Consensus of Melanoma Gene Expression Subtypes Converges on Biological Entities

Melanoma subtypes: genomic profiles, prognostic molecular markers and therapeutic possibilities

“…Classification of primary melanomas by gene expression also resulted in these four classes, which could be collapsed into two classes associated with clinical outcome [62]. The multi-institutional TCGA (The Cancer Genome Atlas) initiative subsequently identified three transcriptomic subclasses, an immune group, a keratin group, and a MITF-low group [4,63]. A subsequent analysis showed that these classifications comprised very similar biological entities (TCGA immune ∼ Lund high-immune, TCGA keratin ∼ Lund normal-like and MITF-high/pigmentation, TCGA MITF-low ∼ Lund MITF-low/proliferative) [63].…”

Phenotype-based variation as a biomarker of sensitivity to molecularly targeted therapy in melanoma

“…Current data indicates the existence of at least 3 melanoma sub-groups as can be defined by transcriptomic analyses, with a preferential up-regulation of either differentiation, proliferation, or immune related genes 1, 2 . Personalized treatment, such as CTLA4 and PD-1 blockade, a most promising treatment for advanced melanoma 8 , is likely to benefit from these classifications as they might help design such therapy on an individual basis 9 . However, melanoma cells per se show transcriptome based heterogeneity when studied in vitro as well, and this is important as it has been shown to correlate with drug sensitivity 10 .…”

Bioinformatic Analysis of Gene Expression for Melanoma Treatment

“…Simultaneously, the multi-institutional TCGA initiative led to a large database from which investigators categorized melanomas into three groups, with related but nonidentical designations: “Immune,” “Keratin,” and “MITF-Low” (Cancer Genome Atlas Network, 2015). Lauss et al (2016) compared these datasets and identified common genes between several of these groups. Specifically, their study revealed overlap between: (i) the High-Immune/Pigmentation (Jönsson dataset) and Immune (TCGA) groups, (ii) the Normal-like/Pigmentation (Jönsson) and Keratin (TCGA) groups, and (iii) the Proliferative (Jönsson) and MITF-Low (TCGA) groups.…”

“…For example, Lauss et al (2016) found that “High-Immune” and “Immune” subtypes predicted better prognosis for patients. However, expression of both archetypal proimmunogenic and immunosuppressive genes can be highly enriched in the immune subtypes.…”

“…In these molecular classification approaches, it is important to determine whether the findings are consistent among those analyses. Lauss et al (2016) attempted to answer this question by comparing the underlying biological processes in their classification schemes with those of The Cancer Genome Atlas Network (TCGA) (Cancer Genome Atlas Network, 2015). …”