Molecular Signature Predictive of Survival in Metastatic Cutaneous Melanoma

• Activity Number: 81 - Contributed Poster Presentations: Section on Statistics in Epidemiology
• Type: Contributed
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Yuna Kim* and Issa Zakeri and Sina Nassiri
• Companies: Drexel University, Dornsife School of Public Health and Drexel University, Dornsife School of Public Health and Bioinformatics Core Facility, The Swiss Institute of Bioinformatics (SIB)
• Keywords: Gene Expression; Cutaneous Melanoma; Dimensionality Reduction; Survival Analysis

Abstract

Cutaneous melanomas are classified into subtypes based on their clinicopathological and/or main genomic alterations. Although beneficial for the therapeutic management of the disease, the existing subtypes demonstrate a poor correlation with patient survival. By making use of both gene expression profile and clinical data from The Cancer Genome Atlas(TCGA), this study aimed to identify molecular subtypes that are biologically meaningful and clinically relevant in predicting patient survival. Semi-supervised Principal Component(SPC) method was applied to develop and validate a gene expression signature comprised of 1051 genes significantly associated with overall survival in patients with metastatic melanoma. We further validated our gene signature in a subset of the TCGA data that was held out in deriving the signature and showed that its association with overall survival is independent of tumor stage and patient’s age. Interestingly, we found that, on average, higher expression of the identified signature is associated with prolonged survival times. Overall, this study provides proof of concept in classifying metastatic melanoma into prognostically distinct subtypes.