Purpose: Gene expression studies in melanoma have been few because tumors are small and cryopreservation is rarely possible. The purpose of this study was to evaluate the Illumina DASL Array Human Cancer Panel for gene expression studies in formalin-fixed melanoma primary tumors and to identify prognostic biomarkers.

Experimental Design: Primary tumors from two studies were sampled using a tissue microarray needle. Study 1: 254 tumors from a melanoma cohort recruited from 2000 to 2006. Study 2: 218 tumors from a case-control study of patients undergoing sentinel node biopsy.

Results: RNA was obtained from 76 of blocks; 1.4 of samples failed analysis (transcripts from <250 of the 502 genes on the DASL chip detected). Increasing age of the block and increased melanin in the tumor were associated with reduced number of genes detected. The gene whose expression was most differentially expressed in association with relapse-free survival in study 1 was osteopontin (SPP1; P = 2.11 106) and supportive evidence for this was obtained in study 2 used as a validation set (P = 0.006; unadjusted data). Osteopontin level in study 1 remained a significant predictor of relapse-free survival when data were adjusted for age, sex, tumor site, and histologic predictors of relapse. Genes whose expression correlated most strongly with osteopontin were PBX1, BIRC5 (survivin), and HLF.

Conclusion: Expression data were obtained from 74 of primary melanomas and provided confirmatory evidence that osteopontin expression is a prognostic biomarker. These results suggest that predictive biomarker studies may be possible using stored blocks from mature clinical trials. (Clin Cancer Res 2009;15(22):693946)

Translational Relevance

This article reports an investigation of new technologies to perform gene expression studies in small formalin-fixed tumor biopsies. The study identified increased expression of osteopontin as a predictor of relapse in melanoma. The study therefore confirms an important potential drug target, but its translational relevance is that the study supports the view that high-throughput gene expression studies are now possible from tumor banks. Stored tissue from mature clinical trials may be accessed to investigate predictive markers using this approach.

The established predictors of outcome for melanoma patients relate to the histologic characteristics of the primary tumor [Breslow thickness, presence of ulceration (1), and mitotic rate (2)], tumor site, sex (2), and age (3). The histologic characteristics are used to estimate prognosis as part of the American Joint Committee on Cancer staging system (1) and, in various algorithms, to give a more personalized estimate (2, 3), but much of the variance in survival remains unexplained. To identify prognostic and predictive biomarkers and to better establish the biological pathways of relevance, genomic studies of primary melanomas are necessary.

However, primary melanomas are small, and as the histologic characteristics conveying prognostic information are often focal within the primary tumor, pathologists are reluctant to cryopreserve tissue. Therefore, genomic expression studies have been relatively few in number and limited in size. Studies published to date have predominantly used PCR techniques to look at single genes (4, 5), often using <25 samples. Larger-scale studies have more recently come from Winnepenninckx et al. who identified a 254-gene signature predictive of survival that included minichromosome maintenance genes in 83 cryopreserved primary tumors (6) and from Kauffmann et al. who identified increased expression of DNA repair genes in metastatic tumors from 60 tumor samples (7).

Although the work by Winnepenninckx et al. (6) represents a major development in the field, the development of approaches to analyze formalin-fixed tumors would allow studies using large numbers of samples stored from melanoma cohorts/clinical trials with long follow-up and potentially with less bias consequent on selected sampling of tumors deemed suitable for cryopreservation. We have used the Illumina DASL (cDNA-mediated annealing, selection, extension, and ligation) assay (8), which has been specifically developed for use in formalin-fixed tissue, to investigate prognostic biomarkers in stored primary melanomas, and we report here an evaluation of the technique and confirmation of the significance of increased osteopontin expression in melanoma. In this large study, we also investigated the value of quality-control measures and sample characteristics to predict performance of RNA samples with the DASL assay.

Patient samples

Formalin-fixed, paraffin-embedded primary melanoma blocks were identified from two study sets (Table 1). In study 1, population-ascertained incident melanoma cases were recruited to a case-control study in a geographically defined area of Yorkshire and the Northern region of the United Kingdom (67 participation rate). All patients gave written informed consent to participation. 961 male and female patients (ages between 18 and 76 years) were diagnosed from September 2000 to December 2005 (9, 10). The cases were identified via clinicians, pathology registers, and the cancer registry to ensure maximal ascertainment. Between September 2000 and December 2001 and from July 2003 to December 2005, patients with Breslow thickness <0.75 mm were not invited to maximize the value of the sample as a cohort looking at prognostic outcomes. Between January 2002 and June 2003, all patients with invasive melanoma were invited to participate. The first 254 blocks identified from participants within the cohort with tumors thicker than 0.75 mm with the longest follow-up comprised the test set. In study 2 (the validation set), patients with melanomas with Breslow thickness 0.75 mm having undergone sentinel node biopsy (SNB) were recruited to a multicenter retrospective case-control study. Five centers from the United Kingdom identified all patients having had SNB from November 1994 until 2006. Cases were melanoma patients with a positive SNB and controls were those with a negative SNB. The number of patients with a negative SNB, being greater than the number with a positive result controls, was randomly selected to be frequency matched by year of SNB and by the center at which the SNB was done. The first 218 blocks identified from participants with the longest follow-up in a study designed to identify predictors of sentinel node positivity and relapse were sampled. In both studies, the blocks sampled were representative of the age, sex ratio, and site distribution of the whole data sets. The Breslow thickness was higher in the study 1 samples than in the whole study, as expected, as we were unable to sample very small tumors (Supplementary Table S3). Both studies were approved by both of the UK national ethics committees (MREC and PIAG). Here, we report expression studies carried out in samples from study 1, which we subsequently sought to validate in samples from study 2.

Table 1.

Descriptive characteristics of the two sample sets

VariableStudy 1Study 2Test statistic and significance value
No. patients 156 198  
Age at diagnosis or at SNB, y, mean (range) 54.9 (19.9-78.5) 52.0 (14.4-88.0) t = 1.87; P = 0.06 
Sex, female, n () 81 (51.9) 95 (48.0) 2 = 0.54; P = 0.46 
Sex, male, n () 75 (48.1) 103 (52.0) 
Site of tumor, n () 
Arm 31 (19.9) 44 (22.2) 2 = 4.13; P = 0.25 
Head and neck 24 (15.4) 17 (8.6) 
Leg 50 (32.0) 69 (34.9) 
Trunk 49 (31.4) 68 (34.3) 
Unknown 2 (1.3) 0 (0.0) 
Breslow thickness, mm, median (range) 1.9 (0.9-12.0) 2.0 (0.7-24.0) Mann-Whitney Z = 0.57; P = 0.57 
Mitotic rate, n () 
<1 27 (17.3) 22 (11.1) 2 = 7.33; P = 0.03 
1-6 76 (48.7) 90 (45.5) 
>6 34 (21.8) 68 (34.3) 
Unknown 19 (12.2) 18 (9.1) 
Ulcerated tumors, n () 40 (25.6) 58 (29.3) 2 = 0.58; P = 0.45 
Relapsers, n () 37 (23.7) 63 (31.8) 2 = 3.48; P = 0.06 
Deaths, n () 21 (13.5) 47 (23.7) 2 = 6.17; P = 0.01 
Follow-up time, mo, median (range) 49.1 (4.9-94.9) 38.4 (0.03-111.7) Mann-Whitney Z = 4.65; P < 0.001 
VariableStudy 1Study 2Test statistic and significance value
No. patients 156 198  
Age at diagnosis or at SNB, y, mean (range) 54.9 (19.9-78.5) 52.0 (14.4-88.0) t = 1.87; P = 0.06 
Sex, female, n () 81 (51.9) 95 (48.0) 2 = 0.54; P = 0.46 
Sex, male, n () 75 (48.1) 103 (52.0) 
Site of tumor, n () 
Arm 31 (19.9) 44 (22.2) 2 = 4.13; P = 0.25 
Head and neck 24 (15.4) 17 (8.6) 
Leg 50 (32.0) 69 (34.9) 
Trunk 49 (31.4) 68 (34.3) 
Unknown 2 (1.3) 0 (0.0) 
Breslow thickness, mm, median (range) 1.9 (0.9-12.0) 2.0 (0.7-24.0) Mann-Whitney Z = 0.57; P = 0.57 
Mitotic rate, n () 
<1 27 (17.3) 22 (11.1) 2 = 7.33; P = 0.03 
1-6 76 (48.7) 90 (45.5) 
>6 34 (21.8) 68 (34.3) 
Unknown 19 (12.2) 18 (9.1) 
Ulcerated tumors, n () 40 (25.6) 58 (29.3) 2 = 0.58; P = 0.45 
Relapsers, n () 37 (23.7) 63 (31.8) 2 = 3.48; P = 0.06 
Deaths, n () 21 (13.5) 47 (23.7) 2 = 6.17; P = 0.01 
Follow-up time, mo, median (range) 49.1 (4.9-94.9) 38.4 (0.03-111.7) Mann-Whitney Z = 4.65; P < 0.001 

Sample preparation

Primary tumor blocks were identified and a H&E-stained slide was examined to identify the deepest part of the tumor having a diameter of >0.8 mm, containing the lowest admixture of inflammatory or stromal cells. This area was marked using a fine-tipped permanent marker and a tissue microarray needle was then used to sample the tumor block horizontally (Supplementary Information). Tissue microarray core needles were used to sample tumors as an efficient approach to obtain sufficient RNA yields from the deepest part of the tumor, in studies potentially using many hundreds of samples while preserving the block for use by pathologists subsequently.

Melanin present in primary melanomas copurifies with DNA resulting in two major problems. Absorption of UV light can lead to unreliable spectrophotometric quantification of nucleic acids (11). More importantly, melanin can inhibit DNA polymerases (12). To allow evaluation of the effect of melanin on gene expression analysis of melanoma samples, slides were graded using a system devised to visually score melanin content of tissue microarray cores (0-3).

RNA extraction

Tissue cores were dewaxed using xylene and two changes of absolute ethanol. RNA was extracted in batches of 24 tissue cores using the High Pure Paraffin RNA kit (Roche Diagnostics) according to the manufacturer's protocol and eluted in 25 L nuclease-free water. For quality-control measures, see Supplementary Information.

DASL expression arrays

The Illumina DASL Human Cancer Panel gene set was used to perform the DASL assay for gene expression profiling of all test and control samples. The Cancer Panel includes 1,536 unique sequence-specific probes targeting 502 genes. Each gene is targeted in three locations by three separate probe pairs designed by a proprietary algorithm (ref. 8; Supplementary Information).

Data pre-processing

The data were normalized using Beadstudio software (Illumina) before exporting to Stata version 10 for statistical analyses. The normalization methods used were background correction, cubic spline smoothing (13), and plate scaling. Normalization was conducted relative to a synthetic reference array, which was created in each study by averaging all melanoma samples (Supplementary Information).

Target validation by quantitative real-time reverse transcription-PCR

Osteopontin (SPP1) expression, identified as significantly associated with relapse or histologic variables that predict relapse, was further investigated by quantitative real-time reverse transcription-PCR on samples from study 1 using probes corresponding to the locations of the DASL probes [Taqman Gene Expression Assays Hs00960942_m1 (exons 1-2) and Hs00959010_m1 (exons 5-6); Applied Biosystems; Supplementary Information]. The comparative Ct (or Ct) method was used to compare relative fold change in expression of two regions of SPP1.

Statistical methodology

The number of genes detected in each sample (probe signal significantly greater than average signal from negative controls; P < 0.05) was used as a measure of the quality of the results. The influence of age of tissue block and melanin level of the tumor on number of genes detected was investigated using Spearman's rank correlation and Kruskal-Wallis tests, respectively.

Methods used to measure quality and quantity of RNA before use in the DASL assay were assessed by correlating the number of genes detected in samples with the quality measure data using either Spearman's rank correlation or Kruskal-Wallis test. Samples with <250 detected genes were classified as failed and excluded from further analysis (Supplementary Table S1). Analysis of sample replicates is detailed in Supplementary Table S2. Mean gene expression was used for the remaining sample replicates.

Differential gene expression and survival analyses were done using log-transformed normalized data (log2). Within the sample sets, mean expression of each gene was compared between samples with histologic features of interest using two-sample t tests and linear regression. Relapse-free survival was defined as the period between diagnosis and date of first relapse at any site. Survival analysis was done using Cox proportional hazards model to calculate hazard ratios and 95 confidence intervals for each gene. These analyses were done unadjusted and adjusted for demographic and histologic factors of prognostic importance in melanoma. Significance values were ranked to identify genes most differentially expressed between groups of interest. Using the Bonferroni method to correct for multiple testing, the significance level was set at 0.0001 for these analyses. All analyses were undertaken using Stata version 10 (StataCorp 2007).

Generation of gene networks using Ingenuity software

The combined data from the studies, fold changes, and significance levels of genes differentially expressed in tumors from patients with reduced relapse-free survival time were analyzed using Ingenuity Pathway Analysis software (Ingenuity Systems). Genes >1.2 times overexpressed or underexpressed with significance levels <0.05 were interrogated by Ingenuity to find genes most related to each other and a network of these relationships was generated.

Descriptive statistics on the sample sets

The two samples sets were similar (Table 1). Participants in study 2 were slightly younger. Study 2 tumors had a significantly higher mitotic rate (Pearson's 2; P = 0.03) and a correspondingly higher relapse rate (23.7 for study 1 and 31.8 for study 2, respectively; Pearson's 2; P = 0.06) and death rate (13.5 for study 1 and 23.7 for Study 2, respectively; Pearson's 2; P = 0.01). The sample sets were broadly representative of the larger study sets from which the samples were derived (Supplementary Table S3). Use of a tissue microarray needle precluded sampling of thin tumors, which accounts for the higher proportion of thicker tumors in the larger study set 1.

RNA yields obtained from tumor samples

Of the 472 formalin-fixed, paraffin-embedded primary tumor blocks identified, 378 (80) were selected for sampling. Reasons for not sampling a block included too little residual tumor after sectioning for clinical purposes or other research projects and tumor cells being mixed with large numbers of normal stromal or inflammatory cells. In 17 of 378 (4.5), a core was taken, but inadequate quantities of RNA were obtained as measured using the Bioanalyzer. Overall, adequate RNA yields were obtained from 361 of 472 (76) of blocks.

Quality-control measures for DASL

Four hundred twenty-three RNA samples including replicate samples were supplied to the Illumina DASL service provider. Less than 250 genes were detected in 6 (1.4) samples, which were classified as failed samples. The failure rate was 2.1 in study 1 and 0.9 in study 2 (Supplementary Table S1).

Supplementary Table S1 summarizes the associations between quality-control measures, age of block, and melanin content of tumor and the number of genes detected. Increased block age was predictive of a reduced number of genes detected in study 2 but not study 1 (the range of block age being much greater in study 2). Increased melanin score was predictive of reduced number of detected genes in study 1. The best quality-control predictors of number of genes detected were RNA concentration, RNA integrity score (14), and the CT value from quantitative real-time reverse transcription-PCR.

Expression data

Study 1

Genes most differentially expressed in tumors from patients with reduced relapse-free survival are presented in Table 2. The gene most predictive of relapse-free survival in study 1 was osteopontin (increased expression was associated with shorter relapse-free survival). In study 1, the hazard ratio for reduced relapse-free survival associated with increased expression was 3.17 (unadjusted; significant at the P = 2.11 106 level). This association persisted when the analysis was repeatedly adjusted for host variables known to predict relapse (age, sex, and tumor site; P = 9.19 106) and when adjusted additionally for Breslow thickness, mitotic rate, and the presence of tumor ulceration (P = 0.001; Table 3). Fold change of expression signal was 1.55 between relapsers and nonrelapsers in unadjusted analysis. Increased osteopontin expression was also predictive of overall survival [hazard ratio, 2.63 (95 confidence interval, 1.38-5.04); P = 0.002] with a fold change of 1.41. This association remains significant after adjusting for age, sex, and tumor site (P = 0.004). Expression signals from all three osteopontin probes on the array were comparably predictive of reduced relapse-free survival in study 1 (hazard ratio range, 2.10-2.84; significance value range, 0.0002-9.91 106).

Table 2.

Top 20 genes differentially expressed in tumors from patients with reduced relapse-free survival in study 1 (unadjusted analysis)

GeneFold change between relapsers and nonrelapsersHazard ratio (95 confidence interval)Significance value
OSTEOPONTIN 1.55 3.17 (1.91-5.26) 2.11 106 
RAD54B 1.39 8.59 (3.17-23.31) 9.40 106 
HMMR 1.43 4.36 (2.08-9.13) 0.00006 
CDKN2B 0.71 0.33 (0.19-0.56) 0.0001 
DEK 1.15 16.94 (4.32-66.45) 0.0001 
TK1 1.23 7.82 (2.70-22.65) 0.0002 
ITGB4 0.74 0.35 (0.20-0.60) 0.0002 
BIRC5 1.30 4.36 (1.82-10.41) 0.0003 
DSP 0.65 0.60 (0.46-0.78) 0.0003 
ING1 1.13 12.81 (3.07-53.47) 0.0003 
TOP2A 1.19 6.86 (2.19-21.49) 0.0003 
AR 0.63 0.25 (0.11-0.55) 0.0003 
E2F5 1.34 3.86 (1.82-8.19) 0.0004 
TGFA 0.66 0.32 (0.16-0.63) 0.0004 
RECQL 1.18 8.64 (2.58-29.00) 0.0004 
BLM 1.24 4.35 (1.86-10.14) 0.0005 
FHIT 1.26 3.99 (1.75-9.10) 0.0006 
GRB7 0.66 0.53 (0.37-0.76) 0.0006 
TFAP2C 0.75 0.36 (0.20-0.64) 0.0006 
RAD51 1.30 4.16 (1.81-9.53) 0.0007 
GeneFold change between relapsers and nonrelapsersHazard ratio (95 confidence interval)Significance value
OSTEOPONTIN 1.55 3.17 (1.91-5.26) 2.11 106 
RAD54B 1.39 8.59 (3.17-23.31) 9.40 106 
HMMR 1.43 4.36 (2.08-9.13) 0.00006 
CDKN2B 0.71 0.33 (0.19-0.56) 0.0001 
DEK 1.15 16.94 (4.32-66.45) 0.0001 
TK1 1.23 7.82 (2.70-22.65) 0.0002 
ITGB4 0.74 0.35 (0.20-0.60) 0.0002 
BIRC5 1.30 4.36 (1.82-10.41) 0.0003 
DSP 0.65 0.60 (0.46-0.78) 0.0003 
ING1 1.13 12.81 (3.07-53.47) 0.0003 
TOP2A 1.19 6.86 (2.19-21.49) 0.0003 
AR 0.63 0.25 (0.11-0.55) 0.0003 
E2F5 1.34 3.86 (1.82-8.19) 0.0004 
TGFA 0.66 0.32 (0.16-0.63) 0.0004 
RECQL 1.18 8.64 (2.58-29.00) 0.0004 
BLM 1.24 4.35 (1.86-10.14) 0.0005 
FHIT 1.26 3.99 (1.75-9.10) 0.0006 
GRB7 0.66 0.53 (0.37-0.76) 0.0006 
TFAP2C 0.75 0.36 (0.20-0.64) 0.0006 
RAD51 1.30 4.16 (1.81-9.53) 0.0007 
Table 3.

Association of osteopontin expression with relapse-free survival in studies 1 and 2

Study 1 raw dataStudy 1 adjusted for age, sex, and site of tumorStudy 1 adjusted additionally for histologic measuresStudy 2 study raw dataStudy 2 adjusted for age, sex, and site of tumorStudy 2 adjusted for age, sex, site of tumor, and SNB statusStudy 2 adjusted additionally for histologic measuresStudy 2 adjusted additionally for histologic measures and SNB status
Mean (SD) signal relapsers 5,396 (2,503) 5,031 (2,385) 4,552 (2,074) 5,812 (2,752) 6,130 (2,701) 4,103 (2,619) 5,907 (2,537) 4,575 (2,485) 
Mean (SD) signal nonrelapsers 3,476 (2,255) 3,060 (2,248) 3,180 (2,127) 4,411 (2,818) 4,533 (2,775) 3,227 (2,622) 4,988 (2,460) 4,051 (2,389) 
Fold change 1.55 1.64 1.43 1.32 1.35 1.27 1.18 1.13 
Hazard ratio (95 confidence interval) 3.17 (1.91-5.26) 3.33 (1.96-5.67) 2.76 (1.49-5.10) 1.60 (1.13-2.27) 1.67 (1.16-2.40) 1.40 (0.97-2.03) 1.24 (0.81-1.90) 1.11 (0.73-1.69) 
Significance value 2.11 106 9.19 106 0.001 0.006 0.006 0.07 0.32 0.62 
Study 1 raw dataStudy 1 adjusted for age, sex, and site of tumorStudy 1 adjusted additionally for histologic measuresStudy 2 study raw dataStudy 2 adjusted for age, sex, and site of tumorStudy 2 adjusted for age, sex, site of tumor, and SNB statusStudy 2 adjusted additionally for histologic measuresStudy 2 adjusted additionally for histologic measures and SNB status
Mean (SD) signal relapsers 5,396 (2,503) 5,031 (2,385) 4,552 (2,074) 5,812 (2,752) 6,130 (2,701) 4,103 (2,619) 5,907 (2,537) 4,575 (2,485) 
Mean (SD) signal nonrelapsers 3,476 (2,255) 3,060 (2,248) 3,180 (2,127) 4,411 (2,818) 4,533 (2,775) 3,227 (2,622) 4,988 (2,460) 4,051 (2,389) 
Fold change 1.55 1.64 1.43 1.32 1.35 1.27 1.18 1.13 
Hazard ratio (95 confidence interval) 3.17 (1.91-5.26) 3.33 (1.96-5.67) 2.76 (1.49-5.10) 1.60 (1.13-2.27) 1.67 (1.16-2.40) 1.40 (0.97-2.03) 1.24 (0.81-1.90) 1.11 (0.73-1.69) 
Significance value 2.11 106 9.19 106 0.001 0.006 0.006 0.07 0.32 0.62 

NOTE: The raw data are presented in column 1. The association was adjusted for sex, patient age, and tumor site (as known predictors of outcome) in column 2 (mean signal values are presented for a 45-year-old female with a tumor on her leg). In column 3, further adjustment is made for known histologic predictors of outcome: Breslow thickness, mitotic rate, and ulceration (mean signal values are presented for a 45-year-old female patient with a nonulcerated tumor on her leg, which has a Breslow thickness of 2.5 mm and a mitotic rate of 1-6/mm2). Study 2 analyses are similarly presented with mean signal values adjusted for SNB status.

Study 2

In these samples, increased osteopontin expression was also associated with reduced relapse-free survival at the P = 0.006 level in unadjusted analyses, with a similar fold change of 1.32 (Table 3). When corrected for age, sex, and tumor site, the significance of the association was P = 0.006. Increased osteopontin expression furthermore was associated with poorer overall survival [hazard ratio, 1.6 (95 confidence interval, 1.1-2.5), P = 0.02] in unadjusted analysis. The fold change between survivors and nonsurvivors was 1.3. Osteopontin remained associated with overall survival after adjusting for age, sex, and tumor site (P = 0.02).

Quantitative real-time reverse transcription-PCR

Quantitative real-time reverse transcription-PCR with probes to exons 1/2 and 5/6 showed that increased expression of osteopontin with fold changes of 1.74 and 1.67, respectively, was associated with reduced relapse-free survival in study 1 (compared with a fold change of 1.55 in the DASL analysis).

Coexpression of genes with osteopontin

The expression of genes most closely correlated with osteopontin expression was studied in the pooled data set for both studies (analysis adjusted for study) and the results are presented in Table 4. We have listed 32 genes whose expression was significantly correlated (either positively or negatively) with that of osteopontin at the 1.0 105 significance level or less and have correlated this further with relapse status. Genes whose upregulation was associated with osteopontin upregulation and with reduced relapse-free survival were BIRC5, IL-8, TK1, HMMR, TOP2A, CCNA2, CDC2, RAD51, NQO1, PTPRH, and MAPK10. We also present a gene network for osteopontin derived using Ingenuity Pathway Analysis (Fig. 1; Table 5). The literature-derived Ingenuity knowledge base identified osteopontin as involved in cell adhesion, cell proliferation, and cell migration. The network shows that osteopontin is the terminal component of many pathways; therefore, overexpression of osteopontin may reflect combined activity in many of these pathways.

Table 4.

Gene expression correlations for osteopontin

GeneCorrelation and P for pooled data setFold change for gene expression between relapsers and nonrelapsersSignificance level for fold changeCorrelation and P for study 1Correlation and P for study 2
PBX1 0.34 (3.1 10110.93 0.07 0.36 (2.9 1060.32 (5.5 106
BIRC5 0.33 (2.0 10101.24 8.81 106 0.40 (2.7 1070.25 (0.0004) 
HLF 0.32 (5.5 10100.81 0.0002 0.35 (7.3 1060.28 (0.00007) 
IL8 0.31 (1.4 1091.26 0.04 0.41 (9.7 1080.22 (0.003) 
HMMR 0.30 (5.8 1091.20 0.004 0.33 (0.00003) 0.27 (0.00009) 
TOP2A 0.29 (1.7 1081.17 0.00001 0.30 (0.0001) 0.28 (0.00007) 
TK1 0.29 (2.0 108 1.17 9.91 107 0.28 (0.0004) 0.30 (0.00002) 
CTSL 0.29 (2.8 1081.06 0.35 0.34 (0.00001) 0.24 (0.0006) 
CCNA2 0.28 (5.6 1081.21 0.00003 0.33 (0.00003) 0.24 (0.0006) 
BCL6 0.28 (8.3 1080.92 0.0007 0.33 (0.00003) 0.24 (0.0007) 
CDC2 0.27 (2.2 1071.29 0.0001 0.31 (0.00008) 0.24 (0.0008) 
RAD51 0.27 (3.3 1071.28 1.03 106 0.29 (0.0002) 0.23 (0.001) 
ERCC5 0.26 (5.1 1070.95 0.08 0.17 (0.03) 0.31 (9.2 106
NQO1 0.26 (5.8 1071.14 0.02 0.28 (0.0005) 0.26 (0.0002) 
CBFA2T1 0.26 (7.6 1070.93 0.22 0.28 (0.0003) 0.24 (0.0007) 
MMP1 0.26 (1.1 1061.11 0.61 0.44 (1.2 1080.13 (0.06) 
PTPRH 0.26 (1.1 1061.24 0.04 0.27 (0.0006) 0.24 (0.0006) 
FGFR2 0.25 (1.2 1060.89 0.05 0.33 (0.00002) 0.17 (0.02) 
EGFR 0.25 (1.2 1060.83 0.01 0.27 (0.0007) 0.23 (0.0009) 
TIMP1 0.25 (1.5 1061.06 0.33 0.18 (0.02) 0.29 (0.00003) 
GAS1 0.25 (1.6 1060.94 0.02 0.27 (0.0006) 0.23 (0.001) 
FLT3 0.25 (1.8 1060.92 0.15 0.24 (0.003) 0.26 (0.0002) 
RBL2 0.25 (2.1 1060.99 0.53 0.27 (0.0007) 0.25 (0.0004) 
ETS2 0.25 (2.5 1060.91 0.003 0.27 (0.0006) 0.20 (0.005) 
NUMA1 0.25 (2.9 1060.97 0.06 0.31 (0.00009) 0.18 (0.01) 
EPHA1 0.24 (4.3 1060.86 0.03 0.23 (0.003) 0.21 (0.002) 
MAP3K8 0.24 (4.7 1060.93 0.06 0.30 (0.0002) 0.20 (0.005) 
VEGF 0.24 (5.0 1061.02 0.50 0.31 (0.0001) 0.20 (0.004) 
CCND3 0.24 (7.5 1060.97 0.04 0.27 (0.0006) 0.21 (0.003) 
AR 0.23 (9.4 1060.83 0.008 0.43 (2.66 1080.15 (0.04) 
FGFR3 0.23 (0.00001) 0.91 0.1 0.24 (0.003) 0.20 (0.006) 
MAPK10 0.23 (0.00001) 1.31 0.01 0.19 (0.02) 0.29 (0.00003) 
GeneCorrelation and P for pooled data setFold change for gene expression between relapsers and nonrelapsersSignificance level for fold changeCorrelation and P for study 1Correlation and P for study 2
PBX1 0.34 (3.1 10110.93 0.07 0.36 (2.9 1060.32 (5.5 106
BIRC5 0.33 (2.0 10101.24 8.81 106 0.40 (2.7 1070.25 (0.0004) 
HLF 0.32 (5.5 10100.81 0.0002 0.35 (7.3 1060.28 (0.00007) 
IL8 0.31 (1.4 1091.26 0.04 0.41 (9.7 1080.22 (0.003) 
HMMR 0.30 (5.8 1091.20 0.004 0.33 (0.00003) 0.27 (0.00009) 
TOP2A 0.29 (1.7 1081.17 0.00001 0.30 (0.0001) 0.28 (0.00007) 
TK1 0.29 (2.0 108 1.17 9.91 107 0.28 (0.0004) 0.30 (0.00002) 
CTSL 0.29 (2.8 1081.06 0.35 0.34 (0.00001) 0.24 (0.0006) 
CCNA2 0.28 (5.6 1081.21 0.00003 0.33 (0.00003) 0.24 (0.0006) 
BCL6 0.28 (8.3 1080.92 0.0007 0.33 (0.00003) 0.24 (0.0007) 
CDC2 0.27 (2.2 1071.29 0.0001 0.31 (0.00008) 0.24 (0.0008) 
RAD51 0.27 (3.3 1071.28 1.03 106 0.29 (0.0002) 0.23 (0.001) 
ERCC5 0.26 (5.1 1070.95 0.08 0.17 (0.03) 0.31 (9.2 106
NQO1 0.26 (5.8 1071.14 0.02 0.28 (0.0005) 0.26 (0.0002) 
CBFA2T1 0.26 (7.6 1070.93 0.22 0.28 (0.0003) 0.24 (0.0007) 
MMP1 0.26 (1.1 1061.11 0.61 0.44 (1.2 1080.13 (0.06) 
PTPRH 0.26 (1.1 1061.24 0.04 0.27 (0.0006) 0.24 (0.0006) 
FGFR2 0.25 (1.2 1060.89 0.05 0.33 (0.00002) 0.17 (0.02) 
EGFR 0.25 (1.2 1060.83 0.01 0.27 (0.0007) 0.23 (0.0009) 
TIMP1 0.25 (1.5 1061.06 0.33 0.18 (0.02) 0.29 (0.00003) 
GAS1 0.25 (1.6 1060.94 0.02 0.27 (0.0006) 0.23 (0.001) 
FLT3 0.25 (1.8 1060.92 0.15 0.24 (0.003) 0.26 (0.0002) 
RBL2 0.25 (2.1 1060.99 0.53 0.27 (0.0007) 0.25 (0.0004) 
ETS2 0.25 (2.5 1060.91 0.003 0.27 (0.0006) 0.20 (0.005) 
NUMA1 0.25 (2.9 1060.97 0.06 0.31 (0.00009) 0.18 (0.01) 
EPHA1 0.24 (4.3 1060.86 0.03 0.23 (0.003) 0.21 (0.002) 
MAP3K8 0.24 (4.7 1060.93 0.06 0.30 (0.0002) 0.20 (0.005) 
VEGF 0.24 (5.0 1061.02 0.50 0.31 (0.0001) 0.20 (0.004) 
CCND3 0.24 (7.5 1060.97 0.04 0.27 (0.0006) 0.21 (0.003) 
AR 0.23 (9.4 1060.83 0.008 0.43 (2.66 1080.15 (0.04) 
FGFR3 0.23 (0.00001) 0.91 0.1 0.24 (0.003) 0.20 (0.006) 
MAPK10 0.23 (0.00001) 1.31 0.01 0.19 (0.02) 0.29 (0.00003) 

NOTE: Fold changes of gene expression between relapsers and nonrelapsers are also presented.

Fig. 1.

Gene network involving SPP1 from Ingenuity in pooled data: cell growth, proliferation, and death (showing only direct interactions, except those involving SPP1). Red, upregulation; green, downregulation.

Fig. 1.

Gene network involving SPP1 from Ingenuity in pooled data: cell growth, proliferation, and death (showing only direct interactions, except those involving SPP1). Red, upregulation; green, downregulation.

Close modal
Table 5.

Correlations and differences in gene expression of genes identified in the Ingenuity network as being linked to osteopontin

GeneCorrelation and P for pooled data setFold change gene expression between relapsers and nonrelapsersSignificance level for fold changeCorrelation and P for study 1Correlation and P for study 2
IL8 0.31 (1.4 1091.26 0.04 0.41 (9.7 1080.22 (0.003) 
CDC25C 0.21 (0.00009) 1.28 0.005 0.18 (0.03) 0.17 (0.02) 
TERT 0.13 (0.02) 1.40 0.0005 0.19 (0.02) 0.09 (0.23) 
RARB 0.11 (0.04) 1.12 0.80 0.07 (0.35) 0.09 (0.20) 
IL3 0.06 (0.25) 1.39 0.009 0.06 (0.42) 0.15 (0.04) 
IL6 0.05 (0.37) 1.27 0.46 0.09 (0.26) 0.01 (0.84) 
E2F5 0.06 (0.25) 1.26 0.004 0.05 (0.50) 0.02 (0.77) 
MCF2 0.02 (0.70) 1.45 0.03 0.01 (0.86) 0.02 (0.79) 
CDKN2B 0.00 (0.93) 0.86 0.004 0.10 (0.21) 0.05 (0.52) 
GeneCorrelation and P for pooled data setFold change gene expression between relapsers and nonrelapsersSignificance level for fold changeCorrelation and P for study 1Correlation and P for study 2
IL8 0.31 (1.4 1091.26 0.04 0.41 (9.7 1080.22 (0.003) 
CDC25C 0.21 (0.00009) 1.28 0.005 0.18 (0.03) 0.17 (0.02) 
TERT 0.13 (0.02) 1.40 0.0005 0.19 (0.02) 0.09 (0.23) 
RARB 0.11 (0.04) 1.12 0.80 0.07 (0.35) 0.09 (0.20) 
IL3 0.06 (0.25) 1.39 0.009 0.06 (0.42) 0.15 (0.04) 
IL6 0.05 (0.37) 1.27 0.46 0.09 (0.26) 0.01 (0.84) 
E2F5 0.06 (0.25) 1.26 0.004 0.05 (0.50) 0.02 (0.77) 
MCF2 0.02 (0.70) 1.45 0.03 0.01 (0.86) 0.02 (0.79) 
CDKN2B 0.00 (0.93) 0.86 0.004 0.10 (0.21) 0.05 (0.52) 

Fortunately, melanoma has a good prognosis in the majority of patients, but advanced disease is extremely difficult to treat. Most chemotherapeutic regimens in use have response rates of 12 to 15 (15); unfortunately, there are no biomarkers in clinical use to identify patients likely to benefit. Poor progress in the development of biomarkers has been at least in part a result of the fact that primary melanomas are physically small, and pathologists are reluctant to cryopreserve tumor. Therefore, we have explored the possibility of using the Illumina DASL assay to produce gene expression profiles from formalin-fixed tumor tissue. The strengths of this study are that it represents much the largest study of gene expression in primary melanoma and benefits from a test sample set and a validation set.

Concerns about the use of fixed tumors derive from the degradation of RNA, which results from delayed time to fixation (16) and time in formalin (17). Increasingly, however, it has been suggested that technical modification can allow profiling of gene expression and microRNA (18,21). The Illumina DASL assay has been shown in other studies to produce comparable results for formalin-fixed and fresh or frozen cells (22, 23). In this article, we report the generation of expression data from 74 of consecutive formalin-fixed melanomas. The paucity of cryopreserved tissue is such that researchers have not been able previously to generate expression data from other than a highly selected proportion of melanoma samples. We have not, in this study, carried out extensive comparisons between results from frozen or fresh and formalin-fixed tumors as previous studies have addressed this (8, 23, 24). Our data suggest that future studies designed to identify predictive or prognostic biomarkers for melanoma would generate results from 75 of samples depending on the age of the blocks and the proportion that were deeply pigmented. We would anticipate that, in predictive biomarker studies, we would achieve results from a higher proportion, as patients undergoing chemotherapy are likely to have larger primary tumors than a significant proportion of the tumors sampled in these studies.

The limitations of this study are related to the presence of a limited number of genes on the DASL Cancer Panel and to sampling of tumors using tissue microarray cores. Using a tissue microarray core does not allow confirmation of the tumor content throughout the core; therefore, there is potentially greater contamination with normal cells than in laser microdissected samples. Furthermore, by using this technique, we were unable to sample very small tumors, so there is a bias toward sampling of larger tumors. The use of this technology, however, has allowed a far greater range of tumors to be examined than in previous research based on cryopreserved tumors. The use of microdissection would address some of these concerns but would be very much more time-consuming for large-scale studies.

Osteopontin was identified as the gene whose increased expression was most strongly associated with reduced relapse-free survival and the validity of this finding was tested by comparison of test and validation sample sets. Quantitative real-time reverse transcription-PCR using probes to the same gene detected similar fold changes associated with relapse. We did not go on to confirm the findings using immunohistochemistry because a large study recently reported that osteopontin staining predicts sentinel node positivity and relapse in melanoma (25). Two small previous studies using cryopreserved tumors also showed a correlation between osteopontin expression and progression in melanoma (26, 27).

Osteopontin is a glycophosphoprotein cytokine with pleiotropic effects. In normal tissues, it plays a role in inflammation, vascular and bone remodeling, and wound repair. It also has a role in cell adhesion, chemotaxis, prevention of apoptosis, invasion, migration, and anchorage-independent growth of tumor cells (28). In terms of inhibition of apoptosis, it is of note that, in our data set, increased expression of osteopontin correlated with increased expression of BIRC5 (survivin; P = 2.0 1010), which was also overexpressed in tumors from patients with poorer relapse-free survival time. Survivin is recognized as a mediator of resistance to apoptosis, increased cell proliferation, and invasiveness in melanoma (29, 30). Osteopontin has a key role in the regulation of cell signaling, which controls neoplastic and malignant transformation and has been identified as a possible drug target (31). It is known to modulate several signaling pathways such as growth factor/receptor pathways via interactions with cell surface receptors such as CD44 and integrins and the metalloproteinases (32, 33). Osteopontin regulates v3 integrinmediated, phosphoinositide 3-kinase/Akt/NF-Bdependent urokinase plasminogen activator and metalloprotein expression, which is associated with tumor cell invasiveness (33). This interrelation between osteopontin and NF-B complex is pictured on the Ingenuity network (Fig. 1). Osteopontin also increases epidermal growth factor receptor activation (34) and is thought to provide the molecular link between degradation of the extracellular matrix, tumor progression, and vascularization (34). In our data set, we saw commensurate increased expression of genes involved in the interaction between tumor cells, the extracellular matrix, and angioneogenesis such as MMP1, IL8, and VEGF.

Increased expression of osteopontin has been shown in several different cancers, and in some, secreted levels in the blood have prognostic value (33, 35). A proportion of melanomas have NRAS mutations (36), and in these, osteopontin transcription may be transcriptionally activated by the RAS oncogene (37). Its expression is also regulated by Wnt/Tcf signaling, steroid receptors, growth factors, Ets, and activator protein-1 transcription factors (38). In our data set, we saw corresponding increased expression of genes involved in cell cycling (CCNA2 and CDC2), DNA replication and repair (TOP2A and RAD51), cell signaling (PTPRH and MAPK10), and cell division and proliferation (BIRC5 and TK1). HMMR is associated with cell motility and the cell cycle and expression levels have been shown to increase with melanoma progression (39). Increased osteopontin expression was associated with reduced expression of the tumor suppressor gene GAS1, which was also underexpressed in tumors from patients who relapsed. GAS1 was recently suggested to be an important tumor suppressor for melanoma (40).

There are in vitro and animal data that suggest a role for osteopontin in melanoma (32), and in a recent large immunohistochemical study of 345 patients, increased osteopontin expression was associated with reduced relapse-free and overall survival and increased probability of sentinel node positivity (25). Our study provides strong corroborative evidence for osteopontin expression as a prognostic biomarker and possibly a drug target in melanoma.

No potential conflicts of interest were disclosed.

Recruitment was facilitated by the UK National Cancer Research Network. We thank the following research staff who collected or managed data over long periods: May Chan, Clarisa Nolan, Susan Leake, Birute Karpavicius, Tricia Mack, Paul King, Sue Haynes, Elaine Fitzgibbon, Kate Gamble, Saila Waseem, Sandra Tovey, Christy Walker, and Paul Affleck.

The following clinicians participated in study J. Lewis, P. Brunyee, C. Walker, Dr. M. Marples, Mr. H. Peach, Leeds; Prof. G.T. Layer, Dr. M.W. Kissin, Dr. M. Green, Dr. E. Wong, Royal Surrey County Hospital, Guildford; J.A. Smallwood, Dr. C. Ottensmeier, Dr. E. Thorne, Dr. G. Theaker, Southampton University Hospital; P. Stanley, Dr. A. Maraveyas, Dr. S. Walton, Dr. A. Roy, Castle Hill Hospital, Hull; Prof. A. Dalgleish, Dr. R. Marsden, Dr. H. Chong, St. Georges Hospital, London.

1
Balch
CM
,
Buzaid
AC
,
Soong
SJ
, et al
. 
Final version of the American Joint Committee on Cancer staging system for cutaneous melanoma
.
J Clin Oncol
2001
;
19
:
3635
48
.
2
Elder
D
,
Murphy
G
. 
Malignant tumors (melanomas and related lesions)
.
Atlas of tumor pathology: melanocytic tumors of the skin
1991
;
2 (third series)
:
103
205
.
3
Cochran
AJ
,
Elashoff
D
,
Morton
DL
,
Elashoff
R
. 
Individualized prognosis for melanoma patients
.
Hum Pathol
2000
;
31
:
327
31
.
4
Kim
J
,
Mori
T
,
Chen
SL
, et al
. 
Chemokine receptor CXCR4 expression in patients with melanoma and colorectal cancer liver metastases and the association with disease outcome
.
Ann Surg
2006
;
244
:
113
20
.
5
Vuoristo
M
,
Vihinen
P
,
Vlaykova
T
,
Nylund
C
,
Heino
J
,
Pyrhonen
S
. 
Increased gene expression levels of collagen receptor integrins are associated with decreased survival parameters in patients with advanced melanoma
.
Melanoma Res
2007
;
17
:
215
23
.
6
Winnepenninckx
V
,
Lazar
V
,
Michiels
S
, et al
. 
Gene expression profiling of primary cutaneous melanoma and clinical outcome
.
J Natl Cancer Inst
2006
;
98
:
472
82
.
7
Kauffmann
A
,
Rosselli
F
,
Lazar
V
, et al
. 
High expression of DNA repair pathways is associated with metastasis in melanoma patients
.
Oncogene
2008
;
27
:
565
73
.
8
Bibikova
M
,
Talantov
D
,
Chudin
E
, et al
. 
Quantitative gene expression profiling in formalin-fixed, paraffin-embedded tissues using universal bead arrays
.
Am J Pathol
2004
;
165
:
1799
807
.
9
Newton Bishop
J
,
Beswick
S
,
Randerson-Moor
J
, et al
. 
Serum 25-hydroxyvitamin D3 levels predict Breslow thickness at presentation and survival from melanoma
.
J Clin Oncol
2009
,
Epub 2009Sept23
.
10
Falchi
M
,
Bataille
V
,
Hayward
NK
, et al
. 
Genome-wide association study identifies variants at 9p21 and 22q13 associated with development of cutaneous nevi
.
Nat Genet
2009
;
41
:
915
9
.
11
Dorrie
J
,
Wellner
V
,
Kampgen
E
,
Schuler
G
,
Schaft
N
. 
An improved method for RNA isolation and removal of melanin contamination from melanoma tissue: implications for tumor antigen detection and amplification
.
J Immunol Methods
2006
;
313
:
119
28
.
12
Eckhart
L
,
Bach
J
,
Ban
J
,
Tschachler
E
. 
Melanin binds reversibly to thermostable DNA polymerase and inhibits its activity
.
Biochem Biophys Res Commun
2000
;
271
:
726
30
.
13
Workman
C
,
Jensen
LJ
,
Jarmer
H
, et al
. 
A new non-linear normalization method for reducing variability in DNA microarray experiments
.
Genome Biol
2002
;
3
:
research0048.1-0048.16
.
14
Schroeder
A
,
Mueller
O
,
Stocker
S
, et al
. 
The RIN: an RNA integrity number for assigning integrity values to RNA measurements
.
BMC Mol Biol
2006
;
7
:
3
.
15
Atkins
MB
. 
The treatment of metastatic melanoma with chemotherapy and biologics
.
Curr Opin Oncol
1997
;
9
:
205
13
.
16
Mizuno
T
,
Nagamura
H
,
Iwamoto
KS
, et al
. 
RNA from decades-old archival tissue blocks for retrospective studies
.
Diagn Mol Pathol
1998
;
7
:
202
8
.
17
Bresters
D
,
Schipper
ME
,
Reesink
HW
,
Boeser-Nunnink
BD
,
Cuypers
HT
. 
The duration of fixation influences the yield of HCV cDNA-PCR products from formalin-fixed, paraffin-embedded liver tissue
.
J Virol Methods
1994
;
48
:
267
72
.
18
Hoefig
KP
,
Thorns
C
,
Roehle
A
, et al
. 
Unlocking pathology archives for microRNA-profiling
.
Anticancer Res
2008
;
28
:
119
23
.
19
Brizova
H
,
Kalinova
M
,
Krskova
L
,
Mrhalova
M
,
Kodet
R
. 
Quantitative measurement of cyclin D1 mRNA, a potent diagnostic tool to separate mantle cell lymphoma from other B-cell lymphoproliferative disorders
.
Diagn Mol Pathol
2008
;
17
:
39
50
.
20
Knudsen
BS
,
Allen
AN
,
McLerran
DF
, et al
. 
Evaluation of the branched-chain DNA assay for measurement of RNA in formalin-fixed tissues
.
J Mol Diagn
2008
;
10
:
169
76
.
21
Linton
KM
,
Hey
Y
,
Saunders
E
, et al
. 
Acquisition of biologically relevant gene expression data by Affymetrix microarray analysis of archival formalin-fixed paraffin-embedded tumours
.
Br J Cancer
2008
;
98
:
1403
14
.
22
Bibikova
M
,
Yeakley
JM
,
Wang-Rodriguez
J
,
Fan
JB
. 
Quantitative expression profiling of RNA from formalin-fixed, paraffin-embedded tissues using randomly assembled bead arrays
.
Methods Mol Biol
2008
;
439
:
159
77
.
23
Ravo
M
,
Mutarelli
M
,
Ferraro
L
, et al
. 
Quantitative expression profiling of highly degraded RNA from formalin-fixed, paraffin-embedded breast tumor biopsies by oligonucleotide microarrays
.
Lab Invest
2008
;
88
:
430
40
.
24
Hoshida
Y
,
Villanueva
A
,
Kobayashi
M
, et al
. 
Gene expression in fixed tissues and outcome in hepatocellular carcinoma
.
N Engl J Med
2008
;
359
:
1995
2004
.
25
Rangel
J
,
Nosrati
M
,
Torabian
S
, et al
. 
Osteopontin as a molecular prognostic marker for melanoma
.
Cancer
2008
;
112
:
144
50
.
26
Zhou
Y
,
Dai
DL
,
Martinka
M
, et al
. 
Osteopontin expression correlates with melanoma invasion
.
J Invest Dermatol
2005
;
124
:
1044
52
.
27
Jaeger
J
,
Koczan
D
,
Thiesen
HJ
, et al
. 
Gene expression signatures for tumor progression, tumor subtype, and tumor thickness in laser-microdissected melanoma tissues
.
Clin Cancer Res
2007
;
13
:
806
15
.
28
Bellahcene
A
,
Castronovo
V
,
Ogbureke
KU
,
Fisher
LW
,
Fedarko
NS
. 
Small integrin-binding ligand N-linked glycoproteins (SIBLINGs): multifunctional proteins in cancer
.
Nat Rev Cancer
2008
;
8
:
212
26
.
29
Raj
D
,
Liu
T
,
Samadashwily
G
,
Li
F
,
Grossman
D
. 
Survivin repression by p53, Rb and E2F2 in normal human melanocytes
.
Carcinogenesis
2008
;
29
:
194
201
.
30
Grossman
D
,
McNiff
JM
,
Li
F
,
Altieri
DC
. 
Expression and targeting of the apoptosis inhibitor, survivin, in human melanoma
.
J Invest Dermatol
1999
;
113
:
1076
81
.
31
Johnston
NI
,
Gunasekharan
VK
,
Ravindranath
A
,
O'Connell
C
,
Johnston
PG
,
El-Tanani
MK
. 
Osteopontin as a target for cancer therapy
.
Front Biosci
2008
;
13
:
4361
72
.
32
Rangaswami
H
,
Kundu
GC
. 
Osteopontin stimulates melanoma growth and lung metastasis through NIK/MEKK1-dependent MMP-9 activation pathways
.
Oncol Rep
2007
;
18
:
909
15
.
33
Tuck
AB
,
Chambers
AF
,
Allan
AL
. 
Osteopontin overexpression in breast cancer: knowledge gained and possible implications for clinical management
.
J Cell Biochem
2007
;
102
:
859
68
.
34
Rangaswami
H
,
Bulbule
A
,
Kundu
GC
. 
Osteopontin: role in cell signaling and cancer progression
.
Trends Cell Biol
2006
;
16
:
79
87
.
35
Rodrigues
LR
,
Teixeira
JA
,
Schmitt
FL
,
Paulsson
M
,
Lindmark-Mansson
H
. 
The role of osteopontin in tumor progression and metastasis in breast cancer
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
1087
97
.
36
Edlundh-Rose
E
,
Egyhazi
S
,
Omholt
K
, et al
. 
NRAS and BRAF mutations in melanoma tumours in relation to clinical characteristics: a study based on mutation screening by pyrosequencing
.
Melanoma Res
2006
;
16
:
471
8
.
37
Guo
X
,
Zhang
YP
,
Mitchell
DA
,
Denhardt
DT
,
Chambers
AF
. 
Identification of a ras-activated enhancer in the mouse osteopontin promoter and its interaction with a putative ETS-related transcription factor whose activity correlates with the metastatic potential of the cell
.
Mol Cell Biol
1995
;
15
:
476
87
.
38
El-Tanani
MK
,
Campbell
FC
,
Kurisetty
V
,
Jin
D
,
McCann
M
,
Rudland
PS
. 
The regulation and role of osteopontin in malignant transformation and cancer
.
Cytokine Growth Factor Rev
2006
;
17
:
463
74
.
39
Ahrens
T
,
Assmann
V
,
Fieber
C
, et al
. 
CD44 is the principal mediator of hyaluronic-acid-induced melanoma cell proliferation
.
J Invest Dermatol
2001
;
116
:
93
101
.
40
Gobeil
S
,
Zhu
X
,
Doillon
CJ
,
Green
MR
. 
A genome-wide shRNA screen identifies GAS1 as a novel melanoma metastasis suppressor gene
.
Genes Dev
2008
;
22
:
2932
40
.

Competing Interests

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Supplementary data