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 (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.

Transcriptional profile and chromatin accessibility in zebrafish melanocytes and melanoma tumors

“…Many studies have utilized next-generation sequencing technologies to evaluate melanoma subtypes based on clinicopathological characteristics ( Cancer Genome Atlas Network 2015 ; Hayward et al 2017 ; Tsoi et al 2018 ; Rabbie et al 2019 ; Cisarova et al 2020 ; Durante et al 2020 ), major mutations ( Cancer Genome Atlas Network 2015 ; Travnickova et al 2019 ), and drug resistance and survival ( Rambow et al 2018 ; Garg et al 2021 ). Prior studies comparing melanocytes and cutaneous melanoma cells have reported on coding mutations, gene expression changes only, or have used human or zebrafish cell lines rather than focusing on in vivo animal models ( Yen et al 2013 ; Haltaufderhyde and Oancea 2014 ; Kaufman et al 2016 ; Badal et al 2017 ; Kunz et al 2018 ; Venkatesan et al 2018 ; Marie et al 2020 ; Wouters et al 2020 ).…”

Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature

“…Secondly, the prognostic prediction model constructed in this study is based on retrospective data, and no prospective clinical studies have been carried out to verify the model. Garg et al proposed a prognostic signature consisting of 121 metastasis-related genes to predict the prognosis of patients with melanoma [ 50 ]. But unfortunately, none of them appeared in both their and our signatures.…”

Machine learning directed sentinel lymph node biopsy in cutaneous head and neck melanoma

“…In the last few years, a few research groups have begun investigated the possibility of utilizing gene expression profiling in conjunction with machine learning to refine predictions for which melanoma patients are likely to have pathologic nodal metastases 30–33 . The preliminary data from these studies are quite promising, and suggests that genetic data may significantly augment our capabilities to predict which melanoma patients are likely to have nodal disease.…”