (traditional and novel) employed to develop an optimal biomarker set for future analyses.