Tumour gene expression signature in primary melanoma predicts long-term outcomes

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 (p_AUROC = 7.03 × 10⁻⁴), or published prognostic signatures (p_AUROC < 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.

The identification of prognostic biomarkers can help stratify cancer patients. Here, the authors apply deep RNA sequencing from primary melanomas coupled with long-term clinical outcome data from a prospective multicentre phase III trial, to develop and validate a 121 metastasis-associated gene signature identifying early-stage melanoma patients at higher risk of metastasis and worse survival.