| Literature DB >> 33602918 |
David J Adams1, Alvis Brazma2, Roy Rabbie3,4, Manik Garg2, Dominique-Laurent Couturier5, Jérémie Nsengimana6,7, Nuno A Fonseca8, Matthew Wongchenko9, Yibing Yan9, Martin Lauss10, Göran B Jönsson10, Julia Newton-Bishop6, Christine Parkinson11, Mark R Middleton12, D Timothy Bishop6, Sarah McDonald13, Nikki Stefanos13, John Tadross13, Ismael A Vergara14,15, Serigne Lo14,15,16, Felicity Newell17, James S Wilmott14,15, John F Thompson14,15,18, Georgina V Long14,15,19, Richard A Scolyer14,15,20, Pippa Corrie11.
Abstract
Adjuvant systemic therapies are now routinely used following resection of stage III melanoma, however accurate prognostic information is needed to better stratify patients. We use differential expression analyses of primary tumours from 204 RNA-sequenced melanomas within a large adjuvant trial, identifying a 121 metastasis-associated gene signature. This signature strongly associated with progression-free (HR = 1.63, p = 5.24 × 10-5) and overall survival (HR = 1.61, p = 1.67 × 10-4), was validated in 175 regional lymph nodes metastasis as well as two externally ascertained datasets. The machine learning classification models trained using the signature genes performed significantly better in predicting metastases than models trained with clinical covariates (pAUROC = 7.03 × 10-4), or published prognostic signatures (pAUROC < 0.05). The signature score negatively correlated with measures of immune cell infiltration (ρ = -0.75, p < 2.2 × 10-16), 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.Entities:
Mesh:
Year: 2021 PMID: 33602918 PMCID: PMC7893180 DOI: 10.1038/s41467-021-21207-2
Source DB: PubMed Journal: Nat Commun ISSN: 2041-1723 Impact factor: 17.694