Influence of HOXA9 in the response of glioblastoma to immune checkpoint inhibitors

Universidade do Minho Escola de Medicina Ana Catarina Mesquita Azevedo setembro de 2019 Influence of HOXA9 in the response of glioblastoma to immune checkpoint inhibitors Ana Catarina Mesquita Azevedo UMinho|2019 Influence of HOXA9 in the response of glioblastoma to immune checkpoint inhibitors Ana Catarina Mesquita Azevedo setembro de 2019 Influence of HOXA9 in the response of glioblastoma to immune checkpoint inhibitors Trabalho efetuado sob a orientação do Doutor Bruno Marques Costa e da Doutora Cláudia Nóbrega Dissertação de Mestrado Mestrado em Ciências da Saúde Universidade do Minho Escola de Medicina II DIREITOS DE AUTOR E CONDIÇÕES DE UTILIZAÇÃO DO TRABALHO POR TERCEIROS Este é um trabalho académico que pode ser utilizado por terceiros desde que respeitadas as regras e boas práticas internacionalmente aceites, no que concerne aos direitos de autor e direitos conexos. Assim, o presente trabalho pode ser utilizado nos termos previstos na licença abaixo indicada. Caso o utilizador necessite de permissão para poder fazer um uso do trabalho em condições não previstas no licenciamento indicado, deverá contactar o autor, através do RepositóriUM da Universidade do Minho. Licença concedida aos utilizadores deste trabalho Atribuição-NãoComercial-SemDerivações CC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/ III Acknowledgements Começo por agradecer aos meus orientadores: Bruno Costa e Cláudia Nóbrega. Ao Bruno por me ter aceitado neste projeto, pela partilha de conhecimento e pelos momentos de aprendizagem. À Cláudia, por todo o apoio, ensinamentos, confiança e motivação. À Céline e à Eduarda por todas as sugestões e ajuda para desenvolver o trabalho aqui apresentado. À Marta, que mesmo longe, sempre me apoiou e me ajudou a aumentar a minha capacidade de trabalho. Ao João, por ter dado literalmente o sangue e suor para me ajudar neste projeto; obrigada por todo o apoio e amparo. Quero deixar também o meu agradecimento ao Agostinho Carvalho e à Cristina Cunha, assim como ao Cláudio, Samuel e Cláudia, por se prontificarem sempre para nos ajudar. À Sónia, à Nathalia, à Carol e à Catarina pela boa disposição no laboratório e por toda a ajuda. A todos os SSRDs, NERDs e PopHealth por toda a partilha de conhecimento e ajuda sempre prestada. À Andreia, Bruna, Joana, Marta, Sofia e Nuno: por todos os desabafos, pelo apoio, pela amizade, pela força e confiança depositada. Obrigada pela partilha dos piores e melhores momentos de sempre. Foram essenciais. Às meninas: Ana, Anabela, Isabel e Marisa, obrigada pelas conversas, pelas alturas em que me obrigam a sair do “buraco” e por estarem sempre presentes. Obrigada pela vossa amizade. Ao Hélder, por me fazer feliz. Por ter estado presente nos melhores e piores momentos. Por me ajudar a ultrapassar todos os obstáculos e me motivar a cada dia. Pela paciência e por me ouvir neste percurso, o que por vezes não foi fácil. Obrigada pelo carinho, incentivo, apoio, conversas e amor. À minha família, por ser a melhor família de sempre. Aos meus pais pelo apoio e amor incondicional, por estarem sempre presentes e por me ampararem nos momentos menos bons e rirem comigo nos bons. Pela paciência, pelo incentivo e pelos sacrifícios que fizeram por mim. Por me ajudarem a crescer e fazerem de mim o que sou hoje. Devo-vos tudo. Ao meu irmão pelo apoio, paciência e por me acompanhar sempre. Para a Paulinha, pela esperança… IV STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Code of Ethical Conduct of the University of Minho. V Influence of HOXA9 in the response of glioblastoma to Immune Checkpoint Inhibitors Glioblastoma (GBM) is the most common and malignant primary central nervous system (CNS) tumour in adults, characterized by high resistance to conventional therapies and a very poor outcome, with a median survival of 15 months. GBM is a highly immunosuppressive tumour, with mechanisms to promote tumour escape from the immune system. Its microenvironment is characterized by the presence of cytokines that inhibit the immune system by suppressing T-cell activation and proliferation, and skewing the immune cells towards a pro-tumour phenotype. Additionally, GBM cells frequently overexpress programmed cell death ligand 1 (PD-L1), an immune checkpoint ligand that binds to programmed cell death 1 (PD1) present in activated T-cells. Recently, immunotherapies using immune checkpoint inhibitors (ICIs) have gained importance by showing promising results in treatment of various cancers. Previous studies showed that HOXA9, a critical transcription factor deregulated in gliomas, is critical in resistance to standard chemotherapy, and global aggressiveness of GBM. Moreover, HOXA9 downregulates mechanisms related to antigen processing and presentation, and to immune responses. This project aims to decipher the relevance of HOXA9 in the immune evasion in GBM, both in treatment-naïve conditions and under ICIs therapy. For this, HOXA9 over-expression and silencing models of human GBM cell lines were used, to understand whether HOXA9 expression modulates the expression of cytokines and of immune checkpoint ligands, and how it influences T-cell responses in the presence or absence of ICIs. Results with human GBM cell lines suggest that expression of HOXA9 is associated with differential expression of immune related cytokines: namely inversely associates with IL1B expression; and with IL8 expression in GL18 cell line; and also associated with CCL2 expression in U251 cell line. At protein level, the silencing of HOXA9 leads to a decrease in PD-L1 expression and to an increase in the PD-L2 expression in the membrane of U251 cells. Moreover, a minor but significant increased sensitivity to antiPD1 therapy is observed in U251 cells, but not in the other cell lines (U87 and U251). Regarding T-cell survival and subpopulations, namely Tregs, no significant differences were obtained. Overall, this work suggests that HOXA9 might increase immunosuppression in GBM, and that a partially effective immune response against GBM cell lines seems to exist. To further complement and clarify these results, it is essential to evaluate the secretion of these cytokines and chemokine; and to extend this study, in vivo GBM models could clarify the roles of HOXA9 in immune cells infiltration and in survival of mice in treatment-naïve conditions or upon treatment with ICIs. Key words: Glioblastoma; HOXA9; Immunosuppression; Immune Checkpoint Inhibitors VI Influência do HOXA9 na resposta do glioblastoma a Inibidores de “Checkpoints” Imunes O glioblastoma (GBM) é o tumor primário mais comum e maligno do sistema nervoso central em adultos, caracterizando-se por uma elevada resistência às terapias convencionais e por um mau prognóstico, com uma sobrevivência mediana de 15 meses. O GBM é muito imunossupressor, com mecanismos para promover a evasão do sistema imunitário. O seu microambiente tumoral é caracterizado pela presença de citocinas que inibem o sistema imunitário, suprimindo a ativação e proliferação das células T e alterando as células imunes para um fenótipo pro-tumoral. Adicionalmente, as células de GBM sobreexpressam frequentemente o “programmed cell death ligand 1” (PD-L1), que se liga ao “programmed cell death 1” (PD1) presente nas células T. Recentemente, imunoterapias que usam inibidores de “checkpoints” imunes (ICIs) ganharam extrema importância, pois mostraram resultados promissores no tratamento de vários cancros. Estudos prévios mostram que o HOXA9, um crítico fator de transcrição desregulado em gliomas, é muito importante na resistência à terapia convencional e na agressividade global do GBM, regulando, ainda, negativamente mecanismos relacionados com o processamento e apresentação de antigénios e com respostas imunes. Este projeto tem como objetivo compreender a importância do HOXA9 na evasão do sistema imunitário no GBM, com e sem ICIs. Para tal, foram usados modelos de sobre-expressão e de silenciamento do HOXA9 de linhas celulares de GBM humanas para perceber se a expressão do HOXA9 modula a expressão de citocinas e de ligandos de “checkpoints” imunes e qual a sua influência na resposta das células T, com ou sem ICIs. Os resultados com as linhas celulares de GBM humanas sugerem que a expressão do HOXA9 está associada a uma diferente expressão de citocinas relacionadas com o sistema imunitário. Assim, está inversamente associada à expressão de IL1B e de IL8 na linha celular GL18 e diretamente associada à expressão de CCL2 na linha celular U251. A nível proteico, o silenciamento do HOXA9 leva à diminuição da expressão de PD-L1 e ao aumento da expressão de PD-L2 na membrana das células U251. Além disso, foi observado um pequeno, mas significativo, aumento da sensibilidade à terapia com anti-PD1 nas células U251, mas estes efeitos não foram observados nas linhas celulares U87 e GL18. Em relação à sobrevivência e subpopulações de células T, nomeadamente Tregs, não foram obtidas diferenças significativas. No geral, estes resultados sugerem que o HOXA9 pode aumentar a imunossupressão no GBM e que parece existir uma resposta imune parcial contra as linhas celulares de GBM. Para complementar e clarificar estes resultados, é essencial avaliar a secreção destas citocinas e da quimiocina. Os modelos in vivo podem clarificar o papel do HOXA9 na infiltração de células imunes e na sobrevivência dos ratinhos, com e sem ICIs. Palavras chave: Glioblastoma; HOXA9; Imunossupressão; Inibidores de “Checkpoints” imunes VII Table of contents 1. Introduction ........................................................................................................................................ 1 1.1 An overview of cancer ................................................................................................................. 1 1.2 Gliomas: classification and clinical significance ............................................................................. 2 1.2.1 Glioblastoma: pathophysiology and clinical features ...................................................................... 4 1.2.1.1 Classification and epidemiology of glioblastoma .................................................................... 4 1.2.1.2 Etiology of glioblastoma ........................................................................................................ 5 1.2.1.3 Current therapies in glioblastoma ......................................................................................... 5 1.2.1.4 The HOXA9 gene ................................................................................................................. 5 1.3 Immune surveillance and immune evasion in cancer ..................................................................... 6 1.3.1 Immune-based anti-tumour therapies .......................................................................................... 12 1.4 Immune evasion in glioblastoma ................................................................................................ 13 1.4.1 Cytokines and chemokines in glioblastoma ................................................................................. 15 1.4.2 Immune checkpoint inhibitors in glioblastoma ............................................................................. 16 2. Objectives ........................................................................................................................................ 17 3. Materials and Methods ...................................................................................................................... 18 3.1 Cell lines and culture conditions ................................................................................................. 18 3.2 Gene expression analysis .......................................................................................................... 18 3.2.1 RNA extraction............................................................................................................................ 18 3.2.2 Complementary DNA synthesis ................................................................................................... 19 3.2.3 Reverse transcriptase - quantitative polymerase chain reaction (RT-qPCR) ................................... 19 3.3 Flow cytometry stain and analysis .............................................................................................. 20 3.3.1 Surface staining .......................................................................................................................... 20 3.3.2 Intracellular staining ................................................................................................................... 20 3.3.3 Annexin V/Propidium Iodide staining .......................................................................................... 21 3.3.4 Data acquisition and analysis ...................................................................................................... 21 3.4 Co-cultures of tumour cells and activated T-cells .......................................................................... 21 3.4.1 Activation and expansion of human peripheral blood mononuclear cells (hPBMCs) ...................... 21 3.4.2 Co-culture of human tumour cells with activated T-cells ............................................................... 22 3.4.3 Activation and expansion of mouse T-cells ................................................................................... 22 3.4.4 Co-culture of mouse tumour cells with activated T-cells ............................................................... 23 VIII 3.5 HOXA9 silencing in mouse GBM cell line .................................................................................... 23 3.6 Statistical analysis .................................................................................................................... 23 4. Results ............................................................................................................................................ 25 4.1 Influence of HOXA9 on the expression of immune related factors in tumour cells ........................... 25 4.1.1 HOXA9 affects the expression of cytokines and chemokines in human GBM cell lines .................. 25 4.1.2 HOXA9 influences the expression of immune checkpoint ligands in tumour cells ......................... 28 4.2 Impact of HOXA9 in the sensitivity of glioblastoma cell lines to T-cell mediated cytotoxicity .............. 32 4.3 Impact of HOXA9 in the sensitivity of glioblastoma cell lines to immune checkpoint inhibitors ......... 34 4.4 Influence of HOXA9 in T-cell survival, in the presence or absence of Immune Checkpoint Inhibitors . 36 4.5 Impact of HOXA9 expression by glioblastoma cells in the percentage of T-cell subpopulations, independently of Immune Checkpoint Inhibitors’ presence ....................................................................... 38 4.6 Studies of HOXA9 impact in glioblastoma immune evasion in a murine model – preliminary data ... 40 4.6.1 Silencing of HOXA9 in a murine GBM cell line ............................................................................. 40 4.6.2 Optimization of mouse T-cell activation and expansion protocol ................................................... 41 5. Discussion ....................................................................................................................................... 47 6. Conclusions and Future Perspectives ................................................................................................. 54 7. References ....................................................................................................................................... 55 8. Supplementary Figures ..................................................................................................................... 68 9. Supplementary Tables ....................................................................................................................... 74 3 Figure 2 – Glia progenitors’ cells and the subsequent classification of gliomas. Represented the glial progenitors’ cells, the differentiated cells and the classification of gliomas. Adapted from Backos D., 2014 (15). According to the World Health Organization (WHO) classification of 2016, gliomas are divided based on histological features taking into account the presumed cell of origin and the levels of differentiation, on their location and on molecular features, into 3 subtypes (16). Thus, histologically, gliomas are divided into astrocytomas, oligoastrocytomas, oligodendrogliomas, based on their similarity to the glial cells (17). Based on tumour malignancy, WHO also divides tumours in grades (grade I to IV), being grade I the ones associated to better prognosis and with slowest tumour growth and grade IV the most malignant with the worst prognosis (17,18). Astrocytomas are the most common type (75% of all gliomas) and can be further divided according to its grade into: grade I (pilocytic astrocytomas), grade II (diffuse astrocytomas, low grade), grade III (anaplastic astrocytomas) and the most common, grade IV (glioblastoma, GBM) (19,20). Moreover, since 2016, isocitrate dehydrogenase 1/2 ( IDH1/2) mutational status, the co-deletion of the short arm of chromosome 1 and long arm of chromosome 19 (1p/19q) and other genetic parameters have been used to classify gliomas (17). 4 1.2.1 Glioblastoma: pathophysiology and clinical features GBM, the most common, aggressive and malignant glioma, is characterized by uncontrolled cell proliferation, diffuse infiltration, necrosis, strong angiogenesis, high resistance to apoptosis and genomic instability (13,21). GBMs have a hugely deregulated tumour genome, with amplification in oncogenes and deletion in tumour suppressor genes involved in several interconnected signalling pathways (22,23). The main signalling pathways deregulated in GBM are: i) p53 signalling pathway, implicated in processes such cell cycle arrest, cell death, cell differentiation, senescence, DNA repair and neovascularization; ii) tumour suppressor retinoblastoma (pRB) signalling pathway, also involved in cell cycle progression processes; iii) PI3K-PTEN-Akt-mTOR signalling pathway, involved in cellular proliferation, growth, apoptosis and cytoskeletal rearrangement; iv) and RAS/MAPK signalling pathway, related with apoptosis and cell transformation processes (22,24). Additionally, other genes are frequently found mutated in GBM, namely: i) loss of tumour suppressor phosphatase and tensin homolog (PTEN); ii) activation of epidermal growth factor receptor (EGFR) and; iii) inactivation of platelet-derived growth factor receptor A (PDGFRA) (21,22,25). Moreover, O-6methylguanine-DNA methyltransferase ( MGMT ) gene methylation status is being considered in treatment decisions, as a predictive biomarker for temozolomide (TMZ) (21). 1.2.1.1 Classification and epidemiology of glioblastoma Regarding the 2016 WHO new classification of gliomas, GBM are now classified as Glioblastoma, IDH - mutant or Glioblastoma, IDH -wildtype (WT). The first one represents about 10% of the cases and corresponds normally to secondary GBM (the ones that arise from a lower-grade glioma previously diagnosed) with incidence mostly in younger patients. The IDH -WT is the most frequent (about 90% of the cases) and is called de novo or primary GBM, and predominates in older patients (17). When evaluation of the IDH gene cannot be performed, the tumour is classified as Glioblastoma, NOS (not otherwise specified) (17). Although they may occur at any age, GBM affects mostly people with 50 to 70 years-old (22). Proposed by Verhaak et al. , using The Cancer Genome Atlas (TCGA), GBM are also divided based on molecular features into neural, proneural, classical and mesenchymal subtypes, although this classification is not used in diagnosis (21,25). In this way, the neural subtype is characterized by the 5 expression of neuron markers such as NEFL, GABRA1, SYT1 and SLC12A5; proneural subtype is distinguished by alterations/mutations on PDGFRA and IDH1 , as well as in TP53; the classical subtype main feature is the high expression of EGFR and the lack of TP53 mutations, but also the amplification of chromosome 7 and the deletion of chromosome 10; and the mesenchymal subtype is associated with mutations in NF1 and PTEN and also with expression of MET that causes epithelial-to-mesenchymal transition (25). 1.2.1.2 Etiology of glioblastoma The etiology of GBM is mostly unknown, being the only established risk factor the exposure to ionizing radiation (26). Studies associating head injuries, foods containing N-nitroso compounds, calcium or antioxidants, tobacco smoking, alcohol consumption and exposure to electromagnetic fields with development of GBM are inconclusive, though association with genetic syndromes ( e.g. neurofibromatosis types 1 and 2, Li-Fraumeni syndrome and Turcot’s syndrome) are reported (26,27). Knowing the risk factors underlying the development of this diseases might have an impact in the prevention and in patient’s prognosis. 1.2.1.3 Current therapies in glioblastoma Nowadays, the standard-of-care treatment for GBM patients consists in a multimodal approach, with surgical resection of the tumour (typically incomplete since GBM is a highly infiltrating tumour), followed by radiotherapy with concomitant and adjuvant chemotherapy, with temozolomide (TMZ), an alkylating agent (28–30). Besides such efforts, patient’s overall survival is still very poor (median of approximately 15 months) (22). GBM presents a high heterogeneity and proliferation and high resistance to the conventional therapies, adding to the difficulty of drugs to cross the blood brain barrier (BBB) and the likelihood of damaging permanently the brain; for all this, it is very difficult to achieve an effective therapy to improve the quality of life and the overall survival of GBM patients (29). 1.2.1.4 The HOXA9 gene Homeobox (HOX) genes are a family of homeodomain containing transcription factors that play a critical role during embryonic development (31,32). There are thirty-nine mammalian HOX genes grouped in four paralogous clusters (HOXA, HOXB, HOXC and HOXD) in different chromosomes (31,33). During 6 development, HOX genes follow both temporal and spatial pattern of expression, specific for each body region (31,34). Several HOX genes are aberrantly expressed in various tumours (35). In particularly, HOXA9 expression has been shown to be associated with more than 50% of acute myeloid leukemias and associated with a poor prognosis (31,36–38). HOXA9 was also reported to be involved in some solid tumours oncogenesis, like colon carcinoma, breast cancer, epithelial ovarian cancer, glioblastoma and non-muscle invasive bladder cancer (39–41). In ovarian cancer, HOXA9 was described to promote an inflammatory microenvironment that leads to tumour growth and allows tumours to escape to immune destruction, through up-regulation of some cytokines as interleukin 6 (IL-6) (37,42,43). Furthermore, HOXA9 induces macrophages to acquire M2like phenotype, promoting immunosuppression in its microenvironment (44). In GBM, HOXA9 is activated through epigenetic modifications, regulated by the PI3K pathway (45). Interestingly, HOXA9 expression promotes resistance to TMZ, the chemotherapeutic agent used in the clinics, and is able to promote malignant transformation in orthotopic mice models (46). HOXA9 is also associated with a shorter overall survival, both in mice and in patients (45–47). Moreover, in GBM, HOXA9 was identified to have a role in cancer-related pathways, for example in cell proliferation, in DNA repair and in stem cell maintenance (46). HOXA9 also promotes cell viability, invasion and proliferation, increases stemness capacity and decreases apoptosis, establishing HOXA9 as an important oncogene in the GBM aggressiveness (45,46). Interestingly, HOXA9 down-regulates genes involved in immune related pathways in GBM (e.g. immune response, inflammatory response and antigen processing and presentation) (46). HOXA9 is also able to increase the expression of PD-L1 in some GBM cell lines (46). Increasing evidences suggest that activation of some oncogenic pathways is associated with a non-responsive tumour microenvironment and with resistance to immunotherapies (48–50). Thus, HOXA9 is a candidate to be a predictive biomarker of the response to immunotherapies in GBM. 1.3 Immune surveillance and immune evasion in cancer The idea that the immune system can have a role in cancer development and progression started early, in the 1950s, when Burnet and Thomas built their cancer immunosurveillance hypothesis (51–54). They postulated that the adaptive immunity act to recognize and eliminate tumour cells (51,54,55). In the next 7 years, numerous studies have established that immunodeficient mice are more susceptible to develop carcinogen-induced tumours than immunocompetent mice (51). In 2001, a study revealed that tumours formed in mice which lack a fully immune system were more immunogenic (with highly immunoreactive clones) than the ones formed in mice with an intact immune system, suggesting that the immune system shapes tumour immunogenicity, besides having a protective function against tumours (51,56). This theory was demonstrated through the transplantation of tumours developed in immunodeficient into immunocompetent mice, in which only half of the mice develop progressive growing tumour, comparing to the mice transplanted with tumours developed in immunocompetent mice, in which all mice developed progressive growing tumours (51). The concept of immunoprotection against tumour cells and the shape of tumour immunogenicity set the basis to understand the cancer immunoediting hypothesis that establish a dual role of immune system in cancer development: host-protective and tumour-promoting (51,55). The cancer immunoediting hypothesis postulates three sequential phases: “elimination” in which innate and adaptive immune system work together to eradicate the tumour growth; “equilibrium” when tumour cells are in a state of dormancy, also in this phase the tumour cells are shaped by the immune system; and “escape” in which the immune system cannot control the tumour cells and promote the tumour grow (51). In ideal conditions (Figure 3), tumour cells release neo-antigens that are captured by dendritic cells (DC), an antigen presenting cell (APC) that migrates to the LN (lymph nodes) to present the antigen to T-cells. T-cells are a population of lymphocytes and can be divided into two populations based on their expression of cell surface markers (CD4+ T-cells and CD8+ T-cells). CD4+ T-cells, also called of helper T-cells, are responsible for providing help to other immune cells, through cell to cell interactions or the secretion of cytokines (57). CD8+ T-cells when effectively primed, mature to cytotoxic T lymphocytes (CTLs), which are the effector cells to eliminate “damaged” cells (57). In order to prime and activate the T-cells to specific cancer antigens, two signals are needed: the binding MHC-antigen complex to T-cell receptor (TCR) and a co-stimulatory signal (binding of CD80 and CD86 molecules to CD28 molecule). These T-cells recognize, using its TCR, the antigens presented by DCs in the context of major histocompatibility complexes (MHC) I and II molecules (only APCs express both MHC-I and MHC-II; all the other cells express only MHC-I) (57,58). DCs express also co-stimulatory molecules (CD80 and CD86) that are necessary to prime and activate T-cells (53,58). Without a co-stimulatory signal, T-cells are not activated and get into a state of anergy, and an anti-tumour immune response is not mounted (53). Then, T-cells traffic from the LN to and infiltrate the tumour microenvironment (tumour infiltrating lymphocytes – TILs), recognizing the tumour cells and killing them (58). The death of tumour cells leads to the release of more tumour- 8 associated neo-antigens that increases the anti-tumour immune response and leads to the resumption of the cycle (58). Despite this well-oiled process, tumours do develop in face of an immune system as tumour cells have acquired several mechanisms to evade this anti-tumour immune response. Genetic instability, tumour heterogeneity and immune selection contribute to tumour cells to acquire the ability to evade the immune system. Tumour cells have different strategies to avoid anti-tumour immune responses (Figure 4). On one hand, tumour cells hide from the immune system, by decreasing the expression of a series of molecules in its membrane. Meaning, to prevent recognition by the immune system, tumour cells downregulate or lose the expression of MHC molecules, co-stimulatory molecules (CD80 and CD86) and adhesion molecules (CD54) (59,60). On the other hand, tumour cells also can scape to immune destruction by becoming resistant to apoptosis and expressing inhibitory molecules to prevent T-cells function (59). Resistance to apoptosis can be achieved by three different mechanism: inhibition of granzyme B, inactivation of death receptors (FAS and TRAIL-R) and BCL-2 overexpression (61,62). To inhibit immune function, tumour cells express in their membranes programmed cell death ligand 1 and/or 2 (PDL1/PDL2), MHC II molecules, CD80/CD86 and HLA-G that bind respectively to programmed cell death 1 (PD1), lymphocyte activation gene 3 (LAG-3), cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) and immunoglobulin-like transcripts (ILT) present in T-cells (59,63). By expressing CD47, cancer cells give a signal to prevent being phagocyted by macrophages (58,59). Also, tumours create an immunosuppressive microenvironment: i) by the secretion of immunosuppressive cytokines [such as interleukin 10 (IL10) and transforming growth factor-beta (TGFβ)]; ii) by inducing immunosuppressive cells like T regulatory (Treg) cells (characterized by CD4+, CD25high, CD127low and FOXP3+), M2-like macrophages and myeloid-derived suppressor cells (MDSC) and; iii) by inducing an exhausted phenotype in effector cells (58,59,64,65). Treg cells are responsible for maintaining selftolerance and immune homeostasis and can supress anti-cancer immunity through CTLA-4, consumption of interleukin 2 (IL2) and production of immune inhibitory cytokines and molecules (66). They infiltrate the tumour microenvironment by chemotaxis: tumour cells and TAMs produce for example chemokine CC motif ligand 2 (CCL2) that recruits Treg cells into tumour tissues (67). In several cancer types, high infiltration of Treg cells is associated with a poor prognosis, since they promote tumour progression (66,68,69). 9 Figure 3 – Cancer immunity cycle. This cycle is divided in seven major steps, that starts with the release of antigens by the tumour cells (1), followed by the presentation of these antigens to T-cells by APCs (2), that results in T-cell priming and activation in the LN (3). Then, T-cells traffic to tumour site (4) and infiltrate into the tumour microenvironment (5). There, tumour cells are recognized (6) and killed by T-cells (7). This is a cyclic process that leads to the generation of amplified responses, by the release of neo-antigens by the tumour death cell (1) and ultimately to immunity to cancer, in ideal conditions. From Chen et al. , 2013 (58). The immune system has mechanisms to control the immune response to maintain immune homeostasis, relying on immune checkpoint proteins to control the function of immune cells. These immune checkpoint proteins present in the surface of T-cells [CTLA-4, PD-1, T-cell immunoglobulin and mucin domain containing protein 3 (Tim-3), LAG-3] are responsible for the regulation of T-cell activation and promote immune tolerance (70). As CTLA-4 and PD-1/PD-L1 axis are two big regulators of the immune response (co-inhibitory molecules) and trigger immunosuppressive responses, their role in cancer is vital. So, understanding their mechanism of action and their function allows to comprehend their roles in cancer and the purpose for an anti-CTLA-4 and an anti-PD-1/anti-PD-L1 therapies. 10 Figure 4 – Cancer immune evasion. Tumour cells have acquired several mechanisms to hide and defend themselves from the immune system and to generate an immunosuppressive microenvironment in order to evade the immune response. Tumour cells hide from the immune system by inducing the down-regulation of MHC molecules ( i.e. leading to limited antigen presentation), of co-stimulatory molecules ( e.g. CD80 and CD86) and of adhesion molecules ( e.g. CD54). Also, tumour cells develop mechanisms to defend themselves from immune surveillance by resisting to immune cells-induced apoptosis ( e.g. upon down-regulation of FAS and TRAIL receptors), by promoting T-cell inhibition ( e.g. upon expression of inhibitory molecules such as PD-L1, PD-L2, CD80, CD86, HLA-G in tumour cells that bind to immune checkpoint proteins present in T-cells), and by inhibiting macrophage’s phagocytosis ( e.g. up-regulation of CD47). Moreover, tumour cells interact with their microenvironment to make it immunosuppressive, by secreting immunosuppressive factors ( e.g. IL-10 and TGF-β) that inhibit the immune system and promote an exhausted phenotype of CTL, polarization of macrophages into M2 phenotype and recruitment of Treg cells. From M. de Charette, 2018 (59). CTLA-4 is involved in early stages of T-cells activation and regulates their response (71). During T cell priming, co-stimulatory signals are needed through the interaction of CD28, present on T-cell surface, with CD80 or CD86, present in APC’s. This interaction drives TCR signal amplification and T-cell activation (72,73). This co-stimulatory signal leads to the expression of CTLA-4 on T-cells membrane, which has a higher affinity towards CD80 and CD86 than CD28, leading to the inhibition the activation signal, and regulation of T cell activation (74). It has also been described that CTLA-4 signalling inhibits CD4+ T-cells 11 and enhances the function of Treg cells (75). CTLA-4 is overexpressed on activated CD4+ T-cells and CD8+ T-cells in tumour microenvironment and can supress T-cell activation by interrupting the co-stimulatory signal (76,77). In the same way, PD-1/PD-L1 axis regulates T-cell immune responses in peripheral tissues during inflammatory processes, mostly to avoid autoimmune diseases (71). PD-1 is present on activated T-cells, B-cells and natural killer (NK) cells, and modulates TCR signalling. It has two ligands (PD-L1 and PD-L2) that are expressed on APC’s surface, but can also be expressed on tumour cells’ membrane (77,78). PDL1 supresses the function and proliferation of CTLs and promotes Treg cells activity by binding to its ligand (PD-1), and decreases the production of some immunostimulatory cytokines, for example interferon gamma (IFNγ) (76,79,80). PD-1/PD-L1 axis is a mechanism for tumours to escape immune surveillance by inducing T-cell disfunction and preventing an effective anti-tumoral immune response (Figure 5) (81). In addition, this mechanism can also stimulate IL10 production in peripheral T-cells, suppress DCs and induce Treg cells differentiation (82). T-cell disfunction is achieved by inducing T-cell anergy, exhaustion and apoptosis (81,82). Figure 5 – Mechanisms for tumours to escape immune surveillance. Tumour cells can express high levels of PD-L1 in its membrane, which leads to several mechanisms to evade immune response: induction of Treg cells, induction of T-cells to produce IL10, T-cell anergy, T-cell exhaustion, T-cell apoptosis, DC suppression and resistant to CTL activity. From Chen et al. , 2015 (82). 12 1.3.1 Immune-based anti-tumour therapies For a long time, the strategy for cancer treatment relied solely on surgery and on radioand chemotherapy, with no significant benefits for patients (83,84). In recent years, therapeutic agents that modulate the immune system to induce or potentiate anti-tumoral responses have shown successful improvements in cancer treatment. Cancer immunotherapies include cytokine treatment (e.g. IFNγ and IL2), adoptive Tcell therapies and T-engineering (e.g. chimeric antigen receptor (CAR) T-cell therapy), cancer vaccines (e.g. dendritic cell therapy and preventive vaccines) and immune checkpoint blockade therapies (85). Expression of the ligands for immune checkpoints by tumour cells lead to tumour immune escape. Thereby, these immune checkpoints and its ligands are attractive as targets for an immunotherapy (Figure 6) (85). As this therapy has successful results in pre-clinical and clinical studies, the US Food and Drug Administration (FDA) have approved a few Immune checkpoint inhibitors (ICIs) for the treatment of these malignancies (85). Nowadays, anti-CTLA-4, anti-PD1 and anti-PD-L1 are the ICIs with more clinical relevance. The FDA has approved the use of the following ICIs for treatment of cancer patients: ipilimumab (anti-CTLA-4) for the treatment of melanoma; pembrolizumab and nivolumab (anti-PD-1) for treatment of metastatic melanoma; nivolumab for treatment of previously treated, advanced or metastatic squamous lung cancer, small cell lung cancer and Hodgkin lymphoma; and atezolizumab (anti-PD-L1) for bladder cancer (86–89). Figure 6 – Immune checkpoint blockade. Representation of the different immune checkpoint inhibitors and their acting site. Anti-CTLA-4 (ipilimumab) act in the LN during T-cell activation, to prevent the inhibition of T-cell activation by CTLA-4. On the other hand, anti-PD1 (pembrolizumab and nivolumab) and anti-PDL1 (atezolizumab, avelumab and durvalumab), act on tumour site to enhance the anti-tumour immune activity. From Abril-Rodriguez, 2017 (90). 19 which was transferred to a new tube. Right after, 0.5 mL of isopropanol was added to precipitate RNA; samples were incubated for 10 minutes and centrifuged at 12 000 g for 10 minutes at 4ºC. Supernatant was decanted and the RNA pellet washed with ethanol 75% and centrifuged at 7 500 g for 5 minutes, twice. RNA pellet was resuspended in RNase free water and the RNA was left for 10 minutes at 55ºC before being stored at -80ºC until being used. 3.2.2 Complementary DNA synthesis One µg of total RNA (quantified by a nanodrop Spectrophotometer ND-1000) was used to be reverse transcribed into complementary DNA (cDNA) using High Capacity cDNA Reverse Transcription Kit (Applied Biosystems), according to manufacturer’s recommendations. This Kit uses RT buffer 1x, dNTPs (0.04 mM), random primers 1x, reverse transcriptase (2,5 U/µL) and water (DNase and RNase free). Synthesis was performed on a thermocycler (Bio Rad) using the following protocol: 25ºC for 10 minutes, 37 ºC for 120 minutes, 85ºC for 5 minutes and at 4ºC until samples were stored at -20ºC. 3.2.3 Reverse transcriptase - quantitative polymerase chain reaction (RT-qPCR) Primers for the RT-qPCR were designed using the online tool “Primer3Plus”. A table with the primers used and the relative melting temperature (Tm) can be found in the Supplementary Table 1. The expression levels of immune related genes and of HOXA9 or Hoxa9 were determined by RT-qPCR, using the TATA-binding protein ( TBP, Tbp ) as reference gene. Depending on the gene and the cell line to be assessed, as depicted in Supplementary Table 1, the KAPA SYBR® FAST qPCR Master Mix (2X) Universal or the PowerUp™ SYBR™ Green Master Mix (Applied Biosystems), were used. Briefly, 1 μL of cDNA, KAPA SYBR® FAST qPCR Master Mix or PowerUp™ SYBR™ Green Master Mix, 0.2 μM of each primer and RNase free H2O was used to prepare the RT-qPCR mix. The reactions were performed in duplicate and ran on a Thermal cycler CFX96 (Bio-Rad) using the program Bio-Rad CFX Manager version 3.1. The conditions of RT-qPCR were as follows: 3 minutes at 95°C; followed by 40 cycles of denaturation, annealing and extension: 3 seconds at 95°C for denaturation, 30 seconds at respective Tm for annealing (supplementary table 1) and 30 seconds at 72°C for extension, for the RT-qPCR performed with KAPA SYBR® FAST qPCR Master Mix; and as follows: 2 minutes at 50°C and 2 minutes at 95°C; followed by 40 cycles of denaturation, annealing and extension: 15 seconds at 95°C for denaturation, 60 seconds at respective Tm for annealing and extension 20 (supplementary table 1), for the RT-qPCR performed with PowerUp™ SYBR™ Green Master Mix. For the melting curve, the dissociation was performed by 5 seconds at 65ºC with increasing the temperature by 1ºC from 65ºC to 95ºC. RT-qPCR products weights were confirmed on 2% agarose gels. Data from the relative expression of the RT-qPCR was analysed using the ΔΔCT method for the analysis of gene relative expression in the cell lines (control ones and the ones with manipulated levels of HOXA9) (115). 3.3 Flow cytometry stain and analysis In this section were described the staining protocols for flow cytometry: surface staining, intracellular staining and annexin v/propidium iodide (Ann/PI) staining. For that, were used the antibodies and dyes depicted in supplementary table 2. 3.3.1 Surface staining The expression of the immune checkpoint ligands, PDL1, PDL2, CD80 and CD86, on the cells’ surface of human GBM cell lines was determined using monoclonal antibodies (supplementary table 2). Cells were detached with trypsin, and total cell numbers were determined using a Neubauer chamber in an inverted microscope (Olympus CKX41). Half million cells were added to each U-shaped well of a 96-well plate, and washed twice with FACS buffer [phosphate buffered saline (PBS) supplemented with 0.3% bovine serum albumin (BSA), 0.01% sodium azide], for 2 minutes, 1 200 rpm, 4 ºC. After incubation of tumour cells with Fc Block (1:50 dilution; eBioscience), cells were incubated with prediluted antibodies for 20 minutes, in the dark and on ice; cells were washed twice with PBS. Live/dead fixable dye (supplementary table 2) was added to the cells, incubated for 30 minutes in the dark, on ice, and washed once with PBS. The samples were acquired on the same day, unless they proceeded for intracellular stain. 3.3.2 Intracellular staining Cells were surface stained (as described in section 5.1) with antibodies directed to CD45, CD3, CD4, CD8, CD127 and CD25. Afterwards, cells were incubated with Fixation/Permeabilization buffer (eBiosciences) for 30 minutes in the dark, on ice and then, washed once with FACs buffer, and once with 21 Permeabilization buffer (eBiosciences). Afterwards, the cells were incubated for 30 minutes in the dark, at room temperature with FoxP3 (diluted in Permeabilization buffer) and then washed twice with Permeabilization buffer and resuspended in PBS 1% BSA. The samples were acquired in the cytometer on the next day. 3.3.3 Annexin V/Propidium Iodide staining After a surface stain with anti-CD45 (section 3.4.1; supplementary table 2), to be able to distinguish tumour cells from leukocytes, cells were washed twice with binding buffer (HEPES 100mM, NaCl 140mM, CaCl2·2H2O 2.5 mM). 50 µL of a mix of Ann and PI was added and incubated in the dark, for 15 minutes at room temperature. Acquisition on the cytometer was performed in the following 15 minutes. 3.3.4 Data acquisition and analysis Single stains for CD80, CD86, PD-L1, PD-L2, CD25 and CD127 were performed using compensation beads (Invitrogen) to obtain clearly defined positive and negative populations for compensation. The samples were acquired on the same day on a BD LSRII flow cytometer (equipped with a blue 488nm, a red 633nm and a violet 405nm lasers), using the FACS Diva Software (Becton Dickinson, Franklin Lakes, NJ, USA). Data were analysed as described in the results section using the FlowJo Software version 10 (Tree Star, Ashland, OR, USA). 3.4 Co-cultures of tumour cells and activated T-cells The cytotoxicity mediated by T-cells was evaluated by co-culture of tumour cells with previously activated T-cells, followed by evaluation of live and dead cells by flow cytometry. The same way, T-cells’ subsets were evaluated after co-culture of tumour cells and activated T-cells. 3.4.1 Activation and expansion of human peripheral blood mononuclear cells (hPBMCs) Ten million fresh hPBMCs from healthy donors, from Hospital de Braga (kindly provided by Dr. Agostinho Carvalho and Dra. Cristina Cunha, ICVS, University of Minho) were cultured in complete RPMI medium (RPMI-1640 with 10% FBS, 1 mM pyruvate and 100 U/mL penicillin and 100 mg/mL streptomycin) supplemented with 30U/mL of human recombinant IL-2 (ImmunoTools), in a T25 flask previously coated 22 for 2h at 37 ºC, with 5 µg/mL of anti-human CD3 (ImmunoTools) and 2.5 µg/mL of anti-human CD28 (ImmunoTools). After 72h, cells were washed and resuspended in fresh complete medium with 30 U/mL of human recombinant IL-2 and cultured for more 24h in a new T25 flask. In the next day, the cells were washed and resuspended in medium without IL-2. 3.4.2 Co-culture of human tumour cells with activated T-cells U87-MSCV/-HOXA9, GL18-shCTRL/-shHOXA9 and U251-shCTRL/-shHOXA9 cell lines, cultured in RPMI medium, were plated at an initial density of 2x105 cells per well in 12-well plates, and incubated overnight. Next day, activated T-cells were added at a ratio of 1:5 of target cells: effector cells (T: E), with or without ICIs [10 µg/mL of anti-CTLA4 (clone BN13; BioXCell) or anti-PD1 (clone J116; BioXCell)]. For each cell line there were 4 conditions: i) tumour cells only; ii) tumour + T-cells + IgG isotype control (10 µg/mL; Sigma Aldrich); iii) tumour cells + T-cells + anti-CTLA4 and; iv) tumour cells + T-cells + anti-PD1. The cells were co-cultured for 48h at 37ºC in 5% CO2 atmosphere. Afterwards, cells in suspension were removed from the well and placed immediately in ice; tumour cells were detached from the wells using trypsin and added to the previously removed cells in suspension, to be counted and stained for flow cytometry. 3.4.3 Activation and expansion of mouse T-cells C57BL/6J mice were euthanized by a lethal dose of anaesthesia and the inguinal, axillary and superficial cervical LN were aseptically removed. Single cell suspension was obtained by gentle disrupting the LN between two notched slide glasses, and, after washing, LN cells were resuspended in complete RPMI medium [RPMI + 10% FBS + 100 U/mL penicillin and 100 mg/mL streptomycin + 1mM sodium pyruvate + 50 µM β-mecaptoethanol (β-me; Sigma)]. After this, the number of cells extracted was obtained using trypan blue exclusion dye in a proportion 1:5, using a Neubauer chamber. For the activation and expansion of T cells, a T25 flask was coated, for 2h at 37ºC, with anti-mouse CD3 (5 µg/mL; clone 145-2C11; BioLegend). After thoroughly washing the flask with PBS, 1x107 million LN cells were cultured with complete RPMI medium. Supplemented with recombinant mouse IL-2 (30U/mL; Invitrogen) and with anti-mouse CD28 (2µg/mL; clone 37.51; BioLegend). After 72h of T-cell activation, cells in suspension were collected, centrifuged at 1 200 rpm, for 10 minutes at 4ºC, and the pellet resuspended in complete RPMI medium supplemented with IL2; cell suspension was transferred to a new T25 flask and cultured for more 24h. Afterwards, cells in suspension were collected, viable counted, 23 washed resuspended in complete RPMI medium. Activation profile of these T-cells was confirmed by flow cytometry, as described above. 3.4.4 Co-culture of mouse tumour cells with activated T-cells GL261 cells, cultured in complete RPMI medium, were plated at an initial density of 5x104 cells per well in a 12-well plate, and incubated overnight; activated mouse T-cells were added to the wells at different ratios of T: E (1:5; 1:10; 1:20). After 48h of culture, cells in suspension were collected and placed on ice; adherent cells were detached from the wells using cell dissociation media (Sigma Aldrich) and added to the first suspension of cells. Total cells were counted and stained for Ann/PI. 3.5 HOXA9 silencing in mouse GBM cell line The GL261 cell line was manipulated to silence HOXA9 with Hoxa9 gene-specific shRNA vector insert (TR500979; clones TR500979A, TR500979B, TR500979C and TR500979D; Origene) or non-effective shRNA (scrambled negative control) inserted in a pRS plasmid (TR30012; Origene). For that, 1.5x105 cells were plated per well in a 12-well plate and when reaching 70-90% confluence the cells were transfected. First, LipofectamineTM 3000 reagent was diluted in Opti-MEMTM Medium (Diluted Lipofectamine 3000) and vortex for 2s. Then, was prepared the master mix of DNA, by diluting DNA (0.5µg) in OptiMEMTM Medium and added P3000 reagent (2 µL/µg) – Diluted DNA. Next, was added the Diluted DNA to the tube of Diluted Lipofectamine 3000 reagent (in 1:1 ratio) and incubated for 15 minutes at room temperature. Finally, the medium was removed from the wells and was added fresh medium (DMEM + 10% FBS) and DNA-lipid complex to the cells and maintained for 48h. The transfected cells were selected with 1 µg/mL of puromycin, since these plasmids present a resistance gene to puromycin. 3.6 Statistical analysis When normality was assumed, unpaired t-test or One-Way ANOVA was performed, but when normality assumptions were not satisfied, the equivalent non-parametric Mann Whitney U test or Kruskal-Wallis test, respectively, was applied. To determine statistical differences between groups was used, first, the Levene’s test to check for equality of variances. If the Levene’s test revealed differences among the homoscedasticity of both groups, Welch’s correction was applied. 24 Overall, the results were expressed as group means ± standard deviation, when the parametric test was used; and as group median ± distance between the first and third quart. Differences were considered statistically significant for p-values below 0.05. The results were analysed using GraphPad Prism version 7 (GraphPad Software, La Jolla, CA, USA). 25 4. Results 4.1 Influence of HOXA9 on the expression of immune related factors in tumour cells The development of an inflammatory microenvironment has long been considered important in the initiation and progression of GBM (116) however, the role of HOXA9 on the modulation of the expression and release of some cytokines has yet to be elucidated. Ligands of immune checkpoints are wildly described to be upregulated in several cancer types, including GBM (91). For this reason, it is important to assess also if HOXA9 can modulate the expression of these proteins. Although there are previous microarray data on the expression of some cytokines and immune checkpoint ligands (117), it was necessary to validate this data through a technique with greater sensitivity, such as RT-qPCR. This work was focused on the expression of the following genes: IL1B , TNFA , IFNG , TGFB1 , IL8 , IL10 , IL6 , CCL2 , CD86 , CD80, PDL1 and PD1 . The expression of these genes was assessed in three different human GBM cell lines: U87-MSCV/HOXA9 (an overexpression model for HOXA9, in which U87-MSCV is the control cell line with low levels of HOXA9 and U87-HOXA9 is the manipulated cell line to express high levels of HOXA9 ); GL18-shCTRL/-shHOXA9 and U251-shCTRL/-shHOXA9 (two silencing models for HOXA9, in which -shCTRL are the control cell lines with high levels of HOXA9, and -shHOXA9 are the ones silenced for HOXA9 ). All of these cells grow preferentially in DMEM, but in order to perform the co-cultures of tumour cells with hPBMCs (which only grow in RPMI), in this work, tumour cells were cultured also in RPMI. As gene expression can change with the culture conditions (118–120), for RT-qPCR evaluation, the expression of immune related genes was assessed in human GBM cell lines, cultured both in DMEM and RPMI. 4.1.1 HOXA9 affects the expression of cytokines and chemokines in human GBM cell lines Numerous cytokines have a role in tumour growth and in the promoting of an immunosuppressive tumour microenvironment. For instance, IL1β and TNFα are the main drivers of inflammation in GBM, promoting tumour growth (106,121), and IFNγ is correlated with the expression of PD-L1 (122); TGFβ1 promote tumour growth and immunosuppression; IL8 and IL6 promote invasion; CCL2 is evolved in Treg cells recruitment and IL10 inhibits T-cell function (94). In order to understand if some immune related genes were altered in the presence of HOXA9 , RT-qPCR were performed directed a set of genes: IL1B , TNFA 26 and IFNG that are pro-inflammatory genes and TGFB1 , IL8 , IL6 , CCL2 and IL10 that are anti-inflammatory genes. Regarding the pro-inflammatory genes, for IL1B , and for both culture media, the expression of this gene decreased in the U87 cell line (i.e. upon HOXA9 over-expression), but for the silencing models these differences were not significant (Figure 8A and B; supplementary table 3). For TNFA , no consistent differences were seen on its expression for the different cell lines (Figure 8C and D; Supplementary Table 4), and in all the cell lines, IFNG expression was is not detected (Supplementary Figure 2). Concerning the anti-inflammatory genes, a decrease of TGFB1 expression was observed in both HOXA9 silencing models when cultured in DMEM, but not in RPMI; additionally, no differences were seen for the U87 cell line overexpressing HOXA9 (Figure 8E and F; Supplementary Table 5). Regarding IL8 expression, an increase of its expression was observed when HOXA9 expression was silenced, though these differences were not consistent in the two silencing models and in the two culture media, but in the overexpression model, U87 cell line, there was no significant differences (Figure 8G and H, Supplementary Table 6). No IL10 expression was detected in any cell line (Supplementary Figure 3). Concerning IL6 expression, in the silencing model GL18 cultured in DMEM, a significant decrease in its expression was observed, but not in the other cell lines tested (Figure 8I and J, Supplementary Table 7). CCL2 expression was significantly decreased in the U251 cell line (silencing model), in both cell culture media, but no differences were seen in other cell lines (Figure 8k and L, Supplementary Table 8). 27 Figure 8– Expression of immune related genes in human GBM cell lines with differential levels of HOXA9 expression. Expression of IL1B (A, B), TNFA (C, D), TGFB1 (E, F), IL8 (G, H), IL6 (I, J) and CCL2 (K, L), in human GBM cell lines. Gene relative quantification, by RT-qPCR, was performed in cell lines cultured in DMEM (A, C, E, G, I, K) and in RPMI (B, D, F, H, J, L). In each graph is represented the logarithm of the fold change of the relative expression of the transformed cell line ( i.e. U87-HOXA9, GL18-shHOXA9 and U251-shHOXA9) to the respective control ( i.e. U87-MSCV, GL18-shCTRL and U251-shCTRL) for each cell line. Each gene’s relative expression is depicted in supplementary tables 3 to 8. T-test was used to compare the fold change logarithm of the expression of immune related genes between the transformed cell line with the respective control. 28 Each column represents the mean ± standard deviation of 3 independent biological samples. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001 4.1.2 HOXA9 influences the expression of immune checkpoint ligands in tumour cells CD80 and CD86 are the ligands for CTLA4 and PD-L1 and PD-L2 are the ligands for PD1, and these ligands are upregulated in tumour microenvironment to promote immune evasion (91). Besides expressing ligands for PD1 and CTLA4, tumour cells can also express the PD1 itself, and depending on the cancer type can promote tumour growth, by binding to its ligand in tumour cells or can block PD1, inhibiting tumour proliferation (123). The expression of PD1 was evaluated by RT-qPCR, though the expression of these gene was not detected in the tested cell lines (Supplementary Figure 4). Besides the presence of bands of interest, the RT-qPCR was contaminated by several unspecific links, make it impossible to quantify the expression of PD1 in these cell lines. By RT-qPCR was observed that silencing of HOXA9 expression, leads to significant decrease in the expression of CD86 only when culture in RPMI (Figure 9A and B; Supplementary Table 9); in the same conditions, and also in the GL18 silencing model cultured in DMEM, an increased expression of CD80 was observed (Figure 9C and D; Supplementary Table 10). Regarding CD80 and CD86 protein expression at the cell surface (assessed by flow cytometry), all the human cell lines analysed were negative for these markers (Figure 10; Supplementary Tables 12 and 13); importantly, and as expected, these antibodies were able to stain human PBMCs or activated T-cells (as positive controls for CD80 and CD86, respectively; Supplementary Figure 5), reinforcing that these tumour cell lines are truly negative for CD80 and CD86. 35 Figure 12 – T-cell mediated cytotoxicity in tumour cells, in treatment naïve conditions or in the presence of ICIs. A, D, G) Depicts the percentage of live (Ann-PI-), apoptotic (Ann+PI-) and dead cells (PI+), in tumour cells only (CTRL), tumour cells with activated T-cells and isotype control (IgG; IC) or in face of anti-CTLA, or of anti-PD1 of U87 (A), GL18 (D) and U251 (G) cell lines. One-way ANOVA was used to compare the percentage of live, apoptotic and dead cells between the conditions with isotype control, anti-CTLA4 and anti-PD1 in the transformed cell lines ( i.e. U87-HOXA9, GL18-shHOXA9 and U251-shHOXA9) and in the control cell lines ( i.e. U87-MSCV, GL18-shCTRL and U251-shCTRL). B, E, H) Represents the fold change logarithm of live cells from tumour cells only condition to tumour cells with activated T-cells (with IC), of U87 (B), GL18 (E) and U251 (G) cell lines. T-test was used to compare the fold change logarithm of the percentage of live, apoptotic and dead cells between the transformed cell line ( i.e. U87-HOXA9, GL18-shHOXA9 and U251-shHOXA9) and the respective control ( i.e. U87-MSCV, GL18-shCTRL and U251-shCTRL). C, F, I) Represents the fold change logarithm of live cells form the tumour cells with activated T-cells (with IC) condition to the conditions with anti-CTLA4 or with anti-PD1, of U87 (C), GL18 (F) and U251 (I) cell lines. T-test was used to compare the fold change logarithm of the percentage of live, apoptotic and dead cells in the conditions with anti-CTLA4 and anti-PD1 between the transformed cell line ( i.e. U87-HOXA9, GL18-shHOXA9 and U251-shHOXA9) with the respective control ( i.e. U87-MSCV, GL18-shCTRL and U251-shCTRL). The average ± standard deviation is represented from 3 independent assays. * p < 0.05. 36 4.4 Influence of HOXA9 in T-cell survival, in the presence or absence of Immune Checkpoint Inhibitors One of the mechanisms of immunosuppression in GBM is the induction of T-cell apoptosis (79). Therefore, it is important to evaluate the effect of HOXA9 expression on T-cell survival in the co-culture assays, and the impact of adding ICIs. For this, the CD45+ population ( i.e. leukocytes) of the co-cultures was analysed (Figure 11A). When comparing co-cultures, without ICIs, of control tumour cell with tumour cells overexpressing (Figure 13A) or with tumour cells silenced for HOXA9 (Figure 13C and E), no differences were observed in the percentage of live, apoptotic and death leukocytes. Also, within the same cell lines, ICIs had no effect on the survival of T-cells, when comparing co-cultures of tumour cells and activated Tcells with isotype control with the same conditions with anti-CTLA4 or anti-PD1 (Figure 13A, C and E). To assess the effect of HOXA9 in the response to ICIs, the fold change of the percentage of viable cells at the control of tumour cell and T-cells with isotype control to the conditions of tumour cell and T-cells with ICIs (anti-CTLA4 or anti-PD1) was calculated. No significant differences were found on the survival of activated T-cells in response to ICIs, when comparing co-cultures of control tumour cells with tumour cells overexpressing (Figure 13B) or silenced for HOXA9 (Figure 13D and F). With this analysis, was demonstrated that HOXA9 expression and ICIs do not influence the T-cell survival in co-cultures of GBM cell lines. 37 Figure 13 – HOXA9 and ICIs impact on T-cell survival. A, C, E) Represents the percentage of live (Ann-PI-), apoptotic (Ann+PI- ) and dead cells (PI+) in tumour cells with T-cells and isotype control (IgG; IC) or in face of anti-CTLA, or of anti-PD1 of U87 (A), GL18 (C) and U251 (E) cell lines. One-way ANOVA was used to compare the percentage of live, apoptotic and dead cells between the conditions with isotype control, anti-CTLA4 and anti-PD1 in the transformed cell lines ( i.e. U87-HOXA9, GL18shHOXA9 and U251-shHOXA9) and in the control cell lines ( i.e. U87-MSCV, GL18-shCTRL and U251-shCTRL); and no significant differences were observed. B, D, F) Represents the fold change logarithm of live T-cells from the condition of the tumour cells and T-cells with isotype control to the condition with anti-CTLA4 or with anti-PD1, of U87 (B), GL18 (D) and U251 (F) cell lines. T-test was used to compare the fold change logarithm of the percentage of live cells in the conditions with antiCTLA4 and anti-PD1 between the transformed cell line ( i.e. U87-HOXA9, GL18-shHOXA9 and U251-shHOXA9) with the respective control ( i.e. U87-MSCV, GL18-shCTRL and U251-shCTRL). The average ± standard deviation is represented from 3 independent assays. 38 4.5 Impact of HOXA9 expression by glioblastoma cells in the percentage of T-cell subpopulations, independently of Immune Checkpoint Inhibitors’ presence GBM is characterized for being an immunosuppressive tumour, with a high percentage of immunosuppressive cells, like Treg cells, and low percentage of effector cells (79,95). For this reason, assess if HOXA9, in treatment-naïve condition or with ICIs treatment, could have an impact in the modulation of the immune system, in vitro , is very important. For this, in the co-culture system, the T-cell profile was further analysed, assessing the percentages of CD4+ T-cells, CD8+ T-cells and Treg cells, by flow cytometry, in the silencing of HOXA9 GBM cell lines models (Figure 14A). In terms of percentage of total T-cells (CD3+), all conditions present about 97% of T-cells as a result of the in vitro activation and expansion of T-cells protocol. Comparing the silenced for HOXA9 expression cell lines ( i.e. GL18-shHOXA9 and U251-shHOXA9) with the respective control cell lines ( i.e. GL18-shCTRL and U251-shCTRL), no differences were observed in the percentages of CD8+ T-cells, CD4+ T-cells and Treg cells in naïve-treatment conditions (Figure 14B-E). Also, when was compared the silenced for HOXA9 expression cell lines ( i.e. GL18-shHOXA9 and U251-shHOXA9) with the respective control cell lines ( i.e. GL18-shCTRL and U251-shCTRL) in the presence of anti-CTLA4 or anti-PD1, no differences were observed regarding in the percentages of CD8+ T-cells, CD4+ T-cells and Treg cells (Figure 14B-E). However, to conclude about the impact of HOXA9 and ICIs (anti-CTLA4 and anti-PD1) in the modulation of the T-cells subpopulations, is mandatory to increase the number of assays, since its represented data just from 2 independent assays. 39 Figure 14 – Influence of HOXA9 expression in tumour cells, in T-cells subpopulations, in the presence or absence of ICIs. A) Representative gating strategy of the T-cells populations on a GL18 co-culture. Selection of a homogeneous sample acquisition over time, plotted for SSC-A vs. time (i). Doublets were excluded (ii), the live leukocytes selected (iii), and then the T-cell population (CD3+). CD4+ T-cells and CD8+ T-cells were selected (v), and inside the CD4+ T-cells, the population of cells expressing low CD127 was selected (vi), followed by the selection of the CD25+Foxp3+ (vii). Tregs were characterized as CD3+CD4+CD127CD25+FoxP3+. T-cell subpopulations were analysed in co-cultures in GBM cell lines with silencing of HOXA9 models: GL18 (B, D, F) and U251 (C, E, G). Represents the percentage of B, C) CD8+ T-cells; D, E) CD4+ T-cells and F, G) Treg cells, in the 40 condition of the tumour cells and T-cells with isotype control (IC), with anti-CTLA4 or with anti-PD1. The average ± standard deviation is represented from 2 independent assays. 4.6 Studies of HOXA9 impact in glioblastoma immune evasion in a murine model – preliminary data As the studies with human GBM cell lines, was next, proposed to perform the same studies but with a murine GBM model and additionally, perform in vivo studies with this model. However, as described in the next section, the silencing of the murine GBM cell line was not successful. Still, the protocol of the Tcell activation and expansion of mouse T-cells was optimized to proceeded with these assays as soon the murine GBM cell line is stablish. 4.6.1 Silencing of HOXA9 in a murine GBM cell line To evaluate the effect of Hoxa9 expression in a murine GBM cell line, on the cytotoxicity mediated by Tcells, Hoxa9 expression in the GL261 cell line had to be modulated. Since the GL261 cell line has high endogenous levels of this gene (mean relative expression of 201.19), the expression of Hoxa9 needed to be silenced. This process would generate a control cell line with high levels of Hoxa9, and its paired silenced cell line with low levels of Hoxa9 . For this, 4 different shRNA constructs were used to stably silence this gene. To confirm the success of the transfection, cells were selected with puromycin, since the constructs used present a resistance gene: tumour cells that were not transfected would die, and transfected cells would survive. It was possible to efficiently transfected the GL261 cell line with each of the 4 constructs (represented a significant example in Figure 15A). Hoxa9 expression was evaluated by RT-qPCR. However, despite being transfected, cells were not successfully silenced, since Hoxa9 relative expression of the cells with the constructs of interest were similar to the cells with the control construct (Figure 15B). To overcome this issue other shRNA construct, with other sequence or a different plasmid, should be tested. As the GL261 cell line was not efficiently silenced, it was not possible to move forward with the experiments in vitro and in vivo in the murine model. 41 Figure 15 – The murine GBM cell line GL261 was not silenced for Hoxa9 , though was transfected. A) Photos of GL261 cell line after 9 days with selection medium containing 1µg/mL puromycin, to confirm the efficient transfection of this cell line. From the left to the right, depicted are the parental cell line (GL261; not submitted to a transfection), then the line cell transfected with the control plasmid (GL261 shCTRL), and the GL261 cell line transfected with one of the tested constructs to silence the Hoxa9 expression (GL261 shD). B) Fold change logarithm of the Hoxa9 relative expression in cell line transfected with each of the 4 different constructs to silence Hoxa9 expression to the cell lines transfected with control vector (shCTRL), evaluated by RT-qPCR. Each column represents the mean ± the standard deviation, from 3 independent biological samples, run in duplicate. 4.6.2 Optimization of mouse T-cell activation and expansion protocol In vivo , T-cell activation is initiated with the interaction of the T-cell receptor (TCR)/CD3 complex with peptide present on the cell surface of APC; interaction of CD28 on the T-cells with CD80 and CD86, on APC, provides the co-stimulatory signal (126). The in vitro activation protocol mimics this process using antibodies that target CD3 and CD28, which have the ability to induce T-cell activation through these molecules’ signalling. IL2 was used to induce T-cell expansion. To perform the co-cultures with murine cells, it was necessary to adjust the T-cell activation and expansion protocol available for hPBMCs to mouse lymph nodes (mLN) cells. Mouse lymph nodes cells were used since this organ presents a similar percentage of T-cells in comparison to hPBMCs (about 60-70%). The use of mouse PBMCs, would require a high number of mice and, since mLN are enriched in T-cells, this reduces the number of mice used per assay. 42 The murine GBM cell line, GL261 grows in DMEM. Because of the co-culture assays to be performed afterwards, the activation and expansion protocol of mLN cells was performed in DMEM; also, in the literature are described some protocols in DMEM (127,128). In a first approach, wells of a 24-well plate were coated with anti-CD3 (5 µg/mL) and anti-CD28 (2 µg/mL) for 2h at 37ºC; afterwards, 2x106cells/mL was added per well, in complete DMEM supplemented with IL-2 (30 U/mL). The mLN cells were maintained in culture for 72h (activation), followed by 24h with complete DMEM and IL-2 in a new, uncoated 24-well plate (expansion). Viable cells were counted, and a very low percentage of the initial number of cells cultured was recovered. As for hPBMCs, the yield is usually around 100%, it was hypothesized that cells were not being properly activated and, due to lack of stimulus, they were dying (129). Co-stimulation with anti-CD28 enhances the proliferative expansion and promotes cell survival during activation (129). In this sense, it would be expected that, by decreasing cell density, and adding soluble anti-CD28, instead of plate bounded anti-CD28, would favour the interaction between T cells and antiCD28, thus enhancing a strong co-stimulatory signal to properly activate T-cells. First, the cell density was decreased from 2x106 cells/mL to 1x106 cells/mL, and soluble CD28 (0.4 µg/mL) was added in the medium. With this alteration on the protocol, a modest increase in the recovery of viable cells was obtained in comparison with the first protocol tested (from 1.48% to 7.00%). Thus, a cell density of 1x106 cells/mL and soluble CD28 was used. Still, the obtained yield was far from the obtained for hPBMCs. The usage of complete DMEM vs . complete RPMI was next tested. Importantly, RPMI was further supplemented with 1mM sodium pyruvate since it is the main source of energy for the cells. Supplementation with β-mercaptoethanol (β-me; 50 µM) was also tested since β-me is a reducing agent, that helps to prevent toxic levels of oxygen radicals and is necessary for the in vitro growth and activation of lymphocytes (130). In the absence of β-me, mLN cells cultured in RPMI presented higher percentages of recovered viable cells in comparison to DMEM (6.63% vs . 1.88%). The presence of β-me increases the percentage of cells recovered, especially for mLN cells cultured in RPMI (6.63% without β-me vs . 92.88% with β-me), but also for cells cultured in DMEM (1.88% without β-me vs . 11.75% with β-me). At this point, it was concluded that culturing mLN cells in RPMI supplemented with β-me is required to properly expand cells and achieve the expected cell recovery. Although the previously results with DMEM were not promising, it was highly desirable to optimize this protocol using DMEM, in order to not to change the growth media of the tumour cell line. A new test, using DMEM and RMPI was performed, using different concentrations of anti-CD3 and anti-CD28. With 43 the same rational that co-stimulation with anti-CD28 enhances the proliferation and promote cell survival during activation, and based on previously described protocols (129,131,132), the concentration of antiCD28 was increased from 0.4 µg/mL to 2 µg/mL. Additionally, it was shown that, with 2.5 µg/mL of anti-CD3 there is more proliferating T-cells (CD4+ and CD8+ cells), and with higher doses ( e.g. 5 µg/mL) the expression of CD3 decreases in CD4+ and CD8+ T-cells (127). Based on this, the stimulation and expansion protocol for mLN cellss was tested in DMEM and RPMI, with 0.4 µg/mL or 2 µg/mL of soluble anti-CD28, and with 2.5 µg/mL or 5 µg/mL of plate-bounded anti-CD3. Results show that in DMEM, the recovery of viable mLN cells was below 20%, irrespectively of the tested condition (Figure 16). Regarding to the mLN cells cultured in RPMI, with 5 µg/mL of anti-CD3 there was an increased recovering of viable mLN cells especially with 0.4 µg/mL of anti-CD28, but also with 2 µg/mL, when compared with 2.5 µg/mL of anti-CD3 (Figure 16). Since the percentage of recovered live cells using RPMI was highest, and although there were better results with the lower concentration of anti-CD28 ( i.e. 0.4 µg/mL), understanding if T-cells were effectively activated was the next step. To this, the activation profile was evaluated, by flow cytometry of the mLN cells cultured in RPMI, in the presence of 5 µg/mL of anti-CD3 and with both concentrations of CD28 (0.4 µg/mL and 2 µg/mL). Figure 16 – Percentage of cells recovered after the activation and expansion of mLN cells protocol with the different conditions tested. Different mediums were tested (DMEM and RPMI with β-me), with different concentrations of plate-bounded anti-CD3 (2.5 µg/mL and 5 µg/mL) and soluble anti-CD28 (0.4 µg/mL and 2 µg/mL), at a cell density of 1x106 cells/mL. The cells were plated in a 24-well plate and were counted using trypan blue. The results presented are referred to an activation and expansion period of 96h (72h + 24h). The percentage of recovered cells was calculated based on the initial number of cells cultured per well. 44 The T-cell activation profile was evaluated using CD44 and CD62L, that allow to distinguish 3 subpopulations [naïve (CD44intCD62L+), effector memory (CD44hiCD62L-) and central memory (CD44hiCD62L+)], CD69 and PD1, which are markers of activation and PD1 also known as an exhaustion marker. A marker to exclude dead cells was used in this stain. A relevant percentage of CD4+CD8+ T-cells were found on the mLN cells stimulated with 0.4 µg/mL of anti-CD28; these percentages were minor when cells were stimulated with 2 µg/mL of CD28 (Figure 17A). Moreover, it has been described that in vitro T-cell stimulation can lead to the generation of CD4+CD8+ T-cells (133) and these may also be found in the mLN, though at low percentages (1 – 10% of total T-cells) (133). Regarding CD44/CD62L expression, in both concentrations of anti-CD28, most of the CD4+ and of the CD8+ T-cells presented a central memory phenotype (CD44hiCD62L+; Figure 17B). In the same way, for both anti-CD28 concentrations tested, the majority of the CD4+ T-cells were positive for the activation markers, CD69 and PD1. Regarding CD8+ T-cells, these present a less activated phenotype when stimulated with 2 µg/mL of anti-CD28 in comparison to 0.4 µg/mL, as seen by the lower percentage of PD1+ and of CD69+ cells (Figure 17C). Overall, there was a higher number of recovered cells using 0.4 µg/mL of anti-CD28, but these presented higher percentages of CD4+CD8+ T-cells. For this reason, the condition with 2 µg/mL of anti-CD28 was chosen for future assays, despite CD8+ T-cells were not as activated as the ones in 2 µg/mL of anti-CD28. 51 is described that tumour cells can release tumour-derived exosomes expressing FAS ligand and also PDL1 that blocks T-cell activation and proliferation and promote T-cell apoptosis (146,147). It would be important to look at these tumour-derived exosomes, and evaluate the expression of PD-L1 and PD-L2, that could be achieved by western blot, after a differential centrifugation to isolate the exosomes. Evaluating the exosomes released by GBM cells with high and low expression of HOXA9 , could give more information about the expression of these markers in GBM. Moreover, antiand pro-inflammatory cytokines can serve as growth and survival factors to stimulate tumour progression (107). Together, in the presence of HOXA9 , the expression of PD-L1, with the expression of anti-inflammatory genes ( TGFB , IL6 and CCL2 ) in GBM cells, may contribute to the immunosuppressive microenvironment of GBM. A study in breast cancer shows that blockade of IL1β leads to an increase in CD8+ T-cells and to its infiltration in the tumour microenvironment, that consequently, leads to tumour regression. Moreover, they show that blocking of IL1β, previous to anti-PD1 therapy, increases the response to this immunotherapy and abrogated tumour progression (148). In the present study, using the U251 silencing model, it was observed an increased PD-L1 expression and a decreased IL1B expression in the presence of HOXA9. This goes along with the observation, for this same cell line (U251), that, an increase expression of HOXA9 is related with an increased sensitivity to anti-PD1 therapy. Immune checkpoint inhibitors have achieved remarkable success in cancer treatment; however, not all patients show clinical benefit. Several factors can affect and predict the efficacy of ICIs, as tumour mutation burden, immune checkpoint ligands (such as PD-L1) expression levels, density of TILs and mismatch-repair deficiency in some cancers (124,125). Nevertheless, it is still needed to find new reliable predictive biomarkers of ICIs response, to allow a precision immunotherapy and to better understand and overcome resistance mechanisms. One study already tried to evaluate the above-mentioned predictive biomarkers in the response of GBM patients to ICIs therapy, using TCGA, but found inconsistent patterns (either suggesting resistance or susceptibility to ICIs), raising the need to find other predictive biomarkers for ICIs in GBM (149). Therefore, in this work was study the HOXA9 predictive value for ICIs therapy. Regarding the immunotherapy with anti-CTLA4, no significant differences were found, neither in the response to ICIs, neither in the effect of HOXA9 to this therapy, consistent with the results from the expression of CD80 and CD86 in the GBM cells. About anti-PD1 therapy, it did not increase the T-cell mediated cytotoxicity, though, in U251 silencing model, there was a minor, but significant decrease in the sensitivity to anti-PD1 therapy with the silencing of HOXA9 , but this effect was not seen in the other GBM cell lines. This suggest that the effect of HOXA9 in anti-PD1 therapy is cell line dependent. 52 Anti-CTLA4 acts by activating anti-tumour immunity by promoting T-cell proliferation. Anti-PD1 blockade can induce T-cells proliferation and cytokine production. Also, it is described that, in melanoma, treatment with anti-CTLA4 can replace the numbers of effector and memory CD4+ and CD8+ T-cells (150). T-cell apoptosis was described as an important mechanism of cancer immune resistance, what can contribute to the decrease of T-cell number in the tumour microenvironment and lead to resistance to immunotherapies (151). In these co-cultures studies, ICIs therapy did not impact on T-cell survival and apoptosis; that could be explained by the percentage of apoptotic and dead T-cells which decrease the number of functional T-cells. It should be noted that the concentrations of anti-CTLA4 and of anti-PD1 used were based on the literature (152–154). As were not tested before in our assays, it is important to perform a dose-response curve, and evaluate the responses of tumour and activated T-cells. Importantly, including a positive control for the ICIs response in the co-culture assays, will confirm the efficacy of the ICIs in each assay, as a melanoma cell line, since it is described that melanomas respond well to ICIs therapy. Moreover, in the co-culture assays, could be tested a combination therapy, in which will be tested anti-CTLA4 and anti-PD1, since it is described that combination of these two ICIs improve the outcome in several cancer patients, namely in melanoma patients (150,155–158). Preclinical studies in melanoma models show that, upon treatment with anti-CTLA4, the Teff/Treg ratio increases in the tumour microenvironment and treatment with anti-PD1 can overcome inhibition by Treg cells (150). Although, no differences were observed regarding HOXA9 effect in Treg cells population with the use of ICIs, but required further confirmation with the increase in the number of independent assays. Evaluate Treg cells phenotype, could give extra information in terms of the immune cells function, as is described that in gliomas, Treg cells expressing higher levels of PD1 show an exhausted phenotype that fails to suppress T-cells proliferation (150). Thus, PD1 expression is used to identify exhausted T-cells (139). To distinguish Treg exhausted cells form T-cell exhaustion, Tregs can be first sorted and then evaluate its exhaustion by RT-qPCR, analysing pathways/genes involved in the PD1/PDL1 axis (e.g. evaluating the recruitment of phosphatases like SHP2) (159). To move forward in this study, murine models are of extreme importance, since allow the exploitation of the GBM microenvironment in an immunocompetent mouse. This model permits the evaluation of the infiltrating immune cells, namely T-cells, the identification of the different sub-populations, and the assessment of the ratio between effector and Treg cells. Moreover, allows the study of mice survival upon ICIs therapy, with all the complexity of an in vivo model and of the tumour. For that, being able to manipulate the Hoxa9 expression levels in a murine GBM cell line would be essential, though this was a 53 goal not achieved in the context of this project. Nevertheless, human GBM cell lines can also be used in in vivo studies upon humanization of immunocompromised mice [such as NOD.CgPrkdc scid Il2rgtm1Wjl/SzJ (NSG) mice] with human, pre-activated PBMCs (160,161). With this model, it is possible, also, to evaluate the infiltration of immune cells, using subcutaneous models, and to evaluate the survival of mice, using intracranial models, in naïve treatment conditions and upon treatment with ICIs. This approach has an advantage of use human cells, but in return this can lead to graft-vs-host disease and make impossible the development of a normal immune response since the PBMCs have to be engrafted already activated and, for example, memory T-cells are more prone to be activated (162). 54 6. Conclusions and Future Perspectives Clinical evidences suggest that activation of some oncogenic pathways is associated with an immunosuppressive microenvironment and resistance to immunotherapy. Previous work showed that HOXA9, an oncogene in GBM, downregulates pathways associated with immune responses, suggesting that this gene may have a role in immune evasion and in the resistance to immunotherapy. However, no link was previously established between the expression of HOXA9 and immune evasion and/or response to immunotherapies. By using in vitro approaches and cell lines overexpressing or silenced for HOXA9 expression, this work suggests that HOXA9 might increases immunosuppression in GBM, by modulating the expression of some cytokines and chemokines, namely IL1B , IL8 and CCL2 ; and also, immune checkpoint ligands, PD-L1 and PD-L2. Moreover, suggests that GBM cells can escape to immune surveillance, although a partially effective immune response against GBM cell lines seems to exist and that HOXA9 leads to a minor, but significant, increase in sensitivity to anti-PD1 therapy, in U251 cell line but not in other cell lines (U87 and GL18). Also, regarding the T-cell population, HOXA9 expression and ICIs therapy did not impact in the survival, neither in its subpopulations. 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Overcoming Current Limitations in Humanized Mouse Research. J Infect Dis. 2013;208(suppl_2):S125–30. 68 8. Supplementary Figures Supplementary figure 1 – Microarray data, regarding immune-related genes validated by RT-qPCR: cytokines, chemokines and immune checkpoint ligands. A) Expression of cytokines and chemokines: IL1B ; TNFA ; IFNG ; TGFB1 ; IL8 ; IL10 ; IL6 and CCL2 . B) Expression of immune checkpoint ligands: PDL1 (PD1 ligand) and CD86 and CD80 (CTLA4 ligands). The depicted data is from Pojo M. et al. , 2015 (46), but was analysed in the context of this thesis to evaluate the gene expression of these cytokines, chemokines and immune checkpoint ligands. 69 Supplementary figure 2 – IFNG is not detected in human GBM cell lines. Represented the agarose gel from expression of IFNG in human GBM cell lines cultured in A) DMEM and B) in RPMI; analysed by RT-qPCR. These human GBM cell lines were considered as negative for IFNG expression since the RT-qPCR was contaminated by several unspecific links, besides the presence of bands of interest, make it not possible to quantify the expression of IFNG in these cell lines. hPBMCs – positive control; neg – negative control for the RT-qPCR reaction (RT-qPCR mix without cDNA). 70 Supplementary figure 3 – IL10 is not detected in human GBM cell lines. Represented the agarose gel from expression of IL10 in human GBM cell lines cultured in A) DMEM and B) in RPMI; analysed by RT-qPCR. These human GBM cell lines were considered as negative for IL10 expression since the RT-qPCR was contaminated by several unspecific links, besides the presence of bands of interest, make it not possible to quantify the expression of IL10 in these cell lines. hPBMCs – positive control; neg – negative control for the RT-qPCR reaction (RT-qPCR mix without cDNA). 71 Supplementary figure 4 – PD1 is not detected in human GBM cell lines. Represented the agarose gel from expression of PD1 in human GBM cell lines cultured in A) DMEM and B) in RPMI; analysed by RT-qPCR. These human GBM cell lines were considered as negative for PD1 expression since the RT-qPCR was contaminated by several unspecific links, besides the presence of bands of interest, make it not possible to quantify the expression of PD1 in these cell lines. hPBMCs – positive control; neg – negative control for the RT-qPCR reaction (RT-qPCR mix without cDNA). 72 Supplementary figure 5 – Positive controls for staining with CD80 and CD86. A) CD80 positive control: activated T-cells were stained with CD80 antibody, according to surface stain for flow cytometry. B) CD86 positive control: fresh hPBMCs were stained with CD86 antibody, according to surface stain for flow cytometry. A-B) The doublets were excluded (i) and the live cells (ii) and the population of cells of interest were selected (iii). Lastly, was selected the population of cells positive for CD80 or CD86 (iv). (v) Represents the FMO (fluorescence minus one) of each antibody. 73 Supplementary figure 6 – Expression of CD45 in human GBM cell lines. The doublets were first excluded (i) and then selected the cell population of interest (ii). Histograms with the expression of CD45 in each human GBM cell line (U87, GL18 and U251 cell lines) are depicted (iii). Blue histograms represent the human GBM cell line stained with CD45 and the red histograms represent the non-stain (ns) control. 74 9. Supplementary Tables Supplementary Table 1 – Primers used for RT-qPCR, with the respective Tm and the length of the products. 75 Supplementary Table 2 – Antibodies panel for the molecular analysis by Flow Cytometry. 76 Supplementary Table 3 – Relative expression of IL1B in human GBM cell lines, obtained by RT-qPCR. 83 Supplementary Table 10 – Relative expression of CD80 in human GBM cell lines, obtained by RT-qPCR. 84 Supplementary Table 11 – Relative expression of PDL1 in human GBM cell lines, obtained by RT-qPCR. 85 Supplementary Table 12 – Mean fluorescence intensities (MFIs) of CD86 in the human GBM cell lines, obtained by flow cytometry. The fold change was not determined, when the MFI of the ns (non-stain) was equal or superior to the MFI of the samples stained. In these cases, cells were considered as negatives for this marker. Supplementary Table 13 – MFIs of CD80 in the human GBM cell lines, obtained by flow cytometry. The fold change was not determined, when the MFI of the ns (non-stain) was equal or superior to the MFI of the samples stained. In these cases, cells were considered as negatives for this marker. 86 Supplementary Table 14 – MFIs of PDL1 in human GBM cell lines, obtained by flow cytometry. NS – non-stain Supplementary Table 15 – MFIs of PDL2 in human GBM cell lines, obtained by flow cytometry. NS – non-stain