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Br J Ophthalmol. 2007 October; 91(10): 1385–1392.
Published online 2007 May 2. doi:  10.1136/bjo.2007.116947
PMCID: PMC2001033

Reduced expression of autotaxin predicts survival in uveal melanoma



In an effort to identify patients with uveal melanoma at high risk of metastasis, the authors undertook correlation of gene expression profiles with histopathology data and tumour‐related mortality.


The RNA was isolated from 27 samples of uveal melanoma from patients who had consented to undergo enucleation, and transcripts profiled using a cDNA array comprised of sequence‐verified cDNA clones representing approximately 4000 genes implicated in cancer development. Two multivariate data mining techniques—hierarchical cluster analysis and multidimensional scaling—were used to investigate the grouping structure in the gene expression data. Cluster analysis was performed with a subset of 10 000 randomly selected genes and the cumulative contribution of all the genes in making the correct grouping was recorded.


Hierarchical cluster analysis and multidimensional scaling revealed two distinct classes. When correlated with the data on metastasis, the two molecular classes corresponded very well to the survival data for the 27 patients. Thirty two discrete genes (corresponding to 44 probe sets) that correctly defined the molecular classes were selected. A single gene (ectonucleotide pyrophosphatase/phosphodiesterase 2; autotaxin) could classify the molecular types. The expression pattern was confirmed using real‐time quantitative PCR.


Gene expression profiling identifies two distinct prognostic classes of uveal melanoma. Underexpression of autotaxin in class 2 uveal melanoma with a poor prognosis needs to be explored further.

About 5% of all melanomas arise from the ocular and adnexal structures and uveal melanoma is the most common primary intraocular malignant tumour with an incidence of approximately 4.3 cases per million per year in the USA.1,2 Although, cutaneous and uveal melanocytes share a common embryologic origin, uveal melanoma and cutaneous melanoma have many differing clinical, epidemiological and prognostic features.3,4

The five‐year survival rates following enucleation, brachytherapy and other methods of treatment of primary uveal melanoma range from 6%–53% depending upon the size of the tumour.5,6,7,8,9,10 Despite achieving great accuracy in diagnosing uveal melanoma,11 mortality from uveal melanoma in the USA has remained unchanged over a period of 25 years from 1973 to 1997.12

Recent studies have identified specific chromosomal changes of prognostic significance such as monosomy 3, alteration of 8q, and 6p13,14,15,16 but the genetic mechanisms involved in the pathogenesis of uveal melanoma remain unknown. Microarray gene profiling of uveal melanoma tumour samples17 and cell cultures18 has revealed two subtypes of uveal melanoma. One previous study has suggested a correlation between molecular classes of uveal melanoma and survival.19 To get a better insight into genes that are involved in uveal melanoma tumorigenesis and metastasis, we analysed gene expression profiles in 27 patients with uveal melanoma with known survival outcome.

Materials and methods

Tumour collection

The tumour specimens were collected from the eyes of patients undergoing enucleation. The tumour samples were snap frozen in liquid nitrogen at the point of collection, and subsequently transferred to −180°C for long‐term storage.

Clinical data

Twenty seven patients with medium to large sized uveal melanoma were included in the study following approval by the institutional review board. The clinical details (age, sex), tumour features (location, largest basal diameter, height), histopathological features (per cent epithelioid component, presence or absence of matrix patterns), and outcome (follow‐up duration, survival status (alive, dead due to metastasis, dead due to other causes)) were recorded for each patient. On the largest tumour face, a periodic acid‐Schiff stain was carried out, without counterstain, to assess the tumour extracellular matrix patterns. Nine morphological patterns of extracellular matrix deposition have been defined for ciliary body or choroidal melanomas.20,21 The presence of extracellular closed loops and networks (a network is defined as at least three back‐to‐back closed loops), is the feature strongly associated with death from metastatic disease.20,21

RNA isolation and target RNA preparation

RNA was isolated from uveal melanoma tumour samples using the RNAqueous kit (Ambion) as described by the manufacturer. Following isolation, the RNA was DNAse treated using the reagents from the DNA‐free kit (Ambion, Foster City, USA), again as described by the manufacturer. The quality of the isolated RNA was verified using an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, USA). Target RNA was generated in a T7 polymerase‐based linear amplification reaction using a modified version of a published protocol.22 Two micrograms of total RNA and 5 pmol of T7‐(dT)24 primer [5′‐GGCCAGTGAATTGTAATACGACTCACTATAGGGAGGCGG‐(dT)24‐3′] in a total volume of 5.5 μl was incubated at 70°C for 10 min and chilled on ice. For first‐strand cDNA synthesis, the annealed RNA template was incubated for 1 h at 42°C in a 10 μl reaction mixture containing first‐strand buffer (Invitrogen, Carlsbad, CA, USA), 10 mM dithiothreitol, 1 unit of anti‐RNase (Ambion) per μl, 500 μM deoxynucleoside triphosphates, and 2 units of Superscript II (Invitrogen) per μl. Second‐strand synthesis was for 2 h at 16°C in a total reaction volume of 50 μl containing first‐strand reaction products, second‐strand buffer (Invitrogen), 250 μM deoxynucleoside triphosphates, 0.06 unit of DNA ligase (Ambion) per μl, 0.26 unit of DNA polymerase I (New England Biolabs, Ipswich, USA) per μl, and 0.012 U of RNase H (Ambion) per µl followed by the addition of 3.3 units of T4 DNA polymerase (3 units per µl; New England Biolabs) and a further 15 min of incubation at 16°C. Second‐strand reaction products were purified by phenol‐chloroform‐isoamyl alcohol extraction in Phaselock microcentrifuge tubes (Eppendorf, Westbury, NY, USA) according to the manufacturer's instructions and ethanol precipitated. In vitro transcription was performed by using the T7 megascript kit (Ambion) according to a modified protocol in which purified cDNA was combined with 1 μl (each) of 10× ATP, GTP, CTP, and UTP and 1 μl of T7 enzyme mix in a 10 μl reaction volume and incubated for 9 h at 37°C. Amplified RNA was purified by using the RNAeasy RNA purification kit (QIAGEN, Valencia, USA) according to the manufacturer's instructions.

cDNA microarray construction

The tumour cDNA array used in this study comprised a subset of sequence‐verified cDNA clones from Research Genetics Inc (Carlsbad, USA), a 40 000‐clone set representing approximately 4000 genes involved in tumorigenesis.23,24,25 The 4000 cDNAs were selected based on their implication in metastasis and cancer development in general from the literature and from the Affymetrix cancer G110 array, the presence of AU‐rich elements in their 3′‐ and/or 5′‐untranslated region25 as well as encoding proteins responsive to cytokines, containing a zinc‐finger or being implicated in apoptosis. The complete list of spotted cDNAs on this tumour array can be downloaded from the site DNA preparation and slide printing were as previously described, except for the use of 40% dimethyl sulfoxide in place of 1.5× SSC as the printing solution.25

RNA labelling

Cy3‐ or Cy5‐labeled cDNA was prepared by indirect incorporation. Two micrograms of amplified RNA, 1 µl of dT12‐18 primer (1 µg per µl; Invitrogen), 2.6 µl of random hexanucleotides (3 µg per µl; Invitrogen), and 1 µl of anti‐RNase (Ambion) were combined in a reaction volume of 15.5 µl and incubated for 10 min at 70°C. Reverse transcription was for 2 h at 42°C in a 30 µl reaction mixture containing annealed RNA template, first‐strand buffer, 500 µM (each) dATP, dCTP, and dGTP, 300 µM dTTP, 200 µM aminoallyl‐dUTP (Sigma), 10 mM dithiothreitol, and 12.7 units of Superscript II per µl. For template hydrolysis, 10 µl of 0.1 M NaOH was added to the reverse transcription reaction mixture and the mixture was incubated for 10 min at 70°C, allowed to cool at room temperature for 5 min, and neutralised by the addition of 10 µl of 0.1 M HCl. cDNA was precipitated at −20°C for 30 min after the addition of 1 µl of linear acrylamide (Ambion), 4 µl of 3 M sodium acetate (pH 5.2), and 100 µl of absolute ethanol, and then resuspended in 5 µl of 0.1 M NaHCO3. For dye coupling, the contents of one tube of N‐hydroxysuccinimide ester containing Cy3 or Cy5 dye (product numbers PA25001 and PA25002; GE Healthcare) was dissolved in 45 µl of dimethyl sulfoxide. Five µl of dye solution was mixed with the cDNA and incubated for 1 h in darkness at room temperature. Labelled cDNA was purified on a QIAquick PCR purification column (QIAGEN) according to the manufacturer's instructions. Eluted cDNA was dried under a vacuum and resuspended in 30 µl of Slidehyb II hybridisation buffer (Ambion). After 2 min of denaturation at 95°C, the hybridisation mixture was applied to the microarray slide under a coverslip. Hybridisation proceeded overnight in a sealed moist chamber in a 55°C water bath. After hybridisation, slides were washed successively for 5 min each in 2× SSC–0.1% SDS at 55°C, then in 2× SSC at 55°C, and finally in 0.2× SSC at room temperature.

Acquisition of data

Data were acquired with a GenePix 4000B laser scanner and GenePix Pro software, version 5.0, as previously described.22

Real‐time quantitative PCR

One μg of total RNA was reverse transcribed using random hexamers (Superscript first‐strand synthesis system; Invitrogen). Real‐time quantitative RTQ‐PCR was performed on the cDNA using a Taqman gene expression assay for autotaxin (Hs00196470 µm1) normalised with a eukaryotic 18S rRNA endogenous control assay (Applied Biosystems, Foster City, CA, USA). Reactions were performed in triplicate on a 7900HT Sequence Detection System instrument (Applied Biosystems) using Real Master Mix Probe ROX according to the manufacturers instructions (Eppendorf). Relative quantification was determined by comparison of each sample to a reference cDNA (Universal human reference RNA, Stratagene, La Jolla, CA).

Data analysis

Two multivariate data mining techniques, Hierarchical cluster analysis performed with 10 000 randomly selected subsets and multidimensional scaling,26 were used to investigate the grouping structure in the gene expression data of the 27 tumour samples. Pearson correlation coefficient was used as the proximity matrix27 in the hierarchical cluster analysis and in multidimensional scaling. Hierarchical cluster analysis with average linkage method26 was used to generate the dendrograms. Multidimensional scaling was also applied to investigate the grouping structure. Tumour metastatic survival data within molecular classes was correlated with Kaplan–Meier survival curves.


Clinical aspects

Twenty seven patients were included in the study. The average age of the patients was 61 years, of whom 13 were female; 16 patients were dead by the follow‐up date. The clinical details, tumour features, histopathological features and outcome are summarised in table 11.

Table thumbnail
Table 1 Clinical and pathologic profile of 27 patients with uveal melanoma

Gene expression profile analysis

No obvious pitfalls were found when we looked at the overall gene expression profiles across the tumour samples, so we analysed all 27 samples provided (fig 11).). Hierarchical cluster analysis revealed two distinct molecular classes (class 1 and class 2). Hierarchical cluster analysis with average linkage method was used to generate the dendrogram (fig 22).). Multidimensional scaling analysis also revealed two molecular classes identical to those found with the hierarchical cluster analysis (fig 33).).

figure bj116947.f1
Figure 1 Box plot of the 27 tumour samples. The colour indicates the metastatic death status (green for alive, red for dead, and blue for death from other cause).
figure bj116947.f2
Figure 2 Dendrogram from the hierarchical cluster analysis using Pearson correlation as distance matrix. Two molecular classes were found, the one in the green box corresponds to class 1 that contains all the patients alive and the other one in ...
figure bj116947.f3
Figure 3 Plot of the multidimentional scaling with Pearson correlation as distance matrix. Two molecular classes, class 1 and class 2, are obvious and the colour indicates the metastatic death (green, alive; red, metastatic death; blue, one death ...

Key genes that are in favour of the grouping structure

We next identified the genes that distinguished the two molecular classes. First, all genes were pre‐filtered and those with data missing were deleted. Wilcoxon rank sum test was performed on the remaining 3514 genes. Based on the p values from the Wilcoxon test, the adjusted p values for controlling the false discovery rate were calculated. We filtered out all the genes with false discovery rate adjusted p values greater than 0.1, leaving 972 genes. Second, cluster analysis was repeatedly performed with 10 000 randomly selected subset of genes, and the cumulative contributions of all the genes in making the correct grouping were recorded. The cumulative contribution (score) was plotted for all 972 genes and we selected the top 32 discrete genes (corresponding to 44 probe sets) with the highest classification contribution (fig 44;; indicated as red dots). The 44 probe sets selected from the above algorithm are listed in table 22.. The 44 probe sets are clustered into two major groups, with either high expression of molecular class 1 and low expression of molecular class 2, or low expression of class 1 and high expression of class 2 (fig 55).).

Table thumbnail
Table 2 Summary of the properties of the 32 key genes (44 probe sets)
figure bj116947.f4
Figure 4 Top key genes selected from the selection algorithm. Gene weights was the average gene selection rate, and 20% was used as the cutoff to select the key genes that were correctly defined the molecular class 1 and class 2.
figure bj116947.f5
Figure 5 Gene profiles of the 44 key genes across the 27 samples with the dendrogram from the samples on the top and the dendrogram from the 44 key genes on the side. Pink indicates the high expression and cerulean blue indicates low expression. ...

Correlation with survival data

The two molecular classes corresponded very well to the Kaplan–Meier survival curves of the two classes. Class 1 had all nine patients alive and class 2 had 17 dead and one alive patient. The prognosis obtained with molecular classification was superior to other well known prognosticators such as tumour size, histopathology, location and extracellular matrix patterns (fig 66).28 Moreover, among the 32 key genes, gene expression pattern of autotaxin (ectonucleotide pyrophosphatase‐phosphodiesterase 2, ENPP2) alone was sufficient to distinguish molecular class 1 and class 2, where class 2 represents poor prognosis uveal melanoma (fig 77).). The expression pattern of autotaxin was confirmed using real‐time quantitative PCR (in triplicate) and was highly concordant.

figure bj116947.f6
Figure 6 Kaplan–Meier survival analysis on molecular classes and seven histopathological and clinical measures. All mortality was melanoma metastasis. Statistical significance is based on log‐rank test and indicated in the plot ...
figure bj116947.f7
Figure 7 Individual profile for ENPP2 expression (autotaxin, 20194). The x axis corresponds to 27 samples that are grouped onto molecular class 1 and class 2, in that order, and the dashed vertical lines indicates the boundary between the two ...


Uveal melanoma cells are shed into circulation at initial presentation and even in adequately treated cases.29 The fate of the uveal melanoma cells in the circulation and hence the features that influence their ability to establish metastasis determines the survival of patients with uveal melanoma. Microarray gene profiling is a powerful tool that can reveal such genetic attributes. In our study, cluster analysis performed with a subset of 10 000 randomly selected genes revealed 44 genes with the highest classification contribution and cumulative contribution (score). Overexpression of E‐cadherin was noted in class 2 uveal melanoma associated with poor prognosis. E‐cadherin is an important transmembrane protein which is indicative of epithelial differentiation.30 This attribute correlates with epithelial cell‐like (epithelioid) morphology of aggressive uveal melanoma.31 Although overexpression of E‐cadherin in class 2 uveal melanoma associated with poor prognosis initially appears to be paradoxical, E‐cadherin is also upregulated in other cancers that spread haematogenously, such as advanced cutaneous melanoma and hepatocellular carcinoma.32

In the circulation, uveal melanoma cells with high HLA class I expression enable the cells to evade natural killer cell‐mediated tumour surveillance.33 Therefore, it is not unexpected that several genes of the histocompatibility complex class I and class II were overexpressed in class 2 uveal melanoma associated with a poor prognosis. It is also likely that the presence of tumour‐infiltrating macrophages and lymphocytes, features of a poor prognosis for uveal melanoma,34 may be the reason for the observed overexpression of immune response genes.

Tissue inhibitor of metalloproteinase 3 (TIMP3) inhibits cell invasion and metastasis by inhibiting degradation of the extracellular matrix.35 TIMP3 also induces apoptosis36 and inhibits angiogenesis.37 Comparative expression profiling of metastasis in uveal melanoma cell lines and primary uveal melanoma cell lines revealed a fivefold lower expression of TIMP3 in metastasis cell lines.38 The lower expression of TIMP3 in class 2 uveal melanoma with a poor prognosis, as observed in our study, indicates pro‐angiogenic and anti‐apoptotic attributes of aggressive melanoma cells that favour establishment of tumour metastatic sites.

Results of gene expression profiling do not always generate reproducible or identical genetic profiles due to several factors including biological and technical variability (table 33).). Previous studies have identified sets of 201 genes,17 7 genes,39 and 3 genes19 sufficient for accurate class prediction in uveal melanoma. It is remarkable that autotaxin was also one of the discriminating genes in two previous studies.19,39 In any event, it is prudent not to focus on one gene; the expression pattern of several genes should be considered for determining the expression profile.40 What is remarkable is the fact that non‐metastasising (class 1) and metastasising (class 2) uveal melanoma can be readily discriminated by their genetic profiles as determined by microarray techniques.

Table thumbnail
Table 3 Comparison of top discriminating genes from published studies that are underexpressed in class 2 (poor prognosis) uveal melanoma

Autotaxin is synthesised as a pre‐pro‐enzyme and after proteolytic cleavage the protein is secreted.41 Autotaxin is identical to lysophospolipase D and generates lysophosphatidic acid by hydrolysing lysophosphatidyl choline.42 Lysophosphatidyl choline enhances cell motility, cell proliferation, and angiogenesis41,43,44 through G protein‐coupled receptors.45 Autotaxin is upregulated in various malignancies, including breast,46 lung,47 thyroid carcinoma48 and glioblastoma multiforme.49

As autotaxin is a tumour cell motility‐stimulating factor, originally isolated from melanoma cell supernatants,50 it is possible that autotaxin may be detected in serum and serve as a biomarker of metastasis. Underexpression of autotaxin in class 2 uveal melanoma with a poor prognosis needs to be explored further.


Grant support: American Cancer Society.

Competing interests: None declared.


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