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Figure 1.
Overview of Driver Mutations in Uveal Melanoma, Gene Expression Profile (GEP) Classification, and Metastatic Status in 81 Uveal Melanomas
Overview of Driver Mutations in Uveal Melanoma, Gene Expression Profile (GEP) Classification, and Metastatic Status in 81 Uveal Melanomas

White boxes indicate mutation absent (wild type); colored boxes, mutation present; and gray box, information not available. For GEP class, blue boxes indicate class 1, and red boxes indicate class 2.

Figure 2.
Molecular Association Plot Summarizing Statistical Associations Between Driver Mutations
Molecular Association Plot Summarizing Statistical Associations Between Driver Mutations

P values indicate statistical significance of association between mutations in 2 genes connected by a given line. Thick orange lines indicate statistically significant inverse associations (P ≤ .05), and gray lines indicate no statistically significant association (P > .05). The thin orange line indicates that mutations in SF3B1 and EIF1AX were almost mutually exclusive, with only one tumor having a mutation in both, but this association did not achieve statistical significance (P = .17).

Table 1.  
Summary of Clinical and Pathologic Features in 81 Primary Uveal Melanomas
Summary of Clinical and Pathologic Features in 81 Primary Uveal Melanomas
Table 2.  
Somatic BAP1 Mutations Previously Unreported in Primary Uveal Melanoma
Somatic BAP1 Mutations Previously Unreported in Primary Uveal Melanoma
Table 3.  
Somatic EIF1AX Mutations Previously Unreported in Primary Uveal Melanoma
Somatic EIF1AX Mutations Previously Unreported in Primary Uveal Melanoma
1.
Ramaiya  KJ, Harbour  JW.  Current management of uveal melanoma.  Expert Rev Ophthalmol. 2007;2(6):939-946.Google ScholarCrossref
2.
Onken  MD, Worley  LA, Ehlers  JP, Harbour  JW.  Gene expression profiling in uveal melanoma reveals two molecular classes and predicts metastatic death.  Cancer Res. 2004;64(20):7205-7209.PubMedGoogle ScholarCrossref
3.
Chang  SH, Worley  LA, Onken  MD, Harbour  JW.  Prognostic biomarkers in uveal melanoma: evidence for a stem cell–like phenotype associated with metastasis.  Melanoma Res. 2008;18(3):191-200.PubMedGoogle ScholarCrossref
4.
Worley  LA, Onken  MD, Person  E,  et al.  Transcriptomic versus chromosomal prognostic markers and clinical outcome in uveal melanoma.  Clin Cancer Res. 2007;13(5):1466-1471.PubMedGoogle ScholarCrossref
5.
Petrausch  U, Martus  P, Tönnies  H,  et al.  Significance of gene expression analysis in uveal melanoma in comparison to standard risk factors for risk assessment of subsequent metastases.  Eye (Lond). 2008;22(8):997-1007.PubMedGoogle ScholarCrossref
6.
Singh  AD, Sisley  K, Xu  Y,  et al.  Reduced expression of autotaxin predicts survival in uveal melanoma.  Br J Ophthalmol. 2007;91(10):1385-1392.PubMedGoogle ScholarCrossref
7.
Onken  MD, Worley  LA, Tuscan  MD, Harbour  JW.  An accurate, clinically feasible multi-gene expression assay for predicting metastasis in uveal melanoma.  J Mol Diagn. 2010;12(4):461-468.PubMedGoogle ScholarCrossref
8.
Onken  MD, Worley  LA, Char  DH,  et al.  Collaborative Ocular Oncology Group report number 1: prospective validation of a multi-gene prognostic assay in uveal melanoma.  Ophthalmology. 2012;119(8):1596-1603.PubMedGoogle ScholarCrossref
9.
Febbo  PG, Ladanyi  M, Aldape  KD,  et al.  NCCN Task Force report: Evaluating the clinical utility of tumor markers in oncology.  J Natl Compr Canc Netw. 2011;9(suppl 5):S1-S32.PubMedGoogle Scholar
10.
Aaberg  TM  Jr, Cook  RW, Oelschlager  K, Maetzold  D, Rao  PK, Mason  JO  III.  Current clinical practice: differential management of uveal melanoma in the era of molecular tumor analyses.  Clin Ophthalmol. 2014;8:2449-2460.PubMedGoogle ScholarCrossref
11.
Harbour  JW, Onken  MD, Roberson  ED,  et al.  Frequent mutation of BAP1 in metastasizing uveal melanomas.  Science. 2010;330(6009):1410-1413.PubMedGoogle ScholarCrossref
12.
Onken  MD, Worley  LA, Long  MD,  et al.  Oncogenic mutations in GNAQ occur early in uveal melanoma.  Invest Ophthalmol Vis Sci. 2008;49(12):5230-5234.PubMedGoogle ScholarCrossref
13.
Van Raamsdonk  CD, Bezrookove  V, Green  G,  et al.  Frequent somatic mutations of GNAQ in uveal melanoma and blue naevi.  Nature. 2009;457(7229):599-602.PubMedGoogle ScholarCrossref
14.
Van Raamsdonk  CD, Griewank  KG, Crosby  MB,  et al.  Mutations in GNA11 in uveal melanoma.  N Engl J Med. 2010;363(23):2191-2199.PubMedGoogle ScholarCrossref
15.
Harbour  JW, Roberson  ED, Anbunathan  H, Onken  MD, Worley  LA, Bowcock  AM.  Recurrent mutations at codon 625 of the splicing factor SF3B1 in uveal melanoma.  Nat Genet. 2013;45(2):133-135.PubMedGoogle ScholarCrossref
16.
Martin  M, Maßhöfer  L, Temming  P,  et al.  Exome sequencing identifies recurrent somatic mutations in EIF1AX and SF3B1 in uveal melanoma with disomy 3.  Nat Genet. 2013;45(8):933-936.PubMedGoogle ScholarCrossref
17.
Roper  N, Stensland  KD, Hendricks  R, Galsky  MD.  The landscape of precision cancer medicine clinical trials in the United States.  Cancer Treat Rev. 2015;41(5):385-390.PubMedGoogle ScholarCrossref
18.
Bauer  J, Kilic  E, Vaarwater  J, Bastian  BC, Garbe  C, de Klein  A.  Oncogenic GNAQ mutations are not correlated with disease-free survival in uveal melanoma.  Br J Cancer. 2009;101(5):813-815.PubMedGoogle ScholarCrossref
19.
Dono  M, Angelini  G, Cecconi  M,  et al.  Mutation frequencies of GNAQ, GNA11, BAP1, SF3B1, EIF1AX and TERT in uveal melanoma: detection of an activating mutation in the TERT gene promoter in a single case of uveal melanoma.  Br J Cancer. 2014;110(4):1058-1065.PubMedGoogle ScholarCrossref
20.
Ewens  KG, Kanetsky  PA, Richards-Yutz  J,  et al.  Chromosome 3 status combined with BAP1 and EIF1AX mutation profiles are associated with metastasis in uveal melanoma.  Invest Ophthalmol Vis Sci. 2014;55(8):5160-5167.PubMedGoogle ScholarCrossref
21.
Field  MG, Decatur  CL, Kurtenbach  S,  et al.  PRAME as an independent biomarker for metastasis in uveal melanoma.  Clin Cancer Res. 2016;22(5):1234-1242.PubMedGoogle ScholarCrossref
22.
Shah  AA, Bourne  TD, Murali  R.  BAP1 protein loss by immunohistochemistry: a potentially useful tool for prognostic prediction in patients with uveal melanoma.  Pathology. 2013;45(7):651-656.PubMedGoogle ScholarCrossref
23.
Johansson  P, Aoude  LG, Wadt  K,  et al.  Deep sequencing of uveal melanoma identifies a recurrent mutation in PLCB4 Oncotarget. 2016;7(4):4624-4631.PubMedGoogle Scholar
24.
Carvajal  RD, Sosman  JA, Quevedo  JF,  et al.  Effect of selumetinib vs chemotherapy on progression-free survival in uveal melanoma: a randomized clinical trial.  JAMA. 2014;311(23):2397-2405.PubMedGoogle ScholarCrossref
25.
Landreville  S, Agapova  OA, Matatall  KA,  et al.  Histone deacetylase inhibitors induce growth arrest and differentiation in uveal melanoma.  Clin Cancer Res. 2012;18(2):408-416.PubMedGoogle ScholarCrossref
Original Investigation
July 2016

Driver Mutations in Uveal MelanomaAssociations With Gene Expression Profile and Patient Outcomes

Author Affiliations
  • 1Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida
  • 2Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida
  • 3Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, Florida
  • 4National Heart and Lung Institute, Imperial College of Science and Technology, London, England
 

Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.

JAMA Ophthalmol. 2016;134(7):728-733. doi:10.1001/jamaophthalmol.2016.0903
Abstract

Importance  Frequent mutations have been described in the following 5 genes in uveal melanoma (UM): BAP1, EIF1AX, GNA11, GNAQ, and SF3B1. Understanding the prognostic significance of these mutations could facilitate their use in precision medicine.

Objective  To determine the associations between driver mutations, gene expression profile (GEP) classification, clinicopathologic features, and patient outcomes in UM.

Design, Setting, and Participants  Retrospective study of patients with UM treated by enucleation by a single ocular oncologist between November 1, 1998, and July 31, 2014.

Main Outcomes and Measures  Clinicopathologic features, patient outcomes, GEP classification (class 1 or class 2), and mutation status were recorded.

Results  The study cohort comprised 81 participants. Their mean age was 61.5 years, and 37% (30 of 81) were female. The GEP classification was class 1 in 35 of 81 (43%), class 2 in 42 of 81 (52%), and unknown in 4 of 81 (5%). BAP1 mutations were identified in 29 of 64 (45%), GNAQ mutations in 36 of 81 (44%), GNA11 mutations in 36 of 81 (44%), SF3B1 mutations in 19 of 81 (24%), and EIF1AX mutations in 14 of 81 (17%). Sixteen of the mutations in BAP1 and 6 of the mutations in EIF1AX were previously unreported in UM. GNAQ and GNA11 mutations were mutually exclusive. BAP1, SF3B1, and EIF1AX mutations were almost mutually exclusive with each other. Using multiple regression analysis, BAP1 mutations were associated with class 2 GEP and older patient. EIF1AX mutations were associated with class 1 GEP and the absence of ciliary body involvement. SF3B1 mutations were associated with younger patient age. GNAQ mutations were associated with the absence of ciliary body involvement and greater largest basal diameter. GNA11 mutations were not associated with any of the analyzed features. Using Cox proportional hazards modeling, class 2 GEP was the prognostic factor most strongly associated with metastasis (relative risk, 9.4; 95% CI, 3.1-28.5) and melanoma-specific mortality (relative risk, 15.7; 95% CI, 3.6-69.1) (P < .001 for both). After excluding GEP class, the presence of BAP1 mutations was the factor most strongly associated with metastasis (relative risk, 10.6; 95% CI, 3.4-33.5) and melanoma-specific mortality (relative risk, 9.0; 95% CI, 2.8-29.2) (P < .001 for both).

Conclusions and Relevance  BAP1, SF3B1, and EIF1AX mutations occur during UM tumor progression in an almost mutually exclusive manner and are associated with different levels of metastatic risk. These mutations may have value as prognostic markers in UM.

Introduction

Uveal melanoma (UM) is the most common primary cancer of the eye and has a propensity for fatal hematogenous metastasis.1 Uveal melanomas can be stratified by gene expression profile (GEP) classification into 2 prognostically significant molecular classes. Class 1 UMs have a low metastatic risk, whereas class 2 UMs have a high metastatic risk.2 Class 1 tumors retain a differentiated melanocytic phenotype, whereas class 2 tumors exhibit a dedifferentiated stem cell–like phenotype.3 After it was shown by multiple groups that the prognostic accuracy of GEP outperforms clinicopathologic features and chromosomal gains and losses,4-6 our group developed a GEP classifier for routine clinical use in which expression of 12 discriminating genes and 3 control genes is measured by quantitative polymerase chain reaction (PCR) on a microfluidics platform after targeted amplification. The result was an ultrahigh-performance assay that accurately measures gene expression from fine-needle biopsy samples that are too small to be reliably assessed using chromosome-based assays.7 A prospective multicenter study8 was performed, which confirmed the assay’s prognostic accuracy and showed it to be superior to chromosome 3 testing. To date, this assay is the only prognostic test for UM ever to undergo prospective multicenter validation, which is required for a cancer biomarker to achieve the highest level I evidence according to the National Comprehensive Cancer Network Task Force on cancer biomarkers and the Tumor Marker Utility Grading System.9

Consequently, this assay has been made commercially available (DecisionDX-UM; Castle Biosciences, Inc), which has become the standard care for molecular prognostic testing in many ocular oncology centers.10 The class 2 profile is strongly associated with inactivating mutations in the BAP1 (OMIM 603089) tumor suppressor gene.11 Four other genes are frequently mutated in UM, including EIF1AX (OMIM 300186), GNA11 (OMIM 139313), GNAQ (OMIM 600998), and SF3B1 (OMIM 605590).12-16 Herein, we describe the associations between mutations in these 5 genes, GEP molecular class, clinicopathologic features, and patient outcomes in 81 primary UMs treated by enucleation.

Box Section Ref ID

Key Points

  • Question What are the associations between driver mutations, gene expression profile classification, clinicopathologic features, and patient outcomes in uveal melanoma?

  • Findings This retrospective study of patients with uveal melanoma treated by enucleation found that BAP1, SF3B1, and EIF1AX mutations were almost mutually exclusive with each other. BAP1 mutations were associated with poor prognostic factors, and EIF1AX and SF3B1 mutations were associated with good prognostic factors.

  • MeaningBAP1, SF3B1, and EIF1AX mutations may have value as prognostic markers in uveal melanoma.

Methods
Tissue Samples

This study was conducted in a Health Insurance Portability and Accountability Act of 1996–compliant manner in accord with the tenets of the Declaration of Helsinki. Approval was obtained from the Institutional Review Board of the University of Miami. Written informed consent was attained from each patient. Tumor samples were taken at enucleation between November 1, 1998, and July 31, 2014, from patients with UMs arising from the ciliary body, choroid, or both. Samples were snap frozen and stored at −80°C. Baseline clinical and pathologic information, as well as patient outcomes, were recorded.

Molecular Analyses

Molecular prognostic class assignments (class 1 or class 2) were obtained using a prospectively validated 12-gene classifier, as previously reported.8 Genomic tumor DNA was prepared for sequencing with a purification kit (Wizard Genomic DNA; Promega) following the manufacturer’s protocol, and target regions were amplified using PCR. The PCR was performed on a thermal cycler (Veriti; Applied Biosystems) in a reaction volume of 25 µL. Thermocycling was performed in the following conditions: initial denaturation at 95°C for 3 minutes and 30 rounds of amplification at 95°C for 15 seconds, touchdown PCR ramping from 65 to 55°C for 30 seconds, 72°C for 45 seconds, and a final extension step at 72°C for 7 minutes. The PCR products were visualized on a 2% agarose gel, and the remaining volume was purified using resin (SOPE; Edge BioSystems). One microliter of clean PCR product was then sequenced using a cycle sequencing kit (BigDye Terminator, version 3.1; Applied Biosystems) according to the manufacturer’s protocol. Products were combined with a plate (Sephadex; GE Healthcare Bio-Sciences AB) for purification and loaded on a sequencer (ABI 3730; Applied Biosystems) for 25 cycles. Mutations in BAP1 and SF3B1 were initially identified with exome sequencing in a subset of tumors, and then the mutation status of additional samples was determined by Sanger sequencing, as previously described.11 For this study, sequencing of BAP1 included all coding regions and splice junctions, as previously described.11 For GNAQ and GNA11, the 2 mutation hot spots at R183 and Q209 were sequenced.14 For SF3B1, the mutation hot spot at R625 was sequenced.15 For EIF1AX, the hot spot regions containing reported mutations within exons 1 and 2 were sequenced.16 Mutation calling was performed by aligning the genomic sequence traces using sequence analysis software to the hg19 genome build (Sequencher, version 4.1.2; Gene Codes).

Statistical Analysis

Statistical analysis was performed using a computer program (MedCalc, version 14.10.2; MedCalc Software bvba). The Fisher exact test was used to evaluate discrete dichotomous variables, Cox proportional hazards modeling was used to assess variables associated with metastasis and melanoma-specific mortality, and multiple regression was used for identifying clinicopathologic features associated with mutations in BAP1, EIF1AX, GNA11, GNAQ, and SF3B1. P ≤ .05 was considered statistically significant.

Results
Baseline Data

Clinicopathologic and patient outcome data are summarized in Table 1. Molecular and genetic data are summarized in Figure 1. Among 81 participants, the GEP classification was class 1 in 35 (43%), class 2 in 42 (52%), and unknown in 4 (5%). Among those with DNA samples available for sequencing, BAP1 mutations were identified in 29 of 64 (45%), GNAQ mutations in 36 of 81 (44%), GNA11 mutations in 36 of 81 (44%), SF3B1 mutations in 19 of 81 (24%), and EIF1AX mutations in 14 of 81 (17%). Sixteen of the mutations in BAP1 were previously unreported in UM or any other cancer (Table 2). Six of the mutations in EIF1AX, found in 10 tumors, were novel in UM, and 2 of the 6 mutations were not previously reported in any cancer (Table 3). One of the novel mutations was a splicing change predicted to lead to loss of exon 2. During a mean follow-up of 34.1 months (median, 24.4 months) among 81 participants, metastasis was detected in 28 (35%), and melanoma-specific mortality occurred in 21 (26%). Among primary tumors that metastasized, mutations were detected in BAP1 in 17 of 24 (71%), GNAQ in 13 of 28 (46%), GNA11 in 12 of 28 (43%), SF3B1 in 3 of 28 (11%), and EIF1AX in none.

Associations Between Mutations

Statistical analysis for the associations between different mutated genes was performed using the Fisher exact test, and the results are shown in Figure 2. Mutations in GNAQ and GNA11 were mutually exclusive (P < .001). Mutations in BAP1 and SF3B1 were almost mutually exclusive (P = .007), as were mutations in BAP1 and EIF1AX (P = .03). Mutations in SF3B1 and EIF1AX were also almost mutually exclusive, with only one tumor having a mutation in both, but this association did not achieve statistical significance (P = .17).

Mutations vs Clinicopathologic Features

The associations between mutations and clinicopathologic features were analyzed for statistical significance using multiple regression analysis (eTable in the Supplement). BAP1 mutations were associated with class 2 GEP (P < .001) and older patient age (P = .007). EIF1AX mutations were associated with class 1 GEP and the absence of ciliary body involvement (P = .03 for both). SF3B1 mutations were associated with younger patient age (P = .006). GNAQ mutations were associated with the absence of ciliary body involvement (P = .008) and greater largest basal diameter (P = .04). GNA11 mutations were not associated with any of the analyzed features.

Survival Analysis

Cox proportional hazards modeling was used to analyze the prognostic value of GEP class, gene mutations, and clinicopathologic features vis-à-vis time to metastasis and to melanoma-specific mortality (eTable in the Supplement). When all of these variables were considered together, class 2 GEP was the prognostic factor most strongly associated with metastasis (relative risk, 9.4; 95% CI, 3.1-28.5) and melanoma-specific mortality (relative risk, 15.7; 95% CI, 3.6-69.1) (P < .001 for both). After excluding GEP class, the presence of BAP1 mutations was the factor most strongly associated with metastasis (relative risk, 10.6; 95% CI, 3.4-33.5) and melanoma-specific mortality (relative risk, 9.0; 95% CI, 2.8-29.2) (P < .001 for both).

Discussion

The identification of driver mutations has become a centerpiece of cancer precision medicine for diagnostic, prognostic, and therapeutic decision making in individual patients with cancer.17 Thus far, only the following 5 genes have been found to be commonly mutated in UM: GNAQ, GNA11, BAP1, SF3B1, and EIF1AX. In this study, we found mutation frequencies of 89% (72 of 81) for GNAQ and GNA11, 45% (29 of 64) for BAP1, 23% (19 of 81) for SF3B1, and 17% (14 of 81) for EIF1AX, which is similar to previous reports.11-16,18,19 Sixteen mutations in BAP1 and 6 mutations in EIF1AX were previously unreported in UM. GNAQ and GNA11 mutations were mutually exclusive with each other, they were present in similar proportions in class 1 and class 2 UMs, and they showed no association with tumor size or patient outcomes, suggesting that these mutations occur early in tumorigenesis.12 Mutations in BAP1, SF3B1, EIF1AX were almost mutually exclusive with each other, suggesting that they may represent alternative downstream molecular events during tumor progression. Of particular interest, BAP1 mutations were associated with poor prognostic factors (class 2 GEP and older patient age) and high metastatic risk. In contrast, EIF1AX and SF3B1 mutations were associated with good prognostic factors (EIF1AX mutations with class 1 GEP and the absence of ciliary body involvement and SF3B1 mutations with younger patient age).

Consistent with previous work,8 class 2 GEP demonstrated prognostic accuracy that was superior to all other variables that were examined. After excluding GEP class, the next most accurate prognostic factor was the presence of BAP1 mutations for both time to metastasis and to melanoma-specific mortality. These findings suggest that mutational analysis of BAP1 may have value as a biomarker for poor prognosis, whereas EIF1AX and SF3B1 may be useful markers of good prognosis, as previously suggested.20 Our group recently reported that expression of the oncogene PRAME identifies class 1 UMs with intermediate metastatic risk, and these class 1 PRAME-positive UMs often harbor SF3B1 mutations.21 As such, we anticipate that SF3B1 mutations will be associated with a newly identified subclass of UM associated with metastatic risk that is intermediate between UMs with BAP1 mutations (high risk) and UMs with EIF1AX mutations (low risk).

A limitation of this study was that it included only UMs treated by enucleation, which was a matter of necessity to obtain adequate amounts of tumor tissue for the various molecular analyses that were performed. As such, the findings of our study and others that are limited to enucleation specimens may not be representative of smaller UMs that are treated by globe-sparing procedures. With recent advances in next-generation sequencing technology, all of these mutations can be detected from a single fine-needle aspiration biopsy sample. A second limitation was that tumor DNA adequate for sequencing all coding regions of the entire BAP1 gene (which was not required for the other 4 genes) was available in only 64 of 81 (79%) participants. However, most other studies have relied on immunohistochemistry for the presence of the BAP1 protein as a surrogate marker for BAP1 mutations,22 which is associated with considerable false-positive and false-negative results, so our study provides important new insights based on validated mutations. A third limitation was that we performed targeted sequencing of only the 5 commonly mutated genes. With multiple groups reporting their whole-exome sequencing results for UM, rare mutations in other genes have been found and will likely continue to be discovered.11,16,23 The importance of these additional infrequent events will need to be assessed in future studies. A fourth limitation was the retrospective design and its effect on the clinical outcomes obtained. We are planning a prospective multicenter study to evaluate these and other genetic abnormalities in UM.

Conclusions

Five common mutations in UM have been identified to date. Two of these mutations (GNAQ and GNA11) occur early in tumor formation and are not associated with prognosis, whereas the other 3 (BAP1, SF3B1, and EIF1AX) likely occur later in tumor progression and are prognostically significant. These findings suggest that the mutation status of BAP1, SF3B1, and EIF1AX is of clinical value in the application of precision medicine in UM. Because the GEP classification was prognostically superior to the mutation status of these genes, the role of mutational analysis for prognostication will likely be as a supplement to GEP. While GEP remains the most accurate prognostic biomarker, it is possible that continued research will show that the inclusion of mutational information with GEP could increase the prognostic accuracy or suggest specific treatment choices, such as an MEK inhibitor for GNAQ and GNA11 mutations or an epigenetic modulator for BAP1 mutations.24,25

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Article Information

Submitted for Publication: November 24, 2015; final revision received February 26, 2016; accepted March 13, 2016.

Corresponding Author: J. William Harbour, MD, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, 900 NW 17th St, Miami, FL 33136 (harbour@miami.edu).

Published Online: April 28, 2016. doi:10.1001/jamaophthalmol.2016.0903.

Author Contributions: Ms Decatur and Dr Harbour had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Garg, Bowcock, Harbour.

Acquisition, analysis, or interpretation of data: Decatur, Ong, Garg, Anbunathan, Bowcock, Field.

Drafting of the manuscript: Decatur, Ong, Field.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Garg, Field, Harbour.

Obtained funding: Bowcock, Harbour.

Administrative, technical, or material support: Decatur, Anbunathan, Bowcock.

Study supervision: Bowcock, Harbour.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Drs Bowcock and Harbour reported being coinventors of intellectual property related to the discovery of BAP1 mutations in uveal melanoma. Dr Harbour reported being the inventor of intellectual property related to the gene expression profile technology used in the study, reported being a paid consultant for Castle Biosciences, Inc (which licensed this intellectual property), and reported receiving royalties from its commercialization. No other disclosures were reported.

Funding/Support: This work was supported by grant R01 CA161870 from the National Cancer Institute (Drs Bowcock and Harbour); by the Melanoma Research Foundation and the Sheila and David Fuente Graduate Program in Cancer Biology (Mr Field); by grant R01 CA125970 from the National Cancer Institute, a Research to Prevent Blindness Senior Scientific Investigator Award, the Melanoma Research Foundation, the Melanoma Research Alliance, the Ocular Melanoma Foundation, and the Sylvester Comprehensive Cancer Center (Dr Harbour); and by Center Core grant P30EY014801 from the National Institutes of Health, a Research to Prevent Blindness Unrestricted Grant, and grant W81XWH-09-1-0675 from Department of Defense (Bascom Palmer Eye Institute).

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

References
1.
Ramaiya  KJ, Harbour  JW.  Current management of uveal melanoma.  Expert Rev Ophthalmol. 2007;2(6):939-946.Google ScholarCrossref
2.
Onken  MD, Worley  LA, Ehlers  JP, Harbour  JW.  Gene expression profiling in uveal melanoma reveals two molecular classes and predicts metastatic death.  Cancer Res. 2004;64(20):7205-7209.PubMedGoogle ScholarCrossref
3.
Chang  SH, Worley  LA, Onken  MD, Harbour  JW.  Prognostic biomarkers in uveal melanoma: evidence for a stem cell–like phenotype associated with metastasis.  Melanoma Res. 2008;18(3):191-200.PubMedGoogle ScholarCrossref
4.
Worley  LA, Onken  MD, Person  E,  et al.  Transcriptomic versus chromosomal prognostic markers and clinical outcome in uveal melanoma.  Clin Cancer Res. 2007;13(5):1466-1471.PubMedGoogle ScholarCrossref
5.
Petrausch  U, Martus  P, Tönnies  H,  et al.  Significance of gene expression analysis in uveal melanoma in comparison to standard risk factors for risk assessment of subsequent metastases.  Eye (Lond). 2008;22(8):997-1007.PubMedGoogle ScholarCrossref
6.
Singh  AD, Sisley  K, Xu  Y,  et al.  Reduced expression of autotaxin predicts survival in uveal melanoma.  Br J Ophthalmol. 2007;91(10):1385-1392.PubMedGoogle ScholarCrossref
7.
Onken  MD, Worley  LA, Tuscan  MD, Harbour  JW.  An accurate, clinically feasible multi-gene expression assay for predicting metastasis in uveal melanoma.  J Mol Diagn. 2010;12(4):461-468.PubMedGoogle ScholarCrossref
8.
Onken  MD, Worley  LA, Char  DH,  et al.  Collaborative Ocular Oncology Group report number 1: prospective validation of a multi-gene prognostic assay in uveal melanoma.  Ophthalmology. 2012;119(8):1596-1603.PubMedGoogle ScholarCrossref
9.
Febbo  PG, Ladanyi  M, Aldape  KD,  et al.  NCCN Task Force report: Evaluating the clinical utility of tumor markers in oncology.  J Natl Compr Canc Netw. 2011;9(suppl 5):S1-S32.PubMedGoogle Scholar
10.
Aaberg  TM  Jr, Cook  RW, Oelschlager  K, Maetzold  D, Rao  PK, Mason  JO  III.  Current clinical practice: differential management of uveal melanoma in the era of molecular tumor analyses.  Clin Ophthalmol. 2014;8:2449-2460.PubMedGoogle ScholarCrossref
11.
Harbour  JW, Onken  MD, Roberson  ED,  et al.  Frequent mutation of BAP1 in metastasizing uveal melanomas.  Science. 2010;330(6009):1410-1413.PubMedGoogle ScholarCrossref
12.
Onken  MD, Worley  LA, Long  MD,  et al.  Oncogenic mutations in GNAQ occur early in uveal melanoma.  Invest Ophthalmol Vis Sci. 2008;49(12):5230-5234.PubMedGoogle ScholarCrossref
13.
Van Raamsdonk  CD, Bezrookove  V, Green  G,  et al.  Frequent somatic mutations of GNAQ in uveal melanoma and blue naevi.  Nature. 2009;457(7229):599-602.PubMedGoogle ScholarCrossref
14.
Van Raamsdonk  CD, Griewank  KG, Crosby  MB,  et al.  Mutations in GNA11 in uveal melanoma.  N Engl J Med. 2010;363(23):2191-2199.PubMedGoogle ScholarCrossref
15.
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