Tumour gene expression signature in primary melanoma predicts long-term outcomes: A prospective multicentre study
Manik Garg, Dominique-Laurent Couturier, Jérémie Nsengimana, Nuno A. Fonseca, Matthew Wongchenko, Yibing Yan, Martin Lauss, Göran B Jönsson, Julia Newton-Bishop, Christine Parkinson, Mark R. Middleton, Tim Bishop, Pippa Corrie, David J. Adams, Alvis Brazma, Roy Rabbie
Received Date: 10th March 20
Adjuvant systemic therapies are now routinely used following resection of stage III melanoma, however accurate prognostic information is needed to better stratify patients. We used differential expression analyses of primary tumours from 446 RNA-sequenced melanomas within a large adjuvant trial, identifying a 121 metastasis-associated gene signature. This signature strongly associated with progression-free (HR=1.7, p=3.44x10-6) and overall survival (HR=1.73, p=7.71x10-6), and validated in 177 regional lymph nodes metastasis as well as two externally ascertained datasets. Machine learning classification models trained using signature genes performed significantly better in predicting metastases than models trained with clinical covariates (pAccuracy=4.92x10-3), or published prognostic signatures. The signature score negatively correlated with measures of immune cell infiltration (ρ=-0.75, p<2.2x10-16), with a higher score representing reduced lymphocyte infiltration and a higher 5-year risk of death in stage II melanoma. Our expression signature identifies melanoma patients at higher risk of metastases and warrants further evaluation in adjuvant clinical trials.
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This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.