Gene-expression profiling reveals distinct expression patterns for ...

Oncogene (2004) 23, 2732–2742

& 2004 Nature Publishing Group All rights reserved 0950-9232/04 $25.00 www.nature.com/onc

Gene-expression profiling reveals distinct expression patterns for Classic versus Variant Merkel cell phenotypes and new classifier genes to distinguish Merkel cell from small-cell lung carcinoma

ONCOGENOMICS

Mireille Van Gele1, Glen M Boyle2, Anthony L Cook3,5, Jo Vandesompele1, Tom Boonefaes4, Pieter Rottiers4, Nadine Van Roy1, Anne De Paepe1, Peter G Parsons2, J Helen Leonard5 and Frank Speleman*,1 1 Center for Medical Genetics, Ghent University Hospital, Ghent B-9000, Belgium; 2Melanoma Genomics Group, Queensland Institute of Medical Research, Brisbane, Queensland 4006, Australia; 3Institute for Molecular Biosciences, University of Queensland, Brisbane, Queensland 4072, Australia; 4Department of Molecular Biomedical Research, Flanders Interuniversity Institute for Biotechnology and University of Ghent, Ghent B-9000, Belgium; and 5Queensland Radium Institute Research Unit, Queensland Institute of Medical Research, Brisbane, Queensland 4006, Australia

Merkel cell carcinoma (MCC) is a rare aggressive skin tumor which shares histopathological and genetic features with small-cell lung carcinoma (SCLC), both are of neuroendocrine origin. Comparable to SCLC, MCC cell lines are classified into two different biochemical subgroups designated as ‘Classic’ and ‘Variant’. With the aim to identify typical gene-expression signatures associated with these phenotypically different MCC cell lines subgroups and to search for differentially expressed genes between MCC and SCLC, we used cDNA arrays to profile 10 MCC cell lines and four SCLC cell lines. Using significance analysis of microarrays, we defined a set of 76 differentially expressed genes that allowed unequivocal identification of Classic and Variant MCC subgroups. We assume that the differential expression levels of some of these genes reflect, analogous to SCLC, the different biological and clinical properties of Classic and Variant MCC phenotypes. Therefore, they may serve as useful prognostic markers and potential targets for the development of new therapeutic interventions specific for each subgroup. Moreover, our analysis identified 17 powerful classifier genes capable of discriminating MCC from SCLC. Real-time quantitative RT–PCR analysis of these genes on 26 additional MCC and SCLC samples confirmed their diagnostic classification potential, opening opportunities for new investigations into these aggressive cancers. Oncogene (2004) 23, 2732–2742. doi:10.1038/sj.onc.1207421 Published online 2 February 2004 Keywords: Merkel cell carcinoma; small-cell lung carcinoma; differential expression profiling; differential diagnosis; Classic/Variant

*Correspondence: F Speleman; E-mail: [email protected] Received 26 August 2003; revised 17 November 2003; accepted 2 December 2003

Introduction Merkel cell carcinoma (MCC) is a rare aggressive skin tumor and is assumed to arise from normal Merkel cells. Merkel cells are neuroendocrine in origin, expressing markers such as neuron-specific enolase and bombesin. These cells are located in the basal layer of the epidermis, where they often function as slow-acting mechanoreceptors (Halata et al., 2003). MCC mostly affects elderly people and occurs predominantly on the sun-exposed areas of the skin, suggesting UV exposure in its etiology (Miller and Rabkin, 1999; Van Gele et al., 2000; Popp et al., 2002). In a previous study, we have determined by comparative genomic hybridization (CGH) the patterns of genomic imbalances which occur in MCC (Van Gele et al., 1998). Interestingly, the observed under- and overrepresentations of partial chromosomal regions were quite similar to those observed in small-cell lung carcinoma (SCLC) (Ried et al., 1994; Levin et al., 1995; Petersen et al., 1997; Van Gele et al., 1998). Both are neuroendocrine tumors with remarkable histopathological similarities, that is, small round cells often containing dense core granules and expressing several identical immunohistochemical markers (Ratner et al., 1993; Metz et al., 1998; Schmidt et al., 1998; Goessling et al., 2002). Therefore, the possibility exists that they arise from a common cell lineage. Further support for this hypothesis comes from the fact that cell lines derived from MCC and SCLC resemble each other in their biochemical, morphological and growth characteristics. Similar to SCLC, MCC cell lines are classified into two groups: ‘Classic’ and ‘Variant’ defined on their biochemical markers and neurosecretory granule status, which are further subdivided into four subtypes (I–IV) based on morphology, colony shape and aggregation (Carney et al., 1985; Gazdar et al., 1985; Leonard et al., 1993, 1995a, 2002; Leonard and Bell, 1997). At present, little is known about the genes associated with these characteristics.

Gene-expression profiling of MCC and SCLC MV Gele et al

2733

In contrast to MCC, numerous molecular genetic studies have been performed on SCLC which have contributed to the understanding of SCLC pathogenesis (Fong et al., 1999; Minna et al., 2002). In addition, histochemical markers and differentially expressed genes distinguishing Classic from Variant SCLC cell lines have been identified and could lead to an improved understanding of the underlying genetic basis responsible for the biological and clinical heterogeneity among smallcell lung cancers (Broers et al., 1985; Zhang et al., 2000). In order to obtain further insights into the complex and heterogeneous molecular pathogenesis of MCC, we determined the gene-expression profiles of 10 MCC cell lines and four SCLC cell lines using Atlas cDNA arrays containing 1891 unique genes involved in many cellular functions. This study offers potential insights into the genes and signalling pathways involved in MCC and SCLC, a prerequisite for the development of new rational therapeutic interventions, which could lead to an improved patient survival or even complete remission. Furthermore, we identified genes not previously implicated in these cancers, whose expression enabled discrimination between MCC and SCLC and may therefore aid in the differential diagnosis of cases where existing markers such as cytokeratin 20 are unable to differentiate between these two neuroendocrine cancers.

Results Validation of atlas cDNA array data The expression level of 1891 genes was measured by the use of Atlas Human and Human Cancer 1.2 arrays for 10 MCC and four SCLC cell lines. After primary data analysis and normalization, the data of 1083 genes which had an expression value above background in at least six of the analysed samples were used for hierarchical clustering. Of these 1083 genes, 206 genes were present on both the Human and Human Cancer 1.2 arrays and a high correlation was found between the expression levels of these common genes for each sample (mean Spearman rank correlation coefficient ¼ 79.3%). Reliability and reproducibility of the array gene-expression data were further supported by (a) comparison of the array gene-expression levels and real-time RT–PCR expression levels for 25 selected genes (mean Spearman rank correlation coefficient ¼ 76.1%) (see below), (b) a highest degree of similarity evidenced by hierarchical clustering for expression patterns of cell lines MCC14/1 and MCC14/2, derived from the same tumor (see Figure 1b) and (c) confirmation of the differential gene expression for ASCL1 in SCLC versus MCC, as reported in the literature (see Discussion). Hierarchical clustering analysis of MCC and SCLC cell lines Hierarchical clustering was used to identify similarities in gene-expression patterns between MCC and SCLC cell lines. Clustering of the 14 samples was based on the

Figure 1 (a) Scaled-down representation of the hierarchical cluster diagram of 1083 selected genes and 10 MCC cell lines and four SCLC cell lines. A row in the cluster indicates expression of a specific gene across all the 14 samples. A column indicates the sample in which the gene is expressed. The color scale (Expression Index) shown at the bottom (3 to þ 3 in log base 2 units) indicates that the relative expression level of the gene is greater, less than or equal to the geometric mean expression across all 14 samples, respectively. (b) Dendrogram representing similarities in the expression patterns between experimental samples from Figure 1a. The symbols below the sample names reflect the different (sub)groups of tumor cell lines (circle: Variant MCC; asterix: Classic MCC; square: SCLC). (c) Real-time-based hierarchical cluster analysis of MCC cell lines and tumor samples for nine genes, which can distinguish between the Classic and Variant MCC subtypes (asterix: Classic MCC cell lines; square: MCC tumors; circle: Variant MCC cell lines. (d) Average linkage hierarchical cluster analysis of MCC and SCLC cell lines and tumor samples for 14 genes identified by SAM as able to differentiate between MCC (1, asterix) and SCLC (2, square) and quantified by real-time PCR. Same color scale as above for both figures, but expressed in log 10 base units

gene-expression levels of the 1083 preselected genes. Figure 1a shows the complete cluster diagram. The dendrogram (Figure 1b) summarizes the degree of similarity in gene expression among the 14 analysed Oncogene


2734

samples. Two major subgroups were observed. Except for MCC cell line UISO, group 1 contained all Variant MCC cell lines (circle:MCC26, MCC13, MCC14/2 and MCC14/1 (see Table 1)), indicating that these samples resemble each other in their gene-expression patterns. Group 2 included UISO, and all Classic MCC cell lines (asterix:MKL-2, MKL-1, T95-45, MCC6 and MCC5) which were mixed with all the four SCLC cell lines (square), of which GLC4 was the only Variant one (see Table 1). From this analysis, we concluded that clustering of MCC cell lines into different subgroups predominantly coincided with the Classic or Variant phenotypes of the respective cell lines. However, hierarchical clustering of the 1083 preselected genes could not unequivocally separate MCC cell lines from SCLC cell lines. This does, however, further support a putative ontogenetic relationship for both tumors.

median ‘false discovery rate’ (FDR) of 2.5%, which means that there are about six false positives on average. Highly differentially expressed significant genes were further selected if a differential expression pattern (4two-fold difference) was present in at least four of the five Classic MCC cell lines as compared to the Variant ones and vice versa. In all, 46 genes with a relative elevated expression in MCC Classic cell lines and 30 genes with an increased expression level in the Variant MCC cell lines were identified (see Table 2). Hierarchical cluster analysis of these 76 genes clearly classified the MCC cell lines in their respective phenotypic groups (data not shown). Genes with kinase activities but also genes encoding for ligand and voltagegated ion channels, neuromediators, GDP/GTP exchangers and signal-transduction receptors were seen at higher expression levels in Classic MCC cell lines relative to Variant ones. Genes with a higher expression in Variant cell lines compared to Classic ones included genes involved in cell cycle control and proliferation (see Table 2).

Identification of differentially expressed genes in Classic versus Variant MCC cell lines Unsupervised analysis of array data enables coherent patterns of gene expression to be identified, but provides little information about the statistical significance. Therefore, we decided to exploit a supervised strategy in order to identify a specific set of genes whose expression pattern could discriminate Classic versus Variant MCC cell lines. To this purpose, Classic (n ¼ 5) and Variant (n ¼ 5) MCC cell lines were predefined as the two sample groups. Subsequently, a two-class SAM analysis on the log transformed data matrix containing 1365 genes (see Material and methods) was performed. A cutoff value delta, depending on an arbitrary falsepositive rate, was chosen to identify significantly differentially expressed genes. For this analysis, a delta value of 0.90572 was used. This led to the identification of a total of 239 differentially expressed genes with a

Identification of differentially expressed genes in MCC versus SCLC Hierarchical clustering analysis performed in this study showed a high degree of similarity between MCC and SCLC. We were interested, however, to search for genes which could distinguish between the two tumor types. Therefore, the same supervised strategy as outlined above was applied to identify genes differentially expressed in MCC versus SCLC cell lines. Due to the small gene-expression level variances between MCC and SCLC, as a result of their similarity, we had to choose a rather low delta (D ¼ 0.33878) in order to include as many genes as possible with significant but sometimes small expression differences in MCC versus SCLC and

Table 1 Characteristics of the MCC and SCLC cell lines used for cDNA array hybridization Morphological type

Colony shape

Colony aggregation

Classificationa

MCC cell lines MCC5 MCC6 MCC13 MCC14/1 MCC14/2 MCC26 UISO MKL-1 MKL-2 T95-45

I I IV IV IV IV IV III III II 3-d

3-db 3-d Flat Flat Flat Flat Flat 2-d 2-d Loose

Tight Tight NAc NA NA NA NA Loose Loose Classic

Classic Classic Variant Variant Variant Variant Variant Classic Classic

SCLC cell lines NCI-H69 NCI-H146 COR-L88 GLC4

II II III III

3-d 3-d 2-d 3-d

Loose Loose Loose Loose

Classic Classic Classic Variant

Cell line

a Classic MCC cell lines express neuroendocrine markers including neuron-specific enolase and Chromogranin A, and contain neurosecretory granules (Leonard et al., 1993). Variant MCC cell lines have a selective loss of neuroendocrine markers including Chromogranin A, and do not contain neurosecretory granules, as evidenced by electron microscopy (Leonard et al., 1995a). Classic SCLC cell lines express levels of L-dopa decarboxylase and bombesin while Variant ones have undetectable levels of L-dopa decarboxylase and bombesin (Carney et al., 1985; Gazdar et al., 1985). b3-d; three-dimensional clusters, 2-d; two-dimensional clusters. cNot applicable, adherent growing cell lines.

Oncogene


2735 Table 2 Spota

GB Accb

Classic specific genes C_E09m D78345

Symbol

List of Classic and Variant MCC classifier genes Gene/protein identity

C_E08k C_D02h H_D01d H_D08e H_D07h H_F06k H_B13d C_E09h C_A10n H_A13n

U78095 U96136 X53179 J05252 L19761 X51405 D10924 AF003521 U72649 U07139

SPINT2 CTNND2 CHRNB2 PCSK2 SNAP25 CPE CXCR4 JAG2 BTG2 CACNB3

H_B05f C_F09a H_D01g C_B10h C_A04m C_D09j C_B13d C_C12k H_C05b H_B14a H_C14e C_B02f C_C02l C_F13b C_B08i C_C02f H_C12k C_F13n C_D04l

X74979 U86759 Y00757 L42374 X80343 AF011466 L07597 U66197 L05500 L14595 M23410 D50925 Y11416 L11931 U27193 L07540 L09561 U96876 Y08110

DDR1 NTN2L SGNE1 PPP2R5B CDK5R1 EDG4 RPS6KA1 FGF12 ADCY1 SLC1A4 JUP PASK TP73 SHMT1 DUSP8 RFC5 POLE INSIG1 LR11

C_C03b H_D07g C_A11f C_B13l H_B03m

U77352 AF040255 AF006484 D84064 X80907

MADD DCX CDK2AP1 HGS PIK3R2

H_E12f H_A05k C_C08e H_C13k H_C08g C_D11e

U04810 U40343 U86529 D38073 M93426 U24497

TROAP CDKN2D GSTZ1 MCM3 PTPRZ1 PKD1

H_C05e C_E05h H_B01a H_A07n H_A08i

X70326 X61157 X91906 U69883 X60188

MLP ADRBK1 CLCN5 KCNN1 MAPK3

Membrane-bound and secreted immunoglobulin gamma heavy chain Kunitz-type serine protease inhibitor 2 Delta catenin Cholinergic receptor nicotinic beta polipeptide 2 Neuroendocrine convertase 2 25-kDa synaptosomal-associated protein Carboxypeptidase H CXC chemokine receptor type 4 Jagged homolog 2 NGF-inducible anti-proliferative protein PC3 Dihydropyridine-sensitive L-type calcium channel beta-3 subunit Epithelial discoidin domain receptor 1 Netrin-2 Secretory granule endocrine protein I Protein phosphatase 2A B56-beta Cyclin-dependent kinase 5 activator G protein-coupled receptor EDG4 Ribosomal protein S6 kinase II alpha 1 FHF-1 Adenylate cyclase type I Neutral amino-acid transporter A Junction plakoglobin KIAA0135 p73 Cytosolic serine hydroxylmethyltransferase Dual-specificity protein phosphatase 8 Replication factor C 36-kDa subunit DNA polymerase II subunit A Insulin-induced protein 1 Low-density lipoprotein receptor-related protein LR11 MAP kinase-activating death domain protein Doublecortin Putative oral tumor-suppressor protein HRS Phosphatidylinositol 3-kinase regulatory beta subunit Trophinin-associated protein Cyclin-dependent kinase 4 inhibitor 2D Glutathione transferase zeta 1 MCM3 DNA replication licensing factor Protein-tyrosine phosphatase zeta Autosomal dominant polycystic kidney disease protein 1 MARCKS-like protein Beta-adrenergic receptor kinase 1 Chloride channel protein 5 Calcium-activated potassium channel HSK1 Mitogen-activated protein kinase 3

VIM FOSL1 AXL CCND1 TIMP1 CASP4 MMP11 CAPN2 CSDA AKAP12 ELK3 ITGB8 HSPD1 SPARC FGFR1 GSTTLp28

Vimentin FOS-like antigen 1 axl oncogene G1/S-specific cyclin D1 Tissue inhibitor of metalloproteinase 1 Caspase 4 Matrix metalloproteinase 11 M-type calcium-activated neutral proteinase Cold shock domain protein A Gravin ets domain protein elk-3 Integrin beta 8 Heat shock 60-kDa protein Secreted protein acidic and rich in cysteine Fibroblast growth factor receptor 1 Glutathione-S-transferase-like protein

Variant specific genes C_F04g X56134 H_A07d X16707 H_A05e M76125 H_A03h X59798 H_F05n X03124 H_C06h U28014 H_A11g X57766 H_C14h M23254 H_E03f M24069 C_F09g M96322 H_D10j Z36715 H_E09g M73780 H_F03b M34664 C_F14a J03040 H_E13k X66945 C_D09m U90313

IGHG3

Fold chc 100

Chrom locd

Core

P-value

14q32.33

ND

ND

19q13.1 5p15.2 1q21.3 20p11.2 20p12–11.2 4q32.3 2q21 14q32 1q32 12q13

83.5 ND ND ND ND ND ND ND 93.4 ND

3.80E-04 ND ND ND ND ND ND ND 1.03E06 ND

8.33 8.33 8.33 8.33 7.69 7.69 7.69 7.14 7.14 6.67 6.67 6.67 6.67 6.67 6.25 6.25 6.25 5.89 5.56

6p21.3 16p13.3 15q13–14 11q12 17q11.2 19p12 3 3q28 7p13–12 2p15–13 17q21 2q37.3 1p36.33 17p11.2 11p15.5 12q24.2–24.3 12q24.3 7q36 11q23.2–24.2

ND ND ND ND ND ND ND ND ND ND 94.5 ND ND ND ND ND ND ND ND

ND ND ND ND ND ND ND ND ND ND 1.12E06 ND ND ND ND ND ND ND ND

5.27 5.26 4.76 4.55 4.55

11p11.2 Xq22.3–23 12q24.31 17q25 19q13.2–13.4

ND ND ND ND ND

ND ND ND ND ND

4.55 4.35 4 4 4 3.57

12p11.1 19p13 14q24.3 6p12 7q31.3 16p13.3

ND ND ND ND ND ND

ND ND ND ND ND ND

3.33 3.33 2.86 2.7 2.38

1p34.3 11q13 Xp11.23–11.22 19p13.1 16p12–11.2

75.8 ND ND ND ND

2.67E03 ND ND ND ND

42.71 25.05 21.14 16.56 16.09 10.57 9.15 9.13 8.31 7.85 7.59 7.32 7.19 7.04 6.78 6.67

10p13 11q13 19q13.1 11q13 Xp11.3–11.23 11q22.2–22.3 22q11.23 1q41–42 12p13.1 6q24–25 12q23 7p21.3 2q33.1 5q31.3–32 8p11.2–11.1 10q24.33

90.8 80.8 90.3 83.9 ND ND ND ND ND ND ND ND ND ND ND ND

7.30E06 8.39E04 9.56E06 2.17E04 ND ND ND ND ND ND ND ND ND ND ND ND

50 38.46 25 20 20 11.11 11.11 11.11 10 9.09

Oncogene


2736 Table 2 a

Spot

b

GB Acc

Symbol

H_B12g

U48959

MYLK

C_B10c H_F02g

L31951 U10117

MAPK9 SCYE1

C_C10b C_E03i H_B11e H_D08i H_F02l H_A02d C_A05i H_A12c C_E11l H_E13g H_F07k

U60520 AF031385 M59371 M13667 D00760 J04101 M25753 V00568 M34671 D13866 X87212

CASP8 CYR61 EPHA2 PRNP PSMA2 ETS1 CCNB1 MYC CD59 CTNNA1 CTSC

(continued )

Gene/protein identity Smooth muscle and nonmuscle myosin light chain kinase Mitogen-activated protein kinase 9 Endothelial-monocyte activating polypeptide II Caspase 8 Cysteine-rich anigogenic inducer 61 Ephrin type-A receptor 2 Major prion protein Proteasome subunit alpha type 2 ets1 proto-oncogene G2/mitotic-specific cyclin B1 myc proto-oncogene CD59 glycoprotein Alpha1 catenin Cathepsin C

Fold chc

Chrom locd

Core

P-value

6.18

3q21

ND

ND

6.16 6.12

5q35 4q24

ND ND

ND ND

5.74 4.97 4.87 4.66 4.23 3.93 3.83 3.67 3.64 3.53 3.33

2q33–34 1p31–22 1p36 20pter-p12 7p13–12 11q23.3 5q12 8q24.12–24.13 11p13 5q31 11q14.1–14.3

ND ND ND ND ND ND ND 94.7 ND ND ND

ND ND ND ND ND ND ND 1.00E06 ND ND ND

a Spot location of each gene on either the Atlas Human (H) or Cancer (C) arrays. bGenBank accession number. cFold change was calculated by dividing the mean expression level of all Classic MCC cell lines to the mean expression level of all Variant MCC cell lines for Classic specific genes (and vice versa for Variant specific genes). dChromosomal location of each gene. eExpression of a number of genes was confirmed by real-time quantitative RT–PCR. The Spearman rank correlation coefficient (%) was calculated between the array gene-expression levels and the real-time RT–PCR levels. ND, not done.

vice versa. This led to the identification of a total of 121 differentially expressed genes with a median FDR of 27.9%, that is, 33.8 false-positive genes on average. In addition, the 121 SAM-identified genes were further selected as highly differentially expressed significant genes in MCC versus SCLC if a differential expression pattern (4two-fold difference) was present in at least four of the MCC cell lines as compared to SCLC or in at least three of the four SCLCs as compared to MCC. In this way, the use of a low delta value merged with a relatively high FDR was justified. Subsequently, 12 and initially seven genes showed higher and lower expression levels respectively in MCC versus SCLC (see Table 3 and footnote f). Genes more highly expressed in MCC included, for example, brain-derived neurotrophic factor, heat shock-related 70-kDa protein 2, neurogranin and intergrin alpha 3. In three of the four SCLC cell lines, elevated levels of the neuronal differentiation marker ASCL1 (achaete-scute homolog 1), the basic helix– loop–helix transcription factor ID2 (inhibitor of DNAbinding protein 2) and GPX2 (glutathione peroxidaserelated protein 2) were observed relative to those seen in MCC cell lines. The lower expression level of ASCL1 in cell line GLC4 was of interest, as this was the only Variant SCLC cell line examined. All others had a Classic phenotype (see Table 1) consistent with ASCL1 being required for the neuroendocrine phenotype of SCLC (Borges et al., 1997). A complete list of SAM genes differentially expressed between MCC and SCLC is given in Table 3. Two genes, FLT1 and EGR1 (Table 3, footnote f), were however excluded from further analysis, as differences in array gene-expression levels were not confirmed by real-time quantitative RT– PCR analysis. The classification potential of the remaining 17 differentially expressed genes between MCC and SCLC was visualized after re-clustering. Each cell line clearly clustered into either the MCC or SCLC subgroup (data not shown). The low number of classifier Oncogene

genes being identified in this analysis illustrated the high degree of similarity at the RNA expression level between MCC and SCLC, and further supports a putative ontogenetic relationship between both tumor types. Validation of array gene-expression levels and classification of additional MCC and SCLC cell lines/ tumor samples by real-time RT–PCR analysis To verify the array gene-expression levels by an independent and sensitive method, we performed realtime quantitative RT–PCR on the same RNA samples of the 14 cell lines used for filter array analysis. In all, 25 SAM selected genes were quantified. Of these, 16 genes (ASCL1, GPX2, ID2, TFAP4, FLT1, IGFBP2, PRKCA, ITGA3, BDNF, ILK, PRKR, CDC25B, EGR1, CHD2, MAP2K3 and HSPA2) were previously selected by their ability to distinguish between MCC and SCLC (see Table 3). The other nine genes selected arbitrarily from the SAM list (SPINT2, MYC, AXL, CCND1, JUP, BTG2, FOSL1, VIM and MLP) had Classic versus Variant classification capability (see Table 2). In general, the quantitative RT-PCR data correlated very well with the array hybridization data (see Tables 2 and 3). Figures 2a and b illustrate relative real-time RT–PCR data and array hybridization data for the genes ASCL1 and IGFBP2, with Spearman rank correlation coefficients of 70.5% (P-value ¼ 4.82E03) and 92.1% (P-value ¼ 2.98E06), respectively. Average linkage hierarchical cluster analysis of 12 MCC cell lines and 10 MCC tumors for nine SAM genes with phenotypic classification potential and quantified by real-time PCR resulted in separation of all (adherent) Variant MCC cell lines, all but one Classic MCC cell line (MCC5) and nine of the 10 MCC tumors (Figure 1c). These results illustrate that, even with a limited selected set of differential genes, a distinction between Classic and Variant MCC cell lines can easily


2737 Table 3 Spota

GB Accb

List of MCC and SCLC classifier genes

Symbol

Gene/protein identity

Fold chc

Chrom locd

Core

P-value

MCC specific H_F09f H_F04a H_C06c H_E06i H_B03i H_C06j H_B12h H_A01k H_E06h H_A08j H_B03g C_A11l

genes M61176 L26336 Y09689 M59911 M22199 M35663 L36719 D84212 M34064 D88435 U40282 M81934

BDNF HSPA2 NRGN ITGA3 PRKCA PRKR MAP2K3 STK6 CDH2 GAK ILK CDC25B

Brain-derived neurotrophic factor Heat shock-related 70-kDa protein 2 Neurogranin Integrin alpha 3 Protein kinase C alpha polypeptide Interferon-inducible RNA-dependent protein kinase Mitogen-activated protein kinase kinase 3 Aurora-related kinase 1 Cadherin 2 Cyclin G-associated kinase Integrin-linked kinase Cell division cycle 25 homolog B

16.67 11.11 7.69 7.14 6.25 4.17 3.7 3.57 2.94 2.86 2.5 2.44

11p13 14q24.1 11q24 17q21.32 17q22–23.2 2p22–21 17q11.2 20q13.2–13.3 18q11.2 4p16 11p15.5–15.4 20p13

92.9 95.1 ND 85.9 75.9 89.9 80.7 ND 83.3 ND 60.9 82.9

4.61E06 2.04E06 ND 8.20E05 1.67E03 1.24E05 4.91E04 ND 2.17E04 ND 2.09E02 2.51E04

SCLC specific H_D01h H_F06a H_D11m C_A08f H_E04d

genesf L08424 X53463 M97796 M35410 S73885

ASCL1 GPX2 ID2 IGFBP2 TFAP4

Achaete-scute homolog 1 Glutathione peroxidase-related protein 2 Inhibitor of DNA binding 2 protein Insulin-like growth factor-binding protein 2 AP4 basic helix–loop–helix DNA-binding protein

70.72 10.43 5.06 3.33 2.68

12q22–23 14q24.1 2p25 2q33–34 16p13

70.5 83.6 87.7 92.1 78.9

4.82E03 1.33E03 3.83E05 2.98E06 7.95E04

a Spot location of each gene on either the Atlas Human (H) or Cancer (C) arrays. bGenBank accession number. cFold change was calculated by dividing the mean expression level of all MCC cell lines to the mean expression level of all SCLC cell lines for MCC-specific genes (and vice versa for SCLC specific genes). dChromosomal location of each gene. eExpression of a number of genes was confirmed by real-time quantitative RT–PCR. The Spearman rank correlation coefficient (%) was calculated between the array gene-expression levels and the real-time RT–PCR levels. ND, not done. fInitially identified SAM genes FLT1 and EGR1 were excluded for further analysis, as their array gene-expression levels were not confirmed by real-time RT–PCR analysis (Cor ¼ 31.0%; P-value ¼ 2.81E01 and Cor ¼ 14.2%; P-value ¼ 6.26E01, respectively).

be made. Originally, the grouping of Classic and Variant phenotypes for MCCs was cell line based (Leonard et al., 1993; Leonard et al., 1995a, 2002; Leonard and Bell, 1997). However, we have observed concordant results between tumor samples and their respective cell lines in several genomic deletion analyses (Leonard and Hayard, 1997; Leonard et al., 2000; Van Gele et al., 2000; Cook et al., 2001) and in immunohistochemical studies. In particular, the transcription factor HATH1, shown to be expressed in normal Merkel cells and in Classic MCC cell lines was also expressed only in those biopsies from which Classic MCC cell lines, were derived (Leonard et al., 2002). In the present study, almost all MCC tumors clustered in one group which showed a higher degree of similarity with the Classic cell lines compared to the Variant cell lines, although they shared some gene-expression features common to both cell line groups. Given the concordance previously seen between tumors and their respective cell lines, these groupings are likely to have clinical significance and only further studies on additional tumors would determine if those examined could be typed as having a ‘Classic’ phenotype. It should be mentioned that cell line MCC19 was recently thought to be a Variant MCC suspension cell line based on the lack of HATH1 expression (Leonard et al., 2002). However, MCC19 still expresses, similar to Classic MCC suspension cell lines, the transcription factor Brn-3c (Leonard et al., 2002) and Chromogranin A, a neuroendocrine marker. Therefore, it is probably not surprising that MCC19 is outlying the Variant cluster group, but shows instead a high degree of similarity with Classic MCC suspension cell lines such as

MKL-1 (Figure 1c). Although PCR-based hierarchical clustering of the nine selected SAM genes analysed in this study could not classify MCC19 as a separate third biological MCC subgroup, as suggested by HATH1 reactivity (Leonard et al., 2002), quantitative PCR analysis of a larger panel of differentially expressed genes followed by clustering with an extended number of Variant MCC suspension cell lines may enable this to occur. In order to confirm the classification strength of the SAM selected genes (MCC versus SCLC), real-time RT– PCR analysis was extended to two further MCC cell lines, 10 MCC tumor samples, 12 additional SCLC cell lines and two SCLC tumors for ASCL1, GPX2, ID2, TFAP4, IGFBP2, PRKCA, ITGA3, BDNF, ILK, PRKR CDC25B, CHD2, MAP2K3 and HSPA2. Average linkage hierarchical cluster analysis based on the Spearman rank correlation coefficient as a similarity measure showed two major clusters (Figure 1d). Except for one SCLC cell line (NCI-N464), cluster 1 contained all MCC cell lines and MCC tumor samples. Cluster 2 consisted of all the remaining SCLC cell lines and SCLC tumors. Real-time PCR-based gene-expression profiling therefore resulted in an almost perfect classification of the different tumors or cell lines into their respective tumor groups. In addition, the cell lines MCC14/1 and MCC14/2 established from the same tumor sample remained clustered next to each other in a subgroup with other Variant MCC cell lines. The cell lines MKL-1 and MKL-1 clone 2, also derived from a same patient, were found in different subclusters. The MKL-1 clone 2 has, however, been grown separately from MKL-1 for a long time, and has clonally evolved, apart from Oncogene


2738

Figure 2 Histogram comparing relative real-time PCR expression levels (gray bars) and array hybridization levels (white bars) of (a) ASCL1 and (b) of IGFBP2 in MCC and SCLC cell lines (ordinate, log10 space). The normalized expression level of each gene was divided by its geometric mean across all 14 samples. The Spearman rank correlation coefficient (Sp r) for both genes is indicated on the histogram

becoming karyotypically unrelated to MKL-1 (Rosen et al., 1987; Van Gele et al., 2002). The value of using cell lines for gene-expression profiling and validation of differentially expressed genes for tumor types such as SCLC where tumor material is very difficult to obtain in sufficient amount or numbers has recently been demonstrated in a global gene expression analysis by Pedersen et al. (2003). Therefore, we believe that, in this study, the use of a limited number of available SCLC tumors combined with a larger number of SCLC cells in culture for validation of SCLC classifier genes is justified and reliable. Discussion In this study, expression profiling of 10 MCC and four SCLC cell lines was performed through analysis of 1891 unique genes. Hierarchical clustering was used in a first general attempt to assess the classification power of the obtained expression data set. Cluster analysis of 1083 preselected genes allowed the MCC cell lines to segregate into two different subgroups mainly associated Oncogene

with their Classic or Variant phenotypes. On the other hand, this analysis could not distinguish between MCC and SCLC, emphasizing their biological/genetic relationship. Therefore, we adopted a supervised data-mining strategy, that is, two-class SAM analysis, in order to identify (a) phenotypic classifier genes which allow to separate Classic from Variant MCC cell lines and (b) diagnostic classifier genes which may aid in the differential diagnosis of MCC and SCLC. This led to the identification of 76 highly differentially expressed significant genes, of which 46 showed higher expression in the Classic cell lines and 30 were more highly expressed in the Variant MCC cell lines. A subset of genes with higher levels of expression in the Classic cell lines are involved in signal-transduction pathways, and could lead to increased cell growth when overexpressed. This is exemplified by genes such as MAPK3 and MADD involved in the mitogen-activated protein (MAP) kinase pathway, and genes such as PI3-K p85 beta in the phosphatidylinositol 3-kinase (PIK3) pathway. In addition, Classic cell lines showed higher levels of expression of genes coding for neuromediators (SGNE1) and neurotransmittors (PCSK2), and proteins involved in neuronal development such as doublecortin (DCX) and MARCKS-like protein (MLP). This is in keeping with the observed neuroendocrine and more differentiated phenotypes associated with the Classic MCC cell lines. Ligand and voltage-gated ion channels and receptors essential for neurotransmission were also expressed in the Classic cell lines. Some of these ion channels are known to play a role during mechanical stimulation of normal Merkel cell receptors (Baumann et al., 2000; Tazaki et al., 2000). Variant MCC cell lines could have lost expression of some of these ion channels. Their specific function in MCC tumor cells, however, has yet to be elucidated. Genes with higher expression in Variant cell lines were involved in cell cycle control (CCND1 and CCNB1) and cell proliferation (HSP60, MMP11, MAPK9, FOSL1, AXL, MYC and ETS1). Some of these genes may correlate with the shorter doubling time and aggressive nature of the Variant MCC cell lines, as illustrated by their higher cloning efficiency in soft agar and their reduced sensitivity to radiation (Leonard et al., 1995b). In addition, we observed high expression of vimentin, a mesenchymal marker, together with FOSL1 (alias FRA1, FOS-related antigen 1). A tight correlation of vimentin and FOSL1 expression was also recently found in highly invasive breast cancer cell lines, pointing to a possible role in tumor progression and enhanced cell migration of these cancer cells (Zajchowski et al., 2001). These two genes could be significant prognostic markers for the more aggressive MCC Variant types. Increased expression of vimentin was also observed by immunohistochemical studies in Variant SCLC cell lines (Broers et al., 1985, 1986), and as a result of a suppression subtractive hybridization experiment comparing a Classic to a Variant SCLC cell line (Zhang et al., 2000). These observations could point at a similar mechanism of tumor progression or metastatic properties between MCC and SCLC Variant phenotypes.


2739

To extend and validate the information from the SAM analysis data set, nine genes able to differentiate between Classic and Variant cell lines were arbitrarily selected from the list and quantified by real-time PCR in 12 MCC cell lines and 10 other MCC tumors. Cluster analysis of the PCR results confirmed the separation of the phenotypically different MCC cell lines, and illustrates that, even with a limited selected set of differential genes, a distinction between Classic and Variant MCC cell lines can be made. In addition, comparison of our results found for MCC with recently published gene-expression profiles of SCLC cell lines by Pedersen et al. (2003) revealed a number of identical genes expressed in the Classic cell lines, but not in the Variant ones of both tumor types. These included the neuroendocrine gene SGNE1 and neuronal markers doublecortin and MACMARKS or MLP. These genes could be used as markers for distinction between Classic and Variant phenotypes of both tumor types. Moreover, the nine selected SAM genes able to distinguish between Classic and Variant MCC cell lines could also be used as classifiers for Classic and Variant SCLC cell lines (personal observation). Two of these nine genes, vimentin (see also above) and SPINT2, were previously found to be differentially expressed between a Classic and more aggressive Variant SCLC cell line after a suppression subtractive hybridization experiment (Zhang et al., 2000). Interestingly, SCLC tumors derived from Variant cell lines are more aggressive and patients have a worse prognosis (Gazdar et al., 1985). Comparable to SCLC, lack of expression of Classic marker genes described here or overexpression of Variant MCC classifier genes could indicate a subset of more aggressive MCCs, for which more intensive treatment and closer follow-up may be warranted in a similar way to our recent results for Brn3c/HATH1 expression (Leonard et al., 2002). Real-time PCR analysis of 10 MCC tumors did not separate them into distinct subgroups. However, the number of Classic versus Variant classifier genes analysed by real-time PCR in this study was limited to nine genes, and extension of this panel and also increased numbers of patients, for which survival data and treatment procedures are also available, might be beneficial. In addition, future investigations of genes or disregulated pathways involved in Classic and Variant MCC and/or SCLC cell lines could lead to potential targets for the development of new therapeutic strategies specific for each (sub)group. A second goal of the study was to identify genes which were able to distinguish MCC from SCLC. This led to the identification of 17 classifier genes whose geneexpression levels showed significant differential gene expression between MCC and SCLC samples. In all, 12 of these genes showed a higher expression in MCC as compared to SCLC. Of particular interest was brainderived neurotrophic factor (BDNF), which is known to stimulate the mechanotransducing properties of normal Merkel cells (Carroll et al., 1998). In addition, overexpression of BDNF in murine skin was shown to be associated with an increase in Merkel cell number

(Botchkarev et al., 1999). Higher expression in MCC as compared to SCLC could contribute, in patients, to an increase in the numbers of Merkel cells typically observed in MCC tumors (Moll et al., 1996). Consequently, if BDNF could be downregulated in MCC patients, this might have an antiproliferative effect. This hypothesis warrants further investigation. Expression of CDC25B was recently observed in SCLC cell lines (Pedersen et al., 2003), and we showed now even higher levels of expression in MCC, suggesting that a possible upregulation of CDC25B in MCC may occur. None of the other 10 genes were previously shown to be implicated in MCC and SCLC biology. One striking finding was the differential expression of the alpha subunit of PKC (PRKCA) in MCC compared to SCLC cell lines. Protein kinase C is a key protein involved in the regulation of cell growth and activation of the MAP kinase pathway (Buchner, 2000), and this finding is in keeping with the observed expression for MAP2K3. Both overexpression and downregulation of PRKCA have been previously observed in different human tumor types and correlated with malignant transformation and proliferative activity of PRKCA (Benzil et al., 1992; Scaglione-Sewell et al., 1998). PRKCA could thus be involved both in MCC and SCLC, albeit through a different mechanism in each of these tumor types. Further investigation of this gene and other MCC and SCLC classifier genes such as ITGA3, HSPA2, CDH2, NRGN, GAK, PRKR and STK6 should elucidate their role in MCC and/or SCLC biology. Five of the 17 SAM-identified MCC and SCLC classifier genes had a higher expression level in SCLC as compared to MCC. Our data confirmed the previously reported expression of the neuroendocrine differentiation marker ASCL1 in (Classic) SCLCs and its lack of expression in MCC cell lines (Bhattacharjee et al., 2001; Garber et al., 2001; Leonard et al., 2002; Pedersen et al., 2003). The ID2 basic helix–loop–helix transcription factor demonstrated higher levels of transcripts in Classic SCLCs compared to MCCs. Gene-expression profile analysis of small-cell lung cancer cells by Pedersen et al. (2003) also detected expression of ID2 in SCLC. ID2 plays a role in cell proliferation and differentiation and is able to disrupt the antiproliferative effects of retinoblastoma family members (Iavarone et al., 1994). It is possible that disruption of the RB1 pathway through increased expression of ID2 could be an important mechanism in neuroendocrine SCLCs which may not occur in MCC. For the three remaining genes (GPX2, IGFBP2 and TFAP4) expressed in SCLC cell lines but not in MCC, no previous involvement in SCLC has been described. Further investigation of these genes should clarify their role in SCLC or MCC biology. Our gene-expression profiling and clustering with only 17 MCC and SCLC classifier genes identified through SAM analysis was extended through real-time RT–PCR on the original cell lines, as well as additional cell lines and tumor samples. Our results showed that the selected genes were able to effectively cluster the samples, providing an additional and simple test to differentiate between MCC and SCLC. Oncogene


2740

In conclusion, we generated a gene expression-based classification of at least two biological and possibly clinically distinct subgroups of MCC. Interestingly, some of the differentially expressed genes are typically classifying Classic and Variant phenotypes of MCC as well as SCLC. Further investigation could result in a more selective therapeutic treatment applicable for both tumor types and improvement of patient outcome. In addition, we demonstrated for the first time a high degree of similarity at the gene-expression level between MCC and SCLC. Furthermore, we were able to identify a subset of genes by supervised analysis, which may be helpful in the differential diagnosis of MCC and SCLC. Our study also serves as the first step towards a further detailed study of differentially expressed genes involved in cell proliferation, signal transduction and neurotransmission, finally leading to more insight into the complex and heterogeneous biology of MCC and SCLC.

Materials and methods Cell lines Merkel cell carcinoma cell lines MCC5, MCC6, MCC13, MCC14/1, MCC14/2 and MCC26 were established at the Queensland Radium Institute Research Unit, Queensland, Australia and have been described in detail previously by Leonard et al. (1993, 1995a). MCC cell line UISO was described by Ronan et al. (1993) and MKL-1 by Rosen et al. (1987). MKL-2 was established at the Robert H Lurie Comprehensive Cancer Center, IL, USA and reported by Van Gele et al. (2002). T95-45 was established at the Center for Medical Genetics, Ghent, Belgium. Most cell lines were previously analysed by CGH and/or multiplex fluorescence in situ hybridization (Van Gele et al., 1998, 2002). Small-cell lung carcinoma cell lines NCI-H69 and NCI-H146 were obtained from the American Type Culture Collection, CORL88 was a gift from Dr P Twentyman, Cambridge, UK and GLC4 was a gift from Dr Marc Maliepaard, Amsterdam, The Netherlands. The morphological characteristics, growth behavior and subgroup classification (Classic versus Variant) of the cell lines are summarized in Table 1. All cell lines were grown to 70% confluency in RPMI 1640 (Invitrogen) supplemented with antibiotics, 10% fetal calf serum and 1% L-glutamine. Cells from 10 tissue culture flasks (75 cm2) were pelleted, quick frozen in liquid N2 and stored at 801C.

according to the Clontech Atlas cDNA Expression Arrays User Manual. Purification of the probe, hybridization and washes were performed by following the manufacturer’s instructions. Each RNA sample was simultaneously hybridized to both filters in the same hybridization bottle. After the washes, membranes were exposed for one to three nights to phosphoimager plates and scanned with a PhosphoImager System using ImageQuaNT (Molecular Dynamics – Amersham Biosciences). Analysis of cDNA arrays The scanned gel images were converted to a 16-bit tagged image file format. Signal intensities were quantified using the VisualGrid software version 2.1 (GPC Biotech). The ArrayAn2 software (T. Boonefaes, P. Rottiers, and J. Grooten. ArrayAn2: optimized algorithms for primary data analysis of cDNA arrays, manuscript in preparation) was used for further primary data analysis. In short, the spot intensities were corrected for the local background signal intensity, followed by a spot quality-control step to exclude spots influenced by intense signals of adjacent spots. The detection limit for expression values above background was calculated based on the variation of the local background intensity. Constitutive genes were selected (50% of the spots showing the lowest coefficient of variation over all arrays) and used for normalization. Expression data analysis Genes with an expression value above the background level in at least six of the analysed samples were selected for further analysis. This resulted in a total of 1083 genes, of which 412 were common genes (i.e. 206 genes were present on both arrays (Human and Human Cancer 1.2). Cluster and Treeview software were used for unsupervised hierarchical clustering and visualization of the data (Eisen et al., 1998). Prior to clustering, genes were mean centered and the expression data matrix was log transformed (base 2). Subsequently, complete linkage clustering using Spearman rank correlation coefficient as similarity metric was performed to the samples and genes. The complete expression data matrix is available as a tab delimited file from the authors on request. We used the Significance Analysis of Microarrays or SAM algorithm (Tusher et al., 2001), which allows supervised identification of significantly differentially expressed genes between predefined sample groups. In order to include less representative genes for the SAM analysis, the filter threshold was lowered by including genes expressed above background in at least four of the analysed samples (1365 genes). Real-time quantitative RT–PCR

cDNA array hybridization Total RNA from cell lines was extracted using Proteinase K and phenol/chloroform (Sigma), followed by a sodium acetate precipitation in ethanol (MCC13, MCC14/1, MCC14/2 and MCC26) or the Atlas Pure Total RNA Labelling System (Clontech – BD Biosciences). The resuspended RNA was subsequently DNase I (Roche) (2 U/50 mg) treated. The quality and integrity of the Dnase-treated RNA were checked by ethidium bromide agarose gel electrophoresis. Expression analysis was performed using the Atlas Human 1.2 (7850-1) and Atlas Human Cancer 1.2 (7851-1) nylon arrays (Clontech – BD Biosciences). Both filters contained 1176 genes, of which 461 were present on both arrays. For each sample, 12.5 mg of total RNA was used in the cDNA probe synthesis with [a-32P]dATP (NEN Life Science Products) and performed Oncogene

In all, 25 SAM identified genes were quantified by real-time quantitative RT–PCR on the same 14 RNA samples as for array hybridizations. In order to validate the array geneexpression data, the normalized array and real-time PCR data were each mean centered for the genes. The Spearman rank correlation coefficient was then calculated between the array gene-expression levels and real-time PCR expression levels for each gene using the Statistical Package for the Social Sciences (SPSS) Version 11.0 software. Primer sequences for all 25 genes were designed with Primer Express 1.0 software (Applied Biosystems) using the default TaqMan parameters, with modified minimum amplicon length requirements (75 bp). The primer sequences are submitted in a public database (RTPrimerDB) for real-time PCR primers and probes (Pattyn et al., 2003) (gene: primer-ID; ASCL1: 373, GPX2: 346, ID2:


2741 102, IGFBP2: 349, FLT1: 348, TFAP4: 347, EGR1: 389, BDNF: 352, HSPA2: 390, ITGA3: 351, PRKCA: 350, PRKR: 354, MAP2K3: 388, CDH2: 387, ILK: 353, CDC25B: 355, VIM: 356, FOSL1: 357, AXL: 360, CCND1: 87, MYC: 18, SPINT2: 358, BTG2: 361, JUP: 359, MLP: 362). In order to confirm the classification potential of the above-mentioned genes, real-time PCR analysis was extended to two further MCC cell lines, 10 MCC tumors, 12 additional SCLC cell lines and two SCLC tumors. Tumor samples (MCCT1, T2, T3, SCLCT1 and T2) were collected at the Department of Dermatology or Pathology, Ghent University Hospital, Ghent, Belgium, and tumor samples MCCT4, T5, T6, T7, T8, T9 and T10 were collected at the Department of Pathology, University Hospital, Leuven, Belgium. Tumor biopsies were homogenized with an Ultra-Turrax T25 (IKAWerke) in 2 ml lysis buffer (Qiagen). Total RNA of biopsies was extracted using the RNeasy Midi Kit (Qiagen), according to the manufacturer’s instructions. Total RNA from MCC cell line MCC19 (Type II, suspension cell line with threedimensional loose colonies thought to be Variant (Leonard et al., 2002)) was isolated at the Queensland Radium Institute Research Unit using Total RNA Isolation Reagent (Applied Biotechnologies) from cells in exponential growth. The RNA of SCLC cell lines NCI-H446, POVD and AFL was a gift from Dr G Sozzi (Milan, Italy), RNA of GLC1, GLC7, GLC28, GLC36 and GLC45 was kindly provided by Dr K Kok (Groningen, The Netherlands) and RNA of NCI-H60, NCIH82, NCI-H250, NCI-N464 and MCC cell line MKL-1 (subclone 2) (Type III, suspension cell line with twodimensional loose colonies classified as Classic) was a gift from H Salwen (IL, USA). All RNAs were quantified using the Ribogreen reagent (Molecular Probes) on a TD-360 fluorometer (Turner Designs). The relative gene-expression levels were determined using an optimized two-step SYBR green I RT– PCR assay, as described by Vandesompele et al. (2002a). The

standard curve method (serial dilutions of a cDNA mixture containing two SCLC and two MCC samples) or the comparative Ct method was used for quantification. PCR reagents were obtained from Eurogentec as SYBR Green I mastermixes, and used according to the manufacturer’s instructions. PCR reactions were run on an ABI Prism 5700 Sequence Detection System (Applied Biosystems). To correct for differences in RNA quantities and cDNA synthesis efficiency, relative gene-expression levels were normalized using the geometric mean of five housekeeping genes (UBC, HPRT1, GAPD, TBP and HMBS) according to Vandesompele et al. (2002b). In order to perform hierarchical clustering, a real-time-based expression matrix was created by dividing the normalized gene-expression level of each gene by its geometric mean across all samples, and data were subsequently log transformed (base 10). Acknowledgements This work was supported by GOA Grant 12051397, FWO Grant G.0028.00 and the Queensland Cancer Fund and the Queensland Radium Institute Research Unit. Anthony L Cook is supported by a University of Queensland Mid Year Scholarship. Jo Vandesompele is sponsored by VEO-grant 011V1302. Nadine Van Roy is a postdoctoral researcher of the Fund for Scientific Research, Flanders. This paper presents the research results of the Belgian program of Interuniversity Poles of attraction initiated by the Belgian State, Prime Minister’s Office, Science Policy Programming. The scientific responsibility is assumed by us. We would like to thank Drs MM Hughes, VM Hinkley, RW Allison, W Cockborn, TJ Harris and O Williams for their support in collecting the Queensland MCC specimens, from which the MCC cell lines were established, and H Salwen for providing MCC cell line MKL-2.

References Baumann KI, Senok SS, Chan E and Yung WH. (2000). Merkel Cells, Merkel Cell Carcinoma and Neurobiology of the Skin, Suzuki H and Ono T (eds) Elsevier Science B.V.: Tokyo, pp 73–81. Benzil DL, Finkelstein SD, Epstein MH and Finch PW. (1992). Cancer Res., 52, 2951–2956. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M, Loda M, Weber G, Mark EJ, Lander ES, Wong W, Johnson BE, Golub TR, Sugarbaker DJ and Meyerson M. (2001). Proc. Natl. Acad. Sci. USA, 98, 13790–13795. Borges M, Linnoila RI, van de Velde HJ, Chen H, Nelkin BD, Mabry M, Baylin SB and Ball DW. (1997). Nature, 386, 852–855. Botchkarev VA, Kief S, Paus R and Moll I. (1999). J. Invest. Dermatol., 113, 691–692. Broers JL, Carney DN, de Ley L, Vooijs GP and Ramaekers FC. (1985). Proc. Natl. Acad. Sci. USA, 82, 4409–4413. Broers JL, Carney DN, Klein Rot M, Schaart G, Lane EB, Vooijs GP and Ramaekers FC. (1986). J. Cell Sci., 83, 37–60. Buchner K. (2000). J. Cancer Res. Clin. Oncol., 126, 1–11. Carney DN, Gazdar AF, Bepler G, Guccion JG, Marangos PJ, Moody TW, Zweig MH and Minna JD. (1985). Cancer Res., 45, 2913–2923. Carroll P, Lewin GR, Koltzenburg M, Toyka KV and Thoenen H. (1998). Nat. Neurosci., 1, 42–46.

Cook AL, Pollock PM, Welch J, Walsh MD, Bowman RV, Baumann KC, Hayward NK and Leonard JH. (2001). Int. J. Cancer, 93, 361–367. Eisen MB, Spellman PT, Brown PO and Botstein D. (1998). Proc. Natl. Acad. Sci. USA, 95, 14863–14868. Fong KM, Sekido Y and Minna JD. (1999). J. Thorac. Cardiovasc. Surg., 118, 1136–1152. Garber ME, Troyanskaya OG, Schluens K, Petersen S, Thaesler Z, Pacyna-Gengelbach M, van de Rijn M, Rosen GD, Perou CM, Whyte RI, Altman RB, Brown PO, Botstein D and Petersen I. (2001). Proc. Natl. Acad. Sci. USA, 98, 13784–13789. Gazdar AF, Carney DN, Nau MM and Minna JD. (1985). Cancer Res., 45, 2924–2930. Goessling W, McKee PH and Mayer RJ. (2002). J. Clin. Oncol., 20, 588–598. Halata Z, Grim M and Bauman KI. (2003). Anat. Rec., 271A, 225–239. Iavarone A, Garg P, Lasorella A, Hsu J and Israel MA. (1994). Genes Dev., 8, 1270–1284. Leonard JH and Bell JR. (1997). Australas. J. Dermatol., 38, 91–98. Leonard JH, Bell JR and Kearsley JH. (1993). Int. J. Cancer, 55, 803–810. Leonard JH, Cook AL, Nancarrow D, Hayward N, Van Gele M, Van Roy N and Speleman F. (2000). Cancer Detect. Prev., 24, 620–627. Oncogene


2742 Leonard JH, Cook AL, Van Gele M, Boyle GM, Inglis KJ, Speleman F and Sturm RA. (2002). Int. J. Cancer, 101, 103– 110. Leonard JH, Dash P, Holland P, Kearsley JH and Bell JR. (1995a). Int. J. Cancer, 60, 100–107. Leonard JH and Hayard N. (1997). Genes Chromosomes Cancer, 20, 93–97. Leonard JH, Ramsay JR, Kearsley JH and Birrell GW. (1995b). Int. J. Radiat. Oncol. Biol. Phys., 32, 1401–1407. Levin NA, Brzoska PM, Warnock ML, Gray JW and Christman MF. (1995). Genes Chromosomes Cancer, 13, 175–185. Metz KA, Jacob M, Schmidt U, Steuhl KP and Leder LD. (1998). Graefe’s Arch. Clin. Exp. Ophthalmol., 236, 561–566. Miller RW and Rabkin CS. (1999). Cancer Epidemiol. Biomarkers Prev., 8, 153–158. Minna JD, Roth JA and Gazdar AF. (2002). Cancer Cell, 1, 49–52. Moll I, Gillardon F, Waltering S, Schmelz M and Moll R. (1996). J. Cutan. Pathol., 23, 109–117. Pattyn F, Speleman F, De Paepe A and Vandesompele J. (2003). Nucleic Acids Res., 31, 122–123. Petersen I, Langreck H, Wolf G, Schwendel A, Psille R, Vogt P, Reichel MB, Ried T and Dietel M. (1997). Br. J. Cancer, 75, 79–86. Pedersen N, Mortensen S, Sorensen SB, Pedersen MW, Rieneck K, Bovin LF and Poulsen HS. (2003). Cancer Res., 63, 1943–1953. Popp S, Waltering S, Herbst C, Moll I and Boukamp P. (2002). Int. J. Cancer, 99, 352–360. Ratner D, Nelson BR, Brown MD and Johnson TM. (1993). J. Am. Acad. Dermatol., 29, 143–156. Ried T, Petersen I, Holtgreve-Grez H, Speicher MR, Schrock E, du Manoir S and Cremer T. (1994). Cancer Res., 54, 1801–1806.

Oncogene

Ronan SG, Green AD, Shilkaitis A, Huang TS and Das Gupta TK. (1993). J. Am. Acad. Dermatol., 29, 715–722. Rosen ST, Gould VE, Salwen HR, Herst CV, Le Beau MM, Lee I, Bauer K, Marder RJ, Andersen R, Kies MS, Moll R, Franke WW and Radosevich JA. (1987). Lab. Invest., 56, 302–312. Scaglione-Sewell B, Abraham C, Bissonnette M, Skarosi SF, Hart J, Davidson NO, Wali RK, Davis BH, Sitrin M and Brasitus TA. (1998). Cancer Res., 58, 1074–1081. Schmidt U, Muller U, Metz KA and Leder LD. (1998). Am. J. Dermatopathol., 20, 346–351. Tazaki M, Tazaki Y, Inoue T and Shimono M. (2000). Merkel Cells, Merkel Cell Carcinoma and Neurobiology of the Skin, Suzuki H and Ono T (eds) Elsevier Science B.V.: Tokyo, pp. 63–71. Tusher VG, Tibshirani R and Chu G. (2001). Proc. Natl. Acad. Sci. USA, 98, 5116–5121. Vandesompele J, De Paepe A and Speleman F. (2002a). Anal. Biochem., 303, 95–98. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A and Speleman F. (2002b). Genome Biol., 3, research0034.001–research0034.0011. Van Gele M, Kaghad M, Leonard JH, Van Roy N, Naeyaert JM, Geerts ML, Van Belle S, Cocquyt V, Bridge J, Sciot R, De Wolf-Peeters C, De Paepe A, Caput D and Speleman F. (2000). Br. J. Cancer, 82, 823–826. Van Gele M, Leonard JH, Van Limbergen H, Van Roy N, Van Belle S, Cocquyt V, De Paepe A, Salwen H and Speleman F. (2002). Int. J. Cancer, 101, 137–145. Van Gele M, Speleman F, Vandesompele J, Van Roy N and Leonard JH. (1998). Cancer Res., 58, 1503–1508. Zajchowski DA, Bartholdi MF, Gong Y, Webster L, Liu HL, Munishkin A, Beauheim C, Harvey S, Ethier SP and Johnson PH. (2001). Cancer Res., 61, 5168–5178. Zhang L, Cilley RE and Chinoy MR. (2000). J. Surg. Res., 93, 108–119.