Role of the AP-1 Transcription Factor FOSL1 in the Mesenchymal Glioblastoma subtype

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UNIVERSIDAD AUTÓNOMA DE MADRID

FACULTAD DE CIENCIAS

D EPARTAMENTO DE B ^IOLOGÍA M ^OLECULAR

R OLE OF THE AP-1 T RANSCRIPTION F ^ACTOR F ^OSL 1

IN THE M ESENCHYMAL G LIOBLASTOMA S ^UBTYPE

C ^AROLINA A ^LMEIDA M ^ARQUES

Madrid, 2019

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D EPARTAMENTO DE B IOLOGÍA M OLECULAR

F ACULTAD DE C IENCIAS

UNIVERSIDAD AUTÓNOMA DE M ^ADRID

R OLE OF THE AP-1 T RANSCRIPTION F ^ACTOR F ^OSL 1

IN THE M ESENCHYMAL G LIOBLASTOMA S ^UBTYPE

C AROLINA A LMEIDA M ARQUES MSc in Biotechnology for Health Sciences

T HESIS DIRECTOR M

ASSIMO

S

QUATRITO

S EVE -B ALLESTEROS F OUNDATION B RAIN T UMOUR G ROUP

S ^PANISH N ^ATIONAL C ^ANCER R ESEARCH CENTRE (CNIO)

Madrid, 2019

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5 Dr. Massimo Squatrito, Head of the Seve-Ballesteros Foundation Brain Tumour Group at the Spanish National Cancer Research Centre (CNIO), certifies that Ms.

Carolina Almeida Marques, Master in Biotechnology for Health Sciences by the Universidade de Trás-os-Montes e Alto Douro from Vila Real, Portugal, has completed her Doctoral Thesis entitled “Role of the AP-1 transcription factor FOSL1 in the Mesenchymal Glioblastoma subtype” and meets the necessary requirements to obtain the PhD degree in Molecular Biosciences. To this purpose, she will defend her Doctoral Thesis at the CNIO. The Thesis has been carried out under my supervision and I hereby authorize its defense in front of the appropriate Thesis Tribunal.

Massimo Squatrito, PhD

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7 This thesis submitted for the degree of Doctor of Philosophy at the Universidad Autónoma de Madrid, has been compiled in the Seve-Ballesteros Foundation Brain Tumour laboratory at the Spanish National Cancer Research Center (CNIO), under the supervision of Dr.

Massimo Squatrito.

This work was supported by a “La Caixa” International PhD Fellowship (2012), by the project PI13/01028, co-founded by Institute of Health Carlos III and European Regional Development Fund (ERDF) and by the Seve-Ballesteros Foundation.

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9

A CKNOWLEDGEMENTS

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11 This thesis is the conclusion of the last 6 years of research that would not have been possible without the support of many people that I would like to thank.

First, I want to thank Dr. Massimo Squatrito for giving me the opportunity to perform my PhD thesis in his laboratory and be one of the first building blocks of his surely successful career as a PI. This project has been quite challenging for both of us and I am grateful that you never gave up on finding the right path. I have learnt a lot from you and I just have to thank you for being such a great mentor in this special period of my scientific career. For pushing me forward in the hard moments and for sharing the joy of those moment when an experiment works and everything makes sense.

I also want to thank the current and former members of the Brain Tumour lab, for the scientific input and all the good moments shared. My special thanks to Barbara, for all the support throughout these years, both scientifically and personally. I value all the scientific discussions, all the suggestions and all the hands-on help you always gave me. You are a true inspiration and I admire you so much, for being such a super mum and a super scientist. I also want to thank Paula Nogales. For all the great memories we keep from the times you were around, but especially for becoming my Spanish best friend.

I want to thank to the current and former members of the Genes, Development and Disease Group, for all the “AP-1 support”. First, to Professor Erwin Wagner, for his availability of sharing with us the tools needed to move this project forward and for the fruitful discussions we had along the way. To Dr. Latifa Bakiri, for having the door always open when I needed a last minute reagent and for the countless ideas that contributed to the success of this project. To Dr. Álvaro Ucero, for solving my doubts, giving me suggestions and hearing my thrills and frustrations so many times. To Lucía Diez, for sharing with me the “meriendas” and the anxiety of the stressful times of thesis writing, I think we learned a lot from each other’s experiences! To the rest of the GDD members, for adopting me as one of the AP-1 family. I also want to thank Pia, for the great moments we had together, it was a blast having you around the last summer!

I want to thank everyone at the CNIO that I crossed paths with and contributed to the development of this project. Special thanks to Flor Díaz, from the Animal Facility, which work and constant availability to help were crucial for the accomplishment of all the mouse-related work performed during this project.

À minha comunidade portuguesa em Madrid, obrigada por fazer desta longa estadia o mais agradável possível. À Bebé e ao Santo, por terem “aberto caminho” e ajudado tanto no início, e por serem os amigos mais queridos há 13 anos. À Gomes, por ser a melhor parceira de cafés que poderia ter encontrado no CNIO, e por se ter tornado na melhor amiga também. À Joana, por ter vindo dar

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12 alegria aos nossos dias no CNIO e às nossas noite de bailarico no Barco. À Catarina, minha marida, por tão bem cuidares de mim e seres a melhor companheira. Ao Ponto, que embora faça parte da comunidade portuguesa em Dublin, as suas incursões madrilenas foram sempre alegria. À Fonte, por ter escutado os meus desabafos em longos lanches e me divertir tanto com as suas histórias. À Nuna, ao Rocha e à Diana, pelos momentos longe do trabalho, rodeados de música e cerveja.

À minha família e amigos, em especial às minhas amigas Joaninha, Raquel, Bibi, Rafa, Carla, Mariana, Ana e Sara. Obrigada por estarem sempre aí e desculpem a minha ausência, muitas vezes mais do que física. Prometo compensar-vos em breve.

Ao Pita, por ter embarcado no que seria o meio desta viagem e ter esperado por mim no que afinal se tornaram os 2/3 mais difíceis da viagem. Pela companhia de todas as noites, mesmo que ao ecrã de um computador, por tantas vezes escutar os meus desafabos, anseios e tristezas e ter sempre uma palavra de aconchego. Por me ensinar a ser melhor pessoa e gostar de mim mesmo quando não sou essa pessoa melhor. Obrigada bilico!

Aos meus Pais, obrigada pelo apoio e pelo carinho de sempre. Por estarem sempre aí para mim, em todos os momentos. As saudades são sempre muitas, mas vocês ajudaram a acreditar que no fim de contas vale a pena. Obrigada por tudo!

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13

S ^UMMARY

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15 Glioblastoma (GBM) is the most lethal form of brain tumor in adults and despite recent advances in genomic profiling that improved our knowledge on the molecular alterations underlying this disease, current therapies remain ineffective and median overall survival of GBM patients is 15 months. The mesenchymal (MES) subtype has the worse prognosis and demands a better characterization of its key molecular players that would potentiate novel therapeutic strategies to benefit patients diagnosed with GBM exhibiting MES features.

AP-1 transcription factors have complex biological functions and have been linked to cancer, acting as oncogenic or tumor suppressive depending on the cellular context and genetic background. FOSL1 (also known as FRA-1), one of the AP-1 members, has been associated with many cancer types, mainly epithelial, in which its overexpression is often linked to aggressive features and the epithelial- to-mesenchymal process.

Here, we studied the role of Fosl1 in a model of MES GBM. By manipulating its expression in Kras mutant (G12V) transformed mouse neural stem cells (NSCs), we found that Fosl1 is responsible for sustaining cell growth in vitro and in vivo and for the maintenance of stem-like properties.

Moreover, we confirmed that Fosl1 has indeed a crucial role in the regulation of the MES subtype. By RNA-seq analysis on Fosl1 knock-out in Kras^G12VNSCs, we observed a downregulation of the Mesenchymal gene signature, while the Proneural gene signature was upregulated. We validated these findings also in Fosl1 loss-of-function xenograft tumors. Additionally, in an inducible Fosl1 model, its overexpression resulted in upregulation the MES signature.

The relevance of our mouse data was further supported by our findings in primary human brain tumor stem cells. The knockdown of FOSL1 expression in human primary MES GBM cells resulted in the downregulation of the MES gene signature.

Our data suggest that FOSL1/FRA-1 is a master regulator of the MES subtype of GBM, contributing to the aggressiveness of these tumors by sustaining cell growth and maintaining the stem cell properties.

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17

R ^ESUMEN

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19 El glioblastoma (GBM) es la forma más letal de tumor cerebral en adultos, y a pesar de los recientes avances en el perfil genómico que contribuyen a ampliar nuestros conocimientos sobre las alteraciones moleculares subyacentes a esta enfermedad, las terapias actuales siguen siendo ineficaces y la supervivencia global media de los pacientes con GBM es de 15 meses. El subtipo mesenquimal (MES) tiene un peor pronóstico y exige una mejor caracterización de las principales bases moleculares que potenciarían nuevas estrategias terapéuticas para mejorar a los pacientes diagnosticados con GBM que presentan características de MES.

Los factores de transcripción AP-1 tienen funciones biológicas complejas y están relacionados con cáncer actuando como oncogenes o supresores de tumores según el contexto celular y los antecedentes genéticos. FOSL1 (también conocido como FRA-1), uno de los miembros de la familia AP-1, se asocia con muchos tipos de cáncer, principalmente epiteliales, en los que su sobreexpresión suele estar relacionada con características agresivas y con el proceso de transición epitelio- mesénquima (EMT).

Aquí, nosotros estudiamos el papel de Fosl1 en un modelo de GBM MES. Al manipular su expresión génica en células madre neurales de ratón (en Inglés Neural Stem Cells, NSCs) transformadas con Kras mutante (G12V), encontramos que Fosl1 es responsable de mantener el crecimiento celular in vitro e in vivo y del mantenimiento de las propiedades troncales de las células madre. Además, confirmamos que Fosl1 tiene un papel crucial en la regulación del subtipo MES. Mediante el análisis de RNA-seq en NSCs Kras^G12V y deficientes en Fosl1, observamos una reducción en la regulación de genes característicos del linaje mesenquimal, mientras que los genes correspondientes al subtipo proneural son sobreexpresados. También validamos estos hallazgos mediante xenotransplante de las células deficientes en Fosl1. Además, en un modelo inducible para Fosl1, su sobreexpresión dio lugar a incremento en la expressión de los genes característicos de MES.

La relevancia de nuestros datos en el modelo murino fue respaldada por nuestros hallazgos en células madre de tumores cerebrales humanos primarios. El silenciamiento de la expresión de FOSL1 en células primarias humanas de GBM MES resultó en la reducción de la expresión de los genes característicos de MES.

Nuestros datos sugieren que FOSL1/FRA-1 es un regulador fundamental del subtipo MES de GBM, que contribuye a la agresividad de estos tumores al sostener el crecimiento celular y mantener las propiedades de las células madre.

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21

T ABLE OF C ONTENTS

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23

L ^{IST OF} A BBREVIATIONS

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27 AP-1 activator protein 1

ASLV avian sarcoma leukosis virus ATF activating transcription factor ATRX ATRX, chromatin remodeler

AU arbitrary units

bFGF basic fibroblast growth factor

bHLH-B2 also known as BHLHE40 basic helix-loop-helix family member e40 BMP4 bone morphogenetic protein 4

BRAF B-Raf proto-oncogene, serine/threonine kinase BrdU 5-bromo-2'-deoxyuridine

BTSC brain tumor stem cell bZIP basic leucine zipper Cas CRISPR-associated

CD44 CD44 molecule (Indian blood group) CDK4 cyclin dependent kinase 4

CDK6 cyclin dependent kinase 6

CDKN2A cyclin dependent kinase inhibitor 2A CDKN2B cyclin dependent kinase inhibitor 2B CEBPB CCAAT enhancer binding protein beta CHI3L1 chitinase 3 like 1

ChIP chromatin immunoprecipitation

CL Classical

CRC colorectal cancer

CRISPR clustered regularly interspaced short palindromic repeats CSC cancer stem cell

Dox doxycycline

EGF epidermal growth factor

EGFR epidermal growth factor receptor ELDA Extreme Limiting Dilution Analysis EMT epithelial-to-mesenchymal transition EphA2 EPH receptor A2

ERK Extracellular Signal-Regulated Kinase FBS fetal bovine serum

FDR false discovery rate

Flk1 also known as Kdr (kinase insert domain receptor) FRA-1 Fos-related antigen 1

FRA-2 Fos-related antigen 2 FUT4 Fucosyltransferase 4

GABRA1 gamma-aminobutyric acid type A receptor alpha1 subunit

GBM glioblastoma

G-CIMP glioma CpG island methylator phenotype GEMM genetic engineered mouse model GFAP glial fibrillary acidic protein GSC glioma stem cell

GSEA Gene Set Enrichment Analysis

Gtv-a tv-a expression under the GFAP promoter

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28 IDH isocitrate dehydrogenase

IDH-mut IDH mutant IDH-wt IDH wildtype

IF immunofluorescence

IHC immunohistochemistry

IL-13Rα2 interleukin 13 receptor subunit alpha 2

IP Immunoprecipitation

JNK Jun N-terminal kinase

KD knockdown

KO knock-out

KRAS KRAS proto-oncogene, GTPase LAC lung adenocarcinoma

LGG low-grade glioma

MAF musculoaponeurotic fibrosarcoma MAPK mitogen-activated protein kinase MDM2 MDM2 proto-oncogene

MDM4 MDM4, p53 regulator

MEK1 MAPK kinase 1

MES Mesenchymal

MET MET proto-oncogene, receptor tyrosine kinase MGMT O-6-methylguanine-DNA methyltransferase MMP1 matrix metallopeptidase 1

MMP9 matrix metallopeptidase 9

MR master regulator

NE Neural

NEFL neurofilament light

NES Normalized Enrichment Score

NF1 neurofibromin 1

NF-κB nuclear factor κ-light-chain-enhancer of activated B cells NRAS NRAS proto-oncogene, GTPase

NSC neural stem cell

NSPC neural stem and progenitor cell

Ntv-a tv-a expression under the Nestin promoter nu/nu immunodeficient nude mouse

OE overexpression

PCA principal component analysis

PDGFRA platelet derived growth factor receptor alpha PDX patient derived xenograft

PI propidium iodide

PI3K Phosphoinositide 3-kinase PLAU plasminogen activator, urokinase

PLAUR plasminogen activator, urokinase receptor

PN Proneural

PROM1 prominin 1

PTEN phosphatase and tensin homolog r Pearson correlation coefficient

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29 RCAS Replication-Competent Avian leukosis virus Splice acceptor

RNAi RNA interference RNA-seq RNA sequencing

RTK receptor tyrosine kinase RT-qPCR Real Time – quantitative PCR rtTA reverse tetracycline transactivator RUNX1 runt related transcription factor 1 SERPINE1 serpin family E member 1

sgRNA single guide RNA

SLC12A5 solute carrier family 12 member 5 SNAI1 snail family transcriptional repressor 1

SOX2 SRY-box 2

SRE serum-response element

STAT3 signal transducer and activator of transcription 3 SYT1 synaptotagmin 1

TAA tumor associated antigen TAM tumor associated macrophage

TAZ tafazzin

TCF ternary-complex factors TCGA The Cancer Genome Atlas TERT telomerase reverse transcriptase TF transcription factor

Tg transgenic

Tgfb1 transforming growth factor, beta 1 TIMP1 TIMP metallopeptidase inhibitor 1 TME tumor microenvironment

TMZ temozolomide

TP53 tumor protein p53

TPA 12-O-tetradecanoylphorbol-13-acetate TRE TPA responsive element

TSG tumor suppressor genes tv-a tumor virus A

TWIST twist family bHLH transcription factor 1 VEGF Vascular endothelial growth factor

WB western blot

WHO World Health Organization

Zeb1 zinc finger E-box binding homeobox 1 Zeb2 zinc finger E-box binding homeobox 2

ZNF238 also known as ZBTB18 zinc finger and BTB domain containing 18

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31

I NTRODUCTION

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33

1. Glioblastoma

1.1. Epidemiology and risk factors

Primary malignant brain tumors are relatively rare tumors compared with brain metastasis and other primary tumors (such as lung, breast, prostate or colorectal) but constitute a significant cause of cancer associated morbidity and mortality, accounting for more than 250,000 newly diagnosed cases annually worldwide (Walsh, Ohgaki and Wrensch, 2016). Gliomas are the most common primary tumor of the central nervous system in adults, with different histologic types that include astrocytomas (grades I-IV), oligodendrogliomas (grades II-III) and oligoastrocytomas (grades II-III) (Ostrom et al., 2014). Glioblastoma (GBM, WHO – World Health Organization – grade IV) account for the majority of gliomas (56.1%), affecting 3.2 per 100,000 people, with higher incidence rate in the range between 75-84 years old (15.28 per 100,000) (2010-2014). Patients diagnosed with GBM present very low survival, with 5-year relative survival rate of only 5.5% (Miranda-Filho et al., 2017;

Ostrom et al., 2017).

Few environmental and heritable risk factors have been established for the development of GBM.

Ionizing radiation, from therapeutic procedures or occupational exposures, is the only recognized environmental factor associated with increased glioma risk (Walsh, Ohgaki and Wrensch, 2016). In fact, brain tumor patients treated with radiotherapy have increased incidence of secondary brain tumors (Salminen, Pukkala and Teppo, 1999). Studies on other environmental factors (exposure to radiofrequency fields from mobile phones or other low frequency fields, viral infections, alcohol consumption and tobacco smoking, allergic conditions, etc.) did not show any association, besides allergic conditions with decreased glioma risk (Wigertz et al., 2007; Cahoon et al., 2014; Wirsching, Galanis and Weller, 2016; Miranda-Filho et al., 2017). Rare inherited genetic mutations are known to confer increased glioma risk within families. Hereditary cancer syndromes such as neurofibromatosis types 1 and 2, Lynch syndrome, Li-Fraumeni syndrome increase the risk of developing gliomas, accounting for a small proportion of glioma incidence (<5%) (Walsh, Ohgaki and Wrensch, 2016;

Lapointe, Perry and Butowski, 2018).

1.2. Histological and molecular characterization of gliomas

Gliomas have been traditionally classified based on their histological features. Considering the cell of origin, gliomas are divided in astrocytic, oligodendroglial, oligoastrocytic (mixed) or ependymal tumors, and assigned a grade (I-V) based on morphological features such as mitotic activity, necrosis and microvascular proliferation. The WHO grading system ranges from low malignancy (grade I) to high malignancy (grade IV), is predictive of the biological behavior of a tumor and is used in the

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34 clinical setting as a major factor influencing the choice of therapies (Louis et al., 2007). Until recently, the histologic analysis was the gold standard for glioma classification and subsequent disease management. Nonetheless, this classification system presents some limitations, as inadequate tissue sampling and imprecise diagnostic criteria, as well as the fact that complex glioma biology cannot be fully represented solely by its histology (Perry and Wesseling, 2016). Recent advances in molecular analysis of brain tumors support the notion that molecular features might correlate better with tumor biology rather than histological diagnosis. In fact, the incorporation of clinically relevant molecular alterations to the classic histologic classification resulted in an updated version of the WHO Classification of Tumors of the Central Nervous System (Louis et al., 2016). This new classification allows, for the first time, to define tumor classes not only by their histomorphologic features, but also by key diagnostic molecular parameters, resulting in a more objective tumor classification (Diamandis and Aldape, 2018). This will hopefully contribute to a better diagnosis, improving patient management with more accurate determinations of prognosis and treatment (Louis et al., 2016). The new WHO 2016 classification separated gliomas into circumscribed gliomas (WHO grade I) and diffusely infiltrating gliomas, often referred as diffuse gliomas (WHO grades II-IV;

whether astrocytic or oligodendroglial) based on their pattern of growth and the presence or not of isocitrate dehydrogenase (IDH) mutation (Figure I.1) (Lapointe, Perry and Butowski, 2018). The inclusion of the IDH mutation status is one of the major changes in the new classification system, highlighting the importance of molecular alterations in distinguishing tumors with different biologic and clinical behaviors (Parsons et al., 2008; Yan, Parsons and Jin, 2009; Weller et al., 2015).

Diffuse gliomas are characterized by diffusely infiltrative growth within the normal brain parenchyma, often accompanied by aggregation of tumor cells around blood vessels, neurons (perineuronal satellitosis) and under the pial membrane (Perry and Wesseling, 2016). In the new classification, both astrocytic and oligodendroglial are included in this type of gliomas and are graded based on growth pattern and histologic features as well as the presence of genetic alterations in IDH genes. This group of gliomas includes diffuse astrocytomas and oligodendrogliomas (grade II), anaplastic astrocytomas and anaplastic oligodendrogliomas (grade III) and glioblastomas (grade IV).

Glioblastomas, the most common and aggressive form of diffuse gliomas, are characterized by high mitotic activity, microvascular proliferation and/or necrosis (usually densely packed and radially oriented – pseudopalisading necrosis) (Louis et al., 2016). These tumors are highly heterogeneous, at the histologic and cytologic level, and this intratumoral heterogeneity also occurs at the molecular level (Sottoriva et al., 2013; Patel et al., 2014)^.

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35 Figure I.1 – Molecular diagnosis of diffuse gliomas. Common genetic alterations considered as diagnostic biomarkers of diffuse gliomas, namely IDH1 or IDH2 mutation, loss of nuclear ATRX expression and 1p/19q co- deletion. Other markers as chromosomal and genetic alterations that may serve as additional diagnostic markers are also represented (adapted from Masui et al., 2016). TERTp – TERT promoter; MGMTp – MGMT promoter; - Chr 10 – deletion of chromosome 10; + Chr 7 – trisomy of chromosome 7.

With the new classification, GBMs are divided into IDH wildtype (IDH-wt) and IDH mutant (IDH-mut), being the first the most frequent (~90% of cases) and associated with primary or de novo lesions, while IDH-mut mainly correspond to secondary tumors which usually develop from pre-existing low- grade gliomas (LGGs) (Ohgaki and Kleihues, 2013). IDH-wt GBMs frequently present additional genetic alterations such as gain of chromosome 7, loss of chromosome 10, CDKN2A and CDKN2B deletions, mutations or deletions of PTEN and mutations of the TERT promoter; gene amplifications involving EGFR, PDGFRA, MET, CDK4 and CDK6, MDM2 and MDM4 are also commonly found in this group of GBM. In contrast, IDH-mut GBM molecularly resemble IDH-mut astrocytomas, presenting frequent mutations on TP53 and ATRX, as well as G-CIMP (glioma CpG island methylator phenotype) (Aldape et al., 2015).

1.3. Transcriptional subtypes of glioblastoma

The advances of large-scale platforms for genomic profiling over the last decades allowed an improved understanding of the molecular pathways involved in gliomagenesis, providing better tools for tumor stratification with implications in patients’ diagnosis, prognosis and treatment (Freije et al., 2004; Phillips et al., 2006; TCGA, 2008; Gravendeel et al., 2009).

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36 The most common genetic alterations in human GBM were identified by molecular studies:

amplification and mutational activation of receptor tyrosine kinase (RTK) genes, activation of the PI3K pathway and inactivation of tumor suppressor genes (TSGs) as p53 and Rb (Figure I.2) (reviewed in Furnari et al., 2007).

Figure I.2 – Main signaling pathways and proteins involved in glioma formation. Intracellular signaling pathways (growth factor receptors, p53 and RB) and proteins (IDH1/2 and MGMT) frequently altered or involved in gliomagenesis (adapted from Ricard et al., 2012).

In 2008, The Cancer Genome Atlas Research Network (TCGA) characterized the genome and transcriptome of 206 glioblastoma samples, integrating data from different molecular platforms:

gene expression, DNA copy number and methylation (TCGA, 2008). This type of integrated analysis provided a detailed view on the GBM genomic landscape, highlighting the major genetic alterations occurring in these tumors, but with some limitations, such as the retrospective nature of the experimental design, the fact that patients involved in these studies were not uniformly treated or the unknown impact of patient selection for tumors with sufficient material for multidimensional profiling (Aldape et al., 2015). Despite these limitations, genome-wide studies are extremely useful, and contribute to a broader knowledge about GBM as a very heterogeneous disease, allowing as well a sub-classification of these tumors according to their molecular features. The most accepted and used molecular subtype classification of GBM divides this tumor type into 4 subclasses:

Proneural (PN), Neural (NE), Classical (CL) and Mesenchymal (MES), each one characterized by specific genetic alterations (Verhaak et al., 2010). For the purpose of this work, CL and NE subtypes will be only briefly discussed while the MES and PN subtypes will be further explored.

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37 The Classical subtype is mainly characterized by chromosome 7 amplification paired with chromosome 10 loss, observed in 100% of the cases, while EGFR amplification was observed in 97%

and EGFR point or vIII mutations were detected in more than 50% of the cases classified as classical.

Other frequent alteration of this subtype is homozygous deletion of 9p21.3 (95%), targeting the TSG CDKN2A (Verhaak et al., 2010).

The Neural subtype is defined by the expression of neuron markers such as NEFL, GABRA1, SYT1 and SLC12A5. However, the separation of this subtype as a well-defined identity has been somehow neglected, probably due to the absence of marked molecular alterations. Indeed, in a recent

“revision”, the authors defined 3 GBM-intrinsic transcriptional subtypes: PN, CL and MES, excluding the NE subtype (Wang et al., 2017). This resulted from more stringent separation of the transcriptome of GBM and non-GBM cells and suggested that the initial classification of the NE subtype arose from contamination of the original samples with non-tumor cells (Sidaway, 2017).

The main feature of the Mesenchymal subtype is the loss or mutation of neurofibromin 1 (NF1), a TSG that negatively regulates the RAS pathway. Focal hemizygous deletion of a region at 17q11.2 containing the NF1 gene was observed in 38% of the cases and 37% of the tumors classified as MES also exhibited NF1 mutations. This subtype is also characterized by high expression of mesenchymal markers such as CHI3L1 (or YKL40) and MET (Phillips et al., 2006; Verhaak et al., 2010). NF1 alterations were found in ~15% of gliomas (Parsons et al., 2008). The transcription factors (TFs) STAT3, CEBPB, FOSL2, RUNX1, bHLH-B2 and ZNF238 have been described as the master regulators (MRs) of this subtype (Carro et al., 2010). In this study, the role of STAT3 and C/EBPβ was validated, with co-expression of the TFs inducing expression of MES markers in neural stem cells (NSCs), resulting in increased migration and invasion capacity of these cells, whereas silencing of these TFs in glioma cells lead to collapse of the MES signature and reduced tumor aggressiveness (Carro et al., 2010). Besides the master regulators of the MES signature, other regulators of this transcriptional subtype have been defined. The transcriptional coactivator TAZ has been shown to have a role in this context, by direct recruitment to a majority of MES gene promoters. Silencing of TAZ in MES glioma stem cells (GSCs) decreased expression of MES markers and cell invasion, self-renewal capacity and tumor formation, while overexpression of TAZ in PN GSCs and mouse NSCs induces MES marker expression, and resulted in high-grade tumors with MES features in a glioma mouse model (Bhat et al., 2011). As previously mentioned, the exclusion of the NE subtype resulted from a recent definition of the tumor-intrinsic gene expression subtypes, which establishes an important role of the tumor microenvironment on the control of tumor cell transcriptome. In this study, the authors report increased infiltration of tumor associated macrophages (TAMs)/microglia in NF1-deficient tumor cells. These GBMs showed reduced tumor purity when compared with GBMs with wildtype

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38 NF1 and specifically within the MES subtype, with a M2 macrophage signature significantly higher in NF1-deficient tumors. These experimental data and others supported the hypothesis that NF1 deactivation in glioma cells promotes macrophages/microglia recruitment in these tumors (Wang et al., 2017).

The Proneural subtype displays two major features: alterations in PDGFRA and point mutations in IDH1, but TP53 mutations and loss of heterozygocity are also frequently found in this subtype.

Alterations in PDGFRA include focal amplification of the locus at 4q12 that harbors PDGFRA (observed in 35% of the cases) concomitant with high levels of PDGFRA gene expression, and PDGFRA mutation was present in 11% of the samples analyzed. Mutations in IDH1 occurred in 30%

of the cases, most of which mutually exclusive with PDGFRA abnormalities (Verhaak et al., 2010).

This subtype is further subdivided according to the promoter DNA methylation profile into G-CIMP positive or negative. G-CIMP positive are samples that exhibit hypermethylation at a large number of foci and are directly linked to IDH1 somatic mutations (Noushmehr et al., 2010).

Several clinical associations can be drawn regarding the established GBM subtypes: PN tumors are more frequent in younger patients, consistent with a high proportion of secondary GBMs, and this subtype is overall associated with better patient outcome. However, when survival was stratified according to the treatment, patients of the PN subtype that underwent aggressive treatment protocols (concurrent chemo- and radiotherapy or more than 3 subsequent cycles of chemotherapy) did not benefit compared with patients receiving nonconcurrent or short chemotherapy regimens.

Patients with GBMs from the MES and CL subtypes, on the other hand, showed significantly improved survival when treated with aggressive protocols (Verhaak et al., 2010). The MES subtype is characterized by high percentage of necrosis and associated inflammation (Cooper et al., 2012), which can have implications in treatment response; macrophage/microglia infiltration can regulate NF-κB pathway activation that in turn induces MES phenotype with increased expression of CD44, and this is correlated with radiation resistance and shorter survival (Bhat et al., 2013).

The two major GBM subtypes, PN and MES, have been reproducibly defined and characterized, shedding light into GBM biology. Although, the application of gene expression signatures in the clinical setting as predictors of patient outcome has not been accomplished (Aldape et al., 2015). In the practical sense, the major limitations are the lack of an effective treatment for any of the subtypes, and the effort and investment needed to develop and validate clinical laboratory assays for RNA expression measurement in formalin-fixed paraffin-embedded tissues, the standard way of processing tumors in most pathology departments (Huse, Phillips and Brennan, 2011). In the theoretical sense, there are also drawbacks, being probably the most challenging the intratumoral heterogeneity of GBM. In 2013, Sottoriva and colleagues developed a surgical multisampling scheme

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39 in order to collect spatially distinct fragments from GBM patients and found, through an integrated genomic analysis, that most patients present different GBM subtypes within the same tumor (Sottoriva et al., 2013). Another research team performed RNA sequencing on single cells derived from five different primary tumors and also found individual cells corresponding to different GBM subtypes in all the tumors (Patel et al., 2014). PN-to-MES switch upon recurrence or after radiation treatment in mouse models has also been reported (Phillips et al., 2006; Halliday et al., 2014; Ozawa et al., 2014) and although this might also confound the applicability of GBM subtypes in the clinics, the frequency and relevance of this phenomenon in glioma progression remains unclear (Wang et al., 2017).

2. Cancer cell of origin

There are two concepts proposed for the origin of cancer: the clonal or stochastic evolution and the hierarchical cancer stem cell (CSC) model (Shackleton et al., 2009). The clonal evolution model postulates that genetic mutations occur randomly over time in a normal cell, giving rise to a cancer cell that clonally expands to form identical copies with identical tumorigenic potential (Nowell, 1976). If the new phenotype confers selective advantage to a given cell, this clone will be selected and will proliferate, resulting in a substantial number of cells in the tumor that are able to sustain tumor growth (Bradshaw et al., 2016). However, the hierarchical CSC model has become the more accepted model of cancer initiation and progression (Batlle and Clevers, 2017). This model proposes that a tumor arises from CSCs generated by mutation in either normal embryonic stem cells (ESCs) or progenitor cells conferring them ability for uncontrolled growth and propagation (Bradshaw et al., 2016). Recent studies also observed the capacity of non-CSC to “de-differentiate” into CSCs in response to epigenetic or environmental factors, contributing to the complexity of tumor biology and treatment (Safa et al., 2015).

2.1. Cell of origin in gliomas

Brain tumors can arise from stem, progenitors and/or more mature cells, and the cell of origin can determine tumor behavior and open possibilities of lineage-specific therapeutic opportunities as well as identification of early malignant events (Azzarelli, Simons and Philpott, 2018). The existence of a cell of origin in brain tumors has been assessed with different genetic engineered mouse models (GEMMs). The use of GEMMs for brain tumor modeling will be further explored in the section 3.2.2, but regarding the study of glioma cell of origin, animal models with neural stem and progenitor cell (NSPC)-related promoters have been used, in combination with activation of oncogenes or inactivation of TSGs, to address the tumorigenic potential of specific neural stem and progenitor

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40 populations (Holland et al., 2000; Alcantara Llaguno et al., 2009; Jacques et al., 2010; Munoz et al., 2013).

Most of these studies report that NSPCs are more prone to transformation than differentiated cells, although it has also been demonstrated that more committed progenitors have also the potential of acting as cells of origin in gliomas (Zong, Parada and Baker, 2015) and that differentiated cells, such as neurons and astrocytes, have also the ability to initiate tumorigenesis upon oncogenic transformation that induces dedifferentiation (Friedmann-Morvinski et al., 2012).

2.2. Glioma Stem Cells

Glioma stem cells (GSCs) are thought to derive from NSPCs upon mutation or from dedifferentiation of normal brain cells such as astrocytes and oligodendrocytes (Seymour, Nowak and Kakulas, 2015;

Bradshaw et al., 2016). GSCs share features with normal NSCs such as self-renewal and ability to differentiate into different cellular lineages (astrocytes, oligodendrocytes and neurons).

Nonetheless, these cells are able to initiate tumors that recapitulate the characteristics of the original tumor (Figure I.3)(reviewed in Safa et al., 2015).

Figure I.3 – Functional characteristics of GSCs. GSCs are defined by functional characteristics, including sustained self-renewal and proliferation and tumor initiation ability. GSCs can also show additional characteristics as low frequency within the tumor, the expression of stem cell markers and the ability to differentiate in multiple lineages (adapted from Lathia et al., 2015).

GSCs have been isolated from human glioblastomas and cultured for further studies for more than a decade (reviewed in Dirks, 2008), mostly based on CD133 expression or ability to grow as

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41 neurospheres in serum-free medium. CD133 (encoded by PROM1) was the first proposed marker of GSCs. It is a cell surface glycoprotein expressed on NSCs and enriches for cells with higher rates of self-renewal and proliferation and increased differentiation ability (Singh et al., 2003). CD133 is still one of the most commonly used markers for GSCs but it has some limitations: (1) its expression decreases with differentiation but its mRNA expression is not regulated with stemness, (2) it is not useful for cells extensively cultured, since it mediates interactions between cells and their microenvironment and (3) its expression can be regulated at the cell cycle level, and slow-cycling NSPCs can lack CD133 expression but still maintain multipotency (Lathia et al., 2015). Other method used to enrich for GSCs is the ability of these cells to grow as neurospheres in serum-free medium. It has been shown that GSCs cultures in this type of medium (serum-free with addition of epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF)) are more similar, phenotypic- and genotypically, to the primary tumor than serum-cultured cell lines (Lee et al., 2006). However, this type of culture promotes a selection of only a small portion of cells from the initial tumor with bias towards stem/progenitor features, discarding the heterogeneity of the original tumor (Lathia et al., 2015).

The most important characteristics of GSCs are their ability to repopulate tumors and their resistance to treatment. The standard therapy for GBM patients consists in maximum resection of the tumor mass, combination of radiotherapy and concomitant chemotherapy with the DNA- alkylating agent Temozolomide (TMZ) followed by adjuvant chemotherapy, with a median survival of 12-15 months (Stupp et al., 2009). The major limitations of the current GBM therapies rely on (1) the difficulty in achieving a complete resection of the tumor due to its highly infiltrative nature, (2) the challenges in penetrating the blood brain barrier, arising pharmacokinetic problems in the accumulation of therapeutic drug concentrations in the tumor and (3) the vulnerability of normal brain tissue to high-dose drug treatment and radiation therapy, that can endanger patient’s quality of life (Shah, 2016). GSCs might play an important role in tumor repopulation after resection and subsequent radio- and chemotherapy, due to their quiescent phenotype and described resistance to both types of therapy (Bao et al., 2006; Liu et al., 2006). Treatment resistance can also be explained using the hierarchical CSCs model, which favors stem-like cells, such as GSCs. Radio- and chemotherapy, besides enhancing the stem-like phenotype by switching the cellular hierarchy toward GSCs (Auffinger et al., 2014; Jackson, Hassiotou and Nowak, 2014), are also known to target highly proliferative cells, therefore ineffective on GSCs that are quiescent and slow-cycling (Seymour, Nowak and Kakulas, 2015).

Given this highly resistant phenotype to treatment, there is a great demand on effective therapeutic strategies to target critical pathways of GSCs, in order to improve conventional therapies through

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42 the elimination of residual GSCs. Efforts have been put in order to understand the signaling pathways involved in GSC biology. Notch and STAT3 pathways, for instance, were described as important in self-renewal and cell growth, respectively. However, the effects of Notch inhibition in vitro were not recapitulated in vivo and STAT3, although with promising results in vivo upon genetic knockdown, is also important for normal stem cells and immune response, thus targeting it might have undesired side effects (reviewed in Seymour, Nowak and Kakulas, 2015). Other features of GSCs such as apoptosis evasion and metabolic reprogramming in response to environmental conditions (hypoxia and limited nutrient availability) have been approached, but so far no definite therapeutic options have been achieved (reviewed in Lathia et al., 2015).

The complexity of GBMs also implies the heterogeneity among GSCs. These cells have been categorized in two distinct subtypes, PN and MES, based on gene expression profiles (Bhat et al., 2013; Mao et al., 2013). Given the distinct features that characterize each GBM subtype, it would be likely that also GSCs from different subtypes exhibit diverse and deregulated pathways, responsible for their phenotypes. Indeed, PN GSCs appear to share similarities with fetal NSCs, while the MES ones more closely resemble adult NSCs, being more aggressive, invasive, angiogenic and resistant to radiotherapy than the PN GSCs (Bradshaw et al., 2016). MES GSCs mainly derive from primary GBMs that arise de novo, while PN GSCs reside in both grade III and GBM (Mao et al., 2013). Regarding GSC subtype-specific markers, although there are not exclusive markers, PN GSCs appear to express CD133 and CD15, while the MES GSCs almost completely lack expression of these markers but instead show high expression of CD44 (Nakano, 2015).

3. Experimental models of glioma 3.1. Human glioma cell lines

The most widely studied established glioma cells lines derived from GBM patients are U87 and U251 (Ponten and Macintyre, 1968; Westermark, Ponten and Hugosson, 1973). They have been extensively used for decades, retaining the genetic aberrations from the original tumors, allowing the study of their contribution to oncogenic signaling pathways, as well as their use as a prescreening tool for testing targeted drugs in a rapid and reproducible fashion. However, years and years of cell culture most likely contributed to acquisition of alterations such as indels, copy number variations and translocations, as shown by the sequencing of the U87 genome (Clark et al., 2010).

The genetic drift caused by different serum culture conditions of different laboratories worldwide, presumably generated a large number of U87 subclones, which might affect experimental reproducibility, demanding for regular cell line authentication (Lenting et al., 2017).

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43 As an alternative of established cell lines cultured in serum, surgically obtained cells from glioma patients can be stably maintained and propagated as neurospheres (or tumorspheres), when cultured in specialized medium containing growth factors (bFGF, EGF, B27, etc.) in the absence of serum (Singh et al., 2003; Galli et al., 2004). This type of culture is usually genetically stable and easier to obtain if derived from more aggressive tumors, although it might underestimate tumor heterogeneity, since probably the most aggressive cells of the original tumor will be represented in these neurosphere cultures (Lenting et al., 2017). Neurosphere cultures usually express stem cell markers (CD133, NESTIN, SOX2, etc.) and upon growth factors withdrawal and serum replacement, cells rapidly lose intercellular connections and undergo phenotypic changes associated with differentiation: cells start to adhere to plastic dishes and flatten, acquiring a more fibroblast appearance and losing stem cell marker expression (Lee et al., 2006). Interestingly, neurosphere cultures can, similar to establish adherent cell cultures, be genetically modified with RNA interference (RNAi) techniques or more recently with CRISPR/Cas9 technology, as it will be discussed later in the section 3.2.2.

3.2. Mouse models

3.2.1. Transplantation models

In vivo transplantation models as xenografts are widely used and involve immunodeficient host animals to allow implantation. However, this can raise a major concern. On one side, the implanted tumors lack the pressure to evade immune destruction, one of the hallmarks of cancer (Hanahan and Weinberg, 2011). On the other side, the fact that the tumor microenvironment (TME) belongs to a different species can mask a normal stromal response (Schuhmacher and Squatrito, 2017).

Intracranial xenografts of human GBM established cell lines lead to well defined tumors and its main advantages are the good reproducibility in terms of engraftment rate and reliable growth and disease progression. Yet, the tumors formed in these models rarely infiltrate and characteristic necrosis and microvascular proliferation are frequently absent. Besides that, the use of human established cell lines have disadvantages as mentioned in the previous section (Huszthy et al., 2012;

Schuhmacher and Squatrito, 2017).

Neurosphere cultures can also be used for orthotopic xenograft models. In addition to the molecular profile of these cells being stable over time and more related to the original tumor (Lee et al., 2006;

Günther et al., 2008), the greatest advantage of transplanting neurospheres over serum-cultured GBM cells is the establishment of extensive infiltrative lesions (Hambardzumyan et al., 2011; Huszthy et al., 2012).

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44 Patient derived xenograft (PDX) models are also useful due to their biological consistency with the tumor of origin. PDX consists of fresh tumor fragments that are orthotopically injected or serially passaged subcutaneously in immunodeficient mice prior to intracranial injection (Schuhmacher and Squatrito, 2017). These tumors are phenotypically stable at histological, transcriptomic, proteomic and genomic levels for several rounds of transplantation, extremely useful for therapeutic response prediction for individual patients, but as all xenografts, lack the contribution of the natural TME of the human tumors (Zhang et al., 2013; Aparicio, Hidalgo and Kung, 2015).

3.2.2. Genetically engineered mouse models (GEMMs)

Increased knowledge of the genomic alterations underlying human glioma biology has contributed to the development of GEMMs of gliomas that in many instances recapitulate the histopathology, etiology and biology of human gliomas, representing an essential experimental tool to gain insight of the genetic alterations driving tumor initiation and progression and to test new therapeutic approaches (Huszthy et al., 2012). A good mouse model that mimics the natural history of a tumor should carry the same mutations found in human tumors, introduced in their endogenous loci and the mutant genes must be silent during embryonic and early postnatal development (except for models of inherited or pediatric tumors). Besides, mutant genes must be expressed in specific target tissues or in selected cell types and mutations must take place in a limited number of cells (Schuhmacher and Squatrito, 2017). Achieving these requirements implies several advantages of GEMMs over xenograft models. GEMMs address specific molecular events, providing new perception into those events and pathways responsible for tumor initiation and progression; tumor- stroma interactions can be modeled, contributing to a better understanding of the role of TME in the tumor biology. GEMMs also allow to dissect the minimum genetic alterations needed for malignant transformation, the sequence of events occurring upon a specific mutation and the crosstalk between different pathways involved in oncogenesis (Huszthy et al., 2012).

Most of the GEMMs involve genome manipulation to create specific genetic changes – typically expression of oncogenic mutations or loss of TSGs, generating the conventional genetic approaches such as transgenic (Tg) and knock-out (KO) models (Miyai et al., 2017). Additionally, conditional strategies have been applied to regulate gene expression in tissue/cell type- and/or time-specific manner, such as the use of site-specific recombinases like Cre (Macleod and Jacks, 1999; Talmadge et al., 2007). In GBM models, the most common Cre Tg strains express Cre recombinase under the glial fibrillary acidic protein (GFAP) promoter – targeting astrocytes and glial precursors – or Nestin promoter – targeting NSCs and intermediate neural progenitor cells (Schuhmacher and Squatrito, 2017). Although extremely useful, these models are costly and time-consuming, requiring extensive

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45 intercrossing of mouse strains. Furthermore, most of these models act by targeting extensive areas, contributing to reduce heterogeneity (Weiss and Shannon, 2003).

An alternative mechanism is the use of viruses for precise gene delivery that allow for simultaneous delivery of multiple genes in a faster way, overcoming the need for multiple Tg or KO mouse lines.

The main limitations of viral systems are the reduced tumor incidence in some models, the size of the genes that can be effectively packaged into the vectors and the delivery of the viruses itself, typically by intracranial injection, can be found as a technical challenge (Huse and Holland, 2009).

One of the most widely used viral systems for brain tumor modeling is the RCAS-Tva system. This system is based on the somatic gene delivery into genetically modified mouse strains that express the receptor (tv-a) for a subgroup-A avian sarcoma leukosis viruses (ASLVs) (Federspiel et al., 1994).

The tv-a receptor can be expressed under the GFAP or Nestin promoters (Gtv-a and Ntv-a, respectively) (Holland and Varmus, 1998; Holland et al., 1998). The DNA fragment of interest is cloned in the RCAS (Replication-Competent Avian leukosis virus Splice acceptor) vector, derived from ASLVs and genetically engineered to accept the insertion of these DNA fragments (Greenhouse et al., 1988). RCAS-producing chicken fibroblasts are then intracranially injected into the brain of newborn or adult mice, infecting the neighboring cells expressing the tv-a receptor (Lenting et al., 2017). The RCAS-Tva system allows the geographic and temporal control of viral delivery, as wells as the selection of the cell type to be infected (astrocytes in Gtv-a mice and glial progenitors in Ntv-a mice).

This model provides a somatic transfer method for a variety of genetic tools: oncogenes, shRNAs to silence TSGs, Cre recombinase, etc. (Schuhmacher and Squatrito, 2017). The transduced gene(s) injected, the cell type expressing the tv-a receptor and other genetic alterations present in the mice influence the subtype, grade and incidence of the generated tumor (Holland et al., 2000; Dai et al., 2001; Ding et al., 2001; Uhrbom et al., 2002; Shih et al., 2004; Wei et al., 2006).

The CRISPR (Clustered Regularly Interspaced Short Palindromic repeats) – Cas (CRISPR-associated) is a powerful genome editing tool that allows specific manipulation of individual cells. This technology can be applied in cancer modeling through the inactivation of TSGs (Platt et al., 2014; Sanchez-Rivera et al., 2014; Zuckermann et al., 2015), generation of somatic point mutations (Xue et al., 2014; Drost et al., 2015) and more complex genomic rearrangements (Blasco et al., 2014; Maddalo et al., 2014;

Torres et al., 2014), in a quite specific manner, being less prone to off-target effects when compared to RNAi or shRNAs techniques. Recently in our laboratory, the RCAS-Tva system was combined with the CRISPR-Cas9 technology for precise modeling of human brain tumors. This novel RCAS-Tva- CRISPR-Cas9 system constitutes an extremely versatile mouse model, combining somatic gene transfer and genome editing to model gliomas with tailored genetic alterations. Using Rosa26-LSL- Cas9 knock-in mice, combined with Ntv-a or Gtv-a mice and through in vivo delivery of RCAS

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46

A B

plasmids carrying gRNAs for TSGs, we efficiently generated high-grade tumors in mice. Among other applications, this combined strategy can also be useful for pre-clinical testing of targeted therapies (Oldrini et al., 2018).

4. AP-1 transcription factor

The AP-1 (activator protein 1) transcription factor is a dimeric complex formed by members of the JUN, FOS, ATF (activating transcription factor) and MAF (musculoaponeurotic fibrosarcoma) subfamilies (Eferl and Wagner, 2003). AP-1 proteins are structurally characterized by a basic leucine zipper (bZIP) domain. The leucine zipper motif is responsible for dimerizarion while the basic domain interacts with the DNA backbone (Figure I.4A) (Eferl and Wagner, 2003; Lopez-Bergami, Lau and Ronai, 2010). Different combinations of AP-1 homo- and heterodimers bind to DNA with varying affinities, have different transactivation efficiencies and therefore regulate different genes (Chinenov and Kerppola, 2001; van Dam and Castellazzi, 2001).

Figure I.4 – AP-1 transcription factor. (A) Schematic representation of an AP-1 dimer bound to a TRE DNA element. (B) Transcriptional and post-translational modifications of AP-1 (adapted from Eferl and Wagner, 2003).

The main AP-1 proteins in mammalian cells are JUN and FOS, that recognize and bind to the DNA response element 5’-TGAC/GTCA-3’, also known as TRE – TPA responsive element – based on their ability to mediate transcription induced by the tumor promoter 12-O-tetradecanoylphorbol-13- acetate (TPA). In addition to tumor promoters, AP-1 complex also binds to the TRE sequence in

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47 response to growth factors, cytokines and oncoproteins, which implies a role of AP-1 in a wide variety of biological processes such as proliferation, survival, differentiation and transformation of cells (Eferl and Wagner, 2003).

The JUN family consists of c-JUN, JUNB and JUND and each of the proteins have distinct characteristics. c-JUN and JUNB are important for cell proliferation, survival and apoptosis and essential for embryonic development while JUND is dispensable in this matter (Eferl et al., 1999;

Mechta-Grigoriou, Gerald and Yaniv, 2001; Vogt, 2001). JUN proteins can form either homo- or heterodimers with FOS proteins, while FOS member only heterodimerize with JUN members. FOS family is composed of c-FOS, FOSB, Fos-related antigen 1 (FRA-1, encoded by FOSL1) and Fos-related antigen 2 (FRA-2, encoded by FOSL2). While c-FOS and FOSB have potent transactivation domains, exhibiting the ability of transforming efficiently cells in culture, FRA-1 and FRA-2 lack transactivation domains, presenting weak transforming activity (Foletta et al., 1994; Bergers et al., 1995). AP-1 activity can be regulated by dimer composition, transcriptional and post-translational events and interactions with other proteins (Figure I.4B) (Eferl and Wagner, 2003). One of the main signaling that interacts with AP-1 is the RAS pathway that cooperates with AP-1 components so they can exert their oncogenic potential (Westwick et al., 1994). It has been shown that cell transformation by activated RAS or MEK1 (MAPK kinase) induces AP-1 protein expression, being FRA-1 and c-JUN the main AP-1 components induced by activated RAS (Mechta et al., 1997). Other MAPK pathways, the Jun N-terminal kinase (JNK) and p38 MAPK pathways, are also involved in the regulation of AP-1 TFs (Wagner and Nebreda, 2009).

Since this work is focused on FRA-1, the next section will only briefly discuss other AP-1 members, while FRA-1 will be further dissected later on.

4.1. AP-1 in cancer

AP-1 can exert pro-oncogenic or anti-oncogenic function, depending on the cell type and its differentiation state, tumor stage and the genetic background of the tumor (Eferl and Wagner, 2003). Genetic alterations in JUN or FOS genes are rare in human tumors, but mutations in FOS and FOSB were recently identified in osteoblastoma (Fittall et al., 2018). Besides, JUN has been found amplified in aggressive human sarcomas (Mariani et al., 2007) and many human cancers exhibit high expression levels of JUN proteins (reviewed in Lopez-Bergami, Lau and Ronai, 2010). This high expression of JUN predominantly results from the activation of upstream oncogenes such as RAS, BRAF and EGFR. For instance, the activating mutations of NRAS or BRAF that occur in >70% of melanomas are responsible for a super-activation of ERK that results in increased c-JUN expression by increasing its transcription and stability (Lopez-Bergami et al., 2007). Regarding FOS, high or low

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48 expression can be associated with poor outcome and tumor progression depending on the tumor type (Bamberger et al., 2001; Papachristou et al., 2003; Jin et al., 2007; Mahner et al., 2008). FRA-1 overexpression, in its turn, has been associated with many human tumors – including lung, colon, breast, brain, head and neck, nasopharyngeal, thyroid, etc. – as well as in mouse and rat induced carcinomas and human and murine carcinoma cell lines (reviewed in Young and Colburn, 2006).

4.2. FOSL1/FRA-1 regulation

As the others AP-1 members, FRA-1 can be regulated at both transcriptional and post-translational levels (Figure I.5). At the transcriptional level, FOSL1 can be regulated by c-FOS and by FRA-1 itself, by binding to the AP-1 site in the first intron (Bergers et al., 1995). The same site is essential for the FRA-1 induction in response to oncogenic RAS in transformed rat thyroid cells, since it is stably occupied by a transcriptionally active FRA-1-containing complex. This autoregulatory mechanism sustains constitutive FRA-1 expression in a RAS transformation context, and it has been associated with a drastic increase in histone H3 acethylation, characteristic of a transcriptionally active state (Casalino, Cesare and Verde, 2003). Besides the effects of RAS/MAPK on FRA-1 regulation, other pathways can be involved in this process, such as PI3K/AKT/SP1 or WNT/β-catenin pathways (reviewed in Verde et al., 2007).

Figure I.5 – Transcriptional and post- translational regulation of FRA-1.

Schematic representation of multiple cellular pathways involved in FRA-1 regulation. Oncogenic RAS (RAS*) induces phosphorylation (P), ubiquitination (Ubi), sumoylation (Sumo), proteasome- dependent degradation (dustbin) and FRA-1 C-terminal destabilizer (yellow-circled d).

The different dimer partners of JUN family are represented by J. Horizontal wavy line indicates Fosl1 mRNA, while wavy lines extending from nucleosome represents histone tails (adapted from Verde et al., 2007).

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49 FRA-1 is also controlled by post-translational modifications, namely through phosphorylation. In RAS-transformed rat thyroid cells it was shown that FRA-1 half-life is strongly affected by this pathway, being highly stabilized by MEK/ERK-dependent phosphorylation (Casalino, Cesare and Verde, 2003). The extent of FRA-1 phosphorylation is also regulated by the cell cycle phase, being further increased in G2/M (Casalino et al., 2007). RAS-dependent FRA-1 phosphorylation can be abrogated inhibiting the MEK/ERK pathway (Verde et al., 2007). Besides, different levels of ERK activation have different effects on FRA-1: while moderate activation of the ERK pathway is sufficient to induce FOSL1 transcription, increased ERK activity prevents proteasome-dependent degradation of FRA-1 in human colon carcinoma cell lines expressing the KRAS and BRAF oncogenes (Vial, 2003).

4.3. FOSL1/FRA-1 in cancer

The prognostic value of FRA-1 has been described for different tumors such as breast cancer (Chiappetta et al., 2007), esophageal squamous cell carcinoma (Usui et al., 2012), oral squamous cell carcinoma (Xu et al., 2017), hepatocellular carcinoma (Gao et al., 2017), prostate cancer (Wu et al., 2015) and lung and pancreatic adenocarcinomas (Vallejo et al., 2017), where FRA-1 overexpression correlated with tumor progression or worse patient survival. This suggests FRA-1 as potential target for cancer therapy. In fact, a DNA vaccine developed against FRA-1 was effectively used in preventing dissemination and eradicating lung metastasis in a mouse model of breast cancer as well as the primary tumor (Luo et al., 2003, 2005). Great efforts have been put to modulate AP-1 activity with small molecule inhibitors. The selective inhibitor T-5224 has been shown to have a beneficial effect in several mouse models for conditions such as lipopolysaccharide-induced liver injury and acute kidney injury (Izuta et al., 2012; Ishida et al., 2015), intervertebral disc degeneration (Makino et al., 2017) and osteoarthritis (Motomura et al., 2018), pathological conditions with a high inflammatory component in which AP-1 TFs have an important role. T-5224 was the only AP-1 inhibitor reaching a phase II clinical trial, for rheumatoid arthritis (Ye et al., 2014) but apparently it has been discontinued and no information regarding the results of this trial can be found. Although this AP-1 inhibitor is considered to be very specific, it inhibits the DNA binding activity of c-FOS/c- JUN without affecting those of other TFs and the protein levels of other FOS family members, as FRA-1 (Aikawa et al., 2008).

Taken all this into account, and considering that no anti-tumor effect of pharmacological FRA-1/AP-1 inhibition has been proven, it is of prime importance to understand how FRA-1 contributes to cancer phenotypes, in processes such as proliferation, apoptosis, cell motility, invasiveness and angiogenesis, in order to target its oncogenic functions. FRA-1 has been described as a promoter of

Role of the AP-1 Transcription Factor FOSL1 in the Mesenchymal Glioblastoma subtype

UNIVERSIDAD AUTÓNOMA DE MADRID

FACULTAD DE CIENCIAS

D EPARTAMENTO DE B IOLOGÍA M OLECULAR

R OLE OF THE AP-1 T RANSCRIPTION F ACTOR F OSL 1

IN THE M ESENCHYMAL G LIOBLASTOMA S UBTYPE

C AROLINA A LMEIDA M ARQUES

Madrid, 2019

D EPARTAMENTO DE B IOLOGÍA M OLECULAR

F ACULTAD DE C IENCIAS

UNIVERSIDAD AUTÓNOMA DE M ADRID

R OLE OF THE AP-1 T RANSCRIPTION F ACTOR F OSL 1

IN THE M ESENCHYMAL G LIOBLASTOMA S UBTYPE

C AROLINA A LMEIDA M ARQUES MSc in Biotechnology for Health Sciences

T HESIS DIRECTOR M

S

S EVE -B ALLESTEROS F OUNDATION B RAIN T UMOUR G ROUP

S PANISH N ATIONAL C ANCER R ESEARCH CENTRE (CNIO)

Madrid, 2019

A CKNOWLEDGEMENTS

S UMMARY

R ESUMEN

T ABLE OF C ONTENTS

Table of Contents

L IST OF A BBREVIATIONS

I NTRODUCTION

1. Glioblastoma

1.1. Epidemiology and risk factors

1.2. Histological and molecular characterization of gliomas

1.3. Transcriptional subtypes of glioblastoma

2. Cancer cell of origin

2.1. Cell of origin in gliomas

2.2. Glioma Stem Cells

3. Experimental models of glioma 3.1. Human glioma cell lines

3.2. Mouse models

3.2.1. Transplantation models

3.2.2. Genetically engineered mouse models (GEMMs)

4. AP-1 transcription factor

4.1. AP-1 in cancer

4.2. FOSL1/FRA-1 regulation

4.3. FOSL1/FRA-1 in cancer

D EPARTAMENTO DE B ^IOLOGÍA M ^OLECULAR

R OLE OF THE AP-1 T RANSCRIPTION F ^ACTOR F ^OSL 1

IN THE M ESENCHYMAL G LIOBLASTOMA S ^UBTYPE

C ^AROLINA A ^LMEIDA M ^ARQUES

UNIVERSIDAD AUTÓNOMA DE M ^ADRID

R OLE OF THE AP-1 T RANSCRIPTION F ^ACTOR F ^OSL 1

IN THE M ESENCHYMAL G LIOBLASTOMA S ^UBTYPE

S ^PANISH N ^ATIONAL C ^ANCER R ESEARCH CENTRE (CNIO)

S ^UMMARY

R ^ESUMEN

L ^{IST OF} A BBREVIATIONS