Metastasis is the leading cause of death in patients with advanced melanoma, yet the somatic alterations that aid tumour cell dissemination and colonisation are poorly understood.,Here, we deploy comparative genomics to identify and validate clinically relevant drivers of melanoma metastasis.,To do this, we identified a set of 976 genes whose expression level was associated with a poor outcome in patients from two large melanoma cohorts.,Next, we characterised the genomes and transcriptomes of mouse melanoma cell lines defined as weakly metastatic, and their highly metastatic derivatives.,By comparing expression data between species, we identified lunatic fringe (LFNG), among 28 genes whose expression level is predictive of poor prognosis and whose altered expression is associated with a prometastatic phenotype in mouse melanoma cells.,CRISPR/Cas9‐mediated knockout of Lfng dramatically enhanced the capability of weakly metastatic melanoma cells to metastasise in vivo, a phenotype that could be rescued with the Lfng cDNA.,Notably, genomic alterations disrupting LFNG are found exclusively in human metastatic melanomas sequenced as part of The Cancer Genome Atlas.,Using comparative genomics, we show that LFNG expression plays a functional role in regulating melanoma metastasis.
Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment.,In this study, we seek to identify a gene signature of metastatic melanoma.,We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples.,Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring.,Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma.,We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities.,From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy).,In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging.,Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10−4) alone remained predictive after adjusting for clinical predictors.