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Shaughnessy et al. J Transl Genet Genom 2018;2:14. I https://doi.org/10.20517/jtgg.2018.25 Page 9 of 13

melanomas with immune and non-immune gene expression signatures may be expected to show different
responses to such immunotherapeutic agents.

[87]
UMs separate into two distinct classes upon GEP . Class 1 uveal melanomas exhibit low-grade behavior, a
[88]
low metastatic risk, and overall good prognosis . In contrast, class 2 tumors show high-grade behavior and
worse prognosis overall, with a 72% 5-year metastatic risk. Class 2 tumors are associated with loss-of-func-
tion BAP1 mutations, which impart additional loss of tumor suppressive function and increased aggressive
tumor behavior. The advancements in our understanding of GEP of UM has resulted in the development of
a commercially available 15-gene FDA-approved qPCR-based assay to risk stratify patients according to class
1 and class 2 uveal melanoma tumor status . The role of prognostic GEP for NACM is currently being ex-
[89]
plored, and further validation may help establish new guidelines for clinical risk-stratification with GEP [90,91] .

Pan-cancer analyses
Thanks to the collaborative efforts and remarkably impactful initial results of TCGA, dozens of further studies
have been published using the entire dataset of 33 cancer types and 1,1000 tumor samples that TCGA initially
[92]
investigated . These pan-cancer analyses, which utilize data across cancer types, are unique in that they con-
textualize melanoma among a comprehensive cancer panel. They have confirmed previously known findings
about melanoma and are unveiling novel connections between genomics, immunology, molecular biology, and
[93]
clinical outcomes in all 33 cancer types [93-96] . In their recent analysis of TCGA data, Bailey et al. confirmed
that UM and NACM have diametrically-opposed genetic features, even when considered amongst the larger
cohort of tumor samples. By demonstrating that NACM harbors one of the largest amounts of overall mu-
tations and UV signature mutations in the cohort, and UM one of the lowest, these results have helped to
characterize the magnitude of the genetic differences among melanocytic tumors. In our pursuit of targeted
treatments for uveal melanoma, perhaps we should focus elsewhere than simply on what is already known
about cutaneous melanoma’s molecular machinery. Pan-cancer analyses that evaluate cancer genomics as
a whole, identifying shared features amongst cancer types, may be able to bridge the gap between UM’s ge-
nomic foundations and its relatively grim prognosis.

CONCLUSION
Our current collective knowledge of melanoma’s genetic landscape has already yielded significant clinical
impact, dramatically altering the prognostic course for countless melanoma patients, and this knowledge
will continue to expand in the coming years. Despite this progress, very few actionable discoveries have
been made of targeted therapeutic strategies for the roughly half of patients whose melanomas are driven by
mutations other than BRAF. Each melanoma subtype offers a unique set of therapeutic targets, only some of
which have proven fruitful. New genes will continue to be identified, and in turn, chosen as possible thera-
peutic targets. Regardless, we will, unfortunately, continue to face a lack of universally-applicable treatments
for metastatic melanoma. Melanoma genomics and the use of immune-mediated treatments provide one
possible solution, but there remains much to be discovered.

DECLARATIONS
Authors’ contributions
Design: Shaughnessy M
Literature research: Shaughnessy M
Manuscript writing, editing, revision: Shaughnessy M, Klebanov N, Tsao H

Availability of data and materials
Not applicable.

Financial support and sponsorship
This activity was supported in part by the U.S. NIH (K24 CA149202 to H.T.).