Midkine in Uveal Melanoma Expression, Mechanism and Clinical Relevance

Midkine in uveal melanoma expression, mechanism and clinical relevance

Der Naturwissenschaftlichen Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg zur Erlangung des Doktorgrades Dr.rer.nat.

vorgelegt von Margarete Maria Karg

aus Schweinfurt Als Dissertation genehmigt von der Naturwissenschaftlichen Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg

Tag der mündlichen Prüfung: 26.10.2018

Vorsitzender des Promotionsorgans: Prof. Dr. Georg Kreimer

Gutachter: Prof. Dr. Manfred Frasch Prof. Dr. Bruce Ksander Contents

Summary VI

Zusammenfassung VIII

1 Introduction 1 1.1 Uveal melanoma ...... 1 1.1.1 Epidemiology ...... 1 1.1.2 Symptoms and diagnostics ...... 2 1.1.3 Risk factors ...... 2 1.1.4 Cytogenetics: Chromosomal aberrations ...... 2 1.1.5 Molecular genetics: Genetic basis and pathophysiology ...... 3 1.1.6 Immunological factors ...... 4 1.1.7 Metastatic disease ...... 4 1.1.8 Treatment options ...... 5 1.1.9 Prognostic markers and patient classiﬁcations ...... 6 1.2 Midkine ...... 7 1.2.1 Molecular genetic characterization and structure ...... 7 1.2.2 MDK Receptors and intracellular signaling ...... 8 1.2.3 Regulation of MDK expression ...... 9 1.2.4 MDK functions ...... 11

2 Aims of this thesis 16

3 Results 17 3.1 MDK qPCR primer validation ...... 17 3.2 MDK antibody validation ...... 19 3.3 MDK expression in uveal melanoma ...... 19 3.3.1 MDK expression and secretion of primary and metastatic tumor cells derived from patient 270 ...... 19 3.3.2 MDK expression and secretion of primary and metastatic uveal melanoma cell lines ...... 20 3.3.3 MDK expression in human uveal melanoma tissue ...... 21 3.4 MDK can be found in the soluble and exosome fraction ...... 23 3.5 MDK is a membrane-bound protein in UM cells ...... 24 3.6 Regulation of MDK gene expression ...... 25 3.6.1 Retinoic acid induces MDK expression in UM cell and healthy melanocytes 25 3.6.2 DNA methylation inﬂuences MDK expression in UM cells ...... 25 3.6.3 Hypoxic conditions induce MDK expression ...... 29 3.7 Downregulation of MDK expression using RNA interference ...... 30 3.8 Generation of cell lines with stable and long term expression of MDK ...... 32 3.8.1 Creation of a MDK expression plasmid ...... 32 3.8.2 Determination of G418 concentration ...... 35 3.8.3 Stable overexpression of MDK in OCM1 ...... 36 3.9 Cellular function of MDK ...... 37 3.9.1 Downregulation of MDK reduces proliferation and viability of UM cells . 37

III Contents

3.9.2 Downregulation of MDK increases apoptosis by increasing caspase3/7 activity ...... 38 3.9.3 MDK promotes migration of UM cells ...... 39 3.10 Mechanism and signaling pathways ...... 39 3.10.1 Receptor expression on UM cells ...... 39 3.10.2 Signaling pathways influenced by MDK ...... 41 3.10.3 Baseline phosphorylation of RPS6 ...... 46 3.11 MDK induces resistance to Akt and mTOR inhibitors in UM cells ...... 47 3.12 Clinical relevance of MDK expression in UM patients ...... 49 3.12.1 MDK expression correlates with overall survival in UM patients ...... 49 3.12.2 MDK expression correlates with metastatic-free-survival ...... 49 3.12.3 MDK expression correlates with time-to-metastasis ...... 50 3.12.4 MDK and tumor classifications ...... 51 3.12.5 MDK expression correlates with chromosome 3 status ...... 53 3.12.6 MDK expression does not correlate with Bap1 and PRAME expression .. 53 3.12.7 MDK expression correlates with immune cell infiltration in UM tumors .. 54 3.12.8 MDK expression correlates with tumor pigmentation but not with composition of UM tumors ...... 55

4 Discussion 56 4.1 Heterogenous expression of MDK in UM cell lines and in tissue ...... 56 4.2 MDK receptors and intracellular signaling ...... 61 4.3 MDK eﬀects on tumor cell survival ...... 64 4.4 MDK and tumor cell migration and metastasis ...... 64 4.5 MDK and the immune system ...... 66 4.6 Clinical relevance of MDK in UM patients ...... 67 4.7 MDK and therapeutic interventions ...... 68

5 Overall conclusion and future perspectives 71

6 Materials and Methods 72 6.1 Cell culture ...... 72 6.2 Cell lines ...... 73 6.3 Isolation of blood mononuclear cells ...... 73 6.3.1 Density gradient centrifugation ...... 73 6.3.2 Magnetic cell separation ...... 73 6.4 Mononuclear cell activation ...... 74 6.5 M1 and M2 macrophages polarization ...... 74 6.6 DNA isolation ...... 74 6.7 RNA isolation ...... 74 6.8 Reverse transcription - cDNA synthesis ...... 75 6.9 Primer design ...... 75 6.10 Polymerase chain reaction (PCR) ...... 75 6.10.1 PCR ...... 75 6.10.2 Real-time quantitative PCR (qPCR) ...... 76 6.10.3 Bisulﬁte conversion and bisulﬁte PCR ...... 76 6.11 Agarose gel electrophoresis ...... 78 6.12 PCR product clean up ...... 78 6.13 Sequencing ...... 78 6.14 Genarray analysis ...... 78 6.15 Enzyme-linked immunosorbent assay (ELISA) ...... 79

IV Contents

6.16 Western blot ...... 79 6.16.1 Lysate preparation ...... 79 6.16.2 Bicinchoninic acid assay (BCA) ...... 80 6.16.3 Gel casting ...... 80 6.16.4 Gel running and blotting ...... 80 6.16.5 Developing and visualization ...... 81 6.17 Exosome isolation ...... 81 6.18 RNA interference ...... 81 6.19 Stable transfection of UM cell lines ...... 82 6.19.1 Construction of MDK expression plasmid ...... 82 6.19.2 Transformation ...... 82 6.19.3 Determination of antibiotic (G418) resistance concentration ...... 83 6.19.4 Transfections of tumor cells ...... 83 6.20 Retinoic acid assay ...... 83 6.21 Demethylation assay ...... 83 6.22 Hypoxia assay ...... 83 ® 6.23 Cell Titer 96 AQueous One Solution Cell Proliferation Assay (MTS) ...... 84 6.24 Apoptosis, Viability and Cytotoxicity assay (ApoTox-Glo™ Triplex Assay) .... 84 6.25 Transwell migration assay ...... 84 6.26 Intracellular Signaling Array ...... 85 6.27 Flow cytometry ...... 86 6.27.1 Surface staining ...... 86 6.27.2 Intracellular staining ...... 86 6.27.3 Phosphorylation staining ...... 86 6.28 Histology ...... 87 6.28.1 Tissue sampling, fixation and paraffin embedding ...... 87 6.28.2 Hematoxilin and Eosin (H & E) staining ...... 87 6.28.3 Depigmentation ...... 88 6.28.4 Immunofluorescence (IF) staining ...... 88 6.29 Analysis Software ...... 89 6.30 Statistics ...... 90 6.31 Bioinformatics ...... 90

Appendix 91 A MDK and immune cells ...... 91 B Sources of UM cell lines ...... 93 C Bleaching histological tissue ...... 93 D MDK expression in mouse models of ocular melanoma ...... 95 E Clinical, Pathological and Molecular Characteristics of UM TCGA Cases (adapted from Robertson et al. 2017) ...... 97

Tables 99

Figures 99

Abbreviations 102

References 104

Acknowledgement 115

Curriculum Vitae 116

V Summary

Uveal melanoma (UM) is a highly aggressive intra-ocular malignancy that lacks any effective targeted therapy. Neither survival, nor prognosis has been improved for the past decades in patients with metastatic UM. UM is the most occurring malignancy of the eye in adults even though it only makes a small percentage of all overall melanoma cases in general. Despite its common origin UM and cutaneous melanoma (CM) are clinically and genetically distinct. Remarkable differences are found at the genetic level, for example in UM specific mutations in G-protein GNAQ and GNA11 and in skin melanoma common mutations in BRAF and NRAS are detected. About half of UM patients develop universally fatal metastatic disease predominantly in the liver. Patients classifications to assess metastatic risk of patients at first diagnosis are available. The American Joint Committee on Cancer (AJCC) classifications of UM patients uses anatomical features, like tumor size, ciliary body involvement and extra-ocular extension making risk analysis possible. Genetic profiling has also shown that certain mutations or gene expression levels allow classification of the patients in class 1 with low metastatic risk and in class 2 with high metastatic risk. Additionally, grade of immune cell infiltration, microvascular density, tumor cell type composition and high proliferation index have been shown retrospectively to be prognostic markers. However, to date, no marker has been identified that can reliable predict metastatic disease. To gain further insight in the liver tropism of metastatic UM and to identify potential biomarkers that can predict disease progression, the gene expression levels of primary tumor cells of patient 270 were compared to four distinct liver metastasis of the same patient. Interestingly, midkine (MDK) was one of the most significantly differentially expressed genes between the primary tumor and all four metastases. MDK is a highly interesting protein due to its previously described functions in cancer progression. MDK is a heparin-binding growth factor that is expressed in many diverse human cancers and plays important roles in tumor cell growth, survival, transformation, migration, and (lymph)angiogenesis. Furthermore, MDK has been shown to be an effective biomarker in multitude of cancers. We therefore hypothesize that MDK, has the function to promote tumor survival, migration and can serve as a biomarker in UM. In this thesis we first confirmed the microarray data and found indeed, that MDK is highly differentially expressed between the primary tumor and the metastatic derived cell lines. An additional set of UM cell lines showed heterogeneous MDK expression levels compared to non- malignant melanocytes. Interestingly, immunohistological analysis of UM primary tumor tissue also showed tumors with low or high MDK expression. Little is known about the upstream factors and signaling mechanisms that regulate MDK gene expression. We found that MDK expression can be induced by retinoic acid treatment, targeting the previously identified retinoic acid response element in the MDK gene promotor. Additionally, MDK expression was shown to be induced under hypoxic conditions. Furthermore, we could show that DNA methylation in the 5’UTR of the MDK gene contributes to MDK expression. To study the function of MDK on UM cells we performed loss-of-function and gain-of-function experiments. Downregulating MDK with specific anti-MDK siRNAs showed reduced tumor cell proliferation, viability and increased apoptotic rate. Stably overexpressing MDK in a low MDK expressing tumor cell line increased tumor cell migratory capabilities. Screening of MDK effects on signaling molecules identified ribosomal protein S6 (RPS6) and proline-rich Akt substrate of 40kDA (PRAS40) both substrates of the Akt and mTOR mediated signaling pathways as potential targets of MDK. Consequently, we could show that MDK contributes to resistance to

VI Summary

Akt and mTOR specific inhibitors. MDK overexpressing cells have a survival benefit compared to wildtype and mock transfected cells. MDK has previously been described to function as an inflammatory cytokine. Interestingly, we could show that activated mononuclear cells (MNCs) increased MDK expression and M1 (pro- inflammatory) have higher expression levels of MDK than M2 (anti-inflammatory) differentiated macrophages. Moreover, we could show that MDK has growth promoting effect on endothelial cells, suggesting a role of MDK in the tumor cell-stroma interaction. Analyzing gene expression data of 80 UM primary tumors (data provided by TCGA database) showed that MDK expression levels in primary UM tumors correlated significantly with overall survival of UM patients. We could show that the time-to-metastasis is significantly shorter in UM patients with primary tumors that express high MDK levels. Interestingly, MDK does also correlate with AJCC classifications. Importantly, MDK expression significantly correlated with chromosome 3 status, which is a standard prognostic indicator of UM. Specifically, primary tumors that are monosomy 3, which are at high-risk for metastatic disease, display higher levels of MDK expression. Overall, data in this thesis indicate that MDK acts as a survival factor, drives metastasis and therapeutic resistance, and prognosticate high-risk for disease progression in UM. Targeting MDK and using MDK to predict high risk patients may contribute to improve prognosis and treatment of UM patients.

VII Zusammenfassung

Das uveale Melanom (UM) ist eine sehr aggressive Tumorerkrankung des Auges, für die bisher keine kurative oder spezifische Behandlung existiert. Weder die durchschnittliche Überleben- szeit noch die Prognosen haben sich für Patienten mit metastasiertem UM in den letzten Jahren verbessert. Obwohl das UM nur einen kleinen Anteil aller Melanome ausmacht, ist es die häu- figste maligne Tumorerkrankung des Auges bei Erwachsenen. Cutanes Melanom (CM) und UM unterscheiden sich trotz gleicher zellulärer Herkunft, genetisch und klinisch deutlich. Bedeutende Unterschiede finden sich auf genetischer Ebene, zum Beispiel gibt es bei dem UM häufig vorkom- mende spezifische Mutationen in den G-Proteinen, GNAQ und GNA11, wobei im Hautmelanom vor allem Mutationen in BRAF und NRAS vorliegen. Bei ungefähr der Hälfte aller UM-Patienten kommt es zur Ausbildung von Metastasen. Hi- erbei haben vor allem Lebermetastasen den größten Anteil, die unweigerlich und rasch zum Tode führen. Zum Zeitpunkt der Erstdiagnose wird die Wahrscheinlichkeit der Metastasierung beurteilt. Die Patientenklassifikation und Riskoanalyse des American Joint Committee on Cancer (AJCC), beeinhaltet verschiedene anatomische Merkmale des primären Tumors, wie die Tumor- größe, die Ziliarkörperbeteiligung und die Ausdehnungen des Tumors in Regionen außerhalb des Auges. Basierend auf dem Expressions- und Mutationsstatus bestimmter Gene ist es zudem möglich genetische Profile zu erstellen, um Patienten in hoch oder niedrig Riskoklassen (Klasse 1 und Klasse 2) hinsichtlich der Metastasierung einzustufen. Zusätzlich konnte retrospektiv gezeigt werden, dass der Grad der Immunzellinfiltration in den Tumor, die Dichte vaskulärer Gefäße, die Zusammensetzung des Tumors und ein hoher proliferativer Index die Krankheitspro- gression vorhersagen können. Zum jetzigem Zeitpunkt ist allerdings kein Biomarker bekannt, der verlässlich die Metastasierung voraussagen kann. Mit dem Ziel den Lebertropismus von UM-Metastasen besser zu verstehen und potenzielle Biomarker, welche die Progression der Tumorerkrankung vorhersagen können, zu identifizieren, wurde das Genexpressionsmuster eines Primärtumors mit dem von vier Lebermetastasen des gleichen Patienten verglichen. Bei der Betrachtung fiel auf, dass Midkine (MDK) eines der Gene war, welches zwischen Primärtumor und Metastasen den signifikantesten Unterschied zeigte. Bei MDK handelt es sich aufgrund der beschriebenen Funktionen im Krankheitsverlauf ver- schiedener maligne Tumorerkrankungen um ein hoch interessantes Protein. MDK ist ein heparin- bindender Wachstumsfaktor, der in zahlreichen verchiedenen humanen Tumoren exprimiert wird und zum Tumorwachstum, zum Überleben, zu Transformationprozessen, zur Tumorzellmigration und (Lymph)angiogenese beiträgt. Zusätzlich ist MDK als effektiver prognostischer Biomarker in verschiedenen Krebserkrankungen beschrieben. Daraus ergibt sich unsere Hypothese, dass MDK auch im UM die Funktion hat, Tumorzel- lüberleben und -migration zu stimulieren und folglich auch als Biomarker genutzt werden kann. Während dieses Dissertationsprojektes konnten wir zuerst die Microarray-Genexpressionsdaten bestätigen und in der Tat zeigen, dass sich die Expression von MDK zwischen dem Primarus und den Metastasen stark unterscheidet. Im Vergleich zu nicht malignen Melanozyten wieß ein weiteres Set an UM-Zelllinen eine heterogene MDK-Expression auf. Interessanterweise zeigten immun-histologische Analysen primärer UM-Tumoren sowohl Tumoren mit niedrigen als auch hohen MDK-Proteinkonzentrationen. Regulierungsmechanismen von MDK sind weitestgehend unbekannt. Wir konnten durch die Behandlung der Tumorzellen mit Retinoläure die MDK-Genexpression induzieren. Hiebei bindet Retinoläure an die bereits bekannte Retinolsäure-Bindungssequenz im MDK-Promoter und ak- tiviert dadurch die MDK-Genexpression. Desweiteren verstärkt Hypoxie die MDK-Expression.

VIII Zusammenfassung

Zusätzlich scheint die DNA-Methylierung der Promoterregion eine entscheidende Rolle bei der Regulierung der MDK-Expression zu spielen. Um die Funktion von MDK im UM näher zu charakterisieren wurden sowohl "loss-of-function"- als auch "gain-of-function"-Experimente durchgeführt. In Zellen, die mit spezifischen anti-MDK siRNAs transfiziert wurden, konnte neben vermindertem Zellwachstum und verminderter Zellvi- ablität auch eine erhöhte Apotoserate detektiert werden. Währenddessen führte die Überexpression von MDK in einer MDK- niedrig exprimierenden Zelllinie zu erhöhtem migratorischen Vermögen. Potenzielle Signalmoleküle, die durch MDK beeinflusst werden, wurden untersucht. Dabei konnten Veränderungen des Phosphorylierungsta- tus des Ribosomal Protein S6 (RPS6) und des Proline-Rich Akt Substrate of 40kDA (PRAS40) identifiziert werden. Bei beiden Molekülen handelt es sich um Substrate des Akt- und mTOR- Signalweges. In der Tat zeigte sich, dass MDK zur Resistenzentwicklung gegen spezifische Akt- und mTOR-Inhibitoren beiträgt. Tumorzellen mit MDK-Überexpression haben einen Über- lebensvorteil gegenüber unveränderten oder mock-transfizierten Zellen. Es ist unter anderem bekannt, dass MDK als entzündungsaktivierendes Zytokin wirkt. Dies belegte sich, da sowohl aktivierte mononukläre Zellen (MNCs) als auch pro-inflammatorische M1-Makrophagen eine erhöhte MDK-Expression im Vergleich zu unstimmulierten MNCs und anti-inflammatorischen M2-Makrophagen aufzeigten. Die Genexpressionsanalyse von 80 UM-Primärtumoren (zur Verfügung gestellt von der TCGA- Datenbank) wießen eine signifikant negative Korrelation zwischen dem MDK-Expressionslevel und dem Überleben der Patienten auf. UM-Patienten mit einer erhöhten MDK-Expression im Primärtumor entwickelten schneller Metastasen als Patienten mit einer niedrigeren MDK- Expression. Interessanterweise konnte auch bei einem erhöhtem Mestasierungsrisiko (nach AJCC-Tumorklassifizierung) auch eine erhöhte MDK-Expression festgestellt werden. Ebenfalls konnte ein Zusammenhang mit dem Chromosom 3-Status, einem Standard-Prognosemarker des UMs, festgestellt werden. Monosomie 3-Tumore zeigten eine erhöhte MDK-Expression. Ab- schließend lässt sich anhand der im Umfang dieser Arbeit gewonnenen Daten zusammenfassen, dass MDK als Tumorüberlebensfaktor wirkt, die Metastasierung beschleunigt, Therapieresisten- zen hervorruft und als prognostischer Marker für die Hochrisikobewertung im UM in der Zukunft dienen kann. MDK-Targeting und die Verwendung von MDK zur Vorhersage von Hochrisikopatienten kön- nten dazu beitragen, die Prognose und Behandlung von UM-Patienten zu verbessern.

IX 1Introduction

1.1 Uveal melanoma

1.1.1 Epidemiology Melanoma arises from melanocytes, melanin producing cells that give color to our skin. DNA damage is one of the main causes of malignant transformation and uncontrolled growth of melanocytes to eventually form a mass of cancerous cells. Four subtypes are used to categorize melanoma based on place of origin. Cutaneous melanoma (CM), the most common melanoma originates in the skin, acral melanomas occur within acral skin of the palm of the hand, the sole of the foot and under nail beds. Mucosal melanoma is occurring in the mucosal membranes lining oral, respiratory, gastrointestinal and urogenital tracts (Hayward et al. 2017). The ﬁnal subgroup ocular melanoma originates in and around in the eye (Fig. 1.1). Ocular melanoma makes up 5%- 12% of all melanoma cases (Tab. 1.1). The majority of all ocular melanoma (85-97%) (Shields et al. 2015; Leyvraz and Keilholz 2012) arises in the uveal tract of the eye. Rarely, about 5% of all ocular melanoma occur in the conjunctiva. The uveal tract is made up of the choroid, iris and ciliary body. Uveal melanoma (UM) can develop in any of these parts and is named accordingly (Fig. 1.1). Choroidal UM is most commonly found, followed by less frequent occurring ciliary body melanoma and iris melanoma (McLaughlin et al. 2005). Around 2000-3000 new cases are diagnosed each year in the United States. (https://www.melanoma.org/understand-melanoma, http://www.cancer.net/cancer-types) which makes it a yearly incidence rate of about 6-9 people per 1 million the United States every year. Incidence rate of 4.5 per million are reported for Ger- many (https://www.aao.org/topic-detail/choroidal-melanoma-europe). The incidence is similar in other Caucasian populations. However, ocular melanoma rates were 8–10 times higher among whites than among blacks (McLaughlin et al. 2005; Inskip et al. 2003; Singh et al. 2011). In Eu- rope increase in incidence correlates with increase of latitude and ranges on the lower spectrum with two per million in southern countries, Spain and Italy, around six per million in central Europe, and greater than eight per million in northern countries, like Denmark and Norway (Virgili et al. 2007). In this thesis the focus is UM.

Melanoma Figure 1.1 –Possibleanatomicalregionswhereocular (conjunctival) melanoma can originate. Uveal (intra-ocular) melanoma includes choroidal, ciliary and melanoma arising from Conjunctiva the iris. Extra-ocular melanoma can be found in Cilary Body the conjunctiva. Adapted from (C) 2007 RelayHealth and/or its aﬃliates. Melanoma (iris) Retina Origin Percentage Choroid cutaneous 91% ocular 5-12% - uveal - 85-97% of ocular - conjunctival - 5% of ocular IrisIris - other ocular sites - 10% of ocular Melanoma Melanoma (choroid) (ciliary) mucosal 1% side unknown 2% Table 1.1 –Distributionofmelanoma

1 1 Introduction

1.1.2 Symptoms and diagnostics Most patients with UM do not experience any symptoms. Some signs or symptoms could include problems with vision, blurriness or sudden loss of vision, spots in the field of vision or flashes of light. Growing dark spot on the iris, change in the size or shape of the pupil, change of position of the eyeball, bulging of the eye and change in the way the eye moves in the socket. Enlarged blood vessels on the outside of the eye could also be a sign. Pain is rare unless the tumor has grown extensively outside the eye (http://www.cancer.org/cancer/eyecancer/detailedguide/ eye-cancer-melanoma-diagnosis). Ophthalmologist or eye specialist are usually able to diagnose UM with a routine eye exam. However imaging techniques or biopsies are used to confirm the diagnosis. Early detection of UM is critical. The ABCDEF guide may help to differentiate a harmless nevus from melanoma. The letters represent: A, age young ( 40 years); B, blood in anterior chamber; C, clock hour of mass inferiorly; D, diffuse configuration; E, ectropion (eyelid is turned outwards away from the eyeball); and F, feathery margins (Shields et al. 2015). Tests to confirm diagnosis of primary tumors include color fundus photography, ultrasonography (USG), fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), optical coherence tomography (OCT), fundus autofluorescence (FAF) and ultrasound biomicroscopy (UBM). Fine-needle aspiration biopsy (FNAB) of the tumor can be performed when the clinical diagnosis is unclear, and the diagnosis can be clarified by the evaluation of an experienced ocular pathologist (Tarlan and Kiratli 2016). Test confirming metastasis include computed tomography (CT), magnetic resonance imaging (MRI), and other imaging scans (76%); biopsy (63%); liver function tests (40%); and physical or clinical examination (39%) (Diener-West et al. 2005). MRI is the most sensitive method for liver imaging of metastatic disease, whereas CT has a difficulty to discriminate metastasis from benign lesions (Servois et al. 2010;TarlanandKiratli2016).

1.1.3 Risk factors Several risk factors have been identiﬁed for ocular melanoma. In common with CM are fair skin and light eyes (Weis et al. 2006). The incidence rate is higher in caucasian population and the geographic north-to-south decreasing gradient supports the protective role of ocular pigmentation (Virgili et al. 2007). However, UM development is less clearly linked to ultraviolet violet radiation as CM. A recent study by Hayward et al. 2017 conﬁrmed with next generation sequencing of melanoma samples a mutation spectrum, with very high mutation rate, caused by UV radiation. In UM lower mutation burden, inconsistent with UV radiation signature are found (Krauthammer et al. 2012; Johansson et al. 2016). Additionally, incidence of UM has remained unchanged from the last 50 years (Singh et al. 2011). Whereas, conjunctival melanoma resembling more its cutaneous counterpart with increasing incidence rates in Europe and in the US (Inskip et al. 2003; Tuomaala and Kivela 2003), possible due to increased exposure to ultraviolet radiation.

1.1.4 Cytogenetics: Chromosomal aberrations Non-random chromosomal abnormalities in UMs are known since the 1990s and originally described by Prescher et al. 1990; Horsman et al. 1990; Sisley et al. 1990. The majority of detected chromosomal anomalies included chromosome 1p loss, chromosome 3 loss, chromosome 6 gain and chromosome 8q gain. In the last 2 decades more and more associations between these cytogenetic abnormalities and metastatic disease were found. Loss of chromosome 3, loss of 6q, and gain of 8q were significantly associated with poor overall survival and a predictor of disease-free survival (Aalto et al. 2001; Kilic et al. 2005; Maat et al. 2008; Nes et al. 2016). Monosomy 3 was also found to be associated with the presence of epithelioid cells and increased levels of tumor-infiltrating macrophages and was connected to inflammatory phenotype with increased HLA class I and II expression (Maat et al. 2008). Interestingly, fluorescence in situ hybridization

2 1 Introduction

(FISH) analysis of paraffin-embedded tissue section of UM chromosome 3 showed heterogeneity of chromosome 3 status in different areas of the tumor, however no significant difference in metastasis-free survival was observed when comparing the UMs with low percentage of monosomy 3 with UMs with monosomy in most nuclei (Mensink et al. 2009). In a study by Damato et al. 2010 of 452 UM patients, non of the study participants with disomy 3, died within 10 years caused by the UM, whereas monosomy 3 and in combination with regular numbers of chromosome 8q lead to only 45% survival and if monosomy 3 was present and 8q gain the disease related survival rate was reduced to 29%. Several studies have also shown highly significant correlation between monosomy 3 and metastasis probability (Shields et al. 2011; Ewens et al. 2014). By comparing benign dermal nevi with non-metastasizing and metastasizing UM, chromosome 3 loss and chromosome 8q gain are significantly correlated with a lower metastasis-free survival (Staby et al. 2017). The most recent and most comprehensive analysis identified four molecular and clinical subsets in UM. Two groups associated with monosomy 3 (poor-prognosis) and two associated with disomy 3 (better-prognosis). Additional information about DNA methylation profiles, transcriptional features and mutation status provided deeper insight into the biology of poor- and better-prognosis subgroups (Robertson et al. 2017).

1.1.5 Molecular genetics: Genetic basis and pathophysiology Activating mutations in G proteins, drive oncogenesis in many cancers by activating multiple intracellular signaling pathways. Therapeutically targeting these G proteins is challenging due to the multitude of pathways involved. Several studies (Dono et al. 2014; Ewens et al. 2014; Staby et al. 2017) have looked at genetic mutations in UM patients and found similar mutations rates of GNAQ (40%-46%), GNA11 (32.6%-37%), SF3B1 (9.7%-10%), EIF1AX (16%-18.9%), In about 50% of all patients BAP1 mutation can be found. Interestingly, somatic mutations in the G-protein pathway-associated GNAQ and GNA11 are mutually exclusive and are found in 82%- 92.5% of UM patients (Ewens et al. 2014; Robertson et al. 2017) In UM patients BAP1 is often mutated. BAP1 is located on chromosome 3 and encodes a nuclear ubiquitin carboxyterminal hydrolase (UCH) and together with BRCA1 and BARD1 it forms a tumor suppressor heterodimer complex. BAP1 mutation highly correlated with monosomy 3. 90% of all UMs with monosomy 3 had BAP1 mutations (Nes et al. 2016). Loss of one chromosome 3 plus additional BAP1 mutation leading to complete loss of BAP1 protein and results in loss of BAP1 tumor suppressor function. BAP1 is mutated in 47% of all (26 of 31 (84%) of class 2, 1 of 29 (2.5%) of class 1) (Harbour et al. 2010). Dono et al. 2014 found 31.5% mutation rate of all studied UM samples. Interestingly, all mutations were found in patients with monosomy 3. CM is driven by MAPK activation through mutations in BRAF, NRAS or loss of function in NFI. These alterations are rarely found in UM. The most common disease driving mutations can be found in G-protein alpha subunits encoded by GNAQ and GNA11. GNAQ mutations result in constitutive activation, turning GNAQ into a dominant acting oncogene. The activating mutations occur on residue R183 or Q209 The GTPase enzyme necessary to inhibit G-protein activity is disrupted. Constitutive activation of GNAQ, leads to activating of the mitogen-activate protein/extracellular signal-regulated kinase/mitogen-activated protein kinase (MEK/MAP-kinase) and PI3K/AKT pathways (Populo et al. 2010; Babchia et al. 2010). It was also shown that GNAQ induces intracellular signaling pathways through ARF6, an immediate downstream effector. Blocking ARF6 reduces the growth of GNAQ-dependent UM cells in vitro and vivo (Yoo et al. 2016). Furthermore, GNAQ/GNA11 mutations have been shown to increase YAP and TAZ activation (Vader et al. 2017) through an alternative pathway utilizing Trio, a guanine nucleotide-exchange factor, and the downstream GTPases Rho and Rac (Krantz et al. 2017). In-depth proteomic profiling of the UM secretome showed that the mTOR/S6 signaling axis was among the most differentially regulated biological processes in UM compared with normal

3 1 Introduction choroidal melanocytes (Angi et al. 2016). In a recent study, the importance of the PI3K/Akt pathway in UM signaling was also shown. They could show that inhibition of c-Met and Akt with small molecule inhibitors abolished HGF induction of PI3K/Akt and reduced tumor growth in nude mice (Z. Wang et al. 2017). The activation of the PI3K/AKT pathway in some UM tumors may be mediated through the loss of the tumor suppressor phosphatase and tensin homolog (PTEN). PTEN encodes a phosphatase protein that antagonises PI3K/Akt/mTOR pathway. About 76% of primary UM tumors exhibit hemizygosity of PTEN and in about 11% of those additional PTEN mutations have been found, resulting in reduced levels of PTEN. Lower levels of PTEN correlated with shorter disease-free survival (Abdel-Rahman et al. 2006).

1.1.6 Immunological factors Tumor infiltrating immune cells often correlate with improved diagnosis in different cancers (Clemente et al. 1996; Galon et al. 2012). In patients suffering from CM, patients with tumor lymphocyte infiltrate had significantly higher 5-year survival rates than patients with low or absent lymphocyte infiltrates (reviewed in Oble et al. 2009). In contrast to CM, in UM a high immune cell infiltrate is associated with worsen outcome (reviewed in Bronkhorst and Jager 2012). A large retrospective histological study (1078 cases) showed that the group (12.4% of the total cohort) with high lymphocyte infiltrate had low survival rate at 15 years with only 36.7% of the patients still alive. The low lymphocytic group showed higher survival rates at 15 years of 69.6% (Cruz et al. 1990). This finding could be confirmed by a study by Whelchel et al. 1993 that could show that fewer T cell infiltration in UM tissue was correlated with longer survival. More specifically, looking at tumor infiltrating macrophages Makitie et al. 2001 could show in a retrospective study of 167 UM patients that CD68 positive macrophages were found in 83% of all cases and the number of CD68 positive cells was significantly associated with larger tumors, heavy pigmentation, epithelioid cells and melanoma specific mortality. Furthermore, high lymphocyte infiltration was associated with the presence of epithelioid cells and with monosomy of chromosome 3. Two markers that are associated with higher metastatic risk and lower survival rate (Bronkhorst et al. 2011). T regulatory cells were found in 12% (Lagouros et al. 2009)up to 24% (Mougiakakos et al. 2010) of studied UM samples and were an independent marker for worse overall survival.

1.1.7 Metastatic disease About half of all patients develop metastatic disease predominantly in the liver. 89% of all metastatic patients have liver metastases. In 46% within the group of metastatic UM patients, the liver was the only site in which metastases was detected. 43% patients had metastasis in the liver and other sites and the rest 11% had metastases in sites other than the liver. The other sites with the most occurring metastases are, lung (29%), bone (17%), skin or subcutaneous tissue (12%), lymph node (11%) brain (5%). 1% of all patients had metastasis in the orbit of eye treated for choroidal melanoma, in the spinal cord and in the other eye or orbit (see table1.2 Diener-West et al. 2005). In only two of 52 (3.8%) of patients diagnosed with choroidal melanoma metastatic melanoma was detectable before treatment (Finger et al. 2005). The median survival, once liver metastasis have developed is about 1 year (Rietschel et al. 2005) and 80% of patients die within a year, and 92% within 2 years (Diener-West et al. 2005). The molecular pathways mediating the predominant liver metastases are not well understood. However, a few molecules and pathways have been identiﬁed that potentially play a role in development of liver metastases, including the hepatocyte growth factor (HGF) and its cognate cMet receptor pathway, IGF-1 and IGF-1R and chemokines and chemokine receptors (CXCR4 and CXCL12) (reviewed in Bakalian et al. 2008).

4 1 Introduction

Site of Metastasis Percentage Liver 89% Lung 29% Bone 17% Skin or subcutaneous tissue 12% Lymph node 5% Brain 1% Orbit of eye treated for choroidal melanoma >1% Other 20% Table 1.2 – Sites of metastasis for patients with metastatic disease reported during follow-up or at the time of death

1.1.8 Treatment options Based on risk and prognostic factors patients without metastatic disease are usually either treated with mainly eye-conserving therapies or enucleation. Eye-conserving therapies include, photoco- agulation, transpupillary thermotherapy (TTT), a diode laser-based method used to treat small and medium-sized melanomas, usually well tolerated, however local relapses are high (Diener- West et al. 2001). Intensive follow-up is often chosen if the patient is to frail for surgical interventions or radiotherapy (Chattopadhyay et al. 2016). The number of patients treated with surgery alone is decreasing over the last 50 years with 93.8% of patients treated with surgery alone in 1973-1975 vs. 28.3% in 2006-2008 and increasing trend in patients treated with radiation 1.8% for 1973-1975 vs 62.5% for 2006-2008 (Singh et al. 2011). Currently, radiotherapy is the most common treatment for UM, especially posterior UM. It is mainly administered in the form of radioactive plaque therapy (also known as brachytherapy), direct irradiation via the application of a radioactive source directly to the tumor (Singh et al. 2011). No difference in survival was observed when I-125 plaque brachytherapy was compared to enucleation. Independently from treatment no changes in the 5-year relative survival rate was observed (Diener-West et al. 2001; Singh et al. 2011). With large tumors surgery, removal of the globe (enucleation) is a option, specifically if radiation therapy would cause severe ocular and visual impairments. Since treatments do have side effects, the challenge is to decide when to treat, especially small tumors could primarily be monitored (watch and wait approach). However, it is unknown whether a tumor will become metastatic before it reaches a size that makes treatment absolute necessary. Interestingly, the development of metastasis seems to be independent of treatment of the primary tumor (Tarlan and Kiratli 2016). Undetectable micrometastatic disease could precede local therapy. Detection of circulating tumor cells in the bloodstream at time of diagnosis support this theory of metastasis development (Torres et al. 2011; Schuster et al. 2011; Bidard et al. 2014; Pinzani et al. 2010). To treat UM metastasis classical chemotherapy or local interventions of metastases, like selective internal radiation therapy (SIRT) or high-dose chemotherapy via liver perfusion are used. However, the survival rates of UM patients with metastasis are disheartening and currently no adjuvant treatment has been shown to be effective (reviewed Jovanovic et al. 2013). New targeted therapeutic approaches based on molecular drivers in UM are promising but still have to prove their effectiveness. Newer targeted therapies include tyrosine kinase inhibitors (TKIs), histone-deacetylase inhibitors (HDACs), (lymph)angiogenesis inhibitors and immunotherapies (reviewed in Woodman 2012; Chattopadhyay et al. 2016). More specifically, targeted therapies could include direct targeting molecular drivers like GNAQ and GNA11 mutations or downstream molecules effected by these mutations. For instance targeting MET/MAPK pathway. On going clinical studies looking at targeting HGF receptor c-Met, IGF-1 and IGF-1 receptor (IGF-1R) or intervening with Akt/mTOR pathway. All pathways

5 1 Introduction reported to be involved in UM tumor progression (Populo et al. 2010; Babchia et al. 2010;Angi et al. 2016; Z. Wang et al. 2017). In recent years multiple immunotherapies have been developed for a wide range of cancers and applications. Clinical trails to combat metastatic UM include dendritic cell vaccinations (Schuler-Thurner et al. 2015; Bol et al. 2016), checkpoint inhibitors (Danielli et al. 2012;Luke et al. 2013; Zimmer et al. 2015; Karydis et al. 2016; Chan et al. 2017; Javed et al. 2017;Heppt et al. 2017) and adoptive cell transfers (Chandran et al. 2017). Unfortunately, many immunotherapy studies have not shown the anticipated improvement in metastatic UM patients.

1.1.9 Prognostic markers and patient classifications Mutation status (see chapter 1.1.5), cytogenetic alterations, especially monosomy 3 (see chapter 1.1.4) and expression profiling allows classification of and stratification of different patient risk groups at first diagnosis: patients with high risk of metastases and patients with low risk of metastases (Shields et al. 2013; Onken et al. 2004; Robertson et al. 2017). The American Joint Committee on Cancer (AJCC) classification has been validated for use in posterior UM. It classifies 4 stages (T1-T4) based on tumor size, additional subgrouping include ciliary body involvement and extraocular extensions (a-e options within a tumor size group) (Shields et al. 2013). The AJCC classification therefore only includes anatomical features of the tumor, chromosomal and genetic informations are not included. Clinical parameters, like tumor size and tumor diameter have also been shown to be significant prognostic marker on disease-free survival and metastatic rate in other previous studies (Kilic et al. 2005; Maat et al. 2008; Diener-West et al. 2005). However, gene expression profiles have made molecular risk classification possible. Onken et al. 2004 could show that UM patients clustered in two distinct molecular classes based on gene expression. Class 1 with low risk and class 2 with high risk of metastatic death. A very recent and most comprehensive study of UM primary tumors clustered gene expression into four molecularly distinct subsets (1-4) with increasing metastatic risk. The classification included unsupervised clustering of somatic copy number alterations (SCNAs), gene expression data, as well as DNA methylation, miRNA and protein expression (Robertson et al. 2017). Additionally, there also seems to be a link between tumors composition and prognosis. Patients with tumors composed exclusively of spindle cells have better prognosis than those containing epithelioid cells in any proportion (Cruz et al. 1990; Griewank et al. 2014). Immune cell infiltration also highly correlates with metastatic disease, see for more details chapter 1.1.6. The microvascular density was also shown to be significant for survival prognosis (Al-Jamal et al. 2003) as well as a high cell proliferation index which is associated with a higher risk of metastatic death (Al-Jamal and Kivela 2006) A few additional prognostic markers have been suggested, including Bap1 status (Ewens et al. 2014; Kalirai et al. 2014) and c-Met expression (Barisione et al. 2015). c-Met is especially interesting since it would be the first soluble biomarker found in serum. Despite good prediction if metastasis will develop and if patients belong to high risk or low risk group no prognostic marker is known to show early micrometastatic disease. Only regular check- ups are available to detect metastasis. Improvements in identification of disease progression and earliest metastatic onset, ideally as a serum biomarker are highly needed.

6 1 Introduction

1.2 Midkine

Midkine (MDK) was first identified during experiments studying the mechanisms of embryonal development. MDK was identified as a gene upregulated during retinoic acid driven differentiation of embryonal carcinoma pluripotent stem cells (Kadomatsu et al. 1988). During mouse embryonal development, MDK expression was undetected on d5, widely expressed all on d7 and thereafter progressively restricted, to certain tissues. These sites include the brain ectoderm around the lens and brain ventricles, the anterior lobe of the pituitary gland, the upper and lower jaw, the caudal sclerotomic half of vertebral column, the limbs, the stomach, and the epithelial tissues of the lung, the pancreas, the small intestine, and the metanephros. These areas include the region where secondary embryonic induction is prominent. These findings indicated that MDK plays a fundamental role in the differentiation of a wide variety of cells (Kadomatsu et al. 1990). Tomomura et al. 1990 early on could show that MDK is processed and exported outside the cell, suggesting a soluble function of MDK. In healthy adult tissue MDK expression is highly restricted (Aridome et al. 1995; Z. Q. Zhao et al. 2012). However, low levels of MDK expression was detected in adult human skin, at the epidermal-dermal junction and in basal cells as well as in normal human keratinocytes (Inazumi et al. 1997)

1.2.1 Molecular genetic characterization and structure The human MDK gene is located on chromosome 11q11 and encodes a 13.4-kDa protein. MDK is a cysteine-rich protein composed of two domains, the N- and C-terminal domains (Fig. 1.2). Each domain is formed from three anti-parallel b sheets which are connected by a ﬂexible linker region.

Figure 1.2 –Midkineproteinstructure:Thedomainorganizationofmidkineandthreedimensionalstructureof the domains. Two heparin-binding sites in the C-domain are encircled. Cited from Muramatsu, T., J. Biochem. 132, 359-371 (2002)

MDK represents one of two members of a family of heparin-binding growth factors. The only other known member is named pleiotrophin (PTN). Both molecules are highly conserved within the diﬀerent species. MDK and PTN share about 45% sequence identity with each other (T. Muramatsu 2002). In drosophila melanogaster, two MDK and PTN homologues have been identiﬁed, named miple1 and miple2. Human and mouse MDK share 87% sequence identity and all cysteine residues in MDK and PTN are conserved in the human and murine system (Fabri et al. 1993). The basic amino acids that form the clusters for heparin-binding are also highly conserved in MDK and PTN (Asai et al. 1997). Despite the structural similarities of MDK and PTN, both molecules show distinct

7 1 Introduction expression patterns and diﬀerential functions.

1.2.2 MDK Receptors and intracellular signaling MDK Receptors MDK has been attributed with multifaceted functions, which make it likely that MDK binds to several receptor. Diﬀerent receptors have been described to interact with MDK, including proteoglycan receptor (receptor-type tyrosine phosphatase⇣ (RPTP⇣)), anaplastic lymphoma kinase (Alk), low density lipoprotein receptor-related protein (LRP), syndecans and Notch2 (reviewed C. Xu et al. 2014). Below, I will brieﬂy introduce the key MDK receptors (see Fig. 1.3).

MDK

RPTPζ ALK Syndecans LRP1 Notch CD138 CD91

signaling

Figure 1.3 –Receptorslinkedtomidkinesignaling.

Proteoglycan receptor (receptor-type tyrosine phosphatase ⇣ (RPTP ⇣)) is a chondroitin sulfate proteglycan with a transmembrane domain and intracellular tyrosine phosphatase domains. At first Maeda et al. 1996 showed PTN (the second member of the heparin-binding growth factors) binding to RPTP⇣ in 1996. Later Maeda et al. 1999 identified RPTP⇣ as receptor also for MDK with high binding affinity to native PTP⇣. Binding competition with PTN inhibited PTP⇣ - MDK binding, indicating that PTN and MDK share a common binding site. Additionally, they could show that MDK has a high (Kd of 0.58nM) and a low (Kd of 3nM) affinity binding sites. In- terestingly, heparin strongly and heparan sulfate moderately inhibited binding of MDK to PTP⇣.

Anaplastic lymphoma kinase (Alk) is a receptor protein-tyrosine kinase within the insulin receptor superfamily that take part in the embryonic development of the nervous system. Many Alk-fusion proteins have been described that result from various chromosomal rearrangements with various implications in the pathogenesis of several diseases. Downstream signaling molecules include RAS, RAF, MEK, ERK1/2 and JAK/STAT (reviewed in Roskoski 2013) During the search for receptors of PTN, Stoica et al. 2001 incubated a phage display human cDNA library against immobilized PTN protein and could isolate an amino acid sequence of the extracellular domain of the Alk receptor. Anti-PTN antibody was able to inhibit the PTN - Alk binding and adding of PTN induced Alk phosphorylation and downstream eﬀector molecules, including PI3K. The binding constant was established at Kd of 32+- 9 pm. According to Stoica et al.(Stoica et al. 2002) a Kd of 170 pm was identiﬁed of binding of MDK to Alk and anti-Alk antibodies inhibited MKD binding to the receptor, preventing MDK stimulated colony formation

8 1 Introduction of SW-13 cells.

The LRP receptor, also known as cluster of differentation 91 (CD91) belongs to the low-density lipoprotein (LDL) receptor super family. The receptor is involved in many areas by being consti- tutively endocytosed with its ligands from the membrane and recycled back to the cell surface. More than 30 different ligands have been described to bind to LRP. Intracellular signal-pathways activated via the LRP receptor leads to increased cell proliferation and migration as well as inte- grin maturation and focal adhesion disassembly (reviewed in Lillis et al. 2005). Muramatsu et al. identified MDK as one of the ligands for LRP by utilizing lectin and MDK affinity chromatog- raphy to identify cell-surface molecules binding to MDK. SDS-page was succeeded by protein sequencing and identified LRP as MDK binding molecule. The calculated Kd of binding between LRP and MK was 3.5nm.

Syndecans are cell surface proteoglycans, expressed on almost all epithelial cells. Four types of syndecans belong to the gene family. Syndecans extracellular domains, are heparan sulfate (HS) and chondroitin sulfate (CS) chains which interact with diverse extracellular matrix (ECM) molecules and cytokines. Syndecan1 is also known as SDC1 or CD138 (reviewed in Gharbaran 2015 and Szatmari et al. 2015). As ECM receptors syndecans play important roles in cell proliferation, cell migration, vascularization and metastasis of cells. It was specifically shown to be influential in myeloma tumors, essential for B cell development, for the survival of long-lived plasma cells and multiple myeloma plasma cells (Reijmers et al. 2013). The HS chains of synde- can1 (CD138) are specifically responsible for its binding to MDK (Kojima et al. 1996).

Notch receptors (Notch1-4) are a family of single-pass transmembrane receptor proteins. MDK was shown to be a ligand of Notch2 by yeast 2-hybrid assays (Y. Huang et al. 2008)andby coimmunoprecipitation assays (Y. Huang et al. 2008; Gungor et al. 2011). Notch2 signaling has been connected to tumor growth, chemoresistance and epithelial-mesenchymal transition (EMT). Taken together, these receptors described above may regulate the biological activities of MDK either independently or cooperatively.

MDK signaling Even though the precise signaling of MDK within the cell is not completely understood, several signaling pathways and signaling molecules have been shown to be inﬂuenced by MDK. Including major kinases like MAP-kinase and PI3-kinases. Both important kinases catalyzing the phosphorylation of many target substrates, to mediate a wide range of signal transduction pathways and control complex cellular functions. Important downstream signaling molecules include Akt, mTor, S6, ribosomal protein S6 as well as and Erk.

1.2.3 Regulation of MDK expression Retinoic acid All-trans-retinoic acid (ATRA) is a physiologically active metabolite of vitamin A, that plays a role in diverse and complex physiological processes in vertebrates. Speciﬁcally, ATRA is involved in fundamental aspects of organ and limp development by regulating the expression of a large number of target genes through a families of retinoic acid receptors (RAR). The transcription of genes is controlled by binding of retinoic acid to RAR-retinoid X receptor (RXR) heterodimers complex and additional binding of co-activators. The whole complex binds to retinoic response elements (RAREs) near target genes. The RARE sequence typically contain hexameric direct repeats (DRs) - (A/G)G(T/G)TCA with interspacing of 5bp (DR5 elements) or 2bp (DR2 elements) (Cunningham and Duester 2015). MDK was ﬁrst discovered during the search for retinoic acid response genes (Kadomatsu et al. 1988) and early on it was observed that MDK expression

9 1 Introduction was upregulated in embryonal carcinoma cell line (F9) treated with retinoic acid (Matsubara et al. 1990). The responsiveness of MDK to ATRA was shown by fusing the 2.3-kb upstream region of MDK with a CAT gene in transfected F9 cells. CAT activity was higher in transfected cells treated with ATRA compared to untreated cells (Matsubara et al. 1994; Pedraza et al. 1995). Later a DR5 RARE-like element in the 5’ upstream region of MDK gene was identiﬁed. Several follow-up studies have shown that MDK expression could be induced by ATRA in a large array of diﬀerent cells, including normal human keratinocytes (Inazumi et al. 1997), alveolar type II cells (H. Zhang et al. 2009), primary cortical neurons (Harvey et al. 2004) and pituitary cells (Maliza et al. 2017)

Epigenetics and DNA methylation Modifications of nucleotides are common occurrence in genomic DNA of mammalians. The most prevalent modification is cytosine methylation. Methylation of cytosines is important in gene expression regulation. Only cytosines followed by a guanine (CpG) in the DNA sequence can be methylated. DNA methylation occurs at the carbon-5 position of cytosine to form 5-methyl cytosine (5-mC). 5-azacytidine and 5-aza-2’-deoxycytidine (Decitabine, 5’aza), when incorporated into DNA, inhibits DNA methylation. 5’aza is structurally similar to cytosine and when incorporated into DNA cytosine methylation is prevented leading to rapid loss of DNA methylation in total. Therefore, treating cells with 5’aza is a useful tool to study the correlation between loss of methylation and activation of the associated genes or the suppressive function of hypermethylation of tumor suppressor genes. In many human tumor types, a correlation could be observed between decreased methylation levels and increased expression of specific genes (reviewed in Christman 2002). Loss of tumor suppressor gene function can occur both through mutation and through gene silencing linked to methylation of CpG island promoters (reviewed in Baylin et al. 1998; Santini et al. 2001). Treatment with 5-azacytidine and 5-aza-2’-deoxycytidine has been shown to reactivate tumor suppressor gene function by demethylating silenced CpG island promoters.

Hypoxia Hypoxia is a driving force of angiogenesis, promotes metastasis, treatment resistance and malignant progression and is related to poor clinical outcome of many cancer diseases. Hypoxia inducible factors (HIF) induces the transcription of more than 60 genes, including vascular endothelial growth factor (VEGF) and erythropoietin. Both are involved in biological processes such as angiogenesis and erythropoiesis, which contribute to oxygen delivery to hypoxic regions. HIF-1↵ was shown to enhance the transcription of MDK by acting on regulatory elements located in the MDK gene promoter. Mutated HIF-1↵ response element in the MDK promoter could block the eﬀects of HIF-1↵ on MDK expression (Reynolds et al. 2004).

Ethanol and other factors A few other substances have been described to induce MDK gene expression. For instance in neuroblastoma cells ethanol (He et al. 2015) as well as morphine injection (Garcia-Perez et al. 2015) was shown to increase MDK gene expression rapidly. In prostate cancer cell lines as well as pancreatic cancer cells MDK expression was induced by treating the cells with TNF-↵, HGF, EGF and serum (You et al. 2008; Rawnaq et al. 2014). Interestingly, the expression of MDK was also increased by MIF and c-Met in normal mouse splenic B and chronic lymphocytic leukemia (CLL) cells (S. Cohen et al. 2012). In lymphocytes treatment with IL-2 or INF- as well as activation of lymphocytes by phytohemagglutinin (PHA) or through the engagement of the CD28 antigen, MDK expression became transiently increased (Hovanessian 2006).

10 1 Introduction

1.2.4 MDK functions A multitude of functions have been attributed to MDK (reviewed in Kadomatsu et al. 2013). Figure 1.4 shows a summary and overview of all areas MDK has been found to be influential. MDK has been reported to play a role in development, inflammation and immunity (reviewed in Sorrelle et al. 2017), in tissue protection and fracture healing (Haffner-Luntzer et al. 2016a; Haffner-Luntzer et al. 2016b), in blood pressure regulation and in cardiac disease (reviewed Woulfe and Sucharov 2017), in neurological disorder (reviewed Sakamoto and Kadomatsu 2012). For the scope of this thesis mainly MDK functions in oncogenesis will be introduced in more detail in the following chapter.

Neurological disorders Biomarker Inflammation & Immunity

(Lymph)angiogenesis MDK Cancer Tissue protection Development Chemoresistance

Figure 1.4 – Summary of MDK functions adapted from Kadomatsu et al. 2013.MDKhasbeenreportedtobe important in neurological diseases, in inﬂammation and immunity, in tissue protection and fracture healing and in lymphangiogenesis. MDK has be attributed to induce chemoresistance, act as tumor growth factor and can be used as a biomarker

MDK and metastasis During development of cancer, particularly development of metastasis the transition of epithelial cells to mesenchymal cells (EMT) is an important process by which the epithelial cells undergo changes from a organized cell layer to mesenchymal cells with more motility and invasiveness, by loosing epithelial cell-cell junction and the upregulation of mesenchymal markers. MDK has been shown to contribute to EMT in immortalized keratinocytes (Y. Huang et al. 2008) and pancreatic cancer (Gungor et al. 2011). MDK seems to be important for macrophage (K. Hayashi et al. 2001) and neutrophil (Weckbach et al. 2011)migration Furthermore, several studies have reported that MDK increases tumor cell migration (Y. Huang et al. 2008; Rawnaq et al. 2014; Erdogan et al. 2017).

MDK and (lymph)angiogenesis Blood vessels deliver oxygen, nutrients to every part of the body, remove waste and provide gateways for patrolling immune cells. In tumors with high proliferation rate oxygen and nutrients are often depleted. To compensate many tumors induce neovascularization by secreting proangiogenic factors. Previously, it was reported that MDK is able to induce neovascularization. In MDK-deficient mice the angiogenic response was highly reduced compared to control animals. Furthermore, endothelial cells were identified as the source of soluble MDK in the vascular system specifically during hypoxia and showed that MDK is important for angiogenesis during ischemia in non- malignant tissue (Weckbach et al. 2012). In addition, MDK induced angiogenesis in the chick chorioallantoic membrane (CAM) assay (H. L. Huang et al. 2015). Treatment with anti-MDK

11 1 Introduction inhibitor reduced tumor growth as well as CD31 blood vessel density within xenografts of oral squamous cell carcinoma (Masui et al. 2016). The role of MDK in endothelial proliferation has been shown in several studies (H. L. Huang et al. 2015; Olmeda et al. 2017; Lautz et al. 2018) to be relevant in the process of arteriogenesis, angiogenesis and lymphangiogenesis. In more detail, MDK has been shown to be a systemic inducer of neo-lymphangiogenesis, by inducing early distal pre-metastatic niches uncoupled from lymphangiogenesis at primary lesions.

MDK promoting resistance to treatment Therapy resistance is one of the main obstacles in the success rate of cancer treatments. MDK has been shown to promote resistance to treatment several cancers (Lorente et al. 2011; Gungor et al. 2011; Tian et al. 2017). For instance, downregulating MDK could restore chemosensitivity in pancreatic cancer cells (Gungor et al. 2011) and a human gastric cell line (Tian et al. 2017). Furthermore, it was reported that MDK promotes resistance to cisplatin (cis- diamminedichloroplatinum, DDP) in oral squamous cell carcinoma (OSCC) cells. High levels of MDK, produced by cancer-associated fibroblasts promoted the cisplatin resistance via elevated expression of lncRNA ANRIL. Downregulation of lncRNA ANRIL in tumor cells inhibited proliferation and induced apoptosis which could be restored by treatment with human MDK (D. Zhang et al. 2017). Several studies have shown that the anti-apoptotic effects of MDK, protected cells from a range of apoptosis inducing mechanisms, caused by inflammation, chemotherapeutics, or control mechanisms getting rid of faulty cells (Q. Wang et al. 2007; Yazihan et al. 2008). It could also be shown that MDK protected glioma cells from THC pro-autophagic and antitumoral functions (Lorente et al. 2011).

MDK as biomarker There is a need for a biomarker in UM that can specifically identify development of early metastasis. Many studies in a large range of different cancers have shown that MDK is a promising prognostic biomarker (reviewed in Jono and Ando 2010). In acute kidney injury, urinary MDK levels have shown to an effective biomarker that may allow the start of prophylactic medication (H. Hayashi et al. 2017; Albert et al. 2017). In cancer, MDK mRNA or protein concentrations of the tumor tissues as well as MDK serum or plasma levels were predictive and associated with poorer outcome in ovarian cancer (Rice et al. 2010), non-small cell lung cancer (Ma et al. 2013; Xia et al. 2016), pancreatic cancer (Yao et al. 2014), breast cancer (F. Li et al. 2015; Cetin Sorkun et al. 2016), bladder cancer (Vu Van et al. 2016), thyroid cancer (Y. Zhang et al. 2014;Shao et al. 2014; Meng et al. 2015; Jia et al. 2017) and CM (Olmeda et al. 2017).

MDK in cancer MDK gene and protein overexpression has been observed in various cancers in tissue, blood and urine (reviewed in (Jones 2014)). MDK up-regulation has been found in colorectal and gastric cancer (Y. Xu et al. 2009; Krzystek-Korpacka et al. 2012; Z. Q. Zhao et al. 2012; Krzystek- Korpacka et al. 2017). In more detail, MDK protein level was higher in cancerous versus non- cancerous tissue in 98% cases and increased levels of MDK correlated with lymph node metastasis (Krzystek-Korpacka et al. 2012). Interestingly, increased MDK levels were also found in non- cancerous tissue surrounding the tumor dependent on the progression of the colorectal cancer (Krzystek-Korpacka et al. 2017). In gastric cancer about 70% of 107 patients had high levels of MDK in the cancerous tissue whereas no MDK expression was detected in adjacent normal gastric mucosa. MDK expression correlated with the gastric tumor size, depth of invasion, lymph node metastasis and pathological stage (Z. Q. Zhao et al. 2012). Downregulating of MDK in gastric cancer cells reduced proliferation, whereas MDK overexressing cells showed increased ERK and Akt phosphorylation and increased proliferation rates (Y. Xu et al. 2009). MDK transfection

12 1 Introduction in gastric cells lead to suppressed NK cell cytotxicity by inhibiting CD107a and Granzyme B expression (S. Zhao et al. 2012), indicating a MDK induced immune evasion mechanism. In pancreatic carcinoma patients higher MDK serum levels were found compared to healthy controls. Depleting MDK caused decreased proliferation and reduced migration of pancreatic carcinoma cells in vitro (Rawnaq et al. 2014) and in vivo (L. Yu et al. 2013). Additionally, reduced metastatic rates and reduced microvessel density were observed in the mouse group that carried pancreatic tumors with downregulated MDK (L. Yu et al. 2013). In non-small cell lung cancer MDK mRNA levels in peripheral blood mononuclear cells (PBMCs) was found to correlate to clinical stage, differentiation and lymph node metastasis (Ma et al. 2013). In lung adenocarcinoma cells downregulation of MDK with siRNAs or treatment with small MDK inhibitor (MDKi; 3-[2-(4-fluorobenzyl)imidazo[2,1-beta][1,3]thiazol-6-yl]- 2H-chromen-2-one) reduced proliferation and induced apoptosis. Interestingly, the small MDK inhibitor, inhibited the PI3K/AKT pathway and lower phosphorylation levels of PI3K and AKT were observed (H. Hao et al. 2013). In many other cancers overexpression of MDK was also detected. Specifically, in pancreatic cancer (Gungor et al. 2011), prostate cancer (Nordin et al. 2013), melanoma (Westphal et al. 2000; Olmeda et al. 2017), thyroid cancer (Y. Zhang et al. 2014), breast cancer (F. Li et al. 2015) and CLL (S. Cohen et al. 2012).

MDK in melanoma Upt to recently not much was known about expression and function of MDK in melanoma. One of the first studies mentioning MDK reported no MDK expression was detected in two melanoma cell lines (Inazumi et al. 1997). However, a larger more comprehensive study in 2000 by Westphal et al. 2000 found high MDK expression in almost all tested melanoma cell lines. Interestingly, the concentration of MDK RNA levels were highly elevated in the BLM and MV3 cell lines. Two cell lines with the highest vascular densities in human melanoma xenografts, as well as the fastest development of metastasis formation. A more recent study found high MDK expression in melanoma tissue compared to normal skin and found that by downregulating MDK in a melanoma cell line an overall reduction of proliferation and migration was observed (Y. Yu et al. 2015). The most comprehensive and recent study, by Olmeda et al. 2017 describes a novel function of MDK in a subset of melanomas. They found that MDK promotes lymphovascular niche formation and tumor metastasis in distant organs. They could identify melanomas in which neo-lymphangiogenesis at distal sites was found prior to or in the absence of notice- able lymphangiogenesis at the site of the primary tumor. Analyzing these sites they found that they were potential pre-metastatic niches. One of the responsible factors was identified to be MDK. Silencing MDK decreased lymphangiogenesis and metastasis into lymph nodes and lungs, while MDK overexpression increased lymphangiogenesis and metastasis in immunodeficient mice. MDK was found to accumulate in association with lymphatic vessels in lungs and liver before tumor colonization and was found to promote mTOR activation, VEGFR3 expression, and cell proliferation in cultures of human lymphatic endothelial cells. Immunohistochemical analysis of melanoma tissues also showed many tumors overexpressed MDK compared to benign nevi and high upregulation of MDK expression occurred in local, lymph node, and distant metastases. High levels of MDK also correlated with shorter disease-free survival times.

MDK in uveal melanoma Previous to this research, described in this thesis, no studies have been published about MDK in UM. The expression levels of MDK in the UM tumors were unknown as well as functionality of MDK, contributions MDK has on tumor growth, metastatic disease development and so forth.

13 1 Introduction

However, developing of the metastatic disease is universally fatal and no effective treatment to improve the survival of patients is currently known, studying development of UM primary tumors as well as metastasis is crucial. To meet this end first generation gen microarrays were performed on tumor derived cell lines of the primary tumor as well as four liver metastasis of UM patient 270. The history of UM patient 270 and establishment of respective cell lines Mel270 (primary; eye) and OMM2.2, OMM2.3, OMM2.5, OMM2.6 (metastases; liver) were recently described in extensio (Jager et al. 2016). As summarized in figure 1.5 (A), UM Patient 270 was first diagnosed in 1977 with a small, slow growing primary UM that was untreated for the next 6 years. In 1983 the patient received scleral plaque 125I radiotherapy that controlled local tumor growth in the eye. In 1992 the patient suffered from reduced vision and a painful eye related to the previous radiotherapy. Ophthalmoscopic examination revealed evidence of tumor growth, and the eye was enucleated. Pathologically, the largest basal diameter of the tumor measured 10mm x 10mm x 12.7mm. Routine histopathological examination of primary tumor tissue sections revealed a mixed cell type. At this point cell line Mel270 was isolated from primary tumor tissue as described (Ksander et al. 1991). Three years later in 1995, the patient presented with abnormal liver function tests, and hepatic masses were observed on hepatic MRI. Laparoscopic biopsies confirmed the presence of multiple UM metastases histologically. At this point cell lines OMM2.2, OMM2.3, OMM2.5, OMM2.6 were isolated from 4 separate liver biopsy samples from multiple different hepatic sites as described (P. W. Chen et al. 1997). As shown in figure 1.5 (B) MDK was identified as one of the most prominently differentially expressed genes between autologous primary (Mel270) and metastatic uM cell lines from multiple different hepatic sites (OMM2.2, OMM2.3, OMM2.5, OMM2.6). Based on this findings, our hypothesis and aims for this study were established and explained in more detail in the following chapter.

14 1 Introduction

MDK MDK

MDK

Gene Gene code Accession # Fold change

Midkine MDK X55110 - 64.28

Figure 1.5 –Genearrayanalysisidentifiesmidkineinuvealmelanoma(UM).(A)UMpatient270timeline of disease events and (B) microarray intensity plots of comparisons between autologous primary (Mel270) and metastatic UM cell lines from multiple different hepatic sites (OMM2.2, OMM2.3, OMM2.5, OMM2.6) identifying MDK as one of the most prominently differentially expressed genes.

15 2Aimsofthisthesis

Rationale There is a need for new therapeutic targets as well as prognostic biomarkers helping to diagnose and treat patients with metastatic disease UM. Analysis of gene expression between primary and metastatic UM identiﬁed MDK as one of the most diﬀerentially expressed genes. In several malignancies, it has been shown that MDK acts as tumor growth factor with cytokine characteristic and that MDK promotes metastasis. In addition, evidence suggests MDK can be used as biomarker to predict disease progression. Therefore, in this thesis, we aimed to further study the expression and role of MDK in UM.

Hypothesis MDK promotes tumor survival, migration and therapeutic resistance in UM. In addition, may serve as biomarker for predicting UM progression.

Aims of this thesis To test our hypothesis following aims were studied. Aims of this thesis were to

• determine the MDK gene and protein expression levels in – UM primary cell lines – UM metastatic cell lines – UM patient tissue

• investigate regulatory mechanism of MDK-expression in UM cells

• identify the function of MDK in UM cells, speciﬁcally eﬀects on tumor cell survival and migration

• assess MDK-receptors expression on UM cells and MDK intracellular signaling

• determine if MDK is a biomarker in UM disease, speciﬁcally analyses of MDK gene expression and its correlations with clinical, pathological and molecular characteristics of UM TCGA cases

16 3Results

3.1 MDK qPCR primer validation

MDK primers, see table 3.1 were designed as described in the material and methods chapter 6.9. Three diﬀerent MDK primers (MDKa, MDKb and MDKc) were chosen amplifying amplicons of 367bp, 172bp and 101bp respectively.

Primer Region Sequence (5’-3’) Amplicon (bp) MDKa forward a GAT AAG GTG AAG AAG GGC GGC 367 reverse a GGC TTG GCG TCT AGT CCT TT MDKb forward b AAG AAG GAG TTT GGA GCC GA 172 reverse b CCT TTG CTT TGG TCT TGG GG MDKc forward c CTC AGT GCC AGG AGA CCA TC 101 reverse c GCT TGG CGT CTA GTC CTT TC Table 3.1 –MDKqPCRprimer

Different transcript variants of MDK are predicted by RefSeq gene predictions from NCBI. In silico predictions of qPCR products of MDK primer a,b and c are visualized on top of the transcript variants (Fig. 3.2). MDK primers a, b and c all amplify all potential transcript variants. The specificity of the primers were validated by melting curve analysis and by visualizing the specific product amplification by gel electrophoresis. The amplification curves showed identical detection levels of MDK by all three primers and the melting curves show specific size dependent peaks (Fig. 3.1 A). The DNA gel validation shows specific amplification of qPCR products with the predicted size of all three primers (Fig. 3.1 B).

A B bp amplification curve melting curve 1,350 916 766 H2O H2O H2O 500 450 MDK a MDK a MDK b MDK b MDK c MDK c 400 367bp 350 300 250 200 172bp 150 101bp 100 50

Figure 3.1 – MDK qPCR primer validation. (A) Three MDK primer were tested in qPCR to amplify MDK (MDKa =red, MDKb= orange, MDKc= green, housekeeping gene 18S =grey). All three primers give the same amplification curve and all three MDK primers have specific melting curves based on the amplicon size. (B) All three primers amplify a specific MDK product with the predicted size.

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MDKa MDKb MDKc

Figure 3.2 –MDKtranscriptvariantsandqPCRprimerproducts

18 3 Results

3.2 MDK antibody validation

MDK belongs to the heparin-binding growth factors family together with one additional molecule called pleiotrophin (PTN). MDK and PTN are 46% homologous. To eliminate any potential cross-reactivity between the anti-MDK and anti-PTN antibodies recombinant MDK (200pg) and PTN (250pg) were analyzed using westernblot. The westernblot membrane was ﬁrst probed with anti-MDK antibody (Fig. 3.3 A), than the blot was stripped and probed with anti-PTN antibody (Fig. 3.3 B). Both antibodies recognized only their speciﬁc substrate and no cross- reactivity could be observed.

A B

20kD 20kD

15kD MDK 15kD PTN

Figure 3.3 –MDKantibodyvalidation.WesternblotanalysistodetectrecombinantMDK(200pg)andPTN (250pg) with (A) anti-MDK antibody or with (B) anti-PTN antibody.

3.3 MDK expression in uveal melanoma

3.3.1 MDK expression and secretion of primary and metastatic tumor cells derived from patient 270 Comparisons between autologous primary (Mel270) and metastatic UM cell lines from multiple different hepatic sites (OMM2.2, OMM2.3, OMM2.5, OMM2.6) identified MDK as one of the most prominently differentially expressed genes (Fig. 1.5 B). To validate these microarray data, we assessed MDK mRNA expression with quantitative PCR (Fig.3.4 A). MDK mRNA expression was highly significantly downregulated in all four liver metastasis in comparison to the primary UM. Additionally, we observed an average of 2.5 fold increase of MDK expression in the primary Mel270 cells compared to MDK mRNA expression in healthy melanocytes. Moreover, this differential expression could be observed by detecting MDK in the supernatant of cultured tumor cells making use of a commercially available ELISA kit (Fig. 3.4 B). The conditioned medium was collected from cells that were adjusted by passage and cell number. Furthermore, MDK protein level of total cell lysates was detected with westernblot analysis (Fig. 1.5 C). The total lysate levels of MDK were low and differential expression of MDK between the primary (Mel270) and the metastatic cells (OMM2.2-OMM2.6) was not very clear. However, when the conditioned medium was analysed by westerblot analysis (Fig. 3.4 D) high levels of secreted MDK was detected from the primary (Mel270) and no MDK secretion was detected from the metastatic cells. Overall, we could show overexpression of MDK levels in primary UM cells (Mel270) compared to non-malignant melanocytes and we could verify the previous findings that MDK is differentially expressed between the primary UM tumor cells and the metastatic cells of the same patient. Interestingly, we could see that the primary Mel270 tumor cell secrete MDK in high concentrations.

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A patient #270 B patient #270 ** *** 4 4 *** *** *** *** *** *** 3 3

2 2

1 ng/ml Midkine 1 (relative to melanocytes)

fold in/decrease MDK expression in/decrease fold 0 0

Mel270 Mel270 OMM2.2OMM2.3OMM2.5OMM2.6 OMM2.2OMM2.3OMM2.5OMM2.6 p m m m m p m m m m C D MDK MDK

ß-actin ß-actin

Figure 3.4 –ExpressionofMDKinuvealmelanomacelllinesofpatient270(primarytumorMel270,livermetas- tasis OMM2.2, OMM2.3, OMM2.5 and OMM2.6. (A) relative mRNA MDK compared to healthy melanocytes normalized to houskeeping gene (GAPDH). (C) The MDK concentrations in the lysat assessed by westernblot. MDK protein concentrations in supernatant assessed by (B) ELISA and (D) westernblot.

3.3.2 MDK expression and secretion of primary and metastatic uveal melanoma cell lines In addition to the MDK expression in cells derived from the primary tumor and metastasis of patient 270 we assessed the expression of MDK in six (four primary, two metastatic) uveal melanoma cell lines derived from six further UM patients (Fig. 3.5). MDK expression levels were again compared to MDK expression levels in non-malignant melanocytes. The mRNA expression levels of MDK were heterogenous with four cell lines overexpressing MDK and two downregulating MDK in comparison to healthy melanocytes (Fig. 3.5 A). The primary and metastatic cells were derived from distinct patients making a direct comparison of MDK expression difficult. Secretion and total lysat levels of MDK (Fig. 3.5 B,D) correspond highly to mRNA levels with the highest levels of secreted MDK from the primary cell line Mel202. To look at the distribution of MDK within the cell, Mel202 cells were cultured on a chamber slide and stained with anti- MDK primary antibody and a secondary antibody linked to an AlexaFluor488 fluorochrome. The immunofluorescence staining showed perinuclear and vesicular distribution of MDK within Mel202 cells (Fig. 3.5 C).

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A UM cell line panel B UM cell line panel 150 20 100 50 15 10 8 10 6

4 ng/ml Midkine 5 (relative to melanocytes) 2

fold in/decrease MDK expression fold in/decrease 0 0

H79 H79 Mel202OCM1OCM3OM431OMM1 Mel202OCM1OCM3OM431OMM1 p p p p m m Melanocytesp p p p m m

C D MDK DAPI MDK ß-actin

Figure 3.5 –ExpressionofMDKinuvealmelanomacelllines,primaryuvealmelanomacelllineMel202, OCM1, OCM3, OMM431 and metastatic cell lines OMM1, H79. (A) Relative mRNA MDK compared to healthy melanocytes normalized to housekeeping gene (GAPDH). (B) MDK protein concentrations in supernatant assessed by ELISA. (C) Immunoﬂuorescence staining of MDK in Mel202 cells. (D) MDK secreted protein concentrations assessed by westernblot.

3.3.3 MDK expression in human uveal melanoma tissue In a retrospective clinical series of paraffin-embedded sections (kindly provided by Prof. Heindl, Köln) of primary human UM samples tumor-associated expression of MDK was confirmed by immunofluorescence microscopy suggesting high, intermediate and low MDK-expressing tumors (Fig. 3.6).

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Hematoxilin & Eosin staining Immunofluorescence no zoom 2x 40x 2x 40x

2x 10x 2x 10x

Figure 3.6 –ExpressionofMDKinUMtissue.(A)ImmunohistologicalvisualisationofMDKexpressionin primary UM tissue. (green: MDK staining assessed with monoclonal anti-MDK antibody counterstained with secondary AlexaFluor488 antibody, blue: Dapi staining). (B) Corresponding hematoxilin and eosin stainings of eye tumors. On the left 2x magnification pictures with white boxes indicate position of further magnified in figures on the right.

22 3 Results

In primary UM tumor cells (UM) directly after isolation from fresh enucleated tumor-tissue, MDK overexpression relative to non-malignant melanocytes was confirmed by qRT-PCR (Fig. 3.7 A). MDK blood serum levels of four UM patients and six healthy donors were assessed by ELISA. We observed no significant difference between the MDK serum levels of the healthy donors (mean 86.63 65.10 pg/ml) and patients (mean 54.32 66.38 pg/ml) (Fig. 3.7 B). ± ± A B 1024 0.5

256 0.4

64 0.3 ns 16 0.2 ng/ml Midkine 4 serum blood in

(relative to melanocytes) 0.1 foldMDK increase expression 1 0.0 UM Mel202 healthy donors UM patients Figure 3.7 –ExpressionofMDKinUMpatients(A)MDKishighlyoverexpressedinUMprimarytumortissue and UM tumor cell line (Mel202) compared to healthy melanocytes. (B) MDK levels in blood serum detected by ELISA of healthy donors and UM patients (T-test, ns = not signiﬁcant).

3.4 MDK can be found in the soluble and exosome fraction

We were able to detect secreted MDK in the supernatant of UM cells (Fig. 3.4 B,D and Fig. 3.5 B). Therefore, we were interested in the mechanism by which MDK is secreted and transported outside the cell. To study if exosomes are involved we isolated exosomes from Mel202 supernatant with ultra centrifugation. Purity of the exosomes was assessed by ﬂow cytometry. The exosome fraction showed high expression of exosome speciﬁc markers (CD9, CD63 and CD81) in comparison to the control beads (Fig. 3.8 A). The exosome fraction and UM cell conditioned medium depleted of exosomes (soluble fraction) were further analyzed by immunoblotting (Fig. 3.8 (B). MDK was found as an exosome cargo of primary UM cells, albeit at lower levels compared to the soluble fraction. Figure 3.8 (C) shows the about 4 fold increase of MDK levels in the soluble fraction relative to the exosome fraction based on densitometry measurements of 2 independent immunoblots.

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B C 5 Mel202 4 MDK 3

CD81 2

exosome soluble increasefold midkine 1

fraction fraction fraction) exosome to (relative 0 soluble fraction

Figure 3.8 – (A) Flowcytometric assessment of the purified exosomes for expression of exosome specific markers CD63, CD9 and CD81. (B) Representative immunoblot analysis showing 5-fold higher MDK expression in the soluble, exosome depleted fraction compared to the purified CD81+ exosome fraction. (C) Densitometry of MDK expression in the exosome and soluble fraction. Values shown are the average (mean+ SD) of 2 independent immunoblots.

3.5 MDK is a membrane-bound protein in UM cells

We performed membrane biotinylation to precipitate membrane bound proteins only. Using this approach we were able to detect MDK expression in the membrane fraction (see ﬁg. 3.9).

membrane fractions rec. MDK non-biotinylated Exp1 Exp2 (pos) (neg) 20kD MDK 15kD

Figure 3.9 – MDK positive signal in the membrane fraction of Mel202 could be shown in two independent experiments (Exp1 and Exp2). Recombinant MDK and non-biotinylated lysate was used as positive control and negative control respectively.

24 3 Results

3.6 Regulation of MDK gene expression

3.6.1 Retinoic acid induces MDK expression in UM cell and healthy melanocytes All-trans-retinoic acid (ATRA) is a physiologically active metabolite of vitamine A, that plays a role in numerous complex physiological processes in vertebrates. Specifically, ATRA is involved in fundamental aspects of organ and limp development by regulating the expression of a large number of target genes through a family of retinoic acid receptors (RAR). The transcription of genes is controlled by binding of retinoic acid to RAR-retinoid X receptor (RXR) heterodimers complex and additional binding of co-activators. The whole complex binds to retinoic response elements (RAREs) near target genes. MDK has been previously described as a retinoic acid responsive gene (Kadomatsu et al. 1988; Pedraza et al. 1995). MDK was identified during the search for new regulators of differentiation whose expression is controlled by retinoic acid. The responsiveness of MDK to ATRA was previously shown by fusing the 2.3-kb upstream region of MDK with a CAT gene. After transfection into F9 EC cells, CAT activity was higher in transfected cells treated with ATRA compared to untreated cells (Pedraza et al. 1995). The RARE sequence typically contain hexameric direct repeats (DRs) - (A/G)G(T/G)TCA with interspacing of 5bp (DR5 elements) or 2bp (DR2 elements) (Cunningham and Duester 2015). The 5’ upstream region of MDK contains a DR5 RARE-like element (Fig: 3.10 A). The DR5 RARE-like element is highlighted in yellow. MDK exons are higlighted in light blue marks and orange highlights the translation starting sequence with ATG marking the translational startcodon. Fig: 3.10 B shows a simplified RXR-RAR complex, binding to the RARE-like element in the 5’UTR of MDK. To study the effect of ATRA on MDK expression in UM cells, we incubated four UM cell lines and non-malignant melanocytes with increasing concentration of 1nM-10µM ATRA for three days. MDK mRNA expression was assessed with qPCR and normed to dimethylsulfoxid (DMSO) control (DMSO concentration was the same as the highest concentration ATRA used). All UM cells and the melanocytes showed dose-dependent increase of MDK expression (Fig: 3.10 C-F) with the strongest fold increase in non-malignant melanocytes.

3.6.2 DNA methylation influences MDK expression in UM cells Treatment with 5’Aza-2-deoxycytidine (5’Aza) increases MDK expression in UM cells Localized DNA methylation of cytosines has been associated with gene silencing. DNA demethylation can be induced with 5’Aza-2-deoxycytidine (5’Aza). The effect of DNA methylation on MDK expression in UM cells has not been studied earlier. However, a previous study showed that rat chondrosarcoma cells increased invasiveness when treated with 5’Aza accompanied by increased, but reversible expression levels of sox2 and MDK (Hamm et al. 2009). This study suggested that MDK expression may be regulated by DNA methylation. To test if DNA methylation influences MDK expression in UM cells, we treated our cells with 10µM 5’Aza for four days. MDK expression levels were assessed with qPCR. The results of the 5’Aza treatment are shown in figure 3.11 (B). We treated two primary UM cell lines, Mel202 (high MDK expressing) and OCM1 (low MDK expressing) as well as the two cell lines derived from patient 270, the primary Mel270 (high MDK expressing) and the metastatic OMM2.3 (low MDK expressing) cell lines with 5’Aza. Fold change of MDK expression was assessed by qPCR and shown relative to MDK expression in the control cells (DMSO treated). In Mel202 a mean fold increase of 3.25 was detected, whereas a mean fold increase of 180 could be detected in OCM1. In Mel270 a mean fold increase of 6,63 and a higher mean fold increase of 26,7 was measured in OMM2.3. The low MDK expressing cells showed a higher sensitivity to 5’Aza treatment than the already high MDK expressing cells. This may be due to the methylation status of the MDK promotor, because high MDK expressing cells may likely have less DNA methylation in the MDK promoter region in comparison to low MDK expressing cells (Fig. 3.11). Therefore, 5’Aza treatment and the resulting demethylation would be expected to have lower effects in higher MDK expressing

25 3 Results

B C D

Melanocytes Mel270 25 10

20 8

all-trans retinoic acid 15 6 (ATRA) 10 4

5 2 (relative to DMSO to control) (relative (relative to DMSO control) to (relative fold in/decreasefold MDK expression 0 MDKexpression in/decrease fold 0 midkine UTC 1nM 100nM 1µM 10µM UTC 1nM 100nM 1µM 10µM retinoic acid retinoic acid E F G Mel202 OCM1 OMM2.3 10 10 10

8 8 8

6 6 6

4 4 4

2 2 2 (relative to DMSO to control) (relative (relative to DMSO to control) (relative DMSO to control) (relative fold in/decreasefold MDK expression 0 in/decreasefold MDK expression 0 in/decreasefold MDK expression 0 UTC 1nM 100nM 1µM 10µM UTC 1nM 100nM 1µM 10µM UTC 1nM 100nM 1µM 10µM retinoic acid retinoic acid retinoic acid

Figure 3.10 –Inﬂuenceofall-transretinoicacid(ATRA)onMDKexpressionlevelsinUMcells:(A)5’UTR region of MDK, yellow = retinoic acid responsive element (RARE), which allows binding of retinoid receptors, lightblue = exon sequences, red=translation start. (B) Structur formula of all-trans retinoic acid and schematic drawing of retinoic acid binding to retinoic acid response elements. (C) UM and non-malignant melanocytes were treated with 1nM - 10µM all trans retinoic acid (ATRA) and fold increase levels of MDK mRNA assessed with qPCR cells than in lower MDK expressing cells. To validate this hypothesis we used bisulﬁte conversion, to assess the actual DNA methylation status of the MDK high expressing cell line Mel202 and the low MDK expressing cell line OCM1.

DNA methylation rate of promoter region of MDK shows correlation with MDK expression To study the DNA methylation status of the promoter region of the MDK high expressing cell line Mel202 and the low MDK expressing cell line OCM1 we used DNA bisulfite conversion followed by sequencing analysis. Incubation of DNA with sodium bisulfite results in conversion of all unmodified cytosines to uracils while leaving the methylated cytosines intact. We used this mechanism to study the methylation pattern of Mel202 and OCM1 cells. The bisulfite conversion and bisulfite PCR was done according to the manufacturers’ instructions and as described in chapter 6.10.3. The bisulfite converted DNA was amplified with bisulfite specific primers and

26 3 Results

A 5'aza-2'-deoxycytidine globally B 5'aza-2'-deoxycytidine treatment demethylates DNA 450 patient #270 300 40 150

10 30 8 20 6 4 10

(relative to DMSOcontrol) to (relative 2 fold increase MDK expression MDK increase fold (relative to DMSOcontrol) to (relative

0 expression MDK increase fold Mel202 OCM1 0 Mel270 OMM2.3 5' Aza-2' deoxycytidine [10µM] 5' Aza-2' deoxycytidine [10µM]

Figure 3.11 – MDK expression following demethylation treatment. (A) Schematic model to illustrate demethylation effect of 5’Aza-2-deoxycytidine (5’Aza). High DNA methylation suppresses gene expression, 5’Aza is an inhibitor of DNA methylation and allows gene expression of originally high methylated genes. (B) Mel202 and OCM1, MDK high and low expressing cells respectively were treated with 10µM5’Aza.FoldincreaseofMDKex- pression was assessed by quantitative PCR. 5’Aza treatment induces MDK expression in all UM cell lines. MDK expression is higher induced in low MDK expressing cells (OCM1 and OMM2.3) than in high MDK expressing cells (Mel202 and Mel270) sequences were compared with untreated DNA. The results are shown in Fig: 3.12.PartAofthe figure shows the overlay of 1. reference sequence (https://www.ncbi.nlm.nih.gov/gene/4192), 2. bisulfite converted reference sequence with all cytosines in CpGs converted, 3. bisulfite converted DNA of Mel202 and 4. bisulfite converted DNA of OCM1. The bisulfite conversions of OCM1 (line 4) and Mel202 (line 3) DNA were successful, since all unmethylated cytosines (cytosines not followed by a guanine within a CpG context), were converted to thymines (see grey marked bases). Interestingly, all potentially methylated cytosines (cytosines followed by a guanine), in the Mel202 sequence were not methylated and converted to thymine. However, all potentially methylated cytosines within the OCM1 sequence were methylated and therefore protected from conversion and stayed cytosines. The black boxed sequence in Fig: 3.12 A is shown as a sanger sequence in Fig: 3.12 B and put in direct comparison to the sequence of untreated DNA. In both, Mel202 and OCM1 untreated DNA, the unmethylated cytosines are marked with a grey triangle and the potentially methylated cytosine with a yellow triangle. Clearly, in the bisulfite converted DNA of Mel202 no cytosine signal, no blue peak is visible detected and all cytosines were converted to thymines (red triangle). In contrast, in the bisulfite converted DNA of OCM1 cells only the cytosines that could not be methylated were converted (red triangle) and all other cytosines were methylated and therefore protected (blue triangles). In summary, treatment with 5’Aza, a DNA methylation inhibitor increased MDK expression significantly higher in lower MDK expressing UM cells than in higher MDK expressing cells. Additionally, we could show with bisulfite conversion DNA sequencing that at least parts of the promoter region of MDK are highly methylated in cells with lower MDK expression levels compared to cells with higher expression levels. Taken together these results indicate that DNA methylation contributes to regulate MDK expression in UM cells.

27 3 Results

1 GCCGATCTAGGGGCTGGGGGCTGGAGGCGGGGGTGGGGGTCTGAGCTGCGTCCTGGGCTCGAGGCGTCCCCCGGGGAGTCGCCTCTTAGCGGTGCGTCCGGGCTAGCGGCGAGGGGCC 2 GTCGATTTAGGGGTTGGGGGTTGGAGGCGGGGGTGGGGGTTTGAGTTGCGTTTTGGGTTCGAGGCGTTTTTCGGGGAGTCGTTTTTTAGCGGTGCGTTCGGGTTAGCGGCGAGGGGTC 3 ------GTttGGgTTTtganTgTTTTTTGGGGAGTTGTTTTTTAGTGGTGTGTTTGGGTTAGTGGTGAGGGGTT 4 ------GGggagTCGTTTTtnngCGgtgCGTTcGGgttaGCGGCGAGGGGTC

1 GCCCCAAGTCTTCCCACCGCCGCCACCTTAGCAGCCCGACTTGGGGCCTGGAAAGTGGAGCACGCGGAGGTGGGAGGGCCCTGCACGCGGCCCCCGGTGGGGAAGGGGACGGGCCAGGG 2 GTTTTAAGTTTTTTTATCGTCGTTATTTTAGTAGTTCGATTTGGGGTTTGGAAAGTGGAGTACGCGGAGGTGGGAGGGTTTTGTACGCGGTTTTCGGTGGGGAAGGGGACGGGTTAGGG 3 GTTttaagTTTTTttaTTGTTGTTATTttngtaGTTTgATTTGGGGTttGgAAAGTGGagtATGTGGAggtGGgaGGgTTTtgTATGTGGTTTTTGgTGGGGaaGGggaTGgnttAGGg 4 GTTttAAGTTTTTTtaTCGTCGttaTTttagtAGTTCGATTTGGGGTTTGGAAAGTGGAGtAcGCGgaGgTGGGaGGGTTTTGTACGCGGTTTTCGgtGGGgAAGGGgaCGGGTTAGGG

1 ATTCAGACTCGGGCTCTCCCCTCAGGATGCAGCACCGAGGCTTCCTCCTCCTCACCCTCCTCGCCCTGCTGGCGCTCACCTCCGCGGTCGCCAAAAAGAAAGGTGATGGGGGATGATCG 2 ATTTAGATTCGGGTTTTTTTTTTAGGATGTAGTATCGAGGTTTTTTTTTTTTTATTTTTTTCGTTTTGTTGGCGTTTATTTTCGCGGTCGTTAAAAAGAAAGGTGATGGGGGATGATCG 3 aTTTAgATTtggTTTTTTTttttnng------4 ATTTaGATTCGGGTTTTTTTTTtaGGATgTannAtcgaGgTTTTTTTTTTTTTTTTTTt------

B original DNA – Mel202 bisulfite converted DNA – Mel202

ACGCGGAGGTGGGAGGGCCCTGCACGCGGCCCCCG ATGTGGAGGTGGGAGGGTTTTGTATGTGGTTTTTG

original DNA – OCM1 bisulfite converted DNA – OCM1

ACGCGGAGGTGGGAGGGCCCTGCACGCGGCCCCCG ACGCGGAGGTGGGAGGGTTTTGTACGCGGTTTTCG

potentially methylated C (CpG) methylated C (CpG) C

unmethylated C unmethylated C T

Figure 3.12 – Excerpt of bisulfite-modified DNA sequences from UM cell lines showing methylated and unmethylated sequences of the MDK promoter. All unmethylated cytosines (red triangles) are converted by the bisulfite modification step to thymidine, whereas, all methylated 5-methylcytosines are resistant to modification and remain cytosines (blue triangles).

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3.6.3 Hypoxic conditions induce MDK expression Tumors often have hypoxic conditions and oxygen supply is frequently disrupted. Hypoxia- inducible factor 1-↵ (HIF1-↵) regulates expression of more than 60 genes that are involved in biological processes such as angiogenesis and oxygen transport to hypoxic regions. Stabilization of HIF factors is highly correlated with hypoxia, therefore detecting HIF is routinely used to screen for hypoxia. Interestingly, hypoxia has been previously, described as a regulator of MDK expression in lung tissue of hypoxia-susceptible mice (Reynolds et al. 2004). To study the effect of hypoxic conditions on MDK expression in UM cells we made use of CoCl2, a stabilizer of HIF1-↵ (Kanaya and Kamitani 2003). We treated OCM1 cells with increasing concentrations of CoCl2 (0- 400µM) and assessed the viability. The viability of the OCM1 cells is significantly decreasing with the highest two doses (200µMand400µM) of CoCl2 (Fig: 3.13 A). HIF1-↵, measured with flow cytometry, increased correspondingly to increasing CoCl2 concentrations (Fig: 3.13 B). Figure 3.13 (D) shows representative flow cytometry dot blots illustrating increasing HIF1-↵ expression coinciding with increasing CoCl2 concentrations. A dip in the dose-dependent increase of HIF1-↵ can be observed with the highest CoCl2. However, the significantly reduction in viability of the cells when treated with 400µM CoCl2 is likely to be the reason for the lower HIF1-↵ expression. Therefore, to assess the MDK expression with qPCR only cells treated with CoCl2 of 0-200µM were used. As seen in figure 3.13 (C) increased MDK expression correlated with increased HIF1- ↵ and increasing concentration CoCl2, modelling increasing hypoxic conditions. In summary, treatment of OCM1 cells with hypoxia inducing CoCl2 leads to dose-dependent stabilization of HIF1-↵ and dose-dependent increase of MDK expression. Therefore, we could show that in OCM1 cells hypoxia is a regulatory mechanism to induce MDK expression.

A viability B percentage HIF1-α C MDK expression 1.2 60 8

1.0

0.8 40 4

0.6 viability 0.4 HIF- % 20 2

0.2 (normedcontrol)toUTC fold increase MDK expressionfold increase 0.0 0 1 UTC 25 50 100 150 200 400 UTC 25 50 100 150 200 400 UTC 25 50 100 150 200 concentration CoCl 2 [µM] concentration CoCl2 [µM] concentration CoCl2 [µM]

D concentration CoCl2 [µM]

UTC 25 50 100 150 200 400

HIF-1α Figure 3.13 –HypoxicconditionsinduceMDKexpression.OCM1cellsweretreatedwithincreasingconcen- trations CoCl2,acompoundthatinduceshypoxicconditionsincells.(A)ViabilityassessedwithOneSolution Cell Proliferation Assay (MTS). (B) HIF1-↵ percentage assessed with ﬂow cytometry as a marker for hypoxia and corresponding (C) MDK expression assessed with qPCR. (D) Representative ﬂow cytometry blots showing increasing HIF-1↵ positive cells with increasing concentration CoCl2.

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3.7 Downregulation of MDK expression using RNA interference

RNA interference (RNAi) is a commonly used tool to specifically target a particular mRNA for degradation by making use of synthetic short interfering RNA (siRNA) duplexes. The leading strand of the 19-23 basepair long siRNA molecules is complementary with the target messenger RNA and causes specific downregulation of the expression of the target gene. This method is widely used in knock-down libraries and to study effects of gene expression. To investigate the function of MDK expression in UM we applied the RNAi method using two specifically designed siRNAs targeting MDK. The first siRNA (siMDK1) has its complementary target sequence in exon 3 and the second siRNA (siMDK2) targets MDK on exon 5 at the C-terminus (Fig. 3.14 A). Furthermore, a scrambled siRNA (siNC) with no specific target was used as a transfection control. To assess the transfection efficiency an additional siRNA linked to an AlexaFluor647 fluorochrome (siAF647) was used. Cotransfection of siNC, siMDK1 and siMDK2 with siAF647 allows flow cytometric assessment of transfection efficiencies of the siRNAs. The results are shown in figure 3.14 (B) and (C). Flow cytometric analysis showed nearly 100% transfection efficiencies of all three siRNAs. No differences in the transfection efficiency were observed. Since the transfection efficiency was equal among the siRNA constructs we were able to assess specific downregulation of MDK using these siRNAs.

A siMDK2 siAF647 siNC siMDK1

MDK

Transfection efficiency ns B ns ns C 100 Mel202

80 siMDK2 + siAF647 60 count

cell siMDK1 + siAF647 40 siNC + siAF647

%transfected cells 20 UTC 0 siAF647 UTC siNC siMDK1siMDK2 Figure 3.14 – siRNA transfection efficiency. (A) Model of MDK gene with target areas for siRNAs downregulating MDK expression (siMDK1, siMDK2). A scrambled siRNA without target is used as negative controle (siNC) and cotransfection with scrambled siRNA labeled with AlexaFluor647 fluorochrome was used to assess the transfection efficiency of all three siRNAs (siNC, siMDK1 and siMDK2). (B) Cotransfections with fluorochrome labeled siRNA resulted in almost 100% transfection efficiencies and no efficiency difference between the different siRNAs was observed. (C) Representative stagged histogram of cotransfected cells showing identical high transfection efficiencies

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To assess MDK downregulation, 24h after seeding 1 105 Mel202 cells in 24 well plates, cells ⇥ were transfected with either siMDK1, siMDK2 or siNC . Three days after transfection the cells and conditioned medium were analysed with qPCR, ELISA, and westernblot. We could show efficient downregulation for MDK expression in Mel202 cells treated with siMDK1 and siMDK2 (Fig. 3.15 A) as well as significant reduction of secreted MDK levels in the supernatant with ELISA (Fig. 3.15 B) and by visualising MDK in the supernatant with westernblot analysis (Fig. 3.15 C) . Additionally we detected specific MDK protein bands in untreated and mock transfected Mel202 cells, whereas significant less MDK protein was detected in the lysate of siMDK1 and siMDK2 (Fig. 3.15 D).

A MDK mRNA expression B MDK secretion 6 1.2 *** *** *** *** 1.0

0.8 4

0.6

0.4 MDK ng/ml MDKexpression (normedsiNC)to 0.2

0.0 0

UTC siNC UTC siNC siMDK1siMDK2 siMDK1siMDK2 C D MDK MDK

ß-actin ß-actin UTC siNC siMDK1 siMDK2 UTC siNC siMDK1 siMDK2

Figure 3.15 –DownregulationofMDKexpressioninMel202makinguseofRNAi.Allshownresultswere measured three days after transfection. Signiﬁcant downregulation of (A) MDK mRNA expression, (B) protein levels in supernatant assessed with ELISA, (D) protein levels in supernatant visualized with westernblot and (C) protein levels in lysate.

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3.8 Generation of cell lines with stable and long term expression of MDK

With the RNAi technology we were able to downregulate MDK in UM cells with high MDK expression to study the effect of reduced concentrations of MDK. In contrast to the downregulation we were also interested to study the effects of overexpression of MDK in UM cells with very low MDK expression. By direct integration of MDK expressing plasmid into the target cells genome, stable and long-term expression of MDK can be achieved. Our analysis of MDK expression in UM cell lines showed that many cell lines overexpress MDK (Fig.3.5). However, some of the cell lines including OCM1 express MDK at low levels. To study the effects of MDK expressions on originally low expression UM cells we therefore transfected the OCM1 cells with MDK expressing plasmid as described in this section.

3.8.1 Creation of a MDK expression plasmid To transfect UM cells an expression plasmid containing full length MDK coding sequence was required. Therefore, the 445bp MDK coding sequence was amplified via PCR using specific primers (described in more detail in section 6.19.1 (Kerzerho et al. 2010)). Mel202 cDNA was generated as described in sections 6.7 and 6.8. The annealing temperatures predicted with UCSC In-Silico PCR (https://genome.ucsc.edu/cgi-bin/hgPcr) were 82 and 73 for the forward and reverse primer respectively. Therefore, the PCR was performed simultaneously with four different annealing temperatures (60,65,70,75). The primers included the added EcoRI and XhoI restrictions sides (Fig. 3.16 B). Fig. 3.16 (C) shows the specific 471bp (445bp MDK coding sequence and 27bp added restriction sites) product from the PCR with annealing temperatures 60,65 and 70. Annealing temperature of 70 showed reduced product yield compared to the lower temperatures and annealing temperature of 75 resulted in no specific product. For further steps only PCR products of the PCR with annealing temperature of 60 and 65 were used. The PCR was performed in duplicates. One duplicate was used for the gel electrophoresis analysis the second duplicate was purified. The concentrations of the cleaned up PCR products were assessed with Nanodrop (MDK cDNA 60:26.5ng/µl; MDK cDNA 65:27.3ng/µl) and products were digested with XhoI and EcoRI. Subsequently, all digested products were analyzed by gel electrophoresis (Fig. 3.16 D). The digested samples were purified and ligated with pcDNA3.1 plasmid. One shot TOP10 chemically competent E.coli were used to amplify the plasmids. Xho1 and EcoRI restriction digestion of plasmid DNA was performed to screen for MDK containing clones (Fig. 3.17 A). Four clones revealed the two expected bands at 5.428 kb (pcDNA3.1 vector) and 445 bp (MDK). All four clones (60-1, 60-2, 65-2 and 65-3) were sequenced in order to verify correct and mutation free insertion of MDK cDNA into the plasmid (Fig. 3.17 B). The sequences were compared to the MDK reference sequence. All four clones had a silent point-mutation, a CTG codon was changed to CTA (both codons code for leucine). No amino acid changes would result of this nucleotide change. The rest of the sequences of clones 60-1, 60-2 and 65-3 were completely complementary with the control sequence. Clone 65-2 had a mutation that would lead to an exchange of serine with threonine and was therefore excluded from further experiments. For all further experiments pcDNA3.1-MDK clone 60-1 was used.

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A MDK primer including restriction sites for EcoRI and XhoI enzymes EcoRI Upstream primer: 5’ GTGGTGGAATTCACCACCATGCAGCACCGAGGCTTCCTC 3’ XhoI Downstream primer: 5’ AAACTCGAGCCAGGCTTGGCGTCTAGTC 3'

B C MDK- cDNA

midkine cDNA C C C C

ATGCAGCACCGAGGCTTCCTCCTCCTCACCCTCCTCGCCCTGCTGGCGCTCACCT ° ° ° ° CCGCGGTCGCCAAAAAGAAAGATAAGGTGAAGAAGGGCGGCCCGGGGAGCGA GTGCGCTGAGTGGGCCTGGGGGCCCTGCACCCCCAGCAGCAAGGATTGCGGC 60 65 70 75 GTGGGTTTCCGCGAGGGCACCTGCGGGGCCCAGACCCAGCGCATCCGGTGCA GGGTGCCCTGCAACTGGAAGAAGGAGTTTGGAGCCGACTGCAAGTACAAGTT TGAGAACTGGGGTGCGTGTGATGGGGGCACAGGCACCAAAGTCCGCCAAGGC ACCCTGAAGAAGGCGCGCTACAATGCTCAGTGCCAGGAGACCATCCGCGTCAC 500bp CAAGCCCTGCACCCCCAAGACCAAAGCAAAGGCCAAAGCCAAGAAAGGGAAGG GAAAGGACTAGACGCCAAGCCTGG

D inserts and plasmid treated with restriction enzymes ld 1 2 3 4 ld

5kbp MDK

MDK MDK C C ° ° 60 65

wt mock MDK 500bp

Figure 3.16 –CreationofMDKexpressionplasmid.(A)SequencesofMDKprimerincludingrestrictionsites for EcoRI (light blue) and XhoI (green). Purple highlighted sequences indicate overlapping sequences with MDK cDNA. (B) Schematic overview how to create stable transfected UM tumor cells overexpressing MDK, making use of amplified MDK cDNA with primers shown in (A), ligated into the pcDNA3.1 plasmid. Transfected cells were selected with neomycin antibiotics resistance transferred with the plasmid. (C) Full length 471bp MDK coding sequence, including linked EcoRI and XhoI restriction sites, was amplified by PCR with annealing temperatures 60,65 and 70.CompleteMDKPCRproductwasseparatedbya1%-agarosegel.Nospecificproduct was produced in PCR with 75 annealing temperatures. (D) Prior to ligation, the pcDNA plasmid (lane 1) and MDK cDNA (lanes 2 and 4) were restricted with EcoRI and XhoI restriction enzymes to create fitting sticky ends. Lane 4 shows the unrestricted pcDNA plasmid as a control.

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A MDK-pcDNA3.1 clones

1 2 - - 60 60

1000bp 1000bp

500bp 500bp

B sequences of MDK-pcDNA3.1 clones Seq ID Clone 60-1 Clone 60-2 Clone 65-2 Clone 65-3 midkine seq consensus silent point-mutation CTA Leucine CTG Leucine Seq ID Clone 60-1 Clone 60-2 Clone 65-2 Clone 65-3 midkine seq consensus mutation in aa sequence AGC Serine ACC Threonine Figure 3.17 – MDK-pcDNA3.1 digested clones. (A) EcoRI and XhoI restriction digestion of pcDNA-3.1-MDK clones after minipreparation to screen for MDK positive clones. Sequences from clones 60-1, 60-2, 65-2 and 65-3 were checked by sequencing. (B) Sequencing analysis showed that all four clones had a silent point mutation compared to the MDK reference sequence. Additionally clone 65-2 had a G-C mutation that would lead to amino acid exchange from serine to threonine and was therefore excluded for further experiments. For all further experiments pcDNA3.1-MDK clone 60-1 was used.

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3.8.2 Determination of G418 concentration Stably-transfected cells can be selected by the addition of drugs to the culture medium, if the expression plasmid carries a drug resistance gene. The pcDNA3.1 plasmid carries a neomycin resistance, which uses resistance to G418 as an antibiotic selection marker. Cells differ in their susceptibility to G418. To determine the necessary concentration of G418 to select only transfected cells, we treated OCM1 cells with increasing concentration of G418. 1 105 cells were ⇥ seeded in 24-well plates and 0.2mg - 1mg G418 per ml was added to the culture medium. Cells were cultured for 7 days and every second day fresh selection medium was substituted. The viability of the cells was measured with the one solution cell proliferation assay as described in section 6.23. Results are shown in Fig. 3.18 (A). The number of living cells decreased dose- dependently to increasing concentration G418. In addition, pictures taken of the cell culture clearly confirm the effects of the increasing concentration of G418 on the viability of OCM1 cells (Fig. 3.18 B). 0.6 mg/ml G418 was used to select for transfected OCM1-mock and OCM1-MDK cells. G418 titration

A B 0mg/ml 0,2mg/ml 0,4mg/ml 1.2

1.0

0.8

0.6

0.4 0,6mg/ml 0,8mg/ml 1mg/ml

absorbance(490nm) 0.2

0.0 0 0,2 0,4 0,6 0,8 1 G418 concentration [mg/ml]

Figure 3.18 – Determination of G418 concentration for drug selection of plasmid transfected OCM1 cells: OCM1 cells treated with 0.2mg/ml - 1mg/ml G418 antibiotics. (A) Cell viability assessed with one solution cell proliferation assay. Absorbance measured at 490nm. (B) Representative pictures of OCM1 cells treated with G418 on day 7.

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3.8.3 Stable overexpression of MDK in OCM1 Stable transfected OCM1 cells that acquired resistance to neomycin (G418) were further analysed by ELISA and qPCR to assess the MDK expression levels. Up to 600 fold increase of MDK mRNA was detected in OCM1-MDK cells compared to OCM1-mock and untransfected (OCM1- wt) cells (Fig. 3.19 A). Additionally, signiﬁcantly higher protein levels of MDK were detected in the supernatant of OCM1-MDK cells compared to OCM1-mock and OCM1-wt cells (Fig.3.19 B). In summary, we were able to create stable MDK expression in OCM1 cells by transfecting the cells with MDK expressing vector.

A MDK mRNA expression B MDK secretion ** 1000 ** 8 *** *** 500 6 10

8 4 6 ng/ml Midkine ng/ml

MDKexpression 4

(normed to mock) to (normed 2 2 0 0

wt wt mock MDK mock MDK

Figure 3.19 –MDKoverexpressionininOCM1cellline.(A)MDKmRNAexpressionand(B)secretion levels are signiﬁcantly increased in pcDNA3.1_MDK (OCM1-MDK) plasmid transfected cells compared to pcDNA3.1_empty (OCM1-mock) and non-transfected OCM1 wildtype (OCM1-wt) cells.

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3.9 Cellular function of MDK

3.9.1 Downregulation of MDK reduces proliferation and viability of UM cells To study the functional effects of loss of MDK-function we looked at the proliferation capability of siMDK transfected Mel202 cells. We made use of a cell proliferation MTS assay based on a colometric method to determine number of living cells. The assay is based on the reduction of the tetrazolium salt MTS to water-soluble formazan dye by metabolically active cells. The absorbance of formazan was measured at 490 nm and is directly proportional to the number of living cells in culture. Three days after transfection absorbance at 490 nm was measured and results are shown in figure 3.20 (A). Lipofectamine transfection with siRNAs reduces the proliferation of Mel202 as seen by the significant reduction in the cells transfected with negative control siNC. However, compared to the siNC control, the specific downregulation of MDK reduces the proliferation significantly. In addition, the viability of the UM cells was determined using the CellTiter-FluortmCell Viability Assay. The assay reagent contains the fluorogenic cell-permeable glycylphenylalanyl-aminofluorocoumarin (GF-AFC) peptide substrate, which is converted intracellularly into the fluorescent product AFC. The fluorescence signal generated is proportional to cell viability. The results are shown in figure 3.20 (B). The measurements show reduced viability when cells are treated with the siRNAs downregulating MDK. Taken together, downregulation of MDK reduces proliferation and viability, suggesting MDK expression is advantageous of UM cells.

A Proliferation B Viability 150 ** 150

* ns ** * 100 100

50 50 viability(%ofsiNC) proliferation(%ofsiNC) 0 0

UTC siNC UTC siNC siMDK1siMDK2 siMDK1siMDK2

Figure 3.20 – MDK downregulation with specific siRNAs reduces proliferation and viability of UM cells. (A) Proliferation rate, based on absorbtion measurements of formazan converted from MTS and (B) cell viability, based on CellTiter-FluortmCell Viability Assay is significantly reduced in cells treated with specific siRNAs downregulating MDK

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3.9.2 Downregulation of MDK increases apoptosis by increasing caspase3/7 activity The effect of MDK to protect tumor cells from apoptosis has been reported in a multitude of studies (Q. Wang et al. 2007; Yazihan et al. 2008; D. Zhang et al. 2017). Apoptosis is a process of programmed cell death and is a highly orchestrated and regulated process. Cell death is induced by activating caspases, which are enzyms degrading proteins. Apoptosis can be measured indirectly by assessing the activity of primary effector caspases 3/7. We used the Caspase-Glo® 3/7 Assay to investigate the effect of MDK on the apoptotic rate of UM cells. The assay is based on the cleavage capability of a proluminescent substrate for caspase 3 and 7, resulting in the release of luminescent aminoluciferin. The amount of luminescence correlates with the caspases activity. The results are shown in figure 3.21 (A). The caspase3/7 activities were normalized to the negative control and were significantly higher in transfected cells in general compared to the untreated cells. However, increased caspase 3/7 activites were observed when cells were treated with siRNAs specifically downregulating MDK compared to the mock transfected cells. Figure 3.21 (B) shows similar findings with increased cleaved caspase 3 levels in cells transfected with siRNAs targeting MDK measured by westernblot analysis of intracellular signaling proteins (Path Scan Intracellular Signaling Array). Caspase-3 is activated by endoproteolytic cleavage at Asp175 and exerts its pro-apoptotic activity through cleavage of multiple cellular targets. Therefore, cleaved caspase 3 is a well known marker for cells undergoing apoptosis. The mean chemiluminescence intensity and therefore, the amount of cleaved caspase 3 was increased in the lysats produced from the cells treated with siRNAs specifically downregulating MDK. Overall, the results from the caspase 3/7 activity assay as well as the path scan intracellular signaling array kit showed increased apoptosis in UM cells with downregulated MDK, suggesting a protective, anti-apoptotic role of MDK in UM cells.

A caspase-3/7 activity assay B Path Scan Intracellular Signaling Array Kit * 1.5 ** 41006

*** 31006 1.0

21006

0.5 Caspase3 11006 (normed to siNC)(normedto Caspase-3/7activity

0.0 luminiscence mean intensity 0

UTC siNC UTC siNC siMDK1siMDK2 siMDK1siMDK2 Figure 3.21 –MDKdownregulationwithspeciﬁcsiRNAsincreasescaspase3/7activity.(A)Thecaspase3/7 activity and (B) levels of cleaved caspase is increased in UM cell with downregulated MDK expression.

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3.9.3 MDK promotes migration of UM cells Several previously published papers have shown that MDK induces migratory capabilities of tumor cells (Y. Huang et al. 2008; Rawnaq et al. 2014; Erdogan et al. 2017) and immune cells (K. Hayashi et al. 2001; Weckbach et al. 2011). Here, we were interested in the effect of MDK on migration behavior of UM cells. To study the influence of MDK on migration we made use of transwell cell culture inserts. The inserts divide the lower and upper part of the well, however 8µm pores allow cell migration. We compared the migratory behavior of OCM1-wt, OCM1- mock and OCM1-MDK cells. The results are shown in figure 3.22. Significant higher number of OCM1-MDK cells (overexpression of MDK) migrated as compared to the OCM1-wt and the OCM1-mock cells. Figure 3.22 (B) shows representative pictures of migrated cells.

Transwell Assay - Cell migration

A 200 B * * 150

OCM1-wt OCM1-mock OCM1-MDK 100

50 number ofmigratedcells number

wt mock MDK

Figure 3.22 –MDKpromotesmigrationofUMcells.(A)ThemigrationabilitiesofOCM1-wt,OCM1-mock and OCM1-MDK cells were determined using transwell chambers. The number of migrated cells on the lower surface of the inserts were stained with DAPI and five microscopic fields (10x) were counted for each group using image j. The results presented are averages of cells counted on five microscopic fields. Data shown represent the means plus standard error of the mean of three independent experiments. (B) Representative pictures of DAPI stained migrated cells.

3.10 Mechanism and signaling pathways

Our results described in chapter 3.9 show that MDK acts as a growth factor with anti-apoptotic and pro-migratory effects. Our findings are in agreement with the literature where MDK is described as a cytokine and growth factor influencing tumor cell differentiation, growth and migratory behavior. All these functions require intracellular signal transduction. To study the signaling of MDK we first looked at MDK receptor expression on UM cells (chapter 3.10.1) and second, we screened a panel of signaling molecules in UM cells with downregulated or overexpressed MDK levels (chapter 3.10.2).

3.10.1 Receptor expression on UM cells Autocrine signaling requires an extracellular mediator, in our case MDK, binding to receptors on the same cell. In a paracrine signaling the receptor is on an additional cell. Several receptors have been described as MDK receptors (reviewed C. Xu et al. 2014) that orchestrate various MDK functions (detailed descriptions in chapter 1.2.2). Studying autocrine and/or paracrine eﬀects of MDK on UM cells we initially looked at expression levels of MDK receptors on the

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UM cells using flow cytometry. We assessed the expression percentages (Fig: 3.23 A) and mean fluorescence intensities (Fig: 3.23 B) of CD91, CD138 and Notch2, on the UM cells derived from patient 270, the primary tumor cell line (Mel270) and the four metastatic cell lines (OMM2.2, OMM2.3, OMM2.5 and OMM2.6). Additionally, we assessed the expression percentages (Fig: 3.24 A) and mean fluorescence intensities (Fig: 3.24 B) of these three known MDK receptors on our UM cell line panel including cell lines derived from primary tumors (Mel202, OCM1, OCM3 and OM431) and cell lines derived from metastatic liver tumors (OMM1 and H79). Expression levels of CD91, CD138 and Notch2 were also assessed on non-malignant melanocytes. All UM cells derived from patient 270, the primary tumor cell line (Mel270) and the four metastatic cell lines (OMM2.2, OMM2.3, OMM2.5 and OMM2.6) have higher percentages of CD91 positive cells than melanocytes. With the exception of OMM2.2 all UM cells showed higher mean fluorescence intensity (mean FI) indicating higher concentration of CD91 receptors on the tumor cell surface in comparison with non-malignant melanocytes. Nearly all tumor cells are CD138 positive, whereas only a low percentage of melanocytes show CD138 expression. This finding is confirmed by the mean FI measurements, with clearly higher CD138 mean FI levels in the tumor cells than in the melanocytes. The third MDK receptor we investigated is the Notch2 receptor. All UM tumor cells were Notch2 positive, whereas about 2/3 of non-malignant melanocytes expressed Notch2. The Notch2 mean FI levels of all tumor cells were higher than in non-malignant melanocytes, indicating upregulation of the Notch2 receptor on the tumor cells.

patient #270 – MDK Receptor expression A CD91% CD138% Notch2% 100 100 100

80 80 80

60 60 60

CD91% 40 40 40 CD138% Notch2%

20 20 20

0 0 0

Mel270 Mel270 Mel270 OMM2.2OMM2.3OMM2.5OMM2.6 OMM2.2OMM2.3OMM2.5OMM2.6 OMM2.2OMM2.3OMM2.5OMM2.6 Melanocytes Melanocytes Melanocytes B CD91-mean CD138-mean Notch2-mean 3000 5000 2500

4000 2000 2000 3000 1500

2000 1000 1000 mean FICD91 mean mean FICD138 mean 1000 Notch2FI mean 500

0 0 0

Mel270 Mel270 Mel270 OMM2.2OMM2.3OMM2.5OMM2.6 OMM2.2OMM2.3OMM2.5OMM2.6 OMM2.2OMM2.3OMM2.5OMM2.6 Melanocytes Melanocytes Melanocytes Figure 3.23 – MDK receptor expression on cell derived from patient270. (A) Percentage (%) and (B) mean ﬂuorescence intensity (mean FI) of CD91, CD138 and Notch2 expression on melanocytes, primary tumor and the four metastatic cell lines from patient270.

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CD91, CD138 and Notch2 receptors are expressed by UM cells and almost all UM cell lines even show higher expression levels of the receptors. This indicates, that autocrine and paracrine signaling upon MDK binding to these receptors may occur. Therefore, we next studied the potential intracellular signaling pathways activated via the MDK-receptor interaction.

UM cell line panel – MDK Receptor expression A CD91% CD138% Notch2% 100 100 100

80 80 80

60 60 60

CD91% 40 40 40 CD138% Notch2%

20 20 20

0 0 0

H79 H79 H79 Mel202OCM1OCM3OM431OMM1 Mel202OCM1OCM3OM431OMM1 Mel202OCM1OCM3OM431OMM1 Melanocytes Melanocytes Melanocytes B CD91-mean CD138-mean Notch2-mean 3000 5000 3000

4000 2000 2000 3000

2000 1000 1000 mean FICD91 mean mean FICD138 mean 1000 Notch2FI mean

0 0 0

H79 H79 H79 Mel202OCM1OCM3OM431OMM1 Mel202OCM1OCM3OM431OMM1 Mel202OCM1OCM3OM431OMM1 Melanocytes Melanocytes Melanocytes Figure 3.24 –HeterogenouscellmembraneexpressionofMDKreceptorsbyprimaryandmetastaticUMcells. (A) Percentage (%) and (B) mean ﬂuorescence intensity (mean FI) of CD91, CD138 and Notch2 expression on melanocytes and on four primary UM cell lines (Mel202, OCM1, OCM3 and OM431) and two metastatic cell lines (OMM1 and H79).

3.10.2 Signaling pathways inﬂuenced by MDK To screen for intracellular signaling pathways aﬀected by MDK we used the PathScan®Intracellular Signaling Array Kit. The kit makes use of the sandwich immunoassay principle and allows the simultaneous detection of 18 signaling molecules when phosphorylated or cleaved. Figure 6.2 shows the layout of the PathScan®Intracellular Signaling Array, including a list of all intracellular signaling molecules and their phosphorylation sites.

Downregulation of MDK reduces phosphorylation of RPS6 and PRAS40 To study the aﬀect of MDK we prepared lysates of the following Mel202 cells: untreated (UTC), transfected with control siRNA (siNC) and transfected with MDK speciﬁc siRNAS (siMDK1 and siMDK2) and analyzed the phosphorylation levels of the signaling molecules with the PathScan®Intracellular Signaling Array. The total protein concentration in the lysates was assessed with the BCA as described in chapter 6.16.2 and equal protein concentrations were used

41 3 Results in the PathScan®Intracellular Signaling Array assay. Figure 3.25 (A) shows the chemiluminescence developed PathScan®Intracellular Signaling Array blots. Visually, the most striking result was detected for the mTOR substrates ribosomal protein S6 (p-RPS6) and phosphorylation of PRAS40 (p-PRAS40). Figure 3.25 (B) shows the relative phosphorylation of RPS6 and PRAS40 based on the luminescence intensity, corrected to the positive loading control (upper left and right corners and lower left corner). Significant lower phosphorylation of RPS6 and PRAS40 was observed in the cells with downregulated MDK expression. No significant difference could be observed for all other signaling molecules on the membrane.

A Mel202 UTC siNC siMDK1 siMDK2

p-RPS6 p-RPS6 p-RPS6 p-RPS6

p-PRAS40 p-PRAS40 p-PRAS40 p-PRAS40

B ** ** 1.2 ** 1.2 * 1.0 1.0

0.8 0.8

0.6 0.6 p-RPS6

0.4 p-PRAS40 0.4

0.2 0.2

0.0 0.0

UTC siNC UTC siNC siMDK1siMDK2 siMDK1siMDK2

Figure 3.25 –DownregulationofMDKexpressioninMel202leadstoreducedphosphorylationofmTOR substrates RPS6 and PRAS40. (A) Phosphorylation of RPS6 and PRAS40 assessed with paths scan intracellular signaling array kit is highly reduced in UM cells treated with siRNAs against MDK. (B) Quantiﬁcation of luminescence density signals of RPS6 and PRAS40 phosphorylation corrected to positive controls (upper left and right corners and lower left corner). Data were normed to cells treated with siNC and presented as mean ± s.e.m.

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We could validate these ﬁndings by using phospho-ﬂow cytometry. Downregulation of MDK reduced the phosphorylation of RPS6 (Fig. 3.26). Figure 3.26 (B) shows a representative stagged histogram displaying lower phosphorylation in the cells with downregulated MDK, compared to cells transfected with the siNC and the untreated cells.

flowcytometric analysis of RPS6 phosphorylation A B 1.5 ** *

1.0 siMDK2

count siMDK1

cell siNC 0.5 UTC unstained p-RPS6(normedsiNC) to 0.0 pRPS6

UTC siNC siMDK1siMDK2 Figure 3.26 –DownregulationofMDKexpressioninMel202leadstoreducedphosphorylationofmTOR substrates RPS6 and PRAS40. (A) Phosphorylation of RPS6 and PRAS40 is significantly reduced in UM cells treated with siRNAs against MDK as assessed with phospho-flow cytometry. (B) Representative stagged histograms of phosphorylation of RPS6 of untreated (UTC), mock transfected (siNC) and UM cells treated siRNAs specifically downregulating MDK. Data were normed to cells treated with siNC and presented as mean ± s.e.m.

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Overexpression of MDK increases phosphorylation of RPS6 and PRAS40 OCM1 cells overexpressing MDK showed higher phophorylation of RPS6 and PRAS40, compared to wildtype (wt) and mock transfected OCM1 cells, shown in figure 3.27. We could visually observe higher intensity of phosphorylated RPS6 and PRAS40 on the PathScan®Intracellular Signaling Array blots in the MDK overexpression cells compared to wt and mock (Fig. 3.27 A). Also, quantification of the mean chemilumniscence intensity, after correction to the positive controls showed significantly higher phosphorylation of these two signaling molecules. No significant difference could be observed for all other signaling molecules on the membrane.

A OCM1 wt mock MDK

p-RPS6 p-RPS6 p-RPS6

p-PRAS40 p-PRAS40 p-PRAS40

B *** ** 2.5 *** 4 **

2.0 3

1.5 2

p-RPS6 1.0 p-PRAS40 1 0.5

0.0 0

wt wt mock MDK mock MDK

Figure 3.27 –MDKoverexpressioninOCM1leadstoincreasedphosphorylationofmTORsubstratesRPS6 and PRAS40. (A) Phosphorylation of RPS6 and PRAS40 assessed with paths scan intracellular signaling array kit is increased in OCM1 cells overexpressing MDK. (B) Quantiﬁcation of luminescence density signals of RPS6 and PRAS40 phosphorylation corrected to positive controls (upper left and right corners and lower left corner). Data were normed to cells treated with siNC and presented as mean ± s.e.m.

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We again could validate these findings by using phospho-flow cytometry to determine the phosphorylation levels of RPS6. Overexpression of MDK increased the phosphorylation of RPS6 (Fig. 3.28). OCM1-MDK show significant higher level of phosphorylation of RPS6 than the OCM1-wt and mock transfected cells. The representative flow cytometry histogram show similar phorphorylation levels of RPS6 in OCM1-wt and OCM1-mock and higher phosphorylation levels in OCM1-MDK cells (Fig. 3.28 B).

flowcytometric analysis of RPS6 phosphorylation A B 2.0 * ** 1.5

MDK

1.0 count mock cell wt 0.5 unstained p-RPS6 (normed to mock) 0.0 pRPS6 wt mock MDK

Figure 3.28 –MDKoverexpressioninOCM1leadstoincreasedphosphorylationofmTORsubstratesRPS6 and PRAS40. (A) Phosphorylation of RPS6 and PRAS40 assessed with phospho ﬂow cytometry is signiﬁcantly increased in OCM1 cells overexpressing MDK. (B) Representative stagged histograms of phosphorylation of RPS6 of wildtype (wt), mock transfected (mock) and OCM1 cells overexpressing MDK. Data were normed to mock transfected cells and presented as mean ± s.e.m.

In summary, we could show that MDK inﬂuences phosphorylation levels of mTOR substrates RPS6 and PRAS40. Speciﬁcally, we showed that downregulation of MDK reduced and overexpression of MDK increased phosphorylation of RPS6 and PRAS40.

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3.10.3 Baseline phosphorylation of RPS6 As described above in chapter 3.10.2 we found a correlation between MDK expression and phosphorylation levels of RPS6. Since we found heterogenous MDK expression levels in UM cells, we assessed the baseline phosphorylation of RPS6 in primary and metastatic UM cells derived from patient 270 (Fig. 3.29 A) and within the UM panel (Fig. 3.29 B). Indeed, the primary tumor ells Mel270has higher baseline of phosphorylated RPS6, which coincides with high levels of MDK expression in these cells. In contrast, the metastasis derived cell lines (OMM2.2, OMM2.3, OMM2.5 and OMM2.6), which express lower levels of MDK, presented lower levels of baseline PRS6 phosphorylation. This pattern was conﬁrmed in the extended cell line panel of UM cells. In summary, the baseline phosphorylation status of RPS6 correspnded with MDK expression levels.

flow cytometric analysis of RPS6 phosphorylation

A patient#270 B UM cell line panel 5000 5000

4000 4000

3000 3000

2000 2000 mean FIp-RPS6 mean mean FI p-RPS6FI mean 1000 1000

0 0

H79 Mel270 Mel202OCM1OCM3 OMM1 OMM2.2OMM2.3OMM2.5OMM2.6 OM431 Figure 3.29 –BaselinephosphorylationofRPS6inUMcelllines.PhosphorylationofRPS6in(A)celllines derived from patient #270 and (B) additional UM cell line panel

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3.11 MDK induces resistance to Akt and mTOR inhibitors in UM cells

As presented above in section 3.10.2, we showed that MDK activates RPS6 an important component of the Akt/mTOR pathway. To study MDKs influence on the Akt/mTOR signaling in more detail, we treated OCM1-wt, OCM1-mock and OCM1-MDK cells with specific Akt (Ak- tIV) and mTOR (Torin) inhibitors. Treating OCM1-wt, OCM1-mock and OCM1-MDK with AktIV dose-escalated (range 1-5 µM) indicated a significant proliferation benefit for MDK overexpressing OCM1-MDK cells (Fig. 3.30 A, B). The phosphorylation of RPS6 was not reduced in OCM1-MDK cells, indicating a maintained activation of the Akt/mTOR pathway despite specific inhibition with AktIV (Fig. 3.30 C,D).

A C 2,5µM AktIV 1.2 OCM1-wt OCM1-mock OCM1-MDK 1.5 * 1.0 * *** 0.8 1.0

0.6 *** 0.4 0.5

0.2 p-RPS6DMSO)(normedto 0.0

proliferationDMSO(normedto control) 0.0 DMSO 1 2,5 5 wt AktIV inhibitor [µM] mock MDK B D OCM1-wt OCM1-mock OCM1-MDK 2,5µM AktIV

DMSO wt counts mock

2,5µM MDK AktIV p-RPS6

Figure 3.30 –MDKpromotesUMcellsurvivalbymaintainingRPS6phosphorylationduringAktinhibition.(A) Proliferation response of UM cells (OCM1) with and without MDK overexpression to increasing concentrations of Akt inhibitor (AktIV). Statistical analyses were performed using 2-way-ANOVA (***P<0.0001, *P<0,05). Data were normed to cells treated with DMSO and presented as mean ± s.e.m (n=4). (B) Representative sections of pictures of OCM1-wt, OCM1-mock and OCM1-MDK cells in culture treated with DMSO or 2,5µMAktIVon day three. Higher conﬂuency of cells was observed in the OCM1-MDK cell culture. (C) Maintainance of RPS6 phosphorylation in MDK overexpressing UM cells upon treatment with 2,5 µMAktIV.Datawerenormedto cells treated with DMSO and presented as mean ± s.e.m (n=4 ). (D) Representative histograms of p-RPS6 by phospho-ﬂow cytometry in cells treated with Akt inhibitor.

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We observed similar results by treating the tumor cells with 25, 250 or 1000 nM mTOR inhibitor (Torin). MDK overexpressing cells (OCM1-MDK) showed a signiﬁcant survival beneﬁt compared to the mock transfected and wildtype cells (Fig. 3.31 A). The OCM1-MDK cells treated with 250nM Torin showed higher phosphorylation levels of RPS6 compared to OCM1-wt and OCM1-mock cells (Fig. 3.31 B,C).

A B C

OCM1_wt OCM1_mock OCM1_MDK 1.2 1.5 * 1.0

0.8 1.0 ** 0.6 wt

0.4 0.5 mock

0.2 MDK p-RPS6(normed DMSO)to

proliferation(normedDMSO to control) 0.0 0.0 DMSO 25 250 1000 Torin inhibitor [nM] wt p-RPS6 mock MDK

Figure 3.31 –MDKpromotesUMcellsurvivalbymaintainingRPS6phosphorylationduringmTORinhibi- tion. (A) Proliferation response of UM cells (OCM1) with and without MDK overexpression to increasing concentrations of mTOR inhibitor (torin). Statistical analyses were performed using 2-way-ANOVA (**P<0.001, *P<0,05). Data were normed to cells treated with DMSO and presented as mean ± s.e.m (n=5). (B) Main- tainance of RPS6 phosphorylation in MDK overexpressing UM cells upon treatment with 25nM Torin. Data were normed to cells treated with DMSO and presented as mean ± s.e.m (n=3 ). (C) Representative histograms of p-RPS6 by phospho-ﬂow cytometry in cells treated with mTOR inhibitor.

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3.12 Clinical relevance of MDK expression in UM patients

Recently, a large integrative analysis analyzed gene expression in tumor tissue of primary UM of 80 UM patients with next-generation sequencing (Robertson et al. 2017). The sequencing data are publicly available on the The Cancer Genome Atlas (TCGA) website (https://cancergenome. nih.gov). With the help of Dr. Christian Büttner 1 we performed statistical analysis of the gene expression data. The following data were generated by extensive analysis of data extracted from the TCGA database with the specific focus on MDK gene expression and its correlation with survival, developing metastasis, tumor classifications, tumor compositions, other described prognostic markers and immune cell infiltration (see appendix E for an overview of the clinical, pathological and molecular characteristics of UM TCGA cases). The findings showed that MDK correlated with overall survival, development of metastasis, chromosome 3 status, immune cell infiltration and degree of pigmentation of the primary tumor. We will present and discuss these data in the following sections.

3.12.1 MDK expression correlates with overall survival in UM patients Of the 80 patients included in the Robertson et al. 2017 study 3 patients died due to unknown reasons and 1 patient died due to non-metastatic reasons and were therefore excluded from the statistical overall survival analysis. The median of MDK concentration was therefore determined based on the remaining 76 patients. The low and high MDK expression group contained all patients with MDK levels below or above the median respectively. Signiﬁcant higher 5-year overall survival was seen in low MDK expression group (Fig. 3.32).

1.0

0.8 p (logrank)= 0.0004

0.6

0.4 overall-survival

0.2 low MDK expression high MDK expression 0.0 0 1 2 3 4 5 Follow-up (years)

Figure 3.32 –MDKexpressioncorrelateswithoverallsurvival.UMpatientswithlowMDKexpression(below median, n=38) in the primary tumor showed signiﬁcantly higher 5-year survival compared to high MDK expression (above median, n=38) (p=0.0004, log-rank (mantel-cox) test).

3.12.2 MDK expression correlates with metastatic-free-survival All 80 patients were included in the progression-free survival analysis. Developing metastasis was set as the progressing factor. Signiﬁcant diﬀerences in disease progression were found between the high MDK expressing group (above MDK median) with 19 of 40 (48%) patients developing metastasis and the low MDK expressing group (below MDK median) with 7 of 40 (18%) patients developing metastasis within the 5 year follow up period (Fig. 3.33 A).

1Dr. Christian Büttner, Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander- Universität Erlangen-Nürnberg, Erlangen, Germany

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Furthermore, MDK expression is signiﬁcantly higher in the group that developed metastases compared to non-metastatic group (Fig. 3.33 B).

Log-rank (Mantel-Cox) Test A B Chi square 9.164 df 1 1.0 10 P value 0.0025 P value summary ** p= 0.0262 Are the survival curves sig different? Yes 0.8 8

Gehan-Breslow-Wilcoxon Test Chi square 7.517 0.6 6 df 1 P value 0.0061 P value summary ** 0.4 4 Are the survival curves sig different? Yes 0.0025

p (logrank)= (log2(FPKM)) MDK expression MDK progression-free-survival 0.2 2 low MDK expression high MDK expression 0.0 0 0 1 2 3 4 5 metastasis non-metastasis Follow-up (years)

Figure 3.33 – MDK expression correlates with metastasis. (A) Kaplan-Meier blot for progression-free survival. Signiﬁcant longer progression free survival in the low MDK expressing group (MDK expression levels below the median) than in the group with high MDK expression (MDK expression level above the median (p=0.0025, log-rank (mantel-cox) test). (B) Box whisker blot showing signiﬁcant higher MDK levels in patients group that develop metastasis (n=26) compared to the patient group with no metastasis (n=54) (p=0.0262).

3.12.3 MDK expression correlates with time-to-metastasis For 16 patients of the 26 patients in the study that developed metastasis information about the time-to-metastasis after initial therapy was available. Patients in the group with high (above median (calculation included the 16 metastatic patients only)) and low MDK expression (below median) developed signiﬁcantly faster metastasis than the group with low MDK levels (Fig. 3.34 A). The mean days without metastasis in the high MDK expression group is 226 days whereas the mean days without metastasis in the low MDK expressing group is 646 days (Fig. 3.34 B).

A B 1.0 low MDK expression high MDK expression 0.8

0.6

high MDK 0.4 p=0.0112 metastasis free survival free metastasis 0.2 low MDK

p (logrank)= 0.0073 0.0 0 500 1000 1500 0 200 400 600 800 1000 Follow-up (days) Figure 3.34 – MDK expression correlates with with time-to-metastasis. (A) Kaplan-Meier blot for patients with metastasis. Patients developed signiﬁcantly faster metastasis in the group with MDK expression above median than the group with below median MDK expression (p=0.0073, log-rank (mantel-cox) test). (B) The metastatic-free days in the high MDK expression group is signiﬁcantly shorter (mean days to metastasis 226d) than in the group with below median MDK expression (mean days to metastasis 646d) (p=0.0112).

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3.12.4 MDK and tumor classifications AJCC tumor staging AJCC tumor staging for malignant melanoma of the uvea is based on tumor size (T1-4), information on ciliary body involvement (a and c without ciliary body involvement, b and d with ciliary body involvement) and extraocular extensions (with extraocular extension c, d and e). The extended patient information of Robertson et al. 2017 include the AJCC staging of all 80 patients. The patients included in the study had tumors classified as T2a/b, T3a/b/c and T4a/b/c/d/e. As shown in figure 3.35 (A) no significant difference of MDK expression was found in tumors of different size categories, ignoring the a-e classification. However, if the tumors are further stratified in AJCC subgroups, depicted in figure 3.35 (B), it seems that as soon as tumors present with ciliary body involvement or extraocular extensions in each size category, a trend to higher MDK expression can be observed. These data suggest that not the larger tumor sizes influence MDK expression but ciliary body involvement and extraocular extensions.

A AJCC staging B AJCC staging (based on tumor size only) (subgroups)

10 10

8 8

6 6

4 4 (log2(FPKM)) (log2(FPKM)) MDK expression MDK MDK expression MDK 2 2

0 0 T2 T3 T4 T2a T2b T3a T3b T3c T4a T4b T4c T4d T4e AJCC staging Figure 3.35 – MDK expression levels in tumor classifications based on tumor size and further AJCC staging. (A) MDK expression in tumors classified to T2 (n=14), T3 (n=32) and T4 (n=34) tumors based on tumor size. (B) MDK expression in tumors stratified to subgroups based on AJCC staging (T2a, n=12; T2b, n=2; T3a, n=25; T3b, n=5; T3c, n=1; T4a, n=20, T4b, n=9; T4c, n=2, T4d, n=2, T4e, n=1).

Therefore, we reorganized the patients based on ciliary body involvement and extraocular extension independently of their tumor size category. The patient group with no extraocular extension include all patients classified with tumor subcategorized a and b, whereas the patients with extraocular extension include all subcategorized with c, d and e. The group with no ciliary body involvement include all tumors classified with a and c, the group with ciliary body involvement all tumors in the subcategories b and d. Figures 3.36 (A) and (B) show that tumors with extraocular extension and with ciliary body involvement have significantly higher mean levels of MDK expression, independently of the tumor size category.

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A extraocular extension B ciliary body involvement

10 p= 0.0052 10 p= 0.0473

8 8

6 6

4 4 (log2(FPKM)) (log2(FPKM)) MDK expression MDK expression MDK 2 2

0 0 extraocular - + ciliary body - + extension involvement

Figure 3.36 – MDK expression levels in tumor classification based on AJCC staging (including extraocular extension and ciliary body involvement). (A) Mean MDK expression is significant lower in tumors without extraocular extension (n=73) compared to tumors with extraocular extension (n=6). The differences are independent of tumor size (p=0.0473). (B) Tumors without ciliary body involvement (n=60) show significant lower mean MDK expression than tumors with ciliary body involvement (n=18) independent of tumor size (p=0.0053).

SCNA clustering Based on an unsupervised clustering of somatic copy number alterations (SCNAs), four groups ordered by increasing chromosomal instability were clustered by Robertson et al. 2017. SCNAs separated the 80 primary UM tumors is four clusters with similar number of patients: 1 (n=15), 2 (n=23), 3 (n=22) and 4 (n=20). Interestingly, MDK expression increased with increasing chromosomal instability, with signiﬁcant diﬀerences between group 1 and group 4 as well as group 2 and group 4 (Fig. 3.37).

SCNA cluster 10 * * 8

4 (log2(FPKM)) MDK expression MDK 2

0 1 2 3 4 Figure 3.37 – MDK expression correlates with SCNASCNA clustering. cluster SCNAs separated 80 primary UM into four clusters: 1 (n=15), 2 (n=23), 3 (n=22), and 4 (n=20), ordered by increasing chromosomal instability. Signiﬁcant higher MDK expression is found between tumors SCNA1 and SCNA4 and between SCNA2 and SCNA4 (*P < 0.05, 1-Way-ANOVA, dunn’s multiple comparison test).

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3.12.5 MDK expression correlates with chromosome 3 status Chromosome 3 status as described in chapter 1.1.4 is highly predictive for metastatic risk in UM patients. Speciﬁcally, UM with monosomy 3 is at high-risk for metastatic disease. Patients with monosomy 3 had signiﬁcantly higher levels of MDK than the patients with disomy 3 (Fig. 3.38). One patient was registered with trisomy 3 and was excluded of the analysis.

chromosome 3 status

10 p=p=0.03560.0159 8

4 (log2(FPKM)) MDK expression MDK 2

0 monosomy disomy

Figure 3.38 –MDKexpressioncorrelateswithchromosome3status.Boxwhiskerblotshowingsigniﬁcant higher MDK levels in patients group with monosomy 3 (n=37) compared to the patient group with disomy 3 (n=42) (p=0.0356).

3.12.6 MDK expression does not correlate with Bap1 and PRAME expression Best-fit values No correlations were found with Bap1Goodness andBest-fit of Fit PRAME values two other factors that have beenGoodness described of Fit Slope -0.1171 ± 0.1233 Slope 0.003354 ± 0.05235 as indicators of high-metastatic riskR square (Fig. 3.39). 0.01143 R square 5.262e-005 Y-intercept when X=0.0 5.104 ± 0.5311 Sy.x Y-intercept when1.337 X=0.0 4.611 ± 0.2174 Sy.x 1.345 X-intercept when Y=0.0 43.60 X-intercept when Y=0.0 -1375 A correlation with B correlation with BAP1BAP1 PRAMEPRAME 10 10 MDK 8 8

6 6

4 4

2 2 MDK expression (Log2(FPKM)) expression MDK MDK expression (Log2(FPKM)) expression MDK 0 0 0 2 4 6 8 0 2 4 6 8 10 BAP1 expression (Log2(FPKM)) PRAME expression (Log2(FPKM))

Figure 3.39 –NocorrelationbetweenMDKexpressionand(A)BAP1or(B)PRAMEexpression

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3.12.7 MDK expression correlates with immune cell infiltration in UM tumors MDK expression in primary UM tumors correlated to the density of tumor infiltrating tumor cells (TILs) and tumor associated macrophages (TAMs) using the data from the TCGA-database. According to Robertson et al. 2017 the infiltration densities were assessed by a panel of five histopathologists with expertise in ocular pathology and melanoma. The infiltration was characterized as mild, moderate or heavy (Robertson et al. 2017). The majority of tumors 89% (71 of 80) presented with mild, 11% with moderate and heavy (moderate: 8,7% (7 of 80), heavy 2,5% (2 of 80)) TIL infiltration. MDK expression is significantly lower in the tumors with mild TIL infiltration than in the group with combined moderate and heavy TIL infiltration (Fig. 3.40 A). Half of the tumors 50% (40 of 80) present with mild, and half with moderate and heavy (moderate: 36% (29 of 80), heavy 14% (11 of 80)) TAM infiltration. Significant lower MDK expression is found in tumors with mild TAM infiltration (Fig. 3.40 B).

A tumor infiltrating B tumor associated lymphocytes (TILs) macrophages (TAMs) 10 10 p= 0.0070 p=0.0409 8 8

6 6

4 4 (log2(FPKM)) (log2(FPKM)) MDK expression MDK MDK expression MDK 2 2

0 0 mild moderate/heavy mild moderate/heavy

Figure 3.40 –MDKexpressioncorrelateswithimmunecellinfiltration.(A)SignificanthigherMDKexpression in tumors with moderate/heavy TIL infiltration (TIL mild n=71, TIL moderate/heavy n=9; p=0.007). (B) Significant higher MDK expression in tumors with moderate/heavy TAM infiltration (TAM mild n=40, TAM moderate/heavy n=40; p=0.0409)

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3.12.8 MDK expression correlates with tumor pigmentation but not with composition of UM tumors Historically, histological analysis of UM defined two cell types present in the tumor tissue: spindle and/or epithelioid cells. Epithelioid cells have been contributed to more aggressive tumors and worse patient outcomes. No significant difference of MDK expression was found between tumors consisting of only spindle cells, tumors having a mixed cell type and consisting of only epithelioid cells (Fig. 3.41 A). The level of tumor pigmentation has also been correlated with the outcome of patients. Pigmentation levels are classified as minimal and marked. The marked group contains all tumors with moderate and heavy pigmentation. Higher MDK mean expression was found in the marked tumors compared to tumors with minimal pigmentation (Fig. 3.41 B).

A tumor composition B tumor pigmentation 10 ns 10 ns ns p= 0.0070 8 8

6 6

4 4 (log2(FPKM)) (log2(FPKM)) MDK expression MDK expression MDK 2 2

0 0 spindle mixed epithelioid mild moderate/heavy

Figure 3.41 –MDKexpressionintumorsgroupedbycelltypeandgradeofpigmentation.(A)MDKexpression in tumors with only spindle cells (n=33), mixed cell type (n=39) and majority epithelioid cells (n=7)(ns=not signiﬁcant). (B) Marked tumors, tumors with moderate (n=27) and heavy pigmentation (n=14) have higher mean MDK expression than minimal pigmented tumors (n=39) (p=0.0070).

55 4Discussion

Uveal melanoma is the most common tumor in the eye of adults. It is caused by alterations in melanocytes in the choroid, ciliary body or iris resulting in accumulation of a malignant mass in the eye. The exact causes of this malignant alteration are unknown. About 50% of patients will develop metastatic disease with the large majority of metastasis found in the liver. The main challenge is that as soon as metastasis are detected no effective therapy is available that can prolong the survival of these patients, which is in general very poor (median 9 months). Some markers to predict metastasis can be used to successfully assess risk of metastasis. Including chromosome 3 status, mutation status of G-coupled proteins GNA11 and GNAQ, immune cell infiltration, composition and size of the tumor. However no clear link between these markers and actual disease progression and timing of metastasis development can be made. To address this challenge, work leading up to this thesis used a first generation microarray to compare gene expression in cells isolated from the primary tumor as well as in cells isolated from four distinct liver metastasis of the same patient (Fig. 1.5). Interestingly, MDK was one of the molecules highly differentially expressed between the primary tumor and the metastasis. Previous studies have shown that MDK has a multitude of functions. For example, it has been reported that MDK acts as a tumor growth factor, stimulates EMT transition and cell migration and induces lymphangiogenesis. Moreover, numerous studies have shown that MDK is a promising biomarker in diverse cancer malignancies. Therefore we hypothesized that MDK promotes tumor survival, migration and therapeutic resistance in UM. In addition, it may serve as biomarker for predicting UM progression. To test our hypothesis following aims were studied. Firstly, we determined the MDK gene and protein expression levels in primary and metastatic UM cell line and UM patient tissue. Then we investigate regulatory mechanisms of MDK expression and studied the function and mechanism of MDK. Finally, to determine if MDK is a biomarker in UM disease, we specifically analyzed the MDK gene expression and its correlations with clinical, pathological and molecular characteristics in UM TCGA cases. In the following sections the main findings will be discussed.

4.1 Heterogenous expression of MDK in UM cell lines and in tissue

We could validate the differential expression of MDK in primary tumor and the metastasis derived cell lines of patient #270 on RNA and protein level (Fig. 3.4). In an additional set of cell lines, heterogenous expression of MDK could be detected (Fig. 3.5). Some cell lines showed high levels of MDK expression on RNA as well as protein level, and others showed low to no expression. Furthermore, MDK stainings in a set of primary UM tissue of ten patients did show MDK positive tumor areas in all ten patients. However, the expression patterns were diffuse with some tumor sections showing low MDK expression and some tumor sections high MDK expression. Moreover, there were always tumor areas with no MDK expression detected. The underlying reason for this diffuse pattern is unclear. It is known that UM tumors can consist of different cell types, including epithelioid and spindle cells. From these ten samples no clear link to one of the cell types could be made. However, these diffuse pattern may explain the heterogenous expression of MDK we found in the cell lines. On the other hand, we did not find a difference in MDK expression in primary tumors independent of the tumor composition (Fig. 3.41). To sample UM tumor tissue fine-needle aspiration biopsy is a successful method. The biopsy is used for molecular genomic analysis to help determine the diagnosis and prognosis in UM (Sellam

56 4 Discussion et al. 2016). However, it is also controversially discussed. In the fine-needle aspiration biopsies only a small section of the tumor can be studied. In a study in 2013 mean numbers of cells evaluated in the biopsy were assessed. The mean number of cells in the biopsy was 273 (range 28 to 529) (Chang et al. 2013). Interestingly, even in this small sample size, cytogenetic heterogeneity for monosomy 3 in the biopsy sample can be found (Chang et al. 2013). This clearly indicates that evaluating fine-needle aspiration biopsies can only assess a selection of the tumor. Heterogeneities could bias the evaluation in a certain direction. Cell lines usually are the product of tumor cells able to proliferate in cell culture. Cells are typically selected when generating cell lines. If the cell line derives from a part of the tumor with no MDK expression, this would lead to a MDK negative cell line even though other areas in the tumor express high levels of MDK. In addition, the expression pattern could be influenced by the tumor microenvironment, including hypoxic conditions or other regulatory mechanisms.

Regulation of MDK expression To answer the question how expression levels of MDK could be regulated, we looked at different regulatory mechanisms. Specifically, we studied MDK expression stimulating molecules like retinoic acid, DNA methylation and hypoxic conditions. Retinoic acid is described in the literature to be able to induce MDK expression. Other groups reported that MDK has a retinoic acid response element in the promoter region and indeed we could see that UM tumor cells treated with increasing concentrations of retinoic acid induced MDK gen expression. Interest- ingly, also non-malignant melanocytes were responsive to retinoic acid and showed increased levels of MDK expression (Fig. 3.10). Retinoic acid plays an important role in organogenesis and differentiation, vision, metabolism and inflammation. It has been shown that during eye development retinoic acid is required in neural crest cells (parental cells of melanocytes) for normal eye formation (Matt et al. 2008). Futhermore, it has been shown that MDK is expressed in the retina and its surrounding region and in the cornea in the early stages, but not in later stages of embryonal development, indicating its importance for eye development (Kadomatsu et al. 1990). Moreover, it has been demonstrated that MDK was able to protect the eye from damaging effects of constant light, by significantly suppressing macrophage infiltration into the eye (Unoki et al. 1994). Upregulation of MDK after retinoic acid treatment was suggested as a therapeutic approach to potentially rescue pulmonary hypoplasia caused by disrupted early lung morphogenesis (Doi et al. 2011) as well as protect against ischemia-related brain injuries (Harvey et al. 2004). Retinoic acid is also highly regulated during inflammation and could regulate MDK at a transcriptional level through altered levels of retinoic acid expression during inflammation. The eye is immunologically privileged characterized by a profoundly immunosuppressive environment. The aqueous humor contains several immunsuppressive factors including TGF- and has been shown to inhibit T cell proliferation, IFN- production, and induction of Tregs. Retinoic acid seems to play an important role in inducing suppressive T cells (Zhou et al. 2011). Retinoic acid has been shown to be beneficial in several oncogenic diseases including leukaemia, cervical cancer, thyroid cancer, breast cancer, squamous cell carcinoma, skin cancer and head and neck cancer alone or in combination with other therapies (reviewed in Schenk et al. 2014). Effects of retinoic acid on UM cells include growth arrest, reduced viability, increased ICAM-expression and augmented sensitivity to killing by cytotoxic T lymphocytes and NK cell-mediated lysis (Vertuani et al. 2007). Retinoic acid has been studied in a wide range of clinical studies as a treatment of cancers with promising results of combinatorial strategies. ATRA as a drug has been patented in 1975 and was approved in 1963. However, the only routine and effective use is for treatment of precancerous lesions such as leukoplakia, actinic keratosis, cervical dysplasia and the acute myeloid leukemia M3 subtype, i.e. promyelocytic leukemia (APL). Overall, retinoic acid plays opposite roles in UM. While retinoic acid directly decreases survival functions and inreases susceptibility to immune cell mediated killing of UM, it also induces MDK expression

57 4 Discussion that promotes survival of UM cells. Interestingly, ICAM-1 expression is upregulated by retinoic acid in UM and this adhesion molecule synergizes with MDK in the ICAM-1/LFA1 interaction (Weckbach et al. 2014). So again we observe opposite roles for retinoic acid in combination with MDK; on the one hand promoting adhesion to endothelium and extravasation leading to metastasis. On the other hand, this interaction can facilitate cell-to-cell contact with immune cells and augment cytotoxicity. Exactly, how this interaction leads to tumor promoting or anti-tumor eﬀects remains to be determined.

Hypoxic conditions are common in tumors. Rapid tumor growth can restrict access to oxygen delivering blood vessels or the outgrowth of faulty vessels are primary causes of hypoxia. To study the eﬀect of hypoxic conditions on MDK expression, we made use of CoCl2 an inducer of the hypoxia-inducible-factor 1-↵ (HIF1-↵) and could see that hypoxic conditions induce MDK expression in UM cell lines (Fig. 3.13). Hypoxia is a driving force to induce neo(lymph)angiogenesis, promotes metastasis and can cause treatment resistance, all functions also contributed to MDK in a wide range of malignancies. Several other studies have also reported correlation between hypoxia and MDK concentration. For instance, in carcinoma tissue high MDK expression was detected under hypoxic conditions (Horiba et al. 2006). Interestingly, increased MDK protein expression was also observed in polymorphonuclear cells (PMNs, monocytes and endothelial cells under hypoxic conditions compared to normoxia (Weckbach et al. 2012). Under hypoxic conditions MDK has been found to have a protective role by preventing hypoxia induced apopotosis via activation of the Akt pathway, heme oxygenase-1 and HIF-1↵ (S. H. Lee et al. 2012) con- necting our ﬁndings that downregulation of MDK increases apoptosis.

As described in section 1.2.3 DNA methylation is highly important to regulate gene expression. Treating UM cells with 5’aza-2’-deoxycytidine, a global DNA inhibitor, resulted in increased levels of MDK mRNA. Interestingly, already high expressing tumor cell line (Mel202 and Mel270) showed significantly lower increase of MDK expression than intrinsic low expressing cell line (OCM1 and OMM2.3) (Fig. 3.11). These results do not directly prove that promoter methylation of MDK influences MDK expression, since global demethylation may induce other factors that could regulate MDK expression. However, we did find a correlation between DNA methylation in the promoter region of MDK and MDK expression levels. With bisulfite conversion PCR we could show that high cytosine methylation in the MDK promoter region was detected in OCM1 cell line that expresses low levels of MDK expression. No cytosine methylation was found in Mel202 cell line that expresses high levels of MDK (Fig. 3.12). These data show a correlation between promoter DNA methylation and MDK expression level. The only other study looking at the influence of DNA methylation on MDK expression was done by Hamm et al. By demethylating rat chondrosarcoma cells with 5’-Aza they could show a significant increase in MDK expression (Hamm et al. 2009). Tumor progression is often linked to loss of tumor suppressors as well as gain of function of oncogenes. Both can occur through mutation and through gene silencing or activation linked to methylation or demethylation of CpG island in promoters (reviewed in Baylin et al. 1998; Santini et al. 2001). Treatment with 5-azacytidine and 5-aza-2’-deoxycytidine has been shown to reactivate tumor suppressor gene function by demethylating silenced CpG island promoters. In UM hypermethylation of tumor-suppressor genes have been found in hTERT (Maat et al. 2007) and RAS associated domain family 1 (RASSF1), which has also shown to significantly correlate with the development of metastatic disease (Moulin et al. 2008). On the other hand higher expression of the gen Deleted in Split hand/Split foot 1 (DSS1) was found in 64.1% of UM tumor tissue compared to normal tissues. It was shown that the overexpression correlated with lower promoter methylation and 5-Aza treatment of OCM-1 and OCM-3 cell lines significantly increased DSS1 gene expression. The promoter hypomethylation of DSS1 and the resulting overexpression was linked to poor prognosis and specifically to metastasis and reduced overall and disease free survival (Venza et al. 2017). Furthermore, the expression of Preferentially

58 4 Discussion

Expressed Antigen in Melanoma (PRAME), a biomarker of metastasis in uveal melanoma is associated with aberrant hypomethylation of the PRAME promoter (Field et al. 2016). These studies clearly show that epigenetics, especially promoter DNA methylation influences the UM tumor development and progression. Interestingly, in UM and CM cell lines DNA demethylation using 5’Aza caused significant reduction of viability and invasiveness and in combination with irradiation 5’Aza treatment reduced metastases from the eye to the lung in a murine cutaneous melanoma xenograft model (Rajaii et al. 2014). However, in case of the DSS1 gene, PRAME and MDK, demethylation correlated with increased expression and with increased tumor cell migration and poor prognosis, indicating that global demethylation plays opposite roles. Today, no therapy based on epigenetics is in clinical use for UM. However, this may change, since miRNAs are an important epigenetic regulatory system that may be targeted as a cancer therapy. Targeting specific miRNAs could be particularly effective in cancers in which miRNAs have been found to confer chemotherapeutic resistance (Y. Li et al. 2017a).

Intracellular and soluble MDK MDK expression is regulated at the gene level, however regulatory mechanisms at the protein level, e.g. post-translational modifications, may play a role as well. MDK is a protein that can be secreted, allowing signal induction in neighboring cells as well as distant organs/tissues. Indeed we showed that the UM cells lines secrete MDK via ELISA and Westernblot. Extracellularly MDK sends growth activating and anti-apoptotic signals via the interaction with cell surface receptors. However, immunofluorescence staining for MDK of UM cells on a chamber slide showed strong perinuclear and intracellular staining (Fig. 3.5), suggesting intracellular accumulation of MDK. The function of MDK within the cell is unclear. A few studies have shown that exogenous MDK can be endocytosed and can be transported to the nucleus. Nucleolin, a major nucleolar protein, which functions as a transport protein between the nucleus and cytoplams was identified as a MDK-binding protein (Take et al. 1994; Shibata et al. 2002). In peripheral blood lymphocytes, co-localization of MDK and nucleolin on the cell surface as well as within the nucleus was found after activation of lymphocytes (Callebaut et al. 2001). It was reported that the C-terminal end of nucleolin, containing repeats of the amino acid motif RGG, is the domain that binds MDK (Said et al. 2002). Additionally, it was demonstrated that exogenous MDK was detected in the nucleus within 20 min after internalization and nuclear targeting of MDK was mediated via CD91/LRP (Shibata et al. 2002). In HepG2 cells, MDK was found to localize to the nucleus as well as to the nucleolus (Dai et al. 2005). The MDK C-terminal tail has been shown to be important in controlling MDK nucleolar accumulation (Dai et al. 2005;Suzuki et al. 2004). Moreover, the nuclear localization was shown to be involved in the transcription of ribosomal RNA (Dai et al. 2008). Interestingly, active and repressive chromatin-associated proteome analysis identified MDK as an H3K4me3-marked chromatin associated protein and pretreatment with MDK inhibitor (MDKi) prevented induced epithelial monolayer permeability (Khan et al. 2016) via PI3K/Akt pathway (Khan et al. 2017). In summary, in UM MDK seems to accumulate at the nucleus and some evidence from other tumors suggest MDK could function as a transcription factor. However, the exact role of MDK in the nucleus remains unclear.

To assess the utility of soluble MDK as a biomarker in UM we tested MDK serum levels of four patients and six healthy donors. Unfortunately, we only had the opportunity to test four UM patients. A larger cohort is needed with additional patient data to actually address the question if MDK serum levels could be a biomarker for UM disease progression as reported in other malignancies. Several studies in diﬀerent tumors have shown signiﬁcantly higher MDK serum levels in patients than in the control groups. The most extensive study looked at ten types of carcinomas, including oesophageal carcinoma, gastric carcinoma, duodenal carcinoma (adenocarcinoma), colon carcinoma, hepatocellular carcinoma, bile-duct and gallbladder carcinoma, pancreatic carcinoma,

59 4 Discussion thyroid carcinoma, lung carcinoma and breast carcinoma. In all cancer patients higher MDK serum levels were detected than in the healthy controls (Ikematsu et al. 2000). In ovarian cancer significantly higher MDK plasma concentrations were detected in the patient group (909 pg/ml patients vs 383 pg/ml healthy control). Adding MDK to a multi-analyte panel, that includes plasma levels of CA125 (best characterized ovarian cancer biomarker) and anterior gradient 2 protein (ARG2; recently reported circulating biomarker for ovarian cancer) significantly improved the diagnostic sensitivity and specificity, clearly showing the usefulness of MDK as a biomarker (Rice et al. 2010). In non-small cell lung cancer higher serum levels of MDK were found in patient group (657.36 496.58 pg/ml) compared to the healthy control group (194.49 ± 122.57 pg/ml)(Xia et al. 2016). Significant higher levels of MDK in serum of oral squamous ± cell carcinoma were found (419.1 97.9 pg/ml healthy control vs 885.9 465 pg/ml) (K. Ota ± ± et al. 2008). In breast cancer mean MDK serum levels prior to tumor removal were (3.68 ± 2.13 ng/ml) significantly higher than in control group (1.77 0.38 ng/ml). After tumor removal ± MDK serum levels dropped significantly (2.47 1 ng/ml) (Cetin Sorkun et al. 2016). In thyroid ± cancer two recent studies have shown significant higher serum levels of MDK in patients than in the control groups. They suggested cut-offs of MDK serum concentrations between 300 and 500 pg/ml to optimally distinguish between healthy and disease status (Meng et al. 2015; Jia et al. 2017). Interestingly, MDK serum levels varied between control groups of different studies. MDK serum levels in control individuals were found between 184 111 pg/ml (Ikematsu et al. 2000), ± 194.49 122.57 pg/ml (Xia et al. 2016), 255.01 126.78 pg/ml (Meng et al. 2015), 383 pg/ml ± ± (Rice et al. 2010) and the highest levels 1.77 0.38 ng/ml (Cetin Sorkun et al. 2016). We ± found a mean serum MDK level of 86.63 65.10 pg/ml in the healthy donors, at the lower ± end of concentrations found in control groups. Different protocols to collect serum as well as different ELISA kits were used in the different studies which could contribute to the detected differences. The composition of the control groups in some studies have not been extensively discussed, therefore differences between the groups could also cause the various MDK serum levels reported. Potentially other confounding factors, such as inflammation, age differences etc. could play a role. Apart from MDK concentrations in serum, also urine levels have been shown to be significantly elevated compared to healthy controls and non-malignant tissue. In bladder cancer, MDK concentrations were significantly higher in urine samples of patients compared to the control group. Data normalized to urinary creatinine, showed mean MDK concentrations of 0.36 µg/creatinine g in the control group and 1.35 µg/creatinine g in the bladder cancer group. Increasing tumor stages correlated with increasing MDK urine concentrations with the highest mean MDK concentration detected (3.58 µg/creatinine g) in the patient group with the highest progressed tumor (Vu Van et al. 2016). The advantage of measuring MDK in serum or urine is the relative uncomplex sample retrieval. However, biopsies of tumors to assess protein or RNA concentrations has been used to determine biomarker function of MDK. Histological or qPCR analysis of MDK expression in tumor tissue was shown to be correlated to poor prognosis of patients with pancreatic cancer (Yao et al. 2014), breast cancer (F. Li et al. 2015), papillary thyroid cancer (Kato et al. 2000; Y. Zhang et al. 2014; Shao et al. 2014), prostate cancer (Nordin et al. 2013), gastrointestinal cancer (Aridome et al. 1995), cervical cancer (Moon et al. 2003), salivary gland tumor (T. Ota et al. 2010) and cutaneous melanoma (Olmeda et al. 2017). Interestingly, also significant higher levels of MDK were found in the B-precursor ALL than in normal bone marrow (Hidaka et al. 2007)andsignificant higher levels of MDK mRNA were detected in PBMCs of patients with primary non-small cell lung cancer compared to patients with benign lung lesions (Ma et al. 2013).

We and others could show that MDK is secreted from tumor cells and is able to act on neighboring cells. We were wondering if MDK simply diﬀuses outside the cell or if an active and

60 4 Discussion regulated transport takes place. We found that exosomes, purified from the supernatant of UM cells contained MDK. However, MDK was found in about 4 fold higher levels in the soluble fraction, depleted of exosomes (Fig. 3.8). The importance of MDK levels in the exosome and in the soluble fraction is unclear and further studies are needed to find out if MDK transport is regulated. Interestingly, recently Olmeda et al. 2017 identified MDK as the top scoring candidate, after studying the cargo of exosomes secreted by melanoma cells in the search for tumor-secreted proteins that influence distant lymphangiogenesis and metastasis. They showed that MDK in exosomes can function at distant sites, by influencing endothelial cells to promote lymphangiogenesis and generation of a premetastatic niche. Therefore, it is warranted to study the cytokine function of secreted MDK either as a soluble protein or as part of exosome cargo for a potential role in inducing metastasis in UM.

4.2 MDK receptors and intracellular signaling

MDK receptors Several receptors for MDK have been described in the literature, which have been attributed with a multitude of functions of MDK in diﬀerent tissues and diseases. We assessed the cell surface expression of CD91, CD138 and Notch2, all MDK receptors introduced in more detail in chapter 1.2.2. High overall expression of these receptors could be detected on the cell surface of UM tumor cells. Interestingly, only low expression of CD91 and CD138 could be detected on the surface of non-malignant melanocytes, making these receptors very interesting targets for therapeutic interventions.

For instance, studies have reported that blocking MDK - CD91/LRP1 interaction inhibited the anti-apoptotic function of MDK on neurons (H. Muramatsu et al. 2000), diminished MDK mediated growth inducing eﬀects (Suzuki et al. 2004) and prevented MDK supported tumor formation of CMT-93 cells in nude mice (S. Chen et al. 2007). Targeting the LRP1 receptor resulted in loss of MDK protective function of hypoxic injuries in embryonic stem cells by blocking MDK- induced Akt phosphorylation and prevented MDK binding to polymorphonuclear neutrophils (PMNs) reducing MDK mediated PMNs adhesion and migration (Weckbach et al. 2014).

Interestingly, in malignant gastric adenocarcinoma, inverse correlations of MDK and CD138 were found. It was also reported, that expression levels of CD138 were negatively correlated with the grade of differentiation and lymph node metastasis. It was hypothesized that high expression levels of MDK indicate a lower stage of differentiation of malignant cells and poor prognosis, whereas CD138 expression indicates a high degree of differentiation, an early clinical stage and a favorable prognosis (Hu et al. 2014). However, if indeed high expression levels of CD138 indicate early clinical stage and loss of CD138 corresponds to progressing disease, the MDK effect in the later stages might occur via signaling through its other receptors. Further- more, we found high levels of CD138 expression on all of the UM cells including cell lines derived from highly progressed liver metastasis. Additionally, in many cancers high expression of CD138 is correlated with unfavorable outcomes and deregulated CD138 expression has been used as a prognostic factor in cancers (reviewed in Gharbaran 2015). Interestingly, in multiple myeloma, cell growth could be increased by the binding of HGF to CD138, which then potentiates MET signaling and resulted in the activation of PI3 kinase- protein kinase B and Ras-MAP kinase pathways (Derksen et al. 2002). These pathways are also activated by MDK. Furthermore, successful targeting CD138 with OC-46F2, a fully humanized anti-CD138 recombinant antibody reduced CD138/VEGFR2 activities in the tumor microenvironment, thereby suppressing vascular maturation and tumor growth in malignant melanoma (Orecchia et al. 2013). These findings make CD138 a highly interesting potential target for new therapeutic interventions in UM.

61 4 Discussion

The MDK - Notch2 interaction was shown to activate Notch2 signaling and to promote EMT in keratinocytes by downregulating epithelial markers and increasing mesenchymal markers (Y. Huang et al. 2008). Furthermore, MDK was shown to induce chemoresistance in pancreatic cancer cells (Gungor et al. 2011) and gastric cancer cells (Tian et al. 2017)viaNotch2signaling. Overall, Notch signaling has been shown to be important in inducing proliferation and invasiveness of UM cells and by inhibiting the Notch pathway reduced melanoma cell growth and invasiveness was observed (Asnaghi et al. 2012). Interestingly, Liu et al. 2015 did ﬁnd a regulatory connection between GNAQ expression and Notch signaling. Downregulation of GNAQ reduced Notch ligand (Jag-1) and target genes (NCID and Hes-1) whereas overexpression of GNAQ increased their expression.

Apart from CD91, CD138 and Notch2 other receptors have been described to interact with MDK, including PTP⇣ and Alk. Although we have not studied expression of these receptors in UM cells, for completeness they are brieﬂy discussed here.

Starting with PTP⇣, Qi et al. 2001 showed that haptotactic migration, which involves cell crawling towards substrate-bound molecules such as extracellular matrix proteins is induced by MDK and showed a link between PTP⇣-MDK binding and the migratory behavior of cells. The anti-apoptotic eﬀect of MDK on embryonic neurons was diminished by digestion with chon- droitase ABC or addition of the antibody to PTP⇣, suggesting that the survival promoting eﬀect of MDK was signaled via the PTP⇣ receptor (Sakaguchi et al. 2003). MDK was also shown to induces cell survival in B cells via the PTP⇣ receptor (S. Cohen et al. 2012).

MDK and PTN (the second heparin-binding growth factor) have been identified by phage display as potential high-affinity ligands for human Alk (Stoica et al. 2002). It was shown that MDK as well as PTN were able to stimulate Alk phosphorylation and activation of PI3K in endothelial cells as well as tumor cells from various origins (He et al. 2015). Recombinant MDK increased phosphorylation of Alk, ERK and STAT3. This effect could be abolished by treating the cells with an Alk inhibitor (TAE684) suggesting MDK signaling via the Alk receptor (He et al. 2015). In addition, it was shown that neuron proliferation depends on MDK and Alk levels and MDK/Alk signaling controls proliferation of neural crest cells of E2 chicken embryos in vitro. In vivo downregulation of MDK and Alk seemed to be compensated and no proliferation differences were observed (Reiffet al. 2011). Several other studies found contradicting evidence. For instance in 2007 Mathivet et al. did show that PTN was not able to activate Alk in neuroblastoma and glioblastoma cells (Math- ivet et al. 2007). Moreover, it was discussed that in drosophila, miple1 and miple2 (drosophila counterparts of PTN and MDK) are not able to control activation of Alk signaling, due to their different spatial expression pattern. Miple1 and miple2 are widely expressed during embryogenesis, whereas Alk activity is spatially restricted (H. H. Lee et al. 2003). Furthermore, it was shown that miple1 and miple2 are neither required to drive Alk signaling during drosophila embryogenesis, nor are these molecules essential for development in the fruit fly. They did also show that neither PTN, nor MDK were able to activate human Alk in vivo, when extopically co-expressed in the fruit fly. Therefore, Alk was disputed as a MDK or PTN receptor (Hugosson et al. 2014). Further investigations could elucidate the specificities of the functional interactions and binding specificities between these receptors and MDK. In addition, this could provide information on the potential usefulness of therapeutically targeting MDK receptors to prevent MDKs growth promoting effect on UM tumor cells.

Intracellular signal transduction Intracellular signal transduction starts with a ligand binding to its cognate receptor triggering a cascade of signaling molecules to become activated or silenced. The targets of such signaling

62 4 Discussion pathways frequently include transcription factors that function to regulate gene expression. In- tracellular signaling pathways thus connect the cell surface to the nucleus, leading to changes in gene expression in response to extracellular stimuli. We used a screening assay to assess 20 important signaling molecules for their role in MDK-mediated intracellular signaling. Inter- estingly, of the 20 molecules the phosphorylation of RPS6 and PRAS40 were clearly linked to MDK expression. Both signaling molecules showed significantly lower phosphorylation in UM cells with downregulated MDK and showed higher phosphorylation in UM cell overexpressing MDK (Fig. 3.25 and Fig. 3.27). Both molecules are important components of the Akt/mTOR signaling pathway. PRAS40 acts at the intersection of the Akt and mTOR mediated signaling pathways. mTOR signaling is linked to regulation of cellular growth, mRNA translation, ribo- some biogenesis, lipid biogenesis, vesicle-mediated transport, autophagy, mitochondrial function and lymphangiogenesis. Activation of mTOR in response to nutrients or growth factors results in phosphorylation of numerous substrates. PRAS40 was first discovered as a Akt substrate, however recently, PRAS40 was identified as both component and substrate of mTOR complex one. Phosphorylation of PRAS40 by Akt or mTOR results in dissociation of PRAS40 from mTOR complex, which could reduce inhibitory signals on mTOR activity (reviewed Wiza et al. 2012). In melanoma it was reported, that levels of phosphorylated PRAS40 increased during melanoma progression, and targeting PRAS40 inhibited tumor growth in mice. Decreased phosphorylation of PRAS40 increased tumor cell apoptosis and decreased chemoresistance (Madhunapantula et al. 2007). To understand PRAS40 functionality in UM, further studies are needed. We could confirm the finding that MDK has an impact on the phosphorylation of RPS6 with phospho-flow cytometry. We again found higher phosphorylation in cells with high levels of MDK and reduced phosphorylation in cells with low levels of MDK (Fig. 3.26 and fig. 3.28). Several other studies have shown MDK acting on the Akt/mTOR pathway. For instance MDK promoted resistance to Tetrahydrocannabinol (THC)-induced autophagy and apoptosis by enhancing activity of Akt and increased phosphorylation of S6 in glioma cells (Lorente et al. 2011). Also, MDK was shown to induce migration of osteoblast-like cells and activation of both PI3- kinases and MAP-kinases, resulting in higher Akt phosphorylation. The MDK induced migration could be abolished by PI3K inhibitors (Qi et al. 2001). Furthermore, treating lung adenocarcinoma cells with small MDK inhibitors (MDKi), reduced proliferation, induced apoptosis and lowered phosphorylation levels of PI3K and Akt (H. Hao et al. 2013). Conversely, overexpression of MDK increased phosphorylation of Akt, Erk and p38 MAPK in gastric cancer cells (Y. Xu et al. 2009; Y. Xu et al. 2012; S. Zhao et al. 2012) and prostate cancer cell lines. Primary B cells treated with recombinant MDK increased Akt phosphorylation (S. Cohen et al. 2012). In lymphatic endothelial cells MDK has been shown to activate the mTOR pathway (Olmeda et al. 2017). Additionally, Akt and Erk phosphorylation was reduced in total heart organ tissue in the MDK knockout mice (Honda et al. 2016). Interestingly, in UM it was shown that mTOR/S6 signaling axis was among the most differentially regulated biological processes compared with normal choroidal melanocytes (Angi et al. 2016). Our data suggest that MDK, by inducing phosphorylation of Akt/mTOR substrates, contributes to the higher activation of these pathways in UM. Based on these findings we propose a model for the intracellular signaling mechanism of MDK in UM cells (Fig. 4.1).

63 4 Discussion

MDK intracelluar signaling

MDK

CD91/CD138/Notch2

Akt P P PRAS40 P mTOR viability P RPS6 chemoresistance migration

mRNA translation apoptose UM tumor cell

Figure 4.1 –ProposedintracellularsignalingmechanismUM.SolubleMDKbindstooneofMDKreceptors on the cell surface of the UM cell, triggering an intracellular signaling cascade, resulting in higher phosphorylated PRAS40 and RPS6 (Akt and mTOR substrates). This activations leads to increased tumor cell survival, migration and induces therapeutic resistance.

4.3 MDK eﬀects on tumor cell survival

To study the biological effects of MDK on UM tumor cells we performed loss-of-function as well as gain-of-function experiments. Loss of MDK significantly reduced proliferation and viability of the tumor cells (Fig. 3.20). this is in agreement with other studies showing that downregulation of MDK with siRNA or small inhibitory molecules reduced tumor cell growth (Dai et al. 2006; Q. Wang et al. 2007; H. Hao et al. 2013). This loss of tumor cell growth was often found in combination with increased apoptosis as well as increased sensitivity to therapeutic agents. We also observed that by downregulating MDK in UM cells increased caspase 3 activity was measured (Fig. 3.21). Apoptosis is induced by activating a cascade of caspases, including primary effector caspases 3 and 7. We measured significant increase in caspase 3/7 activity in MDK downregulated cells. MDKs influence on caspase activity has been reported in other cells in both ways, either downregulation of MDK increases caspase activity and induces apoptosis (Ohuchida et al. 2004; Q. Wang et al. 2007) and on the other hand MDK secretion protects from apoptosis by reduced caspase 3 activity (Yazihan et al. 2008).

4.4 MDK and tumor cell migration and metastasis

Cell motility and migration are highly regulated mechanisms, essential for disease progression and metastasis development. We found that UM cells overexpressing MDK have increased mobility and migratory capabilities (Fig. 3.22). Many previous studies found that MDK enhances EMT in tumor cells driving cell migration and development of metastasis. For instance, increased levels of MDK have been contributed to enhanced EMT of lung adenocarcinoma cells (G. F. Zhao et al. 2012) and pancreatic cells (Gungor et al. 2011). Induced migration and faster wound-healing in scratch assay was seen in pancreatic cancer cell lines (Rawnaq et al. 2014). MDK signaling via the Notch receptor

64 4 Discussion was reported to drive EMT and cell migration (Y. Huang et al. 2008; Gungor et al. 2011). Also in prostate cancer stem cells inhibiting MDK expression significantly reduced migration accompanied with reduced phosphorylation of Akt (Erdogan et al. 2017). The direct influence of MDKs UM metastasis to the liver has not been studied. However, in the 80 UM patients included in the TCGA cohort, we retrospectively found, significant higher levels of MDK expression in primary tumors of patients that developed metastases than in primary tumors of patients that did not develop metastases within the timeframe of the study (3.33 B). Furthermore, patients with high MDK expression in the primary tumor developed metastases faster than patients with low MDK expression (Fig. 3.34). Interestingly, metastasis to the liver could be suppressed by downregulation of MDK in human pancreatic cancer cells (L. Yu et al. 2013) and colorectal cells (Barderas et al. 2013). A recent study has identified MDK as a key factor for early induction of a distal pre-metastatic niches in a mouse model for skin melanoma (Olmeda et al. 2017). They investigated the role of MDK on distant neolymphangiogenesis and the development of metastasis, making use of vascular endothelial growth factor receptor 3 (VEGFR3) reporter mice. VEGFR3 reporter mice were transplanted with CM cells with either MDK downregulated or overexpressed. Xenograft mice with CM tumor cells with downregulated MDK levels showed significant lower induction of VEGFR3 as well as significant reduction in lymphnode and lung metastasis. On the other hand the mice transplanted with CM cells with MDK overexpression showed induction of neolymphangiogenesis and higher numbers of lymphnode and lung metastases. MDKs influence on liver metastasis was not specifically studied. These data show a dual role of MDK and in inducing neolymphangiogenesis as well as metastasis in distant organs. Prior to this study high correlations were found between MDK expression and density of microvessels (T. Ota et al. 2010). MDK inhibition was shown to suppress angiogenesis, inhibits the proliferation of human umbilical vein endothelial cells and suppresses formation of new blood vessels (H. L. Huang et al. 2015). A few other molecules and pathways have been identified that potentially also play a role in development of liver metastases, including the hepatocyte growth factor (HGF) and its cognate c-Met receptor pathway, IGF-1 and IGF-1R and chemokines and chemokine receptors (CXCR4 and CXCL12) (reviewed in Bakalian et al. 2008). HGF is proposed to be an important microen- vironmental element in supporting UM metastasis through activation of c-Met. Studies have shown higher levels of c-Met in metastatic tissue compared to primary tumor tissue (Mallikar- juna et al. 2007; Gardner et al. 2014). c-Met a membrane bound receptor can also be found in a soluble form. Higher serum levels of c-Met were measured in patients with metastatic disease as compared to patients without metastatic disease and healthy donors (Barisione et al. 2015). Interestingly, stable patients had constant serum levels of c-Met, whereas patients with progressive metastatic disease showed further increases in c-Met levels. HGF levels were not assessed in this study (Barisione et al. 2015). In addition, substantial c-Met staining of UM tissues could be associated with epithelioid type tumors, a higher metastasis rate (Kennedy et al. 2014) and enhanced cell migration (Ye et al. 2008). In UM cell lines, it could be shown that siRNA knockdown of c-Met or treatment with HGF inhibitor, did not change the viability of UM cell lines, however, the migratory capability was highly significantly reduced (Surriga et al. 2013; Z. Wang et al. 2017). Yan et al. 2009 found that microRNA, miR-34a, which regulates cMet expression was downregulated in UM cells. Transfection of miR-34a in UM cells led to reduced c-Met expression and caused significantly decreased cell growth and migration. A second microRNA, miR-144 was identified to be downregulated in UM cell lines and UM tissue. One of the functions of miR-144 was inhibiting c-Met mediated cell proliferation and invasion (Sun et al. 2015). Crizotinib, a c-Met inhibitor prevented metastasis, by inhibition of c-Met phosphorylation in a metastatic UM mouse model (Surriga et al. 2013). Taken together, c-Met seems to be a an important contributor to metastasis development. Apart from c-Met and HGF, IGF-1 and IGF-IR are also interesting molecules in the context of predominantly found liver metastasis. The chemokine, IGF-1 is mainly produced in the

65 4 Discussion liver and may explain the preferentially homing of the tumor cells to the liver in UM patients. Immunohistochemical evaluations of IGF-1R expression in UM primary tissue showed correlation with the degree of pigmentation, necrosis, and lymphocyte infiltration (Topcu-Yilmaz et al. 2010). Additionally, chemokine receptors and their ligands seem to be important, since they are involved in a number of cell processes, including normal cell trafficking as well as metastasis in cancer. CXCR4 and CXCL12 (also known as stromal cell derived factor-1) are specifically interesting. CXCL12 has previously been associated with liver metastasis in cancers, previously (Matsusue et al. 2009; Amara et al. 2015). Interestingly, CXCR4 receptor has been found to be expressed on UM cell lines (Di Cesare et al. 2007) and on UM tissue (Dobner et al. 2012). Migration towards the chemokine CXCL12 was greater than towards the negative control and a peptide inhibitor, targeting CXCR4 receptor complex prevented UM cell migration (Di Cesare et al. 2007).

In summary, the liver tropism of UM is not fully understood. Several factors have been identiﬁed that may play a role in metastatic spread from the eye to the liver. One of these factors may be MDK. The notion is supported by data from this thesis indicating increased migration of UM cells with high MDK expression and a shorter time-to-metastasis in patients with high MDK expression in the primary tumor. Therefore, further studies on the mechanistic insights of MDKs functional role in metastasis development is needed.

4.5 MDK and the immune system

MDK a cytokine with pro-inflammatory functions MDK has been described as a cytokine with pro-inflammatory functions. In particular, MDK has been described in auto-immunity diseases and may play a role in chronic inflammatory disorders (reviewed in Weckbach et al. 2011;SakamotoandKadomatsu2012; Sorrelle et al. 2017). Supporting the role of MDK as a pro-inflammatory molecule, we found that in vitro stimulation of mononuclear cells induced MDK expression (Fig. A.1). Interestingly, also in the more pro-inflammatory M1 macrophages higher levels of MDK were detected (Fig. A.2). These results are in line with data from other groups showing that in vitro activated T cells with PHA or with anti-CD3/anti-CD28 antibodies induce MDK expression transiently. MDK expression was also induced upon stimulation through the CD28 receptor alone and treatment with IL-2 or IFN- (Callebaut et al. 2001; Masuda et al. 2017). MDK has been shown to be an indicator of inflammation in renal transplantation (Ozturk et al. 2014). Blocking MDK attenuates acute inflammation in experimental autoimmunity (Brunner et al. 2016) and attenuates lipopolysac- caride induced lung inflammation in mice (Tanino et al. 2016). Additionally, elevated MDK plasma levels have been detected in patients with acute pancreatitis (Y. Li et al. 2017b)andin patients with systemic lupus erythematosus (G. C. Wu et al. 2017). In leukemia significant higher levels of MDK were found in the bone marrow of B-precursor ALL patients compared with healthy donors (Hidaka et al. 2007). Significant higher levels of MDK mRNA were detected in peripheral blood mononuclear cells (PBMCs) of patients with primary non-small cell lung cancer compared to patients with benign lung lesions, which correlated significantly with clinical stages, differentiation and lymph node metastasis (Ma et al. 2013). Interestingly, MDKs pro-inflammatory effects seem to be at least partly due to MDKs effects on regulatory cells. It was shown that MDK deficiency attenuates experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis, because of an expansion of the Treg population in peripheral lymph nodes and decreased number of Th1 and Th17 cells. Parallel in vitro studies showed that MDK decreases the Treg cell population ex vivo in a dose-dependent manner by suppression of STAT5 phosphorylation that is essential for Foxp3 expression (J. Wang et al. 2008). Also, MDK produced under inflammatory conditions, particular by CD4+ T cells suppressed the development of tolerogenic dendritic cells. Inhibition of MDK, increased numbers

66 4 Discussion of tolerogenic dendritic cells and Tregs in draining lymph nodes and suppressed the severity of EAE (Sonobe et al. 2012).

MDK induces immune cell migration As already described, MDK has been shown to induce migration and motility of tumor cells. Interestingly, MDK was also suggested to be important for immune cell migration and infiltration. We found that mean MDK expression levels were higher in primary UM tumor with moderate and heavy levels of TILs and TAMs compared to tumors with mild infiltration (Fig. 3.40). The MDK produced by the infiltrated immune cells could add to the tumor produced MDK levels and potentiate the MDK effect on metastasis and disease progression. High immune cell infiltration has been reported to correlate with worse outcome in UM patients (in detail described in chapter 1.1.6). In particular high infiltration of M2 macrophages, which have more pro-angiogenic and less tumoricidal capacities have been found in UM tumors (Bronkhorst et al. 2011). Other studies have reported that MDK increases macrophage (K. Hayashi et al. 2001)and neutrophil (Weckbach et al. 2011) migration. Furthermore, MDK expression was suggested to be important for increased recruitment of inflammatory cells and migration of vascular smooth muscle cells in neointimal hyperplasia (Narita et al. 2008). In MDK knock-out mice tubuloin- terstinitial damage after renal ischemic reperfusion injury was less sever and the number of infiltrating neutrophils and macrophages was lower, suggesting that MDK enhances migration of inflammatory cells upon ischemic injury (Sato et al. 2001). MDK deficiency in knock-out mice was also shown to prevent the infiltration of inflammatory cells into the lupus kidney. Inflammatory-related molecules, like TNF-↵,IL-1 and INF- were significantly lower in lupus kidneys of MDK-/- mice (Masuda et al. 2017). Furthermore, MDK was suggested to be involved in the development of pulmonary fibrosis by regulating inflammatory cell migration into the lung (Misa et al. 2017) and recently, MDK has been identified as a modulator of tight junctions permeability via the PI3K and Akt pathway (Khan et al. 2017). In summary, MDK has been contributed to the adhesion and migration of inflammatory cells into tissue. Potentially, via the same mechanisms as described in the MDKs effect on migration of tumor cells.

4.6 Clinical relevance of MDK in UM patients

We used next-generation sequencing data produced by Robertson et al. 2017 and publicly provided by the the genome cancer atlas (TCGA) database from the national institute of health (NIH) for analysis of MDK expression and potential clinical correlations. UM primary tumors of 80 patients were sequenced and a large number of additional clinical data were collected (see appendix E for an overview of the clinical, pathological and molecular characteristics of UM TCGA cases). Here, we were able to show for the first time that MDK expression levels in the primary tumor significantly correlated with the overall-survival of UM patients (Fig. 3.32). Kaplan- Meier estimates of 5 year survival in the high MDK expressing group was 20%, whereas the survival rate in the low MDK expressing group was 90%. The lower survival rate is most likely due to MDK influence on inducing metastasis. As previously mentioned we and others could show that MDK overexpression in a UM cell line increased the migratory capability of the tumor cells (Fig. 3.22). We could also show that deficiency of MDK reduces proliferation and viability of the tumor cells as well as increases the apoptotic rate. In the TCGA cohort, higher MDK expression in primary tumors correlated with metastasis (Fig. 3.33). Moreover, we could show that higher MDK levels does not only correlate with metastasis and with lower overall-survival, but also that metastasis develop faster in patients with high levels of

67 4 Discussion

MDK in the primary tumor (Fig. 3.34). In the subgroup of patients with metastasis, the mean time to metastasis was 226 days in the high MDK group and 646 days in the low MDK group. Collectively, these data imply that MDK contributes to metastasis formation.

Grouping of UM tumors according to the the AJCC classification has shown that the tumor size is not does not correlate with MDK expression. Even though a slight trend to increase mean of MDK expression could be observed with increase tumor size (Fig. 3.35) the ranges of MDK expression levels within each tumor size group are very large. Interestingly, by stratifying the tumors not only by size, but also by subcategories, MDK expression levels tend to increase with the involvement of ciliary body as well as extraocular extension in each tumor size category. Indeed, when we combined all tumors of all sizes with ciliary body involvement or with extraoclar extension and compared MDK expression levels in tumors without ciliary body involvement or without extraocular extension respectively, the tumors with ciliary body involvement and extraocular extension showed significant higher mean expression levels of MDK (Fig. 3.36). Ciliary body involvement and extraocular extensions have been associated with to worse outcome. In particular, the extraocular extensions, which lead to a breach in the immune privilege of the eye, allowing immune cell infiltration as well as dissemination of tumor cells. The current AJCC staging uses tumor size, ciliary body involvement, and extraocular growth as its parameters to prognose UM. Therefore, the AJCC classifications is only based on anatomical tumor information and no genetic data contributes to the staging. Recently it was shown that adding chromosome status improved the prognostic power of the UM AJCC staging (Dogrusoz et al. 2017). The SCNA cluster defined by Robertson et al. 2017 does include chromosomal information. Four SCNA groups have been clustered based on increasing chromosomal instabilities and correlated with increased metastatic risk. Interestingly, MDK expression levels also increase in SCNA cluster scoring with the highest mean MDK expression in group 4, the group with the highest risk of metastatic disease. Moreover, higher MDK expression was found in tumors with monosomy of chromosome 3, a well established marker of metastasis development and reduced overall-survival. No correlation was found to BAP1 and PRAME, two other discussed progression markers. In summary, we could show that MDK promotes survival functions of UM tumor cells and induces tumor cell migration. MDK gene expression level in the primary UM tumor is highly correlated with time-to-metastasis and with overall survival. Therefore, targeting MDK, MDK receptors or downstream molecules may provide an effective prevention of metastasis development.

4.7 MDK and therapeutic interventions

MDK and therapeutic resistance Here, we found a survival benefit of UM cells treated with Akt/mTOR inhibitors when overexpressing MDK (Fig. 3.30 and 3.31). MDK has been described to induce resistance to therapy in previous studies. For instance it was shown just by downregulating MDK chemosensitivity for gemcitabine could be restored in pancreatic cancer cells (Gungor et al. 2011)andhumangastric cell line (Tian et al. 2017). Furthermore, it was reported that MDK promotes oral squamous cell carcinoma (OSCC) cells resistance to cisplatin. High levels of MDK, produced by cancer- associated fibroblasts promoted the the cisplatin resistance via elevated expression of lncRNA ANRIL. Downregulation of lncRNA ANRIL in tumor cells inhibited proliferation and induced apoptosis which could be restored by treatment with human MDK (D. Zhang et al. 2017). Sev- eral studies have shown that MDK exerts anti-apototic effects, counteracting apoptosis inducing therapeutica (Q. Wang et al. 2007; Yazihan et al. 2008). It could also be shown that MDK protected glioma cells from THC pro-autophagic and antitumoral functions (Lorente et al. 2011). In human ovarian cancer cell lines high MDK expression levels correlated with higher susceptibility

68 4 Discussion to paclitaxel/cisplatin (X. Wu et al. 2015) in contrast to the studies of Tian et al. 2017 and D. Zhang et al. 2017 which showed that high levels of MDK protected for paclitaxel/cisplatin eﬀects. In summary these ﬁndings indicate that MDK induces resistance to therapy. By targeting MDK directly or indirectly, this resistance to therapies may be overcome.

Direct and indirect targeting of MDK Apart from overcoming resistance to therapies, directly or indirectly targeting MDK could eliminate MDKs effects on tumor cell migration and development of metastasis. To directly target MDK, small nucleotides could be used to downregulated MDK expression. We and others could show that downregulating MDK with specific siRNAs reduces tumor cell growth effectively. However, delivering siRNAs effectively are challenging. Alternatively, the previously described small molecule compound MDK inhibitor commercially available from ChemDiv (San Diego, CA) could be used. It was shown that treating lung adenocarcinoma cells with MDKi reduced proliferation and induced apoptosis. Interestingly, the small MDK inhibitor, inhibited the PI3K/AKT pathway and lower phosphorylation levels of PI3K and AKT were observed (H. Hao et al. 2013). Pretreatment with MDKi prevented induced epithelial monolayer permeability (Khan et al. 2016) via PI3K/Akt pathway (Khan et al. 2017). Lastly, humanized monoclonal anti-MDK antibodies (under development: http://www.cellmid.com.au/content_ common/pg-cancer-treatment-and-detection.seo) could be used to target MDK directly. Indirectly targeting MDK could include blocking MDK cell surface receptors to prevent intracellular MDK signaling and could potentially inhibit, MDKs pro-growth and pro-migratory effects. Furthermore, molecules in downstream signaling pathways of MDK could be targeted. As we and others have shown MDK is connected to the PI3K/Akt/mTOR pathway. Interestingly, two still ongoing clinical studies for UM patients try to assess the effectiveness of MEK or PKC inhibitors in combination with Akt and PI3K↵ inhibitor (NCT01979523 and NCT02273219).

MDK and immunotherapy In the recent years a large number of studies have been done to investigate the effectiveness of immunotherapy for the treatment of metastatic UM. In CM patient the toxicity and efficacy of adjuvant dendritic cell vaccination is highly studied. The use of tumor antigens gp100 and tyrosinase expressed on both CM and UM makes it possible to target UM as well. An open label phase II study included high-risk patients with UM (monosomy 3) without distant metastases. Patients received autologous, monocyte-derived dendritic cells transfected with mRNA encoding the tumor-antigens gp100 and tyrosinase. Conclusion of this study was that adjuvant treatment with dendritic cell vaccination in high-risk patients with UM gives little toxicity and correlates with favorable OS in patients with a detectable tumor antigen-specific immune response after dendritic cell vaccination (Bol et al. 2016). A phase III vaccination study has been started to recruit UM patients to test the effectiveness of patient’s own dendritic cells electroporated with autologous tumor RNA to prevent or delay progression and further metastases (Schuler-Thurner et al. 2015). This is an ongoing actively recruiting clinical trail. The use of immune checkpoint inhibition has led to major improvements in outcome for patients with metastatic CM (C. Hao et al. 2017). Unfortunately, the same was not found in patients with metastatic UM treated with PD-1 (pembrolizumab or nivolumab), PD-L1 antibody (atezolizumab) or anti-CTLA4 (ipilimumab)(Danielli et al. 2012; Luke et al. 2013; Zimmer et al. 2015; Karydis et al. 2016; Chan et al. 2017; Javed et al. 2017). Only a small set op patients seem to benefit from immune checkpoint inhibition. In a recent review, of all clinical trials using immune checkpoint blockade for UM it was found that UM is little responsive to ipili-

69 4 Discussion mumab regardless of dosage schemes. The effects of combination treatments are still unclear and more randomized clinical trail are needed (Heppt et al. 2017). Immunotherapy has been shown to be dramatically less effective in UM compared to reputable response rates in CM patients. Specifically, failure of PD-1 inhibitor therapy may be caused by very raw PD-L1 expression in metastatic UM (5.1%) compared to metastatic CM (26.1%) detected by a recent study. These findings suggests that immune evasion in metastatic UM may occur via alternative mechanisms (Javed et al. 2017). The origin of metastatic UM from the immune privileged eye may also contribute to lower effectiveness of immunotherapy. The recent study by Chandran et al. 2017 and colleagues reports on an effective adoptive transfer of tumor infiltrating lymphocytes (TILs) to metastatic UM patients. Metastasectomies were done to procure tumor tissue and to generate autologous TIL cultures, followed by large scale ex-vivo expansion. Patients were treated with lymphodepleting conditioning chemotherapy followed by a single intravenous infusion of autologous TILs and high-dose IL-2. Seven of 20 (35%) had objective tumor regression. The tumor regression was highly correlated with frequency of tumor-reactive TILs assessed in vitro before administrating the TILs back to the patients. The mean percentage of tumor reactive TILs was 9.4% compared to 0.6% in the non-responder group. Showing first promising results of adoptive cell transfer in metastatic UM patients. Across the board, immunotherapies, however in particular usage of checkpoint inhibitors to treat metastatic UM have been disappointing. Only, a few promising outcomes could be reported. Suggesting, that UM tumor cells somehow develop resistance to the immunotherapies or immunescape mechanisms. In this light, it would be interesting to investigate MDKs role. On the one hand MDK has been reported to induce resistance to therapies, on the other hand MDK is also described as a cytokine with pro-inflammatory functions. Taken together, this would lead to contradicting responses to immunotherapies.

70 5Overallconclusionandfutureperspectives

In conclusion, the data, either gathered by us within the scope of this thesis or by other research groups imply that MDK plays an important role in tumor progression. MDK acts as a tumor cell survival factor, drives metastasis and therapeutic resistance, and prognosticate high-risk for disease progression in UM. In many other cancers it has been shown that the MDK concentration level either in serum, urine or the tumor tissue is predictive of disease progression. We could show that MDK expression levels in primary tumor tissue are predictive for disease progression. A larger cohort study is needed to address the question if MDK serum levels could be a predictive and highly needed biomarker for disease progression in UM patients. Especially, since no improvements in patient outcomes have been made to date. The development of liver-metastasis, is still universally fatal, mechanistically not understood and cannot be prevented. Targeting MDK and using MDK to predict high risk patients may contribute to improve prognosis and treatment of UM.

71 6MaterialsandMethods

6.1 Cell culture

The human UM cell lines (see table 6.3) were grown in RPMI1640 (Pan, Carlsbad, CA, USA) medium supplemented with 10% bovine serum (PAN), 1% penicillin and streptomycin (Gibco), 1% L-Glutamin (Gibco), 1% natriumpyruvat (PAN), 1% non-essential amino acids (PAN) and 0.4% vitamins (PAN). Human epidermal melanocytes (Hema-LP) were obtained from Gibco (Carlsbad). Melanocytes were grown in Medium 354 (Gibco) supplemented with HMGS-2 (PMA- Free human melanocyte growth supplement) (Gibco). Cultures were maintained in 5% CO2 at 37 . Primary blood mononuclear cells (PBMCs) were kept in RPMI1640 (Pan, Carlsbad, CA, USA) medium supplemented with 10% human serum (PAN), 1% penicillin and streptomycin (Gibco), 1% L-Glutamin (Gibco), 1% natriumpyruvat (PAN), 1% non-essential amino acids (PAN) and 0.4% vitamins (PAN).

Reagents for cell culture Reagents Function or use # number company RPMI1640 Medium cell culture medium P04-17500 Pan/Gibco Bovine serum (BS) additive to culture medium Pan Mem (non essential aminoacids) additive to culture medium P08-32100 Pan Penicillin additive to culture medium 15140-122 Gibco Steptomycin additive to culture medium 15140-122 Gibco L-Glutamin additive to culture medium 2503024 Gibco Vitamine additive to culture medium P08-41100 PAN Mercaptoethanol additive to culture medium 31350-010 Gibco Phosphate-buﬀered saline (PBS) wash buﬀer Gibco Table 6.1 – Reagents for cell culture

72 6 Materials and Methods

6.2 Cell lines

Human UM cell lines

Human UM cell lines Cell lines Origin Reference/Source MEL202 primary tumor Prof. B. R. Ksander (Verbik et al., 1997) MEL270 primary tumor Prof. B. R. Ksander (Verbik et al., 1997) OCM1 primary tumor Prof. J. Kan-Mitchell OCM3 primary tumor Prof. J. Kan-Mitchell OMM1 metastasis Dr. M. J. Jager OMM2.2 metastasis Prof. B. R. Ksander (Verbik et al., 1997) OMM2.3 metastasis Prof. B. R. Ksander (Verbik et al., 1997) OMM2.5 metastasis Prof. B. R. Ksander (Verbik et al., 1997) OMM2.6 metastasis Prof. B. R. Ksander (Verbik et al., 1997) H79 metastasis Prof. S. Guèrin Table 6.2 –HumanUMcelllines

Healthy control cells Cells Origin Reference/Source Hema-LP human skin melanocytes Gibco (Carlsbad) MelanA mouse skin melanocytes Prof. Bosserhoﬀ Table 6.3 –Healthycontrolcells

Stable transfected cell lines Cell lines Vector Name OCM1 pcDNA3.1 OCM1-mock OCM1 pcDNA3.1-MDK OCM1-MDK Table 6.4 – Stable transfected cell lines

6.3 Isolation of blood mononuclear cells

6.3.1 Density gradient centrifugation Blood from healthy donors was collected in EDTA containing S-MONOVETTE® (Sarstedt, Nümbrecht, Germany). 1:1 with PBS diluted blood was carefully layered on 20ml lymphocyte separating medium (Pancoll, PAN Biotech, Aidenbach, Germany). Centrifugation for 20min at 20 at 800 x g. The layer of mononuclear cells was transferred in a sterile tube and washed with PBS (centrifugation 700 x g, 10 min at 4). Then, the cell pellet was resuspended in either full medium for PBMCs (see chapter 6.1) or if further cell separation is needed in MACS (manual and automated cell isolation) buﬀer (see table 6.5) and counted.

6.3.2 Magnetic cell separation The mononuclear cells isolated from blood with density gradient centrifugation (see section 6.3.1) and separated by magnetic activated cell sorting (MACS) technology. PBMCs were resuspended

73 6 Materials and Methods in MACS buﬀer (see table 6.5). CD4+ and CD8+ T lymphocyte subpopulations were further separated from total PBMCs with CD4+ T cell isolation kit (Milteny Biotech, Bergisch-Gladbach, Germany) or with CD8+ T cell isolation kit (Milteny Biotech) respectively according to the manufacturer’s instruction. Reagent Volume PBS 487.5ml EDTA 0.375g Human serum albumin 20% 12.5ml Table 6.5 –MACSbuﬀer

6.4 Mononuclear cell activation

Mononuclear cells (MNCs), CD4+ and CD8+ T lymphocytes were stimulated with Dynabeads® human T-activator CD3/CD28 (Thermo Fisher Scientiﬁc, Waltman, USA) in a ratio 1:25 for 3 days. MDK expression was assessed in activated MNCs, CD4+ and CD8+ cells with qPCR and western blot and compared to MDK expression in non-activated cells.

6.5 M1 and M2 macrophages polarization

M1 and M2 differentiated macrophages were kindly provided by Dr. Heiko Bruns of our department. Differentiation to M1 (classical or pro-inflammatory) and M2 (alternative or anti- inflammatory) was achieved by incubating monocytes with granulocyte-macrophage colony stimulation factor (GM-CSF) or macrophage colony-stimulating factor (M-CSF) respectively for 7 days. MDK expression was assessed in M1 and M2 macrophages with qPCR and compared to MDK expression in non-differentiated monocytes.

6.6 DNA isolation

Total genomic DNA was isolated of UM cell lines using QIAamp® DNA blood puriﬁcation assay (Qiagen, Venlo, NL) according to the manufacturer’s protocol. Concentration and quality of RNA and DNA was assessed with NanoDrop 2000 (ThermoScientiﬁc, Walthman, MA, USA).

6.7 RNA isolation

Total RNA was extracted from frozen cells pellets (0.2-1 106 cells) using the RNeasy® Mini ⇥ Kit (Qiagen, Venlo, NL). Cells were lysed in 350µl RLT buﬀer supplemented with 3.5µlß- Mercaptoethanol and homogenized making use of a 1ml syringe and 20-23 gauge needle. Elimi- nating genomic DNA contamination was achieved either by gDNA eliminator spin columns or by treatment of the samples with DNAse. The RNA extraction was further carried out according to the manufacturer’s protocol and RNA was eluated in 25-50µl RNase-free water. RNA was isolated of snap frozen mouse in liquid nitrogen organs using the RNeasy® FibrousTis- sueMiniKit (Qiagen, Venlo, NL). Disruption of tissue samples was carried out manually in liquid nitrogen making use of a mortar and pestle. Maximal 10mg tissue was lysed in 300µl RLT buﬀer supplemented with 3µl ß-Mercaptoethanol followed by homogenization using a syringe and needle as described previously. gDNA elimination, proteinase K treatement and further RNA isolation was carried out according to the manufacturer’s protocol.

74 6 Materials and Methods

6.8 Reverse transcription - cDNA synthesis cDNA was transcribed from 50ng- 500ng of total RNA with the ProtoScript® First Strand cDNA Synthesis Kit (New England Biolabs, Ipswich, MA, USA ) making use of random primers (see table 6.6).

Reagent Volume Total RNA 1-6µl(50ng-500ng) Random Primer 2µl(6µM) Nuclease-Free Water to a total volume of 8µl Table 6.6 –RNA/primermix

RNA was denatured for 5 min at 70 than M-MuLV Reaction Mix and M-MulV enzyme Mix was added (see table 6.7.)

Reagent Volumes RNA/primer mix 8µl M-MuLV Reaction Mix (2x) 10µl M-MulV Enzyme Mix 2µl Table 6.7 –RNA/primer/dNTPandreversetranscriptasemix

The 20µl RNA/primer/dNTP and enzyme mix was preincubated for 5min at 25 before cDNA synthesis reaction took place by incubating the mix at 42for one hour. Enzyme inactivation was achieved by 4min incubation at 80. The cDNA products was stored at -20 or directly used for PCR.

6.9 Primer design

Oligonucleotides used as primers were designed with aid of the NCBI pick primer tool (https: //www.ncbi.nlm.nih.gov/tools/primer-blast/). For quantitative PCR a maximum PCR product size of 300bp was selected. Moreover, the primer pair had to be separated by at least one intron on the corresponding DNA to prevent amplification of potentially genomic DNA impurities. The specificity and melting temperatures were checked with aid of the UCSC In- Silico PCR (https://genome.ucsc.edu/cgi-bin/hgPcr). For human primers the assembly from Dec.2013 (GRCh38/hg38) and mouse primers the assembly from Dec.2011 (GRCm38/mm10) was used as template. Bisulfite primers were designed with aid of Bisulfite Primer Seeker (http://www.zymoresearch.com/tools/bisulfite-primer-seeker). The oligonucleotides were ordered and synthesized by Metabion international AG (Planegg/Steinkirchen, Germany).

6.10 Polymerase chain reaction (PCR)

6.10.1 PCR Polymerase chain reaction was performed with the Pwo SuperYield DNA Polymerase, dNTPack according to the manufacturer’s instruction. See table 6.8 and 6.9 for the PCR mix and the PCR cycling program. The PCR was performed with the Eppendorf Mastercyler (Eppendorf, Wesseling-Berzdorf)

75 6 Materials and Methods

PCR mix volume PCR Buﬀer 2µl 10mM dNTPs 0.4µl GC-rich 4µl Cyle Step Temp () Time evtl. 1 M betaine 4µl Denaturation 94 2min 5µM Forward Primer 1.5µl 95 20sec 5µM Reverse Primer 1.5µl 30- 40 cycles 60 30sec Template DNA 1µl 72 30-60sec Polymerase 0.2µl Extension 72 7min Nuclease-free water 4.4-8.4µl Hold 4 hold Table 6.8 –PCRmix Table 6.9 –PCRcyclingprogram

Primer Region Sequence (5’-3’) Amplicon (bp) MDK cloning forward GTG GTG GAAT TCA CCA CCA TGC 445 AGC ACC GAG GCT TCC TC MDK cloning reverse AAA CTC GAG CCA GGC TTG GCG TCT AGT C Table 6.10 –MDKcloningprimer

6.10.2 Real-time quantitative PCR (qPCR) Quantitative real-time PCR was performed using SYBR® Select Master Mix (Life Technologies) according to manufacturer’s instructions. See table 6.11 and 6.12 for the qPCR mix and the qPCR cycling program. The qPCR was performed with the StepOne Cycler (Invitrogen).

Cyle Step Temp () Time Preincubation 50 2min PCR mix volume Denaturation 95 2min 2x brilliant II SYBR 5µl 40 cycles 95 3sec green 60 30sec 1µM Forward Primer 1µl melt curve stage 1µM Reverse Primer 1µl step1 95 15sec Template DNA 1µl step2 60 1min Nuclease-free water 2µl step3 +0.3 until 95 Table 6.11 –qPCRmix Table 6.12 –qPCRcyclingprogram

6.10.3 Bisulfite conversion and bisulfite PCR Gene expression can be regulated with DNA methylation, predominately methylation of cytosine in CpG dinucleotides. Sodium bisulfite conversion was used to determine the methylation status of the promoter region of MDK. Incubation of DNA with sodium bisulfite results in conversion of all unmodified cytosines to uracils leaving the modified bases intact. The EpiMark® Bisulfite Conversion Kit (NEB, Ipswich, MA) was used to convert 500ng DNA from high MDK expressing UM cell line Mel202 and low expressing cell line OCM1. The DNA was added to the bisulfite mix (see table 6.15) and used for the bisulfite conversion reaction (see table 6.14). The bisulfite conversion reaction is followed by desulphonation reaction and the final clean up of the samples according to the manufacturers instructions.

76 6 Materials and Methods

Primer Region Sequence (5’-3’) Amplicon (bp) 18S forward CTC AAC ACG GGA AAC CTC AC 110 18S reverse CGC TCC ACC AAC TAA GAA CG MDK forward a GAT AAG GTG AAG AAG GGC GGC 367 MDK reverse a GGC TTG GCG TCT AGT CCT TT MDK forward b AAG AAG GAG TTT GGA GCC GA 172 MDK reverse b CCT TTG CTT TGG TCT TGG GG MDK forward c CTC AGT GCC AGG AGA CCA TC 101 MDK reverse c GCT TGG CGT CTA GTC CTT TC Table 6.13 –qPCRprimer

Cyle Step Temp () Time (min) Denaturation 95 5min Incubation 65 30min Denaturation 95 5min Incubation 65 60min Denaturation 95 5min Reagent Volumes (µl) Incubation 65 90min genomic DNA 10µl(500ng) Hold 18 hold bisulfite mix 130µl Table 6.14 –Bisulfiteconversionreaction Table 6.15 –Bisulfiteconversionreactionmix

PCR mix Volume 5X EpiMark Hot Start Taq Reaction 5µl Buffer 10mM dNTPs 0.5µl Cyle Step Temp () Time 10µM Forward Primer (bi1C) 0.5µl Denaturation 95 30sec 10µM Reverse Primer (bi1) 0.5µl 95 15sec Template DNA 3µl 40 cycles 60 30sec EpiMark Hot Start Taq DNA Poly- 0.125µl 68 30sec merase Extension 95 5min Nuclease-free water 15.375µl Hold 4 hold Table 6.16 –BisulfitePCRmix Table 6.17 –BisulfitePCRcyclingprogram

For the end-point bisulfite PCR primers (see table 6.18) were used that amplified bisulfite converted DNA sequences from the 5’UTC region of the MDK gene. The PCR reaction mix includes bisulfite converted DNA, Hot Start Taq Reaction Buffer, dNTPs, the EpiMark Hot Start Taq DNA Polymerase and bisulfite primer (see table 6.16). The PCR cycling protocol was used as described in table 6.17. Amplificate of the promoter region of non bisulfite converted DNA was used as comparison template and produced by PCR reaction using the Pwo SuperYield DNA Polymerase, dNTPack (Roche) with the primer set shown in table 6.18 The success of the bisulfite PCR was assessed with gel eletrophoresis and the methylation status was determined with SangerSequencing (see section 6.13).

77 6 Materials and Methods

Primer Region Sequence (5’-3’) Amplicon (bp) 5’bi1C Forward bisulﬁte GTCGATTTAGGGGTTGGGGGTTGGAGG 350 5’bi1 Reverse 5’ UTC TCCCCCATCACCTTTCTTTTTAAC 5’1 Forward 5’ UTC CCTCTTAGCGGTGCGTCC 405 5’1 Reverse GGAACAGAGTTCAGGACTGGG Table 6.18 –5’UTCPCRprimer

6.11 Agarose gel electrophoresis

For visualization of DNA, horizontal agarose gels were used. To visualize and separate DNA fragments longer than 0.5kb 1% agarose gels were used. For fragments <0.5 kb 1.5% - 2% gels were used. Gels were routinely prepared and run in TBE buffer. 1-2% agarose (Roth) was added to 1 x TBE, heated to the boiling point, and then cooled to 60.5µl Midori Green Advance DNA Stain (Nippon Genetics Europe, Düren) was added to 100ml 1x TBE and the molten agarose was poured into a gel caster with a slot forming comb. After the gel solidified the slot former was removed and gel was placed in gel running tank, filled with 1x TBE. Samples were loaded on gel with 1/5 volume 6x gel loading dye (NEB). DNA was separated by electrophoresis for 30 min - 1 hour with an applied voltage of 100 - 160. DNA ladders (100bp or 1 kb (NEB)) were used to evaluate the size of the separated DNA strands.

6.12 PCR product clean up

PCR clean up was performed the Wizard® SV Gel and PCR Clean-Up System (Promega) according to manufacturers instructions. In brief, equal amount of membrane binding solution was added to the PCR ampliﬁcation. The mix was transferred to the mini column and incubated for 1 minute at RT. Membrane wash solution was used in two washing steps followed by 1 minute centrifugation at 16,000 x g for the ﬁrst washing step and 5 min centrifugation at the same speed for the second washing step. DNA was eluted with 50 µl of nuclease-free water. The column was incubated with the elution water for 1 minute at room temperature, followed by centrifugation for 1 minute at 16,000 x g. DNA was stored at -20.

6.13 Sequencing

Sanger sequencing provided by GATC biotech (Konstanz, Germany) was used to conﬁrm mutation free cloning and to distinguish unmethylated and methylated cytosines following bisulﬁte PCR. The sequencing reaction consisted of a suitable sequencing primer (25µM) and cleaned up PCR product or vector DNA (0.2 - 1µg) in a total volume of 10µl. The sequences were further analyzed with DNA sequencing software Chromas (Technelysium) and DNAMAN software (Lynnon Biosoft).

6.14 Genarray analysis

Mel270, OMM2.2, OMM2.3, OMM2.5 and OMM2.6 were expanded to a total number of 50- 70 106 under standard cell culture conditions as described above. PolyA+ mRNA was isolated ⇥ using a column based method (FastTrack® 2.0 mRNA Isolation Kit, cat Invitrogen, Carlsbad, CA.), according to the manufacturer’s instructions (FastTrack® 2.0 Kit Manual version G). mRNA was analyzed for quantity and quality using RNA 6000 Nano m-RNA biochip analysis on the Agilent Technologies 2100 Bioanalyzer© (Agilent Technologies). Approximately 10-35µgof

78 6 Materials and Methods polyA+ mRNA of sufficient purity was obtained per sample. Subsequently, biotinylated cRNA targets were prepared according to the manufacturer’s instructions (Affymetrix Genechip® Ex- pression Analysis Platform, Affymetrix, Santa Clara, CA). Briefly, 2µgofpolyA+mRNAwas transcribed to double-stranded cDNA using the Gibco BRL Superscript Choice system (Gibco BRL). Included in the reaction was a T7-(dT)24 primer containing a T7 RNA polymerase promotor site (primer sequence: 50 -GGCCAGTGAATTGTAATACGACTCACTATAGGGAGGCGG- (dT)24-30 ). cDNA was in vitro transcribed (IVT) to approximately 26-63µgofbiotinylated cRNA with the BioArray™ HighYield™ RNA Transcript Labeling Kit (Enzo Diagnostics, Farm- ingdale, NY). The cRNA samples were hybridized to HG-U95Av2 oligonucleotide arrays containing 12,500 probe sets. Hybridization, washing, staining, and scanning were performed following standard Affymetrix protocols according to the manufacturer’s instructions. Three independent experiments i.e. UM cell expansions (with equal early passage numbers), mRNA isolation, IVT/cRNA preparations and array hybridizations were performed. Computational analysis of triplicate experiments showed close to equal hybridization parameters. To enable comparison of all of the arrays, the average intensity of all of the genes was set to 1000 before analysis. Expres- sion values (average difference) for each gene was calculated using the Affymetrix Microarray Suite 4.0 analysis software. Expression data were analyzed and annotated to the Rosetta Re- solver Database of H. Sapiens using Rosetta Resolver (Rosetta Inpharmatics). In all analysis procedures, only those genes with fold change > 2 in gene expression and P-value of < 0.01 were finally considered.

6.15 Enzyme-linked immunosorbent assay (ELISA)

MDK secretion in supernatant of UM cells was detected by ELISA (Human Midkine Development Kit 900-K190; Peprotech, Rocky Hill, NJ, USA). The samples were diluted 1:1 in assay diluent and the ELISA was carried out according to the manufacturer’s instructions and OD readings acquired on a SpectramaxM3 (Molecular Devices).

6.16 Western blot

6.16.1 Lysate preparation Cells were washed with PBS and centrifuged for 10 min at 10000g to make pellets. The supernatant was removed and 20µl-100µl working working solution radioimmunoprecipitation assay (RIPA) buﬀer (see tables 6.19 and 6.20) was added. The RIPA cell mixture was thoroughly vortexed and incubated for 10 min on ice for freshly made pellets and for 30 min on ice for frozen samples. Thorough vortexing was followed by centrifugation for 10 min at 10000g at 4. Lysate is collected and total protein concentration is determined with the Bicinchoninic acid assay (BCA).

Stock solution Concen- Volume Working solution Concen- Volume RIPA tration RIPA tration Tris 50 mM 0.606 g RIPA stock solution 500µl NaCl 150 mM 0.876 g Na3VO4 1mM 5 µl H2O 100 ml NaF 5mM 5 µl TritonX 100 1% 1ml Protease Inhibitor 25x 20 µl Table 6.19 –Recipefor100mlstocksolutionRIPA Table 6.20 – Recipe for 500µlworkingsolutionRIPA buﬀer buﬀer

79 6 Materials and Methods

6.16.2 Bicinchoninic acid assay (BCA) The Pierce BCAtm Protein Assay Kit (BCA, ThermoScientific) assay was used to detect and quantify total protein concentrations. The assay is based on bicinchoninic acid and combines the biuret reaction (the reduction of Cu2+ to Cu+) by proteins with purple-colored reaction produced by the interaction of Cu+ with with two molecules of BCA. The created complex exhibits strong absorbance at 562nm that correlates linearly with the protein concentration. The protein concentrations are determined with reference to a standard curve created by a series of dilutions of known concentrations of bovine serum albumin (BSA). 10µl of each unknown sample (1:10 diluted lysates in MiliQ water) and standard is added to flat bottom 96-well plates. 200µl of BCA working reaction solution (50:1, Reagent A:B) are added to each well. The plate is covered and incubated at 37 for 30 min. The plate is then cooled to RT and the absorbance at 562nm is measured by SpectraMax Microplate Reader (Molecular Devices). The measurements are-blank corrected and the standard curve is used to determine the protein concentration of each unknown sample. Desired concentrations of protein lysate were resuspended in sample buffer (see 6.21), boiled (95, 10 min) and stored on ice until they were used.

Sample buﬀer Concentration T ris/HCL ph6 300 mM SDS 12% glycerol 50% bromophenolblue 0.2% Table 6.21 –Recipeforsamplebuﬀer

6.16.3 Gel casting Two clean glasplates were clamped in a holder. The separation gel was casted ﬁrst. APS and Temed were added just before casting. 100% EtOH was added on the gel to create an even border. The gel is left for 20 min to solidify, than the EtOH was removed, the collection gel was prepared and added on top of the separation gel. The comb with the desired size was added and the gel was further solidiﬁed for another 20 min.

Separation gel 12% 10% 7.5% Collection gel Volume 30% AA/BAA 3.2ml 2.67ml 2.0ml 30% AA/BAA 667µl TRIS/HCL 1.5M pH 8.8 2.0ml 2.0ml 2.0ml TRIS/HCL 0.5M pH 6.8 1ml

H2O 2.72ml 3.25ml 3.92ml H2O 2.33ml SDS 10% 80µl 80µl 80µl SDS 10% 40µl APS 10% 60µl 60µl 60µl APS 10% 30µl Temed 6µl 6µl 6µl Temed 4µl Table 6.22 –Separationgel Table 6.23 –Collectiongel

6.16.4 Gel running and blotting The western blot chamber (Biorad) was filled with 1x runningbuffer (kept on 4). Samples were run on 12% SDS-page gels (separation gel: 30% acryl amide, Tris/HCl 1.5 M, pH 8.8, H2O, 10% SDS, 10% APS, 6 µl TEMED; stacking gel: 30% acryl amide, Tris/HCl 0.5 M, pH 6.8, H2O, 10% SDS, 10% APS, 6 µl TEMED) at 100 V for the first 10min and subsequently at 160 V for 60min. Following electrophoresis, proteins were transferred to PVDF transfer membranes

80 6 Materials and Methods

Tris/HCL (1.5M; pH 8.8) Tris/HCL (0.5M; pH 6.8) Tris(hydroxymethyl)- 91 g Tris(hydroxymethyl)- 6.0 g aminomethane aminomethane Millipore-water dilute in 300 ml Millipore-water dilute in 40 ml ph 8.8 adju. with 1N HCl ph 6.8 adju. with 1N HCl Millipore-water ﬁll up to 500 ml Millipore-water ﬁll up to 100 ml Table 6.24 –500mlTris/HCl(1.5M;pH8.8) Table 6.25 –100mlTris/HCl(0.5M;pH6.8)

(Amersham, GE Healthcare, Freiburg, Germany) at 15 V for 50min using a Trans-Blot SD Semi- Dry Transfer Cell (Biorad, München, Germany). Membranes were blocked with 5% non-fat dry milk in wash buffer 1-2h at 4. The membranes were incubated with primary antibodies MDK (rabbit polyclonal; 1:500) and ß-actin (mouse monoclonal; 1:2000) diluted in 2% non-fat dry milk in wash buffer overnight. Secondary antibodies goat anti-rabbit-HRP (BD bioscience; 1:2500 in 3% non fat dry milk wash buffer) and goat anti-mouse IgG-HRP (DAKO; 1:5000 in 3% non- fat dry milk wash buffer) respectively were added to the membrane for 50min at RT. The last 10min 1µl StrepTactin HRP (Biorad 131-0380) was added.

6.16.5 Developing and visualization Bands were developed using ECL detection reagent (Amersham, GE Healthcare, Freiburg, Ger- many) and made visible with FluorChemFC2 (Cell Bioscience, Jena,Germany).

6.17 Exosome isolation

For exosome isolation, Mel202 cells (total 3 106 cells) were expanded in standard cell culture ⇥ medium for 7 days, subsequently washed with PBS and cultured in serum free AIM-V (Thermo Fisher Scientific) for 2 days prior to exosome purification from the supernatant (total cell number range 10 106-20 106 after expansion). Culture supernatants were collected, centrifuged for ⇥ ⇥ 10min at 500 x g, followed by 20min at 16,500 x g, and filtered (0.2 µm). Exosomes were isolated by ultracentrifugation for 70 min at 110,000 x g, 4). After PBS wash (70min at 110,000 x g, 4), exosomes were suspended in PBS and filtered (0.2 µm). Isolated exosomes were assessed for purity by flow cytometry using the exosome CD63 isolation/detection reagent (Thermo Fisher Scientific) according to the manufacturer’s instructions and checked for purity by flow cytometry using exosome marker specific mAbs: CD9-PE, CD63-PE, CD81-PE (all from BD Biosciences). Subsequently, exosomes were processed and analyzed by western blot as described above.

6.18 RNA interference

Cells were seeded and grown to 60-80% confluence prior to transfection. Lipofectamine RNAiMAX (ThermoFisherScientific) reagent was diluted in Opti-MEM medium (Gibco). 0.3µl and 1.5 µl lipofectamine per well was used to transfect cells in 96 well plates or 24 well plates, respectively. 10µM anti-MDK and negative control siRNAs (siNC, AM46118, ThermoFischer- Scientific) (see table 6.26) was diluted in Opti-MEM medium to 1 pmol (96-well) or 5 pmol (24-well). Diluted siRNA was added to diluted Lipofectamine RNAiMAX reagent (1:1 ratio) and incubated for 5min at RT. siRNA lipofectamine mix was added to cells in penicillin- and streptomycin-free medium and incubated at 37 for 24h up to 7 days. Cells were either used for functional analyses, to assess cell proliferation, viability and apoptotic rate or cells were pelleted and snap frozen for further analysis by qPCR or western blot. Supernatant was collected to assess MDK secretion by ELISA. The transfection efficiency was assessed by cotransfecting with fluorescently labeled negative control siRNA (AllStar Neg. siRNA AF647, Qiagen).

81 6 Materials and Methods

siRNA Name Sense(5’-3’) Antisense(5’-3’) AM16708 siMDK1 GGCCAAAGCCAAGAAAGGGtt CCCUUUCUUGGCUUUGGCCtt s8626 siMDK2 CAAUUCCAUUACUAAGAAAtt UUUCUUAGUAAUGGAAUUGtg s8627 siMDK3 GCAAGUACAAGUUUGAGAAtt UUCUCAAACUUGUACUUGCag Table 6.26 –Anti-MDKsiRNAs

6.19 Stable transfection of UM cell lines

6.19.1 Construction of MDK expression plasmid Total RNA was extracted from Mel202 cells and reverse transcribed into cDNA as described in 6.7 and 6.8. MDK cDNA was amplified by PCR (see 6.10.1) with the upstream primer 5’ GTGGTGGAATTCACCACCATGCAGCACCGAGGCTTCCTC 3’ and the downstream primer 5’ AAACTCGAGCCAGGCTTGGCGTCTAGTC 3’ (Kerzerho et al., 2010). The PCR product was purified with the QIAquick PCR purification kit (Qiagen) as described in 6.12.0.6µgof purified MDK PCR product and 2.5µg vector pcDNA3.1 (ThermoFischerScientific) were digested by restriction enzyms EcoR1 (NEB) and Xho1 (NEB) (see table 6.27) by incubating the DNA enzyme mix for 2 h at 37 on heatblock shaker with interval of 3 seconds shaking and 1 minute rest.

Digestion mix volume PCR puriﬁed product or Vector 0.6µgor2.5µg smartcut buﬀer 5µl XHO1 1µl (20,000IU/ml) EcoR1 1µl (20,000IU/ml)

H2O 50-x µl Table 6.27 –VectorandMDKDNAdigestion

The digested inserts and vector were ligated with Rapid DNA ligation kit (Roche). Amount of vector to insert ratio was 1:3, taking into account the diﬀerent sizes of vector (5428nt) and insert (445nt). The amount of insert was calculated according to the following formula 6.1

1 amount vector (ng) amount insert (ng) = size insert (nt) (6.1) 3 ⇤ size vector (nt) ⇤

Ligation mix volume dilution buﬀer 4µl vector 100ng insert 24.6ng ligation buﬀer 10µl

H2O 10-xµl ligase 1µl Table 6.28 –Ligationmix

6.19.2 Transformation One shot TOP10 chemically competent E.coli (Invitrogen) were transformed with the ligated pcDNA3.1_MDK or with the empty pcDNA3.1 vector and plated overnight on LB-plates con-

82 6 Materials and Methods taining 50µg/ml ampicillin. Clones were picked and plasmid production was achieved in LB medium (50µg/ml ampicillin) overnight. The pcDNA3.1_MDK and the pcDNA3.1 plasmids were purified using the QIAprep Spin Miniprep Kit (Qiagen). Plasmid concentrations and puri- ties were assessed with NanoDrop 2000 (ThermoScientific). Digestion tests and SangerSequenc- ing (GATC biotech), confirmed MDK positive and mutation-free pcDNA3.1_MDK plasmids were amplified.

6.19.3 Determination of antibiotic (G418) resistance concentration Stably-transfected cells can be selected by the addition of drugs to the culture medium, if the expression plasmid carries a drug resistance gene. The pcDNA3.1 plasmid carries a neomycin resistance, which uses resistance to G418 as a selection marker. Cells diﬀer in their susceptibility to G418. To determine the minimum level of G418 to be added to the culture medium to prevent cell growth in untransfected cells and select for transfected, plasmid expressing cells, a titration of G418 concentrations was used. 1 105 cells were seeded into 24-well plate containing culture ⇥ medium without G418 (CarlRoth). The next day, the culture medium was replaced with medium containing increasing concentrations of G418 (0.2mg/ml to 1mg/ml). Every 2-3 days cells were feed with culture medium containing G418.

6.19.4 Transfections of tumor cells Prior to transfection to improve transfection eﬃciency the pcDNA3.1_MDK and pcDNA3.1 were linearized making use of restriction enzyme Sca (NEB). 500ng of each linearized DNA3.1_MDK and pcDNA3.1 vector and 1.5µl X-tremeGENE HP DNA transfection reagent (Roche) diluted in 50µl optimem were used to transfect 0.5 105 OCM1 cells. Cells were continuously treated ⇥ with neomycin (G418, 0.8mg/ml) to select stable transfected cell clones.

6.20 Retinoic acid assay

UM cells were seeded 5 104 (OCM1) or 10 104 (Mel202, OMM2.3, Mel270 and Hema-LP) in ⇥ ⇥ 24-well plates in 1ml total medium. Daily 1nM - 10µM all trans-retinoic acid (ATRA)(Sigma), diluted in dimethylsulfoxid (DMSO) was added to the cell culture. DMSO diluted in PBS corresponding to the DMSO of the highest concentration was used as the appropriate negative control. After four days in culture cells were harvested, RNA isolated and MDK expression levels were assessed via quantitative PCR.

6.21 Demethylation assay

UM cells were seeded 5 104 (OCM1) or 10 104 (Mel202, OMM2.3, Mel270 and Hema-LP) in ⇥ ⇥ 24-well plates in 1ml total medium. Daily 10µM 5-aza 2’deoxycytidine (5’Aza)(Merck Milipore) diluted in DMSO was added to the cell culture. DMSO diluted in PBS corresponding to the DMSO of the highest concentration was used as the appropriate negative control. After four days in culture cells were harvested, RNA isolated and MDK expression levels were assessed via quantitative PCR.

6.22 Hypoxia assay

During normoxia hypoxia-inducible factor-1↵ (HIF-1↵) transcription factor is degraded by proteasomal pathways, however during hypoxic conditions HIF factors stabilize and can regulate expression of more than 60 genes that are involved in biological processes such as angiogenesis and oxygen transport. Stabilization of HIF factors is highly correlated with hypoxia, therefore

83 6 Materials and Methods

detecting HIF is routinely used to screen for hypoxia. Cobalt chloride (CoCl2)solutionisa chemical inducer of HIF and can therefore be used to mimic hypoxic conditions in vitro. UM cells were seeded 5 104 (OCM1) in 24-well plates in 1ml total medium. Cells were left to adhere ⇥ for 2h and 25µM-400µM CoCl2solution (Sigma) was added. 72h after treatment the (HIF-1↵) stabilization was assessed with ﬂow cytometry (see section 6.27.2), the viability of the cells was assessed with MTS assay (see section 6.23) and expression of MDK was assessed by qPCR (see section 6.10.2).

® 6.23 Cell Titer 96 AQueous One Solution Cell Proliferation Assay (MTS)

The MTS assay is based on a colorimetric method for determining numbers of living cells and is used for measuring cell viability and proliferation. The assay is based on the potential of metabolically active cells (living cells) to reduce tetrazolium salt MTS to formazan. For this assay cells were seeded in concentrations between 1 103 and 2 104 cells per well in 96-well ⇥ ⇥ plates. Cells were incubated at 37 and at timepoint at interest, 10µl of Cell Titer 96 solution was added to each well. Plates were further incubated for 1-4 h and the absorbance was measured at 490nm. The measured absorbance is directly proportional to the number of living cells.

6.24 Apoptosis, Viability and Cytotoxicity assay (ApoTox-Glo™ Triplex Assay)

72h after transfection cell survival parameters proliferation, viability and apoptosis via caspase 3/7 activity were determined in untreated, siNC, siMDK1 or siMDK2 transfected Mel202 cells using the Caspase-Glo® 3/7 Assay and CellTiter-Fluortm Cell viability assay (all obtained from Promega, Madison, WI, USA) according to the manufacturer’s instructions. The viability assay is based on protease activity of live-cell protease, which is active only in living cells. The activity is measured using the pro-fluoreogenic, cell-permeable peptide substrate glycylphenylalanyl- aminofluorocoumarin (GF-AFC), which is converted intracellularly into the fluorescent product AFC. The fluorescence signal generated is proportional to cell viability. Fluorescence is measurements at 380Ex/505Em nm using a micro-plate reader (SpectramaxM3, Molecular Devices, Sunnyvale,CA, USA). The activity of caspase 3/7 is measured through cleavage of a proluminescent substrate, containing the tetrapeptide DEVD as a recognition sequence. In the detection process, two enzymatic steps occur: firstly, the Caspase-Glo® 3/7 reagent contains a substrate for caspase 3 and 7 (Z-DEVD-aminoluciferin), which is cleaved and releases aminoluciferin. Sec- ondly, the aminoluciferin acts as substrate for a luciferase, which catalyses a luciferase reaction. The hereby produced light is measured by determining the luminescence using a micro-plate reader.

6.25 Transwell migration assay

The following transwell migration assay was used to study the migration behavior of UM cells. OCM1-wt, OCM1-mock and OCM1-MDK cells were starved in culture for 24h in FCS free medium. The OCM1 cells were subsequently seeded 5 104 in 100µL on top of 24-well plate ⇥ transwell insert with 8µM pore sizes in FCS free medium. 650µL either FCS free medium (negative control) or medium with 10% FCS were added in the bottom well. Cells were kept 24h at 37. The inserts were washed with PBS and the non-migrated cells on the top were removed with a cotton swap. Next, the migrated cells on the bottom side of the inserts were fixed by incubating the inserts in 70% ethanol. The inserts were left to dry and the transwell filters were excised with a scalpel and put on glass slide. The filters with the migrated cells were then

84 6 Materials and Methods embedded and stained with DAPI (VECTASHIELD®HardFSet™ Mounting Medium with DAPI, vector laboratories). Five 10x zoom optical ﬁelds were captured with the ﬂuorescence microscope (Keyence, BZ9000, Itasca, IL, USA) and the cell number of migrated cells were counted with the ImageJ software (Fig. 6.1).

Transwell Assay - Cell migration – cell counting A B C D E

DAPI-blue marked. color threshold convert to mask watershed particle counting nuclei of living cells Figure 6.1 – Step by step explanation how cells were counted with ImageJ software. (A) Picture of DAPI (blue) stained migrated cells. (B) The color threshold function identiﬁes cells and colors these red and allows (B) conversion to a binary mask, marking cells in black and background in white. (C) The watershed function separates aggregated cells by adding a 1-pixel line to separate the cells for counting (see blue arrow). (D) The particle counting function then counts the cells. Cells on the outer borders are excluded and cells are identiﬁed by size.

6.26 Intracellular Signaling Array

The PathScan® intracellular signaling array (Cell Signaling Technology, Beverly, MA) is a slide- based antibody array based on the sandwich immunoassay principle. The array kit allows for the simultaneous detection of phosphorylation or activity of 18 signaling molecules (see fig. 6.2). Total cell lysates of untreated, mock or MDK transfected OCM1 cells as well as untreated, siNC, siMDK1 or siMDK2 Mel202 cells were prepared as described above (see chapter 6.16.1). Proteinconcentration was assessed with BCA assay as described above (see chapter 6.16.2). The intracellular signaling protein assay was performed according to the manufacturer’s instructions. Briefly, each well was blocked with 100µl blocking buffer, incubated for 15min at RT. Than the blocking buffer was removed and 100µl (0.5mg/ml) lysate was added to the wells and incubated for 2h at RT. Followed by 3x5min washing steps with 1x wash buffer, provided by the array kit. 75µl 1x detection antibody cocktail was added to each well and incubated for 1h at RT, followed by 4x5min washing steps. 75µl (1x) HRP-linked Streptavidin was added to each well incubated for 30min at RT. Followed again by 4x5min washing steps. Combined LumiGlo® and Peroxide reagents were combined to a 1x solution and was added to the whole array. The arrays were analyzed with FluorChemFC2 (Cell Bioscience, Jena,Germany) and the layout of the PathScan®Intracellular Signaling Array Kit, including a list of all intracellular signaling molecules studied is shown in figure 6.2.

85 6 Materials and Methods

A B Intracellular Signaling Target Phosphorylation Site Modification 1 Positive Control N/A N/A 2 Negative Control N/A N/A 3 ERK1/2 Thr202/Tyr204 Phosphorylation 1 3 3 4 4 1 4 Stat1 Tyr701 Phosphorylation 5 Stat3 Tyr705 Phosphorylation 5 5 6 6 7 7 6 Akt Thr308 Phosphorylation 7 Akt Ser473 Phosphorylation 8 8 9 9 10 10 8 AMPKα Thr172 Phosphorylation 11 11 12 12 13 13 9 Ribosomal Protein S6 Ser235/236 Phosphorylation 10 mTor Ser2448 Phosphorylation 14 14 15 15 16 16 11 HSP27 Ser78 Phosphorylation 12 Bad Ser112 Phosphorylation 17 17 18 18 19 19 13 P70S6 Kinase Thr389 Phosphorylation 14 PRAS40 Thr246 Phosphorylation 1 20 20 2 2 2 15 p53 Ser15 Phosphorylation 16 p38 Thr180/Tyr182 Phosphorylation 17 SAPK/JNK Thr18,/Tyr185 Phosphorylation 18 PARP Asp214 Cleavage 19 Caspase-3 Asp175 Cleavage 20 GSK-3β Ser9 Phosphorylation

Figure 6.2 –LayoutofPathScan®Intracellular Signaling Array Kit. (A) Consecutively numbered signaling molecules in duplicate. (B) Listing of all intracellular signaling molecules, including positive and negative control. Adapted from instruction manual of PathScan®Intracellular Signaling Array Kit (Cell Signaling Technology)

6.27 Flow cytometry

6.27.1 Surface staining Cells were harvested, preferably with versene (trypsin-free dissociation reagent) and transferred to FACS tubes (Sarstedt, Nümbrecht) for surface staining. Cells were washed with PBS and labelled with monoclonal antibodies (see table 6.29) using concentrations recommended to manufacturer’s instructions. Cells were incubated at 4 in the dark for 30min. After a further washing step, cells were resuspended in 100-200µl of PBS or ﬁx solution (CellFIXTM, Becton Dickinson (BD), Heidelberg). Analysis was carried out by FACS Canto II ﬂow cytometer (BD). For evaluation of data software DIVA (BD) or Flowjo (Tree Star, Ashland, USA) was used.

6.27.2 Intracellular staining For intracellular staining, cells were harvested and washed with PBS. After staining of surface molecules, cells were fixed with 250µl fixation/permeabilization solution (BD) for 20min at 4. Cells were washed twice with 1x Perm/Wash (BD) and incubated with intracellular antibody (see table 6.29) for 30 min at room temperature in the dark. After washing with Perm/Wash the cells were resuspended in 100-200µl PBS and analyzed by flow cytometry.

6.27.3 Phosphorylation staining Phospho-flow cytometry is a flow cytometry based analysis to determine phosphorylation levels of signaling molecules in living cells. Prior to the phosphorylation staining, 5 105 cells were ⇥ fixed with 500µl for prewarmed cytofix-fixation-buffer (BD, 554655) for 10min in a prewarmed 37 waterbath. Cells were washed twice with PBS and under vortexing 500µl perm buffer III (BD, 558050) was slowly added. After incubation for 30min on ice in the dark cells were washed twice with stain buffer (PBS + 2% FCS). Then, cells were stained with phospho-antibodies (see table 6.29) using concentrations recommended by the manufacturer for 45min at RT in the dark. Finally, cells were washed with wash buffer twice before flowcytometric analysis.

86 6 Materials and Methods

Antibodies Clone Type Fluorochrome Company syndecan1 (CD138) MI15 cell surface PE BD LRP1 (CD91) A2MR-↵2 cell surface PE BD Notch2 MHN2-25 cell surface PE BD HIF1 alpha 546-16 intracellular PE biolegend pRPS6 N4-41 phosphoﬂow PE BD Table 6.29 –Flowcytometryantibodies

6.28 Histology

6.28.1 Tissue sampling, fixation and paraffin embedding Mouse eye enucleation was performed according to Mahajan et al. 2011. Mouse tissue were dissected by our collaborators (Miriam de Jel)1, and fixed in 1-4% formalin for transportation. Tissue specimens were then dehydrated in ascending series of alcohol solutions in 40 min intervals at RT during continuing slow shaking, followed by cleaning steps in Xylol (Roth). Subsequently, the tissue samples are immersed in liquid paraffin overnight at 58 (table 6.30) and finally embedded with Paraffin Dispenser (Leica EG1120, Leica Biosystems, Nußloch, Germany). Paraffin blocks were stored at 4. For experiments, tissue blocks were cut in 4µm sections. The paraffin sections were put in water bath (40) to flatten out and collected on SuperFrost®Plus tissue slides (R.Langenbrinck GmbH, Emmendingen, Germany)

Concentration (%) Time (min) Dehydration 70% Isopropanol 40min 80% Isopropanol 40min 95% Isopropanol 40min 95% Isopropanol 40min 100% Isopropanol 40min 100% Isopropanol 40min Cleaning Xylol I 40min Xylol II 40min Embedding Paraffin I 120min Paraffin II overnight Table 6.30 – Ascending series of alcohol solutions for embedding tissue in paraffin

6.28.2 Hematoxilin and Eosin (H & E) staining The combination of hematoxilin and eosin was used to stain nuclei and cytoplasm. Hemalaun solution colors nuclei of cells. Hemalaun staining solution is obtained by mixing hematoxylin solution A according to Weigert 1:1 with hematoxylin solution B according to Weigert (Roth). Eosin Y solution 0.5% (Roth) in water is used for counterstaining after hemalaun staining. Eosin stains the cytoplasm of cells as well as extracellular proteins such as collagen. 1 drop of pure acetic acid per 100ml Eosin was used to stop alkaline blueing and to improve contrast staining. Table 6.31 displays the H & E staining protocol. Brieﬂy, the tissue is ﬁrstly dewaxed by incubating in xylol (Roth), rehydrated by a series of descending alcohol solutions, stained and counterstained

1AG Bosserhoﬀ, Biochemistry and Molecular Medicine, University Hospital Erlangen, Friedrich-Alexander- Universität Erlangen-Nürnberg, Erlangen, Germany)

87 6 Materials and Methods with hemalaun and eosin. Then, the stained tissue is dehydrated by a series of ascending alcohol solutions, cleared in xylol and mounted using RotiHistokitt-II (Roth).

Reagent Time (min) Dewaxing Xylol 10min Xylol 5min Xylol 5min Rehydration by a series of Ethanol 100% 3min descending alcohol solutions Ethanol 100% 3min Ethanol 95% 2min Ethanol 95% 2min Ethanol 70% 2min Ethanol 70% 2min Staining Haemalaun 3-5min Rinse with distilled water Aqua dest. 1min Blue in ﬂowing tap water Tap water 10-15min Counterstaining Eosin 0.5% 3min Rinse with distilled water Aqua dest. 1min Dehydrate by a series of Ethanol 95% 1min ascending alcohol solutions Ethanol 95% 1min Ethanol 100% 2min Ethanol 100% 2min Clearing Xylol 10min Xylol 5min Xylol 5min Mounting Table 6.31 –H&Estainingprotocol

6.28.3 Depigmentation UM are mostly highly pigmented. Melanocytes produce melanin, the pigment that gives color to skin, hair and eye colors. Accumulation of high melanin concentration can hinder immunohistochemical and immunoﬂuorescence stainings of melanocytes or melanoma cells. Therefore, bleaching of melanin pigment is necessary preceding further stainings. Dewaxed and rehydrated tissue slides are boiled 1-3 min (depending on pigmentation intensity) in 10% H2O2 (diluted in PBS) directly followed by epitope unmasking.

6.28.4 Immunofluorescence (IF) staining Paraffin embedded tissue Human UM tissue of 10 UM patients was obtained from the Department of Ophthalmology, Uni- versity Hospital Cologne with the corresponding informed consent and approval of the protocols by the local Ethics/Institutional Review Board. After enucleation, the tumor bearing eye was formalin-fixed and paraffin-embedded. 5µm thick sections were prepared and deparaffinized in xylol (Roth). Sections were depigmented as described above (section 6.28.3) followed by antigen unmasking in pH6 citrate buffer (10mM citric acid, 0.05% Tween) for 20min at 99. Sections were subsequently cooled down to RT, blocked with 1.5% goat serum (Vector Laboratories,

88 6 Materials and Methods

Burlingame, CA, USA) diluted in PBS for 20min and stained with MDK antibody (EP1143Y, rabbit monoclonal, Abcam, Cambridge, UK) diluted 1:200 in antibody diluent (DAKO real, Agi- lent, Santa Clara, CA, USA) overnight at 4. After washing in PBS, tissues were incubated with Alexa Fluor 488 goat-anti-rabbit IgG secondary antibody (Jackson ImmunoResearch Laborato- ries, West Grove, PA, USA) diluted 1:500 in antibody diluent (DAKO) for 2h at RT, washed in PBS and mounted in vectashield hard set mounting medium with DAPI (Vector Laboratories). MDK staining was evaluated by ﬂuorescence microscopy (Keyence, BZ9000, Itasca, IL, USA).

Fixed cells in chamber slide 2.5 105 Mel 202 cells were seeded on 8 well glass slide (labtek®II chamber slide system, Lalge ⇥ Nunc international, Naperville, USA) in standard cell medium 24h prior to immunofluorescence staining. Cells were washed in PBS, fixed with 2% formaldehyde for 20min. After washing with PBS, cells were incubated with primary antibody mix, which included anti-human MDK antibody (rabbit polyclonal; dilution 1:50; Abcam ab170820), 0.2% triton X (Sigma) and 1.5% goat serum (Vector Laboratories, Burlingame, CA, USA) for 30min at RT, protected from light. After washing cells with PBS, cells were incubated with Alexa Fluor 488 goat-anti-rabbit IgG secondary antibody (Jackson ImmunoResearch Laboratories, West Grove, PA, USA) diluted 1:500 in PBS. After washing with PBS the chambers were removed and cells on glass slide were mounted in vectashield hard set mounting medium with DAPI nuclear stain (Vector Laboratories) and MDK staining was evaluated by fluorescence microscopy (Keyence, BZ9000, Itasca, IL, USA).

6.29 Analysis Software

Software used for Company FACS Diva flow cytometry BD, Franklin Lakes, USA FlowJo analysis flow cytometry data Treestar, Ashland, USA GraphPad Prism statistical analysis, graphs GraphPd Prism Software Inc., San 5.03 Diego, USA Keyence software microscopic analysis Keyence, Osaka, Japan ImageJ visual data analysis Schneider et al. 2012 Genome browser primer design, genetic analysis https://genome.ucsc.edu/cgi-bin/ hgPcr Chromas DNA sequencing analysis Technelysium, Brisbane, Australia DNAMAN DNA and vector analysis Lynnon Biosoft, San Ramon, USA StepOne Software analysis qPCR Thermo Fisher Scientific, Waltham, USA Photoshop CS5 visual data analysis Adobe Systems, San Jose, USA Microsoft office text and data processing, presen- Microsoft Corporation, Redmond, tations USA Endnote reference manager Thomas Reuters, New York, USA LATEX thesis https://latex-project.org/lppl.txt Table 6.32 – Overview of used software

89 6 Materials and Methods

6.30 Statistics

Data are presented as means standard error of the mean (SEM) of at least three independent ± experiments. Graphpad prism 5 (GraphPad, San Diego, CA) was used to analyze the data. If not otherwise stated, T-tests were used for statistical analysis of differences between two groups. The value P 0.05 was considered statistically significant. If not otherwise stated, the multiple  comparisons were made using One-Way-ANOVA and Bonferroni post-tests. The value P 0.05  was considered statistically significant (ns>0.05, *p 0.05, **p 0.01, ***p 0.001, ***p 0.0001).     6.31 Bioinformatics

Bioinformatical analyses of the TCGA UM data were performed in close collaboration with Dr. Christian Büttner 2. Fragments per kilobase of exon per million reads mapped (FPKM) values of mRNA expression of all 80 UM samples sequenced by the TCGA project (Robertson et al. 2017) were downloaded from (https://portal.gdc.cancer.gov/repository). MDK FPKM values were extracted and log2 transformed (log2(FPKM+1). Subsequent data analysis was performed with GraphpadPrism. Groupwise comparisons of MDK expression were tested for signiﬁcance using T-test or Kruskal-Wallis test followed by Dunn’s multiple comparison test. Survival analysis was performed using the Kaplan-Meier method. The log-rank test statistical test was used to assess diﬀerences in overall survival, progression and metastatic free survival.

2Dr. Christian Büttner, Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander- Universität Erlangen-Nürnberg, Erlangen, Germany

90 Appendix

AMDKandimmunecells

MDK expression is upregulated in activated immune cells Freshly isolated mononuclear cells (MNCs) and MACS separated CD4+ and CD8+ T lymphocytes stimulated with anti-CD3/CD28 beads show increased levels of MDK mRNA levels and protein levels compared to non-activated immune cells (Fig. A.1). MNCs from three healthy donors were used in the experiments. Figure A.1 (A) shows exemplary the MDK PCR product of MNCs with or without stimulation with +anti CD3/CD28 beads. MDK was only detectable in stimulated MNCs. Figure A.1 (B) shows increased MDK gene expression in stimulated MNCs and even higher induction of MDK expression in stimulated CD4+ and CD8+ T lymphocytes. Additionally, MDK protein levels are higher in stimulated MNCs and CD4 and CD8 lymphocytes (Fig. A.1 (C) and (D).

MDK expression in activated mononuclear cells

A MNCs C MNCs CD4 CD8 +αCD3/CD28 - + +αCD3/CD28 - + - + - + MDK MDK GAPDH ß-actin

B 1´10-05 ** D 2.0

8´10-06 1.5 * 6´10-06 1.0 4´10-06 (relativeto 18S)

MDKexpression 0.5

-06 (relativeto ß-actin)

2´10 MDKconcentration

0 0.0 +αCD3/CD28 - + - + - + +αCD3/CD28 - + - + - + MNCs CD4 CD8 MNCs CD4 CD8

Figure A.1 –MDKexpressionisupregulatedinactivatedmononuclearcells(MNCs).(A)MDKPCRproduct of stimulated and naive MNCs. (C) MDK expression is upregulated in activated total mononuclear cells fraction, in CD4 and CD8 lymphocytes in three experiments using three diﬀerent healthy donor cells. (B) Exemplary westernblot showing higher levels of MDK protein expression in activated MNCs, CD4 and CD8 cells. (D) Summary of MDK protein expression upregulation of three independent experiments.

MDK expression is upregulated in M1 macrophages Macrophages have a multitude of functions. Macrophages can be differentiated in M1 (pro- inflammatory) and M2 (anti-inflammatory depending on the content of the differentiation medium. Monocytes have been differentiated to M1 and M2 macrophages from three separate donor. All differentiated macrophages have increased levels of MDK expression compared to non-differentiated monocytes (Fig. A.2). However, in all three donors, higher induction of

91 Appendix

MDK mRNA levels were seen in the M1 compared to the M2 diﬀerentiated macrophages. Taking together, these data show a trend towards higher levels of MDK in M1 macrophages than M2 macrophages (Fig. A.2) (B).

MDK expression in M1 and M2 macrophages A B 1000 1000 M1 M2

100 100

10 10 MDKexpression MDKexpression (relativeto monocytes) (relativeto monocytes)

1 1 d#1 d#2 d#3 M1 M2 Figure A.2 –MDKexpressionisupregulatedindifferentiatedmacrophages.(A)FoldincreaseofMDKex- pression M1 and M2 differentiated macrophages of 3 donors (d#1,d#2 and d#3) compared to non-differentiated monocytes isolated from the respective donor. (B) Fold increase of MDK expression is higher in M1 differentiated macrophages compared to M2 differentiated macrophages (n=3)

92 Appendix

BSourcesofUMcelllines

The majority of the cell lines used in this thesis were isolated from UM tissue in the laboratory of and provided by Prof. Bruce Ksander of Harvard Medical School (Mel202, 270, OMM2.2-2.6). These lines have been extensively characterized. For some cell lines (e.g. Mel202) these data have been made publicly available in the ESTDAB project (European Searchable Tumour Line Database, an EU sponsored cell line database (http://www.ebi.ac.uk/ipd/estdab/directory). OMM1 was isolated from a liver metastasis in the laboratory of Drs. Luiten and Jager of Leiden University. The H79 cell line was isolated from a liver metastasis in the laboratory of Prof. Guerin, Canada (see table 6.2) In addition, we used the OCM1 and OCM3 cell lines, originally obtained from the laboratory of Prof. J. Kan-Mitchell. The OCM1 and OCM3 cells were initially described as UM but were recently found to lack GNAQ or GNA11 mutations and instead contain BRAF V600E mutations which are generally found in CM rather than UM (Calipel et al. 2003;Maatetal.2008). The common UM mutations in GNAQ or GNA11 were not found in the BRAF-mutatd cell lines (Lefevre et al. 2004) Generating UM cell lines from tumor tissue is diﬃcult (with a successrate of <10% (Ksander and Bosch personal communication). Genetic analysis, ﬁngerprinting and karyotyping have demonstrated that several cell lines believed to be derived from distinct sources are actually derived from the same patients. Griewank et al. 2012 studied a set of 19 UM cell lines, including cell lines used in this study. The frequency of BRAF mutations in cell lines derived from UM tumors is interestingly. No BRAF mutations have been found in primary UM tumors (Y. Cohen et al. 2003; Edmunds et al. 2003; Weber et al. 2003) via PCR-based techniques. More sensitive techniques, however, found BRAF mutations in a subset of UM, in only a small population of cells within a tumor (Henriquez et al. 2007; Janssen et al. 2008; Maat et al. 2008). If only a small proportion of the tumor cells harbor a BRAF mutation, this could be skewed under culture conditions especially, since observations described by Griewank et al. 2012 showed that cutaneous melanoma with BRAF mutations generally tend to grow well in culture, relative to cell lines derived from cutaneous melanoma without BRAF mutations (S.E. Woodman, unpublished). Conjunctival melanomas, that arise in the extra-ocular conjunctiva, are biologically more similar to cutaneous melanomas. The origin of cell lines with BRAF mutations could therefore be from the conjunctiva or most likely from tumors that are large with an extra-ocular extension. As soon as the tumor extends beyond the immune-privileged eye, the mutational pattern may skew towards that seen in conjunctival or cutaneous melanoma.

CBleachinghistologicaltissue

Bleaching of high pigmented histological tissue samples In humans skin, hair and the retina can be highly pigmented. The amount and the type of melanin determines the degree and the color of the skin and hair. Melanocytes produce melanin and the accumulation of heavy pigment in malignant melanocytes in (uveal) melanoma can mask and obscure histological stainings. Hematoxylin and Eosin (H+E), immunohistochemical and immunofluorescence stainings are greatly influenced by high melanin content. To address this challenge we developed a protocol to remove the pigmentation of tissue without damaging the tissue morphology or disrupt subsequent stainings. Manicam et al. 2014 concluded, that optimal bleaching was achieved using prewarmed 10% H2O2 diluted in PBS at 65 for 120min. However, in our experiments pretreatment of the tissue for 120min often resulted in damaged and detached tissue samples. Drastically decreasing the incubation time to only 1-2min by simultaneously increasing the incubation temperature from 65 to the boiling point allowed effective melanin depigmentation without disrupting the tissue (Fig. C.3).

93 Appendix

Standard H+E stainings Tissue bleached with 10%

without bleaching H2O2

orthotopic ciliary bleached conjunctival processes ciliary tumor model processes

H+E

bleached choroid choroid

Figure C.3 – H+E staining after depigmentation. Highly pigmented tissue of paraﬃn-embedded mouse ocular tissue of an orthotopic conjunctival tumor model (B57BL/6 injected with HFG-Cdk4 cell) was treated with 10% H2O2 before H+E staining. Bleaching with H2O2 successfully removed dark pigmentation in melanocytes of the choroid and ciliary body without impacting the H+E staining.

Standard IF stainings Tissue bleached with 10%

without bleaching H2O2 CD3: Alexa Fluor 594 CD3: Alexa Fluor 594 transgene Nucleus: DAPI Nucleus: DAPI tumor model (GRM1)

CD3: Alexa Fluor 488 CD3: Alexa Fluor 488 Nucleus: DAPI Nucleus: DAPI human uveal melanoma

Figure C.4 – Immunofluorescence staining after depigmentation. (A) Highly pigmented tissue of paraffin- embedded mouse ocular tissue of an transgenic ocular tumor model (GRM1) was treated with 10% H2O2 before immunofluorescence CD3 staining. Bleaching with H2O2 successfully removed dark pigmentation in melanocytes and makes DAPI stained nuclei as well as CD3 positive lymphocytes stained with AlexaFluor 594 dye linked antibodies detectable. (B) Highly pigmented paraffin-embedded human UM tissue was treated with 10% H2O2 before immunofluorescence CD3 staining. Bleaching with H2O2 successfully removed dark pigmentation in melanocytes, removes the autofluorescence of erythrocytes and makes CD3 positive lymphocytes stained with AlexaFluor 488 dye linked antibodies detectable.

94 Appendix

D MDK expression in mouse models of ocular melanoma

To study diseases suitable mouse models are important for in vivo experiments. Unfortunately, no spontaneous occurring UM in mice has been described. There are some reports of spontaneous/naturally occurring UM in horses, cattle, sheep, cats, dogs, fish, rabbits, rats, and chickens. However, limited numbers of these animals for research use and the disease progression not reflecting human disease are limiting factors of animal research with spontaneous developing UM. Therefore, there have been several studies using orthotopic tumor models were either CM or UM cell lines cells are injected into the uveal layer of mice to study tumor growth, metastatic behavior and treatment efficacies (Surriga et al. 2013; Lattier et al. 2013; Waard et al. 2015; Susskind et al. 2017). Schlereth et al. 2015 established a mouse model for metastatic conjunctival melanoma, whereby HGF-Cdk4 or B16F10 melanoma cells are injected into immune competent C57BL/6 mice. In addition, human primary conjunctival melanoma cells (CM2005.1) were injected in NOD/Scid/IL2- /- mice (Waard et al. 2015). Other models make use of genetic crossbreeding resulting in transgenic mice with phenotypes of the disease of interest. Those transgenic mouse models, however also have challenges, since the disease development is often not completely comparable with disease development in humans. For some preliminary MDK stainings we were able to get material from the transgenic mouse model that mimics spontaneous human melanomas, including UM (Schiffner et al. 2014), generously provide by Miriam de Jel 1. These transgenic mice spontaneously develop melanomas due to an aberrant expression of the metabotropic glutamate receptor 1 (Grm1). The Grm1 receptor is under the control of the melanocyte-specific Dct-promoter and causes development of nodal melanomas on hairless skin areas as well as choroidal thickening and uveal melanocytic neoplasia. The dermal lesions with melanin overproduction express melanoytic markers S100B and MelanA (Pollock et al. 2003; Schiffner et al. 2012; Schiffner et al. 2014). The MDK knockout mice, established by Nakamura et al. 1998 as well as the other ocular melanoma mouse models provide the prerequisites for future studies to investigate MDK in UM in vivo.

1AG Prof. Bosserhoﬀ, Biochemistry and Molecular Medicine, University Hospital Erlangen, Friedrich-Alexander- Universität Erlangen-Nürnberg, Erlangen, Germany

95 Appendix

Preliminary MDK stainings (Fig. D.5) show no MDK expression in MDK knockout mice eyes. Tissue sections of MDK knockout mice were kindly provided by Andreas Uhl 2. MDK expression in eyes of wildtype C57BL/6 mice is mainly found in the high pigmented choroid and the iris. In GRM1 transgenic mice, high MDK expression in the thickened choroid. Additionally, Miriam de Jel (AG Bosserhoﬀ) provided us with cDNA of choroidal melanocytes from GRM1 mice eyes and qPCR analysis showed higher MDK levels in the GRM1 eye cells than in non-malignant control cells.

MDK expression in eyes of different mouse models A B C D MDK -/- C57BL/6 HGF-Cdk4 Grm1

100

10 foldincrease mdk

(relative to non-malignant control cell line) control cell (relativetonon-malignant 1 m-eye (1) m-eye (2)

Figure D.5 –MDKexpressionineyesofocularmelanomamousemodels.ImmunoﬂuorescencestainingofMDK in (A) MDK knockout (B) wildtype C57BL/6, (C) HGF-Cdk4 and (D) Grm1 mice eyes. Fold increase of mdk mRNA from isolated choroidal melanocytes from Grm1 mice relative to non-malignant mouse melanocytes

2AG Prof. Walzog, Institute für Kardiovaskuläre Physiologie und Pathophysiologie, LMU München

96 EC l i n i c a l ,P a t h o l o g i c a la n dM o l e c u l a rC h a r a c t e r i s t i c so fU M

SAMPLE_ID MDK Midkine Copy Vital status Cause of Death Distant metastasis distant_metastasis Pathology TAMS density TILS density Degree of AJCC SCNA

FPKM Log2(FPKM) number chr3 after initial _event_time_after cell_type_three_ pigmentation staging Cluster al. et Robertson from (adapted Cases TCGA treatment _inital_treatment categories TCGA_RZ_AB0B 73.2117 6.2136 1 1 Metastatic Uveal Melanoma metastasis 0 mixed mild mild mild 4 4 TCGA_V3_A9ZX 61.3476 5.9623 1 0 [Not Applicable] metastasis 233 epithelioid moderate moderate moderate 2 4 TCGA_V3_A9ZY 29.6589 4.9382 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 3 1 TCGA_V4_A9E5 17.2200 4.1874 2 0 [Not Applicable] no metastasis Not Applicable mixed moderate mild moderate 4 2 TCGA_V4_A9E7 49.1732 5.6488 1 1 Metastatic Uveal Melanoma metastasis 35 mixed heavy mild heavy 4 4 TCGA_V4_A9E8 13.1522 3.8230 1 1 Metastatic Uveal Melanoma metastasis 557 mixed heavy mild heavy 4 3 TCGA_V4_A9E9 3.5227 2.1772 2 0 [Not Applicable] no metastasis Not Applicable spindle cell heavy mild moderate 4 2 TCGA_V4_A9EA 48.4750 5.6286 2 0 [Not Applicable] no metastasis Not Applicable spindle cell moderate mild moderate 4 2 TCGA_V4_A9EC 15.4336 4.0386 2 0 [Not Applicable] no metastasis Not Applicable mixed mild mild moderate 3 1 TCGA_V4_A9ED 20.1584 4.4032 1 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 3 3 TCGA_V4_A9EE 45.4696 5.5382 1 1 Metastatic Uveal Melanoma metastasis 211 epithelioid moderate mild mild 3 4 TCGA_V4_A9EF 14.9179 3.9926 1 0 [Not Applicable] no metastasis Not Applicable mixed moderate mild mild 4 3 TCGA_V4_A9EH 131.9965 7.0552 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild moderate 4 1 TCGA_V4_A9EI 59.3745 5.9159 1 1 Metastatic Uveal Melanoma metastasis 203 epithelioid moderate mild moderate 4 4 TCGA_V4_A9EJ 6.8608 2.9747 2 0 [Not Applicable] no metastasis Not Applicable spindle cell moderate mild moderate 3 2 TCGA_V4_A9EK 14.4155 3.9463 2 0 [Not Applicable] no metastasis Not Applicable spindle cell heavy mild heavy 3 2 TCGA_V4_A9EL 64.3935 6.0311 1 0 [Not Applicable] metastasis 413 mixed moderate moderate moderate 4 4 TCGA_V4_A9EM 94.2821 6.5741 2 0 [Not Applicable] no metastasis Not Applicable mixed mild mild mild 4 2 TCGA_V4_A9EO 12.8922 3.7962 2 0 [Not Applicable] no metastasis Not Applicable mixed mild mild mild 4 3 TCGA_V4_A9EQ 48.8703 5.6401 1 1 Metastatic Uveal Melanoma metastasis 252 mixed moderate mild moderate 4 3

TCGA_V4_A9ES 7.4802 3.0841 1 0 [Not Applicable] metastasis 1299 mixed mild mild mild 3 3 Appendix TCGA_V4_A9ET 71.5200 6.1803 2 0 [Not Applicable] no metastasis Not Applicable mixed heavy mild moderate 4 2

97 TCGA_V4_A9EU 44.4861 5.5074 1 0 [Not Applicable] no metastasis Not Applicable mixed moderate mild heavy 4 4 TCGA_V4_A9EV 38.5571 5.3059 1 1 Metastatic Uveal Melanoma metastasis Unknown mixed mild moderate mild 4 4 TCGA_V4_A9EW 7.8064 3.1386 2 0 [Not Applicable] metastasis 197 mixed moderate mild mild 4 2 TCGA_V4_A9EX 47.9528 5.6133 1 1 Metastatic Uveal Melanoma metastasis 381 mixed moderate mild heavy 4 4 TCGA_V4_A9EY 16.2283 4.1067 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 4 1 TCGA_V4_A9EZ 15.4372 4.0389 2 0 [Not Applicable] no metastasis Not Applicable mixed heavy mild heavy 3 2 TCGA_V4_A9F0 32.5462 5.0681 1 1 Metastatic Uveal Melanoma metastasis 489 mixed heavy mild heavy 4 3 TCGA_V4_A9F1 8.5418 3.2543 1 0 [Not Applicable] no metastasis Not Applicable mixed heavy mild moderate 3 4 TCGA_V4_A9F2 196.2429 7.6238 2 0 [Not Applicable] no metastasis Not Applicable spindle cell moderate mild heavy 3 2 TCGA_V4_A9F3 42.3787 5.4389 1 1 Metastatic Uveal Melanoma metastasis 329 epithelioid mild mild mild 4 4 TCGA_V4_A9F4 12.9719 3.8045 2 0 [Not Applicable] no metastasis Not Applicable mixed mild mild mild 4 2 TCGA_V4_A9F5 80.5444 6.3495 1 0 [Not Applicable] metastasis 78 mixed mild mild moderate 4 4 TCGA_V4_A9F7 8.6329 3.2680 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 3 1 TCGA_V4_A9F8 39.0723 5.3245 1 0 [Not Applicable] no metastasis Not Applicable mixed moderate mild moderate 4 3 TCGA_VD_A8K7 4.8889 2.5580 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 4 2 2017 TCGA_VD_A8K8 44.1703 5.4973 1 1 Metastatic Uveal Melanoma metastasis Unknown spindle cell mild mild mild 3 4 TCGA_VD_A8K9 4.7353 2.5199 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 2 2 TCGA_VD_A8KA 14.2993 3.9354 2 0 [Not Applicable] metastasis 1008 spindle cell mild mild mild 3 2 TCGA_VD_A8KB 15.5176 4.0459 2 0 [Not Applicable] no metastasis Not Applicable mixed mild mild moderate 3 2 ) TCGA_VD_A8KD 28.0879 4.8623 1 1 Metastatic Uveal Melanoma metastasis 0 mixed mild mild moderate 4 3 TCGA_VD_A8KE 12.0994 3.7114 2 0 [Not Applicable] no metastasis Not Applicable mixed mild mild mild 2 1 TCGA_VD_A8KF 16.6071 4.1381 1 1 Metastatic Uveal Melanoma metastasis Unknown mixed mild mild mild 4 3 TCGA_VD_A8KG 9.3419 3.3704 2 0 [Not Applicable] no metastasis Not Applicable mixed mild mild mild 2 2 TCGA_VD_A8KH 9.5842 3.4038 1 0 [Not Applicable] no metastasis Not Applicable mixed mild mild mild 4 3 SAMPLE_ID MDK Midkine Copy Vital status Cause of Death Distant metastasis distant_metastasis Pathology TAMS density TILS density Degree of AJCC SCNA FPKM Log2(FPKM) number chr3 after initial _event_time_after cell_type_three_ pigmentation staging Cluster treatment _inital_treatment categories TCGA_VD_A8KI 44.3097 5.5017 1 1 Metastatic Uveal Melanoma metastasis Unknown mixed moderate heavy heavy 3 4 TCGA_VD_A8KJ 8.6056 3.2639 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 3 2 TCGA_VD_A8KK 16.4535 4.1254 1 0 [Not Applicable] no metastasis Not Applicable mixed moderate mild moderate 4 3 TCGA_VD_A8KL 28.7625 4.8954 2 0 [Not Applicable] no metastasis Not Applicable mixed moderate mild heavy 3 3 TCGA_VD_A8KM 20.7275 4.4415 1 0 [Not Applicable] no metastasis Not Applicable mixed moderate moderate moderate 3 4 TCGA_VD_A8KN 19.1616 4.3335 1 0 [Not Applicable] no metastasis Not Applicable mixed moderate mild moderate 2 4 TCGA_VD_A8KO 4.1798 2.3729 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 2 1 TCGA_VD_AA8M 41.1822 5.3986 2 0 [Not Applicable] no metastasis Not Applicable spindle cell moderate mild mild 2 1 TCGA_VD_AA8N 9.7301 3.4236 1 0 [Not Applicable] no metastasis Not Applicable mixed moderate mild moderate 3 4 TCGA_VD_AA8O 32.2367 5.0547 2 1 Metastatic Uveal Melanoma metastasis Unknown mixed heavy mild heavy 3 3 TCGA_VD_AA8P 83.6702 6.4038 1 0 [Not Applicable] no metastasis Not Applicable spindle cell moderate mild moderate 3 3 TCGA_VD_AA8Q 25.1551 4.7090 2 1 Metastatic Uveal Melanoma metastasis Unknown spindle cell moderate mild heavy 3 1 TCGA_VD_AA8R 34.9129 5.1664 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 3 1 TCGA_VD_AA8S 35.0325 5.1712 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 2 2 TCGA_VD_AA8T 20.3108 4.4135 1 0 [Not Applicable] no metastasis Not Applicable spindle cell moderate mild mild 2 3 TCGA_WC_A87T 25.6478 4.7359 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 3 1 TCGA_WC_A87U 29.4577 4.9287 2 1 Metastatic Pancreatic Cancer no metastasis Not Applicable spindle cell mild mild mild 3 1 TCGA_WC_A87W 134.9359 7.0868 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 2 2 TCGA_WC_A87Y 56.6223 5.8486 1 1 Metastatic Uveal Melanoma metastasis Unknown mixed mild mild moderate 3 4 TCGA_WC_A880 2.9406 1.9784 2 0 [Not Applicable] no metastasis Not Applicable mixed mild mild mild 4 1

TCGA_WC_A881 9.0148 3.3241 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild moderate 3 2 Appendix TCGA_WC_A882 49.8724 5.6688 1 0 [Not Applicable] no metastasis Not Applicable spindle cell heavy moderate heavy 2 3

98 TCGA_WC_A883 102.7601 6.6971 1 1 [Unknown] no metastasis Not Applicable epithelioid moderate moderate moderate 3 4 TCGA_WC_A884 35.3811 5.1851 2 0 [Not Applicable] no metastasis Not Applicable epithelioid moderate moderate mild 4 1 TCGA_WC_A885 4.2989 2.4057 2 0 [Not Applicable] no metastasis Not Applicable spindle cell moderate mild heavy 2 2 TCGA_WC_A888 46.5945 5.5727 2 1 [Unknown] no metastasis Not Applicable mixed heavy heavy moderate 3 3 TCGA_WC_A88A 26.2335 4.7673 1 1 Metastatic Uveal Melanoma metastasis 0 mixed mild mild moderate 4 4 TCGA_WC_AA9A 9.0622 3.3309 1 1 Metastatic Uveal Melanoma metastasis 371 spindle cell moderate mild mild 2 3 TCGA_WC_AA9E 34.9775 5.1690 2 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 2 2 TCGA_YZ_A980 24.9613 4.6983 1 0 [Not Applicable] no metastasis Not Applicable mixed mild mild mild 3 3 TCGA_YZ_A982 153.6065 7.2725 3 1 Atrial fibrillation complications no metastasis Not Applicable spindle cell moderate mild moderate 4 2 TCGA_YZ_A983 2.0910 1.6281 2 0 [Not Applicable] no metastasis Not Applicable epithelioid mild mild mild 3 1 TCGA_YZ_A984 27.4963 4.8327 2 1 Metastatic Uveal Melanoma metastasis 916 spindle cell mild mild mild 3 3 TCGA_YZ_A985 3.6195 2.2077 1 0 [Not Applicable] no metastasis Not Applicable spindle cell mild mild mild 4 3 Tables

1.1 Distribution of melanoma ...... 1 1.2 Sites of metastasis ...... 5

3.1 MDK qPCR primer ...... 17

6.1 Reagents for cell culture ...... 72 6.2 Human UM cell lines ...... 73 6.3 Healthy control cells ...... 73 6.4 Stable transfected cell lines ...... 73 6.5 MACS buffer ...... 74 6.6 RNA/primer mix ...... 75 6.7 RNA/primer/dNTP and reverse transcriptase mix ...... 75 6.8 PCR mix ...... 76 6.9 PCR cycling program ...... 76 6.10 MDK cloning primer ...... 76 6.11 qPCR mix ...... 76 6.12 qPCR cycling program ...... 76 6.13 qPCR primer ...... 77 6.14 Bisulfite conversion reaction ...... 77 6.15 Bisulfite conversion reaction mix ...... 77 6.16 Bisulfite PCR mix ...... 77 6.17 Bisulfite PCR cycling program ...... 77 6.18 5’UTC PCR primer ...... 78 6.19 Recipe for 100ml stock solution RIPA buffer ...... 79 6.20 Recipe for 500µl working solution RIPA buffer ...... 79 6.21 Recipe for sample buffer ...... 80 6.22 Separation gel ...... 80 6.23 Collection gel ...... 80 6.24 Recipe for 500ml Tris/HCl (1.5M; pH 8.8) ...... 81 6.25 Recipe for 100ml Tris/HCl (0.5M; pH 6.8) ...... 81 6.26 Anti-MDK siRNAs ...... 82 6.27 Vector and MDK DNA digestion ...... 82 6.28 Ligation mix ...... 82 6.29 Flow cytometry antibodies ...... 87 6.30 Ascending series of alcohol solutions for embedding tissue in paraffin ...... 87 6.31 H & E staining protocol ...... 88 6.32 Overview of used software ...... 89

99 Figures

1.1 Possible anatomical regions where ocular melanoma can originate ...... 1 1.2 Midkine protein structure ...... 7 1.3 Midkine receptors ...... 8 1.4 Summary of MDK functions ...... 11 1.5 UM patient 270 history and gene array analysis identifying diﬀerential expression of MDK ...... 15

3.1 MDK qPCR primer validation ...... 17 3.2 MDK transcript variants ...... 18 3.3 MDK antibody validation ...... 19 3.4 Expression of MDK in uveal melanoma cell lines of patient 270 ...... 20 3.5 Expression of MDK in uveal melanoma cell lines ...... 21 3.6 Expression of MDK in UM tissue ...... 22 3.7 Expression of MDK in UM patients ...... 23 3.8 MDK expression in the soluble and exosome fraction ...... 24 3.9 MDK binds to the membrane of UM cells ...... 24 3.10 ATRA treatment induces MDK expression ...... 26 3.11 Demethylation treatment increases MDK expression ...... 27 3.12 DNA sequencing results following bisulfite treatment ...... 28 3.13 Hypoxic conditions induce MDK expression ...... 29 3.14 siRNA transfection efficiency ...... 30 3.15 MDK downregulation with specific siRNAs ...... 31 3.16 Creation of MDK expression plasmid ...... 33 3.17 MDK-pcDNA3.1 clones ...... 34 3.18 Determination of G418 concentration for drug selection of plasmid transfected OCM1 cells ...... 35 3.19 MDK overexpression in OCM1 cell line ...... 36 3.20 MDK downregulation with specific siRNAs reduces proliferation and viability of UM cells ...... 37 3.21 MDK downregulation with specific siRNAs increases caspase3/7 activity ..... 38 3.22 Migration of OCM1 cells ...... 39 3.23 MDK receptor expression on cell membrane of UM cells derived from patient270 40 3.24 MDK receptor expression on UM cell line panel ...... 41 3.25 MDK downregulation with specific siRNAs decreases phosphorylation of mTOR substrates RPS6 and PRAS40 ...... 42 3.26 MDK downregulation with specific siRNAs decreases phosphorylation of mTOR substrates RPS6 and PRAS40 ...... 43 3.27 MDK overexpression increases phosphorylation of mTOR substrates RPS6 and PRAS40 ...... 44 3.28 MDK overexpression increases phosphorylation of mTOR substrates RPS6 and PRAS40 ...... 45 3.29 Baseline phosphorylation levels of RPS6 in UM cell lines ...... 46 3.30 MDK promotes UM cell survival by maintaining RPS6 phosphorylation during Akt inhibition...... 47 3.31 MDK promotes UM cell survival by maintaining RPS6 phosphorylation during mTOR inhibition...... 48

100 Figures

3.32 MDK expression correlates with overall survival ...... 49 3.33 MDK expression correlates with metastasis ...... 50 3.34 MDK expression correlates with time-to-metastasis ...... 50 3.35 MDK expression levels in tumor classification based on AJCC staging ...... 51 3.36 MDK expression levels in tumor classification based on AJCC staging (including extraocular extension and ciliary body involvement) ...... 52 3.37 MDK expression correlates with SCNA clustering ...... 52 3.38 MDK expression correlates with chromosome 3 status ...... 53 3.39 No correlation between MDK expression and BAP1 or PRAME expression .... 53 3.40 MDK expression correlates with immune cell infiltration ...... 54 3.41 MDK expression in tumors grouped by cell type and grade of pigmentation. ... 55

4.1 Proposed intracellular signaling mechanism UM ...... 64

6.1 Cell counting with ImageJ software ...... 85 6.2 Layout of PathScan®Intracellular Signaling Array Kit ...... 86

A.1 MDK expression is upregulated in activated mononuclear cells (MNCs) ...... 91 A.2 MDK expression is upregulated in diﬀerentiated macrophages ...... 92 C.3 H+E staining after depigmentation ...... 94 C.4 Immunoﬂuorescence staining after depigmentation ...... 94 D.5 MDK expression in eyes of ocular melanoma mouse models ...... 96

101 Abbreviations

5’aza 5-aza-2’-deoxycytidine 5-mC 5-methyl cytosine AJCC american joint committee on Cancer Alk anaplastic lymphoma kinase ATRA all-trans-retinoic acid BCA bicinchoninic acid assay BSA bovine albumin serum CAM chorioallantoic membrane CLL chronic lymphocytic leukemia CM cutanous melanoma CoCl2 cobalt chloride CS chondroitin sulfate CT computed tomography DR direct repeat DSS1 deleted in split hand/split foot 1 ECM extracellular matrix EMT epithelial-mesenchymal transition FAF fundus autofluorescence FCS fetal calf serum FFA fundus fluorescein angiography FISH fluorescence in situ hybridization FNAB fine-needle aspiration biopsy FPKM fragments per kilobase of exon per million reads mapped GF-AFC glycylphenylalanyl- aminofluorocoumarin GM-CSF granulocyte-macrophage colony stimulation factor HDAC histone-deacetylase inhibitor HGF hepatocyte growth factor HIF hypoxia inducible factor HS heparan sulfate ICGA indocyanine green angiography IF immunofluorescence LRP low density lipoprotein receptor-related protein MAPK mitogen-activate protein/ kinase M-CSF macrophage colony stimulation factor MDKi midkine inhibitor MDK midkine MEK extracellular signal-regulated kinase MNC mononuclear cells MRI magnetic resonance imaging mTOR mammalian target of rapamycin NIH nationalinstituteofhealth OCT optical coherence tomography OSCC oral squamous cell carcinoma PBMC peripheral blood mononuclear cells PBS phosphate-buffered saline

102 Abbreviations

PCR polymerase chain reaction PHA phytohemagglutinin PRAME preferentially expressed antigen in melanoma PRAS40 proline-richAktsubstrateof40kDA PTEN phosphataseandtensinhomolog PTN pleiotrophin qPCR quantitative real-time polymerase chain reaction RARE retinoic response element RAR retinoic acid receptors RASSF1 RAS associated domain family 1 RIPA buffer radioimmunoprecipitationassaybuffer RPS6 ribosomalproteinS6 RPTP ⇣ proteoglycan receptor (receptor-type tyrosine phosphatase ⇣) RT roomtemperature SCNA somatic copy number alteration SDC1 syndecan 1 SEM standard error of the mean siRNA small interference RNA SIRT selective internal radiation therapy TAM tumor associated macrophages TCGA the cancer genome atlas TIL tumor infiltrated lymphocytes TKI tyrosinekinaseinhibitor TTT transpupillary thermotherapy UBM ultrasound biomicroscopy UCH ubiquitin carboxyterminal hydralase UM uveal melanoma USG ultrasonography VEGF vascular enothelial growth factor

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115 Acknowledgement

This part is to thank everybody who helped me during this thesis project. I grateful to many people for their support and encouragement all the way. Specifically, I would like to thank Prof. Dr. Mackensen for the warm welcome and for the opportunity to work at the Department of Medicine 5, Haematology and Oncology. A very special gratitude goes out to Co, my supervisor of this PhD project. I am especially grateful for his enthusiasm, encouragements and openness. Thank you for teaching me to see the positive sides. I am grateful for Barbaras continuous support and for her precise and accurate expertise in the lab and her ability to ground me in reality when ideas and plans exceeded any feasibility. I would like to thank the german research council and the research unit (FOR2240: (Lymph)- angiogenesis and cellular immunity in inflammatory diseases of the eye) for funding for the work. Furthermore, thank you to Prof. Heindl, Nasrin Refaian, Martina Becker and Dr. Simona Schlereth for their support in acquiring patient and mouse samples and for their warm welcome in Köln. For providing additional mouse samples I would like to thank, Prof. Bosserhoff, Miriam De-Jel, Prof. Walzog and Andreas Uhl. I grateful for Dr. Mandy Wahlbuhl-Becker and Elisabeth Koppmann from the Kinderklinik for their help with the histological stainings and thanks to Susa who always knew who to call or who to introduce me to if I needed something. Furthermore, thanks to Prof. Dr. Weisbach for letting me use of Keyence microscope whenever I needed. A very special thanks goes to Dr. Christian Büttner for his help with the bioinformatical analysis of TCGA databank analysis. And a huge thank you to all my colleagues which I had the pleasure and opportunity to share the lab and the office with. All of you created an extremely enjoyable work environment. Thank you for all your support and for your willingness to work together, your encouragements and all your openness for discussions. Especially thanks to Judith, Luise, Hannah, Tabea, Caro, Anky, Sascha and Martina for listening to me ranting about everything and anything. I had so much fun with all of you, during video shoots, preparing "Maus" presentations and creating Doktorhüte. I will never forget the amazing short city trips nor our longer holidays we did together. Especially, thanks to the juicy-girls Martina, Judith and Caro. You were the best travel companions I could wish for. Furthermore, I would like to thank a few important people in my life. Franzi and Christian for their motivational support and specifically their help with Latex. A special gratitude goes out to my flatmates, Cindy and Edith for our late-night working sessions at home and supporting me on good and bad days. Thank you to Katharina and Theresa, my oldest childhood friends for providing a never-ending stream of chocolate, stress relaxation teas and emotional reinforcements. Zuletzt möchte ich mich noch bei meiner Familie bedanken. Vielen Dank für eure Unter- stützung. Danke, dass ich immer auf euch zählen kann.

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