Chapter 2 Understanding Melanoma Progression by Gene Expression Signatures J. T´ ım´ ar, T. Barbai, B. Gy˝ orffy, and E. R´ as´ o Abstract Malignant melanoma is the most aggressive cancer in humans and under- standing this unique biological behavior may help to design better prognosticators and more efficient therapies. However, malignant melanoma is a heterogenous tumor etiologically (UV-induced or not), morphologically and genetically driven by various oncogens (B-RAF, N-RAS, KIT) and suppressor genes (CDKN2A, p53, PTEN). There are a significant number of studies in which prognostic gene and protein signatures were defined based on either analysis of the primary tumors (metastasis initiating gene set) or melanoma metastases (metastasis maintenance gene set) affecting progression of the disease or survival of the patient. These studies provided prognostic signatures of minimal overlap. Here we demonstrate consensus prognostic gene and protein sets derived from primary and metastatic tumor tissues. It is of note that although there were rare overlaps concerning the composing indi- vidual genes in these sets, network analysis defined the common pathways driving melanoma progression: cell proliferation, apoptosis, motility, and immune mecha- nisms. Malignant melanoma is chemoresistant, the genetic background of which has been unknown for a long time, but new genomic analyses have identified complex genetic alterations responsible for this phenotype involving DNA repair genes and oncogene signaling pathways. The advent of immunotherapy of melanoma placed the previously defined immune signature-associated genomic prognosticators into a new perspective, suggesting that it might also be a powerful predictor. Target therapy of malignant melanoma has changed the standard therapy based on IFN J. T´ ım´ ar () • E. R´ as´ o 2nd Department of Pathology, Semmelweis University, Budapest, Hungary Tumor Progression Research Group, National Academy of Sciences-Semmelweis University, Budapest, Hungary e-mail: jtimar@gmail.com; rasoerzs@gmail.com T. Barbai • B. Gy˝ orffy 2nd Department of Pathology, Semmelweis University, Budapest, Hungary e-mail: tbarbai@gmail.com; zsalab@yahoo.com U. Pfeffer (ed.), Cancer Genomics: Molecular Classification, Prognosis and Response Prediction, DOI 10.1007/978-94-007-5842-1 2, © Springer ScienceCBusiness Media Dordrecht 2013 47