Abstract

Melanoma is one of the most aggressive cancer types whose prognosis is determined by both the tumor cell-intrinsic and -extrinsic features as well as their interactions. In this study, we performed systematic and unbiased analysis using The Cancer Genome Atlas (TCGA) melanoma RNA-seq data and identified two gene signatures that captured the intrinsic and extrinsic features, respectively. Specifically, we selected genes that best reflected the expression signals from tumor cells and immune infiltrate cells. Then, we applied an AutoEncoder-based method to decompose the expression of these genes into a small number of representative nodes. Many of these nodes were found to be significantly associated with patient prognosis. From them, we selected two most prognostic nodes and defined a tumor-intrinsic (TI) signature and a tumor-extrinsic (TE) signature. Pathway analysis confirmed that the TE signature recapitulated cytotoxic immune cell related pathways while the TI signature reflected MYC pathway activity. We leveraged these two signatures to investigate six independent melanoma microarray datasets and found that they were able to predict the prognosis of patients under standard care. Furthermore, we showed that the TE signature was also positively associated with patients’ response to immunotherapies, including tumor vaccine therapy and checkpoint blockade immunotherapy. This study developed a novel computational framework to capture the tumor-intrinsic and -extrinsic features and identified robust prognostic and predictive biomarkers in melanoma.

Highlights

  • Melanoma is one of the most aggressive tumors, with about 160,000 newly diagnosed cases worldwide each year (Schadendorf et al, 2015; Torre et al, 2015)

  • We first examined the prognostic value of each node in the training data and chose the TE-signature (H17) and TI-signature (L7) nodes as the representatives for tumor-extrinsic and -intrinsic features given their performances in predicting prognosis (Methods, Figure 2A)

  • Our results first indicated that the TE signature captured the cytotoxic infiltrating immune cell abundance while the TI signature captured MYC oncogenic pathway activity (Figures 2B–F)

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Summary

Introduction

Melanoma is one of the most aggressive tumors, with about 160,000 newly diagnosed cases worldwide each year (Schadendorf et al, 2015; Torre et al, 2015). Biomarkers Identification in Metastatic Melanoma identifying comprehensive gene signatures that predict the responses to immunotherapy and melanoma patients’ overall survival would facilitate the clinical practices of melanoma patients. Both the tumor cell-intrinsic and cell-extrinsic factors influence the progression and regression of cancer. Four molecular subtypes of metastatic melanoma patients based on the gene expression have been identified and the immune subtype patients had significantly prolonged overall survival (Jönsson et al, 2010) This tumor immune microenvironment can be largely affected by tumor intrinsic features In line with these findings, it has been shown that melanoma patients with high somatic mutation burden, low CNV, or low oncogenic activation are more likely to benefit from immunotherapy (Snyder et al, 2014; Van Allen et al, 2015; Davoli et al, 2017; Lauss et al, 2017)

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