The Role of Sema3a in Inflammation Mediated Tumor Progression Master Thesis KTH Royal Institute of Technology Stockholm, June 2015

The Role of Sema3A In Inflammation Mediated Tumor Progression Master Thesis KTH Royal Institute of Technology Stockholm, June 2015

Emma Nygren

Supervisor

Charlotte Rolny, Cancer Center Karolinska

Co-Supervisors

Majken Wallerius, Cancer Center Karolinska Tatjana Wallmann, Cancer Center Karolinska

Examiner

Peter Nilsson, KTH SciLife Laboratory

Abstract

In the tumor microenvironment there are many different cell types present and among these, immune cells display a large proportion. Central players in the tumor immunity are macrophages that come in two different phenotypes, the M1 and M2 macrophages. M1 polarized macrophages are tumor suppressive while M2 polarized macrophages support tumor growth. The factors that contribute to the skewing of macrophages from one phenotype to another are under investigation. Interestingly, our lab has identified Immune Semaphorin 3A (Sema3A) as a participating player in regulating the accumulation of anti-tumoral M1 macrophages leading to a suppression of tumor growth.

In light of these data this thesis has focused on the role of endogenous Sema3A in the tumor microenvironment. A tumor cell line expressing shRNA against Sema3A mRNA was generated using lentiviral mediated gene therapy. This knockdown cell line showed 72 % lower mRNA expression compared to control and was evaluated in vivo by monitoring tumor progression in female BALB/c mice. The immune cell composition of the tumors was analyzed using flow cytometry. The results from the in vivo experiment show that endogenous Sema3A has a limited effect on tumor progression. A slight shift to a more tumor supportive immune profile was observed in the knockdown tumors. Moreover, a virus for transducing cells to overexpress Sema3A under a suitable promoter for systemic delivery was generated.

1 Sammanfattning

Många olika sorters celler är närvarande i tumörers mikromiljö och immunceller utgör en stor andel av dessa. Makrofager är centrala spelare i tumörimmunförsvaret och dessa kan indelas i olika aktiveringsgrader eller fenotyper, M1 eller M2 makrofager. M1 polariserade makrofager är tumörsuppressiva medan M2 makrofager bidrar till tumörtillväxt. De faktorer som reglerar skiftningen mellan M1 och M2 fenotyperna är under utredning. Vårt labb har identifierat att Immunsemaforinen 3A (Sema3A) spelar en roll i att reglera ackumuleringen av antitumorala M1 makrofager vilket leder till hämmad tumörtillväxt.

Med denna information som bakgrund har detta examensarbete fokuserat på Sema3As roll i tumörmikromiljön. Med hjälp av lentivirusmedierad genterapi skapades en tummörcellinje som uttrycker shRNA mot Sema3AmRNA. Denna cellinjes visade 72 % lägre Sema3A mRNA uttryck jämfört med kontroll och utvärderades sedan in vivo genom att följa tumörtillväxten i BALB/c mushonor. Immuncellsammansättningen i tumörerna analyserades sedan med hjälp av flödescytometri. Resultaten från in vivo experimentet visar att endogent Sema3A har en begränsad effekt på tumörutvecklingen. En något mer tumörgynnande immunprofil observerades i de tumörer där Sema3Auttryck var minskat. Utöver detta skapades också ett lentivirus för att transducera celler så att de överuttrycker Sema3A under en passande promotor för systemisk tillförsel.

Table of Contents Abstract ...... 1 Sammanfattning ...... 2 Table of Contents ...... 3 1. Introduction ...... 4 1.1 The project aims and hypothesis ...... 4 1.2 Cancer as a general concept ...... 5 1.3 Summary of key immune system aspects ...... 7 1.4 The link between the immune system and cancer ...... 7 1.5 The role of macrophages in cancer ...... 8 1.6 Immune Semaphorins as regulators ...... 9 2. Techniques and models ...... 10 2.1 Lentiviral mediated gene therapy ...... 10 Packaging of virus particles ...... 11 Harvest and use of generated virus ...... 12 2.3 Gateway cloning ...... 12 2.4 The use of shRNA for gene knockdown ...... 14 2.5 The cell line and mouse model ...... 15 2.6 Flow Cytometry ...... 16 3. Results ...... 17 Overexpression of Sema3A in tumor cells ...... 17 Knockdown of Sema3A in tumor cells ...... 19 4. Discussion ...... 24 4.1 Knockdown of endogenous Sema3A expression in 4T1 tumor cells ...... 24 4.2 Technical evaluation ...... 24 4.3 Future prospectives ...... 25 Acknowledgements ...... 26 5. References ...... 27 Appendix 1 – Materials and Protocols ...... 30 Gateway cloning ...... 30 shRNA ...... 30 Transfection ...... 31 Transduction ...... 31 mRNA-expression ...... 31 Proliferation Assay ...... 32 In Vivo experiment ...... 32 Flow cytometry ...... 32 Appendix 2 – Vector Maps ...... 34

3 1. Introduction

This master thesis was performed in the field of cancer biology and tumor immunology. In this section an introduction to the subjects that are central is presented along with a description of the aims and hypothesis of the project.

1.1 The project aims and hypothesis The importance of immune cells in the tumor microenvironment has been increasingly acknowledged over the recent decades (1) and there is now an interest of developing immune therapies against cancer. To do this, there is a need to understand the mechanisms behind tumor immunology and therefore it is important to study the interactions between tumor cells and immune cells. The understanding of these mechanisms can be used to develop new therapy strategies to treat cancer. The protein Immune Semaphorin 3A (Sema3A) has been identified to be involved in some of the mechanisms that underlie tumor immunology. The overall aim of this thesis project was to investigate the role of Sema3A in breast cancer progression. The work was divided into two technical sub aims listed below:

1. Cloning of Sema3A into a lentiviral Phosphoglycerate kinase (PGK) promoter vector to produce high quality concentrated virus for systemic delivery. 2. Knocking down of Sema3A in 4T1 tumor cells in order to study the contribution of endogenous Sema3A to tumor progression.

The motivation for the first aim was that in order to evaluate the therapeutic potential of Sema3A, it had to be cloned under a suitable promoter. When using lentiviruses systemically (injected into the blood stream) they have high transduction efficacy in cells of the liver. Since Sema3A is a secreted protein, overexpression of it in the liver leads to elevated blood levels and hence, elevated levels in the tumor tissue as well. The promoter previously used in the lab, CMV, has been reported to work poorly in hepatocytes (liver cells) and therefore, there was a need to overexpress Sema3A under another promoter. For this purpose

4 PGK was chosen since it has been shown to maintain a high activity in hepatocyte cells (2,3).

The motivation for the second sub-aim was to study the importance of endogenous Sema3A. Mazzone et al. showed in 2013 (4) that endogenous Sema3A was induced by hypoxia and promoted tumor progression by trapping macrophages in hypoxic areas and skewing them into an M2 phenotype. These data seem contradictory to previous reported results (5–7), so that it was important to elucidate the role of knockdown of endogenous Sema3A in tumor cells.

For both sub-aims many different bio-techniques in a series of several steps were used. These steps include the production of lentiviral vectors using cloning methods and finally the validation of the generated cell lines in vivo. The techniques and models used are described in section 2. Own recent data point out that Sema3A mediated anti-tumoral response is orchestrated via macrophages and induces M1 macrophage proliferation in the tumor microenvironment. With this body of information, the underlying hypothesis for this master thesis was that by knocking down Sema3A, the opposite effect would occur meaning that with loss of Sema3A, an M1 immune profile of the tumors would not be induced and hence, tumor progression would be sustained.

1.2 Cancer as a general concept Around one third of the population in Sweden will get a cancer diagnosis during their lifetime. In 2013, 60 000 people were diagnosed with cancer and the number increases every year, partly because the population gets older. Today, about 65 percent are cured from cancer but this number varies a lot between different types of the disease. The gender distribution is quite equal, with 51 percent male and 49 percent female patients. For men, the most common type is prostate cancer while breast cancer dominates for women (8).

Breast cancer accounts for 30 percent of all cancer incidences in women and most women who are diagnosed are over 50 years old. If the primary tumor is

5 discovered early, the prognosis for the patient is usually good since the tumor can be removed surgically. Detection of cancer in a later stage, where cancer cells had time to spread and metastasize, is linked to low survival rates. The standard treatments besides surgery include cytostatics, surgery and radiation (9).

Cancer is a disease in which cells of the own body acquire capabilities that enable them to circumvent many of the regulatory processes involved in for example cell cycle and cell death. These capabilities are acquired through genetic alterations. Many cancer-associated mutations are well known and have been heavily explored but many more genetic alterations that are utilized and/or required for malignancies to occur are under investigation (10).

The general notion that cancer is a disease involving malignant cells that grow uncontrollably and create a tumor has been revised in the last decades (Figure 1.1A). Accumulation of point mutations is indeed central to the cancer development but the malignant cells also need to affect and interact with stromal cells for the tumor to progress. Endothelial cells, pericytes and fibroblasts are examples of some of the cells that play important roles in the tumor microenvironment. In the recent years, it has become evident that cells of the immune system are key regulators of tumor progression (Figure 1.1B) (11). The exact role of all these cells and how they are recruited and/or activated by the cancer cells is not fully understood yet and this is a major research subject in the field of tumor immunology. Furthermore, it is known that the tumor microenvironment changes during different stages of cancer development, for example when cancer arises or metastasis occur (1,12). Besides local effects in the close proximity to the primary tumor, cancer cells also have the capability to affect cells and tissues at distant sites of the body (13,14).

Figure 1.1: A. The Hallmarks of Cancer, Hanahan and Weinberg (2011), Cell Review (1) B. Depiction of the tumor microenvironment and the interaction of stromal cells with tumor cells (14).

1.3 Summary of key immune system aspects The immune system is our natural defense mechanism against infections by pathogens and plays an essential role in wound healing and recovery after injury. The cells of the immune system, the leucocytes, are divided into the innate and adaptive immune system. The innate immune system is unspecific and hence, does not require a specific antigen to be presented to get activated. Here, cells of the myeloid linage (e.g. neutrophils, dentritic cells and macrophages) are included. Cells of the lymphoid linage like T-cells, B-cells and NK-cells are part of the adaptive immune system. The adaptive immune system is specific and the cells need to be activated by specific antigens to exert their function. Antigen- presenting cells or APCs (dendritic cells and macrophages) are cells capable of engulfing pathogens, processing them and presenting the associated antigens to lymphocytes to create a response against these pathogens (15).

1.4 The link between the immune system and cancer This report focuses on the immune cells that are associated with the tumor microenvironment. These immune cells normally recognize cancer cells and create an anti-tumoral or tumor-suppressive response to eradicate them. Cells that are usually considered tumor-suppressive are NK-cells and cytotoxic T-cells. However, cancer cells have the capability to avoid immune destruction, “hijack” the immune system and thereby creating a microenvironment supportive for

7 tumor progression (16). Cell types associated with tumor growth and progressions are regulatory T-cells (with a general immune suppressive function) and pro-tumoral macrophages (see following section below). The specific molecular and cellular interactions that determine if there will be an immune response against the tumor or if the tumor has the capability of avoiding immune destruction are under investigation (11,17).

1.5 The role of macrophages in cancer Macrophages are a good example of the complicated relationship between the immune system and cancer. They are part of the innate immunity and have many important roles in different immune responses in the body. One role for macrophages is to take part in the first line of defense against pathogens and other infectious agents. These macrophages, known as having an “M1”- phenotype, are pro-inflammatory, highly phagocytic and present antigens to cells of the adaptive immune system (see description of APCs in the immune system section above). Another type of macrophages is involved in wound healing and promotes tissue regeneration. This type is described as having an “M2”- phenotype (18). Macrophages that are present in the tumor microenvironment are termed tumor associated macrophages (TAMs). TAMs acquire an M2- phenotype, which is tumor promoting and supports tumor growth by providing cancer cells with growth and tissue remodeling factors to facilitate the metastatic process. These M2-macrophages also help the tumor cells to avoid immune destruction by suppressing other immune cells. The factors that are involved in the skewing of macrophages from one phenotype to another include cell-cell interactions, cell-matrix interactions and cytokine stimulations (17,19).

There are various strategies to identify the different phenotypes of macrophages based on the expression of surface molecules. Beside the common expression of F4/80 (member of the G protein-coupled receptors) murine M1 macrophages express high levels of Major Histocompatibility Complex II (MHC class II) while M2 macrophages show high expression of Mannose Receptor C type 1 (MRC1). Moreover, M1 macrophages are also known to express increased levels of Cluster of Differentiation 86 (CD86) and CD80 that are important for antigen

8 presentation. All the markers used for cell identification are listed in Materials and Protocols (Appendix 1).

1.6 Immune Semaphorins as regulators The group of proteins called semaphorins has been identified as participators in the regulation of tumor progression. These proteins are known to have diverse functions and are involved in cell migration control and tissue architecture maintenance. In recent studies, some of the semaphorins have been shown to participate in the regulation of the immune response during inflammation and are referred to as immune semaphorins. Semaphorins are either membrane bound or secreted proteins. Sema3A is a secretory protein and binds to receptor complexes composed of the co-receptor Neuropilin 1 and members of the Plexin A (A1 and A4) receptor family. Neuropilin 1 and Plexin A receptors are expressed by macrophages and tumor cells and upon binding, downstream signaling is transmitted (20,21).

Rolny and colleagues, as well as others have shown that Sema3A inhibits angiogenesis and this is associated with inhibition of tumor progression (5). However, recent data by Giacca and colleagues imply that Sema3A mediates recruitment of monocytes in vitro that have an anti-tumoral effect when injected into tumors (22). On the other hand, Casazza et al. reported that Sema3A recruits macrophages into hypoxic areas and thereby shifting them towards an M2- phenotype (4).

In order to elucidate these controversial data Rolny and colleagues have created a mouse mammary tumor cell line that overexpresses murine Sema3A. The overexpression clone was generated with the use of lentiviral mediated gene therapy. The study by Giacca and colleagues, that implied Sema3A mediated recruitment of monocytes, raised the interest of studying the immune cell profile of Sema3A overexpressing tumors (22). Our data show that the overexpression of Sema3A correlates with inhibited tumor growth by mediating a pro- inflammatory program. Moreover, macrophages adopt an M1 phenotype and promote the recruitment of cytotoxic cells such as NK cells and CD8+ T-cells

9 (Figure 1.2). Interestingly, Sema3A overexpression induces M1 proliferation while it inhibits proliferation of M2 macrophages (7).

Figure 1.2: Interaction of Sema3A with cells of the immune system. (7,20)

2. Techniques and models

In this section, the techniques used throughout the work for the thesis project will be described. Different alternative strategies are also presented and an explanation of why a certain choice of method was made will be given with advantages and disadvantages. This section is intended to give theoretic information and therefore, detailed laboratory protocols and information about the experimental conditions are not stated here but instead presented in Materials and Protocols (Appendix 1).

2.1 Lentiviral mediated gene therapy There are different strategies that can be used to produce cell lines with a specific genetic alteration. One is to use viruses that infect the cells and thereby introduce new genetic material. Viruses from different “families” are being used in biotechnology but for this thesis work lentivirus, a member of the retrovirus family, was used. The advantage of using lentiviruses is that this method results in a stable integration of genetic material into the target cell genome. An alternative approach would be the use of adenoviruses and although the transduction efficacy is high, there is no stable integration of genetic material into the genome and is thus not suitable to generate stable cell lines. Lentiviruses integrate genes into the host genome via reverse transcription mediated by a

10 virally encoded retro transcriptase enzyme. There are other retroviruses used in gene transductions but compared to other members of this family, lentiviruses have the advantage of infecting non-dividing cells. Their pre-integration complex form can pass through an intact nucleus membrane, while other retroviruses only infect dividing cells (23–25).

Packaging of virus particles The initial step when creating lentivirus with the correct target gene is a transient transfection of a cell line that is capable of creating viruses. HEK 293T cells are suitable for this step (Figure 2.1)(26). For them to be able to create a complete virus, they need to be transfected with plasmids coding for viral envelope and packaging proteins along with the vector containing the gene of interest. The transient transfection can be performed using several different methods. The crucial part is to pass the negatively charged DNA constructs through the uncharged cell membrane(27).

Electroporation can be used as one approach. This method creates temporal cavities in the cell membranes allowing the vectors to be taken up. The disadvantages of this method are low cell survival, requirement of specific instrumentation and no control of the proportions of the different vectors taken up. Lipofectamine is a molecule that, upon binding to DNA vectors, neutralizes them and enables the transport through the cell membrane. The technique is simple in use but has the disadvantage of variation in efficacies in different cell types and has even lower efficacy in serum containing medium. The method used in this project was calcium-phosphate co-precipitation. This method has the drawbacks of requiring very precise concentrations of the reagents and reaction pH but the advantages are that the reagents are cheap and a high efficacy can be achieved (28,29).

11 Harvest and use of generated virus After transfection the cells that have taken up the correct plasmids will start to produce virus. Viruses are secreted into the medium and the harvested medium is used for the subsequent viral transduction. If effective viruses have been produced, they will efficiently infect the target cells and insert the gene of interest into the genome of the cells. To achieve the right insertion it is important that the gene of interest has the correct insertion sequence on both sides and that the virus expresses a functional reverse Figure 2.1: Lentiviral packaging using 293T cells transcriptase enzyme. followed by transduction of a target cell.

However, it is also required that the insertion takes place on a suitable site in the genome that is accessible for transcription. To assess if transduction was successful, cells are cultured in an antibiotic containing medium. Since the vector containing the target gene also contains a selection marker for antibiotic resistance only cells that have been successfully transduced will survive (25).

2.3 Gateway cloning As described in section 1.7 of this thesis report, one of the goals of the project was to create efficient lentiviruses capable of transducing mammalian cells for overexpression of Sema3A under a different promoter (PGK instead of CMV). To generate a Sema3A overexpression vector with this promoter, cloning with different vector constructs was performed. One vector construct contains the backbone including the promoter and selection marker as well as sequences needed for lentiviral packaging. The second vector contains the target gene (Sema3A) that has to be inserted into the backbone of the first vector. The most

12 common way for this step is to use restriction enzymes that cut sites flanking the gene of interest in one vector and a sequence to be replaced in the other vector. Subsequent addition of DNA ligase results in the ligation of the gene into the backbone creating the desired product. Although this approach is straightforward there are some significant challenges. One difficulty is that at least two different restriction enzymes are required to ensure the correct insertion and these often need different reaction conditions to work properly. Furthermore, the final ligation step requires the right reaction conditions (e.g. salt concentration, temperature) and therefore, the correct product is not always generated (28).

To facilitate the cloning, there is a commercially available cloning system called Gateway cloning (30). This method was developed based on the naturally occurring site-specific recombination reaction that takes place when phage viruses integrate into the genome of Eshcerichia coili (E. coli). There are certain sites in the genomes of the organisms called attP (in the phage) and attB (in E. coli) that will recombine and generate what is called attL and attR sites with the help of specific enzymes. The process can also take place in the opposite direction when the bacteriophage is exerted from the bacterium genome.

In the Gateway-cloning system, the same sites and enzymes are utilized but some refinements of the process have been developed. To get a direction specific reaction, the att-sites have been optimized so that two versions (1 and 2) of each site exist. The design of these att- Figure 2.2: The Gateway cloning system with its sites ensures that an attB1 only possible reaction pathways as described by the reacts with an attP1 resulting in an manufacturer (31). attL1 site, etc (Figure 2.2). The reaction conditions and the enzymes used have also been optimized to give highest possible yields (31,32). In this thesis work, the “LR-reaction” was utilized. An entry clone with the gene coding for Sema3A

13 flanked by attL1 and attL2 sites was used together with a destination vector containing attR1 and attR2 sites. The destination vector backbone contained the chosen promoter sequence as well as the desired selection marker. Vector maps can be viewed in Appendix 2.

2.4 The use of shRNA for gene knockdown The concept of gene knockdown is central for the second part of this project. It is important to stress out the difference between a knockdown and a knockout of a gene. After a knockout the gene is completely silenced while after a knockdown its expression level is only lowered to some extent. There are a variety of different ways to achieve a knockdown of gene expression of a certain gene. One common method is to utilize small RNA molecules that can bind to and block the translation of mRNAs. These small RNAs initiate the process of RNA interference (RNAi) in the cells to destruct specific mRNA templates. RNAi is a natural process in eukaryotes used in development and protection against invading viruses (33).

RNAi gene silencing is initiated by the transcription of RNA sequences from the genome that form double stranded RNA molecules. In the cytoplasm the enzyme dicer recognizes the long double stranded RNA molecules and cleaves them into short double stranded RNA molecules. These short RNAs come in different types known as microRNAs (miRNAs), short interfering RNAs (siRNAs) and short hairpin RNAs (shRNAs) and differ in their double stranded structure. If one of the two double strands recognizes a sequence in a circulating mRNA, the RNA- induced silencing complex (RISC) is formed. If there is a weak binding the mRNA will be physically blocked from translation but if the interaction is strong enough the catalytically active part of the RISC will cleave the mRNA and degrade it. In both cases, the mRNAs are post transcriptionally silenced and protein expression will be lowered (34,35).

As mentioned in the previous paragraph, one of the RNAi strategies is to use shRNAs. shRNA, as the name indicates, is a short RNA sequence that back folds on itself to create a double stranded molecule containing the matching code of its targeting mRNA. shRNA is more stable compared to mi- or siRNA leading to

14 increased degradation of the target mRNA without losing its own structure during the delivery and RNAi process. Since the shRNAs are small, they only target part of the mRNA of interest, so that several different shRNAs are produced for the same mRNA target (34). The different shRNAs have a varying capability of blocking the mRNA translation through either one of the two described pathways.

The shRNA can be introduced to the cell by different methods. If only a transient silencing is needed, a non-integrating transfection method can be used. However, if a stable knockdown Figure 2.3: Lentiviral mediated gene therapy. From is desired, the gene coding for the virus to mRNA blockage (40). shRNA has to be integrated into the host cell genome. This can be achieved through the lentiviral mediated gene therapy technique (36) (Figure 2.3).

2.5 The cell line and mouse model The cell line used for the in vitro and in vivo experiments in this thesis work is a mouse mammary tumor cell line called 4T1. It was isolated from a mammary carcinoma of a BALB/c mouse and can be successfully transplanted to mice from the same strain. The cell line is highly tumorigenic and gives rise to spontaneous metastases at several sites including lung, liver and brain. Although it is an artificially created primary tumor, the way it spreads and metastasizes is similar to that of human mammary cancers. It is easy to genetically manipulate the 4T1 tumor cells both in vitro and in vivo and they have been used in studies related to immunotherapy (37). Originally the 4T1 tumor cell line was established from the inbred strain BALB/c. This strain does not develop spontaneous mammary

15 tumors in a high extent and is therefore suitable for artificial mammary tumor studies (38). In the in vivo experiment conducted in this thesis project, BALB/c mice were injected with 4T1 tumor cells in the mammary fat pad according to the standard protocol for 4T1 tumors (37). The tumors developed over three weeks and were monitored by measuring the tumor volume. After three weeks mice were sacrificed and tumors were harvested.

2.6 Flow Cytometry Flow cytometry is a laser-based method utilized to analyze the cell content of a sample. The sample is passed through a thin tube at a flow rate that allows for one cell to go through at a time. The cells are passed through lasers and detectors that measure forward and side scattered light as well as fluorescent emitted signals. The forward and side scattering gives information about size and granularity of the passing cells. A fluorescent signal can either be detected from internal fluorescence (e.g. endogenous proteins) or from an externally bound fluorophore linked to an antibody that binds to a certain cell surface receptor (39).

For this project, flow cytometry was applied to cell suspension samples of the 4T1 tumors harvested from sacrificed mice. Four different panels of antibodies coupled to fluorophores were applied. Three panels were used to characterize myeloid cells and one to characterize cells of the lymphocyte linage. For all panels, the gating strategy started by first using side and forward scatter to gate for cells of correct size and ensure single cells. Then, to sort out dead cells the marker 7-AAD (7-aminoactinomycin D) was used, which is internalized in cells with leaky membranes. CD11b identifies cells of myeloid heritage and F4/80 binds to murine macrophages. Lymphocyte antigen 6 complex, locus G (Ly6G) helps to distinguish neutrophils from macrophages since neutrophils are Ly6Ghigh and macrophages are Ly6Glow. Myeloid derived suppressor cells (MDSCs) were separated based on the expression of Ly6C (also known as Gr-1), to distinguish monocytic from granulocytic MDSCs.

16 To further separate macrophages into M1 or M2, markers for activation stages were used. A high expression of MHC class II is related to an M1 phenotype as well as CD11c, CD86 and CD80. MRC1 (also known as CD206) was used as an M2 marker. As mentioned earlier one panel was used to characterize the lymphocyte content of the tumors. Expression of CD45 allows the separation of lymphocytes from tumor cells. Furthermore, CD49b is as a surface protein expressed by NK- cells while CD3 is expressed by all T-cells. Cytotoxic or regulatory T-cells can be distinguished based on the expression of CD8+ or CD4+.

3. Results

Overexpression of Sema3A in tumor cells One of the sub-aims of this project was to generate a high quality lentivirus for highly efficient transduction of cells to overexpress Sema3A under a promoter suitable for systemic delivery. Gateway cloning was performed to generate a lentiviral vector with the gene encoding for Sema3A under the correct promoter (PGK). To test if the gateway cloning had worked, restriction enzyme digestion of the generated construct (PGK-S3A) along with the original PGK plasmid as control plasmid (PGK-CTR) was performed. The enzyme used (Kpn1) would only be able to generate one cut in the original plasmid, linearizing it. In the cloned plasmid however, three cuts would be made generating two smaller fragments (around 2200-2400 bp) and one larger fragment (around 6100 bp). Total sixes of the PGK-CTR and PGK-S3A vectors were 9778bp and 10556 bp respectively. Using agarose gel electrophoresis, generated fragments during restriction could be compared (Figure 3.1A). The uncut PGK-S3A clones were larger than the control and the restriction fragments of correct sizes were generated indicating that the cloning had worked. The reason for two bands occurring in the uncut version of original plasmid is that it also has a supercoiled form represented by one of the bands.

Next, transfection of HEK cells for lentiviral production was performed with PGK- S3A and PGK-CTR vectors. To follow the transfection and transduction success and efficiency, a parallel experiment was conducted using a lentiviral plasmid

17 encoding the gene for Green Fluorescent Protein (GFP). Successfully transfected HEK cells showed GFP expression after 24h and transduced 4T1 cells after 48 hours (Figure 3.1B).

Figure 3.1: (A). Agarose gel electrophoresis of digested PGK-S3A and PGK-CTR vectors. Digested PGK- S3A vectors show three bands and PGK-CTR vector shows one band (left) compared to uncut supercoiled and coiled vectors (right). (B). GFP expression of HEK cells 24 hours after transfection and of 4T1 cells 48 hours after transduction, 20X magnification. (C). Quantitative real-time PCR of Sema3A mRNA expression in 4T1 PGK-S3A and PGK-CTR cells. 4T1 PGK-S3A cells show 22-fold higher Sema3A expression compared to 4T1 PGK-CTR cells.

Transduced 4T1 cells were selected for ten days using the antibiotic neomycin and only cells that integrated the vector DNA providing a resistance gene for neomycin were able to survive. Naïve 4T1 cells were used as a control. After ten days, both 4T1 PGK-S3A and PGK-CTR tumor cells had survived but all naïve 4T1 cells died. Sema3A mRNA levels of selected 4T1 PGK-S3A and PGK-CTR tumor cells were analyzed using qPCR (Figure 3.1C). 4T1 PGK-S3A cells had 22-fold higher mRNA expression compared to the 4T1 PGK-CTR cells, confirming that a successful Sema3A overexpression clone had been generated.

18 Knockdown of Sema3A in tumor cells The second sub-aim of this project was to investigate the role of endogenous Sema3A in breast cancer. Data on Sema3A immunostained human breast cancer tissues showed a decrease in Sema3A expression with tumor progression (Figure 3.2A). Based on this and other findings 4T1 cells with Sema3A knockdown were generated using lentiviral mediated gene therapy as previously applied to generate Sema3A overexpressing 4T1 cells (section 2). 4T1 cells with Sema3A shRNA (4T1 shS3A) and non-target shRNA (4T1 shCTR) were produced and Sema3A mRNA levels were analyzed using qPCR. 4T1 shS3A cells showed 72 % lower Sema3A expression compared to 4T1 shCTR cells (Figure 3.2B). Furthermore, proliferation of 4T1 shS3A and shCTR was assessed in vitro (Figure 3.2C).

There was no significant difference in cell growth between 4T1 sh3A and shCTR cells. These results indicate that endogenous Sema3A expression by tumor cells does not have an effect on cancer cell proliferation. In previously performed experiments (7) it has been shown that overexpression of Sema3A suppressed tumor growth and lowered tumor burden. To assess if Sema3A knockdown in tumor cells also has an effect on tumor progression, an in vivo experiment was performed.

Figure 3.2: (A) Histological analysis of Sema3A in human grade I and III breast cancer samples. Expression of Sema3A is downregulated in grade III tumors. (B) Quantitative real-time PCR analysis of Sema3A mRNA levels in 4T1 shS3A and shCTR. 4T1 sh3A cells show 72 % knockdown of Sema3A compared to 4T1 shCTR cells. (C) Assessment of proliferation of 4T1 shS3A and shCTR cells. There is no significant difference in cell growth. (D) Tumor volume over time measured on injected mice starting on day 7 until sacrification on day 21. There is no difference in tumor volume. (E) Tumor burden of harvested tumors shows no significant difference between 4T1 shS3A and shCTR tumors.

Knockdown of Sema3A in 4T1 tumor cells did not have an effect on tumor growth or on tumor volume compared to control (experimental details described in Appendix 1) (Figure 3.2D and E).

Furthermore, previous results have shown that overexpression of Sema3A in tumors mediates an overall tumor suppressive immune response with an increased accumulation of M1 macrophages and cytotoxic T-cells (7). To investigate if knockdown of Sema3A in tumor cells also affects the immune cell composition, tumors were processed into single cell suspensions and flow cytometry analysis was performed. Panels and gating strategy were applied to all samples as described in section 2.6. First, the gating was applied to single cells alive followed by monocytes and macrophages (Figure 3.3). MHC class II expression allowed to distinguish between M1 and M2 macrophages (Figure 3.3 last graph) and to show the activation state measured with the Mean Fluorescent

20 Intensity (MFI). A higher MFI of MHC class II indicates that macrophages have acquired an M1 phenotype since this is a surface molecule necessary for antigen presentation.

Figure 3.3: The upper row of graphs show the gating strategy used in the beginning of all panels and in the lower row, the gating unique for panel 1 is presented.

Although there is no significant difference, macrophages from 4T1 shS3A tumors tend to express less MHC class II molecules compared to shCTR tumors (Figure 3.4).

Figure 3.4: (A) Histogram and (B) statistical analysis of MHC class II expression by macrophages from 4T1 shCTR and shS3A tumors. Macrophages from 4T1 shCTR tumors express higher levels of MHC class II molecules.

21 Next, CD11c was used to analyze macrophage polarization in the macrophage population (CD11b+F4/80+) (Figure 3.5A). This molecule is an activation marker used in cell-cell interactions. High CD11c expression on macrophages indicates M1 polarization. Overexpressing Sema3A tumors are high in M1 macrophages (7) and to elucidate if knockdown of Sema3A has an effect on macrophage polarization, CD11c expression was analyzed (Figure 3.5B and C) .

Figure 3.5: (A) Gating strategy to identify the macrophage population. Macrophages are CD11b+F4/80+. (B) Histogram and (C) statistical analysis of CD11c expression of macrophages from 4T1 shCTR and shS3A tumors. Macrophages from 4T1 shCTR tumors express significantly higher levels of CD11c compared to macrophages from 4T1 sh3A tumors; star indicates significance *p<0.05 based on unpaired two-sided T-test.

Flow cytometry analysis revealed a significantly higher degree of M1 polarization in 4T1 sh3A tumors compared to shCTR tumors.

Finally, the lymphocyte content of the tumors was analyzed. Since in Sema3A overexpressing tumors an increase in CD8+ T cells along with a decrease in CD4+ T cells was observed, it was interesting to investigate the T cell population in the knockdown experiment as well (7). The total lymphocyte population was determined based on CD45 expression (Figure 3.6A) and additional expression of CD3 allowed to isolate T cells (Figure 3.6B). The total T cell population was then analyzed for CD8 or CD4 expression (Figures 3.6C,E). The percentage values were then represented in graphs and significant differences in both cases were observed with 4T1 shCTR tumors displaying a higher infiltration of CD8+ T cells (Figure 3.6D) while 4T1 shS3A knockdown tumors showed increased presence of CD4+ T cells (Figure 3.6F).

Figure 3.6: (A) Scatter plot showing CD45 expression by Lymphocytes (B) Scatter plot displaying T cells out of all lymphocytes identified by CD3 expression. (C) Scatter plot representing CD8+ T cells separated from the total T cell population. (D) CD8+ T cell populations in control tumors compared to knockdown. (E) CD4+ T-cells separated from total t-cell population. (D) CD4+ T-cells populations in control tumors compared to knockdown. Star indicates significance *p<0.05 (unpaired two-sided T-test).

23 4. Discussion

4.1 Knockdown of endogenous Sema3A expression in 4T1 tumor cells One part of this project was to explore the effect of Sema3A knockdown in 4T1 tumor cells on the microenvironment. An in vivo study was conducted where tumor volume, burden and the immune cell content were analyzed. The results show no profound difference between the knockdown and control tumors. Tumor volume and burden are the same in both conditions and flow cytometry analysis of macrophages and T cells reveals a similar immune profile. This indicates that knockdown of endogenous Sema3A has a moderate effect on the microenvironment in the 4T1 breast cancer model. However, when studying the immune profile a slight effect on macrophages and T cells could be observed. Some markers for M1 macrophage activation were down regulated in knockdown tumors that also showed decreased infiltration of CD8+ T cells and increased accumulation of CD4+ T cells. All in all, knockdown of Sema3A shifts the immune response towards immune suppression. These results are in line with the notion that Sema3A overexpression in 4T1 tumor cells correlates with immune activation. Anyway, knockdown of Sema3A does not lead to elevated tumor volume or burden. This may be due to the fact that endogenous Sema3A expression goes down over time as the tumor progress and hence, the difference in Sema3A level between the knock down and control tumors was quite small. In the overexpression experiments, the difference in Sema3A level compared to control was high and could then mediate an effect on the tumor immune profile that was then reflected on tumor volume and burden.

4.2 Technical evaluation The techniques and methods used in this thesis worked well over all. The goals of the thesis were reached, both for generating overexpression and knockdown of Sema3A in 4T1 cells. Stable cell lines were generated and the knockdown cell line was tested in vivo. However, there were some difficulties in the process of generating the cell lines. Some steps were quite tedious as for example the antibiotic selection for ten days. Furthermore, generating a knockdown or overexpression cell line is a process of several steps, so that it was difficult to conclude where something went wrong if the qPCR analysis in the end showed no

24 success. Anyway, for some steps it was possible to check for the right outcome as the restriction enzyme digestion and gel-electrophoresis performed on the cloned overexpression plasmids. Another evaluation for successful integration of the plasmid DNA into the cells was the parallel transfection and transduction with GFP. Moreover, with the number of GFP+ cells conclusions about transfection and transduction efficacy could be made.

When it comes to flow cytometry analysis, there are some limitations as well. Some cell populations, as the T cell population, can be recognized and gated easily since CD4 and CD8 are two specific markers for T cells. Gating on macrophages is not so simple because other cells types of the myeloid linage often show similar marker expression and the differences are in the expression level rather than the expression itself. If the population cannot clearly be recognized it makes the gating difficult and it takes skills and experience to recognize the cell population of interest. Moreover, M1/M2 macrophages are not distinct cell populations but rather belong to the same cell population with a different polarization spectrum. To facilitate the classification to M1 or M2 different markers in diverse panels were applied to the samples and mean expression levels were measured and compared between the samples rather than looking at absolute cell numbers.

4.3 Future prospectives As a future outlook, the next step for validating Sema3A overexpression in the 4T1 cell line would be the performance of western blot to check for Sema3a protein expression Finally, in vivo experiments would give an insight about the systemic effect of Sema3A on tumor progression and microenvironment. Regarding knockdown of Sema3A, repetition of the in vivo experiment would verify the gained results. Moreover, both overexpression and knockdown in vivo experiments could be performed with other tumor cell lines and breast cancer animal models, which would strengthen the generated data.

In the wider perspective, the tumor microenvironment and immunology field holds a lot of promise for the future. Ideally, increased knowledge will lead to the generation of individualized immune profiles for patients helping clinicians to

25 improve treatments and outcome. The ultimate goal would be the development of effective immunotherapies for cancer patients.

Acknowledgements

I would like to express my gratitude to my supervisor Charlotte Rolny (Cancer Center Karolinska) who gave me the opportunity to perform my thesis work in her lab and also for supporting me during the process of the work, for sharing her experience and expertise and for always answering my questions when I had them. A big thank you to Majken Wallerius and Tatjana Wallmann (Cancer Center Karolinska) for guiding me through all the practical work in the lab and for being there for me whenever I had questions or problems that I needed to sort out. Thank you for your positive energy and true consideration for my work, and for encouraging me throughout this thesis. I would also like to thank Peter Nilsson (KTH SciLife Laboratory) for helping me monitor my work and giving me valuable feedback on presentations and guiding me through the writing process of this thesis.

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29 Appendix 1 – Materials and Protocols

Gateway cloning Purification of Destination vectors pLenti PGK Neo DEST (w531-1) from Addgene TM ® and ORFEXPRESS Gateway PLUS Shuttle Clone (GC-Mm05159) from source bioscience containing murine Sema3A gene was accomplished using MAXIprep ® DNA purification kit (Quiagen Maxiprep kit). The Gateway LR reaction was performed to generate an expression clone with the correct backbone from the destination clone and target gene from the shuttle (entry) clone. 100 ng of entry clone was mixed with 150 ng/µl of destination vector and TE buffer was added up to a total volume of 8 µl. LR Clonase II enzyme mix was thawed on ice, 2 µl were added to the prepared reaction and samples were mixed. Reaction was incubated at 25 °C for 1 hour and 1 µl Proteinase K solution was added and incubated at 37 °C for 10 minutes to terminate the reaction.

One Shot Stabl 3 chemically competent Eshcerichia coili (E. coli) were used for the subsequent transformation. 25 µl of cell mix was used per reaction. 1 µl of DNA from Gateway reaction was added to the cells and mixed by tapping the tube. Tubes were incubated on ice for 30 minutes followed by heat shock in 42 °C water bath for 45 seconds. The reaction vials were placed on ice for 2 minutes. To each vial, 250 µl room tempered Super Optimal Broth with Catabolite repression (S.O.C.) medium was added. Vials were shaken at 37 °C for 1 hour at 225 rpm. 100 µl of each transformation mix was plated on ampicillin selective agar plates and incubated over night at 37 °C. shRNA Competent GC10 E. coli were transformed with the lentiviral vectors coding for shRNA against Sema3A and cultivated under ampicillin selection. The vectors were amplified via Maxi culuture and purified using MAXIprep DNA purification kit (Quiagen Maxiprep kit). Concentrations of vectors were determined using Nanodrop.

30 Transfection 293T/17 (HEK) cells were thawed from –80 °C and cultured in 10 cm plates in DMEM medium (+ 10% heat inactivated fetal bovine serum (FBS), 1% L- Glutamine and 1% penicillin-streptomycin (PenStrep)). The cells were cultured for five days and split every 48 hours. 24 hours before transfection, 3x106 293T/17 cells were seeded in 10 cm plates. Two hours before transfection, medium was changed to IMDM medium (+ 10% heat inactivated FBS, 1% L-

Glutamine and 1% PenStrep). The plasmids were mixed in water (dH2O) to a final volume of 450 µl with each plasmid in the quantity as follows: 3 µg envelope plasmid VSV-G, 5 µg packing plasmid pMDLg/p RRE, 2.5 µg pRSV-REV plasmid and finally 10 µg of the gene transfer plasmid. 50 µl of 2.5 M CaCl2 was added followed by and incubation time of five min at RT. 500 µl of 2x HBS was added drop wise while vortexing at full speed. The precipitate was immediately added to the cells. Plates were incubated at 37 °C over night. Medium was exchanged after 16 hours to DMEM medium. After another 48 hours, the virus containing medium was collected, filtered (40 µm filter) and stored in -80 °C.

Transduction 4T1 cells were thawed from –80 °C and cultered in RPMI-1640 medium (+ 10% heat inactivated FBS, 1% L-Glutamine and 1% PenStrep) for two passages. 60.000 cells/well were seeded in a 6-well plate and filtered virus containing medium was added directly to the cells. 250 µl HEPES and 1 µl Polybrene were added to each well. Medium was changed after 15h to RPMI medium. After cells reached 80- 90% confluency, they were split and transferred to 10cm plates and selected in RPMI medium with 1 µg/ml puromycin for 10 days. mRNA-expression

350 µl RLT Buffer was added to the cells and cells were homogenized using a syringe with a 20-gauge needle. RNA was isolated using RNeasy Mini Kit from Qiagen and cDNA was synthesized using Qiagen QuantiTect Reverse Transcription kit. 1 µg RNA was added to 2 µl gDNA wipeout buffer and made up to a total volume of 14 µl with water. The mix was incubated at 42 °C for 2 min. 6 µl RT buffer, 1 µl primer mix and 1 µl Reverse Transcriptase were added to each

31 sample and the reaction mix was incubated at 42 °C for 15 min followed by an incubation at 95 °C for 3 min. The generated cDNA was diluted with 80 µl nuclease free H2O.

For the qPCR reaction mix 2 µl cDNA solution, 5 µl TaqMAn Universal Master Mix II, 0.5 µl TaqMan Gene Expression Assays (both from Applied Biosystems) and

2.5 µl nuclease free H2O were added per well in triplicates in 96-well plate. qPCR included 40 cycles with denaturation at 95 °C for 15 sec and annealing at 60 °C for 60 sec.

Proliferation Assay Roche Cell Proliferation Kit II (XTT) was used to assess cell proliferation. 2x103 cells were seeded in 96-well plate in 100 µl RPMI medium (+ 10% heat inactivated FBS, 1% L-Glutamine and 1% PenStrep). Four different time points were set for measurement of proliferation: 0h, 24h, 48h and 72h. XTT labeling mixture was prepared by mixing labeling reagent and electron coupling reagent immediately before adding to the cells. 50 µl of XTT labeling mixture were added to each well and cells were incubated at 37 °C for 4h. Cell viability was validated by measuring the absorbance of the generated product at 490 nm (absorbance at 650 nm was used as reference) with ELISA reader.

In Vivo experiment 4x103 cells were injected into the mammary fat pad of 6 weeks old female Balb/c mice (purchased from Charles Rivers) under anesthesia. Tumor volume was monitored for 3 weeks and measured two times per week. After scarification of mice tumor weight was determined. Animal experimentation is approved by Jordbruksverket under ethical number N144/13.

Flow cytometry Harvested tumors were cut into small pieces and incubated in 3 ml dissociation buffer (1:1 TrypLe:Stemcell pro accutase) for 30 min at 37 °C. Single cell suspension was generated by drawing the cells through a syringe with an 18- gauge needle and filtering the suspensions through 70 µm filters and after centrifugation through 40 µm filters. Single cells were re-suspended in 25 µl Fc

32 receptor block solution (0,5 µl in 100 µl PBS with 10% FBS) and incubated at 4 °C for 15 min. Antibodies were added in a ration of 1:50 and cells were incubated at 4 °C for 30 min according to Table 1. 3 ml PBS with 10% FBS were added to each sample and samples were centrifuged at 1250 rpm for 5 min. Cell pellets were re- suspended in 200 µl PBS with 10% FBS and 4 µl 7-ADD. Table 1: Antibody staining panels used for Flow Cytometry samples. Panel 1 Panel 2 Panel 3 Panel 4 Macrophage Macrophage NK-/T-cell PE Ly6c CD86 MHC1 CD49b FITC CD11b CD11b CD80 CD8 Per-Cp 7AAD 7AAD 7AAD 7AAD APC MHC2 CD206 Gr1 CD3 APC-Cy7 F4/80 F4/80 F4/80 CD4 PB Ly6G CD11c CD11b CD45

Samples were run on flow cytometer LSR II from BD Bioscience and the results were analyzed with FlowJo software.

33 Appendix 2 – Vector Maps pLenti PGK Neo DEST (w531-1)

GC-Mm05159 Genecopoiea clone