Abstract ED3-2: Understanding melanoma using GWAS and systems biology
Authors: Bishop Julia Newton 
Published: January 2011

Abstract
In this review I will discuss progress made in understanding the genetic determinants of susceptibility to melanoma and outcome from melanoma using genome wide association studies (GWAS) and approaches based more upon systems biology. In very recent years the first GWAS identifying hereditary susceptibility genes have been published as have the first larger scale gene expression studies. In the next few years, bioinformatics developments will allow integration of data from these and other system-wide approaches, in a multi-disciplinary fashion to better understand complex interactions between biological pathways and both the cellular environment and that of the organism.
Family studies are usually the first approach to identifying high penetrance susceptibility genes for cancer in general and for melanoma these lead to the identification of germline mutations in CDKN2A and CDK4 as the most common genes underlying susceptibility in families with 4 or more cases of melanoma. The melanoma genetics consortium GenoMEL at www.genomel.org is directed towards understanding the clinical relevance of these mutations. More recently, GWAS studies confirmed the role of MC1R variants as the most common low risk susceptibility genes (1), although this was previously recognised as a result of a small candidate gene study in 1996 (2) perhaps showing the value of taking multiple approaches. Other pigment genes have been identified as low risk susceptibility genes using a combination of agnostic and candidate gene approaches (3) and the evolution of new techniques was demonstrated by Brown et al who developed DNA pooling in GWAS which identified a locus near to the agouti signalling protein gene as a melanoma susceptibility gene (4). These GWAS have therefore identified susceptibility loci in genes predicted from epidemiological studies, which identified sun-sensitive phenotypes as associated with melanoma risk. Epidemiological studies also identified large numbers of melanocytic nevi as associated with increased risk and GWAS studies designed to identify nevus genes were therefore carried out to identify both nevus and melanoma susceptibility genes (5). Thus the melanoma field has benefitted from GWAS technology in conjunction with epidemiology to identify pathways involved in susceptibility. It is perhaps disappointing that so far the agnostic approach has failed to identify new biological pathways of note but there are some interesting clues and the GWAS data existing already are being interrogated using newer informatics approaches. It is likely that new relevant biological pathways will be identified in the very near future.
It is crucial to increase our understanding of the genetic determinants of outcome for melanoma patients not least to improve the response to therapies for stage IV melanoma. In recent years genomics has lead to major progress in understanding the biological pathways important to tumour progression, which has lead to the clinical trials with the most hopeful chemotherapeutic agent for melanoma, a specific inhibitor of mutant Braf. Biological complexity is evident however and it seems likely that we require a quantum leap in understanding to provide therapeutic options for all patients. Systems biological approaches will be of fundamental importance here aided by new technologies/platforms which allow us to work with small paraffin embedded melanoma tumours such as the Illumina DASL platform (6).
Citation Information: Clin Cancer Res 2010;16(7 Suppl):ED3-2