Evolution of late-stage metastatic melanoma is dominated by aneuploidy and whole genome doubling.
Ismael A VergaraChristopher P MintoffShahneen SandhuLachlan McIntoshRichard J YoungStephen Q WongAndrew ColebatchDaniel L CameronJulia Lai KwonRory WolfeAngela PengJason EllulXuelin DouClare FedeleSamantha BoyleGisela Mir ArnauJeanette RaleighAthena HatzimihalisPacman SzetoJennifer MooiDaniel S WidmerPhil Fang ChengValerie AmannReinhard DummerNicholas K HaywardJames S WilmottRichard A ScolyerRaymond J ChoDavid BowtellHeather ThorneKathryn AlsopStephen CordnerNoel WoodfordJodie LeditschkePatricia O'BrienSarah-Jane DawsonGrant A McArthurGraham J MannMitchell P LevesqueAnthony T PapenfussMark ShackletonPublished in: Nature communications (2021)
Although melanoma is initiated by acquisition of point mutations and limited focal copy number alterations in melanocytes-of-origin, the nature of genetic changes that characterise lethal metastatic disease is poorly understood. Here, we analyze the evolution of human melanoma progressing from early to late disease in 13 patients by sampling their tumours at multiple sites and times. Whole exome and genome sequencing data from 88 tumour samples reveals only limited gain of point mutations generally, with net mutational loss in some metastases. In contrast, melanoma evolution is dominated by whole genome doubling and large-scale aneuploidy, in which widespread loss of heterozygosity sculpts the burden of point mutations, neoantigens and structural variants even in treatment-naïve and primary cutaneous melanomas in some patients. These results imply that dysregulation of genomic integrity is a key driver of selective clonal advantage during melanoma progression.
Keyphrases
- copy number
- end stage renal disease
- mitochondrial dna
- newly diagnosed
- genome wide
- chronic kidney disease
- ejection fraction
- squamous cell carcinoma
- endothelial cells
- prognostic factors
- dna methylation
- patient reported outcomes
- deep learning
- skin cancer
- electronic health record
- risk factors
- artificial intelligence
- data analysis
- replacement therapy