The new melanoma: A novel model for disease progression · differential TGF-β signalling. Smad activation was present in all melanoma cultures irrespective of the presence of a TGF-β

DISS. ETH NO. 17606

The new melanoma: A novel model for disease progression

A dissertation submitted to

SWISS FEDERAL INSTITUTE OF TECHNOLOGY

ZÜRICH

for the degree of

DOCTOR OF SCIENCES

presented by

Natalie Schlegel Master of Science, Otago University (New Zealand)

born on January 20th 1976

citizen of Zürich (ZH)

accepted on the recommendation of

Professor Sabine Werner, examinor

Professor Reinhard Dummer, co-examinor

Professor Josef Jiricny, co-examinor

2008

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Table of Contents Abstract ...................................................................................................................................... 6 Résumé ....................................................................................................................................... 8 Abbreviations ........................................................................................................................... 10 1. Introduction ...................................................................................................................... 13

1.1 Definition ................................................................................................................. 14 1.2 Clinical features........................................................................................................ 14 1.3 Pathological features and staging............................................................................. 16

1.3.2 Clark’s level of invasion and Breslow’s thickness........................................... 16 1.3.3 TNM staging .................................................................................................... 17

1.4 Epidemiology ........................................................................................................... 18 1.5 Genes and pathways ................................................................................................. 19

1.5.1 Genes involved in familial melanoma.............................................................. 19 1.5.2 PTEN................................................................................................................ 21 1.5.3 MAPK .............................................................................................................. 22 1.5.4 Wnt ................................................................................................................... 23 1.5.5 Microphthalmia-associated transcription factor............................................... 24

1.6 The transforming growth factor-β super-family ...................................................... 25 1.6.1 Receptors and Smad proteins ........................................................................... 27 1.6.2 Transforming growth factor-β.......................................................................... 28 1.6.3 Activin.............................................................................................................. 32

1.7 Id proteins................................................................................................................. 34 1.7.1 Ids, cell cycle regulation and cancer ................................................................ 34 1.7.2 TGF-β and Id proteins...................................................................................... 35 1.7.3 Id proteins and melanoma ................................................................................ 36

1.8 Aim........................................................................................................................... 37 1.9 References ................................................................................................................ 38

2. Metastatic potential of melanomas defined by specific gene expression profiles with no BRAF signature................................................................................................................ 49

2.1 Abstract .................................................................................................................... 52 2.2 Introduction .............................................................................................................. 52 2.3 Results ...................................................................................................................... 54

2.3.1 No correlation between BRAF/NRAS mutations and gene expression........... 54 2.3.2 Microarray analyses reveal three cohorts......................................................... 56 2.3.3 Two groups of co-regulated genes define the cohorts...................................... 57 2.3.4 The cohorts reflect differences in metastatic potential..................................... 60 2.3.5 In vitro tests support the link between cohort distribution and metastatic potential ............................................................................................................ 61 2.3.6 Wnt signalling controls Motif 1 ....................................................................... 62

2.4 Discussion ................................................................................................................ 63 2.5 Material and Methods............................................................................................... 67

2.5.1 Cell Culture and Media .................................................................................... 67 2.5.2 Genotyping ....................................................................................................... 68 2.5.3 Total RNA Extraction and Expression Profiling.............................................. 68 2.5.4 Microarray Data Analysis ................................................................................ 68 2.5.5 Growth Inhibition Assays................................................................................. 70 2.5.6 Motility Assays ................................................................................................ 70 2.5.7 Western Analyses and ELISA.......................................................................... 70 2.5.8 Immunohistochemistry..................................................................................... 71

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2.6 References ................................................................................................................ 72 3. In vitro phenotype validation.. ......................................................................................... 79

3.1 Introduction .............................................................................................................. 80 3.1.1 Modulation of TGF-β signalling ...................................................................... 80 3.1.2 Vasculogenic mimicry...................................................................................... 83

3.2 Results ...................................................................................................................... 84 3.2.1 Confirming TGF-β1 and activin A secretion ................................................... 84 3.2.2 Follistatin secretion does not correlate with activin secretion ......................... 84 3.2.3 Smad2 and Smad3 are activated across all cohorts.......................................... 85 3.2.4 Ski is not responsible for the differential TGF-β signalling ............................ 86 3.2.5 The activation of the MAPK pathways does not correlate with the TGF-β signature ........................................................................................................... 87 3.2.6 Identifying vasculogenic mimicry as a discriminating phenotype................... 88 3.2.7 Phenotype switching ........................................................................................ 89

3.3 Discussion ................................................................................................................ 90 3.4 Material and Methods............................................................................................... 94

3.4.1 Cell culture ....................................................................................................... 94 3.4.2 Preparation of condition media ........................................................................ 95 3.4.3 ELISA............................................................................................................... 95 3.4.4 Preparation of total cell protein extracts .......................................................... 95 3.4.5 Preparation of cytosolic and nuclear protein extracts ...................................... 95 3.4.6 Western blot analysis ....................................................................................... 96

3.5 References ................................................................................................................ 98 4. In vivo switching of human melanoma cells between proliferative and invasive states 101

4.1 Abstract .................................................................................................................. 104 4.2 Introduction ............................................................................................................ 104 4.3 Results .................................................................................................................... 106

4.3.1 Phenotypic assignment of cell lines ............................................................... 106 4.3.2 Mitf is a marker of proliferative phenotype ................................................... 107 4.3.3 Mitf expression reflects signature phenotype................................................. 108 4.3.4 Proliferative cells form fast growing tumours sooner than invasive cells ..... 109 4.3.5 Tumours derived from proliferative or invasive lines are indistinguishable ........ 110

4.4 Discussion .............................................................................................................. 111 4.5 Material and Methods............................................................................................. 117

4.5.1 Melanoma tissues and lines............................................................................ 117 4.5.2 In vitro motility and proliferation assays ....................................................... 117 4.5.3 Recombinant adenovirus vector and siRNA .................................................. 117 4.5.4 Transfection and TGF-β challenge assay....................................................... 118 4.5.5 Western blot analyses..................................................................................... 118 4.5.6 Xenografts ...................................................................................................... 118 4.5.7 Immunohistochemistry................................................................................... 119 4.5.8 Statistical analysis .......................................................................................... 119

4.6 References .............................................................................................................. 120 5. Id2 suppression of p15Ink4b abrogates TGF-β-mediated growth inhibition in melanoma .... ........................................................................................................................................ 123

5.1 Abstract .................................................................................................................. 125 5.2 Introduction ............................................................................................................ 125 5.3 Results .................................................................................................................... 127

5.3.1 Differential Id2 regulation and expression in human melanoma cultures ..... 127

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5.3.2 Id2 overexpression protects proliferative cells from the growth inhibitory effects of TGF-β............................................................................................. 129 5.3.3 Id2 regulates TGF-β-induced G1 cell-cycle arrest......................................... 131 5.3.4 Id2 restricts TGF-β-induced upregulation of p15Ink4b .................................... 132 5.3.5 Id2 expression does not correlate with patient survival in melanoma ........... 132

5.4 Discussion .............................................................................................................. 133 5.5 Materials and Methods ........................................................................................... 135

5.5.1 Cell culture and Adenoviruses ....................................................................... 135 5.5.2 RNA extraction, cDNA synthesis and RT-PCR............................................. 136 5.5.3 Western blot analysis ..................................................................................... 136 5.5.4 Growth inhibition assays................................................................................ 137 5.5.5 Cell cycle FACS analysis............................................................................... 137 5.5.6 Immunohistochemistry, cell culture and tissue array..................................... 137

5.6 References .............................................................................................................. 139 6. Discussion & Conclusions.. ........................................................................................... 143 Appendix A ............................................................................................................................ 151 Acknowledgments.................................................................................................................. 177 Curriculum vitae..................................................................................................................... 178

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Abstract Melanoma is an aggressively metastasising tumour originating from neural-crest derived

melanocytes. It is recognised as being the most dangerous of skin cancers and over the last

three decades its incidence has increased more rapidly than that of any other cancer. Although

primary tumours can easily be removed with surgery, once the cancer has metastasised patient

survival is dramatically low. It is therefore of primary importance to elucidate the

mechanisms driving melanoma progression. The model of melanoma progression in which

molecular lesions progressively accumulate is widely accepted and stands as the dominant

paradigm for molecular studies of the disease. However, this model has some limitations. For

example, although accumulation of irreversible genetic lesions during tumour progression has

been reported for many tumours, the acquired capacity for invasion and metastasis has not

been linked to recurrent mutations but rather to specific gene expression changes. We propose

a new melanoma progression model which we formulated using gene expression array data

acquired from three distinct libraries of melanoma cultures. Our model is based on reversible

transcriptional changes and the concept of cohort specific expression. We contend that

melanoma cells switch between two defined gene expression signatures, each underlying a

distinct cell phenotype, which together drive disease progression. Presented in this thesis are

the in vitro and in vivo experimental validations for this model, the investigation of the role of

TGF-β-like signalling, predominantly its role in growth inhibition, and the identification of

Id2 as a gene involved in TGF-β-induced growth inhibition response. After a literature review

of genes identified to have phenotype-specific expression, we identified Wnt and TGF-β

signalling as drivers of the identified transcriptional signatures. By in vitro characterisation of

phenotypically opposed cells, we identified the two phenotypes as proliferative and invasive.

As well as showing divergent proliferative and invasive behaviour, cell types could be

discriminated based on their growth susceptibility to TGF-β and their capacity for

vasculogenic mimicry. Reduced susceptibility to the growth inhibiting effects of TGF-β and

the capacity for vasculogenic mimicry have both been associated with increased invasive and

metastatic properties of melanoma cells. Our model suggests that both proliferative and

invasive transcriptional signatures are important in disease progression and that each

melanoma cell retains the capacity to express either signature given appropriate signalling.

Our model also accounts for much observed gene expression heterogeneity in melanoma

tumours. This heterogeneity and reversibility of transcription programs were also shown in

vivo using a xenograft mouse model. We also investigated the motive forces behind

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differential TGF-β signalling. Smad activation was present in all melanoma cultures

irrespective of the presence of a TGF-β signature, which suggested Smad-independent TGF-β

signalling. The TGF-β Smad-dependent pathway has long been considered as being central to

TGF-β signalling but it is now recognised that TGF-β signals via crosstalk with alternative

pathways. We investigated alternative pathways but could identify no link between the

activation status of several MAPK pathways and the TGF-β signature. TGF-β is a

multifunctional cytokine which controls aspects of cell proliferation, differentiation,

migration, apoptosis, adhesion, angiogenesis, immune surveillance, and survival. TGF-β was

initially defined as a transforming cytokine but it is now understood that TGF-β has dual roles

both as tumour suppressor and tumour promoter. To better understand the regulation behind

the expression of these opposite behaviours, we studied TGF-β’s cytostatic effect, which

plays an important role in its tumour suppressing function and which is lost as melanoma cells

become more invasive and metastatic. We identified the Id2 gene as differentially regulated

by TGF-β and link the loss of its regulation to acquired resistance to TGF-β in invasive

phenotype cells. We show that TGF-β induces cell cycle arrest through induction of p15Ink4b

and repression of Id2. Furthermore, Id2 overexpression in proliferative phenotype cells

counteracts p15Ink4b induction and consequently protects melanoma cells from TGF-β-

mediated inhibition of proliferation. Treating tumours comprised of cells with variably

expressing transcription signatures presents a difficult challenge. This is because specific

therapies have targeted factors we identify here as being subject to repeated changes in

regulation. It is therefore of primary importance we recognise that the existing paradigm for

melanoma progression is insufficient for the design of effective therapies.

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Résumé Le mélanome est un cancer agressif qui se développe à partir des mélanocytes, cellules

dérivées de la crête neurale. Il est reconnu comme étant un des plus dangereux cancers de la

peau. Depuis les trois dernières décennies, son incidence a augmenté plus que celui de tout

autre cancer. Quoique les tumeurs primaires puissent être aisément retirées chirurgicalement,

l’espérance de vie des patients présentant des métastases est abrégée de façon dramatique. Il

est donc primordial d’élucider les mécanismes responsables de la progression du mélanome.

Le modèle de progression du mélanome présentement accepté et sur lequel les études

moléculaires sont fondées, est basé sur l’accumulation progressive de lésions moléculaires.

Ce modèle a toutefois ses limites. Par exemple, quoique l’accumulation de lésions génétiques

irréversibles au cours de la progression de la tumeur ait été reportée pour un nombre

important de cancers, aucun lien entre l’acquisition des compétences permettant l’invasion et

la métastase n’a été fait avec des mutations récurrentes mais plutôt avec des changements au

niveau de l’expression de certains gènes. Nous proposons ici un nouveau modèle de

progression du mélanome que nous avons formulé à partir d’études moléculaires

d’expressions génétiques réalisées avec des cultures de mélanomes provenant de trois

collections distinctes. Notre modèle est basé sur des modifications réversibles du

transcriptome et sur le concept de la classification des tumeurs fondée sur les profils

d’expressions uniques. Nous proposons un modèle dans lequel les cellules cancéreuses

oscillent entre deux signatures transcriptomiques définissant deux phénotypes distincts, qui

ensembles sont responsables de la progression du cancer. Dans cet ouvrage nous présentons la

validation de ce modèle de façon in vitro et in vivo, l’étude du rôle de la signalisation du

TGF-β, principalement son rôle comme inhibiteur de la prolifération cellulaire, et

l’identification d’Id2 comme étant un gène impliqué dans l’inhibition de la prolifération

cellulaire induite par le TGF-β. À la suite d’une revue littéraire des gènes spécifiques au

phénotype, nous avons identifié la signalisation du Wnt et celle du TGF-β comme étant les

moteurs des signatures transcriptomiques identifiées. Après avoir caractérisé des cellules

phénotypiquement opposées, nous avons identifié les deux phénotypes comme étant

prolifératif et invasif. En plus de posséder des caractères prolifératifs et invasifs divergents,

les groupes de cellules se distinguent par leur résistance aux effets antiprolifératifs exercés par

le TGF-β et par leur compétence pour le “vasculogenic mimicry”. Ces deux caractéristiques

ont été associées avec une aggravation des qualités invasives et métastatiques des cellules de

mélanome. Notre modèle suggère que les signatures transcriptomiques proliférative et

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invasive sont toutes deux importantes pour la progression du mélanome et que chacune des

cellules de mélanome est en mesure de les exprimer en recevant le signal approprié. Notre

modèle explique également l’expression génétique hétérogène observée dans les mélanomes.

Cette hétérogénéité et cette réversibilité des programmes de transcription ont aussi été

démontrées à l’aide d’un modèle de xénogreffes sous-cutanées. Nous avons également tenté

d’identifier les causes de la divergence observée dans la signalisation du TGF-β. Les R-

Smads, Smad2 et Smad3, étaient activés dans toutes les cultures de mélanomes étudiées. Leur

activation était donc indépendante de la présence d’une signature marquée par le TGF-β ce

qui suggère la présence d’une signalisation ne dépendant pas des Smads comme transducteurs

de signal. La famille des protéines Smads représente les messagers hautement spécifiques du

TGF-β, mais il est maintenant évident que d’autres voies de signalisation peuvent être

activées par le TGF-β. Nous avons étudié des voies de signalisation alternatives mais n’avons

trouvé aucun lien entre la signature marquée par TGF-β et l’activation des voies des MAPKs.

Le TGF-β est une cytokine multifonctionnelle qui contrôle un large spectre de réponses

biologiques comme la prolifération, la différenciation, la motilité, l’apoptose, l’adhésion,

l’angiogénèse, la surveillance immunitaire et la survie. Le TGF-β a été initialement décrit

comme un peptide capable de provoquer la transformation réversible de cellules en culture

mais il est maintenant reconnu qu’il joue des rôles antagonistes sur la tumorigénèse. Pour

mieux comprendre la régulation de ces rôles antagonistes, nous avons étudié son effet

antiprolifératif qui est important pour sa fonction suppressive du cancer et qui disparait dans

les mélanomes présentant des caractères plus invasifs et métastatiques. Nous avons identifié

le gène Id2 comme étant un gène contrôlé de façon différentielle par le TGF-β et nous

associons la perte de ce contrôle à la perte de réponse au TGF-β observé chez les cellules de

type invasif. Nous démontrons que le TGF-β provoque un arrêt du cycle cellulaire par

l’induction transcriptionnelle de p15Ink4b et la répression de Id2. De plus, la surexpression

d’Id2 chez les cellules de type prolifératif neutralise l’induction de p15Ink4b et protège les

cellules de mélanome contre l’effet antiprolifératif de TGF-β. Le traitement de tumeurs

formées de cellules comportant des signatures transcriptomiques variables représente un défi

de taille. Les thérapies actuelles ciblent des facteurs que nous avons identifiés comme étant

sujet à de nombreux changements au niveau de leur régulation transcriptionnelle. Il est donc

primordial de reconnaître que le modèle actuel de progression du mélanome n’est pas adéquat

pour la conception de thérapies efficaces.

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Abbreviations ARF alternate reading frame

ALM acral lentiginous melanoma

bFGF basic fibroblast growth factor

bHLH basic helix-loop-helix

BMP bone morphogenic protein

BRAF V-raf murine sarcoma viral oncogene homolog B1

CCND1 cyclin D1

CDKN2A cyclin-dependent kinase inhibitor 2A

CDK cyclin dependent kinase

CM conditioned media

Co-Smad co-mediator Smad

CTGF connective tissue growth factor

E-cadherin epithelial cadherin

EGF epidermal growth factor

ERK extracellular signal-regulated kinase

FAK focal adhesion kinase

FCS foetal calf serum

GSK3 glycogen synthase kinase 3

Id inhibitor of DNA binding/differentiation

INK inhibitor of cyclin-dependent kinase

I-Smad inhibitory Smad

JNK c-jun N-terminal kinase

LAP latent-associated peptide

LEF1 lymphoid enhancer-binding factor 1

LMM lentigo maligna melanoma

LOH loss of heterozygosity

LTBP latent TGF-β binding proteins

MAPK mitogen activated-protein kinase

MC1R melanocortin 1 receptor

MEK MAPK kinase

MEKK MAPK kinase kinase

MH Mad-homology

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Mitf microphthalmia-associated transcription factor

MMP matrix metalloproteinase

MOI multiplicity of infection

N-cadherin neuronal cadherin

NM nodular melanoma

PAI-1 plasminogen activator inhibitor-1

PAX3 paired box gene 3

PBS phosphate-buffered saline

PDGF platelet-derived growth factor

PI3K phosphatidylinositol-3-kinase

PKC protein kinase C

pRb retinoblastoma tumour suppressor protein

PTEN phosphatase and tensin homolog

RGP radial growth phase

R-Smad receptor-regulated Smad

RT-PCR reverse transcription polymerase chain reaction

SBE Smad-binding element

Smad homolog of mothers against decapentaplegic, drosophila

Smurf Smad ubiquitination regulatory factor

SOX10 SRY (sex-determining region Y)-box 10

SOM self-organised map

SSM superficial spreading melanoma

TAK1 TGF-β activated kinase-1

TβR TGF-β receptor

TGF-β transforming growth factor beta

TGIF TGF-β-induced factor

TNM primary tumour/regional lymph node/metastases

tPA tissue-type plasminogen activator

uPA urokinase-type plasminogen activator

UV ultraviolet

VEGF vascular endothelial growth factor

VGP vertical growth phase

Wnt Wingless-type MMTV integration site family

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Chapter 1

1 Introduction

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Chapter 1 Introduction

1.1 Definition

Melanoma is an aggressively metastasising tumour originating from neural-crest derived

melanocytes, specialised pigment cells found predominantly in the skin and eyes. While only

3-5% of melanomas are found in the eye, more than 90% occur in the skin (Houghton and

Polsky, 2002), and these are the focus of this thesis. Cutaneous melanocytes, which account

for approximately 1-3% of cells in the adult epidermis, reside on the epidermal basement

membrane (Hoath and Leahy, 2003). Melanocytes produce melanin in endocytic vesicles

known as melanosomes. Melanosomes are derived from smooth endoplasmic reticulum and

contain a large number of enzymes involved in melanin synthesis (reviewed in Lin and

Fisher, 2007). Skin pigmentation results from the transfer of mature melanosomes from

melanocytes’ dendrites to neighbouring keratinocytes (Boissy, 2003). In keratinocytes,

melanosomes are dispersed in the cytoplasm, and upon UV irradiation form cap-like

structures around the nuclei to protect them from UV-induced DNA damage (reviewed in Lin

and Fisher, 2007).

Figure 1.1. Melanocyte structure and function. Melanocytes reside on the basement membrane where they produce melanin within melanosomes. Skin pigmentation results from the transfer of mature melanosomes from melanocyte dendrites to neighbouring keratinocytes.

1.2 Clinical features

Clinically, primary melanomas present as any of four major subtypes. The first three,

superficial spreading melanoma (SSM), nodular melanoma (NM) and lentigo maligna

melanoma (LMM), were first described by Wallace Clark in 1967 (Clark, 1967). Acral

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lentiginous melanoma (ALM) was described ten years later by Arrington and co-workers

(Arrington et al., 1977). Accounting for almost two thirds of all primary melanomas, SSM is

the most common subtype (World Health Organization, 2006). It may arise in almost any part

of the skin but is most frequent on sites with acute-intermittent sun exposure (World Health

Organization, 2006). SSM presents as an asymmetrical flat skin lesion with irregular

pigmentation and border as well as regression phenomena (Fig. 1.2 A) (Hengge and Dummer,

2006). NM is the second most common subtype and may also originate in any part of the skin,

particularly on the trunk, head and neck (World Health Organization, 2006) It is often a

papular lesion with pigmentation changes and irregularly spreading borders (Fig. 1.2B)

(Hengge and Dummer, 2006). LMM is generally flat in appearance and occurs on chronic

sun-exposed areas of elderly people, mostly on the face but also on extrafacial sites including

the neck, upper back and forearm (Fig. 1.2C) (World Health Organization, 2006). ALM is a

relatively rare subtype occurring in approximately 5% of all cases in caucasians but in more

than 50% of cases in dark-skinned populations (Chu, 1999). This subtype of melanoma occurs

on the palms, soles and subungual sites (Fig. 1.2D) (World Health Organization, 2006). In

addition, there are several rare variants such as amelanotic, desmoplastic, verrucous, and

polypoid melanoma, which together comprise approximately 5% of all cases (Hengge and

Dummer, 2006). Although used clinically as descriptive tools, the different classifications

have little prognostic value or therapeutic significance.

Figure 1.2. Clinical presentation of melanoma. (A) Superficial spreading melanoma. (B) Nodular melanoma. (C) Lentigo maligna melanoma. (D) Acral lentiginous melanoma

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Clinicians have advocated the so called “ABCDE rule” in the early identification of

melanoma. Clinical characteristics of melanoma include: Asymmetry of lesions, Border

irregularity, Colour variations, Diameter greater than 6mm and Evolving (with respect to size,

shape, shades of colour, surface features or symptoms) (Abbasi et al., 2004; Friedman et al.,

1985). However, the diagnosis of pigment lesions is difficult for even the best clinicians and

clinical diagnoses rarely exceed 60% accuracy, histopathological examination must be used

for accurate diagnosis (Houghton and Polsky, 2002).

1.3 Pathological features and staging

1.3.1.1 Radial and vertical growth phase

Location and depth of involvement are key features used in histopathological diagnosis.

Primary melanomas typically progress through two well-defined phases. The initial stage of

tumour progression, the radial growth phase (RGP), is characterised by a flat or plaque-like

lesion expanding horizontally and confined to the epidermis (Clark et al., 1969; Gimotty et

al., 2005). The second stage of progression, the vertical growth phase (VGP), is characterised

by invasion through the basement membrane into the dermis and underlying subcutaneous

tissues (Clark et al., 1975).

1.3.2 Clark’s level of invasion and Breslow’s thickness

Tumour thickness and depth of invasion are the most accurate prognostic features for

melanomas. Clark’s level of invasion classification method correlates the anatomic level of

invasion and the mitotic index (measure of cell proliferation, ratio between the number of

cells in mitosis and the total number of cells) to prognosis (Clark, 1967). Level I is

represented by melanoma in situ (tumour cells confined to the epidermis, RGP). Progression

from RGP to VGP is seen in level II where melanoma cells have expanded to the papillary

dermis. When the latter is filled and has expanded, it is considered level III. Level IV is

marked by the invasion into the reticular dermis. Invasion of the subcutaneous tissue is

characteristic of level V (Clark, 1967) (Fig. 1.3).

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Breslow’s method is a direct measurement of the tumour thickness from the top of the

granular cell layer to the deepest invasive tumour cell (Breslow, 1970) (Fig. 1.3). This second

method has been accepted as a more reliable indicator of prognosis as it is more easily

assessed and more objective than Clark’s method (reviewed in Chin et al., 1998).

Figure 1.3. Pathological classification of melanoma. Breslow thickness measures the melanoma thickness from the granular cell layer to the deepest level of invasion in millimetres. Clark’s classification system correlates prognosis to the anatomical level of involvement.

1.3.3 TNM staging

The widely used TNM staging system was developed by the American Joint Committee on

Cancer and the International Union Against Cancer (AJCC/UICC). In its acronym, T stands

for primary tumour, N for regional lymph nodes, and M for metastases. Stage 0 melanomas

are non-invasive and have not broken the integrity of the epidermal basement membrane.

Stage I (≤ 2 mm according to Breslow’s method) and stage II melanomas are localised

primary tumours. Stage III is characterised by regional spread through lymphatic vessels and

stage IV by distant metastasis (Balch et al., 2004; Thompson, 2002). The complete overview

of this staging system can be found in the references mentioned above and a summary can be

found in Table 1.1.

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Table 1.1. TNM classification (Balch et al., 2004)

T Primary Tumour Tis In situ T1 ≤ 1,0 mm - a) without ulceration / b) with ulceration T2 1.01 – 2.0 mm a) without ulceration / b) with ulceration T3 2.01 – 4.0 mm a) without ulceration / b) with ulceration T4 > 4.0 mm a) without ulceration / b) with ulceration

N Regional Lymph nodes N1 One lymph node a) micrometastasis1

b) macrometastasis2

N2 2-3 lymph nodes a) micrometastasis1

b) macrometastasis2

c) in-transit metastases / satellite metastases ----------------------------------------without metastatic lymph nodes

N3 ≥ 4 metastatic lymph nodes, matted lymph nodes or combinations of in-transit metastases / satellite(s) or ulcerated melanoma and metastatic lymph nodes

M Distant Metastases M1a Distant skin, subcutaneous, or lymph node metastases (normal LDH) M1b Lung metastases (normal LDH) M1c All other visceral (normal LDH)

or any distant metastases (elevated LDH) LDH = Lactate dehydrogenase 1 Micrometastases are diagnosed after elective or sentinel lymphadenectomy. 2 Macrometastases are defined as clinically detectable lymph node metastases confirmed by therapeutic lymphadenectomy or when any lymph node metastasis exhibits gross extracapsular extension.

1.4 Epidemiology

Despite growing awareness of the disease, the incidence of melanoma in developed countries

continues to increase dramatically. Melanoma is recognised as being the most dangerous of

skin cancers and five-year survival of patients with metastases is only 14% (Jemal et al.,

2004).

As is the case for most cancers, the incidence of melanoma increases with age. However,

cutaneous melanoma is the second most common cancer in young British adults aged 20-39

years of age (Giblin and Thomas, 2007). In Switzerland, the incidence of melanoma

compared to other cancers in young adults is also high with an average rate of 13 per 100,000

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men and 19 per 100,000 women (20-49 years of age) for the 2001-2003 period (information

available on www.asrt.ch).

Genetic and environmental factors are involved in the development of melanoma. Skin

pigmentation and geographical location correlate with incidence. Caucasians have a 20-fold

increased risk of developing cutaneous melanoma when compared to darker skin populations,

and the risk increases as Caucasians live in countries with higher UV indices (Giblin and

Thomas, 2007). The latter is well reflected in the incidence of cutaneous melanoma in New

Zealand and Australia, which are the highest in the world (Giblin and Thomas, 2007).

The high incidence of melanoma in Caucasian populations living closer to the equator,

together with the anatomical distribution of tumours, clearly highlights the importance of sun

exposure in the development of the disease (Giblin and Thomas, 2007; Houghton and Polsky,

2002). Interestingly, short bursts of intense UV exposure are believed to be more harmful than

chronic exposure. This would explain the higher incidence observed in professionals working

indoors compared to outdoor workers (Houghton and Viola, 1981). Although sun exposure

has been linked to melanoma incidence, the mechanisms involved remain unclear.

Together with environmental factors, genetic and phenotypic factors contribute greatly to

melanoma incidence. Skin pigmentation, density and type of nevi, susceptibility to sunburn

and a family history of melanoma are all important factors influencing the incidence of

melanoma in a given individual (Houghton and Polsky, 2002).

1.5 Genes and pathways

1.5.1 Genes involved in familial melanoma

Individuals born into families with a history of melanoma have a 30-70% increase in the

chance of developing melanoma (Pho et al., 2006). Although familial melanoma accounts for

only 10% of all melanoma cases, it presents opportunities for understanding the genetic basis

for melanoma susceptibility. Furthermore, diagnostic and prognostic methods for early

detection and application of targeted therapies for all melanoma patients may be developed

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with the understanding of phenotypic and genotypic correlations in familial melanoma

(Hayward, 2003; Pho et al., 2006).

Three melanoma-predisposing genes have been identified. The high-penetrance cell cycle

related gene cyclin-dependent kinase inhibitor 2A (CDKN2A) is the most common mutated

gene. It is located on chromosome 9p21 and encodes two cell cycle related proteins: inhibitor

of cyclin-dependent kinase 4A (p16Ink4a) and ARF (alternate reading frame) through

alternative splicing and translation of the products in different reading frames (Quelle et al.,

1995). Mutations at this locus are present in 20-40% of melanoma families (reviewed in de

Snoo et al., 2007). A second high-penetrance gene, although only present in a minority of

melanoma families, is cyclin-dependent kinase 4 (CDK4) located on chromosome 12q13.

CDK4 is the binding partner of p16Ink4a and mutations only occur at its binding domain

(Soufir et al., 1998; Zuo et al., 1996). A third susceptibility-gene encoding the melanocortin 1

receptor (MC1R) shows low penetrance and therefore confers lower risk (Kennedy et al.,

2001; Palmer et al., 2000; Valverde et al., 1996).

The cell cycle plays a critical role in cancer development and both high-penetrance genes,

CDKN2A and CDK4, are involved in its control. Both mutations affecting p16Ink4a and CDK4

disturb the G1 phase check point. Mutated p16Ink4a is no longer able to inhibit CDK4/6-

mediated phosphorylation of the retinoblastoma protein (pRb) and, resulting in the same

effect, mutated CDK4 is unable to bind functional p16Ink4a (Zuo et al., 1996). ARF induces

cell cycle arrest by inhibiting MDM2-mediated ubiquitylation and degradation of p53

(Pomerantz et al., 1998; Stott et al., 1998).

The third melanoma-predisposing gene, MC1R, is not involved in cell cycle control but is

involved in the regulation of the balanced production of brown/black eumelanin and

yellow/red pheomelanin (Mountjoy et al., 1992). MC1R variants, particularly the ones

associated with red hair, fair complexion, inability to tan and tendency to freckle, shift the

balance to increase the amount of pheomelanin in the skin. Pheomelanin has diminished UV-

light protective capacity, and therefore, melanocytes with such MC1R variants are more

susceptible to the DNA damaging effects of UV radiation (Scott et al., 2002).

A single genetic defect is not solely responsible for familial melanoma but rather a group of

genetic disorders individually contribute to the increase risk of melanoma observed in

20


melanoma-prone families (Pho et al., 2006). Moreover, known germline mutations have not

been identified in 50% of families with familial melanoma, indicating that unknown mutated

loci are still to be identified (Pho et al., 2006).

1.5.2 PTEN

Loss of heterozygosity (LOH) and chromosomal rearrangements on chromosome 10 have

been implicated in a large number of cancers, including melanoma (Wu et al., 2003). Loss of

tumour suppressor genes on chromosome 10, including PTEN, is observed in 30-60% of

sporadic melanomas (Bastian et al., 1998). Mutations or deletions of PTEN have been

observed in 5-15% of uncultured melanomas and 30-40% of established cell lines (Guldberg

et al., 1997; Teng et al., 1997). PTEN germline mutations are associated with three clinically

related inheritable cancer syndromes: Cowden disease, Lhermitte-Duclos disease and

Bannayan-Zonana syndrome (Chin, 2003).

PTEN signals down the phosphatidylinositol-3-kinase (PI3K) pathway through its lipid

phosphatase function (Wu et al., 2003). PTEN negatively regulates PI3K signalling by

dephosphorylating PIP3, inhibiting phosphorylation and activation of AKT. AKT activation

promotes the downregulation of antiapoptotic proteins such as Bcl-2 and the upregulation of

proapoptotic signals. Since activation of AKT enhances cellular survival, growth and

proliferation, loss of PTEN regulation of AKT results in reduced proliferative and apoptotic

control (Wu et al., 2003).

PTEN has another phosphatase function which targets proteins. Dephosphorylation of focal

adhesion kinase (FAK) has been shown to inhibit focal adhesion and migration, and further

decrease PI3K activity (Tamura et al., 1999; Tamura et al., 1998). Additionally, PTEN is

thought to interact with growth factor-stimulated mitogen activated-protein kinase (MAPK)

signalling by dephosphorylating adapter proteins such as Shc and IRS, resulting in reduced

MEK1/2 (MAPK kinase) and ERK1/2 (extracellular signal-regulated kinase) phosphorylation

(Wu et al., 2003).

21


1.5.3 MAPK

Because of its role in the regulation of cell growth, survival and invasion, the mitogen-

activated protein kinase (MAPK) pathway is implicated in a large number of cancers. In

melanoma, activation of this pathway occurs through paracrine and autocrine growth factor

stimulation, adhesion receptor signalling, and activating mutations in signalling molecules or

in growth-factor receptors such as c-Kit (Gray-Schopfer et al., 2007; Meier et al., 2005).

Activating RAS mutations have been detected in approximately 9-15% of melanomas, with

NRAS mutants accounting for the large majority (Meier et al., 2005). However, the most

common mutation in the MAPK pathway is found in BRAF. BRAF somatic missense

mutations have been detected in 66% of malignant melanomas (Davies et al., 2002). All

detected mutations were found in the kinase domain, with a single substitution (V600E)

accounting for 80% of all such cases (Davies et al., 2002). Interestingly, 82% of benign nevi

also present with BRAF mutations, suggesting that although the activating mutation may be

involved in melanoma development, it is not sufficient for malignant transformation (Pollock

et al., 2003).

In vitro studies support an oncogenic role for mutant BRAF. Small interfering RNA against

BRAFV600E was shown to inhibit MAPK activation, induce growth arrest and apoptosis, and

inhibit colony formation in soft agar (Hingorani et al., 2003). Furthermore, expression of

BRAFV600E was shown to transform murine melanocytes and induce tumorigenicity in nude

mice (Wellbrock et al., 2004).

BRAFV600E is not a typical ultraviolet fingerprint mutation; the transversion of T to A

resulting in the substitution at nucleotide 1799 is distinct from UV-induced pyrimidine dimer

formation (Davies et al., 2002). Rather than a direct relationship between UV radiation and

the presence of BRAF mutations, it has been shown that BRAF mutations in melanomas

arising on intermittently sun-exposed skin are statistically more common than melanomas on

chronically sun-damaged skin and tissues relatively or completely unexposed to sunlight

(acral and mucosal melanomas) (Curtin et al., 2005; Maldonado et al., 2003).

Regulation of cell growth by the MAPK pathway involves the downstream target cyclin D1

(CCND1) (Bhatt et al., 2005). Interestingly, Curtin and co-workers showed that focused

22


amplification resulting in increased number of copies of CCND1 was inversely correlated

with mutations in BRAF (Curtin et al., 2005). Also, increased CCND1 protein expression was

exclusively correlated with either mutations in BRAF or NRAS or increased copy number of

CCND1, suggesting an important role for elevated levels of CCND1 in melanoma progression

(Curtin et al., 2005). Furthermore, no mutations in BRAF or NRAS or amplification of CCND1

were detected in tumour samples with CDK4 amplifications, indicating that the functions of

the MAPK and CCND1/CDK4 pathways overlap and their oncogenic functions in melanoma

are independent (Curtin et al., 2005).

Aside from its role in MAPK signalling, RAS is a known positive upstream regulator of the

PI3K pathway (Rodriguez-Viciana et al., 1994; Rodriguez-Viciana et al., 1996). Activation

of RAS activates both the PI3K and the MAPK pathway, abrogating the need for PTEN

inactivation and BRAF activation. Supporting this model, Tsao and co-workers showed that

NRAS was exclusively mutated in melanoma, while BRAF and PTEN mutations were found

simultaneously (Tsao et al., 2004).

1.5.4 Wnt

Wnt signalling promotes pigment cell formation from neural crest cells and is therefore

crucial for the development of melanocytes from their neural crest precursors (Dorsky et al.,

1998). The 19 members of the Wnt family of proteins, with the numerous receptors and co-

receptors, underlie the complexity and diversity of Wnt signalling. Three distinct pathways

have so far been identified; the canonical β-catenin pathway, the Wnt/Ca+ pathway, and the

planar cell polarity pathway (Weeraratna, 2005).

Although a significant number of stabilising β-catenin mutations have shown to contribute to

tumorigenesis in diverse cancers and such mutations were reported to be prevalent in

melanoma cell lines (Rubinfeld et al., 1997), they are much less important in primary

malignant melanoma (Rimm et al., 1999). However, evidence for the activation of the

canonical β-catenin pathway through the immunohistochemical detection of nuclear β-catenin

in a subset of primary melanomas suggests a role for this pathway in melanoma development

(Rimm et al., 1999).

23


The recent evidence implicating WNT5A in melanoma invasion supports a role for the non-

canonical Wnt/Ca+ pathway in melanoma progression. Bittner and co-workers, using

microarray analysis, identified WNT5A as the gene that best characterised melanoma

metastases with a highly motile and invasive in vitro phenotype (Bittner et al., 2000). In a

follow-up study, overexpression of WNT5A was shown to increase the activity of protein

kinase C (PKC), induce actin reorganisation and increase cell adhesion, motility and invasion

(Weeraratna et al., 2002). Furthermore, WNT5A expression in human melanoma biopsies

directly correlated to increasing tumour grade (Weeraratna et al., 2002). In a recent

publication, the Weeraratna group showed that WNT5A/PKC stimulate cell motility through

the regulation of genes associated with an epithelial to mesenchymal transition (EMT), such

as vimentin, Snail and E-cadherin (Dissanayake et al., 2007).

The planar cell polarity pathway, through the activation of the GTPases Rho, Rac and cdc42,

is a major activator of the Rho and the JNK/MAPK pathways. In melanoma, RhoC was

shown to enhance metastasis in vivo (Clark et al., 2000), and cdc42 and Rac mediate the

formation of invadopodia (Nakahara et al., 2003).

1.5.5 Microphthalmia-associated transcription factor

Microphthalmia-associated transcription factor (Mitf) is a member of the Myc-related family

of basic helix-loop-helix leucine zipper transcription factors. It modulates the expression of

various differentiation and cell-cycle progression genes such as Bcl2 and CDK2, as well as

the expression of melanogenic proteins such as tyrosinase, silver homologue (gp100) and

melanoma-associated antigen recognised by T cells-1 (MART-1 or melan-A)(Levy et al.,

2006). Mitf is recognised as the master regulator of melanocyte development, function and

survival (Chin et al., 2006; Levy et al., 2006).

The promoter directing melanocytic-specific expression of Mitf contains regulatory elements

associated with paired box gene 3 (PAX3), SRY (sex-determining region Y)-box 10

(SOX10), lymphoid enhancer-binding factor 1 (LEF1) and Mitf itself (Jacquemin et al., 2001;

Saito et al., 2002; Steingrimsson et al., 2004; Yasumoto et al., 2002). Post-translationally,

Mitf is also modified by the c-Kit/MAPK pathway. Erk kinase phosphorylation of Mitf has

been shown to enhance its affinity for the p300/CBP transcriptional coactivator (Hemesath et

24


al., 1998; Price et al., 1998) as well as trigger its ubiquitylation and degradation (Wellbrock

and Marais, 2005; Wu et al., 2000).

Mitf was first considered as an oncogene after Garraway and co-workers detected

amplification of the Mitf gene in 10% of primary cutaneous and 15-20% of metastatic

melanomas using a high-density single nucleotide polymorphism array (Garraway et al.,

2005). In addition, Mitf amplification was associated with decreased 5-year survival in

patients presenting with metastatic melanoma (Garraway et al., 2005). In genetically modified

human melanocytes, in which the p53 and p16Ink4a/CDK4/pRb pathways were inactivated,

expression of Mitf together with activated BRAFV600E conferred robust factor-independent

and anchorage-independent growth, demonstrating that deregulated Mitf expression could

cooperate with BRAFV600E to transform human melanocytes (Garraway et al., 2005). Also,

reduction of Mitf expression sensitised melanoma cells to conventional chemotherapeutic

agents (Garraway et al., 2005).

Mitf amplification is only seen in a subset of melanomas. Its expression is variable across

specimens, with some studies reporting decreased Mitf expression in advanced melanoma

(Salti et al., 2000; Selzer et al., 2002; Vachtenheim et al., 2001). The existence of different

subsets of melanomas has been suggested to explain the different Mitf expression patterns

observed across specimens (Levy et al., 2006). It has also been proposed that Mitf expression

must be kept within narrow limits as it regulates distinct functions in melanocytic cells at

different levels of expression; cell cycle and differentiation at high levels and cell cycle arrest

and apoptosis at critically low levels (Carreira et al., 2005; Gray-Schopfer et al., 2007).

1.6 The transforming growth factor-β super-family

The members of the transforming growth factor-β (TGF-β) super-family are multifunctional

cytokines controlling diverse cellular processes, including cell proliferation and

differentiation, modification of the microenvironment, as well as cell fate determination and

patterning during embryogenesis (reviewed in Shi and Massague, 2003; Siegel and Massague,

2003). The TGF-β super-family of cytokines comprises over 40 human members including

TGF-βs, activins, nodal, bone morphogenic proteins (BMPs), the Müllerian inhibiting

25


substance (MIS), and a number of other structurally related ligands (reviewed in Massague

and Gomis, 2006).

TGF-β family cytokines signal through two transmembrane serine/threonine kinase receptors,

a type II ligand binding receptor and a type I signal transducing receptor. Upon ligand binding

to a type II receptor at the cell surface, a type I receptor is recruited and phosphorylated by the

type II receptor. Phosphorylation leads to the activation of its receptor kinase domain and

subsequent signalling through the Smad proteins (reviewed in Shi and Massague, 2003;

Siegel and Massague, 2003) (Fig. 1.4A).

Figure 1.4. TGF-β-like signalling. (A) Upon ligand binding, the type II receptor recruits and phosphorylates the type I receptor. Phosphorylation leads to the activation of its receptor kinase domain. The activated type I receptor recruits and phosphorylates receptor-regulated Smads (R-Smads). Phosphorylated R-Smads bind to the co-mediator Smad (Co-Smad) and translocate to the nucleus where the heteromeric complex interacts with other transcriptional factors (TF) as well as co-activators and co-represors (co). SBE: Smad-binding element, TBE: transcription factor binding element. (B) Combinations of type II and type I recepors.

26


1.6.1 Receptors and Smad proteins

Multiple combinations of the seven human type I receptors (ALKs 1-7) and the five type II

receptors (TβR-II, ActR-IIA, ActR-IIB, BMPR-II, and AMHR-II) are responsible for the

transmission of signals initiated by the binding of any of over 40 functionally relevant ligands

identified in the human genome (Roberts and Derynck, 2001). (Fig. 1.4B). Activated type I

receptors recruit and phosphorylate receptor-regulated Smads (R-Smads) for signal

propagation (reviewed in Shi and Massague, 2003; Siegel and Massague, 2003). Smad2 and

Smad3 R-Smads are effectors of TGF-β, activin and nodal signalling, while Smad1, Smad5

and Smad8 are involved in BMP signalling (reviewed in Shi and Massague, 2003; Siegel and

Massague, 2003). Phosphorylated R-Smads bind to the co-mediator Smad (Co-Smad) Smad4

and translocate to the nucleus where the heteromeric complex, in conjunction with other

nuclear factors, regulate the transcription of target genes (reviewed in Shi and Massague,

2003; Siegel and Massague, 2003) (Fig. 1.4A).

Smad proteins are characterised by two conserved Mad-homology (MH) domains, MH1 and

MH2, bound by a variable proline-rich linker region. The MH2 domain is responsible for

interaction with anchors for cytoplasmic retention, receptors for activation, nucleoporins for

nucleocytoplasmic translocation, and partner Smad proteins and other nuclear factors for the

formation of transcriptional complexes (reviewed in Massague and Gomis, 2006). Via their

MH1 domain, Smad3 and Smad4 interact directly with the CAGA sequence, which is known

as the Smad-binding DNA element (SBE) (Jonk et al., 1998; Zawel et al., 1998). R-Smads

share more than 90% homology at the amino acid level, but an extra 30 amino acid insertion

in the MH1 domain of Smad2 inhibits its ability to bind DNA (Dennler et al., 1999). The

affinity of Smad3 and Smad4 for the SBE is too low to confer promoter selectivity, therefore

additional transcriptional co-activators and co-repressors are required for selective

transcription regulation (reviewed in Massague and Gomis, 2006). An additional subclass of

Smad proteins is composed of the inhibitory Smads (I-Smads), Smad6 and Smad7. They have

been shown to antagonise TGF-β signalling by a number of means, including competing with

R-Smads for receptor binding, recruiting E3-ubiquitin ligases (known as Smad ubiquitination

regulatory factor 1 and 2 (Smurf1 and Smurf2) leading to type I receptor degradation or by

recruiting factors leading to type I receptor dephosphorylation (reviewed in Dijke ten and

Hill, 2004).

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1.6.2 Transforming growth factor-β

Three mammalian TGF-β isoforms, TGF-β1-3, have been identified. Although encoded by

three distinct genes, they share 76-80% amino acid sequence homology. The TGF-β cytokine

is synthesised as a pre-proTGF-β containing the C-terminal mature TGF-β and an N-terminal

pro-domain, the latent-associated peptide (LAP), which are cleaved in the Golgi apparatus by

a furin-like endoproteinase (reviewed in Koli et al., 2001). After cleavage, the LAP dimer

remains associated with the mature TGF-β dimer through non-covalent interaction. This

complex, known as the small latent complex, is secreted, or in most cases associates

covalently through the LAP to one of four latent TGF-β binding proteins (LTBP) to be

secreted as a large latent complex. Release of the mature TGF-β molecule is induced by

cleavage of the LAP by various proteases or by physical interactions of the LAP with other

proteins (reviewed in Javelaud and Mauviel, 2004; Koli et al., 2001). TGF-β then mediates its

effect through the heteromeric receptor complex consisting of TβR-II and ALK5.

1.6.2.1 Transforming growth factor-β and cancer

TGF-β is a multifunctional cytokine which controls aspects of cell proliferation,

differentiation, migration, apoptosis, adhesion, angiogenesis, immune surveillance, and

survival. TGF-β was initially defined as a transforming cytokine, hence it’s name, based on its

ability to induce anchorage independent growth of normal fibroblasts (Todaro et al., 1980).

On the other hand, TGF-β was also considered a tumour suppressor when it was shown to

have growth-suppressive effects in a number of cell types (reviewed in Wakefield and Sporn,

1990). It is now understood that TGF-β has dual roles both as tumour suppressor and tumour

promoter.

TGF-β’s tumour suppressor role is well exemplified by its cytostatic function. TGF-β

regulates a number of important proteins involved in controlling cell cycle progression from

G1 to S phase. Among the activated TGF-β target genes identified are the cyclin-dependent

kinase (CDK) inhibitors p21Cip1 (Datto et al., 1995), p27Kip1 (Kamesaki et al., 1998) and

p15Ink4b (Hannon and Beach, 1994). p15Ink4b interacts and inactivates CDK4 and CDK6, and

thus prevents progression through the G1/S restriction point (Hannon and Beach, 1994). It

also binds cyclin D complexes of CDK4 or CDK6, inactivating them and displacing p21Cip1

28


and p27Kip1 which are then free to bind and inactivate CDK2 complexes with cyclin A or E

(Polyak et al., 1994). p21Cip1 also directly inhibits DNA synthesis by interacting with

proliferating cell nuclear antigen (PCNA), a polymerase delta accessory factor (Waga et al.,

1994). Activation of the gene encoding p15Ink4b is further enhanced by the downregulation of

c-Myc. TGF-β signalling represses the expression of c-Myc and therefore inhibits its

recruitment to the p15Ink4b promoter, relieving c-Myc induced repression of p15Ink4b (Seoane

et al., 2001; Staller et al., 2001). p57Kip2, another member of the Kip/Cip family of CDK

inhibitors, has been shown to play similar roles as p21Cip1 and p27Kip1 in hematopoietic cells,

in which it is transcriptionally induced by TGF-β (Scandura et al., 2004).

In addition to inhibiting proliferation, TGF-β may also induce apoptosis (reviewed in Schuster

and Krieglstein, 2002). For example, TGF-β has been shown to regulate the expression of

pro- and anti-apoptotic molecules, including p53, Bad, Bax, Bik, Bcl-2 and Bcl-XL (reviewed

in Jakowlew, 2006).

Although only a small fraction of tumour cells resistant to TGF-β cytostatic effects have

mutations in genes encoding TGF-β receptors or Smad proteins, the presence of these

inactivating mutations in certain cancers suggests a tumour suppressor role for TGF-β.

Inactivating mutations in the gene for TβR-II are found in colon and gastric cancers, as well

as in glioma (Chung et al., 1996; Izumoto et al., 1997; Ku et al., 2007; Markowitz et al.,

1995). Inactivating mutations in the gene encoding ALK5 are less frequent but are found in

breast, colorectal, ovarian, head and neck, and pancreatic cancers, and T-cell lymphoma

(Chen et al., 1998; Chen et al., 2001a; Chen et al., 2001b; Goggins et al., 1998; Ku et al.,

2007; Schiemann et al., 1999).

Mutations in Smad2 are relatively rare and are limited to a few cases in colon, lung, gastric,

and head and neck cancers (Han et al., 2004; Qiu et al., 2007; Riggins et al., 1996; Tsang et

al., 2002; Uchida et al., 1996). Smad4 mutations occur in approximately half of cases of

pancreatic cancer and are also detected at a lesser frequency in other carcinomas including

colon carcinomas (Hahn et al., 1996; Schutte et al., 1996). Loss of Smad4 also correlates

with cancer progression and metastasis (Losi et al., 2007; Luttges et al., 2001; Xu et al.,

2000). Recently, the first Smad3 mutation inhibiting the nuclear translocation of the encoded

29


protein and contributing to human carcinogenesis was detected in colorectal cancer (Ku et al.,

2007).

Genetically modified mouse models have strengthened the evidence for a tumour suppressor

role for TGF-β signalling. For example, re-expression of TβR-II or ALK5 in cancers with low

levels of the corresponding receptors reduced tumorigenicity in a colon cancer mouse model

(Wang et al., 1996; Wang et al., 1995).

Experimental and clinical observations also support a pro-oncogenic role for TGF-β

signalling. Interestingly, TGF-β’s tumour suppressive and pro-oncogenic roles are not

dependent on specific molecular etiology of the tumour or the specific cell type of origin.

Tang and co-workers have shown that TGF-β signalling can switch roles during the course of

carcinogenic progression in a given cell lineage with a defined initiating oncogenic event

(Tang et al., 2003). The introduction of a dominant negative TβR-II could cooperate with an

initiating oncogenic lesion to increase the frequency of malignant transformation of breast

epithelial cells and increase the aggressiveness of a low-grade breast carcinoma (Tang et al.,

2003). Conversely, the loss of TGF-β signalling did not affect primary tumorigenesis, but

suppressed metastasis in a high-grade breast carcinoma (Tang et al., 2003). Strengthening the

“switch hypothesis”, TGF-β’s dual role in carcinogenesis has also been demonstrated in a

single animal model. In a murine Neu-induced mammary tumorigenesis model, Siegel and co-

workers showed that activation of TGF-β signalling delayed the appearance of primary

mammary tumours, but enhanced the frequency of lung metastasis (Siegel et al., 2003). On

the other hand, expression of the dominant negative TβR-II decreased the latency of tumour

formation while significantly reducing the incidence of extravascular lung metastases (Siegel

et al., 2003). Interestingly, TGF-β seems to use the same effector proteins for its two opposite

functions, as Tian and co-workers showed that interference with endogenous Smad2/3

signalling enhanced tumorigenesis but also strongly suppressed lung metastasis of breast

cancer cell lines (Tian et al., 2003). Together, these data suggest that while TGF-β signalling

suppresses proliferation processes it drives metastatic processes.

30


1.6.2.2 Transforming growth factor-β and melanoma

Melanomas produce and secrete all three TGF-β isoforms (Krasagakis et al., 1999; Rodeck et

al., 1994; Van Belle et al., 1996). While melanomas express TGF-β constitutively,

melanocytes require exogenous growth factor stimulation for TGF-β production (Rodeck et

al., 1994). Comparable to what is described for other cancers, normal melanocytes are growth

inhibited by TGF-β, while melanomas show various degrees of resistance to TGF-β-induced

growth inhibition (Krasagakis et al., 1999; Rodeck et al., 1994). However, in addition to

decreased susceptibility some melanomas show increased proliferation rates in response to

TGF-β (Rodeck et al., 1999). In contrast to some epithelial malignancies which have acquired

their resistance to the cytostatic effect of TGF-β through mutations of TGF-β signalling

components, melanomas present intact Smad-dependent regulation of gene expression,

regardless of their proliferative response to TGF-β (Rodeck et al., 1999). In the same study, a

number of melanomas presented constitutive Smad-dependent transcription, which is partly

thought to be the result of endogenously produced TGF-β (Rodeck et al., 1999).

As is the case for other cancers, TGF-β has both tumour suppressor and tumour promoter

roles in melanoma. On one hand, TGF-β has been shown to strongly induce plasminogen

activator inhibitor-1 (PAI-1) synthesis, leading to a significant decrease in tissue-type

plasminogen activator (tPA) and urokinase-type plasminogen activator (uPA) secretion,

which in turn led to tumour growth inhibition in a mouse melanoma model (Ramont et al.,

2003). On the other hand, metastasis has also been shown to be facilitated by TGF-β’s

induction of matrix metalloproteinase-9 (MMP9) and β1 and β3 integrins, downregulation of

E-cadherin expression (Janji et al., 1999), as well as by enhancement of melanoma cell

adhesion to the endothelium (Teti et al., 1997). Also, TGF-β stimulation of neighbouring

stromal fibroblasts which translated into an increased production and deposition of

extracellular matrix proteins was shown to increase survival and metastasis of melanoma in

mice (Berking et al., 2001).

Overexpression of the inhibitory Smad protein Smad7 has also been shown to inhibit

tumorigenicity in vitro and in vivo (Javelaud et al., 2005). Stable expression of Smad7

reduced MMP-2 and MMP-9 secretion which resulted in reduced in vitro invasion without

affecting motility or adhesion. Furthermore, it reduced both anchorage-independent growth in

vitro and the capacity to form a primary tumour in a xenograft transplantation model

31


(Javelaud et al., 2005). Using the same Smad7-overexpressing melanoma cell line, Javelaud

and co-workers demonstrated the role of TGF-β in promoting bone metastasis (Javelaud et al.,

2007).

TGF-β signalling has also been shown to play a role in melanogenesis. Using an immortalised

mouse melanocyte cell line, Kim and co-workers demonstrated that TGF-β reduced Mitf

promoter activity, resulting in decreased Mitf, tyrosinase, tyrosinase-related protein-1 (Trp-1)

and Trp-2 protein production, which in turn led to reduced tyrosinase activity and melanin

synthesis (Kim et al., 2004). The inhibition of these effects by a specific Erk-pathway

inhibitor suggests this pathway mediates TGF-β’s regulation of melanogenesis (Kim et al.,

2004).

1.6.3 Activin

Activin, another TGF-β-family cytokine, was initially isolated based on its ability to stimulate

follicle-stimulating hormone release by the anterior pituitary, hence its name (Ling et al.,

1986). Activin has now been shown to regulate a variety of events, including cell

proliferation, differentiation, apoptosis, homeostasis, immune response, wound healing and

endocrine function (reviewed in Chen et al., 2006). Activins are dimeric proteins composed of

two β subunits linked by a single disulfide bond. In human, the two most common subunits

are βA and βB which, when linked, form the two homodimers activin A and activin B, and the

heterodimer activin AB. Additional β subunits (βC, βD and βE) have been identified, however

the in vivo functions of the encoded polypeptides are still poorly defined. Activin signals

through the type II activin receptors (ActRII and ActRIIB) and the type I receptors Alk2,

ALK4, and Alk7, with Alk4 being the most important signal transducing receptor (reviewed

in Chen et al., 2006).

1.6.3.1 Activin and cancer

As has been described for TGF-β, activin’s effects on growth and apoptosis have been linked

to a putative role in cancer development. In human hepatoma cells, activin has been shown to

induce cell cycle arrest through the induction of p15Ink4b expression (Ho et al., 2004) and to

32


down-regulate the expression of the anti-apoptotic gene Bcl-xL (Kanamaru et al., 2002) in a

Smad-dependent manner. Also, in breast cancer cells, activin has been shown to enhance the

expression of p15Ink4b, reduce the expression of cyclin A and phosphorylate pRb (Burdette et

al., 2005). Activin also inhibits proliferation of cells derived from other human tumours,

including cells from gall bladder, prostate and pituitary gland (reviewed in Chen et al., 2006).

However, activin has been shown to increase proliferation of ovarian cancer cell lines (Steller

et al., 2005), highlighting a cell specific characteristic of activin’s effects on proliferation.

Interestingly, activin A has also been suggested to inhibit TGF-β1-mediated inhibition of

growth in differentiated human endometrial adenocarcinoma cells (Tanaka et al., 2004).

In opposition to the pro-angiogenic role described for TGF-β, activin is thought to function as

an inhibitor of angiogenesis. Reintroduction of the activin gene in neuroblastoma cells has

been shown to reduce the proliferation rate and induce the formation of smaller tumours with

reduced vascularity in a xenograft mouse model (Panopoulou et al., 2005). Furthermore, both

ALK4 overexpression and activin treatment of endothelial cells resulted in an increase in the

expression of p15Ink4b, p21Cip1 and p27Kip1, and a decrease in the expression of vascular

endothelial growth factor receptor 2 (VEGF receptor-2), a key angiogenic factor (Panopoulou

et al., 2005).

A role for activin in cell invasion has also been suggested. Overexpression of activin A in

esophageal carcinoma was shown to increase the expression of N-cadherin, a feature

associated with depth of invasion and poor prognosis (Yoshinaga et al., 2004). Furthermore,

activin A expression was significantly associated with N-cadherin in clinical samples

(Yoshinaga et al., 2004).

1.6.3.2 Activin and melanoma

While the interest in the role of TGF-β in melanoma is significant, interest given to the role of

the closely related activin molecule is limited. Hashimoto and co-workers reported on the

differential expression of the βB subunit in high- and low-metastatic melanoma cell lines and

consequently suggested that activin may play a role in metastasis (Hashimoto et al., 1996).

Stove and co-workers identified follistatin as a secreted molecule inhibiting activin signalling

in melanoma cells and characterised the activin/activin receptor system in a number of

33


melanoma cell lines and melanocytes (Stove et al., 2004). They reported that all studied

melanocytes and melanoma cell lines expressed mRNA for activin receptors and for the β

subunits, primarily the βA subunit, suggesting a role for activin A. They further showed that

melanocytes but not melanomas were growth inhibited by activin, and suggested that the

secretion of follistatin by melanoma cells may represent an effective way to circumvent

activin’s negative regulatory effects by binding to activin and preventing it from accessing its

cell surface receptor (Stove et al., 2004).

1.7 Id proteins

Inhibitor of DNA binding/differentiation (Id) proteins were first recognised as regulators of

differentiation but their role in a number of other biological processes, such as proliferation,

cell-cycle regulation, angiogenesis, invasion and migration, has now been well documented

(reviewed in Lasorella et al., 2001).

Id proteins act as dominant negative inhibitors of basic helix-loop-helix (bHLH) transcription

factors. As opposed to the positively acting members of the bHLH family of transcription

factors to which they bind, Id proteins lack a basic DNA binding domain and therefore

prevent the dimers from binding to DNA and regulating transcription (reviewed in Lasorella

et al., 2001). Id proteins can also bind to and alter the activities of other regulatory proteins,

including the ternary complex factor subfamily of ETS-domain transcription factors (Yates et

al., 1999) and the Pax-2/5/8 subfamily of transcription factors (Roberts et al., 2001). In

addition, one member of the Id proteins, Id2, directly interacts with pRb and the related

proteins p107 and p130 via its HLH domain (Iavarone et al., 1994; Lasorella et al., 1996).

1.7.1 Ids, cell cycle regulation and cancer

Id proteins are positive regulators of cell growth and play a critical role in promoting G1/S

cell cycle progression. Their role in the regulation of cell proliferation is thought to be driven

by two mechanisms. First, they interfere with bHLH, Ets and Pax factors and consequently

regulate the expression of their target genes such as the immediate early genes c-Fos and Egr-

34


1 (Yates et al., 1999), and the CDK inhibitors p16Ink4a (Alani et al., 2001; Ohtani et al., 2001),

p21Cip1 (Prabhu et al., 1997), p57Kip2 (Rothschild et al., 2006) and probably p27Kip1 (Lyden et

al., 1999; Mori et al., 2000). Secondly, Id2 binds to the tumour suppressor proteins of the Rb

family and when in large excess, abolishes their growth-suppressing activity by causing the

release of E2F transcription factors required for cell cycle progression (Iavarone et al., 1994;

Lasorella et al., 1996). In normal cells, Id2 is a downstream target of pRb and its family

members which restrain its functions on natural targets. However, Id2 overexpressed by

tumour cells can override the Rb pathway and deprive the cell of its most important cell cycle

checkpoint (Lasorella et al., 1996). Furthermore, Rb pathway inhibition is believed to be

achieved directly through the binding of Id2 but also indirectly through CDK inhibitor

downregulation by all Id members (Ohtani et al., 2001).

Id2 has been shown to be a direct target of Myc transcription factors (Lasorella et al., 2000).

The ability of Myc to promote cell cycle entry in the absence of growth factors and its ability

to transform normal fibroblasts in cooperation with RAS have been shown to be strictly

dependent on the presence of Id2 (Lasorella et al., 2000). Also, human neuroblastoma

tumours presenting N-Myc amplification were shown to invariably overexpress Id2 (Lasorella

et al., 2000). Furthermore, Lasorella and co-workers demonstrated the dependence of Myc on

Id2 to overcome Rb-induced cell cycle block (Lasorella et al., 2000).

Id mRNA and protein levels have been reported to be elevated in a number of human tumours

including carcinomas of the prostate, breast, ovary, colon, rectum, pancreas, liver,

endometrium and thyroid, in squamous cell carcinomas of the nasopharynx, esophagus and

oral cavity, in neural tumours, Ewing’s sarcoma, leukaemia, as well as melanoma (reviewed

in Perk et al., 2005). In some cases, highs levels are associated with disease severity and poor

prognosis.

1.7.2 TGF-β and Id proteins

Members of the TGF-β super-family of cytokines are known regulators of the Id genes. BMPs

have been shown to upregulate the expression of Id genes in a wide range of cell lines and in

embryonic stem cells (reviewed in Miyazono and Miyazawa, 2002). Conversely, TGF-β and

35


activin A have been shown to repress the expression of Id genes in a number of epithelial cell

lines and in keratinocytes (Kang et al., 2003; Kondo et al., 2004; Kowanetz et al., 2004; Ling

et al., 2002; Rotzer et al., 2006). Furthermore, TGF-β-mediated repression of Id1 expression

has been suggested to be involved in TGF-β-induced cell growth inhibition in keratinocytes

and prostate epithelial cells (Di et al., 2006 ; Kang et al., 2003).

1.7.3 Id proteins and melanoma

In melanoma, Id levels have only been reported for Id1 and only limited publications report

on Id2 and melanoma. Polsky and co-workers have demonstrated a correlation between Id1

expression and loss of p16Ink4a expression in early melanoma (Polsky et al., 2001). In a recent

publication, the same group evaluated melanoma cell lines from different stages of

progression for protein levels of Id1 with unconvincing results (Ryu et al., 2007). While two

out of three RGP melanomas expressed high levels of Id1, only one out of four VGP

melanomas but two out of three metastatic melanomas expressed high levels of Id1 (Ryu et

al., 2007). Using tissue microarray, Straume and Akslen evaluated the expression of Id1 in

119 cases of nodular melanoma and showed that strong Id1 expression was significantly

associated with increased tumour thickness and reduced survival (Straume and Akslen, 2005).

Id2 was first mentioned in association with melanoma when it was identified as a

downregulated gene in melanomas with BRAFV600E or NRASQ61R mutations (Bloethner et al.,

2005) and later in melanomas with homozygous deletion of the CDKN2A locus genes

(Bloethner et al., 2006) in two global gene expression studies. In a microarray gene

expression analysis of uveal melanoma, which generated two subgroups representing tumours

with low and high risk of metastatic death, Id2 was one of the top class discriminating genes

(Onken et al., 2006). Id2 was strongly downregulated in melanomas with high risk of

metastatic death and suppression of Id2 expression in tumours with low risk of metastatic

death rendered them more aggressive (Onken et al., 2006).

36


1.8 Aim

The aim of this thesis is to validate a new melanoma progression model we formulated from

gene expression arrays performed on three distinct sets of melanoma cultures and to

investigate the role of TGF-β-like signalling as a major player in this newly defined model.

37

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48

Chapter 2-

2 Metastatic potential of melanomas defined by specific gene

expression profiles with no BRAF signature

49

This paper is the fruit of the first years of research of our newly founded melanoma research

group. Keith Hoek, as the key initiator of this project, generated the microarray data on which

this new model of melanoma progression is founded. My task involved the establishment of a

range of in vitro assays and the validation of this new model.

I optimised conditions for the generation of RT-PCR results shown in figure 2.3 and also

performed a number of Western blot analyses, three of which are shown in figure 2.4. Figure

2.5 shows motility assays which I undertook and I carried out the TGF-β-mediated growth

inhibition assays shown in table 2.2. Finally, I proofread the manuscript to ensure that the

proper use of English was employed and that experimental results and concepts were

displayed in a clear manner.

50

Metastatic potential of melanomas defined by specific gene

expression profiles with no BRAF signature

Keith S. Hoek1, Natalie C. Schlegel1, Patricia Brafford2, Antje Sucker3, Selma Ugurel3, Rajiv Kumar4, Barbara L. Weber5, Katherine L. Nathanson6, David J. Phillips7, Meenhard Herlyn2, Dirk Schadendorf3, Reinhard Dummer1

1Department of Dermatology, University Hospital of Zürich, 8091 Zürich, Switzerland 2The Wistar Institute, Philadelphia PA 3Skin Cancer Unit of the German Cancer Research Center, University Hospital Mannheim,

Mannheim, Germany 4Division of Molecular Genetic Epidemiology, German Cancer Research Center, Heidelberg,

Germany 5Abramson Family Cancer Research Insitute, University of Pennsylvania Cancer Center,

Philadelphia PA 6Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 7Monash Institute of Medical Research, Monash University, Clayton, Victoria, Australia

Pigment Cell Research (2006)19, 290-302

51

Chapter 2 Abstract/Introduction

2.1 Abstract

The molecular biology of metastatic potential in melanoma has been studied many times

previously and changes in the expression of many genes have been linked to metastatic

behaviour. What is lacking is a systematic characterisation of the regulatory relationships

between genes whose expression is related to metastatic potential. Such a characterisation

would produce a molecular taxonomy for melanoma which could feasibly be used to identify

epigenetic mechanisms behind changes in metastatic behaviour. To achieve this we carried

out three separate DNA microarray analyses on a total of 86 cultures of melanoma.

Significantly, multiple testing correction revealed that previous reports describing correlations

of gene expression with activating mutations in BRAF or NRAS were incorrect and that no

gene expression patterns correlate with the mutation status of these MAPK pathway

components. Instead, we identified three different sample cohorts (A, B and C) and found that

these cohorts represent melanoma groups of differing metastatic potential. Cohorts A and B

were susceptible to TGF-β-mediated inhibition of proliferation and had low motility. Cohort

C was resistant to TGF-β and demonstrated high motility. Meta-analysis of the data against

previous studies linking gene expression and phenotype confirmed that cohorts A and C

represent transcription signatures of weakly and strongly metastatic melanomas, respectively.

Gene expression co-regulation suggested that signalling via TGF-β-type and Wnt/β-catenin

pathways underwent considerable change between cohorts. These results suggest a model for

the transition from weakly to strongly metastatic melanomas in which TGF-β-type signalling

upregulates genes expressing vasculogenic/extracellular matrix remodeling factors and Wnt

signal inhibitors, coinciding with a downregulation of genes downstream of Wnt signalling.

2.2 Introduction

Cutaneous melanoma is an often aggressively metastatic and fatal neoplasm that accounts for

most skin cancer deaths (Balch et al., 2001). The early steps of melanoma progression are

marked by molecular changes in networks of adhesion molecules such as those involving the

cadherins, which mediate calcium-dependent intercellular adhesion through homotypic

interaction (Takeichi, 1991). Epithelial cadherin (E-cadherin) is lost very early in

transformation from melanocytes to melanoma. E-cadherin function is critical to melanocyte

52


homeostasis and its loss precipitates changes in the expression of other adhesion factors

(Haass et al., 2005; Hsu et al., 2000b; Li and Herlyn, 2000). Neuronal cadherin (N-cadherin)

is upregulated and mediates gap junction formation with N-cadherin-expressing dermal

fibroblasts (Hsu et al., 2000a), while placental cadherin is downregulated (Tsutsumida et al.,

2004). Another early event in progression of melanoma is the upregulation of matrix

metalloproteinase 2, which affects basal membrane integrity and dermal invasion (Vaisanen et

al., 1996). Dermal invasion itself correlates with the production of immune modifying factors

including interleukins, chemokines, and TGF-β. Later stages of metastasis see the induction

of a host of additional growth factors including CTGF, VEGF, bFGF, and PDGF, which

affect vascularisation and the growth of both melanoma and stromal cells (Bar-Eli, 2001; Hsu

et al., 2002b) (Hsu et al., 2002a). Recent work has uncovered the role of transcription factors

in orchestrating these changes. For example, the AP-2 transcription factor, associated with

regulating adhesion factors and c-Kit, has been shown to be downregulated with malignant

progression (Bar-Eli, 2001). In addition, microphthalmia-associated transcription factor (Mitf)

regulates tissue-specific expression of cyclin-dependent kinase 2, a relationship that may be

critical for melanoma growth (Du et al., 2004). Apart from changes in gene expression,

melanoma progression also is marked by other changes. For example, a change in response to

TGF-β marks a turning point in malignant progression. In some cancers, including melanoma,

early stages are characterised as being susceptible to TGF-β-mediated growth inhibition,

while later stages are increasingly less affected (Elliott and Blobe, 2005). Multiple studies

show that melanomas resistant to TGF-β-mediated growth inhibition are more aggressively

invasive and metastatic than variants that retain the growth-inhibited response (Heredia et al.,

1996; Krasagakis et al., 1994; Roberts et al., 1985). In other cancer types, such as breast and

colon, loss of growth control by TGF-β corresponds with increased motility and expression of

factors promoting invasion and metastasis (Derynck et al., 2001; Gold, 1999; Padgett, 1999;

Wright and Huang, 1996).

Recent analyses have focused on the serine/threonine kinase BRAF, which frequently

undergoes an activating mutation in melanoma (Davies et al., 2002). This likely lies in

BRAF’s role as a mediator of c-Kit activated RAS-ERK signalling (Linnekin, 1999) which is

a regulator of melanocytic cell proliferation (Wellbrock and Marais, 2005). BRAF gene

mutations, the most common being an activating V600E mutation, can occur early in

melanoma

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Chapter 2 Introduction/Results

progression, as they are frequently recorded in lesions prior to neoplastic transformation

(Pollock et al., 2003). Transformation of mouse melanocytes with mutant BRAF has yielded

phenotypically altered cells with in vivo tumorigenicity when transplanted into nude mice

(Wellbrock et al., 2004). The documented existence of patients with wild-type BRAF primary

lesions who later develop BRAF-mutant metastases indicates that BRAF mutations are

associated with metastasis (Shinozaki et al., 2004).

Melanomas exhibit a wide variety of characteristics and at least some are likely reflected in

their gene expression patterns. The construction of a transcription signature taxonomy for

melanoma and its association with elements of melanoma pathology would have significant

value and contribute to understanding the gene regulation of melanoma. Our laboratories

separately obtained the transcriptional profiles of three panels of melanoma cell cultures,

uncovered the transcriptional variations responsible for intrapanel sample differences, and

identified the coregulating gene sets among these. We then compared the results from

clustering analyses of the two cell culture panels and found congruence for both cluster group

(cohort) specific expression and co-regulation patterns in 223 genes. One of our laboratories

(Zürich) went on to further characterise one panel of melanoma lines where we uncovered a

relationship between sample organisation and in vitro characteristics for increased metastatic

capacity.

2.3 Results

2.3.1 No correlation between BRAF/NRAS mutations and gene expression

The results from DNA microarray analyses carried out by individual groups working on

similar problems often deviate appreciably due to differences in platform choice,

methodology, and sample selection. We addressed this by combining post-clustering results

from three laboratories working on different melanoma sets and filtering out data that did not

qualitatively agree in all. One set contained the transcriptional profiles of three melanocyte

cultures and 12 cell cultures derived from metastatic melanomas (Zürich data set). A second

set comprised 29 melanoma cell cultures obtained from 12 in situ melanomas, 14 metastatic

melanomas and three additional samples of unidentified origin (Philadelphia data set). A third

54

Chapter 2 Results

set comprised 45 melanoma cell cultures obtained from metastatic melanomas (Mannheim

data set). The transcriptional profiles were normalised within data sets and subsequent

analyses were performed separately for each data set. These data sets have been deposited in

NCBIs Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and are

accessible using GEO Series accession GSE4845.

We were particularly interested in whether gene expression profiles could be used to

determine pathways that were important in defining melanomas with metastatic potential and

especially the potential involvement of activating mutations in BRAF and NRAS in

determining metastatic potential. Having sufficient information concerning BRAF and NRAS

mutation status in many of our samples we attempted to test the findings of others (Bloethner

et al., 2005; Pavey et al., 2004) who describe melanoma gene expression patterns correlating

with activating mutations in either or both of BRAF and NRAS genes. The Zürich data set

provided four samples which were wild-type for both BRAF and NRAS, four samples which

carried the BRAFV600E mutation, and four samples which carried either the NRASQ61K or

NRASQ61R mutations. The Philadelphia dataset provided four samples which were wild-type

for BRAF and ten samples which carried the BRAFV600E mutation, no NRAS mutation data is

available for this set. The Mannheim dataset provided eleven samples which were wild-type

for both BRAF and NRAS, fifteen samples which carried the BRAFV600E mutation only, and six

samples which carried either the NRASQ61K or NRASQ61R mutations (Appendix A,

Supplementary data 1). For each of the three data sets we applied ANOVA selection and

multiple-testing correction (Benjamini et al., 2001) to look for gene expression changes

correlating with activating mutations of BRAF or NRAS. We did not find any genes for which

expression changes correlated with activating mutations in either of BRAF and NRAS. We

obtained the data set used by Pavey et al. (2004) to identify genes which were co-regulated

with BRAF. We used an identical protocol to the one they describe and we found a similar

group of genes as they did. However, when we included multiple testing controls in their

protocol we found only one gene whose expression changes correlated with BRAF mutation

status (data not shown). We considered the possibility that correlations may instead be found

within cohorts, analyses performed using samples within the same cohort would not be

influenced by genes with cohort-specific expression. The Mannheim data set is large enough

to look within each of the cohorts for correlations between BRAF mutation status and gene

expression, however we did not find any statistically significant correlations. We find no

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http://www.ncbi.nlm.nih.gov/geo/

Chapter 2 Results

genes whose changes in expression consistently correlate with activating mutations in either

BRAF or NRAS across the data sets we tested.

2.3.2 Microarray analyses reveal three cohorts

Given the absence of a relationship between gene expression and activating mutations of

BRAF or NRAS, we were interested in comparing the sample organisation between each of the

three data sets. For the Zürich data set, normalisation and fold-change selection of probe sets,

followed by unbiased hierarchical clustering of sample profiles revealed that the samples

encompassed four stable cohorts (cohorts are here defined as groups of samples which have

closely related transcriptional signatures): one cohort of melanocyte samples and three

distinct cohorts of melanoma samples. An identical protocol applied to both the Philadelphia

and Mannheim data sets yielded three stable melanoma cohorts each.

Figure 2.1. Identification of three distinct melanoma cohorts. DNA microarray data collected for melanocyte cultures and melanoma cultures were subjected to an unbiased hierarchical clustering protocol. Multiple clustering analyses using multiple pools of data showed that melanoma samples consistently cluster within one of three groups termed cohorts A (dark blue), B (yellow), and C (cyan). Displayed above are sample and gene clusterings performed using 296 probe sets with cohort-specific expression common to all data sets: Zürich (tree A; which includes the melanocyte cluster indicated by a red bar), Philadelphia (tree B) and Mannheim (tree C). Probe set coloring reflects normalised signal intensity, red being the highest and green the lowest.

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Chapter 2 Results

Figure 2.1 shows the three data sets’ cohort distributions after reclustering using genes with

cohort-specific expression (see next section). These data indicated that, from a transcriptional

context, melanoma cell cultures are divisible into three recognisably discrete groups, which

we call cohorts A, B, and C.

2.3.3 Two groups of co-regulated genes define the cohorts.

Having identified the cohort structure of the data sets we wanted to know if the genes which

determined these similar distributions were the same. We used multiple testing controlled

statistical methods to select probe sets with cohort-specific expression patterns and in this

way identified 1973 probe sets in the Zürich data set, 1398 in the Philadelphia data set and

5753 in the Mannheim data set. Comparing these data sets identified 296 common probe sets,

equivalent to 223 individual genes (Appendix A, Supplementary Data 2A). With a pool of

shared genes having cohort specific expression patterns we were interested in clustering

similarly regulated genes together so that we might determine the signalling pathways

underlying the regulation of their expression. In order to identify which genes are co-

regulated and how they are co-regulated with respect to the cohorts we used a clustering

algorithm, self-organised map (SOM) analysis, to sort the cross-sample expression patterns in

the Zürich data set. This revealed two major expression patterns of strong co-regulation

involving many different genes (Fig. 2.2).

Figure 2.2. Self-organised map clustering. The 296 probe sets showing cohort-specific expression in both Zürich and Philadelphia data sets were subjected to SOM clustering. Two major expression patterns were revealed: Motif 1 (bottom right cell) and Motif 2 (top left cell), containing 70 and 77 probe sets, respectively. Displayed here is the SOM analysis performed using the Zürich samples. Data is presented as mean normalised signal intensity profile with 95% confidence boundaries. The Y-axis refers to normalised probe set signal intensity (0.1 to 100) and the X-axis denotes sample order arranged as depicted in Figure 2.1. The number of probe sets clustering within a cell is noted in its top-left corner.

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Chapter 2 Results

For the 223 common genes we found that 105 were tightly linked to one of two distinct motifs

(a motif is here defined as a pattern of expression shared by a number of correlating probe

sets as observed across the samples), the members of which are likely co-regulated in

melanoma (Appendix A, Supplementary Data 2B). These two patterns of co-regulated gene

expression are sufficient to differentiate between the identified melanoma cohorts. Motif 1

shows downregulation of 51 co-regulated genes in cohort C compared to those in cohorts A

and B. Motif 2 shows upregulation of 54 co-regulated genes in cohorts B and C compared to

those in cohort A (Fig. 2.2.). These results were particularly interesting because many of the

co-regulated genes within Motif 1 are involved in melanocytic and neural crest differentiation

(Table 2.1).

Table 2.1. Motif genes regulated by Mitf/Sox10 (Motif 1) or TGF-β (Motif 2). Gene name Symbol Regulator

2.3.3.1.1.1 Reference tyrosinase related protein 2 DCT Mitf (Yasumoto et al., 2002) ocular albinism 1 GPR143 Mitf (Vetrini et al., 2004) absent in melanoma 1 MATP Mitf (Du and Fisher, 2002) melan-A MLANA Mitf (Du et al., 2003) gp100 / Pmel17 SILV Mitf (Du et al., 2003) melastatin TRPM1 Mitf (Miller et al., 2004) tyrosinase TYR Mitf (Hou et al., 2000) tyrosinase related protein 1 TYRP1 Mitf (Fang et al., 2002) endothelin receptor type B EDNRB Sox10 (Zhu et al., 2004) c-erbB3 ERBB3 Sox10 (Britsch et al., 2001) myelin basic protein MBP Sox10 (Stolt et al., 2002) microphthalmia-associated transcription factor Mitf Sox10 (Potterf et al., 2000) proteolipid protein 1 PLP1 Sox10 (Stolt et al., 2002) biglycan BGN TGF-β (Ungefroren et al., 2003) N-cadherin CDH2 TGF-β (Tuli et al., 2003) collagen type V, alpha 1 COL5A1 TGF-β (Lawrence et al., 1994) connective tissue growth factor CTGF TGF-β (Igarashi et al., 1993) fibrillin 1 FBN1 TGF-β (Lorena et al., 2004) basic fibroblast growth factor FGF2 TGF-β (Finlay et al., 2000) interleukin 6 / interferon β2 IL6 TGF-β (Park et al., 2003) inhibin βA INHBA TGF-β (Hubner and Werner, 1996) lysyl oxidase LOX TGF-β (Green et al., 1995) PAI-1 SERPINE1 TGF-β (Macfelda et al., 2002) transgelin TAGLN TGF-β (Chen et al., 2003) TGF-β-induced, 68kDa TGF-BI TGF-β (Skonier et al., 1992) thrombospondin 1 THBS1 TGF-β (Hugo, 2003) tumor necrosis factor receptor 11b TNFRSF11B TGF-β (Thirunavukkarasu et al., 2001) tropomyosin 1 (alpha) TPM1 TGF-β (Tada et al., 2000) tropomyosin 2 (beta) TPM2 TGF-β (Bakin et al., 2004)

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Chapter 2 Results

On the other hand, members of the Motif 2 transcription profile are involved in modifying

extracellular environments and a large fraction of these genes, as well as others in the motif,

are subject to TGF-β regulation (Table 2.1). This is particularly interesting because many of

these genes are thought to be involved in invasive and metastatic behaviours. To confirm gene

expression trends shown by the motifs, we used reverse transcription polymerase chain

reaction (RT-PCR) to validate relative expression levels for three Motif 1 genes and three

Motif 2 genes (Fig. 2.3).

Figure 2.3. RT-PCR validation of select genes. Three genes from Motif 2 (BGN, CTGF, SERPINE1) and three from Motif 1 (MLANA, TYR, SOX10) were selected for validation by lightcycler RT-PCR. Samples come from the Zürich data set.

For the two regulatory proteins we hypothesised to have functional significance for the

observed cohort-specific expression patterns, Motifs 1 and 2, we conducted western analyses.

For Motif 1, in which expression changes are correlated with neural crest and pigmentation

genes, we studied the Microphthalmia-associated transcription factor Mitf. We chose Mitf as

it is a transcription factor crucial to processes of neural crest differentiation, proliferation and

survival (Carreira et al., 2005; Garraway et al., 2005; Loercher et al., 2005). We find that Mitf

protein levels correlate closely with Mitf gene expression (Fig. 2.4). For Motif 2, in which

expression changes are correlated between genes regulated by TGF-β, we chose to examine

the levels of secreted activin A into conditioned media. Activin A, a cytokine which functions

similarly to TGF-β (Phillips et al., 2005), is a homodimer of a polypeptide encoded by the

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Chapter 2 Results

INHBA gene present in Motif 2. We found that activin A secretion correlated well with

INHBA gene expression (Fig. 2.4). The genes identified suggest that the cohorts may reflect

differences in metastatic potential between melanomas as regulated by changes in Wnt and

TGF-β-like signalling.

Figure 2.4 Comparison of expression and western blot patterns. Cell extracts, taken from the Zürich data set cell cultures, were subjected to western blotting against Mitf, while conditioned media (24 h) were similarly assessed for TGF-β1 and Activin A secretion. Activin A was also assessed by ELISA. Normalised signal intensity data for the Mitf and INHBA genes, extracted from Zürich data set DNA microarray experiments, are also shown.

2.3.4 The cohorts reflect differences in metastatic potential

Because the genes most responsible for the cohort distributions in our data sets seemed to

include factors known to be involved in melanoma metastatic potential, we investigated the

literature to find factors with known expression differences correlating with metastatic

potential or patient prognosis and compared them with our dataset. We found references to

134 different genes (Appendix A, Supplementary Data 3b). We then examined these 134

genes for expression in the Zürich data set and generated Student’s two-sample t-test P-values

for their differences in expression between cohorts A and C. We found that, of these, 54 genes

were significantly (P<.05) and appropriately up- or downregulated between cohorts A and C

for a gene expression model in which these cohorts are regarded as weakly and strongly

metastatic, respectively (Appendix A, Supplementary Data 3a). Additionally, we find that 20

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Chapter 2 Results

genes are present in the 223 gene intersection from ANOVA analyses of the Zürich,

Philadelphia and Mannheim data sets. The probability of at least 20 genes being selected by

chance, as well as their having appropriate expression patterns, is calculated using

hypergeometric distribution to be less than 2.7 x 10-27. This strongly suggests that the cohort

distributions in our data sets are reflections of differences in metastatic potential.

2.3.5 In vitro tests support the link between cohort distribution and metastatic potential

In order to better understand the characteristics of the different cohorts and provide further

evidence for their association with metastatic potential, the Zürich cell cultures were assayed

for their in vitro motility and their proliferation in the presence and absence of TGF-β. In the

case of motility, tumour cell motility is a well understood aspect of metastatic potential (Raz,

1988) and cultures of aggressively metastatic melanomas have been reported to show greater

in vitro motility relative to cultures of weaker metastatic character (Quinones and Garcia-

Castro, 2004). A scratch wound assay was used to assess motility. In vitro motility assays

showed that a cleared field repopulated within 24 hours with melanomas from cohorts A and

C, while cohort B melanomas were appreciably slower (Fig. 2.5A-F). Time-lapse

photography showed that melanomas from cohort A, rather than migrating across the

substrate, repopulated the cleared field by detaching from the culture plate and diffusing over

the field before reattaching (data not shown). No detachment/reattachment activity was

observed with melanomas from cohorts B or C.

Figure 2.5 In vitro motility and in vivo Mitf/β-catenin. (A–F) Cultured melanomas from each of the three DNA microarray–identified cohorts were investigated for the potential to migrate into a cell-free scratch region. Shown here is migration at 0 and 24 h for cohort A (A,D), cohort B (B,E) and cohort C (C,F). (G–J) Paraffin-embedded tissue samples, the original sources for cohort B (G,H) and cohort C (I,J) were examined by histochemical staining for Mitf (G,I) and β-catenin (H,J). The cohort B melanoma shows nuclear staining for Mitf coinciding with strong staining for β-catenin, whereas the cohort C melanoma shows no nuclear staining for Mitf and little for β-catenin. Nuclear staining for Mitf coincides with strong peripheral staining for β-catenin.

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Chapter 2 Results

Several researchers have shown the link between melanoma resistance to TGF-β and

metastatic behaviour (Krasagakis et al., 1999; Medrano, 2003; Rodeck et al., 1994). While

TGF-β stimulation of neural crest stem cells precipitates their transformation into smooth

muscle cells (Shah et al., 1996), it induces apoptosis in neural crest derived melanocytes

(Alanko and Saksela, 2000). Similarly, it has also been shown that TGF-β can inhibit the

growth of some melanomas (Ladanyi et al., 2001). Heredia and coworkers showed that as

melanoma cell lines become more differentiated (i.e. they resemble a neural crest derivative

less and less), they are less sensitive to TGF-β-mediated growth inhibition (Heredia et al.,

1996). The proliferation assays showed that in comparison to cohort A melanomas, most

cohort C melanomas were only weakly or not at all inhibited by 5 ng/ml of exogenous TGF-β

(Table 2.2). We also found that base growth rates of cohort A melanomas were almost two

fold greater than cohort C melanomas (Table 2.2). These in vitro tests support the hypothesis

that the identified cohorts are linked to metastatic potential.

Table 2.2. Cohort distribution, TGF-β-mediated growth inhibition and base proliferation rate.

Sample Cohort % inhibition Relative proliferation

M000921 46 1 M010817 53 0.53 M980513

A 36 0.49

M000216 28 0.34 M990514 19 0.48 M000907

B 11 0.44

M010119 3 0.21 M991121 10 0.18 M010322 51 0.91 M010308 0 0.12 M990115 0 0.29 M010718

C

0 0.39

2.3.6 Wnt signalling controls Motif 1

The identity of the genes contributing to the Motif 1 expression pattern (Appendix A,

Supplementary data 2b) suggest that in melanoma this gene set is regulated by Wnt signalling.

To explore the possible relationship between Wnt signalling and neural crest/pigmentation

gene expression we used immunohistochemistry. We compared the levels of β-catenin and

Mitf in paraffin-embedded biopsy samples of one melanoma from each of cohorts B and C

that show differential Mitf expression. We found that melanoma nuclear staining for Mitf

correlated with strong immunoreactivity for β-catenin in cohort B, whereas in cohort C

melanoma there was very little staining for Mitf and β-catenin (Fig. 2.5G-J). This is evidence

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Chapter 2 Results / Discussion

that in cohort C melanomas β-catenin is subject to increased turnover, suggesting that in these

cells Wnt signalling has been deactivated.

2.4 Discussion

The patterns of gene regulation we identified and the signalling pathways they suggest, lead

us to construct a model for the gene regulation of melanoma metastatic potential in melanoma

(Fig. 2.6). Proliferative and weakly metastatic cells maintain a neural crest-like transcriptional

signature through Wnt signalling. However, the induction of TGF-β-like signalling, possibly

through microenvironmental changes brought about by hypoxia or inflammation, drives the

expression of factors inhibitory to Wnt signalling and resulting in cells which are less

proliferative but have high metastatic potential (Fig. 2.6).

Figure 2.6. A gene regulation model for melanoma metastatic potential. Left panel, the neural crest phenotype of cohort A cells is set by Wnt signalling which diverts β-catenin from ubiquitination to the nucleus where it participates in the regulation of neural crest genes, resulting in proliferative cells with weak metastatic potential. Right panel, the TGF-β-like signalling apparent in cohort C cells upregulates factors which drive a positive feedback signal, change the extracellular environment and inhibit Wnt signalling. Subsequent β-catenin degradation interrupts expression of neural crest genes, resulting in weakly-proliferative cells with high metastatic potential.

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Chapter 2 Discussion

There have been two publications which have described correlations between activating

mutations in BRAF and/or NRAS and changes in gene expression (Bloethner et al., 2005;

Pavey et al., 2004). Interestingly, the gene lists these groups choose to highlight in their

studies are entirely different. In contrast to their findings, we instead discovered that when

multiple testing correction (Benjamini et al., 2001) is included in the analyses, activating

mutations in BRAF and/or NRAS do not correlate with any gene expression changes in

melanoma. We further discovered that when multiple testing correction is applied to the

analysis originally performed by Pavey et al. (2004) on their data, no correlating genes are

identified. Our findings indicate that the contribution to melanoma pathology by activating

mutations of BRAF or NRAS does not involve their direct regulation of gene expression.

Subsequent to this we looked for a consistent molecular taxonomy of melanoma by assessing

86 different cultures of melanoma divided between three groups separately performing DNA

microarray experiments. We found that melanomas can be categorised within a small number

of cohorts based on their transcriptional signature. The cohorts distinguish melanomas which

maintain a neural crest-type transcription signature (cohorts A and B) from melanomas which

are significantly differentiated from this signature (cohort C). Identification of the genes with

cohort-specific expression shows that twenty of them have previously recorded associations

with metastatic potential (Appendix A, Supplementary Data 3a). This in combination with our

in vitro experiments shows that the cohorts are representative of differences in metastatic

potential. Additionally, our study has identified an additional 203 genes as having expression

patterns linked to metastatic potential. An earlier study also used DNA microarrays to

characterise the molecular signature of invasive melanomas (Bittner et al., 2000), identifying

several genes which we also find similary regulated in melanomas we characterise as having

high metastatic potential. Likewise, Seftor and co-workers compared the expression profiles

of highly invasive and poorly invasive uveal melanoma cell cultures and observed many

genes for whom expression was different in the two groups (Seftor et al., 2002). de Wit and

co-workers also used expression profiling to compare two human melanoma cell cultures of

differing capacity to metastasise to the lungs of nude mice (de Wit et al., 2002). We expand

upon these findings, which help to confirm our assignment of cohort distribution to variations

in metastatic potential, to show that many of these genes are co-regulated and that the

signalling pathways behind the regulation of their expression are identifiable.

64


Our analysis of co-regulated gene expression, which specifically assessed pattern correlation,

found that most of the cohort-differentiating genes were split between only two expression

patterns (motifs) which dominate the cohort-specific data (Fig. 2.2). Close correlation of

many different genes’ expression patterns implies that such genes are, within the system being

studied, subject to a single transcriptional mechanism. This mechanism necessarily includes

both the transcription factors and the signalling cascades which activate them. Our data

implies that only two transcriptional programs underlie the molecular differences between

strongly and weakly metastatic melanomas. Furthermore, these motifs are approximate

mirror-images, which suggests that the transcriptional program of one may influence the

other. To determine what these transcriptional programs might be we revisited the literature to

evaluate what transcriptional regulators might link the genes in question together.

Motif 1 focuses on genes critical to neural crest differentiation and cell cycle control. Recent

data suggest a connection between cell cycle control and melanoma genetics as the

microphthalmia-associated transcription factor (Mitf), a master regulator for melanocytic

differentiation (Steingrimsson et al., 2004), regulates the CDK2 gene in primary melanoma

and subsequently plays an important role in melanoma cell cycle regulation (Du et al., 2004;

Garraway et al., 2005). We found that CDK2 and all known melanocytic Mitf-regulated genes

share a Motif 1 profile (Table 1), as does the Mitf gene itself. Having found that Mitf

expression was also downregulated in cohort C melanomas, we examined the data to identify

upstream transcription factors. Among the many other genes sharing the Motif 1 expression

pattern, the neural crest cell regulator SOX10 was identified as a candidate for regulating Mitf

expression and subsequent regulation of the CDK2/melanocytic differentiation set. Previous

studies have shown that ectopically expressed SOX10 regulates Mitf expression (Huber et al.,

2003; Lee et al., 2000; Potterf et al., 2000) and other SOX10-regulated genes are present

within the Motif 1 expression pattern group (Table 1). Several other factors in the Motif 1

group likely are regulated by one or both of SOX10/Mitf. Among these is Rab38, which is

expressed in melanocytes and abrogation of its function is responsible for oculocutaneous

albinism in mice (Osanai et al., 2005). As the SOX10/Mitf axis is likely central to the

established regulatory pattern of Motif 1, we then investigated the genes that might be

regulating SOX10/Mitf. The downregulation of SOX10 and Mitf in cohort C melanomas is

probably due to an interruption in Wnt/β-catenin signalling. In Xenopus, ectopic Xwnt-1

enhanced SOX10 expression and ectopic glycogen synthase kinase 3 (GSK3), an antagoniser

of Wnt signalling, blocks SOX10 expression (Aoki et al., 2003; He et al., 1995). Also,

65


canonical Wnt signalling through the Wnt/β-catenin pathway is a critical activator of Mitf

expression (Dorsky et al., 2000; Shibahara et al., 2001; Yasumoto et al., 2002). Most

convincingly, it was shown that Mitf expression in melanoma can be regulated by β-catenin

(Widlund et al., 2002). Candidate genes for interrupting the Wnt/β-catenin pathway include

several genes with the Motif 2 expression pattern. Perhaps the most interesting factor that can

negatively regulate the Wnt signalling pathway is connective tissue growth factor, the gene

for which is one of the most strongly expressed in cohort C melanomas. It was shown to bind

the Wnt co-receptor LDL receptor–related protein 6 and inhibit Wnt signalling (Mercurio et

al., 2004). Other candidate inhibitors whose genes are present in Motif 2 include Wnt-5a

(Ishitani et al., 2003), Dickkopf 1 (Zorn, 2001), Dickkopf 3 (Mao et al., 2001), and cysteine-

rich angiogenic inducer 61 (Latinkic et al., 2003). A further sign that the Wnt/β-catenin

pathway has been disregulated in cohort C melanomas is the presence of the gene encoding

transcription factor AP-2α (TFAP2A) in Motif 1. AP-2α has previously been shown to be

downregulated in metastatic melanomas (Bar-Eli, 2001). Luo and co-workers linked Wnt

signalling with increased TFAP2A expression in Xenopus development (Luo et al., 2003).

For Motif 2 genes (upregulated in melanoma of high metastatic potential) we found many

regulated by TGF-β-type signalling (Table 1). This finding indicates that a difference exists in

TGF-β-type signalling between cohorts. However, in the transcription record, we found no

cohort-specific expression patterns among the TGF-β receptor or Smad genes. Likewise, no

cohort-related change exists in expression of the three TGF-β variants (TGF-B1, TGF-B2, and

TGF-B3). Furthermore, conditioned media samples showed little difference in TGF-β1

production across the samples (Fig. 2.4). While TGF-β production has been shown previously

by several groups (Krasagakis et al., 1999; Moretti et al., 1997; Reed et al., 1994), and while

increased resistance to TGF-β is associated with increased metastatic behaviour (Heredia et

al., 1996), no group has clearly linked TGF-β production with increased metastatic behaviour.

However, other autocrine effectors with appropriate expression patterns and TGF-β-like

effects are present in our data. For example, activin A is a TGF-β-family signalling molecule

that does have a cohort-specific expression pattern. The protein is a homodimer of inhibin

beta A polypeptide whose gene (INHBA) is a member of the Motif 2

group (Data Supplement 1). The cohort specificity of this expression was supported by

experiments that measured activin A secretion into conditioned media (Fig. 2.4).

Additionally, the gene for thrombospondin 1 (THBS1), another member of Motif 2, encodes a

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Chapter 2 Discussion / Material and Methods

secreted matricellular factor that mediates changes in cell-cell and cell-matrix interactions

(Bradshaw and Sage, 2001). Thrombospondin 1 also activates TGF-β, binding latent TGF-β

and changing its conformation to an activated form (Schiemann et al., 2003). The secretion of

these factors into the media is likely to contribute to an activated TGF-β-like signal.

We present here data that strongly suggests a transcriptional taxonomy for melanoma not

related to neoplastic transformation (Hoek et al., 2004) or stage progression (Smith et al.,

2005), but rather is separately concerned with metastatic potential. We note that

immunohistochemical analyses of melanoma biopsies, using antibodies targeting the products

of genes which we mention, often yield heterogeneous staining patterns among otherwise

morphologically similar cells. This suggests to us that individual melanoma growths are

comprised of cells from across the cohort spectrum, including populations which are

proliferative but less metastatic than others less proliferative and yet more invasive, TGF-β-

resistant, and secreting factors which change microenvironmental architecture and encourage

neovasculogenesis. We suspect that microenvironmental cues, such as hypoxia and

inflammation, may allow melanoma cells to switch epigenetically between cohort

transcriptional signatures.

2.5 Material and Methods

2.5.1 Cell Culture and Media

Surplus material from cutaneous melanoma metastases removed by surgery were obtained

after written informed consent. Clinical diagnosis was confirmed by histology and

immunohistochemistry. Melanoma cells were released from tissue sections and grown as

previously described (Geertsen et al., 1998). Melanocytes from infant foreskins were cultured

in Medium 254 with Human Melanocyte Growth Supplement (Cascade Biologics, Portland,

OR) at 37°C in an atmosphere of 5% CO2.

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Chapter 2 Material and Methods

2.5.2 Genotyping

DNA was extracted from each cell line using a QIAamp DNA Mini kit (Qiagen, Valencia,

CA). Codons 597 and 600, and codons 12, 13, and 61 of BRAF and NRAS, respectively, were

amplified using primers and amplification protocols as described by Pavey et al. (Pavey et al.,

2004). Purified PCR products were submitted for sequencing to Microsynth (Balgach,

Switzerland).

2.5.3 Total RNA Extraction and Expression Profiling

Total RNA was extracted from melanocyte and melanoma cell cultures using Trizol according

to manufacturer instructions (Invitrogen, Carlsbad, CA). When melanin was present in the

extract, we used a filter-based method, specifically the RNA-4PCR kit (Ambion, Austin, TX),

to separate melanin from the total RNA. Extracted total RNA was used to synthesize poly(T)-

primed double-stranded cDNA to provide a template for the transcription of biotin-labeled

RNA according to recommendations provided by the manufacturer (Affymetrix, Santa Clara,

CA). The biotin-labeled RNA was hybridized to Affymetrix HG-U133 set oligonucleotide

microarrays following the manufacturer’s protocol. The Zürich group used both HG-U133A

and HG-U133B chips, the Philadelphia group used the HG-U133A chip only, and the

Mannheim group used the HG-U133 plus 2.0 chip. After hybridization, the microarrays were

washed and stained using a GeneChip Fluidics Station 400 (Affymetrix), and scanned using a

GeneArray Scanner (Agilent Technologies, Palo Alto, CA). The raw signal intensity data was

scaled to an arbitrary mean value of 500 by MAS 5.0 software (Affymetrix). For select genes

we performed real time quantitative PCR to validate gene expression using primers and

conditions as outlined in Supplementary Data 4 (Appendix A).

2.5.4 Microarray Data Analysis

The Zürich, Philadelphia and Mannheim data sets were processed separately using identical

protocols. All normalisations and analyses were performed using GeneSpring GX 7.3

(Agilent Technologies, Palo Alto, CA). Probe set data values below 0.01 were set to 0.01 and

each measurement was divided by the 50th percentile of all measurements in that sample.

Finally, each probe set measurement was divided by the median of its measurements in all

68


samples. For unbiased sample clustering, hierarchical clustering was performed many times

using different pools of probe sets and the results compared to find stable clusters of samples.

Pools of probe sets were generated by using individual samples to act as denominators for

fold-change comparison against the remaining samples. Probe sets with a two-fold change in

normalised signal intensity between the denominator sample and at least half the remaining

samples were selected. All samples were used in this way to generate separate pools of probe

sets which were then individually subjected to sample clustering analyses. Hierarchical

clustering of samples was performed using Standard, Pearson, and Spearman correlation

algorithms for each pool in order to control for similarity measure artifacts. The results of

hierarchical clustering experiments were compared to identify stable clusters of samples

which were then termed “cohorts.” To determine the gene expression patterns differentiating

between sample cohorts, a statistical analysis (ANOVA) was used to identify probe sets with

cohort-specific expression patterns. The statistical analysis used a parametric test in which all

variances were assumed to be equal, a p-value cut-off of 0.05 was used and the Benjamini

Hochberg false discovery rate (Benjamini et al., 2001) was employed for multiple testing

correction. Post Hoc testing was performed using the Tukey test to determine statistical

significance of differences between the probe sets of specific cohort pairs. All statistical tests

were two-sided. Cohort-specific expressing probe sets common to the Zürich, Philadelphia

and Mannheim data sets were subjected to gene tree clustering using a Standard correlation

algorithm. Self organized map (SOM) analysis was used to sort cohort-specific expression

patterns across a 4×3 matrix over 15 000 iterations using a neighbourhood radius of 4. This

process randomly distributes probe set expression patterns among a 4×3 matrix of cells. The

signal values across the samples for each probe set are converted into a single n-dimensional

vector (where n is the number of samples). The angular distance between probe set vectors

within a cell is calculated and the probe sets are divided into tow equal groups. The first group

represents the 50% of probe sets within the cell which form the population with the least

angular difference from the centroid, the other group represents the remaining 50% of probe

sets within the cell. The first group is retained within the cell and the remaining probe sets are

randomly distributed between neighbour cells in the SOM. The reach of this distribution is

determined by the user. This process is repeated over many thousand iterations, with closely

similar vectors remaining closely linked in the SOM while dissimilar vectors tend to separate

widely. The SOM was used to identify the major expression Motifs (across-sample expression

patterns of groups of correlating probe sets) responsible for defining sample cohorts. Probe set

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identifications were confirmed using the NetAffx Analysis package accessible from

Affymetrix (http://www.affymetrix.com).

2.5.5 Growth Inhibition Assays

Aliquots of 2×104 cells were seeded into 24-well microplates with 400 µL of standard media

containing 0 or 5 ng/mL of TGF-β1 (Biosource, Camarillo, CA) and incubated for four days

under conditions previously outlined in the cell culture section. Cell metabolic activity was

determined with a standard colorimetric assay measuring 3-(4,5-dimethyldiazol-2-yl)-2,5

diphenyl tetrazolium bromide (MTT; Sigma–Aldrich, St Louis, MO) reactivity and used as an

approximation of cell proliferation. Growth inhibition was expressed as a percentage of

growth inhibition compared against growth in the absence of TGF-β1. Each experiment was

performed four times.

2.5.6 Motility Assays

Cell migration in cultures was measured using a two-dimensional in vitro scratch motility

assay. Cells were grown to confluence on tissue culture dishes under standard growth

conditions. A wound of several millimetres in length and approximately 1 mm wide was

made by scratching through the monolayer using a sterile Gilson 200-µL pipette tip. After

washing with phosphate-buffered saline (PBS) and replacing the growth medium, cells were

incubated under standard conditions for 24 h and then observed for repopulation of the

cleared field.

2.5.7 Western Analyses and ELISA

Growth-conditioned media was prepared by growing cells to confluency in a 75-cm2 flask

under previously described conditions(Geertsen et al., 1998), these were then washed with

PBS and placed into fresh serum-free media for 24 h. Growth-conditioned media was

collected, filtered through a 0.22-µm filter and stored at -80°C. 100 µL of growth-conditioned

media was concentrated 10-fold by acetone precipitation. Proteins were separated by SDS-

PAGE under reducing conditions and transferred onto nitrocellulose membrane (Invitrogen).

70


Western blotting used rabbit polyclonal to TGF-β1 used at 1:200 (Abcam) and rabbit anti-

inhibin βA (gift of Prof. S. Werner) used at 1:2 000. Bound antibodies were detected using

horseradish peroxidase-conjugated anti-rabbit secondary antibody (BioRad, Hercules, CA)

and developed using ECL (GE Healthcare, Piscataway, NJ). Activin A was also measured in

conditioned media samples using a specific enzyme linked immunosorbent assay (Knight et

al., 1996) according to the manufacturer’s instructions (Oxford Bio-Innovations, Oxfordshire,

UK), with some modifications for media samples as described previously (Buzzard et al.,

2003). The average intra- and interplate coefficients of variation for this assay are routinely

<8% and the lower limit of detection is 10 pg/ml.

Cells were grown to confluency as previously described, washed twice with cold phosphate-

buffered saline and lysed at 4°C in lysis buffer containing 20 mM Tris-HCl (pH 7.5), 1%

Triton X-100, 150 mM NaCl, 10% glycerol, protease inhibitors (Roche) and phosphatase

inhibitors (Sigma). After quantification, 10 µg of each sample was separated by SDS-PAGE

under reducing conditions and transferred onto nitrocellulose membrane (Invitrogen).

Western blotting used mouse anti-Mitf AB-1(C5) used at 1:1 000 (Lab Visions). Goat

polyclonal antibody against actin (Santa Cruz Biotechnology) was used at 1:1 000 as a

control. Bound antibodies were detected using horseradish peroxidase-conjucated anti-mouse

(Abcam) or anti-goat secondary antibodies (SantaCruz) and developed using ECL (GE

Healthcare).

2.5.8 Immunohistochemistry

Paraffin-embedded tissue sections were stained using the alkaline phosphatase-anti-alkaline

phosphatase technique and counterstained using hemotoxylin. Antibodies used were directed

against β-catenin (Transduction Laboratories, Lexington, KY) and Mitf (DakoCytomation,

Glostrup, Denmark).

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Ungefroren, H., Lenschow, W., Chen, W.B., Faendrich, F. and Kalthoff, H. (2003) Regulation of biglycan gene expression by transforming growth factor-beta requires MKK6-p38 mitogen-activated protein Kinase signaling downstream of Smad signaling. J Biol Chem, 278, 11041-11049.

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Vetrini, F., Auricchio, A., Du, J., Angeletti, B., Fisher, D.E., Ballabio, A. and Marigo, V. (2004) The microphthalmia transcription factor (Mitf) controls expression of the ocular albinism type 1 gene: link between melanin synthesis and melanosome biogenesis. Mol Cell Biol, 24, 6550-6559.

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Chapter 3

3 In vitro phenotype validation..

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3.1 Introduction

We have characterised two different transcription signatures for melanoma cell lines which,

based on known functions of the genes involved, defined their respective contributions to

metastatic potential as either proliferative or invasive (Hoek et al., 2006). We have further

identified two patterns of co-regulated gene expression, so called motifs, which are sufficient

to differentiate between the identified melanoma cohorts. Motif 1 comprises a number of co-

regulated genes involved in melanocytic and neural crest differentiation and is thought to be

controlled by Wnt signalling (Hoek et al., 2006). On the other hand, members of the Motif 2

transcription profile are involved in modifying extracellular environments and a large fraction

of these genes, as well as others in the motif, are known to be subject to TGF-β regulation

(Hoek et al., 2006).

We have identified TGF-β response as a phenotype discriminating between melanoma cells

with differing metastatic potential (Hoek et al., 2006). There is now a large body of published

evidence showing that canonical Smad signalling does not account for all TGF-β effects and

that a number of pathways, including MAPK pathways, may be either activated by TGF-β or

modulate its signalling (reviewed in Javelaud and Mauviel, 2005; Massague, 2003).

Furthermore, Smad signalling is modulated by nuclear repressors such as Ski (Xu et al.,

2000).

We used motility and TGF-β susceptibility as in vitro phenotypes to support the link between

transcription signature cohort distribution and metastatic potential (Hoek et al., 2006).

Additionally, vasculogenic mimicry is another phenotype discriminating between highly

invasive and poorly invasive melanoma (Maniotis et al., 1999).

3.1.1 Modulation of TGF-β signalling

3.1.1.1 Crosstalk between TGF-β signalling and the MAPK pathways

Interaction between mitogen activated protein kinase (MAPK) pathways and TGF-β

signalling is two-fold. On one hand, Smad-dependent signalling is not the only way that TGF-

β regulates cellular functions, as TGF-β activates other pathways including MAPK pathways.

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On the other hand, Smad-dependent signalling is not solely regulated through TGF-β

receptors, but can be activated by other pathways such as the MAPK pathways.

TGF-β activation of ERK, p38 and c-jun N-terminal kinase (JNK) MAPK pathways is

complex. While it can lead to Smad activation in some instances, it may also induce responses

unrelated to Smad-dependent transcription in others (Derynck and Zhang, 2003). For

example, slow kinetics observed in the activation of these pathways suggest Smad-dependent

transcription responses, while rapid activation (5-15 minutes) similar to those observed

downstream of cytokine receptors suggests activities independent from transcription

(reviewed in Derynck and Zhang, 2003). Furthermore, the existence of Smad-independent

MAPK activation is demonstrated by the activation of MAPK pathways in Smad4 deficient

cells and cells expressing dominant negative Smads (reviewed in Javelaud and Mauviel,

2005). Further evidence of Smad-independent MAPK activation comes from reports showing

TGF-β activation of different MAPK pathways using a mutated ALK5 which, although

retaining kinase activity, was unable to activate Smads (Itoh et al., 2003; Yu et al., 2002).

The mechanisms by which different MAPK pathways are activated by TGF-β are not well

characterised. Activation of Ras has been suggested to be important in TGF-β induction of the

ERK MAPK pathway (Yue and Mulder, 2000). On the other hand, JNK and p38 MAPK

pathways are activated by various MAPK kinase kinases, including TGF-β activated kinase-1

(TAK1) which can stimulate both (Yamaguchi et al., 1995). The identification of TAK1

provided some of the earliest evidence for crosstalk between MAPK and TGF-β pathways.

Furthermore, MAPK kinase kinase-1 (MEKK1) is also thought to mediate TGF-β activation

of the JNK MAPK pathway (Brown et al., 1999). Repression, as well as activation, of MAPK

pathways by TGF-β signalling has also been observed. For example, activin was shown to

inhibit cell growth in human erythroleukemia cells by inhibiting ERK signalling and

activating the p38 pathway (Huang et al., 2004).

MAPK pathways are not only activated by TGF-β but, by phosphorylating R-Smads, they

also interfere in Smad-dependent transcriptional activation. ERK has been shown to

phosphorylate Smad1, Smad2 and Smad3 at specific sites in their linker regions to inhibit

nuclear translocation (Kretzschmar et al., 1997; Kretzschmar et al., 1999). Furthermore,

inhibition of Ras-induced Smad3 phosphorylation by mutation of MAPK phosphorylation

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sites rescued the growth-inhibitory response to TGF-β in Ras-transformed cells (Kretzschmar

et al., 1999). This mechanism was proposed to explain silencing of antimitogenic TGF-β

functions in hyperactive Ras cells. In contrast, hepatocyte growth factor (HGF) and epidermal

growth factor (EGF) signalling were shown to mediate Smad-dependent reporter gene

activation and induce rapid phosphorylation of Smad1 and Smad2 by kinases downstream of

MEK1 (de Caestecker et al., 1998).

JNK has also been shown to phosphorylate R-Smads. JNK phosphorylation of the Smad3

linker region facilitates both activation by TGF-β and nuclear accumulation (Engel et al.,

1999). A recent report suggested that Ras transformation suppressed TGF-β-mediated Smad3

phosphorylation responsible for antimitogenic TGF-β responses and stimulated JNK signal-

driven phosphorylation of the Smad3 linker region, leading to upregulation of PAI-1, MMP-1

MMP-2, and MMP-9 expression and increased tumour invasion (Sekimoto et al., 2007).

Modulation of TGF-β responses by MAPK pathways is also seen in the nucleus. EGF

stimulation of the Ras-MEK-ERK pathway leads to phosphorylation and stabilisation of the

Smad co-repressor TGF-β-induced factor (TGIF) which competes with the co-activator p300

for Smad2 association (Lo et al., 2001). Also, activator protein-1 (AP-1) family transcription

factors, downstream components of MAPK signalling, interact with R-Smad/Smad4

complexes in the nucleus (Zhang et al., 1998). For example, Smad3/Smad4 has been shown to

cooperate with c-Jun/c-Fos to mediate TGF-β-induced transcription (Zhang et al., 1998).

Conversely, c-Jun and JunB have been shown to interact with Smad3 by forming off-DNA

complexes, inhibiting Smad3-dependent transcription (Verrecchia et al., 2000; Verrecchia et

al., 2003).

3.1.1.2 Ski

Ski is a negative regulator of TGF-β signalling. It has been shown to recruit various

transcriptional co-repressors to TGF-β targeted promoters, as well as disrupt the binding of

the Smad transcriptional complex to the coactivator p300/CBP, leading to repression of

Smad-dependent TGF-β gene regulation (reviewed in Luo, 2004). Furthermore, as the Ski-

binding surface on Smad4 significantly overlaps with the surface required for binding the R-

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Smads, Ski interferes with the interaction between Smad4 and phosphorylated R-Smads (Wu

et al., 2002).

In melanoma cells Ski counteracts TGF-β-induced growth inhibition by preventing the

induction of p21Cip1 and by binding to pRb, repressing its activity (Reed et al., 2001).

Moreover, when overexpressed, Ski has been shown to induce oncogenic transformation

dependent on its ability to repress Smad regulation (He et al., 2003). Furthermore, Ski protein

levels are reported to correlate with human melanoma progression. Its subcellular localisation

changes from exclusively nuclear in preinvasive melanomas (melanomas in situ), to nuclear

and cytoplasmic in primary invasive and metastatic melanomas (Reed et al., 2001). In this

context, Ski was shown to bind Smad3 and inhibit its nuclear translocation (Reed et al.,

2001).

3.1.2 Vasculogenic mimicry

Maniotis and colleagues reported the presence of patterned networks of interconnected loops

of extracellular matrix in metastatic melanomas (Maniotis et al., 1999). Red blood cells were

detected in the hollow channels formed by these networks, but endothelial cells were not

identified within these matrix-embedded channels. They further reported the formation of

patterned solid and hollow matrix channels in three-dimensional cultures of highly invasive

primary and metastatic melanoma cells in Matrigel or dilute Type 1 collagen (Maniotis et al.,

1999). It was hypothesised that aggressive melanoma cells may generate such channels to

facilitate tumour perfusion independent of angiogenesis. This phenomenon was called

vasculogenic mimicry (Maniotis et al., 1999). Interestingly, these in vitro channels are only

formed by highly invasive cells and not by melanocytes or poorly invasive melanomas.

Furthermore, the presence of microcirculatory loops and networks in uveal melanoma

tumours correlated with a decreased survival rate for the patients (Maniotis et al., 1999).

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Chapter 3 Results

3.2 Results

3.2.1 Confirming TGF-β1 and activin A secretion

The identification of TGF-β signalling as a driving force for Motif 2, described in chapter 2

and published in 2006 (Hoek et al., 2006), prompted us to investigate the mechanisms

regulating this signalling pathway in cultured melanoma cells. Firstly, to complement the

published data, secretion of activin A and TGF-β1 was measured in the conditioned media

(CM) of an additional six cell lines derived from melanoma metastases and obtained from the

Mannheim melanoma cell culture bank (Hoek et al., 2006) (Fig 3.1). Confirming the

previously published data, little difference in TGF-β1 secretion across the samples was

observed, while activin A was only secreted by cells belonging to what was described as

cohort C or as being of “high metastatic potential” and which is now referred to as the

invasive cohort (Hoek et al., 2008). Cohort A, which was referred to as weakly metastatic

melanoma in chapter 2, is here described as the proliferative cohort (Hoek et al., 2008).

Figure 3.1. TGF-β1 and activin A secretion. Western blotting with antibodies against TGF-β1 and the inhibin βA subunit of 10x concentrated conditioned media (CM) of melanoma cultures representing the proliferative and invasive cohorts separated on a reducing gel. Six cell cultures from the Mannheim melanoma cell culture bank (MaMel) and four cell cultures from the Zürich melanoma cell culture bank are shown.

3.2.2 Follistatin secretion does not correlate with activin secretion

Follistatin has been reported to be secreted by melanoma cell lines and it was suggested that

its secretion represented an effective way to neutralise activin’s effects (Stove et al., 2004). In

order to verify if our cell cultures secreted follistatin, we performed an Enzyme-Linked

ImmunoSorbent Assay (ELISA) to measure the levels of follistatin secretion in the CM. Only

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Chapter 3 Results

four of our 12 cell cultures secreted detectable levels of follistatin (lower detection limit of

0.97 ng/ml) and the secretion of follistatin did not correlate with activin secretion (Fig.3.2).

Figure 3.2. Follistatin and activin A secretion in CM. ELISA measuring follistatin and activin A secretion in CM of 12 melanoma cultures representing the proliferative, intermediate and invasive cohorts.

3.2.3 Smad2 and Smad3 are activated across all cohorts

After confirming activin A secretion by cells belonging to the invasive cohort, we further

investigated the activation status of Smad2 and Smad3. Phospho-specific antibodies against

Smad2 and Smad3 were used to detect carboxy-end phosphorylation in the two proteins.

Surprisingly, phosphorylated Smad2 and Smad3 proteins were detected in all samples (Fig.

3.3), suggesting that the identified TGF-β signature was not dependent on Smad2 and Smad3

activation.

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Chapter 3 Results

Figure 3.3. Smad2 and Smad3 activation status. Western blot analysis of nuclear protein extracts of melanoma cell cultures representing the proliferative, intermediate and invasive cohorts. Phospho-specific antibodies against Smad2 and Smad3 were used to evaluate their carboxy-terminus phosphorylation status. The detection of β-actin was used as a loading control.

3.2.4 Ski is not responsible for the differential TGF-β signalling

As Smad2 and Smad3 were found to be phosphorylated in all melanoma cultures regardless of

the cohort they belonged to, the presence of Ski, a negative regulator of TGF-β, was

investigated. Ski interacts with Smad proteins and could explain the apparent lack of TGF-β

signalling in cells of the proliferative cohort. As Ski’s activity is concentrated in the nucleus,

western blotting was performed with the nuclear extracts of all 12 melanoma cell cultures. All

12 cell cultures expressed Ski and we therefore excluded Ski as being involved in the

differential TGF-β signature detected.

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Chapter 3 Results

Figure 3.4. Ski expression. Western blot analysis with an anti-Ski antibody of the nuclear proteins of 12 melanoma cell cultures representing the proliferative, intermediate and invasive cohorts. The detection of β-tubulin was used as a loading control.

3.2.5 The activation of the MAPK pathways does not correlate with the TGF-β

signature

To investigate the possible involvement of MAPK pathways in the TGF-β signature identified

in our invasive cohort melanoma cell cultures, the phosphorylation status of three major

MAPK factors was determined across our samples. The activation of the ERK pathway,

which is induced by mitogens and growth factors via RAS and RAF, was determined using a

phospho-specific antibody directed against ERK1 and ERK2 (Fig 3.5). Although the ERK

phosphorylation status varied across our 12 melanoma cell cultures, no correlation with the

cohort-specific TGF-β signalling activation could be seen. Activation of the two stress-

induced MAPK pathways, p38 and JNK, was also investigated by western blotting with

phospho-specific antibodies. Again, phosphorylation of p38 and JNK did not correlate with

cohort-specific TGF-β signalling activation (Fig. 3.5).

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Chapter 3 Results

Figure 3.5. MAPK activation. Western blot analysis of 12 melanoma cell cultures representing the proliferative, intermediate and invasive cohorts. Phospho-specific antibodies against ERK1/2. JNK and p38 were used to evaluate the activation of the MAPK pathways. The detection of β-tubulin was used as a loading control.

3.2.6 Identifying vasculogenic mimicry as a discriminating phenotype

TGF-β susceptibility and in vitro motility are two phenotypes discriminating between the

proliferative and invasive cohorts discussed in the paper in chapter 2. To identify a third

discriminating phenotype, which could be used in subsequent assays, the capacity of cells to

form networks when seeded on Matrigel, a phenomenon referred to as vasculogenic mimicry,

was investigated. When seeded on Matrigel, all cells belonging to the invasive cohort formed

networks within 24 hours of seeding, while cells from the proliferative cohort formed

dispersed clusters (Fig. 3.6.).

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Chapter 3 Results

Figure 3.6. Vasculogenic mimicry. When seeded on Matrigel, invasive cells form networks, characteristic of vasculogenic mimicry (B), while proliferative cells form clusters (A). Cells were photographed 24 hours after being seeded in a Matrigel-coated well.

3.2.7 Phenotype switching

In contrast to other models implying “one-way” changes in gene expression leading to growth

and metastasis of melanoma, we suggest a dynamic model in which gene expression is altered

from a proliferative to an invasive signature and vice versa (Fig. 3.7.). This gene expression

oscillation is thought to be caused by a signal induced by changes in the microenvironment

and translates into phenotypic changes enabling the cells to proliferate or invade.

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We therefore explored the possibility of “switching” cells from one cohort to an other by

inducing TGF-β signalling in the proliferative cohort or inhibiting TGF-β signalling in the

invasive cohort. We considered activin A as a candidate effector as it is the only member of

the TGF-β family of cytokines whose expression profile correlates with cohort distribution.

Proliferative cells were treated with TGF-β1 and activin A and invasive cells with follistatin

and SB431542 (an ALK4/5/7 inhibitor). Cells were grown on Matrigel for up to 72 hours but

no treatment-induced change in phenotype was observed.

Figure 3.7. An integrated model for gene regulation of melanoma metastatic potential and progression. Early phase melanoma cells expressing the proliferative signature gene set proliferate to form the primary lesion. Subsequently, an unknown signal switch, likely brought about by altered microenvironmental conditions (e.g. hypoxia or inflammation), gives rise to cells with a significantly different invasive signature gene set. Invasive signature cells escape and, upon reaching a suitable distal site, revert to the proliferative state and nucleate a new metastasis where the cycle is repeated. Each switch in phenotype (state change) is accompanied by an exchange in expressed gene sets from proliferative to invasive and vice versa.

3.3 Discussion

TGF-β signalling has been extensively studied in cancer and its role in melanoma has been

highlighted in a number of studies. On the other hand, the role of activin in melanoma has

received very limited attention. In our recent publication in which we presented two different

transcription signatures for melanoma cell cultures which, based on known functions of the

genes involved, defined their respective contributions to metastatic potential as either

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proliferative or invasive, we proposed a role for activin A in maintaining the TGF-β signature

(Hoek et al., 2006). In this chapter, we confirm some of the results presented in chapter 2 and

our results suggest that a number of pathways that are regulated by TGF-β signalling are not

involved in TGF-β signature modulation in melanoma.

In our recent paper, we demonstrated the cohort specificity of activin A secretion by detecting

the presence of the inhibin βA subunit in the conditioned medium of our cell cultures (Hoek et

al., 2006). Our results contrasted with those presented by Stove and coworkers who could not

detect activin A concentrations exceeding 0.1 ng/ml in the CM of any of the 12 melanoma

cell lines they studied (Stove et al., 2004). Therefore, we confirm those results using another

set of cell cultures obtained from the Mannheim melanoma cell culture bank. Although our

assay was ten times more sensitive than the one they used, we detected concentrations ranging

from 0.326 ng/ml to 1.201 ng/ml, concentrations which should have been detected with the

system they described (Hoek et al., 2006). As their RNA expression data supports the

expression of the βA subunit and hence of activin A in their melanoma cell lines, the authors

suggest the possible rapid degradation of the cytokine or its association with the cell surface

to explain the unsuccessful detection of activin A in the CM. Our results do not support such

claims but support their RNA expression data which suggests that a number of melanoma cell

lines produce activin A.

Stove and coworkers further looked at the secretion of follistatin by their melanoma cell lines

and suggested that its secretion may inhibit activin’s negative regulatory effects. We could

detect follistatin in only four of our 12 studied melanoma cell cultures (lower detection limit

of 0.97 ng/ml) and its secretion did not correlate with the secretion of activin A. We therefore

exclude follistatin as an important player in our model.

Nodal, a potent embryonic morphogen from the TGF-β family, has been presented as a key

regulator of melanoma plasticity and tumorigenicity (Topczewska et al., 2006). Nodal, like

TGF-β and activin, signals through Smad2, Smad3 and Smad4, and, like activin, binds to

ActR-IIB which dimerises with ALK4 and ALK7. Arguing that aggressive tumour cells share

many properties with embryonic cells, Topczewska and co-workers used a zebrafish embryo

model as a biosensor for metastatic melanoma expressing a plastic, stem cell-like phenotype

to modulate an embryonic microenvironment. (Topczewska et al., 2006). They showed that a

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subpopulation of aggressive melanoma cells transplanted into the blastula-stage embryo

secreted Nodal and consequently induced ectopic formation of the embryonic axis. They

further demonstrated that Nodal was absent in normal human skin, absent or weakly present

in primary lesions, but present in 60% of metastases examined, suggesting a correlation

between Nodal expression and melanoma progression (Topczewska et al., 2006).

Furthermore, inhibition of Nodal signalling promoted the reversion of melanoma cells

towards a melanocytic phenotype and reduced invasiveness, colony formation in vitro and

tumorigenicity in vivo (Topczewska et al., 2006). In our initial microarray analysis including

the data from our collaborators in Mannheim, the expression of nodal does not differ between

cohorts. It would be interesting to compare the gene targets of nodal and activin A and to

hypothesise that activin A and nodal share similar effects on melanoma tumorigenicity in

their respective models.

Because TGF-β and activin signal through Smad2 and Smad3, we investigated the possibility

of their differential activation between cohorts. Also, as phosphorylated Smads need to

translocate to the nucleus to regulate transcription and that nuclear translocation can be

inhibited by a number of factors such as Ski (Reed et al., 2001) or MAPKs (Xu, 2006), we

looked for phosphorylated Smad2 and Smad3 in the nucleus. Surprisingly, Smad2 and Smad3

were phosphorylated and nuclear in all investigated cell cultures, suggesting a TGF-β

signature independent from the Smad-dependent pathway. It is possible that Smad activation

is induced by growth factors present in the complete growth medium in which the cells were

grown. For example, Rodeck and coworkers reported variable levels of Smad activation in

melanoma grown in reduced-serum medium (Rodeck et al., 1999). However, the same growth

conditions were used for the generation of the expression profiling data in which the different

cohorts were identified, indicating that the observed TGF-β signature is not due to exogenous

growth factors.

To explain the TGF-β signature highlighted in our microarray analysis, we also examined

pathways known to interact with and modulate TGF-β signalling. Cross-signalling between

the MAPK pathways and TGF-β signalling has been well described in diverse cell types and

its implications in carcinogenesis are now clear (Javelaud and Mauviel, 2005). Activation of

the MAPK pathways by TGF-β is not well characterised, but examples of TGF-β-dependent

activation of the ERK, p38 and JNK pathways have been reported (Brown et al., 1999;

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Yamaguchi et al., 1995; Yu et al., 2002). Furthermore, not only is the nuclear translocation of

Smads regulated by non-TGF-β signalling including the MAPK pathways, but these pathways

also regulate the ability of Smads to regulate gene transcription (Xu, 2006). We therefore

looked at the activation status of several MAPK pathways, but could not correlate them with

our TGF-β signature. However, we cannot rule out the presence of complex interactions

between different MAPKs and the TGF-β pathway, which could not have been detected with

the methods presented in this chapter. Furthermore, it is conceivable that multiple MAPKs

could yield similar results, therefore a simple correlation between TGF-β signalling and one

particular MAPK member might not be possible.

Ski’s capacity to disturb Smad signalling makes it another candidate for the source of the

differential TGF-β signature. As for the MAPK pathways, Ski can both sequester R-Smads in

the cytoplasm and also interfere with Smad transcription regulation in the nucleus. If Ski

interference was responsible for differential TGF-β signalling, Ski would be expected to be

differentially expressed and localised across cohorts. However, as Ski was present in the

nucleus of melanomas of all cohorts, it had to be excluded as a candidate for the source of the

differential TGF-β signature.

As we hypothesise that the transcription signatures of our two main cohorts, proliferative and

invasive, represent distinct yet interchangeable states and that we are interested in identifying

the source of the “switch” between them, in vitro phenotyping of cells was of primary

importance for all subsequent experiments. Vasculogenic mimicry has been shown both in

vivo and in vitro in melanoma (Maniotis et al., 1999). As Maniotis and coworkers have

described vasculogenic mimicry as being a characteristic of invasive cells, we compared the

ability of melanoma cells from invasive and proliferative cohorts to form networks when

seeded on Matrigel. Interestingly, while all invasive cells formed distinct network patterns,

proliferative cells generally formed clusters. This showed there was a strong link between

gene expression patterns and vasculogenic mimicry, and confirmed that the cohorts were

descriptive of proliferative or invasive phenotypes.

Maniotis and coworkers tried, without success, to induce or inhibit the formation of

vasculogenic mimicry with CM of phenotypically opposed cells (Maniotis et al., 1999). They

treated poorly invasive cells with basic fibroblast growth factor (bFGF), vascular endothelial

93


growth factor (VEGF), platelet-derived growth factor (PDGF), tumour necrosis factor alpha

(TNF-α) and TGF-β, individually or in combination, but failed to induce the formation of

networks.

With the TGF-β signature now characterised, we were interested in identifying how cells

“switch” between cohort phenotypes. Using vasculogenic mimicry to identify the “switch”,

we tested the possibility of inducing cell phenotype changes by activating TGF-β signalling in

the proliferative cohort or by inhibiting it in the invasive cohort. Although Topczewska and

coworkers reported the prevention of the formation of embryonic-like vasculogenic networks

using SB431542 (Topczewska et al., 2006), we could not prevent or induce the phenotype by

inhibiting or inducing TGF-β signalling, respectively. The different experimental conditions,

for example the type I collagen versus the Matrigel matrix, and the different appearance of the

phenotype, the three- versus the two-dimensional phenotype, could explain their success.

The data presented in this chapter illustrates the difficulty of identifying the source of

transcriptional regulation identified by microarray analysis. This is further complicated as the

transcriptional signature is associated with a multi-functional molecule such as TGF-β. Cross-

interactions with this pathway are multiple and complex, and there may not be a unique

modulator responsible for regulating TGF-β signature across melanoma cell phenotypes.

However, the linking of a third discriminating phenotype, vasculogenic mimicry, supports the

model as represented by two distinct melanoma cell phenotypes with distinct transcriptional

signatures.


3.4.1 Cell culture

Melanoma cell cultures were established from surplus material from cutaneous melanoma

metastases removed by surgery after having obtained written informed consent of the patient.

Clinical diagnosis was confirmed by histology and immunohistochemistry. Melanoma cells

were released from tissue sections and grown as previously described (Geertsen et al., 1998).

Cells were grown in RPMI 1640 (GIBCO, Invitrogen, Carlsbad, CA, USA) supplemented

94


with 10% foetal calf serum (FCS), 4nM glutamine (Biochrom, Berlin, Germany) and 1nM

sodium pyruvate (GIBCO), at 37°C in an atmosphere of 10% CO2.

3.4.2 Preparation of condition media

Growth-conditioned media was prepared by growing cells to confluency in a 75-cm2 flask in

normal growth conditions, these were then washed with PBS and placed into fresh serum-free

media for 24 hours. Growth-conditioned media was collected, filtered through a 0.22-µm

filter and stored at -80°C. Growth-conditioned media was concentrated 10-fold by acetone

precipitation.

3.4.3 ELISA

Activin A and follistatin were measured in conditioned media samples using a specific

enzyme-linked immunosorbent assay (Knight et al., 1996) according to the manufacturer’s

instructions (Oxford Bio-Innovations, Oxfordshire, UK), with some modifications for media

samples as described previously (Buzzard et al., 2003). The average intra- and interplate

coefficients of variation for this assay are routinely <8% and the lower limit of detection is 10

pg/ml for activin A and 1 ng/ml for follistatin.

3.4.4 Preparation of total cell protein extracts

Cells were washed twice with cold PBS and lysed at 4°C in lysis buffer containing 20 mM

Tris-HCl (pH 7.5), 1% Triton X-100 (Sigma-Aldrich, Buchs, Switzerland), 137 mM NaCl,

10% glycerol, protease inhibitors (Complete Mini +EDTA, Roche, Basel, Switzerland) and

phosphatase inhibitors (Sigma Phosphatase inhibitor cocktail 1 + 2). After rotating samples

for 15 minutes and centrifugating for 20 minutes at 4°C, the supernatant was collected.

3.4.5 Preparation of cytosolic and nuclear protein extracts

Cells were washed twice with cold PBS and lysed at 4°C. Cells were lysed in lysis buffer A

containing 10 mM HEPES (pH 7.9), 10 mM KCl, 1.5mM MgCl2, 0.625% NP-40, protease

inhibitors (Complete Mini +EDTA, Roche) and phosphatase inhibitors (Sigma Phosphatase

95


inhibitor cocktail 1 + 2). After a ten-minute incubation on ice, cells were centrifuged at 5,000

rpm for 5 minutes and the cytosolic fraction (supernatant) was transferred to a new tube. This

fraction was centrifuged an additional three times to obtain maximal purity. For nuclear

fraction extraction, the pellet was washed three times in buffer A before it was resuspended in

lysis buffer B containing 20 mM HEPES (pH 7.9), 1.5mM MgCl2, 420mM NaCl, 25%

glycerol, protease inhibitors (Complete Mini +EDTA, Roche) and phosphatase inhibitors

(Sigma Phosphatase inhibitor cocktail 1 + 2). After rotating samples for 60 minutes and

centrifugating for 20 minutes at 4°C, the nuclear fraction (supernatant) was collected. Purity

of nuclear fractions were verified by western blot analysis using an antibody against the

cytosolic protein copper and zinc-containing Superoxide Dismutase (Cu/Zn SOD) (Fig 3.8.).

Figure 3.8. The nuclear protein fraction was not contaminated. Western blot analysis of nuclear and cytosolic protein fractions probed with an antibody against cytosolic-specific Cu/Zn SOD.

3.4.6 Western blot analysis

Proteins were separated by SDS–PAGE under reducing conditions and transferred onto

nitrocellulose membranes (Invitrogen, Basel, Switzerland). Membranes were probed with a

specific primary antibody (Table 3.1.) followed by an appropriate horseradish peroxidase-

conjugated goat anti-rabbit (Bio-Rad, Reinach, Switzerland), rabbit anti-goat (abcam,

Cambridge, UK) or rabbit anti-mouse (SantaCruz, La Jolla, CA, USA) secondary antibodies.

Bound antibodies were detected by chemiluminescence (ECL, GE Healthcare,

Buckinghamshire, UK).

96


Table 3.1. Antibodies and Western blot conditions Immunogen Host Supplier # Conditions

TGF-β rabbit abcam (Cambridge, UK) Ab9758 1:800, 2h, 5%milk

Inhibin βA rabbit Gift from S. Werner (ETH,

Zürich)

--- 1:2000, 2h, 5% milk

p-Smad2 rabbit Chemicon (Billerica, MA,

USA)

AB3849 1:1000, O/Na, 5% milk

p-Smad3 rabbit Cell Signaling (Danvers,

MA, USA)

9514 1:1000, O/Na, 5% BSA

Ski rabbit Upstate (Lake Placid, NZ,

USA)

07-060 1:400, O/Na, 5% milk

p-ERK1/2 rabbit abcam (Cambridge, UK) ab4819 1:2000, 2h, 1% milk

p-p38 rabbit abcam (Cambridge, UK) ab4822 1:1000, 2h, 1% milk

p-SAPK/JNK rabbit Cell Signaling (Danvers,

MA, USA)

9251 1:1000, O/Na, 5% BSA

Actin goat Santa Cruz (La Jolla, CA,

USA)

sc-1616 1:1000, 2h, 5% milk

β-Tubulin mouse Sigma (St-Louis, MO, USA) T4026 1:500, 2h, 5% milk

p84 mouse abcam (Cambridge, UK) ab487 1:1000, 2h, 5% milk

Cu/Zn SOD rabbit Stressgen (Victoria, Canada) SOD-100 1:400, 2h, 5% milk

a)O/N = overnight

97


3.5 References

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Buzzard, J.J., Farnworth, P.G., De Kretser, D.M., O'Connor, A.E., Wreford, N.G. and Morrison, J.R. (2003) Proliferative phase sertoli cells display a developmentally regulated response to activin in vitro. Endocrinology, 144, 474-483.

de Caestecker, M.P., Parks, W.T., Frank, C.J., Castagnino, P., Bottaro, D.P., Roberts, A.B. and Lechleider, R.J. (1998) Smad2 transduces common signals from receptor serine-threonine and tyrosine kinases. Genes Dev., 12, 1587-1592.

Derynck, R. and Zhang, Y.E. (2003) Smad-dependent and Smad-independent pathways in TGF-beta family signalling. Nature, 425, 577-584.

Engel, M.E., McDonnell, M.A., Law, B.K. and Moses, H.L. (1999) Interdependent SMAD and JNK signaling in transforming growth factor-beta-mediated transcription. J Biol Chem, 274, 37413-37420.


He, J., Tegen, S.B., Krawitz, A.R., Martin, G.S. and Luo, K. (2003) The Transforming Activity of Ski and SnoN Is Dependent on Their Ability to Repress the Activity of Smad Proteins. J. Biol. Chem., 278, 30540-30547.

Hoek, K.S., Eichhoff, O.M., Schlegel, N.C., Döbbeling, U., Kobert, N., Schaerer, L., Hemmi, S., Dummer, R. and (2008) In vivo switching of human melanoma cells between proliferative and invasive states. Cancer Res, in press.

Hoek, K.S., Schlegel, N.C., Brafford, P., Sucker, A., Ugurel, S., Kumar, R., Weber, B.L., Nathanson, K.L., Phillips, D.J., Herlyn, M., Schadendorf, D. and Dummer, R. (2006) Metastatic potential of melanomas defined by specific gene expression profiles with no BRAF signature. Pigment Cell Res, 19, 290-302.

Huang, H.-M., Chang, T.-W. and Liu, J.-C. (2004) Basic fibroblast growth factor antagonizes activin A-mediated growth inhibition and hemoglobin synthesis in K562 cells by activating ERK1/2 and deactivating p38 MAP kinase. Biochemical and Biophysical Research Communications, 320, 1247-1252.

Itoh, S., Thorikay, M., Kowanetz, M., Moustakas, A., Itoh, F., Heldin, C.-H. and ten Dijke, P. (2003) Elucidation of Smad Requirement in Transforming Growth Factor-beta Type I Receptor-induced Responses. J. Biol. Chem., 278, 3751-3761.

Javelaud, D. and Mauviel, A. (2005) Crosstalk mechanisms between the mitogen-activated protein kinase pathways and Smad signaling downstream of TGF-[beta]: implications for carcinogenesis. Oncogene, 24, 5742-5750.

Knight, P.G., Muttukrishna, S. and Groome, N.P. (1996) Development and application of a two-site enzyme immunoassay for the determination of 'total' activin-A concentrations in serum and follicular fluid. J Endocrinol, 148, 267-279.

Kretzschmar, M., Doody, J. and Massagu, J. (1997) Opposing BMP and EGF signalling pathways converge on the TGF-[beta] family mediator Smad1. Nature, 389, 618-622.

Kretzschmar, M., Doody, J., Timokhina, I. and Massague, J. (1999) A mechanism of repression of TGFbeta/ Smad signaling by oncogenic Ras. Genes Dev, 13, 804-816.

Lo, R.S., Wotton, D. and Massague, J. (2001) Epidermal growth factor signaling via Ras controls the Smad transcriptional co-repressor TGIF. Embo J, 20, 128-136.

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Luo, K. (2004) Ski and SnoN: negative regulators of TGF-[beta] signaling. Current Opinion in Genetics & Development, 14, 65-70.

Maniotis, A.J., Folberg, R., Hess, A., Seftor, E.A., Gardner, L.M., Pe'er, J., Trent, J.M., Meltzer, P.S. and Hendrix, M.J. (1999) Vascular channel formation by human melanoma cells in vivo and in vitro: vasculogenic mimicry. Am J Pathol, 155, 739-752.

Massague, J. (2003) Integration of Smad and MAPK pathways: a link and a linker revisited. Genes Dev, 17, 2993-2997.

Reed, J.A., Bales, E., Xu, W., Okan, N.A., Bandyopadhyay, D. and Medrano, E.E. (2001) Cytoplasmic localization of the oncogenic protein Ski in human cutaneous melanomas in vivo: functional implications for transforming growth factor beta signaling. Cancer Res, 61, 8074-8078.

Rodeck, U., Nishiyama, T. and Mauviel, A. (1999) Independent regulation of growth and SMAD-mediated transcription by transforming growth factor beta in human melanoma cells. Cancer Res, 59, 547-550.

Sekimoto, G., Matsuzaki, K., Yoshida, K., Mori, S., Murata, M., Seki, T., Matsui, H., Fujisawa, J.-i. and Okazaki, K. (2007) Reversible Smad-Dependent Signaling between Tumor Suppression and Oncogenesis. Cancer Res, 67, 5090-5096.

Stove, C., Vanrobaeys, F., Devreese, B., Van Beeumen, J., Mareel, M. and Bracke, M. (2004) Melanoma cells secrete follistatin, an antagonist of activin-mediated growth inhibition. Oncogene.

Topczewska, J.M., Postovit, L.M., Margaryan, N.V., Sam, A., Hess, A.R., Wheaton, W.W., Nickoloff, B.J., Topczewski, J. and Hendrix, M.J. (2006) Embryonic and tumorigenic pathways converge via Nodal signaling: role in melanoma aggressiveness. Nat Med, 12, 925-932.

Verrecchia, F., Pessah, M., Atfi, A. and Mauviel, A. (2000) Tumor Necrosis Factor-alpha Inhibits Transforming Growth Factor-beta /Smad Signaling in Human Dermal Fibroblasts via AP-1 Activation. J. Biol. Chem., 275, 30226-30231.

Verrecchia, F., Tacheau, C., Wagner, E.F. and Mauviel, A. (2003) A Central Role for the JNK Pathway in Mediating the Antagonistic Activity of Pro-inflammatory Cytokines against Transforming Growth Factor-beta -driven SMAD3/4-specific Gene Expression. J. Biol. Chem., 278, 1585-1593.

Wu, J.W., Krawitz, A.R., Chai, J., Li, W., Zhang, F., Luo, K. and Shi, Y. (2002) Structural mechanism of Smad4 recognition by the nuclear oncoprotein Ski: insights on Ski-mediated repression of TGF-beta signaling. Cell, 111, 357-367.

Xu, L. (2006) Regulation of Smad activities. Biochimica et Biophysica Acta (BBA) - Gene Structure and Expression, 1759, 503-513.

Xu, W., Angelis, K., Danielpour, D., Haddad, M.M., Bischof, O., Campisi, J., Stavnezer, E. and Medrano, E.E. (2000) Ski acts as a co-repressor with Smad2 and Smad3 to regulate the response to type beta transforming growth factor. Proc Natl Acad Sci U S A, 97, 5924-5929.

Yamaguchi, K., Shirakabe, K., Shibuya, H., Irie, K., Oishi, I., Ueno, N., Taniguchi, T., Nishida, E. and Matsumoto, K. (1995) Identification of a member of the MAPKKK family as a potential mediator of TGF-beta signal transduction. Science, 270, 2008-2011.

Yu, L., Hebert, M.C. and Zhang, Y.E. (2002) TGF-beta receptor-activated p38 MAP kinase mediates Smad-independent TGF-beta responses. Embo J, 21, 3749-3759.

Yue, J. and Mulder, K.M. (2000) Requirement of Ras/MAPK Pathway Activation by Transforming Growth Factor beta for Transforming Growth Factor beta 1 Production in a Smad-dependent Pathway. J. Biol. Chem., 275, 30765-30773.

Zhang, Y., Feng, X.-H. and Derynck, R. (1998) Smad3 and Smad4 cooperate with c-Jun/c-Fos to mediate TGF-[beta]-induced transcription. Nature, 394, 909-913

99

.

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Chapter 4

4 In vivo switching of human melanoma cells between

proliferative and invasive states

101

For this second paper, I participated in the generation of in vitro growth curves and in the

optimisation of motility assays shown in figure 4.1. I also took part in the optimisation of

western blotting and TGF-β-mediated growth inhibition assays, as well as the setting up of

the Mitf RT-PCR assay presented in figure 4.3. Finally, I constructed the pAd-H1∆lacZ-lnk1

by replacing the CMV promoter of pAd-CMV∆lacZ-lnk1 with the H1 promoter.

102

In vivo switching of human melanoma cells between

proliferative and invasive states

Keith S. Hoek1*, Ossia Eichhoff1, Natalie C. Schlegel1, Udo Döbbeling1, Nikita Kobert1, Leo

Schaerer2, Silvio Hemmi3, and Reinhard Dummer1.

1Department of Dermatology, University Hospital of Zurich, 8091 Zurich, Switzerland. 2Dermatohistopathologischen Gemeinschaftspraxis, 88048 Friedrichshafen, Germany. 3Institute of Molecular Biology, University of Zurich, 8057 Zurich, Switzerland.

Cancer Research (2008) in press

103

Chapter 4 Abstract/Introduction

4.1 Abstract

Metastatic melanoma represents a complex and heterogeneous disease for which there are no

therapies to improve patient survival. Recent expression profiling of melanoma cell lines

identified two transcription signatures respectively corresponding with proliferative and

invasive cellular phenotypes. A model derived from these findings predicts that in vivo

melanoma cells may switch between these states. Here, DNA microarray-characterised cell

lines were subjected to in vitro characterisation before subcutaneous injection into

immunocompromised mice. Tumour growth rates were measured and post-excision samples

were assessed by immunohistochemistry to identify invasive and proliferative signature cells.

In vitro tests showed that proliferative signature melanoma cells are faster growing but less

motile than invasive signature cells. In vivo proliferative signature cells initiated tumour

growth in 14 ± 3 days post injection. By comparison, invasive signature cells required a

significantly longer (p < 0.001) period of 59 ± 11 days. Immunohistochemistry showed that

regardless of the seed cell signature, tumours showed evidence for both proliferative and

invasive cell types. Furthermore, proliferative signature cell types were detected most

frequently in the peripheral margin of growing tumours. These data indicate that melanoma

cells undergo transcriptional signature switching in vivo likely regulated by local

microenvironmental conditions. Our findings challenge previous models of melanoma

progression which evoke one-way changes in gene expression. We present a new model for

melanoma progression which accounts for transcription signature plasticity and provides a

more rational context for explaining observed melanoma biology.

4.2 Introduction

Metastatic stage melanoma is an aggressive disease that few patients survive for more than

two years. Compounding this, scores of clinical trials testing different adjuvant therapies have

brought no significant improvement in the survival outlook for these patients (Sasse et al.,

2007). One possible explanation for this is that melanoma is a heterogeneous collection of

different cells, and the differences between them are sufficient that some are missed by

targeted therapies. The variety of phenotypic and behavioural features melanomas present

range from distinct organ specificities during metastasis to changes in motility and

invasiveness (Fidler and Kripke, 1977). Furthermore, melanoma tissues have various

104


morphologies, from assorted macroscopic lesional structures to multiple microscopic cellular

forms, which often complicate assessments of diagnosis and prognosis (Levene, 1980).

Additionally, immunohistochemical staining regularly yields heterogeneous results. While

most melanoma lesions will stain for a number of melanocytic markers, this is not necessarily

true for all melanoma cells within a given lesion (Banerjee and Harris, 2000). Finally, DNA

microarray examination of different lesions and melanoma cell line collections reveal among

them consistent taxonomies of genomic aberrations and transcriptional signatures (Curtin et

al., 2005; Haqq et al., 2005; Hoek et al., 2006). The source of heterogeneity is thought to rest

in the combination of how melanoma cells respond to different microenvironments and the

reciprocal influence of their own molecular states. This was an idea first conceptualised in

Stephen Paget’s “seed and soil” model after his observation that particular cancer cells

demonstrated tumorigenic preference for certain tissues over others (Paget, 1889; Ribatti et

al., 2006). By comparison, current molecular models for melanoma progression are

homogeneous. A generally accepted hypothesis assumes that progression is driven by a steady

evolution of molecular changes, and this hypothesis provides the dominant paradigm for

molecular studies (Miller and Mihm, 2006).

Of recent interest has been the activity of the microphthalmia-associated transcription factor

(Mitf) in regulating melanoma cell proliferation. In normal melanocytes Mitf is critical for

melanocytic differentiation, expression of melanogenic enzymes and upregulating cyclin-

dependent kinase inhibitors to drive cell cycle exit (Carreira et al., 2005; Loercher et al., 2005;

Steingrimsson et al., 2004). However, in melanoma Mitf is required for proliferation and has

been identified as a “lineage survival” factor prone to amplification (Carreira et al., 2006; Du

et al., 2004; Garraway et al., 2005). While the contrast in the activities of Mitf in normal and

transformed cells remains unexplained, there is little doubt concerning its central role in

melanoma biology.

We recently explored heterogeneity of gene expression in melanoma cells. Bittner and

coworkers first suggested that there may be specific transcriptional signatures delineating

melanoma cell subgroups (Bittner et al., 2000). We characterised two different transcription

signatures for melanoma cell lines which, based on known functions of the genes involved,

defined their respective contributions to metastatic potential as either proliferative or invasive

(Hoek et al., 2006). We further hypothesised that the transcription signatures represent

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Chapter 4 Introduction/Results

distinct yet interchangeable states regulated by signalling from the microenvironment.

Critically, Mitf expression is a central feature of the proliferative signature which is absent

from the invasive form. Others’ in vitro work concerning Mitf gene regulation have

corroborated the hypothesis that its expression is important for differentiating between

proliferative and invasive states (Carreira et al., 2006). To test the validity of the proliferative

signature we examined Mitf’s role in the proliferative signature phenotype and compared the

in vivo tumorigenicity of these cells against those with an invasive signature. At the same time

we used immunohistochemistry to monitor Mitf and the Ki67 antigen in the resulting tumours

to provide evidence of in vivo switching between signatures.

4.3 Results

4.3.1 Phenotypic assignment of cell lines

To study the in vivo tumorigenic behaviour of melanoma cell lines with different

transcriptional signatures, we selected pairs of proliferative and invasive signature melanoma

cell lines based on previous genome-wide transcription profiling experiments (Hoek et al.,

2006). We performed supervised hierarchical clustering of these samples using normalised

signal intensity data from 105 genes shown to be tightly linked to signature (Fig. 4.1A).

Earlier experiments had shown that proliferative signature lines were significantly less motile

than invasive signature lines. Also, TGF-β challenge showed that proliferative signature cells

were significantly more susceptible to TGF-β-mediated growth inhibition than invasive

signature cells (Hoek et al., 2006). We performed additional motility and proliferation

experiments to expand this range of in vitro characterisations. Cell growth experiments

showed a significant (p < 0.001) difference in proliferation rates between proliferative and

invasive signature cell lines (Fig. 4.1B). Conversely, invasive signature cell lines plated at

subconfluent densities on microporous transwell filters migrated in significantly (p < 0.001)

higher numbers towards the lower chamber than identically plated proliferative cell lines (Fig.

4.1C). With these experiments we concluded that signature assignments given to cell lines

according to their gene expression signature correlate with in vitro data in the context of our

model.

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Chapter 4 Results

Figure 4.1. In vitro correlations with gene expression signatures. M980513 and M000907 proliferative signature melanoma lines, as well as M991121 and M010308 invasive signature melanoma lines, were chosen for this study. (A) A gene expression heatmap, generated by clustering samples based on the normalized expression of 105 metastatic potential genes, highlights subtype-specific signatures. In vitro growth (B) and motility (C) experiments correlate appropriately with proliferative and invasive signature assignments. Error bars indicate standard deviation.

4.3.2 Mitf is a marker of proliferative phenotype

To follow cell signatures in vivo, we selected immunohistochemical markers according to

their signature specificity. Previous analysis indicated that Mitf mRNA and protein levels are

high in proliferative signature lines and at low or undetectable levels in invasive signature

samples (Hoek et al., 2006). We confirmed this by performing immunohistochemistry on

paraffin-embedded cultures of proliferative and invasive signature melanoma lines.

Immunohistochemical staining of the different signature cell line pellets with anti-Mitf

antibodies showed that in proliferative signature cell lines 93% of cells were positive for

nuclear staining for Mitf while invasive signature cell lines showed no positivity (data not

shown). Because invasive melanoma cells have downregulated genes responsible for the

melanocytic phenotype observable in proliferative signature melanoma cells there are no

immunohistochemical markers which unequivocally identify them. Instead, the differential in

growth rates for the signatures indicated that a general proliferation marker may be useful for

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Chapter 4 Results

immunohistochemical identification of signature type in vivo. While examination of

previously published gene expression data shows that between transcriptional signature types

there is no significant differential in the expression of mRNA encoding the proliferation

marker Ki67 antigen, the significant difference in in vitro proliferation rates suggest that Ki67

antigen is likely to show a difference at the protein level. Accordingly, staining for Ki67

antigen showed that 94% of proliferative signature cells and 45% of invasive signature cells

had positively stained nuclei (Fig. 4.2). These results indicate that Mitf is a good marker for

specific identification of proliferative signature cells and that Ki67 antigen is a suitable

marker for identifying regions undergoing differential rates of proliferation.

Figure 4.2. Immunohistochemical marker correlations with gene expression signatures. Immunohistochemical analysis of paraffin-embedded cell lines shows that proliferative and invasive signature lines have differential staining for Mitf (93% and 0%, respectively) and Ki67 antigen (94% and 45%, respectively).

4.3.3 Mitf expression reflects signature phenotype

To confirm that Mitf expression is functionally linked to signature phenotype we used siRNA

to knockdown Mitf protein levels and assessed the effects in vitro. One in vitro characteristic

which distinguishes between proliferative and invasive signature melanoma cells is a

differential in susceptibility to TGF-β-mediated inhibition of proliferation, with proliferative

signature cells being more sensitive to TGF-β than invasive signature cells (Hoek et al.,

2006). Because proliferative signature cells express Mitf and invasive signature cells do not,

we hypothesised that Mitf expression mediated the growth inhibitory effect of TGF-β on

proliferative signature cells. We performed anti-Mitf siRNA knockdown experiments in a

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Chapter 4 Results

proliferative signature melanoma line and confirmed knockdown by western blot analyses

(Fig. 4.3A). We found that Mitf-depletion from proliferative signature melanoma cells made

them less susceptible to TGF-β-mediated growth inhibition (Fig. 4.3B), showing that Mitf

mediates the growth inhibitory effect of TGF-β. Further, we showed that TGF-β treatment

reduces Mitf mRNA and protein expression (Fig. 4.3C), suggesting that TGF-β-mediated

growth inhibition may be effected by reduction of Mitf expression. This demonstrates that

Mitf function is closely linked to the relationship between transcription signature and in vitro

phenotype, confirming it as a useful in vivo marker for identifying different signature cells.

Figure 4.3. siRNA knockdown of Mitf protects against TGF-β-mediated growth inhibition. (A) siRNA-mediated knockdown of Mitf in a proliferative signature melanoma cell line (M000921) was confirmed by Western blot analysis. (B) The ratio of TGF-β-mediated inhibition of growth in cells treated with siRNA targeting Mitf over cells treated with a control siRNA is compared between proliferative (M000921) and invasive (M991121) signature melanoma cell lines. This shows that Mitf knockdown promotes resistance to TGF-β-mediated growth inhibition in a proliferative signature melanoma cell line while identical treatment does not change susceptibility in an invasive signature line. TGF-β-treatment of proliferative signature lines (M980513, M000907) results in reduction of Mitf mRNA (C).

4.3.4 Proliferative cells form fast growing tumours sooner than invasive cells

To test the relationship of cell line signature assignments with in vivo behaviour we

performed subcutaneous injection of cell lines into the flanks of immunocompromised mice

and recorded tumour growth characteristics. We found that proliferative melanoma lines

consistently initiated tumours, measured as the time at which tumour volume exceeded 100

mm3, about 14 ± 3 days after being injected into the flanks of athymic nude mice. This was

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Chapter 4 Results

significantly (p < 0.001) shorter than for invasive lines which took 59 ± 11 days (Fig. 4.4).

These data provide in vivo evidence for the significance of a proliferative signature in

melanoma cells as predicted by in vitro experiments. The proliferative signature-seeded

tumours all initiated growth at nearly the same time point. Contrasting this, initiation times for

the invasive signature-seeded tumours was spread over a wider period. This suggests that,

unlike proliferative signature-seeded initiation, invasive signature-seeded initiation may be

dependent on microenvironmental variation.

Figure 4.4. Xenograft tumour growth. Human melanoma cell lines (M980513, M000907, M991121, M010308) were injected into both flanks of immunocompromised nude mice. Proliferation of melanoma cells led to tumour growth which was monitored daily. Proliferative melanoma cells (M980513, M000907) formed tumours rapidly, while invasive melanoma cells (M991121, M010308) took weeks longer to initiate tumour growth.

4.3.5 Tumours derived from proliferative or invasive lines are indistinguishable

Because both transcription signature melanoma cell types yielded tumours we were interested

in examining these for signature-specific differences. Upon excision the tumours were stained

for Mitf and Ki67 antigen expression. Tumours derived from invasive signature cell lines,

which did not stain for Mitf, revealed melanoma cells with nuclei which were Mitf-positive

and melanoma cells with nuclei which were Mitf-negative (Fig. 4.5A-E). Tumours derived

from proliferative signature cell lines, which stained for Mitf, showed the same patterning of

stained and unstained melanoma cell nuclei (Fig. 4.5F-J). Additionally, we found that Mitf-

stained nuclei tended to concentrate within the peripheral margins of the tumours. Ki67

antigen staining patterns were similarly indistinguishable in tumours derived from

proliferative or invasive signature lines. Also, it was apparent that tumour regions showing

Mitf-positive nuclei were also enriched for Ki67-positive nuclei. These findings showed that

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Chapter 4 Results/Discussion

after removal tumours seeded with invasive or proliferative signature cell lines were not

distinguishable and that homogeneous in vitro staining patterns yielded strikingly

heterogeneous patterns in vivo, showing that signature patterns of melanoma cells change bi-

directionally.

Figure 4.5. Immunohistochemistry of melanoma xenograft tumours. Human melanoma cell lines (M980513, M000907, M991121, M010308) were injected into the flanks of immunocompromised nude mice and allowed to grow tumours for a maximum of 75 days. After a tumour had formed it was removed and subjected to immunohistochemical analysis. (A) A day 75 tumour resulting from an invasive signature melanoma (M010308). (B) Mitf and Ki67 stains of fields 1 and 2. (C) A day 22 tumour resulting from a proliferative signature melanoma (M980513). (D) Mitf and Ki67 stains of fields 3 and 4. Black horizontal bars represent 200 µm.

4.4 Discussion

A feature of current models for gene expression involvement in melanoma progression is their

explicitly one-way nature. It is typical to present gene expression changes proceeding

concomitantly with stage progression, where a gene either increases or decreases expression

as the disease evolves through clinically recognised stages to metastasis (Miller and Mihm,

111


2006). However, models of this design do not account for the broad molecular heterogeneity

apparent in melanoma. Indeed it is clear that, in many melanoma cells within a given lesion,

genes expected to be downregulated in late stages are found active and others expected to be

upregulated are not. One possible answer is that many genes associated with metastatic

potential do not undergo one-way modification of regulation and instead retain the potential

to reverse changes in their expression.

Investigations into the gene expression signatures of melanoma cell lines taken from late

stage tumours show that a given cell line will usually express one of two major transcription

programs. It was also determined that the genes whose expression patterns respectively

delineated the two signatures were likely involved in melanoma metastatic potential (Bittner

et al., 2000; Hoek et al., 2006). One of these signatures (identified by us as proliferative) has

Mitf and other melanocytic genes (e.g. TYR, DCT, MLANA) upregulated along with a number

of additional neural crest-related factors (e.g. SOX10, TFAP1A, EDNRB). This signature is

associated with high rates of proliferation, low motility and sensitivity to growth inhibition by

TGF-β. A second signature (identified by us as invasive) downregulates these genes and

instead upregulates others whose secreted products (e.g. INHBA, COL5A1, SERPINE1) are

known to be involved in modifying the extracellular environment. This signature is associated

with lower rates of proliferation, high motility and resistance to growth inhibition by TGF-β.

Having identified these genes we found that many of the proliferative signature were frequent

responders to Wnt signalling, and those of the invasive signature were commonly TGF-β

signal-driven, and we proposed that the balance in activity of these signalling pathways are

responsible for the different transcription signatures observed (Hoek et al., 2006). Among

genes comprising the invasive signature are several (e.g. WNT5A, DKK1 and CTGF) known

to negatively regulate Wnt signalling (Ishitani et al., 2003; Mercurio et al., 2004; Zorn, 2001),

suggesting that activation of TGF-β signalling may precipitate deactivation of Wnt signalling.

Similar cross-talk opposition between TGF-β and Wnt signalling has already been noted in

gastrointestinal cancer (Mishra et al., 2005). This possible link between the signatures

indicated they may be reversible given appropriate signals and further suggested that

proliferation and invasion are program states which melanoma cells activate according to

microenvironmental cues (Hoek et al., 2006).

The results of our in vitro proliferation and motility analyses were consistent with signature

assignments inferred from earlier DNA microarray experiments (Hoek et al., 2006). In order

112


to immunohistochemically differentiate signatures in vivo we used nuclear Mitf as our marker

for the proliferative signature and nuclear Ki67 antigen as a general indicator for proliferation

activity. We found in vitro that while both invasive and proliferative signature melanoma cells

expressed Ki67 antigen, less than half of the invasive signature cells expressed it, correlating

with the relative growth differences observed between proliferative and invasive cells in vitro.

Because Ki67 antigen is not detected in G0 (Braun et al., 1988; Bruno and Darzynkiewicz,

1992), and it may be an absolute requirement for cell proliferation (Schluter et al., 1993), we

conclude that invasive signature cells spend more time in G0 (quiescence). Increasing Mitf

expression in melanoma has been shown to be a proliferative factor and involved in Cdk2

production and activity (Carreira et al., 2006; Du and Fisher, 2002; Widlund et al., 2002). Our

data also supports a proliferative role for Mitf in vitro as we find that Mitf-positive lines

proliferate faster (Fig. 4.1) and express Ki67 antigen with greater frequency than Mitf-

negative lines (Fig. 4.2).

We performed siRNA knockdown of Mitf in a proliferative signature cell line to show that

this confers a TGF-β-resistance phenotype which others have shown is characteristic of

invasive signature line melanomas (Heredia et al., 1996; Krasagakis et al., 1994; Roberts et

al., 1985). Our DNA microarray data suggested that Mitf gene expression is central to the

proliferative signature and may therefore have a role in mediating the growth-inhibitory

response to TGF-β. After knockdown of Mitf expression in a proliferative signature line the

cells gained resistance to TGF-β-mediated inhibition of proliferation (Fig. 4.3). This

correlates with experiments by others who have shown that invasive characteristics are

increased in melanoma cells treated with siRNA targeting Mitf expression (Carreira et al.,

2006; Lekmine et al., 2007). Together these combined findings indicate that regulation of

Mitf expression is critical to signature membership and supports our contention that in vivo

changes in nuclear Mitf staining indicate proliferative/invasive signature switching.

Our xenograft experiments showed that tumour growth patterns correlated appropriately with

the gene expression signatures. Proliferative signature lines initiated tumours about two weeks

after injection while invasive signature lines lay dormant for an average of eight weeks before

tumour growth began (Fig. 4.4). While these and the in vitro experiments further support the

different signature assignments to different melanoma cell lines, immunohistochemical

examination of the tumours showed evidence for signature switching during tumorigenesis.

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Comparison of in vitro Mitf staining patterns with in vivo Mitf staining patterns shows that

resultant tumours deriving from either proliferative signature or invasive signature lines

reveal the presence of both Mitf-positive and Mitf-negative melanoma cells (Fig. 4.5).

Concurrently, Ki67 antigen staining, while found throughout the tumours, was more frequent

in regions positive for Mitf staining than in regions absent of Mitf. Furthermore, the

distribution of Mitf staining and increased frequency of Ki67 antigen positivity shows a

distinctly peripheral pattern. This confirms that, as expected, melanoma cells proximal to the

interface between host tissues and the tumour are actively undergoing increased rates of

proliferation. To address concerns that our invasive signature lines were contaminated with

proliferative signature cells we monitored in vitro proliferation rates for invasive signature

cells serially passaged over the same time frame as conducted for the xenograft experiments.

We observed no change in proliferation rates (data not shown), and therefore we believe that

the change in tumour growth in invasive signature line-seeded xenografts is not due to prior

contamination with proliferative signature cells.

The long lag-time for tumour initiation observed with invasive signature cells and the

presence of Mitf-positive nuclei in resulting tumours indicate that tumour growth was

probably preceded by a switch in some cells to the proliferative signature type. Similar

microenvironment-driven signature switching has been shown previously in other

experiments. Recent studies investigating the effect of embryonic environments on melanoma

cells has shown how these environments can affect the aggressive phenotype. Kulesa and co-

workers, using the aggressive C8161 melanoma line, showed that transplantation of C8161

cells into chick embryonic tissues stimulated re-expression of melanocytic markers similar to

poorly aggressive cells (Kulesa et al., 2006). Complimentary in vitro studies in which poorly

aggressive cells, grown on 3D matrices preconditioned by aggressive lines, showed

upregulation of extracellular matrix modifying genes and increased invasive ability (Seftor et

al., 2006). Additional experiments in zebrafish revealed that in embryonic environments

inhibition of the morphogen Nodal switched melanoma cells to a less aggressive phenotype,

suggesting that Nodal signalling (which acts through TGF-β family receptors) was important

to maintaining aggressive phenotypes in melanoma cells (Topczewska et al., 2006). Signature

switching of cells in response to the microenvironment would explain why our xenograft

tumours deriving from different signature lines were immunohistochemically

indistinguishable.

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That rapidly proliferating cells are found closer to the periphery of growing tumours also

supports a role for the microenvironmental determination of activity. However, this location

of proliferative phenotype melanoma cells at the tumour periphery directly contradicts the

long-held assumption that these cells represent the tumour’s invasive front. This assumption

is based primarily on observations linking primary tumour thickness and the frequency of

subsequent metastatic disease (Breslow, 1978). We offer that the thickness of the primary

tumour may, rather than serve to bring its peripheral cells closer to vascular egress, determine

the extent to which cells deeper within it experience microenvironmental change which (as we

contend) drives the switch to a more invasive phenotype.

Perhaps more interesting is the comparison of our model with the identification of Mitf as a

lineage-specific oncogene. Garraway and coworkers have shown Mitf gene amplification in a

fraction of melanomas and demonstrate its correlation with a poorer prognosis. They

speculated that increased Mitf gene dosage may in part compensate affected melanoma cells

in settings where Mitf activity is normally lost (Garraway et al., 2005). This suggests that with

amplification of its gene Mitf expression in our xenograft model would be preserved in all

cells. However while increased gene dosage may play a role when the gene is activated (and

precipitate a worsened prognosis in patients), this does not mean the gene is necessarily

immune from otherwise normal signalling responses.

These data are critical pieces of the melanoma progression puzzle because they suggest not

only that invasion and proliferation are divisible aspects of metastatic potential, but that these

different transcriptional states are interchangeable programs between which melanoma cells

oscillate during progression in response to changing microenvironmental cues (Fig. 4.6).

What these microenvironmental cues precisely are remains unknown, but there is growing

evidence that hypoxia may be one (Holmquist et al., 2006) and inflammation another (de

Visser et al., 2006). The model we use to describe melanoma progression is presented in

binary terms of invasive versus proliferative. However this is not to deny that intermediate

transcription signatures exist between the described archetypes and we have indeed

characterised other melanoma lines with intermediate signatures in our earlier study (Hoek et

al., 2006). Therefore we believe that the two signatures discussed here represent opposite ends

of a signature continuum between which melanoma cells slide in response to

microenvironment-linked changes in signalling.

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Figure 4.6. An integrated model for gene regulation of melanoma metastatic potential and progression. Early phase melanoma cells expressing the “proliferative signature” gene set proliferate to form the primary lesion. Following this an unknown signal switch, likely brought about by altered microenvironmental conditions (e.g. hypoxia or inflammation), gives rise to cells with a significantly different “invasive signature” gene set. Invasive signature cells escape and, upon reaching a suitable distal site, revert to the proliferative state and nucleate a new metastasis where the cycle is repeated. Each switch in phenotype (state change) is accompanied by an exchange in expressed gene sets from proliferative to invasive and vice versa.

The relevance of this model to clinical aspects of melanoma is that it may explain why

metastatic melanoma is so refractory to chemo- and immunotherapeutic strategies. During

treatment of metastatic melanoma so-called mixed responses are often observed and this

phenomenon may derive from a heterogeneous distribution of proliferative and invasive

signature cells within metastases. The heterogeneity inherent in tumours containing

melanoma cells with biological activities dependent on the microenvironment suggests that

while proliferating cells are susceptible to chemotherapy there are populations of cells which,

though not proliferating, have the capacity to switch back to a proliferative program and

successfully drive tumour progression once therapy has ceased. Finally, while our state-

switching model for melanoma progression offers attractive answers for why the disease

behaves as it does, we acknowledge the caveat that much of our hypothesis rests on

expression signatures obtained in vitro and thus may not fully recapitulate melanoma's in vivo

biology. Therefore, until the in vivo situation is resolved, we cannot yet advocate the

abandonment of models which favour step-wise accumulation of genetic lesions as a driver

for melanoma progression.

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4.5.1 Melanoma tissues and lines





Cell lines were chosen according to their transcription pattern signatures as previously

described (Hoek et al., 2006). Two proliferative signature (M980513, M000907) and two

invasive signature (M991121, M010308) melanoma lines were used.

4.5.2 In vitro motility and proliferation assays

For the motility assays 2 x 104 melanoma cells were seeded on 8 µM transwell microporous

filters (Becton Dickinson, Franklin Lakes, NJ) in 200 µl RPMI. As a chemoattractant RPMI

containing 10% FCS was added to the lower chamber. After 18 hours of incubation cells on

the upper side of the filter were removed with cotton swab. The membrane was then stained

using a standard hematoxylin and eosin protocol and the cells were counted under a light

microscope. For the proliferation assay melanoma cells were seeded to a density of 5 x 104 in

each well of a six-well plate. After 24, 72 and 96 hours cells were counted in a Neubauer

chamber to estimate cell-doubling times.

4.5.3 Recombinant adenovirus vector and siRNA

Recombinant first-generation, E1/E3-deleted Ad5-based vectors Ad-H1-siMitf and Ad-H1-

siControl were generated as described previously (Hemmi et al., 1998). Briefly, homologous

recombination was performed in human embryonic retinoblast line 911 cells between a

transfer plasmid pAd-H1-siMitf encoding the Mitf-specific siRNA sequence under the control

of the H1 promoter and a genomic ClaI DNA fragment isolated from AdMLP-lacZ. To

construct pAd-H1-siMitf, the CMV promoter of pAd-CMV∆lacZ-lnk1 was replaced with the

H1 promoter (Hasuwa et al., 2002). The H1 promoter fragment was PCR-amplified from

genomic DNA of human 293T cells and cloned into SfiI/BamHI-restricted pAd-CMV∆lacZ-

Ink1. Subsequently, oligonucleotides for the silencing cassette (Saydam et al., 2005)

containing a 19-nucleotide siRNA sequence targeting Mitf (Busca et al., 2005) were cloned

117


into NheI/SalI-restricted pAd-H1∆lacZ-lnk1 (Ad-H1-siMitf). For a mock control (Ad-H1-

siControl), the siRNA sequence of Mitf was scrambled and blasted to ensure no human

sequence is targeted. Recombinant adenoviruses were plaque-purified, amplified, and CsCl

purified. Viral titers were determined by plaque assay, using 911 cells, and were 1.8 x 1010

PFU/ml for Ad-H1-siMitf and 1.3 x 1010 PFU/ml for Ad-H1-siControl.

4.5.4 Transfection and TGF-β challenge assay

Melanoma cells were seeded to a density of 4 x 104 cells in a 24-well plate one day before

infection. The next day medium was changed to RPMI containing 2% FCS and cells were

either infected with virus particles carrying the pAd-H1-siMitf or pAd-H1-siControl. For

assessment of susceptibility to growth inhibition by TGF-β, cells were challenged with 5

ng/ml recombinant TGF-β (Biosource, Camarillo, CA) 24 hours after virus transduction.

After a further 56 hours cell growth was estimated using a standard MTT assay.

4.5.5 Western blot analyses

Cells were solubilised in lysis buffer containing 20 mM Tris-HCl pH 7.5, 1% TritonX-100,

150 mM NaCl, 10% glycerol and Complete mini protease inhibitor (Roche Diagnostics

GmbH, Mannheim, Germany). Proteins were separated on a NuPAGE 10% Bis-Tris gel

(Invitrogen, Carlsbad, CA) under denaturing and reducing conditions followed by transfer

onto a Nitrocellulose membrane (Invitrogen). Mitf protein was detected with a mouse anti-

Mitf MAb (clone C5; LabVision, CA, USA) diluted 1:100 in 3% BSA at 4oC overnight.

Secondary rabbit-anti-mouse antibodies (Abcam, Cambridge, UK) conjugated with

peroxidase was used at a dilution of 1:10000. Detection by chemiluminescence used an

enhanced chemiluminescence system (GE Healthcare, Buckinghamshire, UK).

4.5.6 Xenografts

For each melanoma line a total of 3 x 106 cells were injected into both flanks of eight-week

old female athymic nude mice. Mice were kept in individually ventilated cages for a

maximum of 75 days post injection. Volume of tumours was measured using vernier callipers

(V = W2 x L x 0.5) once every 3-7 days until linear growth was detected, after which

measurements were taken every 1-2 days. If at least one xenograft tumour reached 1 cm3 the

mouse was sacrificed and tumours removed. If the condition of the mouse deteriorated (e.g.

118


listlessness, loss of weight) the mouse was sacrificed and tumours removed. All remaining

mice were sacrificed on the 75th day and tumours removed. Effective tumour initiation time

was calculated on the day tumour volume reached 100 mm3. Tumours not reaching 100 mm3

within 75 days were not considered.

4.5.7 Immunohistochemistry

Cell lines were prepared for immunohistochemistry as follows. Briefly, cells were cultured,

washed in PBS (Biochrom, Berlin, Germany) and then put into suspension by incubating in 2

mL trypsin/EDTA solution (Biochrom) at 37° C. Trypsin was inactivated by adding 18 mL of

FCS-containing growth medium. Cell suspensions were centrifuged for 5 min at 2000 rpm.

After removing the supernatant, four drops of plasma were added to the pellet and the solution

was mixed. One drop of thrombin was added and after five minutes the coagulated material

was encapsulated for fixation in 4% formalin and embedded in paraffin. Excised xenograft

samples were fixed in 4% formalin and embedded in paraffin. Slides were cut from paraffin

blocks and immunohistochemically stained using the alkaline phosphatase-anti-alkaline

phosphatase technique and counter-stained using hematoxylin. Antibodies used were directed

against Mitf (clone D5; DakoCytomation, Glostrup, Denmark) or Ki-67 (clone MIB-1;

DakoCytomation). Counting of stained and unstained nuclei was done on a PC using the free

UTHSCSA ImageTool program (developed at the University of Texas Health Science Center

at San Antonio, Texas and available from the Internet by anonymous FTP from

ftp://maxrad6.uthscsa.edu).

4.5.8 Statistical analysis

For all quantitative sample comparisons Student’s two-sample heteroscedastic t-test was used

to calculate a t-statistic for comparison against a significance cutoff of p = 0.05.

119


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Breslow, A. (1978) Tumour thickness in evaluating prognosis of cutaneous melanoma. Ann Surg, 187, 440.

Bruno, S. and Darzynkiewicz, Z. (1992) Cell cycle dependent expression and stability of the nuclear protein detected by Ki-67 antibody in HL-60 cells. Cell Prolif, 25, 31-40.

Busca, R., Berra, E., Gaggioli, C., Khaled, M., Bille, K., Marchetti, B., Thyss, R., Fitsialos, G., Larribere, L., Bertolotto, C., Virolle, T., Barbry, P., Pouyssegur, J., Ponzio, G. and Ballotti, R. (2005) Hypoxia-inducible factor 1{alpha} is a new target of microphthalmia-associated transcription factor (MITF) in melanoma cells. J Cell Biol, 170, 49-59.

Carreira, S., Goodall, J., Aksan, I., La Rocca, S.A., Galibert, M.D., Denat, L., Larue, L. and Goding, C.R. (2005) Mitf cooperates with Rb1 and activates p21Cip1 expression to regulate cell cycle progression. Nature, 433, 764-769.

Carreira, S., Goodall, J., Denat, L., Rodriguez, M., Nuciforo, P., Hoek, K.S., Testori, A., Larue, L. and Goding, C.R. (2006) Mitf regulation of Dia1 controls melanoma proliferation and invasiveness. Genes Dev, 20, 3426-3439.

Curtin, J.A., Fridlyand, J., Kageshita, T., Patel, H.N., Busam, K.J., Kutzner, H., Cho, K.H., Aiba, S., Brocker, E.B., LeBoit, P.E., Pinkel, D. and Bastian, B.C. (2005) Distinct sets of genetic alterations in melanoma. N Engl J Med, 353, 2135-2147.

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Hasuwa, H., Kaseda, K., Einarsdottir, T. and Okabe, M. (2002) Small interfering RNA and gene silencing in transgenic mice and rats. FEBS Lett, 532, 227-230.

Hemmi, S., Geertsen, R., Mezzacasa, A., Peter, I. and Dummer, R. (1998) The presence of human coxsackievirus and adenovirus receptor is associated with efficient adenovirus-mediated transgene expression in human melanoma cell cultures. Hum Gene Ther, 9, 2363-2373.



Holmquist, L., Lofstedt, T. and Pahlman, S. (2006) Effect of hypoxia on the tumour phenotype: the neuroblastoma and breast cancer models. Adv Exp Med Biol, 587, 179-193.

Ishitani, T., Kishida, S., Hyodo-Miura, J., Ueno, N., Yasuda, J., Waterman, M., Shibuya, H., Moon, R.T., Ninomiya-Tsuji, J. and Matsumoto, K. (2003) The TAK1-NLK mitogen-activated protein kinase cascade functions in the Wnt-5a/Ca(2+) pathway to antagonize Wnt/beta-catenin signaling. Mol Cell Biol, 23, 131-139.

Krasagakis, K., Garbe, C., Schrier, P.I. and Orfanos, C.E. (1994) Paracrine and autocrine regulation of human melanocyte and melanoma cell growth by transforming growth factor beta in vitro. Anticancer Res, 14, 2565-2571.

Kulesa, P.M., Kasemeier-Kulesa, J.C., Teddy, J.M., Margaryan, N.V., Seftor, E.A., Seftor, R.E. and Hendrix, M.J. (2006) Reprogramming metastatic melanoma cells to assume a neural crest cell-like phenotype in an embryonic microenvironment. Proc Natl Acad Sci U S A, 103, 3752-3757.

Lekmine, F., Chang, C.K., Sethakorn, N., Das Gupta, T.K. and Salti, G.I. (2007) Role of microphthalmia transcription factor (Mitf) in melanoma differentiation. Biochem Biophys Res Commun, 354, 830-835.

Levene, A. (1980) On the histological diagnosis and prognosis of malignant melanoma. J Clin Pathol, 33, 101-124.

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Mercurio, S., Latinkic, B., Itasaki, N., Krumlauf, R. and Smith, J.C. (2004) Connective-tissue growth factor modulates WNT signalling and interacts with the WNT receptor complex. Development, 131, 2137-2147.

Miller, A.J. and Mihm, M.C., Jr. (2006) Melanoma. N Engl J Med, 355, 51-65. Mishra, L., Shetty, K., Tang, Y., Stuart, A. and Byers, S.W. (2005) The role of TGF-beta and

Wnt signaling in gastrointestinal stem cells and cancer. Oncogene, 24, 5775-5789. Paget, S. (1889) The distribution of secondary growths in cancer of the breast. Lancet, 1, 571-

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Sasse, A.D., Sasse, E.C., Clark, L.G., Ulloa, L. and Clark, O.A. (2007) Chemoimmunotherapy versus chemotherapy for metastatic malignant melanoma. Cochrane Database Syst Rev, CD005413.

Saydam, O., Glauser, D.L., Heid, I., Turkeri, G., Hilbe, M., Jacobs, A.H., Ackermann, M. and Fraefel, C. (2005) Herpes simplex virus 1 amplicon vector-mediated siRNA targeting epidermal growth factor receptor inhibits growth of human glioma cells in vivo. Mol Ther, 12, 803-812.

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Chapter 5-

5 Id2 suppression of p15Ink4b abrogates TGF-β-mediated growth

inhibition in melanoma

123

Id2 suppression of p15Ink4b abrogates TGF-β-mediated growth inhibition of melanoma

NC Schlegel1, OM Eichhoff1, S Hemmi2, D Mihic3, S Werner4, R Dummer1 and KS Hoek1

1Department of Dermatology, University Hospital of Zürich, 8091 Zürich, Switzerland 2Institute of Molecular Biology, Faculty of Mathematics and Natural Sciences, University of

Zürich, 8057 Zürich, Switzerland 3Institute of Clinical Pathology, University Hospital of Zürich, 8091 Zürich, Switzerland 4Institute of Cell Biology, Department of Biology, ETH Zurich, 8093 Zurich, Switzerland

Manuscript to be submitted to Oncogene

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Chapter 5 Asbtract / Introduction

5.1 Abstract

Growth inhibition resistance to transforming growth factor β (TGF-β) is a turning point in the

malignant progression of many cancer types. In melanoma this resistance is associated with a

more aggressive metastatic potential. Here, using proliferative and invasive phenotype pairs,

we explored molecular responses involved in modulating susceptibility to TGF-β-mediated

inhibition of proliferation. We identified the Id2 gene as differentially regulated by TGF-β

and link the loss of this regulation to acquired resistance to TGF-β in invasive phenotype

cells. We show that TGF-β induces cell cycle arrest through induction of p15Ink4b and

repression of Id2 gene expression. Furthermore, Id2 overexpression in proliferative phenotype

cells counteracts p15Ink4b induction and consequently protects melanoma cells from TGF-β-

mediated inhibition of proliferation. We find that although Id2 expression modulates

susceptibility to TGF-β, tissue microarray analysis of its expression shows no link with

patient survival. We conclude that transition to increased aggressiveness in melanoma cells

requires Id2 upregulation to overcome its suppression by TGF-β and thus circumvent TGF-β-

mediated inhibition of proliferation

5.2 Introduction

Transforming growth factor β (TGF-β) has been shown to have both tumour suppressing and

promoting activities. Its tumour suppressing function is demonstrated by its important role as

a negative regulator of proliferation for most cell types. Evasion from cytostatic

responsiveness to TGF-β is important in a number of cancers, including melanoma. Reduced

susceptibility to the growth inhibiting effects of TGF-β has been associated with increased

invasive and metastatic properties of melanoma cells (Heredia et al., 1996; Krasagakis et al.,

1999; Rodeck et al., 1994).

Most of our understanding of the mechanisms involved in growth control by TGF-β derives

from studies on epithelial cells. A number of these findings are also relevant for other cell

types. TGF-β regulates genes involved throughout the cell cycle, but its cytostatic effects have

been primarily attributed to the regulation of factors targeting G1 events (Flores et al., 1996;

Massague and Gomis, 2006). TGF-β induces the expression of cyclin-dependent kinase

125


(CDK) inhibitors (Massague and Gomis, 2006) and represses growth-promoting transcription

factors such as c-Myc (Pietenpol et al., 1990) and inhibitor of DNA binding (Id) proteins

(Kowanetz et al., 2004; Ling et al., 2002)

Id proteins are positive regulators of cell growth and play a critical role in promoting G1/S

cell cycle progression. Their role in the regulation of cell proliferation is thought to be driven

by two mechanisms. Firstly, they interfere with bHLH, ETS and Pax factors and consequently

regulate the expression of their target genes such as the CDK inhibitors (Alani et al., 2001;

Lyden et al., 1999; Mori et al., 2000; Ohtani et al., 2001; Prabhu et al., 1997; Rothschild et al.,

2006). Secondly, Id2 binds tumour suppressor proteins of the Rb family and when in large

excess, abolishes their growth-suppressing activity by causing the release of E2F transcription

factors required for cell cycle progression (Iavarone et al., 1994; Lasorella et al., 1996). In

normal cells, Id2 is itself a downstream target of pRb and its family members, which inhibit

its function against natural targets. However, Id2 overexpressed by tumour cells can saturate

the Rb pathway and deprive the cell of its most important cell cycle checkpoint (Lasorella et

al., 1996). Rb pathway inhibition is believed to be achieved directly through the binding of

Id2 and indirectly through Id2-mediated downregulation of CDK inhibitor expression (Ohtani

et al., 2001).

In melanoma, Id levels have primarily been reported for Id1 and few publications report on

Id2 and melanoma. For example, Polsky and co-workers have demonstrated a correlation

between Id1 expression and loss of p16Ink4a expression in early melanoma (Polsky et al.,

2001). Using tissue microarrays, Straume and Akslen evaluated the expression of Id1 in 119

cases of nodular melanoma and showed that strong Id1 expression was significantly

associated with increased tumour thickness and reduced survival (Straume and Akslen, 2005).

Id2 was first associated with melanoma when it was identified as the product of a down-

regulated gene in melanomas with homozygous deletion of the CDKN2A locus genes in a

global gene expression study (Bloethner et al., 2006). In a microarray gene expression

analysis of uveal melanoma, which generated two subgroups representing tumours with low

and high risk of metastatic death, Id2 was one of the top class discriminating genes (Onken et

al., 2006). Id2 was shown to be strongly downregulated in melanomas with high risk of

metastatic death (Onken et al., 2006).

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Chapter 5 Introduction / Results

Bittner and co-workers first suggested that there may be specific transcriptional signatures

delineating melanoma cell subgroups (Bittner et al., 2000). We recently characterised two

different transcription signatures for melanoma cell cultures which, based on known functions

of the genes involved, suggested that their respective contribution to metastatic potential were

either proliferative or invasive (Hoek et al., 2006). We hypothesised that melanoma cells may

be able to switch back and forth between these phenotypes and thereby drive melanoma

progression. Subsequently, we demonstrated phenotype switching in vivo using an

immunocompromised mouse model (Hoek et al., 2008). Many of the genes expressed by

invasive phenotype melanoma cells had previously been characterised as being positively

responsive to TGF-β-like signalling in other systems. Furthermore, TGF-β-mediated growth

inhibition, which has previously been shown to be absent or less pronounced in aggressively

invasive melanoma cells, was also shown to be a discriminating phenotype for the phenotypes

we characterised (Hoek et al., 2006). Here, we identify Id2 as a TGF-β regulated gene

involved in the TGF-β growth inhibitory response and discuss its relevance for the invasive

phenotype.

5.3 Results

5.3.1 Differential Id2 regulation and expression in human melanoma cultures

In previous DNA microarray experiments we identified two in vitro transcription phenotypes,

proliferative and invasive, which together drive melanoma progression (Hoek et al., 2008;

Hoek et al., 2006). We also found that proliferative and invasive phenotypes were

differentially susceptible to TGF-β-mediated growth inhibition (Hoek et al., 2006).

We selected pairs of proliferative and invasive phenotype melanoma cell cultures. Supervised

hierarchical clustering of these samples was performed using normalised signal intensity data

from 105 genes whose expression was shown to be tightly linked to phenotype (Fig. 5.1A).

We also performed TGF-β-susceptibility assays to establish that our candidates were

appropriately susceptible or resistant to TGF-β-mediated growth inhibition. These culture

pairs represent opposing TGF-β responses observed in melanomas; at least 50% growth

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Chapter 5 Results

inhibition was observed in proliferative phenotype cells while no inhibition or even a slight

growth stimulation was seen in invasive phenotype cells (Fig. 5.1B).

Figure 5.1. Melanoma cell phenotype characteristics and endogenous Id2 expression levels. (A) A gene expression heatmap, generated by clustering samples based on the normalised expression of 105 metastatic potential genes, highlights phenotype-specific signatures of our four cell cultures. This shows that M000921 and M010817 (proliferative phenotype) cluster separately from M990115 and M010119 (invasive phenotype). (B) 96 hours after the addition of 5ng/ml TGF-β1 cell proliferation was assessed by a colorimetric assay and expressed as a percentage of proliferatiom relative to untreated cells. Proliferative phenotype cells are susceptible to the growth inhibitory effects of TGF-β while invasive phenotype cells are not. (C) Id2 RNA expression measured by RT-PCR and (D) western blot analysis of Id2 protein expression in proliferative (M000921; M010817) and invasive (M990115; M010119) cell cultures. For RT-PCR Id2 mRNA levels are expressed as ratios to GAPDH expression, and for western blotting beta-actin protein expression is shown as a loading control. Proliferation assays were performed three times in quadruplicate and RT-PCR experiments were performed three times in triplicate. Error bars represent ± standard deviation.

After confirming the opposing phenotypes, we looked for differential endogenous expression

of Id2 mRNA and protein in our representative cell cultures. Using reverse transcription-

polymerase chain reaction (RT-PCR) and western blotting we showed that Id2 expression was

diminished in proliferative phenotype cells when compared to invasive phenotype cells

(Figure 5.1C-D).

As Id proteins have been shown to be regulated by TGF-β in epithelial cells (Kowanetz et al.,

2004; Ling et al., 2002), we used RT-PCR to further investigate TGF-β modulation of Id2 in

proliferative and invasive phenotype cells. As shown by RT-PCR, TGF-β1 downregulated Id2

mRNA expression in proliferative (M010817; M000921) but not in invasive (M990115;

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Chapter 5 Results

M0100119) phenotype cell cultures (Fig. 5.2A). To confirm that Id2 protein is also

differentially regulated, we showed Id2 protein expression on two cohort-representative TGF-

β-treated cell cultures (Fig. 5.2B). Consistent with the mRNA data, Id2 protein was down-

regulated after a 24h treatment with TGF-β1 in proliferative phenotype cells.

Figure 5.2. TGF-β suppresses Id2 expression only in proliferative phenotype melanoma cells. (A) Id2 expression was measured by RT-PCR as a ratio to GAPDH expression and expressed in terms of TGF-β-treated cells relative to untreated cells at 24h normalised against data acquired at 0h. This shows that Id2 levels are more susceptible to TGF-β suppression in the proliferative phenotype than in the invasive phenotype. Data represent averages of three independent experiments performed in triplicate with standard deviations (error bars). (B) Western blot analysis of Id2 protein expression in M010817 and M010119 after the addition of TGF-β; beta-actin protein expression is shown as a loading control.

5.3.2 Id2 overexpression protects proliferative cells from the growth inhibitory effects of TGF-β

Given that Id2 is differentially expressed and regulated across melanoma cell phenotypes, we

next hypothesised that Id2 overexpression could counteract the negative effects of TGF-β on

the growth of proliferative phenotype cells. We used a myc-tagged wild-type Id2 adenoviral

construct (Ad-Id2) as used by others (Gleichmann et al., 2002; Kowanetz et al., 2004; Toma

et al., 2000). We infected proliferative phenotype cells with either Ad-Id2 or a control

adenoviral construct (Ad-luciferase) at a multiplicity of infection (MOI) of 3 prior to

treatment with 5 ng/ml TGF-β1 followed by assessment of cell proliferation. Overexpression

of Id2 protein was confirmed by western blot analysis (Fig. 5.3B). Confirming our hypothesis,

proliferative phenoptye cells overexpressing Id2 were less growth inhibited upon addition of

TGF-β compared to the cells transfected with the control construct (Fig. 5.3A). We therefore

conclude that overexpression of Id2 in proliferative phenotype cells protects the cells from the

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growth inhibitory effects of TGF-β by inhibiting the TGF-β-induced downregulation of its

expression.

Furthermore, we suppressed Id2 expression in invasive phenotype cells using an Id2-specific

siRNA. Although Id2 protein expression could not be repressed by more than 50% (Fig.

5.3D) we could still significantly reduce TGF-β-induced growth in invasive phenotype cells

(Fig. 5.3C). Taken together, as it has been shown for epithelial cells (Kowanetz et al., 2004),

our results indicate that repression of Id2 is necessary for TGF-β-induced growth control in

melanoma cells.

Figure 5.3. Id2 modulates TGF-β growth inhibition in melanoma. (A,B) Proliferative phenoptye melanoma cells were infected with an Id2 overexpressing adenovirus or a control adenovirus Ad-luciferase at an MOI of 3. Id2 protein levels in transfected cells were determined by western blot analysis. β-actin staining served as a loading control. (B). Id2 overexpression resulted in reduced TGF-β-mediated inhibition of cell growth (A). (C,D) Invasive phenotype cells were transfected with siRNA duplexes targeting Id2 or non-targeting negative control. Id2 protein levels in transfected cells were determined by western blot analysis. β-actin staining served as a loading control (D). 24h post-infection or 48h post-transfection, cells were treated with 5 ng/ml recombinant TGF-β1 and 50 h post-treatment, cell proliferation was evaluated. Growth inhibition (A) or stimulation (C) was determined by colorimetric assay and expressed as growth inhibition/stimulation relative to untreated cells infected with the respective adenovirus or transfected with the respective siRNA. Data was then normalised to the growth inhibition/stimulation calculated for cells infected with Ad-luciferase or transfected with the control siRNA. For each cell culture, data represents the results of four independent experiments performed in quadruplicate with standard deviations (error bars). Student’s paired t-test was used to calculate significance.

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5.3.3 Id2 regulates TGF-β-induced G1 cell-cycle arrest

TGF-β-induced growth inhibition has been attributed to G1 cell-cycle arrest in a number of

cell types including melanoma (Flores et al., 1996; Massague and Gomis, 2006). In order to

investigate if Id2 modulation directly impacts on TGF-β-induced G1 cell-cycle arrest, we

analysed the cell cycle distribution of TGF-β-treated proliferative phenotype cells transduced

with overexpressing Id2 or control adenoviruses. 24 hours after infecting M010817 cells with

Ad-Id2 or Ad-luciferase (MOI 3), we followed this with 5 ng/ml TGF-β1 for 48 hours and

propidium iodide staining to analyse their DNA content using flow cytometry. We detected a

reduction of cells in S phase and an increase in cells in G1 following TGF-β addition to

control cells (Fig. 5.4A). Confirming our hypothesis, the G1 arrest was reversed when cells

were initially transduced with an overexpressing Id2 construct.

Figure 5.4. Id2 regulates TGF-β-induced G1 cell-cycle arrest and suppresses TGF-β-induced induction of p15Ink4b. (A) Proliferative phenotype cells (M010817) were infected at an MOI of 3 with Ad-Id2 or Ad-luciferase in serum-reduced medium (3%). 24h post-infection, cells were treated with 5 ng/ml TGF-β1 and 48h post-treatment, cells were harvested and their DNA content was analysed by flow cytometry. Data is expressed as TGF-β-induced change in cell cycle content expressed in percentage and is represents averages of three independent experiments with standard deviations (error bars). Student’s paired t-test was used to calculate significance. (B) Proliferative phenotype cells (M010817) were infected at an MOI of 3 with Ad-Id2 or Ad-luciferase in serum-reduced medium (3%). 24h post-infection, cells were treated with 5 ng/ml TGF-β1 and 1, 8 and 24 h post-treatment cells were harvested for RNA extraction and CDK inhibitors expression was measure by RT-PCR. CDK inhibitors mRNA levels were calculated as a ratio to GAPDH expression and data was then normalised to untreated samples transduced with Ad-luciferase at 1 hour. TGF-β induces the expression of the CDK inhibitor p15Ink4b but not p21Cip1, p27Kip1 or p57Kip2. Moreover, Id2 represses the expression of p15Ink4b and concurrently restricts the induction of p15Ink4b by TGF-β. Data represent averages of three independent experiments performed in triplicate.

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5.3.4 Id2 restricts TGF-β-induced upregulation of p15Ink4b

Having shown that Id2 regulates TGF-β-induced G1 arrest, we next wanted to identify the

mechanism of this interaction. The G1 cell cycle arrest induced by TGF-β has been partially

attributed to its role in driving the expression of cyclin-dependent kinase (CDK) inhibitors,

which are negative regulators of the cell cycle (Massague and Gomis, 2006). Contrasting this,

Ids have been shown to positively regulate cell cycle progression by inhibiting the expression

of CDK inhibitors (Alani et al., 2001; Lyden et al., 1999; Mori et al., 2000; Ohtani et al.,

2001; Prabhu et al., 1997; Rothschild et al., 2006). We therefore hypothesised that Id2

suppressed the induction of CDK inhibitors by TGF-β.

To verify this, we transduced proliferative phenotype cells with Ad-Id2 or Ad-luciferase,

followed by TGF-β treatment and analysed the mRNA levels of CDK inhibitors after 1, 8 and

24 hours of treatment. Contrasting with previously published melanoma reports (Florenes et

al., 1996; Reed et al., 2001), p21Cip1 expression was not induced by TGF-β treatment in our

melanoma cells (Fig. 5.4B). Similarly, we did not detect significant induction of p27Kip1 or

p57Kip2. However, we did find a significant increase in p15Ink4b expression by TGF-β

treatment. Interestingly, Id2 overexpressing cells showed significantly reduced levels of

p15Ink4b, which were not rescuable by TGF-β treatment. From this data we conclude that Id2

dampens TGF-β-induced upregulation of p15Ink4b and the consequent G1 arrest.

5.3.5 Id2 expression does not correlate with patient survival in melanoma

We were next interested in correlating Id2 expression with patient survival. Using cell culture

and tissue arrays derived from metastatic tumours, we evaluated Id2 expression by

immunohistochemistry. Id2 expression was scored as being less than 10%, between 10% and

85% or more than 85% present in 51 metastatic melanoma tumours (Fig. 5.5A). After

comparing Id2 expression with survival data from the tumour donors we could conclude that

survival was not significantly different between the three groups (Fig. 5.5B). To look more

closely if the presence of Id2 was important for patient survival, we scored Id2 expression as

being absent (<10%) or present (>10%) but again, did not find a correlation between Id2

expression and patient survival.

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Figure 5.5. Id2 expression in melanoma organ metastases. (A) Representative tissue microarray spots stained for Id2. Id2 expression was scored as being less than 10%, between 10% and 85% or more than 85%. (B) Kaplan-Meier analysis of Id2 expression in melanoma organ metastases. It shows that Id2 expression does not correlate with patient survival in melanoma.

5.4 Discussion

In the present study we show that TGF-β represses Id2 expression in proliferative phenotype

melanoma cells, which are susceptible to the growth inhibitory effects of TGF-β. In contrast,

invasive phenotype melanoma cells, which continue to thrive in the presence of TGF-β,

express higher levels of Id2, which are not repressed by TGF-β. Although a number of reports

have demonstrated the modulation of Id proteins by TGF-β in epithelial cells (Di et al., 2006 ;

Kang et al., 2003; Kondo et al., 2004; Kowanetz et al., 2004), we show differential

modulation of Id2 expression between melanoma cell phenotypes. Moreover, elevated

expression of Id mRNA and protein have been reported for many different human tumours,

including carcinomas, neural tumours, leukaemia, as well as melanoma, and in some cases

high levels were associated with increased disease severity and poor prognosis (Perk et al.,

2005). Complementing those findings, we saw that Id2 expression correlated with the two

different transcription signatures we previously identified (Hoek et al., 2006), where Id2

expression is increased in invasive phenotype cells compared to proliferative phenotype cells.

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We next demonstrated that we could protect proliferative phenotype cells from TGF-β-

mediated growth inhibition by overexpressing Id2 to circumvent TGF-β-driven Id2

repression. To complement this, we mimicked TGF-β-induced repression of Id2 with human

Id2-specific siRNA in Id2-expressing invasive phenotype cells. We found that silencing Id2

inhibited the weak TGF-β-driven induction of proliferation characteristic of these cells but did

not contribute to measurable TGF-β-like growth inhibition. This suggests that Id2

downregulation may not be sufficient to induce a TGF-β-like suppression of growth. Rather,

this complex response is likely to involve the modulation of multiple genes, which may

compensate for Id2 loss.

The high levels of Id2 expression and the inability of TGF-β to modulate it in our invasive

phenotype cells, correlate well with the idea that aggressive cancer cells lose their

susceptibility to the growth inhibitory effects of TGF-β while retaining their responses to

tumour-promoting TGF-β-induced effects such as invasion, evasion of immune surveillance

and metastasis. Adding to this concept, we can hypothesise that the loss of Id2 modulation

favours an invasive behaviour in melanoma as Id proteins have been shown to play a role in a

number of cancer promoting processes, such as proliferation, angiogenesis, invasion and

migration ( reviewed in Lasorella et al., 2001).

While TGF-β is a negative regulator of the cell cycle, Id proteins are positive regulators of

cell growth and play a critical role in promoting G1/S cell cycle progression. TGF-β-induced

growth suppression has been associated with increased expression of CDK inhibitors and a

concomitant G1 cell cycle arrest (Massague and Gomis, 2006). In various cell types, TGF-β

has been shown to transcriptionally induce p21Cip1 (Datto et al., 1995), p27Kip1 (Kamesaki et

al., 1998) p57Kip2 (Scandura et al., 2004) and p15Ink4b (Hannon and Beach, 1994) expression.

In contrast, Id proteins have been shown to regulate the expression of CDK inhibitors, for

example p16Ink4a (Alani et al., 2001; Ohtani et al., 2001), p21Cip1 (Prabhu et al., 1997), p57Kip2

(Rothschild et al., 2006) and p27Kip1 (Lyden et al., 1999; Mori et al., 2000). Using

proliferative signature cell types, which were significantly growth inhibited by TGF-β, we

could show that TGF-β induced the expression of the gene coding for p15Ink4b but not p21Cip1,

p27Kip1 or p57Kip2. Furthermore, reinforcing the link between TGF-β-induced growth

inhibition and TGF-β-induced downregulation of Id2, we could show that only p15Ink4b was

downregulated by Id2 overexpression. We therefore conclude from these results that Id2

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subdues TGF-β-induced upregulation of p15Ink4b, leading to attenuated TGF-β-induced

growth-inhibition response in our cell lines.

Although p15Ink4b has not directly been shown to be regulated by Id proteins, analysis of the

promoter regions of p15Ink4b, p16Ink4a and p21Cip1 reveals the presence of E-boxes, which

renders these genes competent for Id-mediated repression (Pagliuca et al., 2000). We also

highlight the importance of p15Ink4b as a TGF-β target gene involved in the cytostatic effects

observed in melanoma and reveal that upregulation of p21Cip1 is not, as shown by others,

necessary for this response (Florenes et al., 1996; Reed et al., 2001). It is important to note

that the two groups reporting on the relevance of p21Cip1 used cell lines lacking p15Ink4b in the

first case and expressing undetectable levels in the second (Florenes et al., 1996; Reed et al.,

2001). This suggests that TGF-β may induce the expression of both CDK inhibitors and that

their functions are redundant.

Evaluating Id2 expression in cell culture and tissue arrays, we found no association between

Id2 expression and survival in melanoma. We observed Id2 expression heterogeneity in most

samples of the tissue microarrays. This observation highlights questions about the validity of

using tissue arrays to evaluate phenotype-specific protein expression in melanoma samples.

The transcription profile phenotype switching (proliferative and invasive), which drives

progression and is described in our melanoma model, allows melanoma cells to change their

gene expression programs to favour disease progression via alternating states of proliferation

and invasiveness (Hoek et al., 2008; Hoek et al., 2006). This model predicts that melanomas

are heterogeneous with respect to the phenotypes, and suggests that tissue microarrays (in

sourcing extremely limited samples of tumours) may therefore represent an inappropriate

medium for exploring phenotype-specific factors.

5.5 Materials and Methods

5.5.1 Cell culture and Adenoviruses




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Cell lines were chosen according to their transcription pattern signatures as previously

described (Hoek et al., 2006). Two proliferative signature (M000921, M010817) and two

invasive signature (M990115, M010119) melanoma lines were used. Myc-tagged Ad-Id2 was

a generous gift from F.D. Miller, Toronto, Canada. Ad-luciferase and Ad-Id2 were amplified

and titrated in 293 cells as described previously (Hemmi et al., 1998).

5.5.2 RNA extraction, cDNA synthesis and RT-PCR

Total RNA was extracted from melanoma cell cultures using TRIzol according to

manufacturer instructions (Invitrogen, Carlsbad, CA, USA). One microgram total RNA was

used for cDNA synthesis using Promega’s Reverse Transcription System according to

manufacturer instructions (Promega, Madison, WI, USA). PCR was performed on 1 µg

template cDNA using Roche’s LightCycler DNA Master SYBR Green kit (Roche, Basel,

Switzerland). Primers were 5’-CTGCCCAAGCTCTACCTTCC-3’ and 5’-

CAGGTCCACATGGTCTTCCT-3’ (p21Cip1); 5’-CGTGCGAGTGTCTAACGGGAGC-3’

and 5’-TGCGTGTCCTCAGAGTTAGCC-3’ (p27Kip1); 5’-GCGGCGATCAAGAAGCTGTC-

3’ and 5’-CCGGTTGCTGCTACATGAAC-3’ (p57Kip2). Primers for p15Ink4b were purchased

at Qiagen, QT00203147 (Qiagen, Hombrechtikon, Switzerland).

5.5.3 Western blot analysis

Cells were washed twice with cold phosphate-buffered saline (PBS) and lysed at 4°C in lysis

buffer containing 20 mM Tris-HCl (pH 7.5), 1% Triton X-100 (Sigma-Aldrich), 137 mM

NaCl, 10% glycerol and protease inhibitors (Roche). Proteins were separated by SDS–PAGE

under reducing conditions and transferred onto nitrocellulose membranes (Invitrogen, Basel,

Switzerland). Membranes were probed with a rabbit anti-Id2 monoclonal antibody (Zymed

Laboratories, Invitrogen, San Francisco,CA, USA) or a goat anti-actin polyclonal antibody

(SantaCruz, Biotechnology, La Jolla, CA, USA) followed by horseradish peroxidase-

conjugated goat anti-rabbit or rabbit anti-goat secondary antibodies (Bio-Rad, Reinach,

Switzerland and SantaCruz, respectively). Bound antibodies were detected by

chemiluminescence (ECL, GE Healthcare, Buckinghamshire, UK).

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5.5.4 Growth inhibition assays

Cells were seeded in 24-well microplates and left to settle overnight. Cells were then infected

at an MOI of 3 with Ad-Id2 or Ad-luciferase in serum-reduced medium (3%). For siRNA

treatment, cells were transfected with siRNA duplexes targeting Id2 (sense:

GGUGGAGCGUGAAUACCAGtt and antisense: CUGGUAUUCACGCUCCACCtt) or non-

targeting negative control (scrambled sequence) using INTERFERin (PolyPlus, Illkirch,

France) according to manufacturer instructions. 24-hours post-infection or 48-hours post-

transfection cells were treated with 5 ng/ml recombinant TGF-β1 (BioSource, Camarillo, CA,

USA) and 50 hours post-treatment cell metabolic activity was determined with a standard

colorimetric assay measuring 3-(4,5-dimethyldiazol-2-yl)-2,5 diphenyl tetrazolium bromide

(MTT; Sigma-Aldrich, Buchs, Switzerland) reactivity and used as an approximation of cell

proliferation. Growth inhibition was expressed as a percentage of growth inhibition compared

against growth in the absence of TGF-β1.

5.5.5 Cell cycle FACS analysis

Cells were seeded in 24-well microplates and left to settle overnight. Cells were then infected

at an MOI of 3 with Ad-Id2 or Ad-luciferase in serum-reduced medium (3%). 24-hours post-

infection, cells were treated with 5 ng/ml recombinant TGF-β1 (BioSource) and 48 hours

post-treatment, cells were harvested by trypsinisation and fixed in 70% ethanol at 4°C

overnight. Cells were then washed with PBS, followed by incubation in 300 µl of 0.1% Triton

X-100 (Sigma-Aldrich), 50 µg/ml propidium iodide (Fluka BioChemica, Switzerland) and

200 µg/ml DNase-free RNase A (Sigma-Aldrich) for 30 minutes before analysis on

FACSCalibur (Becton-Dickinson, Switzerland).

5.5.6 Immunohistochemistry, cell culture and tissue array

Paraffin embedded tissue of 51 metastases was used for the preparation of a melanoma

metastases tissue microarray. A morphologically representative region of the paraffin “donor”

blocks was chosen. The representative region was taken with a core tissue biopsy (diameter

0.6mm, height 3-4mm) and precisely arrayed into a new “recipient” paraffin block using a

customer built instrument (Kononen et al., 1998). After the block construction was completed,

4.0µm sections of the resulting tumour tissue microarray block were cut and

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immunohistochemically stained for Id2 using an anti-Id2 monoclonal antibody (Zymed

Laboratories, Invitrogen).

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Bloethner, S., Hemminki, K., Thirumaran, R.K., Chen, B., Mueller-Berghaus, J., Ugurel, S., Schadendorf, D. and Kumar, R. (2006) Differences in global gene expression in melanoma cell lines with and without homozygous deletion of the CDKN2A locus genes. Melanoma Res, 16, 297-307.

Datto, M.B., Li, Y., Panus, J.F., Howe, D.J., Xiong, Y. and Wang, X.F. (1995) Transforming growth factor beta induces the cyclin-dependent kinase inhibitor p21 through a p53-independent mechanism. Proc Natl Acad Sci U S A, 92, 5545-5549.

Di, K., Ling, M.-T., Tsao, S.W., Wong, Y.C. and Wang, X. (2006 ) Id-1 modulates senescence and TGF-beta1 sensitivity in prostate epithelial cells.

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J.M. (1996) TGF-beta mediated G1 arrest in a human melanoma cell line lacking p15INK4B: evidence for cooperation between p21Cip1/WAF1 and p27Kip1. Oncogene, 13, 2447-2457.

Flores, J.F., Walker, G.J., Glendening, J.M., Haluska, F.G., Castresana, J.S., Rubio, M.-P., Pastorfide, G.C., Boyer, L.A., Kao, W.H., Bulyk, M.L., Barnhill, R.L., Hayward, N.K., Housman, D.E. and Fountain, J.W. (1996) Loss of the p16INK4a and p15INK4b Genes, as well as Neighboring 9p21 Markers, in Sporadic Melanoma. Cancer Res, 56, 5023-5032.


Gleichmann, M., Buchheim, G., El-Bizri, H., Yokota, Y., Klockgether, T., Kugler, S., Bahr, M., Weller, M. and Schulz, J.B. (2002) Identification of inhibitor-of-differentiation 2 (Id2) as a modulator of neuronal apoptosis. J Neurochem, 80, 755-762.

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Iavarone, A., Garg, P., Lasorella, A., Hsu, J. and Israel, M.A. (1994) The helix-loop-helix protein Id-2 enhances cell proliferation and binds to the retinoblastoma protein. Genes Dev., 8, 1270-1284.

Kamesaki, H., Nishizawa, K., Michaud, G.Y., Cossman, J. and Kiyono, T. (1998) TGF-{beta}1 Induces the Cyclin-Dependent Kinase Inhibitor p27Kip1 mRNA and Protein in Murine B Cells. J Immunol, 160, 770-777.

Kang, Y., Chen, C.-R. and Massague, J. (2003) A Self-Enabling TGF[beta] Response Coupled to Stress Signaling: Smad Engages Stress Response Factor ATF3 for Id1 Repression in Epithelial Cells. Molecular Cell, 11, 915-926.

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Kowanetz, M., Valcourt, U., Bergstrom, R., Heldin, C.H. and Moustakas, A. (2004) Id2 and Id3 define the potency of cell proliferation and differentiation responses to transforming growth factor beta and bone morphogenetic protein. Mol Cell Biol, 24, 4241-4254.


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Onken, M.D., Ehlers, J.P., Worley, L.A., Makita, J., Yokota, Y. and Harbour, J.W. (2006) Functional Gene Expression Analysis Uncovers Phenotypic Switch in Aggressive Uveal Melanomas

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Pagliuca, A., Gallo, P., De Luca, P. and Lania, L. (2000) Class A Helix-Loop-Helix Proteins Are Positive Regulators of Several Cyclin-dependent Kinase Inhibitors' Promoter Activity and Negatively Affect Cell Growth. Cancer Res, 60, 1376-1382.

Perk, J., Iavarone, A. and Benezra, R. (2005) Id family of helix-loop-helix proteins in cancer. Nat Rev Cancer, 5, 603-614.

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Polsky, D., Young, A.Z., Busam, K.J. and Alani, R.M. (2001) The Transcriptional Repressor of p16/Ink4a, Id1, Is Up-Regulated in Early Melanomas. Cancer Res, 61, 6008-6011.


Reed, J.A., Bales, E., Xu, W., Okan, N.A., Bandyopadhyay, D. and Medrano, E.E. (2001) Cytoplasmic localization of the oncogenic protein Ski in human cutaneous melanomas in vivo: functional implications for transforming growth factor beta signaling. Cancer Res, 61, 8074-8078.



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Chapter 6

6 Discussion & Conclusions..

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Chapter 6 Discussion & Conclusions

Melanoma is recognised as being the most dangerous of skin cancers, and over the last three

decades its incidence has increased more rapidly than that of any other cancer (Giblin and

Thomas, 2007). Although primary tumours can be safely removed, once the cancer has

metastasised patient outlook is dramatically reduced; five-year survival of metastatic

melanoma patients is only 14% (Jemal et al., 2004). It is therefore of critical to elucidate the

mechanisms driving melanoma progression.

The aim of this thesis was to validate a new melanoma progression model we formulated

from gene expression arrays performed on three distinct sets of melanoma cultures, and to

investigate the role of TGF-β-like signalling as a major factor in this newly defined model.

Clinically, melanoma progression is described in terms of increasing primary tumour

thickness and extent of metastatic spread (Balch et al., 2004). A model of melanoma

progression in which molecular lesions progressively accumulate enjoys universal acceptance

and stands as the dominant paradigm for molecular studies of the disease (Miller and Mihm,

2006). However, this model has some limitations. For example, although accumulation of

irreversible genetic lesions during tumour progression has been reported for many tumours,

acquired invasion and metastasis have not been linked to recurrent mutations but rather to

specific gene expression changes (reviewed in Feinberg et al., 2006).

We have proposed a new model in which melanoma cells switch between two defined gene

expression signatures translating into phenotypes, which favour disease progression (Hoek et

al., 2008; Hoek et al., 2006). Contrasting with the classical model, our model is not based on

the accumulation of irreversible genetic lesions, but rather allows and requires reversible

transcriptional changes, which are not dictated by genetic alterations. While genetic

alterations, such as mutations, deletions, and amplification, likely play a role in melanoma, it

should be remembered that genes affected by amplification or activating mutations are not

necessarily exempt from transcriptional control.

Our model suggests that both proliferative and invasive transcriptional signatures are

important in disease progression and that a single cell is capable of expressing either signature

given appropriate signalling. Our model also accounts for gene expression heterogeneity in

tumours and is supported by immunophenotypic variations observed in melanoma (Banerjee

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and Harris, 2000). This heterogeneity and the reversibility of transcription programs presented

in our model may also offer an explanation for therapeutic failure against metastatic

melanoma. In our model, any given targeted treatment may be successful against one

phenotype of cells but leaves the other untouched and free to switch back to the targeted

phenotype when treatment has ceased. We also hypothesise that primary tumours initially

consist of proliferative signature cells and that by inhibiting transition to the invasive

signature, we may inhibit escape of cells from the primary tumour and formation of

metastases. However, since primary tumours are easily removed by surgery, and because

metastases are often present at time of diagnosis, it is of primary importance to identify

targets, which inhibit both groups of cells without enhancing the aggressive properties,

proliferative or invasive, of one or the other.

In vitro experiments investigating the motility and proliferative properties of melanoma cells,

as well as their capacity for vasculogenic mimicry, confirmed the phenotypic

characterisations of proliferative and invasive that we had attributed to the two opposite

transcriptional signatures (Hoek et al., 2006). Observed evasion from cytostatic

responsiveness to TGF-β, an important characteristic in a number of cancers including

melanoma, also supported our model; invasive signature cells were, in general, less sensitive

to the growth inhibitory effect of TGF-β compared to proliferative signature cells. Reduced

susceptibility to the growth inhibiting effects of TGF-β has been associated with increased

invasive and metastatic properties of melanoma cells (Heredia et al., 1996; Krasagakis et al.,

1999; Rodeck et al., 1994).

After a thorough literature review of genes with cohort-specific gene expression and the

subsequent identification of Wnt and TGF-β signalling as drivers of the identified

transcriptional signatures, we were interested in understanding the motive forces behind the

differential TGF-β signalling. To our surprise, Smad2 and Smad3 proteins were

phosphorylated in all melanoma cultures, irrespective of the presence of a TGF-β signature.

Consistent Smad activation in melanoma cultures has been observed by others (Mauviel A.

(Paris), personal communication) and suggests that the observed TGF-β signature is not

dependent on Smad activation. Although the Smad-dependent pathway has long been

considered as being central to TGF-β signalling, it is now recognised that TGF-β signals

through alternative pathways such as the MAPK, PI3K or Rho pathways (Derynck and

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Zhang, 2003). We could identify no link between the activation status of several MAPK

pathways and the TGF-β signature. Analysis of phosphorylation and cellular signalling events

by flow cytometry is an alternative method to western blotting and should be considered for

further analyses of multiple interacting pathways as it offers simultaneous correlation of

multiple active kinases involved in signalling cascades in rapid and scalable assays (Krutzik

et al., 2004).

TGF-β’s cytostatic effect plays an important role in its tumour suppressing function. As

mentioned above, when melanoma cells become more invasive and metastatic their

responsiveness to TGF-β-induced inhibitory growth response is suppressed. Using both cell

phenotypes, we investigated molecular mechanisms involved in the differential response

observed between them. We identified Id2 as a TGF-β target gene differentially regulated in

phenotypically opposed melanoma cells. Id proteins are positive regulators of cell

proliferation and have been shown to regulate the expression of proteins involved in cell cycle

regulation, such as the CDK inhibitors (Alani et al., 2001; Lyden et al., 1999; Mori et al.,

2000; Ohtani et al., 2001; Prabhu et al., 1997; Rothschild et al., 2006). We showed that TGF-

β represses the expression of Id2 only in TGF-β susceptible cells and that Id2 can repress

TGF-β-induced upregulation of p15Ink4b in proliferative cells. As Id2 is a negative regulator of

transcription by inhibiting transcriptional activation induced by bHLH, Ets and Pax factors, it

would be of interest to identify its binding partners, which could help identify further

regulated genes.

However, despite the role Id2 plays in modulating TGF-β susceptibility, loss of Id2 regulation

is likely more a consequence and not a driver of the phenotypic switch. We have hypothesised

that an environmental stimulus is responsible for triggering transcriptional change. The

identification of the drivers of both signatures, marked by TGF-β and Wnt signalling, will

hopefully bring us closer to the identification of this stimulus. We suggested activin A, whose

expression shows cohort specificity, as the driving force of the TGF-β signature (Hoek et al.,

2006). Although a tremendous overlap exists between activin and TGF-β transcriptomes (Ryu

and Kern, 2003), different phenotypes are produced by engineered knockout of their

respective receptor genes in mice (Gu et al., 1998; Oshima et al., 1996) Furthermore,

melanoma cells are variably affected by activin A and the effects, in contrast to the TGF-β-

induced growth inhibition, are not cohort specific. Also, exogenous activin A, as opposed to

146


TGF-β, did not suppress Mitf, whose expression is a central feature of the proliferative

signature, in proliferative signature cells. The expression of activin A in invasive signature

cells may also need to be considered as a consequence and not a driver of the TGF-β

signature, in particular since TGF-β was shown to induce the expression of activin A in

different cell types (Hubner and Werner, 1996)

TGF-β’s dual role as a tumour suppressor and tumour promoter has attracted significant

attention. The multiple cytokines, receptors and interacting pathways contributing to the

complexity of TGF-β signalling and the mechanisms involved in the expression of its dual

role in cancer, enhance the challenge in developing therapeutics interfering with any element

of this labyrinthine signalling complex. As discussed above when describing the challenge in

treating cells with variably expressing transcription signatures without enhancing the

tumorigenic behaviour of one group of cells over another, impairing TGF-β signalling raises

similar concerns. The mechanisms involved in the change of response to TGF-β, which could

also be responsible for the switch described in our model, need to be carefully elucidated to

develop a safe and effective treatment.

147


References Alani, R.M., Young, A.Z. and Shifflett, C.B. (2001) Id1 regulation of cellular senescence

through transcriptional repression of p16/Ink4a. Proceedings of the National Academy of Sciences, 98, 7812-7816.

Balch, C.M., Soong, S.J., Atkins, M.B., Buzaid, A.C., Cascinelli, N., Coit, D.G., Fleming, I.D., Gershenwald, J.E., Houghton, A., Jr., Kirkwood, J.M., McMasters, K.M., Mihm, M.F., Morton, D.L., Reintgen, D.S., Ross, M.I., Sober, A., Thompson, J.A. and Thompson, J.F. (2004) An evidence-based staging system for cutaneous melanoma. CA Cancer J Clin, 54, 131-149; quiz 182-134.

Banerjee, S.S. and Harris, M. (2000) Morphological and immunophenotypic variations in malignant melanoma. Histopathology, 36, 387-402.

Derynck, R. and Zhang, Y.E. (2003) Smad-dependent and Smad-independent pathways in TGF-beta family signalling. Nature, 425, 577-584.

Feinberg, A.P., Ohlsson, R. and Henikoff, S. (2006) The epigenetic progenitor origin of human cancer. Nat Rev Genet, 7, 21-33.

Giblin, A.V. and Thomas, J.M. (2007) Incidence, mortality and survival in cutaneous melanoma. J Plast Reconstr Aesthet Surg, 60, 32-40.

Gu, Z., Nomura, M., Simpson, B.B., Lei, H., Feijen, A., van den Eijnden-van Raaij, J., Donahoe, P.K. and Li, E. (1998) The type I activin receptor ActRIB is required for egg cylinder organization and gastrulation in the mouse. Genes Dev, 12, 844-857.




Hubner, G. and Werner, S. (1996) Serum Growth Factors and Proinflammatory Cytokines Are Potent Inducers of Activin Expression in Cultured Fibroblasts and Keratinocytes. Experimental Cell Research, 228, 106-113.

Jemal, A., Tiwari, R.C., Murray, T., Ghafoor, A., Samuels, A., Ward, E., Feuer, E.J. and Thun, M.J. (2004) Cancer statistics, 2004. CA Cancer J Clin, 54, 8-29.


Krutzik, P.O., Irish, J.M., Nolan, G.P. and Perez, O.D. (2004) Analysis of protein phosphorylation and cellular signaling events by flow cytometry: techniques and clinical applications. Clinical Immunology, 110, 206-221.


Miller, A.J. and Mihm, M.C., Jr. (2006) Melanoma. N Engl J Med, 355, 51-65. Mori, S., Nishikawa, S.I. and Yokota, Y. (2000) Lactation defect in mice lacking the helix-

loop-helix inhibitor Id2. Embo J, 19, 5772-5781.

148



Oshima, M., Oshima, H. and Taketo, M.M. (1996) TGF-beta receptor type II deficiency results in defects of yolk sac hematopoiesis and vasculogenesis. Dev Biol, 179, 297-302.




Ryu, B. and Kern, S.E. (2003) The essential similarity of TGFbeta and activin receptor transcriptional responses in cancer cells. Cancer Biol Ther, 2, 164-170.

149

150

Appendix A

Supplementary Data to Chapter 2 Metastatic potential of melanomas defined by specific gene expression profiles

with no BRAF signature

151

Zürich data set Melanoma cultures

BRAF status

NRAS status cohort

M980513 V600E wt A M010817 wt Q61R A M000921 V600E wt A M990514 wt Q61K B M000907 wt wt B M000216 V600E wt B M991121 wt wt C M990115 wt wt C M010718 wt Q61K C M010322 V600E wt C M010308 wt Q61K C M010119 wt wt C Philadelphia data set Melanoma cultures

BRAF status cohort

WM1321 WT A WM1382 unknown A WM1617 V599E A WM164 unknown A WM1727A unknown A WM1799 unknown A WM1819 unknown A WM239A unknown A WM3268V unknown A WM39 V599E A WM51 unknown A WM88 unknown A WM983A V599E A WM983B V599E A WM983C unknown A WM115 unknown B WM3248 unknown B WM3451 unknown B WM35 unknown B 1205 Lu V599E C WM1346 unknown C WM1361A WT C WM1361B V599E C WM1366 WT C WM278 V599E C WM3211 WT C WM793B V599E C WM852 unknown C WM858 V599E C

Mannheim data set Melanoma cultures

BRAF status

NRAS status cohort

Ma-Mel 12 unknown unknown A Ma-Mel 15 wt wt A Ma-Mel 27 wt G12D A Ma-Mel 30 D594N unknown A Ma-Mel 35 wt wt A Ma-Mel 36 V600E wt A Ma-Mel 40 wt wt A Ma-Mel 46 V600E wt A Ma-Mel 48a G469R wt A Ma-Mel 52 V600K wt A Ma-Mel 57 V600E wt A Ma-Mel 59a V600E wt A Ma-Mel 63a V600E Q61R A Ma-Mel 67 V600K wt A Ma-Mel 69 V600K wt A Ma-Mel 76 wt wt A

Ma-Mel 79b wt Q61K hom. A

Ma-Mel 80a V600E wt A Ma-Mel 82 wt G12D A Ma-Mel 39a wt wt B Ma-Mel 60 wt Q61K B Ma-Mel 61a V600E wt B Ma-Mel 71 wt wt B Ma-Mel 73a wt wt B Ma-Mel 84 unknown unknown B Ma-Mel 86b V600E wt B KNUD V600E wt C Ma-Mel 06 V600E wt C Ma-Mel 07 V600E wt C Ma-Mel 25 wt wt C Ma-Mel 26a wt Q61R C Ma-Mel 42a wt wt C Ma-Mel 45a V600E wt C Ma-Mel 50b unknown unknown C

Ma-Mel 53a wt Q61K, R68T C

Ma-Mel 54a V600E wt C Ma-Mel 58 wt wt C Ma-Mel 65 wt Q61K C Ma-Mel 66b unknown unknown C

Ma-Mel 74 wt Q61R het. C

Ma-Mel 85 V600E wt C Ma-Mel 86a V600E wt C

152

Supplementary Data 2A 223 genes which have cohort-specific expression patterns in cultures of melanoma. Probe Set ID Gene Title Gene Symbol 212387_at --- --- 208161_s_at ATP-binding cassette, sub-family C (CFTR/MRP), member 3 ABCC3 43427_at acetyl-Coenzyme A carboxylase beta ACACB 204638_at acid phosphatase 5, tartrate resistant ACP5 202952_s_at 213790_at ADAM metallopeptidase domain 12 (meltrin alpha) ADAM12

222108_at adhesion molecule with Ig-like domain 2 AMIGO2 203002_at angiomotin like 2 AMOTL2 221009_s_at angiopoietin-like 4 ANGPTL4 205082_s_at aldehyde oxidase 1 AOX1 203299_s_at 203300_x_at adaptor-related protein complex 1, sigma 2 subunit AP1S2

205265_s_at aortic preferentially expressed gene 1 APEG1 204416_x_at apolipoprotein C-I APOC1 213553_x_at 203381_s_at 203382_s_at apolipoprotein E APOE 212884_x_at 210980_s_at 213702_x_at 213902_at

N-acylsphingosine amidohydrolase (acid ceramidase) 1 ASAH1

205673_s_at ankyrin repeat and SOCS box-containing 9 ASB9 220948_s_at ATPase, Na+/K+ transporting, alpha 1 polypeptide ATP1A1 202685_s_at 202686_s_at AXL receptor tyrosine kinase AXL

217867_x_at beta-site APP-cleaving enzyme 2 BACE2 220488_s_at breast carcinoma amplified sequence 3 BCAS3 205681_at BCL2-related protein A1 BCL2A1 213905_x_at biglycan /// serologically defined colon cancer antigen 33 BGN /// SDCCAG33 210538_s_at baculoviral IAP repeat-containing 3 BIRC3 220451_s_at baculoviral IAP repeat-containing 7 (livin) BIRC7 221534_at basophilic leukemia expressed protein BLES03 Bles03 213246_at chromosome 14 open reading frame 109 C14orf109 217118_s_at chromosome 22 open reading frame 9 C22orf9 210944_s_at 211890_x_at 214475_x_at

calpain 3, (p94) CAPN3

206837_at cartilage paired-class homeoprotein 1 CART1 204306_s_at CD151 antigen CD151 204726_at cadherin 13, H-cadherin (heart) CDH13 203440_at 203441_s_at cadherin 2, type 1, N-cadherin (neuronal) CDH2

204252_at cyclin-dependent kinase 2 CDK2 204995_at cyclin-dependent kinase 5, regulatory subunit 1 (p35) CDK5R1 204029_at cadherin, EGF LAG seven-pass G-type receptor 2 CELSR2 204266_s_at choline kinase alpha CHKA 204591_at cell adhesion molecule with homology to L1CAM CHL1 207144_s_at Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal domain, 1 CITED1 200998_s_at cytoskeleton-associated protein 4 CKAP4 201734_at Chloride channel 3 CLCN3 209235_at chloride channel 7 CLCN7 211343_s_at collagen, type XIII, alpha 1 COL13A1 203325_s_at 212488_at 212489_at

collagen, type V, alpha 1 COL5A1

153

202110_at cytochrome c oxidase subunit VIIb COX7B 206256_at carboxypeptidase N, polypeptide 1, 50kD CPN1 202551_s_at cysteine rich transmembrane BMP regulator 1 (chordin-like) CRIM1 221541_at cysteine-rich secretory protein LCCL domain containing 2 CRISPLD2 209716_at colony stimulating factor 1 (macrophage) CSF1 209101_at connective tissue growth factor CTGF 204925_at cystinosis, nephropathic CTNS 209774_x_at chemokine (C-X-C motif) ligand 2 CXCL2 210764_s_at cysteine-rich, angiogenic inducer, 61 CYR61 209569_x_at 209570_s_at DNA segment on chromosome 4 (unique) 234 expressed sequence D4S234E

203139_at death-associated protein kinase 1 DAPK1 205337_at 205338_s_at 216512_s_at 216513_at

dopachrome tautomerase DCT

219113_x_at dehydrogenase/reductase (SDR family) member 10 DHRS10 221031_s_at hypothetical protein DKFZp434F0318 DKFZP434F0318 204602_at dickkopf homolog 1 (Xenopus laevis) DKK1 202196_s_at 214247_s_at dickkopf homolog 3 (Xenopus laevis) DKK3

212838_at dynamin binding protein DNMBP 219648_at dilute suppressor DSU 204271_s_at 206701_x_at endothelin receptor type B EDNRB

206580_s_at EGF-containing fibulin-like extracellular matrix protein 2 EFEMP2 201983_s_at 201984_s_at epidermal growth factor receptor EGFR

221870_at 45297_at EH-domain containing 2 EHD2

222294_s_at Eukaryotic translation initiation factor 2C, 2 EIF2C2 214446_at elongation factor, RNA polymerase II, 2 ELL2 202454_s_at v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) ERBB3 204363_at coagulation factor III (thromboplastin, tissue factor) F3 203206_at family with sequence similarity 53, member B FAM53B 202765_s_at 202766_s_at fibrillin 1 (Marfan syndrome) FBN1

201178_at F-box protein 7 FBXO7 204421_s_at 204422_s_at fibroblast growth factor 2 (basic) FGF2

219620_x_at hypothetical protein FLJ20245 FLJ20245 208614_s_at filamin B, beta (actin binding protein 278) FLNB 207876_s_at filamin C, gamma (actin binding protein 280) FLNC 218881_s_at FOS-like antigen 2 FOSL2 206307_s_at forkhead box D1 FOXD1 213056_at FERM domain containing 4B FRMD4B 204948_s_at follistatin FST 210220_at frizzled homolog 2 (Drosophila) FZD2 212256_at polypeptide N-acetylgalactosaminyltransferase 10 (GalNAc-T10) GALNT10 203397_s_at polypeptide N-acetylgalactosaminyltransferase 3 (GalNAc-T3) GALNT3 207116_s_at glyceraldehyde-3-phosphate dehydrogenase, spermatogenic GAPDHS 1598_g_at 202177_at growth arrest-specific 6 GAS6

217167_x_at glycerol kinase GK 207034_s_at GLI-Kruppel family member GLI2 GLI2 221447_s_at glycosyltransferase 8 domain containing 2 GLT8D2 35820_at GM2 ganglioside activator GM2A 204187_at guanosine monophosphate reductase /// guanosine monophosphate reductase GMPR 209167_at 209168_at 209169_at

glycoprotein M6B GPM6B

154

209170_s_at

201141_at glycoprotein (transmembrane) nmb GPNMB 206673_at putative G protein coupled receptor GPR 206696_at G protein-coupled receptor 143 GPR143 206582_s_at 212070_at G protein-coupled receptor 56 GPR56

203632_s_at G protein-coupled receptor, family C, group 5, member B GPRC5B 205862_at GREB1 protein GREB1 210963_s_at 210964_s_at glycogenin 2 GYG2

212822_at 213069_at HEG homolog 1 (zebrafish) HEG1

204670_x_at 209312_x_at 215193_x_at

major histocompatibility complex, class II, DR beta 1 HLA-DRB1

222020_s_at neurotrimin HNT 203914_x_at hydroxyprostaglandin dehydrogenase 15-(NAD) HPGD 54037_at Hermansky-Pudlak syndrome 4 HPS4 219985_at heparan sulfate (glucosamine) 3-O-sulfotransferase 3A1 HS3ST3A1 218971_s_at HSPC049 protein HSPC049 202070_s_at isocitrate dehydrogenase 3 (NAD+) alpha IDH3A 203851_at insulin-like growth factor binding protein 6 IGFBP6 205207_at interleukin 6 (interferon, beta 2) IL6 202859_x_at 211506_s_at interleukin 8 IL8

210511_s_at inhibin, beta A (activin A, activin AB alpha polypeptide) INHBA 205376_at inositol polyphosphate-4-phosphatase, type II, 105kDa INPP4B 204562_at interferon regulatory factor 4 IRF4 201389_at integrin, alpha 5 (fibronectin receptor, alpha polypeptide) ITGA5 203723_at inositol 1,4,5-trisphosphate 3-kinase B ITPKB 201362_at 201363_s_at 206245_s_at

influenza virus NS1A binding protein IVNS1ABP

221584_s_at potassium large conductance calcium-activated channel, M alpha 1 KCNMA1 204401_at potassium intermediate/small conductance calcium-activated channel N 4 KCNN4 212456_at KIAA0664 protein KIAA0664 213478_at kazrin KIAA1026 212942_s_at KIAA1199 KIAA1199 218651_s_at La ribonucleoprotein domain family, member 6 LARP6 208949_s_at lectin, galactoside-binding, soluble, 3 (galectin 3) LGALS3 221880_s_at 51158_at hypothetical gene supported by AK075564; BC060873 LOC400451

209679_s_at small trans-membrane and glycosylated protein LOC57228 204298_s_at 213640_s_at 215446_s_at

lysyl oxidase LOX

202997_s_at 202998_s_at lysyl oxidase-like 2 LOXL2

219042_at 47550_at leucine zipper, putative tumor suppressor 1 LZTS1

221760_at Mannosidase, alpha, class 1A, member 1 MAN1A1 212233_at Microtubule-associated protein 1B MAP1B 207323_s_at 209072_at myelin basic protein MBP

221620_s_at hypothetical protein MGC4825 MGC4825 211026_s_at monoglyceride lipase MGLL 212472_at 212473_s_at microtubule associated monoxygenase, calponin and LIM domain containing 2 MICAL2

207233_s_at microphthalmia-associated transcription factor MITF 206426_at 206427_s_at melan-A MLANA

218211_s_at melanophilin MLPH

155

205413_at metallophosphoesterase domain containing 2 MPPED2 37408_at mannose receptor, C type 2 MRC2 222153_at myelin expression factor 2 MYEF2 204527_at myosin VA (heavy polypeptide 12, myoxin) MYO5A 204114_at nidogen 2 (osteonidogen) NID2 202238_s_at nicotinamide N-methyltransferase NNMT 210510_s_at 212298_at neuropilin 1 NRP1

204589_at NUAK family, SNF1-like kinase, 1 NUAK1 212605_s_at nudix (nucleoside diphosphate linked moiety X)-type motif 3 NUDT3 207303_at phosphodiesterase 1C, calmodulin-dependent 70kDa PDE1C 214582_at phosphodiesterase 3B, cGMP-inhibited PDE3B 218718_at platelet derived growth factor C PDGFC 202273_at platelet-derived growth factor receptor, beta polypeptide PDGFRB 210976_s_at phosphofructokinase, muscle PFKM 204604_at PFTAIRE protein kinase 1 PFTK1 213638_at phosphatase and actin regulator 1 PHACTR1 207938_at peptidase inhibitor 15 PI15 207469_s_at pirin (iron-binding nuclear protein) PIR 219584_at phospholipase A1 member A PLA1A 207943_x_at 209318_x_at pleiomorphic adenoma gene-like 1 PLAGL1

214866_at plasminogen activator, urokinase receptor PLAUR 210198_s_at proteolipid protein 1 PLP1 206470_at 213241_at plexin C1 PLXNC1

201578_at podocalyxin-like PODXL 207808_s_at protein S (alpha) PROS1 204262_s_at presenilin 2 (Alzheimer disease 4) PSEN2 207177_at prostaglandin F receptor (FP) PTGFR 206157_at pentraxin-related gene, rapidly induced by IL-1 beta PTX3 218931_at RAB17, member RAS oncogene family RAB17 209514_s_at 209515_s_at 210951_x_at

RAB27A, member RAS oncogene family RAB27A

219412_at RAB38, member RAS oncogene family RAB38 206617_s_at renin binding protein RENBP 210138_at regulator of G-protein signalling 20 RGS20 221127_s_at regulated in glioma RIG 214663_at receptor interacting protein kinase 5 RIPK5 220425_x_at ropporin, rhophilin associated protein 1B ROPN1B 204633_s_at 204635_at ribosomal protein S6 kinase, 90kDa, polypeptide 5 RPS6KA5

221523_s_at 221524_s_at Ras-related GTP binding D RRAGD

212647_at related RAS viral (r-ras) oncogene homolog RRAS 205334_at S100 calcium binding protein A1 S100A1 204268_at S100 calcium binding protein A2 S100A2 218854_at squamous cell carcinoma antigen recognized by T cells 2 SART2 200958_s_at syndecan binding protein (syntenin) SDCBP 202627_s_at 202628_s_at serpin peptidase inhibitor, clade E 1 SERPINE1

209848_s_at silver homolog (mouse) SILV 203123_s_at 203124_s_at solute carrier family 11, member 2 SLC11A2

209610_s_at 212810_s_at 212811_x_at

solute carrier family 1 (glutamate/neutral amino acid transporter), member 4 SLC1A4

220245_at 221644_s_at solute carrier family 45, member 2 SLC45A2

216092_s_at solute carrier family 7 (cationic amino acid transporter, y+ system), member 8 SLC7A8

156

209897_s_at slit homolog 2 (Drosophila) SLIT2 204466_s_at 204467_s_at 207827_x_at 211546_x_at

synuclein, alpha (non A4 component of amyloid precursor) SNCA

208127_s_at 209647_s_at suppressor of cytokine signaling 5 SOCS5

209843_s_at SRY (sex determining region Y)-box 10 SOX10 38918_at SRY (sex determining region Y)-box 13 SOX13 202935_s_at SRY (sex determining region Y)-box 9 SOX9 210942_s_at 213355_at ST3 beta-galactoside alpha-2,3-sialyltransferase 6 ST3GAL6

203438_at 203439_s_at stanniocalcin 2 STC2

209238_at syntaxin 3A STX3A 205547_s_at transgelin TAGLN 222116_s_at TBC1 domain family, member 16 TBC1D16 205993_s_at T-box 2 TBX2 203753_at 212382_at 212385_at 222146_s_at

transcription factor 4 TCF4

212758_s_at transcription factor 8 (represses interleukin 2 expression) TCF8 213361_at tudor domain containing 7 TDRD7 204653_at transcription factor AP-2 alpha (activating enhancer binding protein 2 alpha) TFAP2A 201506_at transforming growth factor, beta-induced, 68kDa TGFBI 201107_s_at 201108_s_at 201109_s_at 201110_s_at

thrombospondin 1 THBS1

208850_s_at 208851_s_at 213869_x_at

Thy-1 cell surface antigen THY1

201666_at TIMP metallopeptidase inhibitor 1 TIMP1 216997_x_at transducin-like enhancer of split 4 (E(sp1) homolog, Drosophila) TLE4 204137_at transmembrane 7 superfamily member 1 (upregulated in kidney) TM7SF1 213096_at transmembrane and coiled-coil domain family 2 TMCC2 204932_at 204933_s_at tumor necrosis factor receptor superfamily, member 11b (osteoprotegerin) TNFRSF11B

209354_at tumor necrosis factor receptor superfamily, member 14 TNFRSF14 207643_s_at tumor necrosis factor receptor superfamily, member 1A TNFRSF1A 203476_at trophoblast glycoprotein TPBG 206116_s_at 206117_at 210986_s_at 210987_x_at

tropomyosin 1 (alpha) TPM1

204083_s_at tropomyosin 2 (beta) TPM2 202369_s_at translocation associated membrane protein 2 TRAM2 213293_s_at tripartite motif-containing 22 TRIM22 206479_at transient receptor potential cation channel, subfamily M, member 1 TRPM1 206630_at tyrosinase (oculocutaneous albinism IA) TYR 205694_at tyrosinase-related protein 1 TYRP1 209946_at vascular endothelial growth factor C VEGFC 221532_s_at WD repeat domain 61 WDR61 205990_s_at wingless-type MMTV integration site family, member 5A WNT5A 221029_s_at wingless-type MMTV integration site family, member 5B WNT5B 217781_s_at zinc finger protein 106 homolog (mouse) ZFP106

157

Supplementary Data 2B Genes with co-regulated cohort specific expression patterns (motifs). Motif 1 (the neural crest signature) Probe Set ID Gene Title Gene Symbol 203300_x_at adaptor-related protein complex 1, sigma 2 subunit AP1S2 204416_x_at apolipoprotein C-I APOC1 203381_s_at 203382_s_at apolipoprotein E APOE

205681_at BCL2-related protein A1 BCL2A1 211890_x_at 214475_x_at 210944_s_at

calpain 3, (p94) CAPN3

206837_at cartilage paired-class homeoprotein 1 CART1 204995_at cyclin-dependent kinase 5, regulatory subunit 1 (p35) CDK5R1 207144_s_at Cbp/p300-interacting transactivator with Glu/Asp-rich carboxy-terminal domain 1 CITED1 209235_at chloride channel 7 CLCN7 209570_s_at DNA segment on chromosome 4 (unique) 234 expressed sequence D4S234E 216513_at 216512_s_at 205337_at 205338_s_at

dopachrome tautomerase DCT

221031_s_at hypothetical protein DKFZp434F0318 DKFZP434F0318 219648_at likely ortholog of mouse dilute suppressor DSU 204271_s_at 206701_x_at endothelin receptor type B EDNRB

202454_s_at v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) ERBB3 203397_s_at polypeptide N-acetylgalactosaminyltransferase 3 (GalNAc-T3) GALNT3 217167_x_at glycerol kinase GK 204187_at guanosine monophosphate reductase GMPR 209169_at 209168_at 209170_s_at 209167_at

glycoprotein M6B GPM6B

206696_at G protein-coupled receptor 143 GPR143 206582_s_at 212070_at G protein-coupled receptor 56 GPR56

203632_s_at G protein-coupled receptor, family C, group 5, member B GPRC5B 205862_at GREB1 protein GREB1 210964_s_at glycogenin 2 GYG2 205376_at inositol polyphosphate-4-phosphatase, type II, 105kDa INPP4B 204562_at interferon regulatory factor 4 IRF4 203723_at inositol 1,4,5-trisphosphate 3-kinase B ITPKB 47550_at 219042_at leucine zipper, putative tumor suppressor 1 LZTS1

221644_s_at 220245_at membrane associated transporter MATP

207323_s_at 209072_at myelin basic protein MBP

207233_s_at microphthalmia-associated transcription factor MITF 206426_at 206427_s_at melan-A MLANA

213638_at phosphatase and actin regulator 1 PHACTR1 207469_s_at pirin (iron-binding nuclear protein) PIR 219584_at phospholipase A1 member A PLA1A 210198_s_at proteolipid protein 1 PLP1 206470_at 213241_at plexin C1 PLXNC1

219412_at RAB38, member RAS oncogene family RAB38 206617_s_at renin binding protein RENBP 210138_at regulator of G-protein signalling 20 RGS20 221524_s_at Ras-related GTP binding D RRAGD

158

205334_at S100 calcium binding protein A1 S100A1 209848_s_at silver homolog (mouse) SILV 211546_x_at 207827_x_at 204466_s_at

synuclein, alpha (non A4 component of amyloid precursor) SNCA

209843_s_at SRY (sex determining region Y)-box 10 SOX10 213355_at 210942_s_at ST3 beta-galactoside alpha-2,3-sialyltransferase 6 ST3GAL6

204653_at transcription factor AP-2 alpha (activating enhancer binding protein 2 alpha) TFAP2A 209354_at tumor necrosis factor receptor superfamily, member 14 TNFRSF14 206479_at transient receptor potential cation channel, subfamily M, member 1 TRPM1 206630_at tyrosinase (oculocutaneous albinism IA) TYR 205694_at tyrosinase-related protein 1 TYRP1

Motif 2 (the TGF-β-like signal signature) Probe Set ID Gene Title Gene Symbol 208161_s_at ATP-binding cassette, sub-family C (CFTR/MRP), member 3 ABCC3 213790_at 202952_s_at a disintegrin and metalloproteinase domain 12 (meltrin alpha) ADAM12

222108_at amphoterin induced gene 2 AMIGO2 204589_at AMP-activated protein kinase family member 5 ARK5 202686_s_at AXL receptor tyrosine kinase AXL 213905_x_at biglycan /// serologically defined colon cancer antigen 33 BGN /// SDCCAG33 204726_at cadherin 13, H-cadherin (heart) CDH13 203440_at cadherin 2, type 1, N-cadherin (neuronal) CDH2 212489_at 203325_s_at 212488_at

collagen, type V, alpha 1 COL5A1

209101_at connective tissue growth factor CTGF 209774_x_at chemokine (C-X-C motif) ligand 2 CXCL2 210764_s_at cysteine-rich, angiogenic inducer, 61 CYR61 204602_at dickkopf homolog 1 (Xenopus laevis) DKK1 214247_s_at dickkopf homolog 3 (Xenopus laevis) DKK3 201983_s_at epidermal growth factor receptor EGFR 45297_at 221870_at EH-domain containing 2 EHD2

214446_at elongation factor, RNA polymerase II, 2 ELL2 204363_at coagulation factor III (thromboplastin, tissue factor) F3 202765_s_at 202766_s_at fibrillin 1 (Marfan syndrome) FBN1

204421_s_at 204422_s_at fibroblast growth factor 2 (basic) FGF2

206307_s_at forkhead box D1 FOXD1 222020_s_at neurotrimin HNT 219985_at heparan sulfate (glucosamine) 3-O-sulfotransferase 3A1 HS3ST3A1 205207_at interleukin 6 (interferon, beta 2) IL6 211506_s_at 202859_x_at interleukin 8 IL8

210511_s_at inhibin, beta A (activin A, activin AB alpha polypeptide) INHBA 212942_s_at KIAA1199 KIAA1199 221541_at LCCL domain containing cysteine-rich secretory protein 2 LCRISP2 213640_s_at 215446_s_at 204298_s_at

lysyl oxidase LOX

202997_s_at 202998_s_at lysyl oxidase-like 2 LOXL2

212472_at 212473_s_at flavoprotein oxidoreductase MICAL2 MICAL2

204114_at nidogen 2 (osteonidogen) NID2 202238_s_at nicotinamide N-methyltransferase NNMT 210510_s_at 212298_at neuropilin 1 NRP1

159

218718_at platelet derived growth factor C PDGFC 202273_at platelet-derived growth factor receptor, beta polypeptide PDGFRB 209318_x_at pleiomorphic adenoma gene-like 1 PLAGL1 201578_at podocalyxin-like PODXL 207177_at prostaglandin F receptor (FP) PTGFR 206157_at pentaxin-related gene, rapidly induced by IL-1 beta PTX3 218854_at squamous cell carcinoma antigen recognized by T cells 2 SART2 202628_s_at 202627_s_at serine (or cysteine) proteinase inhibitor, clade E 1 SERPINE1

203438_at stanniocalcin 2 STC2 205547_s_at transgelin TAGLN 222146_s_at 212387_at transcription factor 4 TCF4

201506_at transforming growth factor, beta-induced, 68kDa TGFBI 201107_s_at 201110_s_at 201109_s_at 201108_s_at

thrombospondin 1 THBS1

213869_x_at 208850_s_at 208851_s_at

Thy-1 cell surface antigen /// Thy-1 co-transcribed THY1 /// LOC94105

204932_at 204933_s_at tumor necrosis factor receptor superfamily, member 11b (osteoprotegerin) TNFRSF11B

206117_at 206116_s_at 210986_s_at 210987_x_at

tropomyosin 1 (alpha) TPM1

204083_s_at tropomyosin 2 (beta) TPM2 209946_at vascular endothelial growth factor C VEGFC 205990_s_at wingless-type MMTV integration site family, member 5A WNT5A 221029_s_at wingless-type MMTV integration site family, member 5B WNT5B

160

Supplementary Data 3a. Genes described by others to be downregulated in melanomas with greater metastatic potential and are significantly downregulated from cohort A to cohort C (P<.05). Symbol Gene Affy ID Folda P<b Reference A2M alpha-2-macroglobulin 217757_at 22 0.002 (de Wit et al., 2002)

APAF1 apoptotic protease activating factor 211554_s_at 2.4 0.011 (Baldi et al., 2004; Fujimoto et al., 2004)

BCL2 B-cell CLL/lymphoma 2

203684_s_at

203685_s_at

244035_at

2.4

2.5

2.8

0.024

0.010

0.038

(Vlaykova et al., 2002)

CDH1 E-cadherin 201131_s_at 3.4 0.019 (Andersen et al., 2004)

CDKN2A cyclin-dependent kinase inhibitor 2A 211156_at 2.1 0.004 (Straume and Akslen, 1997)

CHL1 c cell adhesion molecule with homology to L1CAM 234583_at

204591_at

8.2

18

0.003

0.004 (Seftor et al., 2002)

DCT c dopachrome tautomerase

216513_at

205338_s_at

216512_s_at

205337_at

23

37

95

>100

0.002

0.001

0.001

0.002

(Takeuchi et al., 2003)

EDNRB c endothelin receptor type B

204271_s_at

204273_at

206701_x_at

22

24

34

0.001

0.001

0.001

(Smith et al., 2002)

EEF1A2 eukaryotic translation elongation factor 1 alpha 2 204540_at 2.4 0.023 (de Wit et al., 2002)

GPM6B c glycoprotein M6B

209168_at

209169_at

209170_s_at

209167_at

16

52

77

92

0.001

0.001

0.001

0.001

(Seftor et al., 2002)

ICAM1 intercellular adhesion molecule 1 202637_s_at

215485_s_at

3.6

2.5

0.004

0.005

(Anastassiou et al., 2000)

MITF c microphthalmia-associated transcription factor

240555_at

207233_s_at

206426_at

12

19

27

0.001

0.001

0.001

(Salti et al., 2000)

MLANA c melan-A 206426_at

206427_s_at

45

77

0.003

0.004 (Berset et al., 2001)

NME1 non-metastatic cells 1, protein (NM23A) expressed in 201577_at 2.5 0.002 (Mao et al., 2001) (Sarris et al., 2004)

PCNA proliferating cell nuclear antigen 201202_at 2.9 0.012 (Evans et al., 1992)

S100A1 c S100 calcium binding protein A1 205334_at >100 0.001 (Juergensen et al., 2001) (Korabiowska et al., 1994)

TF transferrin 220109_at

203400_s_at

5.0

18

0.029

0.022 (Seftor et al., 2002)

TFAP2A c transcription factor AP-2 alpha 210669_at 4.2 0.019 (Karjalainen et al.,

2000a)

161

204654_s_at

204653_at

9.1

20

0.001

0.001 TOP2A topoisomerase (DNA) II alpha 170kDa 201291_s_at 7.2 0.049 (Mu et al., 2000)

TRPM1 c transient receptor potential cation channel, subfamily M, member 1

240386_at

206479_at

237070_at

237069_s_at

14

32

95

>100

0.006

0.001

0.001

0.001

(Duncan et al., 2001)

TYR c tyrosinase 206630_at 40 0.001 (Takeuchi et al., 2003)

aCalculated using normalized Zürich data. bTwo tailed T-test statistic , assuming variance is equal. c Also present in the 223 gene intersection of the Zürich, Philadelphia and Mannheim cohort-specific expression data sets.

162

Genes described by others to be upregulated in melanomas with greater metastatic potential and are significantly upregulated from cohort A to cohort C (P<.05). Symbol Gene Affy ID Folda P<b Reference ALCAM activated leukocyte cell adhesion molecule 240655_at 5 0.015 (van Kempen et

al., 2000)

CDH2 c N-cadherin

203440_at

237305_at

203441_s_at

15

4.9

3.9

0.001

0.015

0.001

(Sanders et al., 1999)

CTGF c connective tissue growth factor 209101_at 6.6 0.033 (Maniotis et al., 1999) (Seftor et al., 2002)

EGFR c epidermal growth factor receptor

201983_s_at

232541_at

201984_s_at

224999_at

233044_at

232120_at

42

22

11

7.8

3.2

1.8

0.001

0.002

0.002

0.001

0.013

0.034

(Hurks et al., 2000)

EPAS1 endothelial PAS domain protein 1 200878_at

235963_at

2.5

2.5

0.013

0.005

(Giatromanolaki et al., 2003)

FBLN5 fibulin 5 203088_at 49 0.001 (Wang et al., 2004)

HIF1A hypoxia-inducible factor 1, alpha subunit 238869_at 2.1 0.016 (Giatromanolaki et al., 2003)

HLA-B major histocompatibility complex, class I, B 211911_x_at

209140_x_at

2.2

1.6

0.026

0.038 (Real et al., 1999)

IL8 c interleukin 8 202859_x_at

211506_s_at

18

11

0.001

0.004

(Nurnberg et al., 1999) (Rofstad and Halsor, 2000)

ITGB1 integrin, beta 1 215878_at

211945_s_at

2.8

1.6

0.023

0.047

(Nikkola et al., 2004)

KCNK1 potassium channel K1 204679_at

204678_s_at

6.9

5.6

0.001

0.018


KRT18 keratin 18 201596_x_at 9.3 0.017 (Fuchs et al., 1992) (Wang et al., 2004)

KRT7 keratin 7 209016_s_at

214031_s_at

>100

6.8

0.001

0.015


LTBP2 latent TGFβ binding protein 2 223690_at

204682_at

22

13

0.001

0.001


MME membrane metallo-endopeptidase 203434_s_at

203435_s_at

19

8.3

0.010

0.012

(Bilalovic et al., 2004)

MMP1 matrix metalloproteinase 1 204475_at 57 0.003 (Nikkola et al., 2002; Seftor et al., 2001)

MMP2 matrix metalloproteinase 2 201069_at 11 0.003 (Seftor et al., 2002) (Vaisanen et al., 1996)

MMP3 matrix metalloproteinase 3 205828_at 14 0.007 (Nikkola et al., 2002)

NRP1 c neuropilin 1 212298_at >100 0.001 (Straume and

Akslen, 2003)

163

210510_s_at

239519_at

233626_at

210615_at

233701_at

242677_at

33

17

5.9

3.6

2.8

2.8

0.001

0.001

0.001

0.034

0.015

0.007

PAX8 paired box gene 8 227474_at 6.7 0.021 (Seftor et al., 2002)

PLAU plasminogen activator, urokinase 211668_s_at

205479_s_at

4.5

3.1

0.041

0.010

(Maniotis et al., 1999; Seftor et al., 2002)

PLOD2 procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2 202619_s_at

202620_s_at

8.5

13

0.010

0.018

(Goldberg et al., 2003)

PRDX2 peroxiredoxin 2

39729_at

201006_at

211658_at

99

7.6

7.4

0.001

0.001

0.001

(de Wit et al., 2002)

PTGS2 prostaglandin-endoperoxide synthase 2 2204748_at 46 0.003 (Figueiredo et al., 2003)

RIG c regulated in glioma 221127_s_at 15 0.002 (Seftor et al., 2002)

SPARC osteonectin 212667_at 3.4 0.006 (Massi et al., 1999)

THBS1 c thrombospondin 1

201107_s_at

201109_s_at

201110_s_at

239336_at

201108_s_at

235086_at

215775_at

>100

67

67

41

28

10

1.8

0.001

0.001

0.001

0.003

0.001

0.001

0.004

(Straume and Akslen, 2003)

THBS2 thrombospondin 2 203083_at 23 0.001 (Kunz et al., 2002) TMSB10 thymosin, beta 10 217733_s_at 1.6 0.05 (Liu et al., 2004)

TNFRSF1A c tumor necrosis factor receptor superfamily 1A 207643_s_at 2.8 0.005 (Galvani et al., 2000)

VEGF vascular endothelial growth factor 212171_x_at 3.2 0.033 (Bayer-Garner et al., 1999) (Osella-Abate et al., 2002)

VEGFC c vascular endothelial growth factor C 209946_at 18 0.001 (Seftor et al., 2002)

WNT5A c wingless-type MMTV integration site family, member 5A

213425_s_at

231227_at

205990_s_at

45

23

20

0.002

0.001

0.001

(Bittner et al., 2000)

aCalculated using normalized Zürich data, bTwo tailed T-test statistic , assuming variance is equal. c Also present in the 223 gene intersection of the Zürich, Philadelphia and Mannheim cohort-specific expression data sets.

164

Supplementary Data 3b Survey of literature exploring 134 factors of possible prognostic value in melanoma.

Factor RegulationaReference

A2M Down (de Wit et al., 2002) ALCAM Up (van Kempen et al., 2000) AOC3 Down (Forster-Horvath et al., 2004) APAF1 Down (Baldi et al., 2004; Fujimoto et al., 2004) APOC2 Up (de Wit et al., 2002) APOD Up (Miranda et al., 2003) ARF6 Up (Tague et al., 2004) ARHGDIB Up (Seftor et al., 2002) ATOX1 Up (Wang et al., 2004) BCL2 Down/Upb (Hernberg et al., 1998; Vlaykova et al., 2002) BIRC1 Down (Seftor et al., 2002) BIRC5 Up (Gradilone et al., 2003) BNC1 Up (Seftor et al., 2002) CASP6 Up (Woenckhaus et al., 2003) CCL2 Down (Su et al., 2000) CCND1 Up (Errico et al., 2003) CCND3 Up (Florenes et al., 2000) CD44 Down/Up (Dietrich et al., 1997; Karjalainen et al., 2000b) CDH1 Down (Andersen et al., 2004) CDH2 Up (Sanders et al., 1999) CDH3 Down (Sanders et al., 1999) CDH5 Up (Hendrix et al., 2001) CDKN1A Up (Karjalainen et al., 1999) CDKN1B Down (Florenes et al., 1998; Woenckhaus et al., 2004) CDKN2A Down (Straume and Akslen, 1997; Straume et al., 2000) CEACAM1 Up (Thies et al., 2002a) CENPE Up (Goldberg et al., 2003) CHL1 Down (Seftor et al., 2002) CKB Up (de Wit et al., 2002) CPS1 Up (Seftor et al., 2002) CSF2 Up (Ciotti et al., 1999) CTGF Up (Maniotis et al., 1999; Seftor et al., 2002) CTSD Up (Podhajcer et al., 1995) CXCL1 Up (Bordoni et al., 1990) DCT Down (Takeuchi et al., 2003) DKKL1 Up (Goldberg et al., 2003) EDNRB Down (Smith et al., 2002) EEF1A2 Down (de Wit et al., 2002) EGFR Up (Hurks et al., 2000) EPAS1 Up (Giatromanolaki et al., 2003) EPHA1 Up (Easty et al., 1999) EPHA2 Up (Maniotis et al., 1999; Seftor et al., 2002) EPHB6 Down (Hafner et al., 2003) ETS1 Up (Keehn et al., 2003) F2R Up (Tellez and Bar-Eli, 2003) FAP Down (Ramirez-Montagut et al., 2004) FASN Up (Innocenzi et al., 2003) FBLN5 Up (Wang et al., 2004) GJA1 Down (Su et al., 2000) GPM6B Down (Seftor et al., 2002) GSTM1 Up (Depeille et al., 2004) HIF1A Up (Giatromanolaki et al., 2003) HLA-A Up (Blom et al., 1997) HLA-B Up (Blom et al., 1997) HLA-G Up (Real et al., 1999) ICAM1 Down/Up (Anastassiou et al., 2000; Ciotti et al., 1999; Haritopoulos et al., 2003) IGFBP2 Up (Wang et al., 2003)

165

IL8 Up (Rofstad and Halsor, 2000) (Nurnberg et al., 1999) IL24 Down (Ellerhorst et al., 2002) ILK Up (Dai et al., 2003) IRF1 Down (Lowney et al., 1999) ITGA4 Up (Schadendorf et al., 1993) ITGA6 Down (Schadendorf et al., 1993) ITGAV Down (Nikkola et al., 2004) ITGB1 Down/Up (Nikkola et al., 2004; Vihinen et al., 2000) ITGB3 Up (Hieken et al., 1996) KCNK1 Up (Seftor et al., 2002) KRT7 Up (Seftor et al., 2002) KRT18 Up (Fuchs et al., 1992; Wang et al., 2004) L1CAM Up (Fogel et al., 2003; Thies et al., 2002b) LAMC1 Up (Seftor et al., 2001) LCP1 Up (de Wit et al., 2002) LTBP2 Up (Seftor et al., 2002) MDM2 Down (Polsky et al., 2002) MET Up (Cruz et al., 2003) MIA Up (Juergensen et al., 2001) MITF Down (Salti et al., 2000) MKI67 Up (Hazan et al., 2002) MLANA Down (Berset et al., 2001) MME Up (Bilalovic et al., 2004; Seftor et al., 2002) MMP1 Up (Nikkola et al., 2002; Seftor et al., 2001) MMP2 Up (Seftor et al., 2001; Vaisanen et al., 1998; Vaisanen et al., 1999; Vaisanen et al., 1996)MMP3 Up (Nikkola et al., 2002) MXI1 Down (Ariyanayagam-Baksh et al., 2003) MYLK Down (Seftor et al., 2002) NME1 Down (Mao et al., 2001; Sarris et al., 2004) NOS2A Down/Up (Ekmekcioglu et al., 2000; Tschugguel et al., 1999) NRP1 Up (Straume and Akslen, 2003) NTRK1 Up (Florenes et al., 2004) NUP88 Up (Zhang et al., 2002) PAWR Down (Lucas et al., 2001) PAX8 Up (Seftor et al., 2002) PCNA Down (Evans et al., 1992) PECAM1 Down (Hendrix et al., 2001) PLAT Down (Ferrier et al., 2000) PLAU Up (Maniotis et al., 1999; Seftor et al., 2002) PLK1 Up (Kneisel et al., 2002) PLOD2 Up (Goldberg et al., 2003) POU3F2 Up (Thomson et al., 1995) PRDX2 Up (de Wit et al., 2002) PTEN Down (Stahl et al., 2003; Tsao et al., 2003) PTGS2 Up (Figueiredo et al., 2003) RB1 Down (Korabiowska et al., 2001) RELA Up (Kashani-Sabet et al., 2004) RIG Up (Seftor et al., 2002) S100A1 Down (Juergensen et al., 2001; Korabiowska et al., 1994) S100A4 Up (Andersen et al., 2004) S100B Up (Banfalvi et al., 2002; Banfalvi et al., 2003) SEMA4D Up (Seftor et al., 2002) SERPINE1 Down (Su et al., 2000) SPARC Up (Massi et al., 1999) STHM Down (Seftor et al., 2002) TAP1 Down (Kamarashev et al., 2001) TCEB3 Up (Seftor et al., 2002) TF Down (Seftor et al., 2002) TFAP2A Down (Karjalainen et al., 2000a) TGM1 Up (Seftor et al., 2002) THBS1 Down/Up (Grant et al., 1998; Straume and Akslen, 2003) THBS2 Up (Kunz et al., 2002) TIE Up (Hendrix et al., 2001; Maniotis et al., 1999; Seftor et al., 2002)

166

TM4SF1 Up (Seftor et al., 2002) TMSB10 Up (Liu et al., 2004) TMSB4X Up (Cha et al., 2003) TNFRSF1A Up (Galvani et al., 2000; Ocvirk et al., 2000; Redondo et al., 2002) TOP2A Down (Mu et al., 2000) TP53 Up (McGregor et al., 1993) TRIP Down (Wang et al., 2004) TRPM1 Down (Duncan et al., 2001) TXNIP Down (Goldberg et al., 2003) TYR Down (Takeuchi et al., 2003) VEGF Up (Bayer-Garner et al., 1999; Osella-Abate et al., 2002) VEGFC Up (Seftor et al., 2002) WNT5A Up (Bittner et al., 2000) WWP2 Up (Seftor et al., 2002)

aRegulation associated with poor prognosis or metastatic aggressiveness. bDifferent researchers reporting opposing results.

167

Supplementary Data 4. Total RNA extracted from samples was subjected to reverse transcription PCR using the 1st Strand cDNA Synthesis Kit for PCR (Roche) and amplifications were perfomed using the LightCycler FastStart DNA Master SYBR Green I (Roche) on a LightCycler 2.0 instrument (Roche) according to manufacturers instructions. Primers and conditions. Gene Primers (sense & antisense) Denaturation Annealing Elongation Cycles MgCl2

BGN CGGGACCTTGCTGTCTTCTC

CCCGGCAAGAACCTGAAAG 95°C / 15s 55°C / 5s 72°C / 17s 50 5 mM

CTGF GCGTCTGCGCCAAGCA

TGGACCAGGCAGTTGGCT 95°C / 1s 56°C / 2s 72°C / 17s 55 3 mM

GAPDH ACGGATTTGGTCGTATTGGG

CGCTCCTGGAAGATGGTGAT 95°C / 10s 56°C / 2s 72°C / 17s 45 4 mM

MLANA ATGCCAAGAGAAGATGCT

GGAGAACATTAGATGTCTG 95°C / 15s 55°C / 5s 72°C / 17s 50 4 mM

SERPINE1 TGCTGGTGAATGCCCTCTACT

CGGTCATTCCCAGGTTCTCTA 95°C / 15s 60°C / 5s 72°C / 18s 35 3 mM

SOX10 GGCGGCGGCCGGGGGCGA

TCAGGGCAGGAGCCAGACAGAAA 95°C / 10s 66°C / 2s 72°C / 14s 35 2 mM

TYR ACAACAGCCATCAGTCT

CCTGTACCTGGGACATT 95°C / 0s 60°C / 15s 72°C / 13s 40 4 mM

168

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Acknowledgments

I wish to thank Professor Reinhard Dummer for giving me the chance to conduct my research

in a clinical environment I was not familiar with. Thank you for all the freedom in research

you have given me….. this freedom has surely contributed to making me a better researcher.

Also, many thanks to Professor Sabine Werner, without whom, the quality of this thesis

would not have been the same. Thank you for being always so involved even though we met

only a few times per year.

Thank you to Keith who introduced me to melanoma research. Thank you for sharing your

ideas on which the new melanoma model could be built. I have to also say thank you for

putting up with me when stress overwhelmed me…. it was only the two of us for two

years….. it’s a chance we are still talking to each other! And I therefore have to thank Ossia

who joined our group in 2005, bringing fresh ideas and plenty of energy. Your ideas and

technical expertise was a real gift to our small group.

To the girls in F14, Giulia, Julia and Ossia, thank you for the continuous support… we had

laughs and we had tears… thank you for always being there. Moving to F14 was a great idea!

I would also like to thank Niki for all the great work he has done in the lab and for listening to

all our small requests….. F14 has been a quiet place to write and it could not have been

without your help. Thank you to all the technicians and the members of the Department of

Dermatology for their support.

Un énorme merci à ma famille qui depuis le Québec m’a toujours soutenue. Merci de n’avoir

jamais perdu patience lors de mes crises d’angoisse…..je crois vraiment que cette thèse sera la

dernière……

Merci aussi à mes amies qui ont toujours gardé contact…merci d’avoir cru en la “nerd” que je

suis!

I also send a warm thank you to Patrick’s family in New Zealand… thank you for caring so

much.

And finally, I would like to thank Patrick for having been so present and patient….. thank you

for all the love, understanding and support you have given me...thank you…

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Curriculum vitae Natalie Schlegel Kirchenfeld 37 8052 Zürich, Switzerland Telephone: +41 (0)43 268 9804 E-mail: [email protected]

Date of Birth: 20.01.1976 Nationalities: Swiss and Canadian Academic Profile 2004-2008 Doctorate of Philosophy in Biology Subject: Cell signalling in melanoma University Hospital Zürich, Zürich, Switzerland Swiss Federal Institute of Technology (ETH) Zürich, Switzerland 2002-2003 Doctorate of Philosophy in Microbiology (interrupted) Subject: Legionella pneumophila pathogenesis Swiss Federal Institute of Technology (ETH), Zürich 1999-2001 Master of Science in Pharmacology with distinction Project conducted in Molecular Pathology Thesis entitled "Genetic susceptibility to migraine in families with bipolar disorder" University of Otago, Dunedin, New Zealand 1996-1999 Bachelor of Science in Biochemistry McGill University, Montreal, Canada Professional Experience 2001- 2002 Junior Research Fellow Microbiology Department University of Otago, Dunedin, New Zealand 2000-2001 Medical testing supevisor School of Medicine University of Otago, Dunedin, New Zealand

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Natalie Schlegel Page 2 2000 Demonstrator (teaching assistant)

Pharmacology Department University of Otago, Dunedin, New Zealand

1995-1999 Volunteer work towards patients Montreal Children’s Hospital Montreal, Canada Scientific Presentations 4th International Melanoma Congress, 1-4 Nov 2007, New York, USA 37th Annual European Society for Dermatological Research (ESDR) Meeting, 6-8 Sept 2007, Zürich, Switzerland ISREC Conference on Cancer Research, 11-13 Oct 2006, Lausanne, Switzerland 36th Annual ESDR Meeting, 7-9 Sept 2006, Paris, France 2nd Cancer research retreat, Cancer Network Zürich, 1-3 Sept 2006, Ascona, Switzerland 35th Annual ESDR Meeting, 22-24 Sept 2005, Tübingen, Germany Swiss Molecular Microbiology Workshop 2003, June 23-25 2003, Cartigny, Switzerland Scientific Publications Hoek, K.S., Schlegel, N.C., Lin, W.M., Mnich, C., Zipser, M., Kobert, N., Storz, M.,Mihic, D., Moch, H., Garraway, L.A., and Dummer, R.. Prognostic loss of heterozygosity patterns in melanoma and the effect of MITF amplification in vivo. Manuscript. Schlegel, N.C., Eichhoff, O.M., Hemmi, S., Mihic, D., Werner, S., Dummer, R., Hoek, K.S.. (2008) Id2 suppression of p15Ink4b in melanoma abrogates TGF-β-mediated anti-proliferation. Manuscript. Hoek, K.S., Eichhoff, O.M., Schlegel, N.C., Döbbeling, U., Kobert, N., Schaerer, L., Hemmi, S. and Dummer, R.. (2008). In vivo switching of human melanoma cells betweenproliferative and invasive states. Cancer Res in press. Hoek, K.S., Schlegel, N.C., Brafford, P., Sucker, A., Ugurel, S., Kumar, R., Weber, B.L.,Nathanson, K.L., Phillips, D.J., Herlyn, M., Schadendorf D. and Dummer R.. (2006). Metastatic potential of melanomas defined by specific gene expression profiles with noBRAF signature. Pigment Cell Res 19, 290-302.

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The new melanoma: A novel model for disease progression · differential TGF-β signalling. Smad activation was present in all melanoma cultures irrespective of the presence of a TGF-β

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