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Tumor-associated glycan structures: friend or foe in immunity to cancer? Cornelissen, L.A.M.

2019

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Download date: 09. Oct. 2021 Tumor-associated glycan structures: friend or foe in immunity to cancer?

Lenneke A.M. Cornelissen

VRIJE UNIVERSITEIT

Tumor-associated glycan structures: friend or foe in immunity to cancer?

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad Doctor of Philosophy aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus prof.dr. V. Subramaniam, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de Faculteit der Geneeskunde op dinsdag 2 juli 2019 om 11.45 uur in de aula van de universiteit, De Boelelaan 1105

door

Lenneke Alexandra Maria Cornelissen geboren te Boxmeer

promotor: prof.dr. Y. van Kooyk copromotor: dr.ing. S.J. van Vliet

Promotiecommissie

Promotor Prof. Dr. Y. van Kooyk Amsterdam UMC

Copromotor Dr. Ing. S.J. van Vliet Amsterdam UMC

Overige leden Prof. Dr. G.J. Adema Radboudumc

Dr. T.J. Boltje Radboud University

Prof. Dr. K. de Visser Netherlands Cancer Institute

Prof. Dr. M. van Egmond Amsterdam UMC

Dr. V.L. Thijssen Amsterdam UMC

Prof. Dr. M. Wuhrer Leiden University Medical Center

The research presented in this thesis was performed at the Department of Molecular Cell Biology and Immunology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam Infection & Inflammation Institute, Amsterdam, The Netherlands. The studies described in this thesis were financially supported by Amsterdam UMC.

Contents

Chapter 1 Introduction

Chapter 2 Transcriptional activation of fucosyltransferase (FUT) genes using the CRISPPR-dCas9-VPR technology reveals potent N-glycome alterations in colorectal cancer cells

Chapter 3 A bitter sweet symphony: immune responses to altered O-glycan epitopes in cancer

Chapter 4 Tn antigen expression contributes to an immune suppressive microenvironment and drives tumor growth in colorectal cancer

Chapter 5 Tumor sialylation impedes T cell mediated anti-tumor responses while promoting tumor associated-regulatory T cells

Chapter 6 Disruption of sialic acid metabolism drives tumor growth through CD8+ T cell apoptosis

Chapter 7 Discussion

Appendices Summary Acknowledgements Curriculum vitae List of publications, grants and awards Portfolio

Chapter 1

Introduction

Lenneke A.M. Cornelissen

The multistep development of cancer

Cancer cells divide in an uncontrolled manner. Although this is probably the most fundamental trait of cancer, cancer cells must acquire more biological abnormalities to become an invasive tumor. These different characteristics of tumor development were first defined in 20001 as ‘The Hallmarks of Cancer’ and later revised and updated in 20112. At the start of development, cancer cells receive high levels of growth- promoting signals secreted by stromal cells, allowing cancer cells to proliferate in a unrestrained manner1. However, sustained cell proliferation activates cellular programs that negatively regulate cell proliferation, which cancer cells need to circumvent3. In addition, sustained cell growth triggers physiological stress that provokes counteracting responses, including cell senescence and apoptosis. Hence, cellular apoptosis functions as a natural barrier for cancer development4,5. As a consequence, next to evading growth suppressors, cancer cells need to resist cell death to maintain their chronic proliferation state. Non-malignant cells can only undergo a limited number of division cycles, controlling cell growth. Thus to sustain chronic proliferation, cancer cells must acquire replicative immortality6. But, as with normal cells, replication requires nutrients and oxygen, while metabolic waste and carbon dioxide need to be cleared. Neo-vasculature, driven by angiogenesis, is crucial for the tumor to maintain its expansion7, especially once a tumor reaches a bulky size. Moreover, local invasion and distant metastasis allows cancer cells to spread throughout the body8. Metastasis is a feature of late stage tumors in many, if not all, tumor types and reduces the probability of cure considerably. However, besides the changes in expression of cell-cell adhesion molecules, including E-Cadherin9, the main regulators of tumor invasion and metastasis are still largely undescribed and form subjects for future research. The multistep development of cancer enables malignant cells to proliferate, survive and disseminate. Nevertheless, two more hallmarks have recently been added to the revised paradigm of the ‘hallmarks of cancer’2. The first one involves the capability of the cancer cell to reprogram its cellular metabolism in order to deal with nutrients as efficiently as possible to support the enhanced cell proliferation10,11. Secondly, increasing bodies of research have proven that cancer cells evade immune destruction, by dampening the attempts of the immune system to eradicate the tumor cells12. Altogether, the combined hallmarks of cancer portray the complexity of the disease, which clarifies the challenges that come with cancer treatment. Additionally, tumors are very heterogeneous, varying between tumor types as well as within the tumor13, which urges the need to apply cancer treatment in a personalized manner. In the present thesis, we have studied two types of cancer, namely colorectal cancer and melanoma. Therefore, these two types are being introduced in more detail.

Colorectal cancer

Colorectal cancer (CRC) is a carcinoma of the large intestine, comprising the colon and the rectum. CRC is the third most common cancer in the Netherlands with more than 13.000 new cases every year (www.kwf.nl/kanker). The prevalence of CRC is strongly associated with age and with a western lifestyle including diet, obesity and smoking14. A rapid increase in CRC incidence and mortality is observed in South America, Asia and Eastern Europe, while numbers are declining in more developed countries including USA, Australia, New Zealand and Western Europe. This decline is likely the result of early detection and prevention programs15. Next to alcohol, red as well as processed meat consumption strongly increases the risk of CRC15,16. The exact mechanism underlying the association of meat consumption and the prevalence of CRC are not totally understood. Possible mechanisms include mutagenic and/or carcinogenic compounds

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present in animal meat17. In addition, red meat is enriched with a monosaccharide that is absent in the human body, but is incorporated in the tissue through dietary intake. Early life exposure of red meat could trigger production of antibodies that are cross reactive against this incorporated monosaccharide. As a consequence, these antibodies can cause inflammation at a later age, which can trigger and promote tumor progression18. CRC typically starts with a polyp that progresses into a carcinoma. The staging of CRC is defined by the size and the extension of the tumor into and through the wall of the large intestine (T1, 2, 3 and 4). In addition, the presence of the cancer cells in the lymph nodes (N0, 1 or 2) and whether it has metastasized (M0 or 1) also determine the staging of CRC. For instance, a tumor, which has grown through the mucosa in the submucosa (T1) and has spread to lymph nodes (N2), but not to distance sites (M0) is categorized as CRC stage III tumor. In the Netherlands, the three year overall survival of patients with stage I and II CRC is 85-95% and 80% for stage III, however the prognosis steeply decreases when entering stage IV, leading to an overall survival of only 20%. Early detection is therefore essential to increase the overall survival rates of CRC patients

Melanoma

Melanoma is the rarest, but most aggressive type of skin cancer rising from melanocytes present in the skin. Melanoma is the most prevalent cancer type in the Netherlands with more than 15.000 new cases each year (www.kwf.nl/kanker). The primary cause of skin cancer is unprotected exposure to ultraviolet radiation from the sun or artificial sources. Ultraviolet radiation can damage DNA, induce gene mutations, inflammation and oxidative stress, all of which are known to promote tumorigenesis19. Protection can be achieved by either sunscreen or natural pigmentation in the skin (melanin). Melanoma staging is dependent on the thickness of the tumor as well as the presence of ulceration, mitosis, and metastasis. Till stage II the primary melanoma thickness is less than 4 mm with or without ulceration. When regional metastasis is present, stage III is reached and with distant metastasis stage IV. The 5 year overall survival rates decline from 65-93% to less than 28% in stage II to stage IV, respectively20.

Cancer treatment options

The most applied treatment options for cancer are surgery, radiotherapy, chemotherapy and immunotherapy. Surgery is the oldest treatment applied and has been used from the 16th century onwards. Halfway of the 19th century radical mastectomy was introduced, whereby the surgeon, in case of breast cancer, removed the entire breast including surrounding muscles in order to remove the cancer as well as its roots. Only later in 1977, research showed that a less aggressive type of surgery is equally effective21. Therefore, radical mastectomy is rarely applied nowadays. For both CRC and melanoma, surgery is often the first line of treatment independent of the cancer’s stage. Delivery of high doses of radiation started after the discovery of radium by Marie and Pierre Curie at the end of the 19th century. Radiotherapy was born and shown to be very effective and is still applied widely in the clinic to eradicate tumor cells. In case of CRC and melanoma, radiation is applied next to surgery in patients with advanced cancer. During the first world war, a chemical weapon named mustard gas was released in order to eliminate enemy soldiers. Soldiers that had exhaled the gas, displayed reduced numbers of leukocytes in their blood. Leukemia is a non-solid cancer beginning in the bone marrow and resulting in elevated numbers of leukocytes in the blood. The observed effects of mustard gas on leukocyte numbers in the blood led to the idea to use similar chemicals to treat

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leukemia patients. Thus, paradoxically, chemotherapy is literally inspired by war22. One of the first developed chemotherapeutics with activity against a wide range of solid tumors was fluoropyrimidine 5-fluorouracil (5- FU)23. 5-FU is an antimetabolite drug that inhibits essential biosynthetic pathways. Since the 1950s 5-FU is used to treat various cancer types, however, the largest impact has been reported for CRC24. As with radiotherapy, chemotherapy is used in addition to surgery for both melanoma and CRC in patients with advanced cancer. Although these cancer treatment options are very effective, they have one major disadvantage in common as they cannot discriminate between healthy non-malignant cells and cancer cells, and cause damage to the healthy tissues. Nevertheless, our body harbors a defense mechanism that is able to specifically interact and damage tumor cells only: the immune system. An elucidated immune response is specifically targeted towards an epitope or antigenic determinant. Therefore, providing the immune system with instructions to recognize cancer antigens and subsequently eliminate cancer cells is a new, fast-growing, approach to cure cancer. The idea that the immune system can be of help in the treatment of cancer is not new and has already been explored in 1891 by William B. Coley. This surgeon injected streptococcal bacteria into an inoperable tumor with success25. By injecting a pathogen into the tumor, the immune system is alerted and activated. As a consequence, a specific immune response towards the pathogen is evoked, but also, because of the general inflammation, beneficial side response is created towards the tumor. Despite the promising results, this new approach to treat cancer through microbial activation of the immune system received excessive criticism. Moreover, at that time people believed more in the successes obtained with radiotherapy. It took almost 125 years before immunotherapy was appreciated again and was awarded as “the breakthrough of 2013” by Science26. In general, cancer immunotherapy aims to strengthen the patient’s own immunity against the cancer. Although having the same tumor type, not all patients respond similarly to cancer treatment due to multiple, not yet completely understood, factors, including the heterogeneous characteristics of tumors and the patient’s own physical background. For instance, the composition of the gut microbome determines your response to cancer immunotherapy27. Therefore, personalized medicine, selecting the optimal therapy based on patient’s genetic background or other molecular or cellular analysis, may hold new promising results.

Immunity to cancer

Immunity is a host defense mechanism that protects the body from infectious diseases, but also from malignant cells that can arise in the body. However, during cancer development, cancer cells acquire several mechanisms to evade immune destruction through the formation of an immune suppressive tumor microenvironment. The transition of immune surveillance to immune evasion has been defined as cancer immunoediting12 whereby cancer cells become less immunogenic over three defined phases, the elimination, equilibrium and escape phase. Next to evasion from immune-mediated destruction, chronic inflammation is present from the start of tumor development and has proven to be an essential component of tumorigenesis28. This is further illustrated by the fact that every risk factor to develop cancer has been related to some form of chronic inflammation29. For instance, constituents of cigarette smoke induce chronic inflammation at mucosal surfaces and alter host immune response to exogenous antigens30, sunburn by UV radiation causes DNA damage which provokes inflammatory responses31 and also obesity is characterized by chronic inflammation. Thus, anti-tumor immunity and tumor-promoting inflammation co-exist during cancer development. Overall, depending on the activation state and cues received from the environment,

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immune cells can either contribute to or suppress anti-tumor immunity. It is a matter of switching this balance towards the anti-tumor immune response to cure cancer with cancer immunotherapy.

Immune cells contributing to tumor destruction Killing of cancer cells is a pivotal role of the immune system to hamper tumor growth. Two immune cell subsets that are experts in eliminating cancer cells are: natural killer cells (NKs) and cytotoxic CD8+ T cells (CTLs). Once activated and in close proximity to a cancer cell, both NK and CTLs release cytosolic granzymes, including perforin and serine proteases. Perforin forms pores in the cell membrane of the target cell, creating a channel for the granzymes to enter the cell, which leads to the initiation of apoptosis. Additionally, NK cells and CTLs also exploit granzyme-independent mechanisms to kill target cells via secretion of the cytokine TNF-related apoptosis-inducing ligand (TRAIL) and upregulation of first apoptosis signal ligand (FasL). Five death receptors are known to bind TRAIL, including DR4 and DR5, all leading to apoptosis32. However, early clinical trials targeting TRAIL have shown disappointing results, likely due to TRAIL resistance by tumor cells33. FasL binds to the death receptor FasR present on the target cell, also leading to cellular apoptosis. NK cells generally express inhibitory and activating receptors, of which the combined triggering determines whether NK cells will move into action. In contrast to CTLs, NK activation occurs in an antigen- independent manner, allowing NKs to react quickly. Moreover, it also allows NKs to eliminate cancer cells that merely present self-antigens in the MHC-I molecule and thus evade CTL-mediated elimination. To generate an effector CTL response, naïve CD8+ T cells need be differentiated into CTLs with the help of antigen presenting cells (APCs). APCs ingest tumor cells or tumor particles and cross-present these tumor- antigens in the context of MHC-I to CD8+ T cells. The T cell receptor (TCR)-MHC-I interaction forms an immunological synapse, which combined with APC-given co-stimulation via ICAM-I binding to LFA-1 or CD80/CD86 binding to CD28, differentiates naïve CD8+ T cells into CTLs. CD4+ T helper 1 cells (Th1) promote CTL responses through secretion of cytokines or through expression of CD40 ligand (CD40L) that binds CD40 present on APC and thereby licenses the APC to become more efficient at differentiating naïve CD8+ T cells into CTLs34. Once activated, CTLs are able to form a second immunological synapse with the MCH-I present on the target cell and subsequently eliminate the target without additional requirement of co-stimulation or Th1 help. Eventually, activated CTLs will upregulate immune checkpoint receptors including PD-1, TIM-3 and CTLA-4 to become sensitive to resolution of the initiated cytotoxic immune cell response. Cancer cells evade CTL-mediated killing by upregulating ligands for these immune checkpoint receptors, including programmed death-ligand 1 (PD-L1) that hampers ongoing CTL responses. In addition, cancer cells downregulate MHC-I, preventing the possibility to form an immunologic synapse with the CTL. Conversely, the MHC-I molecule is a ligand for the murine and human NK cell inhibitory Ly-49 and killer immunoglobulin-like receptor (KIR) receptors respectively35,36. Therefore, MHC-I downregulation by cancer cells releases the break on NK cell activation and provokes NK-mediated tumor killing. To date, NK cells are the only immune cells that lack tumor-promoting traits.

Many tumor types are heavily infiltrated with large numbers of macrophages37. Due to their plasticity and polarization abilities, macrophages have a dual role in cancer progression. Here, the anti-tumor characteristics of macrophages are described, while their tumor-promoting features will be discussed in the next section. Firstly, macrophages are able to contribute to tumor elimination through secretion of reactive oxygen species, leading to significant cancer cell damage, resulting in tumor cell death38. Secondly,

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macrophages are phagocytic cells that are able to engulf and ingest cancer cells. In turn, cancer cells evade macrophage-mediated phagocytosis by overexpressing CD47. CD47 serves as a ligand for SIRP-α, an inhibitory macrophage receptor39,40. Upon CD47 ligation, SIRP-α negatively affects the phagocytic capacity of macrophages, hence, CD47 acts as a ‘don’t eat me’ and checkpoint-like signal39. Oppositely, secretion of INFγ by NK cells, T helper 1 (Th1) and CTLs enhances the phagocytic capacity of macrophages and augments MHC-II expression by macrophages, thereby contributing to tumor elimination and supporting effector Th1 responses, respectively. As activated macrophages secrete IL-12, which in turn can activate NK and Th1 cells, an immune-activating feedback loop is initiated. In colorectal cancer, macrophages have been described to adopt a pro-inflammatory phenotype, through secretion of chemokines to attract T cells and priming of Th1 responses41.

Whereas IL-12 is crucial for the differentiation of Th1 effector cells, the absence of IL-12 together with the presence of IL-4 promotes T helper 2 (Th2) responses. Th2 cells indirectly contribute to tumor immunity through the IL-4 and IL-13-mediated recruitment of eosinophils42. Eosinophils augment CTL tumor infiltration by supporting tumor vasculature normalization and the polarization of anti-tumorigenic macrophages43. However, the contribution of Th2 cells to anti-tumor immunity is still under debate and might be context dependent. For instance, Th2-derived IL-5 enhances tumor invasiveness in bladder cancer44 and in lung cancer it facilitates metastasis45. Located in the spleen, T follicle helper cells (Tfh) are directly responsible for the differentiation of naïve B-cells into antibody producing plasma cells46. Antibodies contribute to tumor immunity by facilitating antibody-dependent cellular cytotoxicity (ADCC) mediated by NK cells, macrophages, neutrophils and eosinophils. Besides cell-dependent cytotoxicity, antibodies can also cause cell lysis via complement activation (complement-dependent cytotoxicity, CDC). Furthermore, the antigen-specific characteristic of antibodies are exploited by immunotherapy to neutralize immune checkpoints and/or growth factor receptors, including nivolumab (anti-PD-1), ipilimumab (anti-CTLA-4) and trastuzumab (anti-human epidermal growth factor receptor 2, HER2) among others47,48. In addition to these naked antibodies, conjugated antibodies such as radiolabeled or chemolabeled antibodies can be used to deliver toxic substances to the tumor. Moreover, as antibodies can induce ADCC, antibodies have also been explored to potentiate immune-mediated tumor destruction in hematologic cancers such as B-cell lymphoma49. Here, the antibody (Rituximab) targets CD20 expressed by B-cells, resulting in malignant B-cell depletion50. In Figure 1 an overall overview of immune cells contributing to tumor destruction is shown.

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Figure 1. Immune cells contributing to tumor destruction Schematic representation of immune cell subsets capable of directly eliminating tumor cells (green arrows) or contributing to tumor destruction (black arrows). Abbreviations used; T helper 1 (Th1), antigen presenting cell (APC), cytotoxic T cell (CTL), macrophage (MQ), natural killer cell (NK), neutrophil (Neu), B cell (B), T helper 2 (Th2), nitric oxide (NO), reactive oxygen species (ROS), first apoptosis signal ligand (FasL), TNF-related apoptosis-inducing ligand (TRAIL).

Immune cells contributing to tumor progression Despite the fact that anti-tumor immunity has received increased appreciation over the last decade, the immune system is also well recognized for its tumor-promoting characteristics. Cancer-related inflammation promotes the recruitment of inflammatory immune cells that aid tumor survival and proliferation as well as angiogenesis and metastasis28,51. In this respect, the tumor-promoting inflammation shows similarities to inflammation during wound healing52. Besides cancer-related inflammation, immune cells themselves can also hamper ongoing anti-tumor immune responses directly.

In contrast to most immune cell subsets, appreciation for neutrophils and their role in tumor immunity had long been lacking. Nowadays, an increasing body of research shows that tumor-associated neutrophils

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(TANs) correlate with decreased recurrence-free overall survival53,54, and therefore, TANs are identified as a new prognostic factor in diverse tumor types including colorectal cancer55 and melanoma56. Neutrophils are the first immune cell subset recruited to the tumor microenvironment, where they support tumor progression in distinct ways. First of all, TANs contribute to angiogenesis during early tumor development through secretion of MMP-9, which is involved in the breakdown of extracellular matrix as well as through the induction of VEGF release57,58. Secondly, elastase derived from TANs, present in the tumor microenvironment, augments tumor proliferation via degradation of insulin receptor substrate-1 (IRS-1), leading to increased interaction between phosphatidylinositol 3-kinase (PI3K) and mitogen platelet-derived growth factor receptor (PDGRR)59,60. Finally, TANs release reactive oxygen species, causing DNA damage that further supports inflammation-induced tumorigenesis61. More details about the emerging functions of TANs in cancer as well as during homeostasis and infection were reviewed recently by Nicolas-Avila et al62.

In addition, neutrophils belong to the more comprehensively studied heterogeneous group of myeloid- derived suppressor cells (MDSCs). MDSCs are recruited from the bone marrow and once present in the tumor microenvironment, sub-classified into monocytic MDSCs (mMDSCs) and granulocytic MDSCs (gMDSCs), which encompasses neutrophils. The development of MDSCs occurs in two stages. The first step is the expansion of immature myeloid cells, initiated by tumor cells and bone marrow stroma that secrete GM-CSF, G-CSF, M-CSF and VEGF63. Secondly, the pathological activation by pro-inflammatory cytokines including INFγ, IL-6 and TNFα64, results in a tumor suppressive phenotype. Tumors are able, among other mechanisms, to recruit MDSCs through enhanced levels of CXCR2 ligands65,66. MDSCs are well defined for their immune suppressive actions, including suppression of T cell function, mediated by enhanced expression of Arginase 1 (ARG1) and iNOS enzymes as well as reactive oxygen species (ROS) production67. ARG1 consumes L-arginine, an amino acid that is fundamental for T cell surival. Thereby, ARG1 indirectly hampers T cell-mediated anti-tumor immunity by scavenging L-arginine away from the local environment68. High levels of the iNOS enzyme and nitric oxide (NO) synthesis by MDSCs have been correlated to inhibition of immune cell responsiveness to interferon (IFN)69. Moreover, immune cells cultured in vitro in the presence of NO displayed decreased STAT1 IFN responses when compared to cultures without NO. Altogether, MDSCs seem to negatively influence immune cell responsiveness to IFN by nitration of STAT1. Next to NO, MDSCs isolated from tumor bearing mice release higher levels of ROS than cells isolated from control mice. Also here, arginase activity is crucial for the increased ROS pool via the accumulation of H2O2. Interestingly, inhibition of MDSC-mediated ROS production completely abrogated the inhibitory effects of MDSCs on CD8+ T cells, strongly indicating that ROS production is an effector mechanism of MDSCs to dampen T cell immunity70. In addition to ARG1, NO and ROS, MDSCs also produce

prostaglandin E2 (PGE2). PGE2 is known for its broad spectrum of immune suppression, including inhibition of cellular immunity, blocking the effector functions of macrophages and neutrophils as well as by 71 promoting regulatory T cell (Treg) and Th2 responses . Therefore, PGE2 secretion can be recognized as another strategy employed by MDSCs to support tumor-promoting immunity. Interestingly, MDSCs are also

recipients of PGE2 themselves, which reinforces MDSC maintenance. As cyclooxygenase 2 (COX2) is the key regulatory enzyme in the synthesis of PGE2, a positive feedback between COX2 and PGE2 exists, leading to reprogramming of dendritic cells (DCs) towards MDSCs72. Finally, MDSCs contribute to immune suppression via Treg recruitment as well Treg expansion73, with a crucial role for CD40 expression on the MDSCs 74.

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Macrophages are phenotypically and functionally different from MDSCs but, like MDSCs, they belong to the myeloid immune cell compartment. Macrophages are abundantly present within the tumor microenvironment (tumor-associated macrophages, TAMs) and are well known for their tumor-supportive features. TAMs are differentiated from circulating monocytes, whereby CCL2 (also referred to as monocyte chemoattractant protein 1, MCP1) is the most prominent chemokine to recruit monocytes towards the tumor microenvironment. The effects of CCL2 are biphasic, whereby both low and high levels of CCL2 are responsible for TAM accumulation75. Cytokines present in the tumor microenvironment, such as IL-4, IL-10 and IL-13, support the cancer-promoting functions of TAMs76-78. Since IL-4 and IL-13 are cytokines secreted by Th2 cells, Th2 cells contribute indirectly to tumor progression through stimulation of TAMs78,79. TAMs inhibit DC maturation via secretion of anti-inflammatory cytokines, like IL-10 and TGFβ. Next to affecting DCs, TAMs subvert tumor-infiltrating CD4+ T cells towards a immunosuppressive phenotype, similar to Tregs, showing an increased IL-10 secretion and enhanced expression of immune checkpoints PD-1 and TIM-380. The immune suppressive actions of TAMs are comparable to those employed by MDSCs. TAMs are, like

MDSCs, also able to increase ARG1 enzyme activity and PGE2 secretion to further dampen T cell responses. Moreover, TAMs can express PD-1, whereby PD-1 expression is negatively correlated with the phagocytic activity of the macrophage81. In general, macrophages, as well DCs, express CD80 and/or CD86 upon maturation, which can bind the immune checkpoint CTLA-4 expressed by tumor-associated CD8+ T cells to inhibit CTL responses. Upregulation of CTLA-4 can be another mechanism exploited by TAMs to dampen cellular immunity. Proangiogenic and pro-metastatic functions of TAMs are achieved through secretion of VEGF as well as different pro-inflammatory cytokines, including TNFα and IL-6. Moreover, TNFα activates the NF-κB signaling pathway in cancer cells to assists in TNFα- and NF-κB-mediated cancer cell survival and proliferation82.

Although the myeloid compartment including TANs, MDSCs and TAMs plays a substantial role in promoting tumor development, also Tregs are essential in dampening ongoing anti-tumor immune responses. Cancer cells exploit Tregs and recruit a substantial amount of these cells to tumor microenvironment83. In addition, also de novo induction of Tregs in the tumor microenvironment occurs, mediated by TGFβ-secreting tolerogenic DCs84. Present in the tumor microenvironment, Tregs are capable of dampening effector T cells through the constitutive expression of CTLA-4 and the secretion of immune suppressive cytokines, such as TGFβ, IL-10 and IL-35. Moreover, both effector T cells as well as Tregs require IL-2 for their proliferation and maintenance. However, expression of the IL-2 receptor (CD25) is much higher on Tregs compared to effector T cells. Hence, Tregs can inhibit effector T cell survival through competition for IL-285. Finally, Tregs stimulate B7-H1 expression on MDSCs, which allows MDSCs to inhibit CD4+ T cell immune responses86.

DCs are the key players in priming T cell responses, including CTLs. Therefore, DCs are a major target in cancer vaccination strategies. Via the DC, T cells can be instructed to differentiate into activated T effector cells, which can eventually eliminate cancer cells. The cytokine imbalance present within the tumor

microenvironment (low levels of GM-CSF, IL-4, IL-12 and IFNγ and high levels of IL-6, IL-10, PGE2 and VEGF) induces the differentiation of so-called tolerogenic DCs. Tolerogenic DCs are characterized by low expression of MHC molecules and co-stimulatory receptors, including CD80 and CD86, and high expression of PD- L187,88. Thus, despite the high potential of DCs to promote anti-tumor immune responses, DCs present in the tumor microenvironment are defective in their T cell differentiation and activation capacity, resulting in

17 poor stimulation of the anti-tumor immune response89. Furthermore, DCs contribute to tumorigenesis through the expression of indoleamine 2,3-dioxygenase (IDO). IDO is the rate limiting enzyme of tryptophan catabolism along the kynurenine pathway, resulting into tryptophan depletion. Tryptophan is an essential amino acid used for protein biosynthesis and thus crucial for cell survival including immune cells90. IDO has been shown to negatively affect T and NK cell activities and to promote the development of functional Tregs and MDSCs91. In Figure 2 an overview of immune cells promoting tumorigenesis is shown.

Figure 2. Immune cells contributing to tumor progression Schematic representation of immune cell subsets capable of dampening immune effector functions (red arrows) or promoting tumor immune evasion (black arrows). Abbreviations used; dendritic cell (DC), natural killer cell (NK), tumor-associated macrophage (TAM), regulatory T cell (Treg), myeloid-derived suppressor cell (MDSC), neutrophil (Neu), tumor-associated neutrophil (TAN), macrophage

(MQ), interleukin (IL), transforming growth factor beta (TGFβ), arginase 1 (ARG1), prostaglandin E2 (PGE2), reactive oxygen species (ROS), indoleamine 2,3-dioxygenase (IDO), vascular endothelial growth factor (VEGF), tumor necrosis factor alpha (TNFα), matrix metallopeptidase 9 (MMP-9).

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Cancer immunotherapy Advancements in our understanding on how tumor cells avoid immune destruction have led to the development of effective cancer immunotherapies. Blocking antibodies targeting the immune inhibitory receptors PD-1 and CTLA-4 are classical examples of clinical successes. The success behind these blocking antibodies is a prolonged CTL-mediated tumor elimination. For example, patients with metastatic melanoma that were treated with a combination of anti-PD-1 and anti-CTLA-4 immunotherapy displayed a 3 years survival of 58%. While only 10 years ago, when these therapies were not applied yet, the median survival of the same patient group was less than 1 year92,93. Also, cellular therapies, including adoptive T cell transfer, but in particular genetically engineered T cells expressing chimeric antigen receptors (CAR T cells) have shown promising results94,95. While these new treatments represent a proof of concept that it is possible to use the patient’s own immune system to combat cancer, only a minority of these patients benefits from currently available cancer immunotherapies. Considering CRC, anti-CTLA-4 as a single-agent therapy initially showed interesting results but in the end, was unsuccessful in this type of cancer96. New approaches are required to further explore the usability of the immune system as a weapon in the fight against cancer. The key challenge is to understand the different mechanisms of how cancer cells are able to evade tumor immunity. Compared with their healthy counterparts, tumor cells display an aberrant glycosylation profile. Interestingly, the receptors that bind these glycan structures are mainly expressed by immune cells, suggesting that tumor cells can communicate and likely dampen anti-tumor immunity through expression of immune suppressive glycan structures. Therefore, investigating and understanding the interaction between tumor-associated glycans and immune cells may pinpoint to new inhibitory immune receptors that can be used as targets for cancer immunotherapy.

The glycosylation machinery

The altered glycosylation profile of cancer cells has been associated with malignant transformation and progression. Hence, cancer glycosylation has generated considerable recent research interest. Glycosylation is a post-translational modification of proteins and lipids. Glycosylation is not template-driven, instead glycosylation is influenced by the metabolic state of the cell, availability of the sugar donors, activation status of glycosyltransferases, glycosidase and sugar transporters. Glycan structures are made up of single or multiple monosaccharides of which more than fifteen different types exist in humans (see Figure 3 for few common glycan structure examples). The variation in glycan chain length, linkage possibilities as well as chemical group substitutions all contribute to the highly complex and diverse nature of the human glycome.

Figure 3. Examples of O-linked and N-linked oligosaccharides. Abbreviations used; threonine (Thr), serine (Ser), tyrosine (Tyr), asparagine (Asn).

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Glycans impact various biological functions, ranging from protein folding and function, to intracellular adhesion and signaling. The entire set of glycan-mediated biological roles has been summarized very elaborately by Ajit Varki97. Worth mentioning, several glycan-associated functions involve the immune system. Examples include the human blood group system, complement activation via the lectin pathway, discrimination between self and non-self and leukocyte trafficking mediated by glycosaminoglycan binding to chemokines as well as selectin-glycan interactions. Central to these immune-related functions is protein glycosylation, which can be sub-classified into O- and N-glycosylation depending on the attachment of the glycan using an OH-group of serine, threonine or tyrosine (O) or a nitrogen of asparagine (N). Cancer cells are frequently aberrantly glycosylated, meaning they express other and/or different expression levels of glycans compared to non-malignant cells. More specifically, the most common tumor-associated changes in glycosylation are enhanced fucosylation, sialylation and O-glycan truncation98.

Tumor-associated fucosylation Fucosylation comprises the attachment of a fucose residue from a guanosine diphosphate-fucose (GDP- fucose) donor to an acceptor substrate by fucosyltransferase (FUT) enzymes. Two types of fucosylation exists: core fucosylation and terminal fucosylation, which differ in the position where the fucose is added. In cancer, enhanced fucosylation results predominantly in expression of Lewisx (CD15) and Lewisy (CD174) and sialylated Lewisx and Lewisa (sLex and sLea) glycans structures99-102. To date, tumor-associated fucosylation has received more attention, because of its association with worse prognosis as well as the development of new therapies. Fucosylated carbohydrate antigen 125 (CA-125) levels are associated with worse prognosis and survival of ovarian cancer patients, hence, CA-125 can be used as a biomarker for the early prognosis103,104. But also in healthy conditions, fucosylated antigens serve as biomarkers, such as Lex epitope that is used for the identification of neural stem cells105. New therapies targeting FUTs can be applied to resensitize malignant cells for cancer treatment, since increased fucosylation is associated with a multidrug resistance phenotype. As an example, FUT4+Lewisx+ overexpressing metastatic CRC cells exhibit resistance to anti-EGFR and anti-VEGF chemotherapeutic agents106. Treatment with MEK inhibitors suppressed FUT4 expression, thus counteracting the resistance to anti-EGFR and anti-VEGF drugs. Together, tumor-associated fucosylation contributes to drug resistance, but also has great potential for designing new tumor107.

Tumor-associated sialylation Sialic acid is a generic term for more than forty different neuraminic acid derivatives. The name neuraminic acid was introduced by scientist E. Klenk in 1941 after he discovered sialic acid-expressing glycolipids in the brain (gangliosides)108. Later on, sialylation was shown to contribute to the structure and function of the brain109. Sialic acids share a nine-carbon backbone whereof the hydroxyl group can be substituted by acetyl, glycolyl, lactyl, methyl, sulfate and phosphate groups. In total twenty sialyltransferases (ST) are described that link sialic acid via their second carbon (C2) to the carbon atom at position 3 (C3, ST3Gal-I-VI), C6 (ST6Gal-I-II and ST6GalNAc-I-VI) or C8 (ST8Sia-I-VI) of the underlying monosaccharide, leading to an α2-3, α2-6 or α2-8 sialic acid linkage. Both the multiple chemical substitutions as well as linkage varieties contribute to the tremendous structural diversity of sialoglycans, defined as the “Sialome” in 2010110. Within the sialome, the most common sialic acid is N-acetylneuraminic acid (Neu5Ac) that carries a acetyl group at the C5 position. Another prominent sialic acid is the N-glycolylneuraminic acid (Neu5Gc) with a glycolyl group at the C5 position. Although Neu5Gc is prominently expressed in vertebrates, human

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are incapable of generating Neu5Gc due to an evolutionary loss of the CMAH gene, which is responsible for Neu5Gc biosynthesis. Once a sialic acid is added to the glycan backbone, the structure is not elongated anymore, leading to sialylated capping of glycan structures. Concerning this prominent position of sialic acid, sialic acids participate in various physiological processes in cell-cell and cell-extracellular matrix interactions, including adhesion, migration and immune cell recognition. In addition, sialic acids are negatively charged allowing the heavily glycosylated mucin proteins to become polyanionic. Through this, the positively charged water can be retained, thus preventing mucus dehydration. Tumor cells of various origins feature enhanced expression of sialylated glycoproteins and glycolipids on their membrane and on secreted proteins present in the tumor microenvironment. The main mechanism underlying tumor hypersialylation is increased expression and/or activity of sialyltransferases, resulting in more sialoglycans76. For instance, the oncogenes Ras and c-Myc control transcription of ST6Gal-I and STGal- I, -II and –IV, leading to α2-6 sialylated β-1 integrin and sLex and sLea antigens respectively, all promoting tumor progression111,112. Additionally, sialic acids derivates in the tumor micro-environment can be taken up by tumor cells, thereby positively influencing tumor sialylation with enhanced integrin-mediated cell mobility as a consequence113.

Tumor-associated truncated O-glycans O-glycosylation is the attachment of a sugar molecule to OH-group present in the amino acids serine, threonine and tyrosine114, which occurs in Golgi apparatus during later stages of protein synthesis. Various O-glycosylation pathways exist whereof O-Acetylgalactosamine glycosylation (O-GalNAcylation) is the most-widely studied in cancer. The O-GalNAcylation pathway starts with the addition of a N- acetylgalactosamine (GalNAc) to a serine or threonine residue and is mediated by one of twenty homologous polypeptide N-Acetylgalatosamine-transferases (GalNAc-Ts) that have their own protein substrate specificities115. Further elongation of the glycan chain occurs through addition of a Galactose, sialic acid or N-Acetylglucosamine (GlcNAc) to the GalNAc residue. More monosaccharides can be added to generate complex O-glycans. However, this is hampered in tumor cells, leading to de novo expression of truncated O-glycans. As mucin glycoproteins contain high densities of O-glycosylation sites, the role of truncated O-glycans on cancer progression is predominantly affecting epithelial cancer types. A comprehensive summary about tumor-associated O-glycosylation and the impact of truncated O-glycans on tumor progression can be found in Chapter 3.

Immune receptors involved in the recognition of tumor-associated glycan structures

Lectins are glycan-binding proteins, which were first discovered in plants more than 120 years ago. By the end of 19th century, evidence started to accumulate that extracts of plants could agglutinate red blood cells of various animal species. The proteins were referred as hemagglutinins. Later, ‘hemagglutinis’ were discovered for their ability to select human blood groups based on their specific recognition of blood group- associated glycan structures. Therefore, hemagglutinins were renamed lectin proteins (Latin for “select”)116. Today, the same plant lectins are widely used in glycobiology research to analyze the quantity as well the specificity of glycan expression on tissues, cells or other biological molecules. Despite their broad application in research, the biological role in plants still remains unclear. In mammals, lectin receptors are predominantly expressed by immune cells. Interestingly, as the glycosylation profile is affected by the physiological state

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of the cell and differs between host and microbial cell surfaces, it is tempting to speculate that the glycosylation machinery in general assists the immune system in its decision to mount, including the type of immune response, or whether it should remain silent. See Table 1 for an overview of receptors known to bind tumor-associated glycan structures, which will be explained in more detail in the next section.

Lectin receptors are subclassified in groups whereof the C-type, I-type and Galectins are the most studied lectin families in immunity nowadays. Lectins described to bind fucosylated antigens are dendritic cell- specific intracellular adhesion molecule-3-grabbing non-integrin (DC-SIGN, CD209), Langerin (CD207) and mouse macrophage galactose-type lectin 1 (MGL-1). DC-SIGN and Langerin are Ca2+-dependent lectin receptors belonging to the C-type lectin family and expressed in humans by antigen presenting cells in the skin117. DC-SIGN specifically recognizes non-sialylated Lewis antigens (Lex, Lea, Ley and Leb), whereas Langerin binds the terminal fucose of Leb and Ley 118. The main function of Langerin is to internalize antigens into the Birbeck granules and target them for degradation. In contrast, DC-SIGN appears to have multiple functions within the immune system. Besides acting as an adhesion molecule, DC-SIGN is an endocytic receptor that can internalize antigens and modulate TLR-induced immune activation119. Mice possess eight different DC-SIGN homologs, however, no clear human DC-SIGN orthologue is present in the mice120. A murine lectin receptor specific for Lex and Lea antigens is MGL-1. As with human DC-SIGN, MGL-1 is expressed on murine antigen presenting cells121,122.

Sialic acids are recognized by multiple lectin receptors and additionally, by the complement Factor H. Factor H is a complement control protein circulating in the plasma, which contains polyanionic binding sites that allows it to bind the negatively charged sialoglycans. Its principal function is to regulate the alternative pathway of the complement system by directing it to pathogens and other dangerous threats instead of host tissues. Removal of sialic acids, using sialidases, has been reported to sensitize tumor cells also to complement-mediated lysis123. The lectins receptors with specificity for sialic acids are the selectins and the Sialic acid-binding immunoglobulin-type lectins (Siglecs). Selectins are a family of cell adhesion molecules that belong to the C-type lectin family. They mediate leukocyte trafficking and homing in chronic and acute inflammatory processes. Three selectin family members exist, which are named for the cell that mainly express the receptor: E-selectin (endothelial cells, CD62E), L-selectin (lymphocytes, CD62L) and P-selectin (platelets, CD62P). An important ligand for selectins and especially E-selectin is sLex. Siglecs are the only well characterized group of I-type lectins. To date, nine and fifteen receptors have been described in mice and human, respectively. Although mainly expressed on leukocytes, Siglec-4 is expressed by Schwann cells present in the central nervous system. The majority of the Siglec receptors have an immunoreceptor tyrosine-based inhibitory motif (ITIM) embedded in their intracellular domain and therefore, Siglecs are generally thought to downregulate immunity.

The major lectin recognizing truncated O-glycans is MGL (Macrophage galactose-type lectin, CD301). Murine species possess two MGL homologues; MGL-1 (CD301a) and MGL-2 (CD301b). MGL-2 has a comparable binding specificity as human MGL, while MGL-1 is dissimilar recognizing LeX and Lea, instead of truncated O-glycans. The MGL receptor and its functions are described in more detail in Chapter 3 of this thesis.

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Table 1. Overview of immune receptors involved in recognition of tumor-associated glycan structures

Glycan structure Lectin type Human Mouse

Fucosylated antigens C-type DC-SIGN (CD209), Langerin (CD207) MGL-1 Siglec-1, -2, -3, -4, -5, -6, -7, -8, -9, - 10, -11, - Siglec-1, -2, -3, -4, -E, -F, -G and -H I-type 12, -14, -15 and -16 Sialylated antigens E-, L-, and P-Selectin (CD62E, CD62L and E-, L-, and P-Selectin (CD62E, CD62L and C-type CD62L) CD62L) Truncated O-glycans C-type MGL MGL-2 Abbreviations used: dendritic cell-specific intracellular adhesion molecule-grabbing non-integrin (DC-SIGN), mouse macrophage galactose-type lectin (MGL), sialic acid-binding immunoglobulin-type lectins (Siglec).

Effects of fucosylation on immunity Several clinical studies have established that tumor-associated Lewis antigen expression is associated with poor prognosis, independent of the tumor type102,124-126. Whether Lewis antigens contribute to tumor progression via inhibition of anti-tumor immunity, has not been fully elucidated. In an infection model, fucose-specific DC-SIGN signaling skews DC-initiated adaptive immunity towards Th2 responses that contributes to pathogen clearance, but might be disadvantageous in a tumor setting127. Indeed, monocyte- derived DCs co-cultured with the colon cancer cell line SW116 bearing Lewis antigens displayed DC-SIGN- dependent induction of the tumor-promoting cytokines IL-6 and IL-10128. Contradictory, Lex modified ovalbumin (OVA) re-directs OVA to the MGL-1 receptor and favors Th1 skewing as well as an enhanced cross-presentating ability of the DCs, thereby augmenting the cross-priming of CD8+ T cells129. The effect of tumor-associated Lex on anti-tumor immunity might therefore differ between human and murine species. Although it is clear that Lewis antigens are correlated with human tumor progression, the precise mechanisms how fucosylated antigens support tumor developments needs further elucidation.

Effects of sialylation on immunity Sialic acids are constitutively expressed on cells and moreover, sialic acids are recognized by multiple lectin receptors present on immune cells. Hence, it is not surprising that sialic acids affect immunity in a multifarious manner. Sialic acids are expressed on self-antigens, therefore, the current dogma states that sialic acids act as a shield to prevent inappropriate immune reactions against self-antigens. This led to the concept that sialoglycans represent a self-associated molecular pattern (SAMP) recognized by self-pattern recognition receptors130. A major group of sialic acid binding receptors are the Siglecs receptors that are renowned for their immune regulatory functions affecting various immune cell subsets131. Siglec-2 (CD22) and Siglec-10 (mouse orthologue is Siglec-G) are well-studied regulators of autoimmunity132-134. Expressed by B-cells, Siglec-2 and Siglec-10 regulate B cell tolerance by binding to cis-ligands and building up a threshold for B cell activation, hence preventing overactivation of humoral immune responses135. Sialoglycan-mediated activation of Siglec-7 and Siglec-9 on NK cells results in hampered NK cytotoxicity136. Siglec-15 is expressed by tumor- associated macrophages in various human cancer tissues and preferentially binds the sialyl-Tn antigen. Upon triggering of the receptor, Siglec-15 induces enhanced TGF-β secretion trough the DAP12-Syk pathway, which may contribute to tumor progression137. Sialylated MUC1 glycoproteins induce PD-L1 expression on macrophages through engagement of Siglec-9138, which skews these macrophages towards a tumor- associated macrophage-like phenotype that dampens cytotoxic T cell immunity138. The murine homologue of Siglec-9 is Siglec-E and the regulatory role of Siglec-E has been investigated by our group by using sialic

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acid-modified OVA (Sia-OVA) to stimulate Siglec-E on murine bone marrow-derived DCs. Compared to naked OVA, Sia-OVA-conditioned DCs induced more de novo differentiation of functional regulatory T cells, which was indeed dependent on Siglec-E139. Pathogens hijack the immune suppressive features of sialoglycans either by scavenging sialic acids from the host or through de novo synthesis to subsequently decorate their cell surfaces with sialic acid140. Furthermore, pathogens expressing sialic acids evade complement-mediated eradication by recruiting the sialic acid-binding Factor H, which functions as a complement inhibitory factor141. Cancer cells are believed to exploit the sialic acid pathway, in a manner similar to pathogens, to evade immune destruction through enhanced immune suppression mediated by Siglecs. Supportive data for this hypothesis is that hypersialylation of cancer cells benefits tumor growth and correlates with poor prognosis in cancer patients142,143. Hence, new therapeutics aimed at inhibiting the sialic acid-Siglec axis are being developed and investigated for their capability to boost immune-mediated tumor destruction. Intriguingly, sialic acid inhibition suppresses tumor growth in multiple murine tumor models144-146. The hampered in vivo tumor growth could be attributed to an altered immune cell composition of the tumor with enhanced NK and CD8+ T cells and diminished Tregs numbers144. Until recently, the role of sialic acid binding to tumor-infiltrating T cells had not been investigated, since Siglec expression was supposed to be absent on naïve as well as activated T cells. However, unlike T cells activated in vitro, tumor-infiltrating CD4+ and CD8+ T cells from non-small cell lung cancer were shown to express Siglec-3, Siglec-5, Siglec-7, Siglec-9 and Siglec-10147. Moreover, enhanced Siglec-9 expression on tumor-infiltrating T cells was also observed in CRC and ovarian cancer patients, providing evidence that the de novo expression of Siglec-9 on T cells is a broad phenomenon and tumor-type independent. In line with these findings, Siglec-9 has now been put forward as a new inhibitory immune checkpoint and thus a target for cancer immunotherapy. Besides Siglecs, selectins form the second group of lectin receptors that can bind sialic acids. Although the interaction between sialic acids and selectins in cancer has received less scientific attention than sialic acid-Siglec axis, it cannot be neglected. Selectins function as cell adhesion molecules, involved in cellular trafficking. Indeed, several studies have shown that hypersialylation of cancers is correlated with tumor aggressiveness and their ability to metastasize and invade surrounding tissues148,149. In addition, inhibition of sialic acid metabolism of tumor cells did not only affect the adhesive capacity of the tumor cells, it also hampered the metastatic spread of the tumors150. Taken together, sialic acids influence multiple aspects of tumor growth through Siglec and selectin binding, which has been reviewed by others in more detail143,151,152.

Effects of truncated O-glycans on immunity Within the variety of immune cell subsets, truncated O-glycans specifically affect antigen presenting cells (APCs) as the immune receptor recognizing truncated O-glycans, MGL, is solely expressed on APCs. Through the MGL receptor, APCs are able to distinguish healthy mucins from tumor-derived mucins153. Moreover, APCs that are positive for MGL, are characterized with an immature and tolerogenic phenotype154, both supporting tumor-promoting immunity and thereby tumor growth. Indeed, expression of MGL ligands has been correlated with worse prognosis in late stage CRC patients155. The role of tumor-associated O-glycans on anti-tumor immunity, including the effects of aberrantly glycosylated mucins on immunity as well as the initiation of adaptive immunity is discussed in detail in Chapter 3 of the thesis.

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Thesis outline

Cancer is an ancient disease that, unfortunately, the majority of people will be confronted with during his or her life. Major improvements have been made in cancer treatment, nevertheless, a substantial amount of patients can still not be cured and will die from the disease. The appreciation for cancer heterogeneity between tumor types as well as within a tumor has enforced the development of personal medicine to treat the cancer, with high costs as an disadvantage. Nonetheless, cancer cells do have features in common that can be employed in designing novel cancer therapeutics. Among others, one of these common features is that cancer cells express a different glycosylation profile compared to their non-malignant counterparts. Therefore, cancer research has directed its focus on how tumor-associated glycan structures support tumor progression. Interestingly, glycan structures can be recognized and bound by glycan-specific receptors, of which the majority is expressed by immune cells. As tumor cells are able to actively evade immune-mediated destruction, the goal of the present thesis was to explored how tumor-associated glycan structures modulate anti-tumor immunity. Tumor-associated glycan structures are characterized by enhanced expression of truncated O-glycans, enhanced fucosylation and hypersialylation as some of the main features of cancer glycosylation. In Chapter 3, we review in detail the role of truncated O-glycans on tumor immunity, including the immune receptors involved in the recognition of these glycans as well as the effect of aberrantly glycosylated mucins on tumor immunity. To study the effect of tumor-associated glycan structures on anti-tumor immunity, we required a tumor model that exhibits differential expression of our glycans of interest. As CRISPR/Cas9 has been defined as a new and very robust genetic engineering tool156, we implemented CRISPR/Cas9 as a new technique in the department to alter the glycosylation profile of our tumor cell lines. In Chapter 2, we implemented the CRISPR-dCas9-VPR gene activation tool to activate fucosyltransferase (FUTs) genes in order to induce de novo expression of Lex epitopes on the murine colorectal cancer cell line MC38, while in Chapter 4 and 6, CRISPR/Cas9 was implemented to knock out the O-glycosylation and sialylation pathway respectively, in order to initiate truncated O-glycosylation and to desialylate the MC38 cell line. In our experience, both gene activation as well as gene knock out was relatively easy to apply in MC38 cell line as well as, which supplied us with a powerful tool to continue with in vitro and in vivo experiments. In Chapter 4, we explored the effect of truncated O-glycans on tumor immunity in vivo. Our findings reveal that of the presence of truncated O-glycans on MC38 cells enhanced tumor growth in vivo, which could be attributed to an accumulation of immune suppressive myeloid-derived suppressor cells (MDSC) in the tumor-microenvironment, while the tumor-eliminating cytotoxic CD8+ T cells (CTLs) were diminished. In Chapter 5 and 6 we investigated the role of tumor sialylation on in vivo tumor growth and anti-tumor immunity in murine models of melanoma and CRC. In Chapter 5 we reduced sialic acid expression on the murine melanoma cell line B16 with the use of shRNA mediated knock down of the sialic acid transporter, encoded by the SLC35A1 gene (B16-SiaSLC35A1). B16-SiaSLC35A1 tumors displayed a reduced in vivo tumor growth, which was characterized by higher frequencies of CD8+ T cell and lower regulatory T cell frequencies in the tumor microenvironment. To evaluate whether the tumor-promoting effects of tumor sialylation were melanoma-specific or also applicable to other tumor types as well, we turned our focus to the the murine MC38s model for colorectal cancer. In Chapter 6, we knocked out a key enzyme in the sialylation pathway, namely the CMAS gene, which resulted in complete desialylation of the MC38 cell line (MC38-Sianull). Interestingly, MC38-Sianull cells displayed an enhanced tumor growth in vivo, which could be attributed to decreased CD8+ T cell tumor infiltration. Moreover, in vitro assays showed that MC38-Sianull supernatants

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were able to induce CD8+ T cell apoptosis, providing an explanation for the reduced CD8+ T cell infiltration in the MC38-Sianull tumors. The contradictory results of Chapter 5 and Chapter 6 proves that the origin of the tumor, the genetic engineering tool used and/or the choice for your target gene may influence the outcome of executed research. Additionally, since the type of sialic acid influences receptor recognition, the difference in relative abundance of α2-3 and α2-6 sialic acid may have affected the outcome as well. The discrepancy observed illustrates that the role of tumor sialylation on tumor immunity is much more complex than initially thought. Whether sialic acids can be regarded as the ‘good’ or ‘bad’ guys within tumor immunity is discussed in Chapter 7.

Overall, the present thesis demonstrates the relevance of CRISPR/Cas9 as a tool to manipulate tumor glycosylation in order to study the role of glycosylation on tumor immunity. The obtained results broaden our knowledge on how tumor-associated glycan structures affect tumor growth in vivo and the immune cell networks in the tumor microenvironment in both melanoma and colorectal cancer models.

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Chapter 2

Transcriptional activation of fucosyltransferase (FUT) genes using the CRISPR-dCas9-VPR technology reveals potent N-glycome alterations in colorectal cancer cells

Athanasios Blanas Lenneke A.M. Cornelissen Maximilianos Kotsias Joost C. van der Horst Henri van de Vrugt Hakan Kalay Daniel I.R. Spencer Radoslaw P. Kozak Sandra J. van Vliet

Glycobiology 29, 137-150, doi:10.1093/glycob/cwy096 (2019)

Abstract

Aberrant fucosylation in cancer cells is considered as a signature of malignant cell transformation and it is associated with tumor progression, metastasis and resistance to chemotherapy. Specifically, in colorectal cancer cells, increased levels of the fucosylated Lewisx antigen are attributed to the deregulated expression of pertinent fucosyltransferases, like fucosyltransferase 4 (FUT4) and fucosyltransferase 9 (FUT9). However, the lack of experimental models closely mimicking cancer-specific regulation of fucosyltransferase gene expression has, so far, limited our knowledge regarding the substrate specificity of these enzymes and the impact of Lewisx synthesis on the glycome of colorectal cancer cells. Therefore, we sought to transcriptionally activate the Fut4 and Fut9 genes in the well-known murine colorectal cancer cell line, MC38, which lacks expression of the FUT4 and FUT9 enzymes. For this purpose, we utilized a physiologically relevant, guide RNA-based model of de novo gene expression, namely the CRISPR-dCas9-VPR system. Induction of the Fut4 and Fut9 genes in MC38 cells using CRISPR-dCas9-VPR resulted in specific neo-expression of functional Lewisx antigen on the cell surface. Interestingly, Lewisx was mainly carried by N-linked glycans in both MC38- FUT4 and MC38-FUT9 cells, despite pronounced differences in the biosynthetic properties and the expression stability of the induced enzymes. Moreover, Lewisx expression was found to influence core- fucosylation, sialylation, antennarity and the subtypes of N-glycans in the MC38-glycovariants. In conclusion, exploiting the CRISPR-dCas9-VPR system to augment glycosyltransferase expression is a promising method of transcriptional gene activation with broad application possibilities in glycobiology and oncology research.

Key words: Colorectal Cancer, CRISPR-dCas9-VPR, fucosylation, gene activation, N-glycans

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Abbreviations

2F-PF: 2F-Peracetyl Fucose AAL: Aleuria Aurantia Lectin BLAST: Basic Local Alignment Search Tool BSA: Bovine Serum Albumin CLR: C-type Lectin Receptor CRISPR: Clustered Regularly Interspaced Short Palindromic Repeats DC-SIGN: Dendritic Cell-Specific ICAM3-Grabbing Non-integrin EGFR: Epidermal Growth Factor Receptor EPD: Eukaryotic Promoter Database ESI: Electrospray Ionization FBS: Fetal Bovine Serum FLR: Fluorescence Fut4: Fucosyltransferase 4 gene FUT4: Fucosyltransferase 4 protein Fut9: Fucosyltransferase 9 gene FUT9: Fucosyltransferase 9 protein GlcNAc: N-acetylglucosamine gRNA: guide RNA HBSS: Hank’s Balanced Salt Solution HILIC: Hydrophilic Interaction Chromatography HPLC: High Performance Liquid Chromatography IL-10: Interleukin-10 LTA: Lotus Tetragonolobus Agglutinin MAL-I: Maackia Amurensis Lectin-I MAL-II: Maackia Amurensis Lectin-II Mgat5: Golgi beta1,6N-acetylglucosaminyltransferase V gene MGL-1: Macrophage Galactose-type lectin-1 MGL-2: Macrophage Galactose-type lectin-2 mRNA: Messenger RNA MS: Mass Spectrometry NK: Natural Killer PPMP: 1-Phenyl-2-Palmitoylamino-3-Morpholino-1-Propanol Siglec: Sialic acid-binding Immunoglobulin-type Lectin SNA: Sambucus Nigra Agglutinin SSEA-1: Stage-Specific Embryonic Antigen-1 TGF-β: Transforming Growth Factor-β Th: T helper Treg: T regulatory UEA-I: Ulex Europaeus Agglutinin-I VPR: VP64-p65-Rta

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Introduction

Over the last few years, genome engineering through the clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 system has received increased attention and is gradually becoming a method of choice for studying various biological processes, including protein and lipid glycosylation. To date, the CRISPR-Cas9 technology is considered as the most precise, efficient and cost-effective gene editing tool available1. Despite the vast complexity of the mammalian glycome, glycosyltransferases have been genetically classified as members of homologous isoenzyme families that influence glycosylation globally2. Therefore, different applications of the CRISPR-Cas9 system can serve as the ultimate toolbox for investigating the biological functions of glycans, through genetic abrogation of the respective biosynthetic pathways in primary cells and cell lines3. For example, the gene knock-out application of CRISPR-Cas9 has already assisted to a better understanding of how glycosylation influences human leukocyte adhesion to endothelial cells4 or the transport of galectins to the cell surface5. Besides the classical gene editing (e.g gene knock-out) applications, tools for successful regulation of gene expression using the CRISPR-Cas9 technology have been also introduced, such as the CRISPR-dCas9- VPR system6. In sharp contrast with traditional methods of gene overexpression with the use of selected cDNA clones, the CRISPR-dCas9-VPR toolkit facilitates de novo gene transcription that occurs physiologically within the nucleus of the cell and its native chromosomal context. In this case, one or multiple guide RNA (gRNA) sequences specifically target the promoter region of the gene of interest, resulting in direct recruitment of the catalytically inactive Cas9 nuclease (called defective or deactivated Cas9) to this site. However, a major difference compared to the CRISPR-Cas9 gene editing tools is that the dCas9 protein is now fused to a hybrid tripartite activation domain (VP64-p53-Rta), known as VPR. The subsequent interaction between the VPR activation unit of dCas9 and the RNA polymerase II and/or other transcription factors eventually drives the expression of the gene of interest (Figure 1A). We hypothesized that induction of gene expression using the CRISPR-dCas9-VPR system could be reliably applied to glycobiology research through the efficient and specific transcriptional programming of glycosyltransferase genes. Importantly, by employing CRISPR-dCas9-VPR, all the critical regulatory mechanisms associated with glycosyltransferase gene expression can be easily unraveled, since they are still active in this model and not simply bypassed. In the past, significant changes in glycosylation due to the use of cDNA clones have been observed7. Moreover, complex epigenetic modifications of genes involved in protein and lipid glycosylation8,9 that are often completely missed or undermined when cDNA clones are used can be now further assessed with CRISPR-dCas9-VPR10. This is of utmost importance for dissecting the mechanisms that lead to an aberrant expression profile of certain glycosyltransferases under pathological conditions, as in the case of cancer. In general, tumor cells are characterized by a tremendous change in their cell surface glycome, as a result of genetic or epigenetic alterations in the expression of particular glycosyltransferase genes. Specifically, cancer cells exhibit elevated levels of fucosylation, sialylation and branched N-linked glycans compared to their healthy counterparts, due to overexpression of certain genes involved in the respective biosynthetic pathways11. Therefore, exploitation of the CRISPR-dCas9-VPR system for transcriptional reprogramming of genes related to cancer glycosylation could provide a better insight into the role of individual glycosyltransferases, as well as the impact of tumor-associated glycans on cancer cell biology. One well-known tumor-associated glycan motif is the fucosylated Lewisx trisaccharide (Galβ1-4(Fucα1- 3)GlcNAcβ1), alternatively known as the stage-specific embryonic antigen 1 (SSEA-1) or CD15 antigen. It is

38 a member of the human histo-blood group antigen system, broadly known as the Lewis antigen system. Overexpression of this epitope has been reported in many types of cancer, including colorectal cancer12. Lewisx is recognized by designated C-type lectin receptors (CLRs) expressed on antigen-presenting cells, such as DC-SIGN (CD209) in humans13 and MGL-1 (CD301a) in mice14. Although different fucosyltransferases are responsible for its synthesis, fucosyltransferase 4 (FUT4) and fucosyltransferase 9 (FUT9) are the most competent ones in synthesizing Lewisx15. Notably, both enzymes are strongly associated with colorectal cancer progression, metastasis and resistance to chemotherapy16,17. However, the biosynthetic properties of the FUT4 and FUT9 enzymes and the potential effect of increased Lewisx expression on the glycosylation status of colorectal cancer cells have not been fully investigated yet. To this end, we are the first to apply the CRISPR-dCas9-VPR targeting system to transcriptionally activate the Fut4 and Fut9 fucosyltransferase genes in MC38 cells, a murine colorectal adenocarcinoma cell line that is commonly used in pre-clinical mouse models for this disease18,19. Following this approach, we successfully generated FUT4- or FUT9-expressing MC38 glyco-engineered cell lines and examined changes in their respective glycosylation profiles, focusing on biosynthesis of the fucosylated Lewisx determinant and its impact on the cancer cell glycome. We believe that this novel methodology of gene expression can be further applied both to human and murine glycosyltransferases involved in tumorigenesis or other disorders and thus set the framework to elucidate the exact implication of these enzymes (or their synthesized glycan structures) in different aspects of disease pathogenesis. Moreover, we consider our study as a representative example of how advances in the CRISPR technology can benefit research investigations focused on glycosylation, thus highlighting its role in health and disease.

Materials and methods

pX330-Delta_Puro cloning The parental pX330 sgRNA Cas9 expression vector from the Zhang lab was obtained from Addgene (ID42230, Addgene, Cambridge, USA). The Puromycin resistance cassette from pX260 (Zhang lab, ID42229, Addgene) was PCR amplified with Phusion High Fidelity polymerase (New England BioLabs, Massachusetts, USA) using forward primer: 5’GGTCGCCCGACGCCCTTACGTCCAGCCAAGCTTAG3’, and reverse primer: 5’TCACTGAGGCCGCCCCGTACTATGGTTGCTTTGAC3’ (Sigma Aldrich, Missouri, USA). The PCR product encoding the Puromycin marker was cloned into the pX330 vector after backbone linearization using two neighboring SmaI restriction sites (New England BioLabs) and InFusion cloning (Clontech, California, USA). Next, the Cas9 coding sequence was removed from the vector backbone by NcoI and EcoRI (New England BioLabs) digests. A linker consisting of hybridized oligonucleotides was cloned into the NcoI and EcoRI sites to circularize the vector using InFusion cloning. Oligonucleotide sequences used were, sense: 5’ATCGCTATTACCATGGAACAATGAGTCTGCATCAAG3’, anti-sense: 5’CGAGCTCTAGGAATTCTTGATGCAGACTCATTGTTC3’ (Sigma Aldrich). gRNA design and cloning As previously described6, the principle behind Cas9-mediated transcriptional programming is that one or multiple gRNA sequences specifically target the promoter region of the gene of interest in close proximity to the transcriptional start site, resulting in direct recruitment of the catalytically inactive dCas9 nuclease to this site. The subsequent interaction between the VPR (VP64-p65-Rta) tripartite activation domain and RNA polymerase II and/or other transcription factors drives the induction of expression of the (glyco)gene of

39 interest (Figure 1A). For this reason, the Eukaryotic Promoter Database (EPD) (https://epd.vital- it.ch/index.php) was used for selecting the promoter regions of the murine Fut4 and Fut9 genes. 20- nucleotide gRNAs sequences complementary to the promoter of each target fucosyltransferase gene were designed and validated with the E-CRISP (http://www.e-crisp.org/E-CRISP/) and the CRISPR design prediction tools (http://crispr.mit.edu/), respectively (Supplementary Table I). The Basic Local Alignment Search Tool (BLAST) (https://blast.ncbi.nlm.nih.gov/Blast.cgi) and the transcription factor binding-site prediction tool (Tfsitescan) (http://www.ifti.org/cgi-bin/ifti/Tfsitescan.pl) were exploited for quality control of the selected gRNAs and to avoid interference with major transcription factor-binding sites (Figure 1B). Selected gRNAs (Supplementary Table I) were ordered (Invitrogen, California, USA) and cloned afterwards into the pX330-Delta_Puro expression vector, as described by Ran et al.20. Finally, the SP-dCas9-VPR plasmid from the Church lab (ID63798, Addgene) was used for expression of the dCas9-VPR protein.

Cell culture and transfections Wild type MC38 cells (murine colon carcinoma), Panc02 (mouse pancreatic ductal adenocarcinoma) (gift from prof. dr. M. van Egmond, Amsterdam UMC, Vrije Universiteit Amsterdam, dept. of Surgery and Molecular Cell Biology and Immunology, Cancer Center Amsterdam), LL/2 (Lewis lung carcinoma) (gift from prof. dr. T. Freire, UdelaR, Montevideo, Uruguay) and glyco-engineered cell lines were maintained in high glucose Dulbecco’s Modified Eagle’s Medium (Gibco, Life Technologies, California, USA) supplemented with 10% FBS premium (Biowest, Nuaillé France) and penicillin/streptomycin (Lonza, Bazel, Switzerland). Cells o were maintained at 37 C and 5% CO2 in a humidified incubator and tested for mycoplasma infection monthly. Cells were transfected in 24-well plates seeded with 50.000 cells per well. 400 ng of the SP-dCas9- VPR-expressing plasmid plus 80 ng of the empty (MC38-MOCK cells) or 80 ng of the FUT4/FUT9 gRNA- expressing PX330-Puro_Delta plasmid (MC38-FUT4/FUT9 cells) were delivered simultaneously to the corresponding wells (protocol adapted from Chavez et al.) together with Lipofectamine LTX (Invitrogen), according to manufacturer’s instructions. During transient transfections, cells were grown for 48 hours before being assayed by flow cytometry. Stable cell lines were selected with 6 µg/mL Puromycin (Sigma Aldrich) for expression of the corresponding gRNA construct, which was applied to the cells 48 hours post- transfection.

Enrichment of Lewisx+ MC38 cells Manual cell separation (MACS) with the use of anti-CD15 (Lewisx) microbeads (Miltenyi Biotec, San Diego, USA) was performed according to manufacturer’s instructions to enrich for the Lewisx+ MC38-FUT4 and MC38-FUT9 glyco-engineered cells 10 days after initial transfection. From this point and due to deviation in the stability of the Fut4 gene expression levels, enrichment of the Lewisx+ fraction of MC38-FUT4 cells specifically was conducted every 2-3 days in order to maintain Lewisx neo-expression in the MC38-FUT4 until the completion of our study. MC38-FUT9 were stable throughout our analysis and did not require any further enrichment steps. qRT-PCR analysis MC38, Panc02 and LL/2 cell-lysis and mRNA isolation was performed with the mRNA capture kit (Roche, Bazel, Switzerland) and cDNA synthesis was performed using the Reverse Transcription System Kit (Promega, Wisconsin, USA), according to manufacturer’s instructions. Synthesized cDNA samples together with the KAPA SYBR® FAST Universal 2X qPCR Master Mix and specific qPCR primers were utilized for each qRT-PCR

40 reaction. Precisely, qPCR primer sequences used for cDNA amplification were as follows: GAPDH forward primer: CCTGCACCACCAACTGCTTAG; GAPDH reverse primer: CATGGACTGTGGTCATGAGCC; FUT4 forward primer: CAGCCTGCGCTTCAACATC; FUT4 reverse primer: CGCCTTATCCGTGCGTTCT; FUT9 forward primer: ATCCAAGTGCCTTATGGCTTCT; FUT9 reverse primer: TGCTCAGGGTTCCAGTTACTCA. qRT-PCR for individual genes was ran and analyzed on the CFX96 Real-Time PCR Detection System (BIORAD, California, USA), with all target gene expression levels normalized to GAPDH (M. Musculus) mRNA levels.

Flow cytometry The primary antibodies used were: anti-Lewisa (clone T174, Calbiochem, San Diego, USA), anti-Lewisb (clone T218, Calbiochem), anti-Lewisx (clone P12, Calbiochem), anti-Lewisy (clone F3, Abcam, Cambridge, United Kingdom), anti-sialyl-Lewisx (clone CSLEX, BD Pharmingen, San Jose, USA) and anti-CD65s (clone VIM-2, LabNed, the Netherlands). The biotinylated lectins used were: LTA (Lotus Tetragonolobus Agglutinin α-1,3-linked fucose), UEA-I (Ulex Europaeus Agglutinin-I, α-1,2-linked fucose), AAL (Aleuria Aurantia Lectin, α-1,6-linked and α-1,3-linked fucose), MAL-I (Maackia Amurensis Lectin-I, α-2,3-linked sialic acid residues on N-glycans), MAL-II (Maackia Amurensis Lectin-II, α-2,3-linked sialic acid residues on O-glycans) and SNA (Sambucus Nigra Agglutinin, α-2,6-linked sialic acid residues), all ordered from Vector Labs (Burlingame, USA). The plant lectin specificity provided above was confirmed by the Functional Glycomics Database (CFG): http://www.functionalglycomics.org/. The C-type lectin-Fc constructs were generated as previously described13 14. Cells were harvested, washed and resuspended in phosphate buffered saline (PBS) or Hank’s balanced salt solution (HBSS) (Gibco, Life Technologies) containing 0.5% bovine serum albumin (BSA) at 1x106 cells/mL. 50.000 cells were added to each well of a 96-well V-bottom plate and incubated with 5 µg/mL of the respective primary antibodies, biotinylated lectins or 10 µg/mL of the C-type lectin-Fc constructs for 30 minutes at 4oC or 37oC, respectively. Cells were washed and resuspended in 50 µL PBS or HBSS (0.5% BSA) containing the corresponding secondary reagents and incubated for 30 minutes at 37oC. Goat anti-mouse IgG FITC was used for anti-Lewisa (Jackson Labs, California, USA) and goat anti-mouse IgM FITC (Jackson Labs, USA) was used for the other primary antibodies. Streptavidin-APC (Biolegend, California, USA) was used to detect plant lectin binding and goat anti-human IgG-Fc FITC (Jackson Labs) for the C-type lectin-Fc constructs. Cells were washed with 100 µL PBS or HBSS (0.5% BSA) and subsequently resuspended in 100 µL PBS or HBSS (0.5% BSA). Fluorescence intensities were measured using either the FACSCaliburTM (BD Bioscience, New Jersey, USA) or the CyAn ADP (Beckman Coulter, Brea, USA) flow cytometers and cytometric data analysis was performed with the FlowJo V10 software.

Inhibitors of glycosylation MC38 cells were treated with 10 µg/mL Kifunensine (mannosidase I inhibitor) (Sigma Aldrich), 2 mM Benzyl-α-GalNAc (O-glycosylation inhibitor) (Sigma Aldrich), 1 μM 1-phenyl-2-palmitoylamino-3- morpholino-1-propanol (PPMP, glucosphingolipid synthesis inhibitor) (Enzo Life Sciences, Brussels, Belgium) or 0-100 µg/mL 2F-Peracetyl Fucose (2F-PF) (general fucosyltransferase inhibitor) (Merck, New Jersey, USA) according to manufacturer’s instructions for 48 hours before cells were analyzed by flow cytometry.

N-glycan release MC38 cells were washed three times with PBS and dried cell pellets (3x106/cell line) were frozen until further use. Upon thawing, the cell pellets were resuspended in 100 μL pure water and were homogenized

41 for 45 minutes in a sonication bath in order to disrupt the cell membranes. After sonication, the samples were centrifuged (500 x g, 15 minutes) and 17.5 µL of pure water was added to each sample and mixed. 5 µL of reaction buffer (250 mM sodium phosphate buffer; pH 7.5) and 1.25 µL of denaturation buffer (2% SDS in 1 M β-mercaptoethanol) was added. The samples were incubated for 10 min at 100oC to denature the proteins. After cooling the samples to room temperature, 1.25 µL of Triton X-100 and 500 units of PNGase F (QABio, California, USA) were added to each sample. Samples were vortexed and incubated overnight at 37oC. The released glycans were then converted to aldoses with 0.1% formic acid, filtered through a protein- binding membrane and dried21.

Glycan labelling Released N-glycans were fluorescently labelled by reductive amination with procainamide as described previously 22 using LudgerTagTM Procainamide Glycan Labelling Kit (LT-KPROC-24). Briefly, samples in 10 µL of pure water were incubated for 60 min at 65oC with procainamide labelling solution. The procainamide labelled samples were cleaned-up using a Ludger clean plate (LC-PROC-96). The purified procainamide labelled N-glycans were eluted with pure water (100 μL). The samples were dried by vacuum centrifugation and resuspended in pure water (100 μL) for further analysis.

Liquid Chromatography Mass-Spectrometry (LC-MS) Procainamide labelled samples and system suitability standards were analyzed by HILIC-(U)HPLC-ESI-MS with fluorescence detection. Samples were injected in 24% pure water/76% acetonitrile (injection volume 25 µL) onto an ACQUITY UPLC® BEH-Glycan 1.7 μm, 2.1 x 150 mm column at 40°C on a Ultimate 3000 UHPLC instrument (Thermo Scientific, Massachusetts, USA) with a fluorescence detector (λex = 310nm, λem = 370nm). The running conditions used were: Solvent A was 50 mM ammonium formate pH 4.4 made from Ludger Stock Buffer (Ludger), and solvent B was acetonitrile. Gradient conditions were: 0 to 38.5 min, 76 to 58% B; 38.5 to 40.5 min, 58 to 40.5% B at a flow rate of 0.4 ml/min; 40.5 to 42.5 min, 40% B at a flow rate of 0.25 ml/min; 42.5 to 44.5 min, 40 to 76% B at a flow rate of 0.25 ml/min; 44.5 to 50.5 min, 76% B at a flow rate of 0.25 ml/min; 50.5 to 51.5 min, 76% B at a flow rate of 0.25 ml/min; 51.5 to 55 min, 76% B at a flow rate of 0.4 ml/min. The UHPLC system was coupled on-line to an AmaZon Speed ETD electrospray mass spectrometer (Bruker Daltonics, Bremen, Germany) with the following settings: source temperature 250°C, gas flow 10 L/min; Capillary voltage 4500 V; ICC target 200,000; maximum accumulation time 50 ms; rolling average 2; number of precursors ions selected 3, release after 0.2 min; Positive ion mode; Scan mode: enhanced resolution; mass range scanned, 180-1500; Target mass, 700. A glucose homopolymer ladder labelled with procainamide (Ludger) was used as a system suitability standard as well as an external calibration standard for GU allocation. ESI-MS and MS/MS data analysis was performed using Bruker Compass DataAnalysis V4.1 software and GlycoWorkbench software23. The relative abundance of identified N-linked glycans was determined as follows: (Number of N-glycans matching the formulas listed in Table I / total number of N-glycans identified for each cell line) * 100%.

Statistical analysis All statistical analyses were performed with the Prism software (GraphPad V8 Software). Statistical significance was determined by a Student’s unpaired t test with Welch’s correction: *p<0.05, **p<0.01, ***<0.001.

42

Results

Design, selection and quality control of the murine Fut4 and Fut9 gene targeting gRNA sequences A key factor for precise, but also efficient, gene targeting using the CRISPR-dCas9-VPR system is the design of the corresponding gRNA sequences. To date, several prediction tools have been developed for this purpose20,24-26, providing detailed lists of proposed gRNAs to the user. However, the final decision about the exact gRNAs that should be used remains a big challenge and a protocol for precisely narrowing down all the possible options is still missing. Therefore, we here present the workflow followed by us for the selection and quality control of the designed gRNAs targeting the murine Fut4 and Fut9 genes (Figure 1B). Importantly, we believe that this process can be easily adapted and utilized for the selection of gRNA sequences that specifically target any other (glyco)gene of interest. In more detail, we initially reviewed the literature giving emphasis to the promoter regions as well as the known regulatory elements that have been implicated in determining the expression of the Fut4 and Fut9 fucosyltransferase genes. Then, we designed 20-nucleotide gRNAs targeting sequences in close proximity to the transcriptional start site of each gene, since transcriptional gene activation is most effective when the target sequence is within the range -400 and +100 bp of the corresponding transcriptional start site27-29. The gRNA sequences were designed using the E-CRISP platform (http://www.e-crisp.org/E-CRISP/)24 and we carefully selected only those gRNAs that could be validated by a second prediction tool, such as the CRISPR design (http://crispr.mit.edu/)20. In order to exclude non-specific gRNAs with potential off-target effects from our analysis, we exploited the Basic Local Alignment Search Tool (BLAST) and further selected those ones that had the minimum complementarity score (<15) with respect to the whole-genome. Finally, by using the transcription factor binding-site prediction tool Tfsitescan (http://www.ifti.org/cgi- bin/ifti/Tfsitescan.pl), we ended up with two gRNAs for each FUT gene (Supplementary Table I), targeting transcription factor binding-free sequences in close proximity to the transcriptional start site and fulfilling the remainder of the criteria mentioned above. These gRNA sequences were considered as ‘optimal’ according to our established gRNA quality control system and were cloned into the appropriate expression vector for the generation of the final gRNA constructs (Figure 1B).

Transcriptional activation of the Fut4 and Fut9 genes leads to specific neo-expression of the Lewisx antigen in MC38-glycovariants Although high expression of fucosylated epitopes has been observed in many human colorectal cancer cell lines30, our MC38 wild type cells did not express any fucosylated type I or type II Lewis antigens on their cell surface (Supplementary Figure 1A) and were completely devoid of Fut4 and Fut9 gene expression (Supplementary Figure 1B). Therefore, we considered MC38 as an ideal candidate cell line for screening the functionality of the designed Fut4 and Fut9 gene targeting gRNAs in vitro. Expression of Lewisx was used as a collective readout for efficient induction of Fut4 or Fut9 gene transcription and subsequent fucosyltransferase function. Specifically, we examined the efficiency of the selected gRNA sequences by co- transfecting MC38 cells with the Sp-dCas9-VR plasmid and one of the Fut4- or Fut9-targeting gRNA constructs (Supplementary Table I). Two days after transfection, MC38 cells were harvested and stained with an anti-Lewisx specific antibody (clone P12) and the expression levels of cell surface Lewisx was assessed by flow cytometric analysis (Supplementary Figure 1E). Interestingly, MC38 cells that were transiently transfected with only one of the two Fut4-targeting gRNAs (gRNA#4-1 or gRNA#4-2) or the Fut9-targeting gRNAs (gRNA#9-1 or gRNA#9-2) displayed higher expression of Lewisx compared to MC38 transfected with

43

Sp-dCas9-VPR construct only (control). This implied that all our four selected gRNAs were functional by means of promoter targeting and that the efficiency of the CRISPR-dCas9-VPR system was successfully translated into functional glycosyltransferases. In addition, we further confirmed the functionality of the designed gRNA sequences by transiently transfecting two different murine cancer cell lines negative for Fut4 and Fut9 gene expression, Panc02 (pancreatic cancer) and LL/2 (Lewis Lung cancer) cells (Supplementary Figure 1C, D, F, G), following exactly the same procedure as mentioned above and again obtained de novo expression of Lewisx.

Figure 1. Model and experimental design for the CRISPR-dCas9-VPR system. (A) Principle of transcriptional gene activation using the CRISPR-dCas9-VPR technology. One or multiple guide RNA (gRNA) sequences that specifically target the complementary promoter region of the (glyco)gene of interest, result in direct recruitment of the catalytically inactive Cas9 nuclease (known as defective or deactivated Cas9) to this site. The following interaction between VPR (VP64-p65-Rta chimeric activator fused to the C-terminus of dCas9) and RNA polymerase II drives the induction of target gene expression. (B) Overview of the five-step experimental design applied for transcriptional activation of the murine Fut4 and Fut9 genes using the CRISPR-dCas9-VPR technology.

44

The small discrepancies observed in the efficiency of Lewisx induction, though, clearly indicate the sequence- specific and cell line-dependent variation of the different gRNAs used in our experiments. As gRNA#4-1 and gRNA#9-1 exhibited slightly higher targeting efficiencies in MC38 cells specifically, using Lewisx expression as a final readout, we proceeded with only these two single gRNAs for the remainder of our studies. Our next step was to generate stable MC38-FUT4 or MC38-FUT9 expressing cells and to evaluate possible changes in their cell surface glycosylation profiles. In this case and compared to MC38-MOCK cells, we observed >200 fold induction of the Fut4 mRNA levels in MC38-FUT4 cells (Figure 2A) and >300 fold induction of the Fut9 mRNA levels in MC38-FUT9 cells (Figure 2B), respectively. Also, no induction of Fut9 gene expression was observed in MC38-FUT4 cells and vice versa, thus showing the specificity of our gRNAs and the CRISPR-dCas9-VPR system. Subsequently, stably-transfected MC38 glycovariants were stained with an anti-Lewisx specific antibody and the expression levels of cell surface Lewisx were assessed using flow cytometry. Consistent with the increased mRNA levels of the Fut4 and the Fut9 genes upon gene activation, significant neo-expression of the Lewisx epitope could be detected on the cell surface of both MC38-FUT4 (>100 fold induction) and MC38-FUT9 cells (>150 fold induction), but not on the MC38-MOCK cells (Figure 2C, E). These results indicate that besides transcriptional gene activation, the CRISPR-dCas9-VPR system can be directly coupled to physiologically relevant fucosyltransferase function, represented in this case by the biosynthesis and the elevated cell surface expression of the Lewisx determinant in MC38-FUT4 and MC38- FUT9 glycovariants. In order to confirm the specificity of the de novo Lewisx expression, cells were treated with different concentrations of the fucosyltransferase inhibitor 2F-Peracetyl Fucose (2F-PF) 31 and stained with the anti- Lewisx antibody. As expected, expression of Lewisx gradually decreased on the surface of MC38-FUT4 and MC38-FUT9 cells upon escalating doses of 2F-PF, with a maximal effect observed at an inhibitor concentration of 100 µg/mL (Figure 2D). Noticeably, Lewisx expression was totally absent on MC38-MOCK cells, irrespective of the inhibitor treatment or not. Besides Lewisx, no other fucosylated type I or type II Lewis antigens (structurally related to Lewisx) could be recorded on the surface of MC38 glycovariants (Figure 2F), confirming that transcriptional activation of the Fut4 and Fut9 genes in MC38 cells specifically results in increased levels of the Lewisx epitope only. During our studies, we noticed that in MC38-FUT4 cells specifically, expression of the Lewisx antigen gradually decreased over time. This prompted us to examine the Fut4 and Fut9 gene expression levels at different time points in MC38 cells. Interestingly, elevated mRNA levels of the Fut4 gene (compared to MC38-MOCK cells) could be observed only within the first 48 hours after isolation of the Lewisx+ cell population in MC38-FUT4 cells, followed by a dramatic decrease 96 hours later (Supplementary Figure 2A). This could probably explain the subsequent decrease in cell-surface expression of Lewisx 96 hours after the initial enrichment (Supplementary Figure 2C). On the contrary, mRNA levels of the Fut9 gene and the expression of Lewisx in MC38-FUT9 cells remained stable over time, since we were able to detect exactly the same expression pattern even 30 days after sorting of the Lewisx+ cell population in these cells (Supplementary Figure 2B, C). Noticeably, the gradual decrease in Lewisx neo-expression in MC38-FUT4 cells (with both a Lewisx+ and a Lewisx- cell population being present at the same time) was not accompanied by a subsequent increase in the cell surface levels of other Lewis antigens (Supplementary Figure 2D), limiting the possibility of Lewisx masking by other fucosyltransferases. Thus, loss of expression of the Fut4 gene, in contrast to the Fut9 gene, suggests that the expression of these specific fucosyltransferase genes may be differentially regulated in our cells. However, whether this loss of FUT4 is really due to unknown

45 genetic or epigenetic factors influencing fucosyltransferase-specific gene expression levels in the context of colorectal cancer, remains to be elucidated by future studies.

Figure 2. Specific neo-expression of the Lewisx epitope on the surface of MC38-FUT4 and MC38-FUT9 glycovariants generated by the CRISPR-dCas9-VPR technology. (A and B) RT-PCR-based assessment of the Fut4 and Fut9 mRNA levels in MC38-glycovariants generated upon transcriptional gene activation using the CRISPR-dCas9-VPR system. Expression was normalized to the housekeeping gene GAPDH (M. musculus). Statistical differences relative to MC38-MOCK cells are depicted. (C) Flow cytometric analysis of the Lewisx antigen expression on the surface of MC38 glycovariants using a mouse anti-Lewisx monoclonal antibody (clone P12). Dotted lines represent staining with the secondary antibody alone, whereas solid lines represent the Lewisx staining. Histograms representative of at least three independent experiments are shown. (D) MC38-glycovariants were cultured in vitro for 48 hours with different concentrations of the fucosyltransferase inhibitor 2F-Peracetyl Fucose (2F-PF). Expression levels of Lewisx were assessed by flow cytometry. (E) Expression of Lewisx on the surface of MC38-FUT4 and MC38-FUT9 cells is abrogated after treatment of cells with 100 µg/mL of the fucosyltransferase inhibitor 2F-Peracetyl Fucose (2F-PF). Mean fluorescent intensities of three independent experiments were normalized to the binding of the secondary antibody alone. (F) Expression of other known type I and type II Lewis antigens (structurally related to Lewisx) on the cell-surface of MC38-glycovariants, as analyzed by flow cytometry. Mean fluorescent intensities of three independent experiments were normalized to the binding of the secondary antibody alone. Statistical significance was determined by a Student’s unpaired t test with Welch’s correction (**p<0.01).

The Lewisx antigen is mainly carried by N-linked glycans and influences α2-3 sialylation in MC38- FUT4 and MC38-FUT9 cells After the generation and characterization of MC38 glyco-engineered cell lines, we wondered whether induction of the Fut4 and Fut9 genes, would affect the global cell surface glycosylation patterns of MC38

46 cells. For this purpose, we exploited a number of commonly used fucose- and sialic acid-recognizing plant lectins and assessed their binding to our MC38 glycovariants using flow cytometry (Figure 3A-F). As expected, we observed significantly higher binding of LTA (Lotus Tetragonolobus Agglutinin with specificity for terminal α1-3 fucosylation) to MC38-FUT4 and MC38-FUT9 cells compared to MC38-MOCK cells, which was completely reversed upon treatment of the cells with 2F-PF, confirming the presence of Lewisx on these cells. However, no statistically significant differences in UEA-I (Ulex Europaeus Agglutinin-I recognizing terminal α1-2 fucosylation) or AAL binding (Aleuria Aurantia Lectin with specificity for core fucosylation and terminal α1-3 fucosylation) could be discerned among the examined MC38 cell lines, although a trend of decreased AAL binding to MC38-FUT4 and MC38-FUT9 cells was noted in the absence of 2F-PF treatment. At this point, due to the overlapping specificity of AAL for core fucosylation and terminal α1-3 fucosylation, we could not draw any conclusions from the AAL binding experiments. To our surprise, MC38-FUT4 and MC38-FUT9 cells displayed decreased binding of MAL-I (Maackia Amurensis Lectin-I recognizing α2-3 sialylation on N-glycans), while MAL-II (Maackia Amurensis Lectin-II with specificity for α2-3 sialylation on O-glycans) and SNA (Sambucus Nigra Agglutinin with specificity for α2-6 sialylation) displayed equal binding to all glycovariants. In addition, MAL-I binding to MC38-FUT4 and MC38-FUT9 cells could be rescued upon treatment of the cells with 2F-PF. Based on these results, we hypothesized that the Lewisx trisaccharide might be carried by N-linked glycan structures on the surface of MC38-FUT4 and MC38-FUT9 cells, thereby competing with α2-3 sialylation on N-glycans in these cells. Therefore, we treated MC38 glycovariants with different inhibitors of glycosylation and we examined changes in the surface expression of Lewisx (Figure 3G, H). Interestingly, Lewisx expression in both MC38-FUT4 and MC38-FUT9 cells was almost completely abrogated upon treatment with Kifunensine (N-glycosylation inhibitor), providing strong evidence that the Lewisx epitope is predominantly carried by N-linked glycans.Of note, the decrease in Lewisx expression due to Kifunensine treatment was comparable to the one observed after treatment of MC38 glycovariants with the general fucosyltransferase inhibitor, 2F-PF. Nevertheless, treatment of MC38 glycovariant cells with either Benzyl-α-GalNAc (O- glycosylation inhibitor) or PPMP (glucosphingolipid synthesis inhibitor) revealed unique differences in the substrate specificity of the FUT4 and FUT9 enzymes, as Lewisx appeared to be present, although to a lesser extent, also on O-glycans and glycosphingolipids in MC38-FUT4 and MC38-FUT9 cells, respectively.

N-glycoprofiling of MC38 engineered cells reveals potent changes in their N-glycome composition Given that the Lewisx antigen is mainly carried by N-linked glycans in MC38-FUT4 and MC38-FUT9 cells, we proceeded to detailed mass spectrometric N-glycoprofiling aimed at the identification of FUT4-, FUT9- or in general, Lewisx-specific alterations in the N-glycan composition of these cells. N-glycans released from whole MC38 cells with the use of PNGase F were labelled with procainamide and subsequentlyanalyzed by HILIC-(U)HPLC-FLR-ESI-MS. All our MC38 glycosylation variants expressed core-fucosylated (fucose residue added in an α1-6 linkage to the innermost GlcNAc of the oligosaccharide core) N-glycans, as indicated by the AAL binding (Figure 3C). Nevertheless, the N-glycoprofiling analysis revealed that distinct di- or tri-fucosylated N-linked glycans bearing both core-fucose and the Lewisx antigen (fucose residue added in an α1-3 linkage to distal GlcNAc residues of the oligosaccharide chain) were detected in MC38- FUT4 and MC38-FUT9 cells only, but not in MC38-MOCK cells (Figure 4A, B, C), validating our previous characterization of MC38-glycovariants using either the anti-Lewisx specific antibody or the LTA plant lectin.

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Figure 3. Lewisx expressed on the surface of both MC38-FUT4 and MC38-FUT9 cells is mainly carried by N-linked glycans. (A- F) MC38-glycovariants, cultured for 48 hours in the presence or absence of the fucosyltransferase inhibitor 2F-Peracetyl Fucose (2F-PF), were incubated for 30 min at 37oC with the indicated biotinylated plant lectins. The levels of antennary α1-3 fucosylation (LTA binding) (A), antennary α1-2 fucosylation (UEA-I binding) (B), core-fucosylation/ α1-3 fucosylation (AAL binding) (C), α2-3 sialylation on N- linked glycans (MAL-I binding) (D), α2-3 sialylation on O-linked glycans (MAA-II binding) (E) and α2-6 sialylation (SNA binding) (F) were examined by flow cytometry. (G and H) MC38-FUT4 (G) and MC38-FUT9 (H) cells were treated with different inhibitors of glycosylation for 48 hours and changes in the cell-surface expression of Lewisx were assessed by flow cytometry. Relative binding (A- F) or expression (G, H) represents the mean fluorescent intensities of three independent experiments after normalization to the binding of the corresponding secondary antibody alone. Statistical differences relative to the untreated condition are shown in G and H. Statistical significance was determined by a Student’s unpaired t test with Welch’s correction (*p<0.05, **p<0.01, ***p<0.001).

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Figure 4: N-glycoprofiling of MC38-glycovariants using HILIC-(U)HPLC-FLR-ESI-MS. FLR chromatograms (10-45 min) corresponding to MC38-MOCK (A), MC38-FUT4 (B) and MC38-FUT9 (C) cells. Representative examples of di- or tri-fucosylated glycans bearing the Lewisx antigen are depicted. The most abundant peaks of the FLR chromatograms represent the high-mannose N-glycans (H5N2/Man5, H6N2/Man6, H7N2/Man7, H8N2/Man8, H9N2/Man9) identified through our analysis. H= Hexose (Hex); N= N-acetylhexosamine (HexNAc); F= Fucose (Fuc); S= Sialic/Neuraminic acid (NeuAc).

Furthermore, we assessed the relative abundance of the identified N-linked glycans (Supplementary Table II) for each cell line separately, focusing on specific traits related to N-glycosylation (Table I), and classified the possible glycan compositions according to the paper by Holst et al.30. In agreement with our earlier observations, MC38-FUT4 and MC38-FUT9 cells displayed a higher relative abundance of di- or tri- fucosylated N-linked glycans compared to MC38-MOCK cells (Figure 5A), highlighting once again the presence of Lewisx in these cells (representative example of the MS/MS analysis of MC38-FUT9 cells is provided as Supplementary Table III).

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Also, a decrease in the relative abundance of mono-fucosylated glycans (corresponding to core- fucosylation) was observed in MC38-FUT4 and MC38-FUT9 cells compared to MC38-MOCK cells, providing an explanation for the reduced AAL binding obtained before. Additionally, the decreased abundance of sialylated glycans (especially the di- and tri-sialylated ones) in MC38-FUT4 and MC38-FUT9 cells (Figure 5B) was consistent with the reduced MAL-I binding to these cells in comparison to MC38-MOCK cells. Therefore, these results imply that the biosynthesis and increased expression of Lewisx have a negative impact both on core-fucosylation and on the N-linked sialic acids.

Table I. Formulas used for calculation of the relative abundance of specific traits defining the N-glycome of MC38-glycovariants, including fucosylation, sialylation, antennarity and glycan subtype. The number of N-glycans matching the provided formulas was transformed into a relative abundance by dividing it to the total number of N-glycans identified for each cell line and multiplying it with 100%. Hex= Hexose, H; HexNAc= N-acetylhexosamine, N; Fuc= Fucose, F; NeuAc= Sialic/Neuraminic acid, S.

Examined traits Calculation Fucosylation Mono- ∑(Fuc=1) Di- ∑(Fuc=2) Tri- ∑(Fuc=3)

Sialylation Mono- ∑(NeuAc=1) Di- ∑(NeuAc=2) Tri- ∑(NeuAc=3)

Antennarity Mono- ∑(HexNAc≤3) Di- ∑(Hex=5 and HexNAc=4) Di/Tri- ∑(Hex≤5 and HexNAc=5) Tri- ∑(Hex=6 and HexNAc=5) Tri/tetra- ∑(Hex≤6 and HexNAc=6) Tetra- ∑(Hex=7 and HexNAc=6) Tetra/poly- ∑(Hex≤7 and HexNAc=7) Poly- ∑(Hex>7 and HexNAc>6)

Glycan subtype High-mannose (HighMan) ∑(Hex4-10HexNAc2) Pauci-mannosidic (PauciMan) ∑(Hex1-3HexNAc2) Hybrid ∑(Hex-HexNAc>2) Complex ∑(Hex-HexNAc≤1)

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Figure 5. Relative abundance of distinct N-linked glycans in MC38-glycovariants identified by HILIC-(U)HPLC-FLR-ESI-MS. The possible N-glycan compositions were classified based on the specific traits examined, such as fucosylation (A), sialylation (B), antennarity (C) and glycan subtype (D), and the relative abundance was calculated for each cell line as indicated in Table I. “Other” represents the N- linked glycan compositions that could not be assigned according to the formulas provided in Table I.

Next, we explored potential differences among the MC38-glycovariants in terms of N-glycan antennarity and specific glycan subtypes. Interestingly, compared to MC38-MOCK cells, MC38-FUT4 and MC38-FUT9 cells were characterized by a higher relative abundance of di- and tri-antennary N-linked glycans and by lower relative abundance of tetra- or tetra-/poly- N-linked glycans (Figure 5C). In addition, a small percentage of pure poly-antennary N-linked glycans could be identified only in MC38-FUT4 and MC38-FUT9 cells and not in the MC38-MOCK cell line. Furthermore, a switch in the balance of the known N-glycan types among the examined MC38 cells could be discerned, with MC38-FUT4 and MC38-FUT9 cells exhibiting a higher relative abundance of complex and a lower relative abundance of high-mannose or hybrid N-glycans compared to MC38-MOCK cells (Figure 5D). Collectively, these findings indicate that the emergence of Lewisx on MC38-FUT4 and MC38-FUT9 cells induces potent changes in the cancer cell N-glycome, influencing antennarity and the respective glycan subtypes. Finally, we were able to pinpoint a number of FUT4 and FUT9-specific N-glycan compositions through our analysis (Table II). For example, the composition H7N6F3 appeared only in the MC38-FUT9 cells and not in the MC38-MOCK or MC38-FUT4, while the composition H5N4F3S1 seemed to be exclusively expressed in the MC38-FUT4 cells. Together our data indicate that FUT4 and FUT9 may have overlapping, yet also unique biosynthetic properties regarding the modification of the substrate N-glycome in tumor cells.

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Table II. List of possible N-linked glycans differing among the examined MC38-glycovariants. N-glycans unique to only one of our glycovariant cell lines are highlighted in grey; (+) indicates presence, (-) indicates absence. Hex= Hexose, H; HexNAc= N- acetylhexosamine, N; Fuc= Fucose, F; NeuAc= Sialic/Neuraminic acid, S.

Possible composition MC38-MOCK MC38-FUT4 MC38-FUT9 Neutral H3N6 - + + H4N8 - - + H6N3 + + - H6N4 + - + H6N5 - - + H6N7 + - - H7N3 + + - H7N4 + - - H7N5 + - - H7N8 - + - H8N7 - - + H9N3 - - + H9N8 - + - H10N2 + - + Fucosylated H3N9F1 - + - H5N2F1 - + + H5N3F1 - + + H5N5F1 - + - H7N6F1 + + - H9N5F1 + - - H3N3F2 - + + H4N3F2 - + + H4N4F2 - + + H4N5F2 - + + H4N6F2 + - - H5N4F2 - + + H4N4F3 - + + H4N5F3 - + + H5N4F3 - + + H5N5F3 - + - H7N6F3 - - + Sialylated H3N4S1 + - - H4N3S1 - + + H4N4S1 - + + H4N5S1 - + + H7N6S1 + - - H4N4S2 + - - H4N5S2 - + + H5N5S2 - + - H7N6S2 + - + H5N5S3 - - + Mixed H4N3F1S1 + - - H4N4F2S1 - + + H5N4F2S1 - + + H5N4F3S1 - + - H5N5F1S1 - + - H7N6F1S1 + - + H8N5F1S1 + - - H5N4F1S2 + + - H5N4F3S2 - - + H6N5F1S2 + - - H7N5F1S2 + - + H7N7F1S2 - - + H6N5F1S3 + - - H8N7F1S4 - - +

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Lewisx antigen neo-expression is functionally relevant and can be recognized by designated lectin receptors. After our thorough examination of the N-glycome of the MC38-FUT4 and MC38-FUT9 glycovariant cell lines, we next determined the functional relevance of cell surface Lewisx and evaluated the binding of specific C-type lectin receptors (CLRs) to our MC38 glycovariants in vitro. For this purpose, we used human DC-SIGN (hDC-SIGN)32 and mouse MGL-1 (MGL-1)33, as these CLRs are well-known Lewisx-recognizing receptors that are physiologically expressed on antigen-presenting cells, such as dendritic cells and macrophages. As a negative control, we chose mouse MGL-2 (MGL-2)14, taking advantage of its lack of specificity for the Lewisx determinant. Compared to the MC38-MOCK cells, a significant increase in the binding of hDC-SIGN (Figure 6A) and MGL-1 (Figure 6B), but not MGL-2 (Figure 6C), could be observed to MC38-FUT4 and MC38-FUT9 cells. Since there was no CLR binding to MC38-MOCK cells lacking Lewisx and the binding was completely lost upon treatment of MC38-FUT4 and MC38-FUT9 cells with 2F-PF, we concluded that the receptor-ligand interactions were CLR-specific and Lewisx-mediated. Taken together, our results signify that the Lewisx determinant synthesized by the FUT4 and FUT9 enzymes following CRISPR-dCas9-VPR-induced gene activation is expressed on the cell-surface in a physiological manner, thus maintaining the full range of its functional and biologically relevant properties.

Figure 6. Binding of C-type lectin receptors to MC38-glycovariants. MC38 cells, cultured for 48 hours in the presence or absence of the fucosyltransferase inhibitor 2F-Peracetyl Fucose (2F-PF), were incubated for 30 minutes at 37oC with human DC-SIGN-Fc (DC-SIGN) (A), mouse MGL-1-Fc (MGL-1) (B) or mouse MGL-2-Fc (MGL-2) (C) and lectin binding was assessed by flow cytometric analysis. Relative binding represents the mean fluorescent intensities of three independent experiments after normalization to the binding of the secondary antibody alone. Statistical significance was determined by a Student’s unpaired t test with Welch’s correction (***p<0.001).

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Discussion

Since the initial discovery of clustered regularly interspaced short palindromic repeats (CRISPR) as the adaptive immune system of bacteria and archaea34, the CRISPR-Cas9 system has been rapidly transformed to a powerful genome-engineering tool with various research applications, including gene editing (gene knock out/in) and gene expression programming (transcriptional gene activation/suppression) among others. The greatest advantage of this system is that the Cas9 nuclease (wild type or deactivated) can be directed to virtually everywhere in the genome with the use of short guide RNAs which, based on DNA sequence complementarity, specifically target the genomic region of interest35. Undoubtedly, research investigations focused on protein and lipid glycosylation can be vastly facilitated by the availability of the CRISPR-Cas9 technology. Besides the direct glycan analysis techniques that have been developed and mainly used so far, the glycosylation machinery within the cells and various functional properties of the glycome can be now further explored through the genetic dissection of distinct glycan biosynthetic pathways. For example, a GlycoCRISPR resource became recently available3, including a validated gRNA library for CRISPR-Cas9 targeting of the human glycosyltransferase genome and enabling potential gene knock-out applications suitable for 167 human enzymes involved in glycan biosynthesis. In the present study, we exploited the CRISPR-dCas9-VPR system for transcriptional activation of certain α1-3 fucosyltransferase genes, Fut416 and Fut917, playing a major role in colorectal cancer pathogenesis. According to our findings, transcriptional activation of the Fut4 and Fut9 genes in MC38 cells resulted in specific neo-expression of the Lewisx antigen on the cell surface. With this, we concluded that the CRISPR- dCas9-VPR-mediated transcriptional gene activation of FUT4 and FUT9 in MC38 cells can be reliably linked to the tumor-associated fucosyltransferase function, with Lewisx emerging as the main fucosylated epitope that these enzymes synthesize in common in the context of colorectal cancer. Regarding the substrate specificity, it is known that FUT9 preferentially fucosylates the GlcNAc residue at the distal lactosamine unit of the polylactosamine chain, whereas FUT4 transfers a fucose to the GlcNAc residue at the inner lactosamine unit36. However, the preferred carrier molecules of Lewisx, synthesized by FUT4 and FUT9 in colorectal cancer cells, have been scarcely studied so far. In our MC38 model system, Lewisx was predominantly carried by N-linked glycans in both MC38-FUT4 and MC38-FUT9 cells. Nevertheless, critical differences in the substrate specificity of the FUT4 and the FUT9 enzymes were also detected in our analysis, since O-glycans and glycosphingolipids appeared to be the secondary preferential glycan carriers of Lewisx in MC38-FUT4 and MC38-FUT9 cells, respectively37,38. Defining the cancer-specific biosynthetic properties of FUT4 and FUT9 is fundamental for the interpretation of the biological functions that these enzymes exert within a tumor cell. Strikingly, N- and O- glycans, as well as glycosphingolipids, have been associated with different functional properties during malignant cell transformation or metastasis39. For example, N-glycans regulate the protein conformation as well as the intracellular signaling properties of tumor-associated growth factor receptors, such as the epidermal growth factor receptor (EGFR) and the transforming growth factor β (TGF-β) receptor40. Therefore, N-glycans have been recently proposed as therapeutic targets in colorectal cancer41. O-glycans have specialized functions in cell adhesion and lipid metabolism42, whereas glycosphingolipids play a major role in anti-cancer drug resistance43 and have been implicated in the maintenance of cancer stemness44. More specifically, we demonstrate here that de novo synthesis of the fucosylated Lewisx antigen by FUT4 and FUT9 leads to potent alterations in the N-glycome of colorectal cancer cells, with a negative impact exerted on core-fucosylation and the α2-3 sialylation levels. This observation highlights existing monosaccharide-, enzyme- and site-specific antagonistic effects occurring during the early stages of N-

54 glycan biosynthesis, the dynamics of which are commonly missed or undermined due to the lack of techniques that are sensitive enough for detection of real-time changes in the cancer cell glycome. In more detail, changes in the N-glycan composition of cancer cells have been shown to affect the subsequent interaction with immune cells45. For instance, hypersialylated tumor cells can engage different Siglec (sialic acid-binding immunoglobulin-type lectins) receptors expressed on myelomonocytic cells and inhibit successful immunosurveillance, depending on the stage of tumor growth and the surrounding microenvironment46. However, it is still unknown whether increased expression of FUT4 and FUT9, influencing the abundance of α2-3-linked sialic acids on N-glycans, is associated with decreased Siglec binding to colorectal cancer cells and modulation of the anti-tumor immune response in vivo. Conversely, the FUT4- and FUT9-mediated synthesis of DC-SIGN and MGL-1 ligands containing the Lewisx epitope hints to potential implication of these enzymes in the induction of an immunosuppressive tumor microenvironment in colorectal cancer. This is supported by the fact that DC-SIGN binding to Lewis antigens overexpressed by cancer cells, results in induction of T helper 2 (Th2) and regulatory T (Treg) cells, decreased activity of natural killer (NK) cells and increased production of anti-inflammatory cytokines, such as interleukin-10 (IL-10)47. Although the exact involvement of MGL-1 in the tumor growth remains elusive, expression of MGL-1 by macrophages has been associated with increased production of IL-10 and acquisition of an anti-inflammatory role in a mouse model of experimental colitis48. Additionally, the induction of complex N-glycan synthesis in MC38 cells upon FUT4 and FUT9 induction raises a possible role for these fucosyltransferases in the metabolic control of colorectal cancer cells. So far, the contribution of complex N-glycans to the altered metabolic status of cancer cells, especially through sensitization to different growth factors, has been merely linked to the expression of Mgat5 (Golgi beta1,6N- acetylglucosaminyltransferase V)49, although more glycosyltransferases might be directly or indirectly involved in this process50. Moreover, LacNAc stretches on complex N-glycans are major ligands for Galectins involved in tumorigenesis, such as Galectin-1, -3 and -851. Nevertheless, whether the observed changes in N-glycan antennary and abundance of complex N-glycans in MC38-FUT4 and MC38-FUT9 cells influence the metabolic or Galectin binding properties52 of colorectal cancer cells, requires further investigation. In summary, we here show that application of the CRISPR-dCas9-VPR technology to augment glycosyltransferase expression in primary cells or cell lines can provide a better insight into potential alterations occurring during malignant cell transformation, and thus, unravel multiple aspects related to the structural or functional properties of the mammalian glycome in health and disease.

Funding This work was supported by the European Union (Marie Curie European Training Network), GlyCoCan project, grant number 676421 to A.B, M.K, D.I.R.S., R.P.K, the Amsterdam UMC to L.A.M.C and H.J.v.d.V, and the Dutch Cancer Society (KWF), grant number 6779-VU-2014 to J.C.v.d.H and S.J.v.V.

Acknowledgements

We thank prof. dr. M. van Egmond for kindly providing the wild type MC38 and Panc02 cells, prof. dr. T. Freire for kindly providing the wild type LL/2 cells, T. Konijn and T. O’Toole for excellent technical support at the O2 Flow Facility (Amsterdam UMC), and prof. dr. Y. van Kooyk for the useful discussions and critical reading of the manuscript.

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Supplementary information

Supplementary Figure 1. Functionality of the designed Fut4- and Fut9-targeting gRNA sequences in different mouse cancer models. Expression of type I (Lewisa, Lewisb) and type II (Lewisy, Lewisy, sialyl Lewisx and VIM-2) Lewis antigens on the surface of MC38 (A), Panc02 and LL/2 (C) wild type cells. Mean fluorescent intensities were normalized to the binding of the secondary antibody alone. Assessment of the relative mRNA levels of the Fut4 and Fut9 genes in MC38 (B), Panc02 and LL/2 (D) wild type cells. Expression was normalized to the housekeeping gene GAPDH (M. musculus). Expression of Lewisx on the surface of MC38 (E), Panc02 (F) and LL/2 (G) cells that were transiently (for 48 hours) transfected with Sp-dCas9-VPR and the selected Fut4- or Fut9-targeting gRNA sequences (listed in Supplementary Table I). As a control, cells were transfected with the Sp-dCas9-VPR-expressing plasmid alone (no gRNA).

Supplementary Figure 2. Stability of the Fut4 and Fut9 transcriptional gene activation and Lewisx neo-expression in MC38- glycovariants. (A) Assessment of the Fut4 gene expression (mRNA levels) in MC38-FUT4 cells 48 and 96 hours after enrichment of the Lewisx+ cell population using anti-CD15 magnetic microbeads. Statistical differences relative to MC38-MOCK cells are depicted. (B) Assessment of the Fut9 gene expression (mRNA levels) in MC38-FUT9 cells 48 hours and 30 days after isolation of the Lewisx+ cell population using anti-CD15 magnetic microbeads. Statistical differences relative to MC38-MOCK cells are depicted. (C) Lewisx expression on the surface of MC38-FUT4 and MC38-FUT9 cells at different time points (48, 96 hours or 30 days) after initial enrichment of the Lewisx+ cell population. (D) Gradual loss of Lewisx neo-expression in MC38-FUT4 cells is not accompanied by elevated levels of other relevant type I or type II Lewis antigens. The cell surface expression of Lewis antigens was examined in MC38-FUT4 cells one week after isolation of the Lewisx+ cell population using anti-CD15 magnetic microbeads, when both a Lewisx+ and a Lewisx- cell fraction are still present.

Supplementary Table I. List of selected and validated gRNA targeting sequences in close proximity to the transcriptional start site (TSS) of the murine Fut4 and Fut9 genes, respectively.

Target Length Targeted Targeted genomic Distance from gene gRNA gRNA sequence (nt) chromosome location (bp) TSS (bp) Murine gRNA#4-1 GAG GTA TCC AGT GCA AGG CG 20 9 14752290 - 14752309 -187 Fut4 gRNA#4-2 GCC AGC GGG GCG CTG TTT CT 20 9 14752204 - 14752223 -101 Murine gRNA#9-1 GCA TAT CGG AGA CGC AGC AA 20 4 25800227 - 25800246 -1 Fut9 gRNA#9-2 GCC TCC CGA CTC AAC ACA CG 20 4 25800140 - 25800159 +86

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Supplementary Table II. List of possible N-linked glycan compositions identified in MC38-glycovariants using HILIC-(U)HPLC-FLR-ESI- MS. Information about the corresponding mass [m/z]+, charge [H+] and GU value is provided for all the glycan compositions. For each cell line, the possible N-linked glycans were categorized into four groups, including neutral (non-fucosylated/non sialylated), fucosylated (mono-, di- or tri-fucosylated), sialylated (mono-, di- or tri-sialylated) and mixed (fucosylated and sialylated) compositions. Hex=Hexose, H; HexNAc=N-acetylhexosamine, N; Fuc=Fucose, F; NeuAc=Sialic/Neuraminic acid, S.

MC38-MOCK Possible Registered Calculated Charge Average composition Mass [m/z]+ Mass [m/z]+ [H+] GU value Neutral H2N2 484.77 484.73 2 2.73 H3N2 565.77 565.76 2 3.22 H3N3 667.32 667.30 2 3.80 H3N4 768.83 768.84 2 4.33 H3N7 716.28 715.97 3 8.36 H4N2 646.81 646.78 2 4.03 H4N3 748.30 748.32 2 6.63 H4N4 849.83 849.86 2 5.22 H4N7 770.29 769.99 3 9.06 H5N2 727.88 727.81 2 5.01 H5N3 829.34 829.35 2 5.49 H5N4 620.94 620.93 3 6.01 H6N2 808.82 808.84 2 5.73 H6N3 607.25 607.25 3 6.38 H6N4 674.97 674.95 3 6.79 H6N7 878.31 878.03 3 7.58 H7N2 889.80 889.86 2 6.48 H7N3 661.29 661.27 3 7.27 H7N4 728.96 728.96 3 7.93 H7N5 796.65 796.66 3 8.24 H8N2 970.83 970.89 2 7.43 H9N2 1051.86 1051.92 2 8.36 H10N2 1132.90 1132.94 2 9.06 Fucosylated H2N2F1 557.77 557.76 2 2.79 H3N2F1 638.80 638.79 2 3.65 H3N3F1 740.33 740.33 2 4.21 H3N4F1 841.85 841.87 2 4.72 H3N5F1 629.28 629.27 3 5.37 H3N6F1 696.95 696.97 3 5.79 H4N2F1 719.95 719.81 2 6.30 H4N3F1 821.34 821.35 2 4.92 H4N4F1 922.87 922.89 2 5.49 H5N4F1 669.60 669.61 3 6.38 H6N4F1 723.64 723.63 3 7.21 H7N4F1 777.64 777.65 3 8.08 H7N5F1 845.53 845.34 3 9.77 H7N6F1 912.63 913.04 3 9.36 H8N5F1 899.31 899.36 3 9.23 H9N5F1 953.31 953.38 3 10.26 H4N6F2 799.65 799.67 3 11.59 Sialylated H3N4S1 610.39 609.93 3 4.66 H5N3S1 650.27 650.27 3 6.18 H5N4S1 717.97 717.96 3 6.75 H6N3S1 704.30 704.29 3 6.99 H6N4S1 771.98 771.98 3 7.43 H7N6S1 721.51 721.29 4 8.85 H4N4S2 761.30 760.97 3 9.36 H5N4S2 814.99 814.99 3 7.27 H6N5S2 702.34 702.78 4 10.96 H7N6S2 794.05 794.06 4 9.52

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Mixed H4N3F1S1 966.85 966.89 2 5.62 H4N4F1S1 712.43 712.63 3 6.26 H4N5F1S1 585.39 585.49 4 11.34 H5N4F1S1 766.65 766.65 3 6.99 H6N4F1S1 820.66 820.66 3 7.77 H6N5F1S1 888.30 888.36 3 8.08 H7N6F1S1 757.44 757.80 4 9.43 H8N5F1S1 996.34 996.39 3 9.65 H5N4F1S2 863.66 863.68 3 7.58 H6N5F1S2 739.28 739.29 4 8.57 H7N5F1S2 779.54 779.81 4 9.52 H7N6F1S2 830.65 830.58 4 10.04 H6N5F1S3 811.70 812.07 4 9.43 H7N4F1S3 802.07 801.81 4 8.85

MC38-FUT4 Possible Registered Calculated Charge Average composition Mass [m/z]+ Mass [m/z]+ [H+] GU value Neutral H2N2 484.76 484.73 2 2.71 H3N2 565.77 565.76 2 3.20 H3N3 667.32 667.30 2 3.78 H3N4 768.83 768.84 2 4.32 H3N6 648.39 648.28 3 9.29 H3N7 716.28 715.97 3 7.93 H4N2 646.80 646.78 2 4.02 H4N3 748.32 748.32 2 6.63 H4N4 849.83 849.86 2 5.21 H4N7 770.29 769.99 3 9.06 H5N2 727.82 727.81 2 4.57 H5N3 829.35 829.35 2 5.60 H5N4 620.93 620.93 3 6.00 H6N2 808.81 808.84 2 5.47 H6N3 910.36 910.38 2 6.37 H7N2 889.82 889.86 2 6.48 H7N3 661.28 661.27 3 7.26 H7N8 750.49 750.05 4 7.52 H8N2 970.84 970.89 2 7.66 H9N2 1051.91 1051.92 2 8.36 H9N8 830.32 831.08 4 11.64 Fucosylated H2N2F1 557.79 557.76 2 2.71 H3N2F1 638.82 638.79 2 3.64 H3N3F1 740.32 740.33 2 4.10 H3N4F1 841.85 841.87 2 4.71 H3N5F1 629.26 629.27 3 5.11 H3N6F1 696.96 696.97 3 5.73 H3N9F1 900.31 900.05 3 9.44 H4N2F1 719.95 719.81 2 6.31 H4N3F1 821.52 821.35 2 4.91 H4N4F1 922.86 922.89 2 5.47 H5N2F1 801.20 800.84 2 7.38 H5N3F1 601.90 601.92 3 6.24 H5N4F1 669.61 669.61 3 6.37 H5N5F1 737.32 737.31 3 6.98 H6N3F1 655.95 655.94 3 7.20 H6N4F1 723.64 723.63 3 7.26 H6N5F1 791.30 791.33 3 7.66 H7N4F1 777.64 777.65 3 7.38 H7N5F1 845.33 845.34 3 8.60

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H7N6F1 912.65 913.04 3 8.85 H8N5F1 899.66 899.36 3 9.29 H3N3F2 813.36 813.35 2 4.91 H4N3F2 894.33 894.38 2 5.79 H4N4F2 995.94 995.92 2 6.24 H4N5F2 731.98 731.98 3 6.74 H5N4F2 717.96 718.30 3 6.63 H6N4F2 771.97 772.32 3 7.38 H6N5F2 839.71 840.01 3 9.81 H4N4F3 712.63 712.97 3 6.31 H4N5F3 780.64 780.66 3 7.93 H5N4F3 767.0 766.99 3 6.90 H5N5F3 834.31 834.68 3 7.52 H6N4F3 820.65 821.00 3 8.85 H6N5F3 888.32 888.69 3 8.24 Sialylated H4N3S1 894.33 893.87 2 5.73 H4N4S1 664.30 663.94 3 6.31 H4N5S1 731.98 731.64 3 6.63 H5N3S1 650.28 650.27 3 6.24 H5N4S1 717.96 717.96 3 6.48 H6N3S1 704.29 704.29 3 7.12 H6N4S1 771.97 771.98 3 7.47 H6N5S1 839.71 839.67 3 8.09 H4N5S2 828.32 828.67 3 8.85 H5N4S2 815.32 814.99 3 7.52 H5N5S2 882.30 882.69 3 9.81 H6N5S2 702.95 702.78 4 8.85 Mixed H4N4F1S1 712.64 712.63 3 6.21 H4N4F2S1 761.31 761.31 3 9.06 H4N5F1S1 585.48 585.49 4 7.93 H5N4F1S1 766.64 766.65 3 6.98 H5N4F2S1 815.32 815.33 3 7.52 H5N4F3S1 863.66 864.02 3 7.38 H5N5F1S1 834.31 834.34 3 8.85 H6N4F1S1 820.65 820.66 3 7.82 H6N5F1S1 888.32 888.36 3 8.50 H6N5F2S1 702.95 703.03 4 10.03 H5N4F1S2 863.66 863.68 3 7.66 H7N6F1S2 830.33 830.58 4 9.81 H7N4F1S3 802.26 801.81 4 9.29

MC38-FUT9 Possible Registered Calculated Charge Average composition Mass [m/z]+ Mass [m/z]+ [H+] GU value Neutral H2N2 484.77 484.73 2 2.72 H3N2 565.78 565.76 2 3.22 H3N3 667.31 667.30 2 3.80 H3N4 768.84 768.84 2 4.33 H3N6 648.32 648.28 3 9.29 H3N7 716.41 715.97 3 7.93 H4N2 646.80 646.78 2 4.03 H4N3 748.33 748.32 2 6.64 H4N4 849.85 849.86 2 5.12 H4N7 770.28 769.99 3 8.86 H4N8 628.50 628.51 4 5.01 H5N2 727.82 727.81 2 4.58 H5N3 829.34 829.35 2 5.48 H5N4 620.94 620.93 3 6.01

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H6N2 808.69 808.84 2 5.48 H6N4 674.95 674.95 3 6.91 H6N5 742.64 742.64 3 7.27 H7N2 889.84 889.86 2 6.64 H8N2 970.84 970.89 2 7.38 H8N7 740.27 739.80 4 11.66 H9N2 1051.86 1051.92 2 8.36 H9N3 769.29 769.31 3 9.44 H10N2 1132.89 1132.94 2 9.07 Fucosylated H2N2F1 557.77 557.76 2 2.79 H3N2F1 638.82 638.79 2 3.68 H3N3F1 740.33 740.33 2 4.21 H3N4F1 841.85 841.87 2 4.72 H3N5F1 629.26 629.27 3 5.12 H3N6F1 696.31 696.97 3 5.73 H4N2F1 719.33 719.81 2 6.32 H4N3F1 821.33 821.35 2 5.37 H4N4F1 922.84 922.89 2 5.48 H5N2F1 801.26 800.84 2 7.38 H5N3F1 601.91 601.92 3 6.01 H5N4F1 669.62 669.61 3 6.38 H6N3F1 655.94 655.94 3 7.19 H6N4F1 723.64 723.63 3 7.27 H6N5F1 791.31 791.33 3 7.67 H7N4F1 777.56 777.65 3 8.09 H7N5F1 845.32 845.34 4 8.61 H8N5F1 899.66 899.36 3 9.29 H3N3F2 813.36 813.35 2 4.92 H4N3F2 894.38 894.38 2 5.88 H4N4F2 995.91 995.92 2 6.32 H4N5F2 731.73 731.98 3 6.64 H5N4F2 717.97 718.30 3 6.99 H6N4F2 772.31 772.32 3 7.38 H6N5F2 839.65 840.01 3 9.81 H4N4F3 712.67 712.97 3 6.32 H4N5F3 586.19 585.75 4 6.64 H5N4F3 766.64 766.99 3 6.99 H6N4F3 820.65 821.00 3 8.86 H6N5F3 888.33 888.69 3 9.81 H7N6F3 757.78 758.06 4 9.44 Sialylated H4N3S1 893.98 893.87 2 5.80 H4N4S1 663.94 663.94 3 6.23 H4N5S1 731.73 731.64 3 6.64 H5N3S1 650.61 650.27 3 7.27 H5N4S1 717.97 717.96 3 6.64 H6N3S1 704.30 704.29 3 7.13 H6N4S1 772.31 771.98 3 7.45 H6N5S1 839.98 839.67 3 8.09 H4N5S2 828.32 828.67 3 8.86 H5N4S2 815.30 814.99 3 7.67 H6N5S2 936.67 936.70 3 8.86 H7N6S2 794.30 794.06 4 9.97 H6N5S3 775.66 775.55 4 9.29 Mixed H4N4F1S1 712.67 712.63 3 6.23 H4N4F2S1 761.32 761.31 3 9.07 H4N5F1S1 585.55 585.49 4 7.93 H5N4F1S1 766.64 766.65 3 7.13 H5N4F2S1 815.30 815.33 3 7.54 H6N4F1S1 820.99 820.66 3 7.75

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H6N5F1S1 888.65 888.36 3 8.50 H6N5F2S1 936.26 937.04 3 9.07 H7N6F1S1 757.78 757.80 4 9.44 H5N4F3S2 960.85 961.05 3 9.44 H7N5F1S2 779.81 779.81 4 9.29 H7N6F1S2 830.35 830.58 4 10.47 H7N7F1S2 881.31 881.35 4 9.81 H7N4F1S3 802.24 801.81 4 9.97 H8N7F1S4 854.20 854.13 5 12.42

Supplementary Table III. Example of HILIC-(U)HPLC-FLR-ESI-MS/MS analysis with fragments of m/z 766.64 (possible composition H5N4F3) from the MC38-FUT9 cell line.

MC38-FUT9 Registered Calculated MS/MS Fragment Intensity Mass [m/z]+ Mass [m/z]+ (%)

366.06 366.14 94.30

441.46 441.27 44.30

512.21 512.20 79.87

528.27 528.19 10.75

587.55 587.33 19.66

644.35 645.01 13.84

674.39 674.25 10.43

790.63 790.41 13.23

952.50 952.46 6.39

1039.33 1039.38 8.32

1114.61 1114.51 21.10

1201.35 1201.44 8.71

1276.34 1276.57 13.06

65

1347.37 1347.49 4.65

1641.75 1641.70 2.92

1787.88 1787.76 5.27

66

67

68

Chapter 3

Bitter sweet symphony: Immune responses to altered O-glycan epitopes in cancer

Lenneke A.M. Cornelissen Sandra J. van Vliet

Biomolecules 6, doi:10.3390/biom6020026 (2016).

Abstract

The appearance of aberrant glycans on the tumor cell surface is one of the emerging hallmarks of cancer. Glycosylation is an important post-translation modification of proteins and lipids and is strongly affected by oncogenesis. Tumor-associated glycans have been extensively characterized regarding their composition and tumor-type specific expression patterns, nevertheless whether and how tumor-associated glycans contribute to the observed immunomodulatory actions by tumors has not been extensively studied. We here provide a detailed overview of the current knowledge on how tumor-associated O-glycans affect the anti- tumor immune response, thereby focusing on truncated O-glycans present on epithelial tumors and mucins. These tumor-associated O-glycans and mucins have been shown to bind a variety of lectin receptors on immune cells to facilitate the subsequently induction of tolerogenic immune responses. We therefore postulate that tumor-associated glycans not only support tumor growth, but also actively contribute to immune evasion.

70

Introduction

Glycosylation is one of the most important post-translational modification of proteins and lipids and it is estimated that over 95% of all cell surface proteins are elongated with glycans. Glycosylation plays a fundamental role in various cellular processes, including cell-cell recognition, cell-matrix interactions as well as intracellular signaling. Glycans regulate the folding of newly synthesized proteins and is therefore crucial for protein stability 1. The glycosylation status of a cell is highly dynamic and dramatically alters upon oncogenesis, whereby cancer cells, compared to their healthy counterparts, express more branched N- glycans, higher levels of fucosylated and sialylated glycans and a truncated O-glycan phenotype 2. This abundant and aberrant cancer glycosylation profile is currently widely accepted as a distinct hallmark of cancer. Although the immune system is capable of attacking and eradicating cancer cells, it is often suppressed within the tumor microenvironment. In this review we focus on the short O-glycans and highlight the current state of knowledge on the effects of these tumor-associated glycans on ongoing anti-tumor immune responses. We discuss how these truncated O-glycans structures , through their interaction with glycan-binding receptors on immune cells mislead anti-tumor immunity and thereby facilitate immune escape by the tumor.

Tumor-associated O-glycosylation

The O-glycosylation pathway starts with the addition of a single N-acetylgalactosamine (GalNAc) to a Serine or Threonine residue, thus forming the Tn antigen epitope. Tn antigen can be further elongated with galactose to form the T antigen (also known as Core 1 or Thomsen–Friedenreich antigen) or with N- acetylglucosamine (GlcNAc) to form Core 3. O-glycans in healthy cells are generally even further extended to complex branched Core 2 or Core 4 structures, whereby the truncated Tn antigen is only expressed at high levels during embryogenesis. Complex Core 3 and Core 4 structures are exclusively present in the intestinal tract. In contrast to healthy cells, tumor cells are characterized by the appearance of truncated O- glycans, such as the Tn antigen and T antigen, on the cell surface. These truncated structures can be sialylated creating sialyl-Tn (sTn) and sialyl- of disialyl- -T (sT), which prevents further elongation of the glycan structure (Figure 1). Synthesis of Tn antigen is mediated by an UDP-GalNAc:polypeptide GalNAc-transferase (ppGalNAcT) that transfers the α-GalNAc to a Serine or Threonine residue. Expression of ppGalNAcTs is dynamic and these enzymes have been shown to relocate from the Golgi to the ER in certain tumor types 3. This relocation facilitates a prolonged mode of action, leading to enhanced expression of Tn antigen on the tumor. T antigen is formed through the action of the glycosyltransferase T-synthase (Core 1 β3-galactosyltransferease) and its essential chaperone COSMC 4,5. COSMC function seems supportive for cancer progression, since loss of T antigen-derived O-glycans decrease breast tumor development in mice 6. In hepatocellular cancer, COSMC mRNA and protein are frequently overexpressed and this correlates with metastasis and poor survival 7. Contradictory, knockdown of COSMC in the pancreatic cell line T3M4 actually induced oncogenic features with enhanced growth and tumor invasion 8. Together, these results indicate that the impact of COSMC function, and thus Tn or T antigen expression, on oncogenesis might be tumor type-specific, whereby, depending on the tumor type, either Tn or T antigen plays a more dominant role. Tn and T antigen and their sialylated counterparts, sTn and sT antigen, are expressed by multiple tumor types, especially those of epithelial origin. Among these, expression levels of Tn antigen and T antigen seem quite comparable, ranging from 87.5% for breast cancer to 71% in ovarian cancer for

71

Figure 1. Overview of the O-GalNAc glycosylation pathway. Glycosyltransferases are shown in red and O-glycan structures in blue.

Tn antigen and 87.5% for breast cancer, to 66% for ovarian cancer and 60% for colon cancer for T antigen 9- 11. The protein that carries T antigen depends on the tumor type, and includes several mucins and mucin- type protein, such as CD44 on colorectal cancer, MUC1 on breast cancer and CD164 in gastric and prostate cancer 12. Bladder cancer is characterized by the overexpression of sTn, whereby 75% and 20% of the high- and low-grade tumors respectively express sTn. Correlation studies clearly point out that truncated O- glycans affect tumor progression, demonstrating that sTn expression is associated with a high risk of recurrence and progression in the high-grade bladder tumors 13. Moreover, in lung adenocarcinomas Tn antigen is a significant and independent prognostic factor for overall and relapse-free survival 14. Given the fact that tumor progression is based on clonal selection of the fittest cells within the heterogeneous cancerous population and that truncated O-glycans are specifically associated with malignant transformation, suggests that tumor cells expressing truncated O-glycans are preferentially selected and likely promote tumor cell survival. Indeed, colorectal carcinoma patients carrying T antigen positive tumors show a significantly higher risk to develop metastasis, as exemplified by increased expression of T antigen in liver metastasis (91%) compared to the primary tumor (60%) 9. Although Tn and T antigen seem to act via different, tumor-type specific mechanisms , truncated O-glycans in general affect tumor progression and their presence is strongly correlated to bad prognosis. For more insight into truncated O-glycans and tumor development, we refer the reader to an excellent and extensive review by Chia et al.15. Overall, the regulation of tumor-associated O-glycans, such as Tn, T antigen and their sialylated counterparts, is complex and cannot be fully attributed to under- and overexpression of certain

72 glycosyltransferases16,17. The strong correlation between tumor-associated O-glycans and bad prognosis points to the crucial role glycans and their mucin-type carrier proteins play in tumor progression and the development of metastasis. The broad impact of glycosylation on (cancer) cell function indicates that glycans not only play a fundamental role in cancer, but might also contribute to other more immune-related diseases (Box 1). In addition, truncated O-glycans may contribute to tumor progression through evasion of anti- tumor immune responses.

Box 1. Aberrant O-glycosylation in other (immune-related) diseases Aberrant O-glycans are involved in a variety of diseases, including autoimmunity and cancer and demonstrate the multi- disciplinary action of glycans and the importance of glycosylation in maintaining health. 1. IgA nephropathy (IgAN): IgAN is a very common glomerulonephritis that is characterized by deposition of IgA immune complexes in the glomerulus. The O-glycans in the hinge-region of IgA-1 antibodies isolated from glomeruli deposits lack galactose and thus display high amounts of Tn antigen 18-20. The aberrantly glycosylated IgA antibodies form immune complexes with anti-glycan autoantibodies, thus classifying IgAN as an autoimmune disease. The high quantity of Tn antigen on the IgA may facilitate binding to and subsequent signaling of the C-type lectin macrophage galactose-type lectin ( MGL) on antigen presenting cells (APCs). MGL triggering has been shown to augment production of IL-10 21, which could result in stimulation of IgA-producing B cells, thus aggravating the disease. The role of IL-10 in disease progression is further supported by the finding that compared to healthydonors, whole blood cultures from IgAN patients are more prone to produce IL-10 after stimulation with lipopolysaccharide or phytohaemagglutinin 22. 2. Tn syndrome: Tn syndrome is characterized by Tn antigen expression on all major blood cell lineages. Patients with Tn syndrome are not clinically affected, except for minor signs of hemolysis or thrombocytopenia 23. Tn syndrome is associated with a somatic mutation of COSMC, thereby preventing T-synthase function 24. Interestingly, only 1-2 % of T cells are affected. As binding of MGL to Tn antigenpos CD45 on T cells induces T cell apoptosis 25, it is tempting to speculate that Tn antigenpos T cells are cleared from the circulation and thus undetectable in these patients. 3. Inflammatory bowel disease: Also in an inflammatory autoimmune setting O-glycans appear to be important for disease progression. Mice with an intestinal epithelial cell-specific loss of Core 1-derived O-glycans spontaneously develop colitis, suggesting a protective role of Core 1 in preventing intestinal inflammation 26. Indeed, also patients with active ulcerative colitis show impaired expression of intestine glycans and an accompanying increase in truncated glycans. An increased amount of the sTn antigen could be detected in 18% of the patients compared to 2% in the control patients. Interestingly, the aberrant glycosylation profile was shown to be reversible upon remission and was significantly correlated to the extent of inflammation 27.

The interplay between tumor cells and the immune system

The tumor microenvironment is a complex and intricate network of tumor cells, stromal cells and infiltrating immune cells. The immune system is able to control cancer development to a certain extent, since immunodeficient mice and immunocompromised humans, such as AIDS patients, develop tumors more readily 28,29. These findings have led to the immunosurveillance theory 30,31, which assumes that the immune system constantly detects and eradicates evolving tumors, even before they become clinically visible. Nevertheless, immunosurveillance is not sufficient as malignant tumors do develop and therefore the updated concept of tumor immunoediting 32 is a more complete explanation on the role of immune cells in tumor progression. This concept conveys that tumor cells acquire the potency to evade ongoing immune responses by reducing immune recognition, by increasing their resistance against immune attack and by creating an immune suppressive tumor microenvironment. As the host’s immune system appears to be a crucial factor in tumor progression and metastasis formation, the immune status of a particular patient has become indispensable for predicting prognosis and response to therapy. This has led to the classification using the immunoscore, first introduced by Galon et al 33. The immunoscore is based on the immune infiltration in the tumor microenvironment and can be

73 applied to identify high-risk patients who would benefit from immunotherapy. In a variety of solid tumors, high frequencies of tumor-infiltrating lymphocytes (TILs) were correlated with increased survival 34-38, whereby high frequencies of memory T cells are particularly associated with a lack of early metastatic invasion 39. Moreover, in a longitudinal study high cytotoxic activity of peripheral-blood lymphocytes is negatively correlated with cancer incidence 40. Indeed, increased frequencies of anti-tumor cytotoxic CD8+ T cells (CTLs) at the center and the invasive margin of the tumor are positively correlated with increased survival 36,37. Like CTLs, NK cells are able to lyse tumor cells, however the hypoxic microenvironment of the tumor, reduces expression of the major activating NK-cell receptors, causing impaired NK cell-mediated tumor kill 41. Dendritic cells (DCs) capture, process and (cross-) present antigens to naïve CD4+ and CD8+ T cells, and are therefore the main instigators in initiating adaptive immunity. However, the number of DCs in the blood of breast, head and neck, and lung cancer patients are reduced and their maturation capacity is impaired compared to healthy blood DCs 42. In agreement with this, tumor infiltration of mature DCs has been correlated with a better clinical outcome 43. Key players in the suppression of anti-tumor immunity are the regulatory T cells (Tregs) and indeed a low CTL/Treg ratio has been associated with poor clinical outcome in ovarian 44 and gastric cancer 45. Tumor- associated macrophages (TAMs) can promote tumor progression by suppressing effector T cell responses through the production of anti-inflammatory cytokines such as IL-10 and TFGβ. Accordingly, TAM infiltration is also correlated with bad prognosis 46. In addition, the tumor cells themselves contribute to immune suppression through the secretion of IL-10 and TGFβ and chemokines that recruit Tregs to the tumor site. These immune-related cancer evasion strategies were recently reviewed in more detail by others 43,47.

Immune receptors involved in the recognition of tumor-associated O-glycans

The interaction between the immune system and the truncated O-glycans present on tumor cells is mediated by a diverse set of carbohydrate-binding receptors, commonly known as lectins. These glycan- binding receptors have been shown to be important immune regulators both in health and disease 48. Especially antigen presenting cells (APCs) express a wide variety of carbohydrate-binding receptors, including the C-type lectin receptors (CLRs) and the Sialic acid-binding immunoglobulin-type lectins (Siglecs). CLRs are able to internalize their ligands for processing and presentation to T cells. In addition, CLRs possess signaling properties to actively modify DC and macrophage function. The human CLR macrophage galactose-type lectin (MGL, CD301) recognizes terminal GalNAc moieties (Table 1), such as Tn and sTn antigen and is therefore a prime receptor for the aberrant O-glycans in cancer 49-51. In mice two MGL homologs with different binding specificities exist. Mouse MGL-1 (MGL-1) primarily recognizes Lewis X, while mouse MGL-2 (MGL-2) interacts with Tn antigen and, in contrast to human MGL, with GlcNAc- GalNAc, T antigen and Core-2 structures 52 (Table 1). MGL is mainly expressed by immature and tolerogenic dendritic cells and macrophages 53, suggesting that MGL may play a role in immune regulation 54. Moreover, MGL-1 and MGL-2 are markers for alternatively activated macrophages 55, while in humans high MGL expression is considered a marker for TAMs 56. Research into the MGL expression on TAMS demonstrated that MGL levels on human TAMs are three times higher compared to in vitro generated macrophages 57. An immunomodulatory role of MGL is further supported by the finding that high MGL binding in stage III colon cancer patients is associated with a poorer disease-free survival 58. Ligands for MGL are mainly expressed on mucins produced by epithelial cancers. Healthy epithelial cell- derived mucins are characterized by an extended Core 2 glycan phenotype and they do not carry the cancerous truncated O-glycans. MGL is able to interact with various types of cancer-related-mucins from

74 which two are extensively studied, namely mucin 1 (MUC1) and mucin 2 (MUC2). More importantly, APCs are able to distinguish between healthy and tumor-derived mucins through the MGL receptor 59-61. The binding of MGL to Tn antigen on MUC1 appears to be independent of the sialylation status of Tn antigens, as MGL binds Tn-MUC1 and sTn-MUC1 with similar affinity 54.

Table 1. Immune receptors involved in the recognition of tumor-associated O-glycans

Species Receptor O-glycan structure

human hMGL Tn antigen

Sialyl-Tn antigen

mouse MGL-1 Lewis X

MGL-2 Tn antigen

T antigen

Core-2

GlcNAC-GalNAc

human hSiglec-3 Sialyl-Tn antigen

hSiglec-9 Sialyl-Tn antigen

Sialic acid-binding immunoglobulin-type lectins (Siglecs) specifically recognize sialic acids, which are generally the outermost sugars on a multitude of carbohydrate structures. Siglecs are widely expressed within the immune system and the majority of them contain an immunoreceptor tyrosine-based inhibitory motif (ITIM) in their cytoplasmic domain, again indicative of an immune regulatory function. In contrast to MGL, Siglecs recognize and bind normal self- antigens, which are usually decorated with sialic acids. Thus, the inhibitory function of Siglecs might be a way to tolerate the immune system to self-glycans. Sialylated glycans therefore have been proposed to act as self-associated molecular patterns (SAMPs) to maintain immune homeostasis and to dampen reactivity following an immune response 62. Several pathogens, including Neisseria gonorrhoeae and group B Streptococccus, evade the immune system by decorating themselves with sialic acids, thus mimicking the host’s self-glycans. As CD33-related Siglecs can dampen host’ inflammatory immune responses, engagement of Siglec-5 and Siglec-9 by the bacterial sialic acids could thus be considered an immune manipulatory mechanism. It is therefore generally believed that Siglecs underwent rapidly evolutionary changes to combat with sialic acid-expressing pathogens 63. In line with this, the hypersialylation, often observed on tumor cells, might be one of the mechanisms by which tumors evade immune attack. However, which Siglecs are involved in the recognition of tumor- associated sTn and sT antigen and whether binding subsequently induces Siglec-mediated signaling is not extensively studied. To date, there are at least 13 different Siglecs described in humans and 9 in mice. In contrast to the CD33-related Siglecs, Siglec-1 (also known as Sialoadhesion and CD169) has no ITIM motif

75 and could therefore be an important antigen capture receptor to promote anti-tumor immunity. Indeed, selective targeting of Siglec-1+ macrophages induces an efficient CD8+ T cell response 64 and promotes germinal B-cell responses in mice 65. Siglec-1 is also able to engage MUC1 from breast cancer cell lines in a sialic acid-dependent manner and Siglec-1+ macrophages are found in close contact with breast carcinoma cells 66. However, whether Siglec-1-mediated signaling is promoting or inhibiting tumor progression is currently not known. Siglec-2 (also known as CD22) is expressed by B cells and has been shown to bind mucins derived from colon cancer cells. Upon mucin binding, the phosphorylation of CD22 and ERK-1/2 was decreased, indicating a downregulation of B cell receptor signal transduction 67. Additionally, Siglec-3 and Siglec-9 expressed by monocyte-derived DCs (moDCs) have been demonstrated to bind MUC2 via the sTn antigen epitope (Table 1), although the subsequent response is different. Binding of MUC2 to Siglec-3 induces apoptosis of moDCs, while binding to Siglec-9 on moDCs decreases IL-12 production, while leaving IL-10 production unaffected 68,69. To summarize, immune cells can specifically recognize tumor-associated O-glycans through expression of CLRs, like MGL, and Siglecs. Based on their expression patterns and inhibitory signaling, these receptors have been implicated in the suppression of the anti-tumor immune response. Because of their extensive O- glycosylation, especially tumor-derived mucins carry a dense array of truncated O-glycan ligands for both the MGL and Siglec receptors.

Effect of aberrantly glycosylated mucins on immunity

The high-molecular-weight mucin proteins are the most abundant carriers of O-glycans. They are produced by epithelial cells and from the at least 20 different mucin types known, especially MUC1 and MUC2 are well studied in regard to their immunomodulatory properties. Mucins consist of 5-500 tandem- repeats domains enriched in Serine, Proline and Threonine amino residues. Each tandem repeat contains at least 5-100 potential glycosylation sites, resulting in a heavily O-glycosylated mucin protein 70. Mucins produced by healthy cells mainly contain extended Core 2-based complex glycans, while the tumor-derived mucins mainly express truncated and immature O-glycans 71.

Uptake and processing of mucins by DCs The main immunological function of DCs is to instruct and activate naïve CD8+ and CD4+ T cells through the presentation of immunogenic peptides in MHC class I and II molecules respectively. The glycosylation status of a peptide does not necessarily interfere with antigen presentation as both MHC class I and II molecules are able to bind and present glycosylated peptides 72,73. Synthetic Tn antigen containing MUC1 glycopeptides, representing the tumor-associated MUC1, are processed to smaller fragments for loading into the MHC class II molecule without removing the glycans attached to the original MUC1 peptide 74. Strikingly, the degradation of MUC1 glycopeptides is not affected by the length of the glycan 74 but the peptide cleavage sites are qualitatively and quantitatively influenced by O-gycosylation, 75. Unlike the presentation of MUC1 glycopeptides in MHC class II, published data on presentation in MHC class I are contradictory. Tn-MUC1 glycopeptides derived from CHO-transfected cell lines 60 as well as MUC1 glycopeptides from pooled ascites fluid from patients with metastatic breast and pancreatic cancer 76 are processed and presented in both MHC class I and II molecules in DCs, supporting the ability of DCs to specifically initiate adaptive immunity to tumor-associated mucins. Nevertheless, Madson et al demonstrated that Tn glycosylation of an Ovalbumin (OVA)-MUC1 fusion peptide inhibited the presentation of the fusion peptides by MHC class I and abolished MUC1-specific CD8+ T cell responses. The same fusion

76 peptide did, however, promote presentation by MHC class II and elicited a specific antibody response 77. Because Tn-OVA conjugates are able to induce increased CD8+ T cell proliferation compared to the unconjugated OVA 78, the observed contradiction is likely not due to the use of OVA as a backbone in the OVA-MUC1 fusion construct. Since the degradation of glycopeptides depends on the attachment site of the glycans, glycosylation might also affect the cross-presentation pathway of DCs and consequently presentation in the MHC class I molecule, thus providing an explanation for the observed contradictory results. As tumor cells express and, in case of MUC2, secrete mucins, DCs are more likely to encounter whole mucin proteins instead of mucin glycopeptides. DCs are equally capable of endocytosing MUC1 glycoproteins, but in contrast to MUC1 glycopeptides, the MUC1 glycoproteins are not transported to late endosomes or MHC class II loading compartments for processing and binding to the MHC class II molecule 76. It has been postulated that abundant mannose structures present on MUC1 glycoproteins bind the mannose receptor and prevent dissociation of MUC1 in the early endosomes, thus leading to entrapment of MUC1 in this compartment 76. In contrast, Tn antigen-containing MUC1 is internalized through MGL and subsequently accumulates in MHC class II loading compartments 60, supporting the idea that the addition of Tn antigen averts binding to mannose receptor and thereby entrapment in the endosome. Co-localization of the Tn-MUC1 glycoprotein with MHC class I is not observed 60, hence it is unlikely that DCs are able to process and present Tn-MUC1 glycoproteins in MHC class I molecule. Clearly, mucin glycoproteins undergo a different intracellular routing in DCs than mucin glycopeptides, and depending on their glycosylation pattern the routing of mucin glycoproteins is re-adjusted to different intracellular compartments.

Influence of mucin engagement on adaptive immunity The maturation status of the DCs is crucial for the subsequent naïve T cell differentiation and involves expression of co-stimulatory and inhibitory molecules and production of pro- or anti-inflammatory cytokines. DCs are thus able to master the balance between inflammatory responses and immune suppression. The influence of mucin-type proteins on DC maturation has been primarily studied using co-culture systems. For instance, MUC2-stimulated monocyte-derived DCs decrease their secretion of the IL-12 cytokine in a glycosylation-dependent manner 69. In DCs cultured with recombinant sT-MUC1, the expression of several co-stimulatory molecules, such as CD86 and CD40 and antigen presenting molecules CD1d and HLA-DR was decreased. In contrast, CD1a and mannose receptor levels, were increased. The cytokine profile of sT-MUC1-stimulated DCs is characterized as IL-10high and IL-12low 79,80, together suggesting that sT-MUC1 prevents DC maturation and induces a regulatory DC phenotype. Indeed, the sT-MUC1-stimulated DCs were defective in triggering pro-inflammatory immune responses in both allogeneic and autologous settings 80. Together these studies show that engagement of tumor-associated mucins by DCs prevents effective induction of subsequent immune responses, pointing to a suppressive action of mucins on adaptive immunity through the induction of tolerogenic DCs. T cells and B cells are the two main players in adaptive immunity. Natural antibodies, produced by plasma B cells, are present in the body without prior external immune activation. Such natural antibodies directed against MUC1 glycopeptides can be detected in the serum of breast cancer patients. Interestingly, these natural antibodies have a binding preference for glycosylated MUC1 peptides compared to the unglycosylated MUC1 81. Antibodies play a crucial role in suppressing tumor growth by binding tumor cells, thereby facilitating recognition and destruction by the immune system via antibody-dependent cell-

77 mediated cytotoxicity. In breast cancer patients, a strong anti-sT-MUC1 antibody response was associated with reduced rate and a delay in metastases formation 82, suggesting that antibodies could indeed play a role in dampening breast cancer progression. As plasma B cells are the sole producers of antibodies, the effect of mucins on B cells was investigated in in vivo tumor models. Mice bearing a mucin-producing mammary tumor had a reduced number of splenic B cells compared to mice bearing non-producing tumors. This effect was probably due to the presence of mucins in the bloodstream which can engage CD22 on splenic B cells, thereby downregulating B cell receptor signal transduction, as discussed previously 67. Although efficient CTL cytotoxicity is crucial for tumor immune attack, not much is known about the direct effect of mucins on the CD8+ T cell responses. This may be explained by the finding that DCs are not able to efficiently process and present mucin glycoproteins in the MHC class I molecule.

Effect of tumor-associated O-glycans on APCs and the initiation of adaptive immunity

Because truncated O-glycans are specifically recognized by lectins, such as MGL and the Siglecs, the immunomodulatory capacity of these receptors has been addressed by targeting these receptors through antibodies or O-glycans coupled to model tumor antigens. Also knock down of glycosyltransferases in in vivo tumor models has been used to investigate the effect of tumor glycosylation on the immune system. The results of these studies are described below.

Immune modulation by MGLpos APCs Immature DCs continuously sample their environment for invading pathogens. Upon pathogen encounter, DCs mature and migrate to the lymph node to initiate adaptive immunity by activating naïve T cells. The C-type lectin MGL is only expressed on immature DCs, where it hampers the DC migratory response 49. Tumor cells might take advantage of this by expressing high levels of MGL ligands that in turn prevent the migration of the tumor-infiltrating DCs to the lymph nodes. Normally, mature DCs in the lymph node are able to skew naïve CD4+ T cells towards distinct T helper cell populations, such as T helper 1 and 2 cells. The differentiation of the T helper cell subsets is amongst others dependent on the DCs subset. Murine dermal DCs (dDCs) specifically express the MGL-2 lectin 83. In contrast to human MGLpos DCs, the migratory capacity of these MGL-2pos dDCs is not affected, as MGL-2pos dDCs are capable of transporting protein antigens, injected in the footpad, efficiently to the skin draining lymph nodes. Moreover, the percentage of MGL2pos dDCs in the skin draining lymph node increases dramatically when papain is used as an adjuvant. Papain is a potent T helper 2-inducing adjuvant, indicating that MGL2pos dDCs are required for the development of antigen-specific T helper 2 responses. Nevertheless, MGL2pos dDCs are not required for the differentiation of T follicular helper cells or germinal center responses induced by pathogens, such as Nippostrongylus brasiliensis 83. However, Freire et al observed a strong germinal B cell reaction after intradermal immunization of Tn-dendrimers in mice, resulting in anti-Tn antibodies 84. Since Tn antigen is a ligand for MGL-2, Tn antigen might play a role in the development of T helper 2 responses including a strong B cell activation. The role of Tn antigen on T helper 2 response in mice is further supported by a study of Chen et al. When the T-synthase enzyme was knocked down in the murine posterior tibialis muscle, leading to high levels of Tn antigen, the splenic CD4+ T cells showed an enhanced production of T helper 2 cytokines, namely IL-10 and IL-4, after transplantation of skin grafts 85. However, whether the observed effects were MGL-2 mediated or not was not addressed. Together these results suggest that Tn antigen directs the activation of T helper 2 responses through MGL-2 in mice. Whether immune responses directed to Tn

78 antigen positive tumors are more prone to generate detrimental T helper 2-type reactions, and thereby indirectly preventing the preferential anti-tumor T helper 1 immunity, is, as far we know, not described in literature. The DC maturation program is initiated after pathogen binding to specific pattern recognition receptors, such as the Toll-like receptors (TLRs), on the DC. In addition, TLRs can recognize specific danger molecules released or expressed by tumor cells. CLR-glycan interactions have already been shown to modify TLR- mediated signaling, leading to a fine-tuning of the pathogen-specific immune responses 86. The MGL-Tn/sTn interaction could therefore also potentially modify the DCs’ ability to respond to danger signals originating from the tumor. Indeed, upon MGL engagement, DCs enhance their TLR-2-mediated signaling resulting in an increased secretion of the anti-inflammatory cytokine IL-10 and a tolerogenic DC phenotype 87. Furthermore, these MGL-licensed DCs are able to specifically promote the differentiation of regulatory T cells 88. The tolerogenic nature of the human MGLpos DCs is even further supported by a study investigating the interaction between DCs and T cells. Interestingly, effector T cells carry MGL ligands on their CD45 molecules. Binding of MGL to CD45 on effector T cells induces T cell apoptosis, which is dependent on concomitant T cell receptor triggering 25. As highly activated effector T cells increase their Tn antigen expression 87, MGLpos APCs in the tumor microenvironment could be crucial in dampening cytotoxic reactions by inducing apoptosis of tumor-infiltrating CD8+ T cells. As discusses earlier, mouse and human MGL appear to be quite different regarding their immunological function. Singh et al observed enhanced antigen-specific CD4+ and CD8+ T cell responses with the use of OVA coupled to Tn antigen, compared to unmodified OVA proteins. Here, the effects were MGL-2-mediated, as Tn-OVA conjugates were endocytosed presented in MHC class I and II molecules in a MGL-2-dependent manner 78. For both human and mice, targeting of MGL with an antibody-conjugate has been shown to increase antigen presentation by DCs 84,89, indicating that the observed differences are not due to inefficient antigen presentation.

In conclusion, MGL-2pos dDCs play a role in T helper 2 immune responses and targeting of MGL-2 with Tn-OVA glycoconjugates induces enhanced CD4+ and CD8+ T cell responses (Figure 2). In contrast, human MGLpos DCs are hampered in their migratory capacity and adopt a tolerogenic phenotype upon engagement of MGL (Figure 2). That human MGL appears to have an immune suppressive action through the specific induction of effector T cell apoptosis may provide an explanation for thecorrelation between MGL binding and reduced survival in colon cancer patients 58.

Sialylated O-glycans and immunity As depicted in Figure 1, truncated O-glycans can be sialylated and thereby form sTn and sT antigen structures. Sialylated glycan structures can be recognized by Siglec receptors, which through their intracellular ITIM motifs have the potential to dampen anti-tumor immunity. To investigate the role of sTn on immune modulation, human moDCs were co-cultured with a bladder cancer cell line (the MCR cell line) that was transduced with ST6GalNAc-I enzyme to induce sTn expression (MCR-sTn). moDCs co-cultured with MCR-sTn cells had an immature phenotype characterized by lower expression of MHC class II, CD80 and CD86 compared to the moDCs co-cultured with control MCR cells. Interestingly, the moDCs from the MCR- sTn co-culture were unresponsive to further maturation stimuli and produced only low levels of IL-12 and TNFα 90.Both the reduction in maturation markers and the low levels of pro-inflammatory cytokines suggests the generation of tolerogenic DCs upon contact with sTn-expressing cells. This is further supported by the

79 observation that MCR-sTn-stimulated moDCs were not able to activate T cells as measured by the expression of the early T cell activation marker CD69. Furthermore, these T cells displayed a Foxp3high, IFNγlow phenotype, representing a typical regulatory T cell profile 90. Importantly, similar results were obtained using a sTn positive breast cancer cell line, arguing that the sTn antigen effect on DC immunity is irrespective of the cancer type. Blockade of CD44 and MUC1 was able to prevent the tolerance-inducing effect on DCs, indicating a pivotal role of CD44 and MUC1 in the observed immune modulatory effects by sTn antigen 90.

Figure 2. Immune modulation by human and mouse MGLpos dendritic cells. Depicted are the MGL-mediated effects on DC and T cell responses after MGL engagement by tumor-associated glycoproteins and O-glycans.

The Treg-promoting capacity of sialic acids is further exemplified in the mouse melanoma (B16) cancer model. Isogenic B16 cell lines with either high or low expression of sialic acids were generated through knockdown of the sialic acid transporter SLC35A1. In vivo the growth of the B16 sialic acid low tumors was significantly decreased compared to B16 sialic acid high tumors. More importantly, in the B16 sialic acid low tumors more effector T cells and a diminished number of regulatory T cells were detected at the tumor site 91. Intriguingly and similar to DCs co-cultured with sTn antigen positive bladder cancer cells 90, DCs primed with sialic acid-OVA conjugates skewed naïve T cells towards antigen specific Foxp3high IFNylow regulatory T cells in a Siglec-E-dependent manner 92. As Siglec-E is not only expressed by DCs, but also on mouse neutrophils, monocytes, and macrophages, the sialic acid-Siglec-E interaction might modify additional immune-related pathways. As discussed earlier, sTn present on MUC2 is recognized by human Siglec-3 and Siglec-9 expressed by moDCs and failed to induce high production of IL-12 68,69. Triggering of the Siglec-9 receptor on macrophages increased IL-10 and decreased TNFα production after stimulation with the TLR ligand

80 lipopolysaccharide or peptidoglycan 93. Another sTn-binding Siglec is Siglec-15, which is expressed by macrophages and DCs, and interestingly, also found on TAM in various human carcinoma tissues including lung, rectal and hepatocellular carcinoma . Engagement of Siglec-15 on macrophages with sTn augments the TGF-β production by these cells 94, suggesting that Siglec-15 on macrophages may contribute to tumor progression by TGF-β-mediated immune modulation.

Overall, a concept emerges in which sialic acids present on tumor cells play a central role in immune suppression (Figure 3) and thereby tumor immune evasion, likely through their interaction with inhibitory Siglec receptors. Remarkably, no research is dedicated to the role of sialyl-T and di-sialyl T antigen on immune modulation, while the unsialylated T antigen and the responsible glycosyltransferases T-synthase and COSMC are well studied. It is highly probable that the sialylated T antigens are also recognized by certain Siglec receptors on immune cells. However, whether this interaction contributes to tumor immune evasion is subject for future studies.

Figure 3. Immune modulation by human and mouse Siglec+ dendritic cells. Depicted are the Siglec-mediated effects on DC and T cell responses after Siglec engagement by tumor-associated glycoproteins and sialylated O-glcyans, or through co-culture with sialylated tumor cells. In mice, these effects are mediated by Siglec-E, whereas in humans Siglec-3 and Siglec-9 are known to modulate DC immunity.

Effect of tumor-associated O-glycans on natural killer cells

Besides DCs and macrophages, not much is known about the role of O-glycans on the other innate immune cell subsets. Natural killer cells (NK cells), as the name already implies, are able to eliminate infected as well damaged and malignant cells very efficiently. Their cytotoxic action is balanced by signaling through activating and inhibitory receptors on the NK cell surface. Already in the 1980s, NK cells were first implicated in tumor immunosurveillance, when researchers observed a higher incidence of defective NK cell function

81 in individuals with cancer 95,96. Early experiments using co-cultures of blood mononucluear cells, containing NK cells, and K562 tumor cells, a classical NK target, revealed that NK cells were less able to kill tumor cells in the presence of sialic acids, while other glycans such as mannose and galactose had no effect 97. Although not a direct proof, it does suggest that sialic acids participate in preventing NK cytotoxicity. More specifically, mucins derived from sheep, bovine and porcine submaxillary glands that carry sTn are able to inhibit NK- mediated tumor cell killing, while mucins derived from human breast and lung cancer cells that lack sTn antigen have no effect. Together these results suggest that sTn antigen can directly modulate NK cell function 98. A more recent study revealed that desialylated tumor cells (from a mouse 3-methylcholanthrene-induced fibrosarcoma) induce higher secretion of IFNγ by NK cells, indicative of a more active NK response 99. Moreover, the NK activating receptor NKG2D was shown to be responsible for the interaction between NK cells and the desialylated ligands on the tumor cells. In vivo, desialylated tumor cells show a reduced tumor growth compared with to fully sialylated wild type tumor cells 99, which is a NK-mediated phenomenon91. The interaction between sialylated tumor cells and NK cells occurs via the inhibitory Siglec-7 and Siglec- 9 receptors on the human NK cell surface. Ligands for Siglec-7 and Siglec-9, which are distinct sialoglycan determinants such as gangliodise GD3, are broadly expressed on different human cancer types and protect the cancer cells from NK cell-mediated cytotoxicity 100. Moreover, increasing the sialylation status of multiple cancer lines decreases their susceptibility to antibody-dependent NK cytotoxicity, through the recruitment of Siglec-7 101. Although commonly accepted that NK cells are instrumental in eliminating tumor cells, the effect of tumor cell glycosylation on NK cell function has not been extensively studied and forms material for future research.

Concluding remarks

Avoiding immune destruction is one of the hallmarks of cancer. We postulate that the aberrant glycosylation of tumors is one of the major factors underlying the development of an immune suppressive tumor microenvironment. Tumor-associated O-glycans induce a tolerogenic programming of tumor- resident DCs, characterized with low expression of co-stimulatory molecules, high IL-10 secretion and an incapacity to induce of effector T cell responses. Engagement of truncated O-glycans by lectin receptors on tumor-associated macrophages likewise promotes production of anti-inflammatory cytokines, such as IL-10 and TGFβ. Cytotoxic NK cells are crucial in eliminating tumor cells, but surprisingly, the role of tumor- associated O-glycans on the function of NK cells is scarce. Clearly, more research is needed to fully understand the impact of tumor glycans on anti-tumor immunity. Some of the outstanding questions that await answering, include: clarification of the specific contribution of each aberrant O-glycan structure, Tn, sTn, T and sT, including the importance of the peptide/protein backbone, to tumor immune evasion, the cells and receptors involved and elucidation molecular mechanisms by which tumor cells employ glycans to avoid anti-tumor immunity. The impact of the tumor type may be one of the confounding factors and should be taken into consideration when addressing these issues. Increasing our understanding of tumor cell glycosylation and its regulation may create novel strategies to alleviate tumor-mediated immune suppression by directly interfering with the tumor glycome and its interaction with antigen presenting cells.

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Acknowledgments

We thank Yvette van Kooyk for critically reading of the manuscript. L.A.M. Cornelissen was supported by a grant from the VU University Medical Center and S.J. van Vliet by project grant VU 2014-6779 from the Dutch Cancer Society (KWF).

Author Contributions

L. A. M. Cornelissen wrote the manuscript and designed the figures. S. J. van Vliet revised and corrected the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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80 Rughetti, A. et al. Recombinant tumor-associated MUC1 glycoprotein impairs the differentiation and function of dendritic cells. J Immunol 174, 7764-7772 (2005). 81 von Mensdorff-Pouilly, S. et al. Reactivity of natural and induced human antibodies to MUC1 mucin with MUC1 peptides and n-acetylgalactosamine (GalNAc) peptides. Int J Cancer 86, 702-712 (2000). 82 Blixt, O. et al. Autoantibodies to aberrantly glycosylated MUC1 in early stage breast cancer are associated with a better prognosis. Breast Cancer Res 13, R25, doi:10.1186/bcr2841 (2011). 83 Kumamoto, Y. et al. CD301b(+) dermal dendritic cells drive T helper 2 cell-mediated immunity. Immunity 39, 733-743, doi:10.1016/j.immuni.2013.08.029 (2013). 84 Freire, T. et al. Glycosidic Tn-based vaccines targeting dermal dendritic cells favor germinal center B-cell development and potent antibody response in the absence of adjuvant. Blood 116, 3526-3536, doi:10.1182/blood-2010-04-279133 (2010). 85 Chen, H. D. et al. Knockdown of core 1 beta 1, 3-galactosyltransferase prolongs skin allograft survival with induction of galectin-1 secretion and suppression of CD8+ T cells: T synthase knockdown effects on galectin-1 and CD8+ T cells. J Clin Immunol 32, 820-836, doi:10.1007/s10875-012-9653-8 (2012). 86 Gringhuis, S. I., den Dunnen, J., Litjens, M., van der Vlist, M. & Geijtenbeek, T. B. Carbohydrate-specific signaling through the DC-SIGN signalosome tailors immunity to Mycobacterium tuberculosis, HIV-1 and Helicobacter pylori. Nat Immunol 10, 1081-1088, doi:10.1038/ni.1778 (2009). 87 van Vliet, S. J. et al. Human T cell activation results in extracellular signal-regulated kinase (ERK)-calcineurin- dependent exposure of Tn antigen on the cell surface and binding of the macrophage galactose-type lectin (MGL). J Biol Chem 288, 27519-27532, doi:10.1074/jbc.M113.471045 (2013). 88 Li, D. et al. Targeting self- and foreign antigens to dendritic cells via DC-ASGPR generates IL-10-producing suppressive CD4+ T cells. J Exp Med 209, 109-121, doi:10.1084/jem.20110399 (2012). 89 Napoletano, C. et al. Targeting of macrophage galactose-type C-type lectin (MGL) induces DC signaling and activation. Eur J Immunol 42, 936-945, doi:10.1002/eji.201142086 (2012). 90 Carrascal, M. A. et al. Sialyl Tn-expressing bladder cancer cells induce a tolerogenic phenotype in innate and adaptive immune cells. Mol Oncol 8, 753-765, doi:10.1016/j.molonc.2014.02.008 (2014). 91 Perdicchio, M. et al. Tumor sialylation impedes T cell mediated anti-tumor responses while promoting tumor associated-regulatory T cells. Oncotarget, doi:10.18632/oncotarget.6822 (2016). 92 Perdicchio, M. et al. Sialic acid-modified antigens impose tolerance via inhibition of T-cell proliferation and de novo induction of regulatory T cells. Proc Natl Acad Sci U S A, doi:10.1073/pnas.1507706113 (2016). 93 Ando, M., Tu, W., Nishijima, K. & Iijima, S. Siglec-9 enhances IL-10 production in macrophages via tyrosine- based motifs. Biochem Biophys Res Commun 369, 878-883, doi:10.1016/j.bbrc.2008.02.111 (2008). 94 Takamiya, R., Ohtsubo, K., Takamatsu, S., Taniguchi, N. & Angata, T. The interaction between Siglec-15 and tumor-associated sialyl-Tn antigen enhances TGF-beta secretion from monocytes/macrophages through the DAP12-Syk pathway. Glycobiology 23, 178-187, doi:10.1093/glycob/cws139 (2013). 95 Morizane, T., Watanabe, T., Tsuchimoto, K. & Tsuchiya, M. Impaired T cell function and decreased natural killer activity in patients with liver cirrhosis and their significance in the development of hepatocellular carcinoma. Gastroenterol Jpn 15, 226-232 (1980). 96 Sullivan, J. L., Byron, K. S., Brewster, F. E. & Purtilo, D. T. Deficient natural killer cell activity in x-linked lymphoproliferative syndrome. Science 210, 543-545 (1980). 97 Van Rinsum, J., Smets, L. A., Van Rooy, H. & Van den Eijnden, D. H. Specific inhibition of human natural killer cell-mediated cytotoxicity by sialic acid and sialo-oligosaccharides. Int J Cancer 38, 915-922 (1986). 98 Ogata, S., Maimonis, P. J. & Itzkowitz, S. H. Mucins bearing the cancer-associated sialosyl-Tn antigen mediate inhibition of natural killer cell cytotoxicity. Cancer Res 52, 4741-4746 (1992). 99 Cohen, M. et al. Sialylation of 3-methylcholanthrene-induced fibrosarcoma determines antitumor immune responses during immunoediting. J Immunol 185, 5869-5878, doi:10.4049/jimmunol.1001635 (2010). 100 Jandus, C. et al. Interactions between Siglec-7/9 receptors and ligands influence NK cell-dependent tumor immunosurveillance. J Clin Invest 124, 1810-1820, doi:65899 [pii]10.1172/JCI65899 (2014). 101 Hudak, J. E., Canham, S. M. & Bertozzi, C. R. Glycocalyx engineering reveals a Siglec-based mechanism for NK cell immunoevasion. Nat Chem Biol 10, 69-75, doi:nchembio.1388 [pii] 10.1038/nchembio.1388 (2014).

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Chapter 4

Tn antigen expression contributes to an immune suppressive microenvironment and drives tumor growth in colorectal cancer

Lenneke A.M. Cornelissen Athanasios Blanas Joost C. van der Horst Laura Kruijssen Anouk Zaal Tom O’Toole Yvette van Kooyk Sandra J. van Vliet

Manuscript submitted

Abstract

Expression of the tumor-associated glycan Tn antigen (αGalNAc-Ser/Thr) has been correlated to poor prognosis and metastasis in multiple cancer types. However, the exact mechanisms exerted by Tn antigen to support tumor growth are still lacking. One emerging hallmark of cancer is evasion of immune destruction. Although tumor cells often exploit the glycosylation machinery to interact with the immune system, the contribution of Tn antigen to an immune suppressive tumor microenvironment has scarcely been studied. Here, we explored how Tn antigen influences the tumor immune cell composition in a colorectal cancer (CRC) mouse model. CRISPR/Cas9-mediated knock out of the COSMC gene (C1GalT1C1) resulted in elevated Tn antigen levels on the cell surface of the mouse CRC cell line MC38 (MC38-Tnhigh). MC38-Tnhigh tumors displayed increased tumor growth in vivo that could be correlated to reduced tumor immune cell infiltration, specifically characterized by enhanced accumulation of myeloid derived suppressor cells and reduced levels of cytotoxic CD8+ T cells. Interestingly, no systemic differences in immune cell subsets were observed. Our data demonstrate for the first time that Tn antigen expression in the tumor microenvironment of CRC alters the local immune repertoire in vivo.

Keywords: Glycosylation, Tn antigen, colorectal cancer, myeloid derived suppressor cells, CD8+ T cell

Highlights

• High Tn antigen levels on colorectal cancer cell line MC38 (MC38-Tnhigh) enhanced tumor growth in vivo • MC38-Tnhigh tumors displayed enhanced accumulation of myeloid derived suppressor cells while CD8+ T cells were diminished. • Tn antigen expression in the tumor microenvironment of CRC alters the local immune repertoire in vivo.

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Introduction

Tumor cells are characterized by an aberrant glycosylation profile, hence, current research is focusing on how tumor-associated glycan structures support tumor progression. Glycosylation is a post-translational modification of proteins and lipids, which is not template-driven, but instead, is influenced by the metabolic state of the cell and the availability of the sugar donors1. Glycan structures are known to drive diverse biological functions, among others, protein folding, cell-cell and cell-matrix adhesion and cell signaling2. Moreover, the ability of glycan structures to modulate immune responses has led to the hypothesis that tumor-associated glycan structures are responsible for skewing the tumor microenvironment towards an immune suppressive phenotype3. However, our knowledge on how tumor-associated glycan structures actually affect anti-tumor immunity is still quite limited. Compared to their non-malignant counterparts, tumor cells express much higher levels of the Tn antigen glycan structure4,5. Expression of Tn antigen is a prognostic factor for overall and relapse-free survival in lung adenocarcinoma6 and correlates with metastatic potential in colorectal cancer7. Tn antigen is made up of one N-acetylgalactosamine (GalNAc) monosaccharide and represents the initiation of mucin-type O- glycosylation. In humans, a repertoire of twenty GalNAc-transferases (GalNAcTs) is responsible for the initiation of O-glycosylation8 and thus Tn antigen synthesis. The expression of GalNAcTs is dynamic and these enzymes have been shown to relocate from the Golgi to the ER in certain tumor types9. This relocation enables prolonged mode of action, leading to overexpression of Tn antigen, thereby promoting cancer cell invasiveness. Since epithelial cells produce high levels of mucin proteins that are heavily O-glycosylated, high expression of Tn antigen predominantly occurs in epithelial cancer types, including colorectal cancer (CRC), where 86% of primary and metastatic human CRC tissues express the Tn epitope10. Tn antigen overexpression has been shown to directly induce oncogenic features including enhanced cell proliferation, decreased apoptosis, increased adhesion and migratory capacities 11-13. Since immune cells highly express receptors recognizing glycan structures14, inhibition of anti-tumor immunity might be an additional mechanism by which Tn antigen supports tumor growth. The lectin specifically binding Tn antigen is the macrophage galactose-type lectin (MGL)15. However, whether cancer cells exploit Tn antigen in vivo to evade immune attack has never been thoroughly investigated. In the present study we assessed for the first time the impact of Tn antigen on in vivo tumor growth and the immune cell composition present at the tumor site using CRISPR/Cas9 glyco-engineered mouse colorectal cancer MC38 cells. We report that overexpression of Tn antigen drives tumor growth in CRC, which coincided with reduced tumor immune infiltration, increased myeloid-derived suppressor cells and decreased CD8+ T cell infiltration. Together, these data suggest that Tn antigen may promote an immune suppressive tumor microenvironment, which could contribute to tumor immune evasion and thus tumor progression.

Materials & Methods

CRISPR/Cas9 constructs CRISPR/Cas9 constructs were made using the pSpCas9(BB)-2A-Puro plasmid, a gift from Feng Zhang (Addgene #62988), according the previously described protocol16. Sequences of the gRNA strands for murine C1GalT1C1 were as follows: top strand CACCGGTTTTCTTACCTCCAAA; bottom strand CCAAAAGAATGGAGGTTTCAAA. The gRNA cloned plasmid was used for transformation of XL1-Blue

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Sublconing-Grade competent bacteria (Stratagene). Nucleobond Xtra Midi kit (Macherey-Nagel) was used to purify the plasmid according manufacturer’s protocol.

Generation of the MC38-Tnhigh cell line MC38 cells were cultured in DMEM supplemented with 10% heat inactivated fetal calf serum (FCS, Biowest), 1% penicillin and 1% streptomycin. MC38 cells were transfected with CRISPR/Cas9 constructs either targeting the C1GalT1C1 gene (MC38-Tnhigh) or an empty CRISPR/Cas9 construct (MC38-MOCK). For transfection, Lipofectamine LTX with PLUSTM reagent (ThermoFisher Scientific) was used and applied according to the manufacturer’s protocol. Transfected MC38 cells were selected in bulk based on their Tn antigen cell surface profile as described below. Transfected MC38 cells were incubated with 5 µg/mL of the biotinylated α-N-acetylgalactosamine-specific Helix Pomotia agglutinin (HPA, Sigma) for one hour on ice, washed with medium and subsequently incubated with streptavidin-PE (Jackson ImmunoResearch) again for one hour on ice. Cells were washed with medium and sorted in bulk on HPA high binding cells. The sorting procedure was performed twice to obtain the final MC38-Tnhigh cell line.

Surveyor assay To obtain genomic DNA, fresh cells were harvested and DNA was isolated with the Quick-DNATM kit (Zymo research) according to manufacturer’s instructions. The C1GalT1C1 gene was amplified with qPCR (forward primer: CTGGCGGTCTGCCTGAAATA, reverse primer: TGTACAAGCAGACTTCAATG). The qPCR products (416 bp) were hybridized and treated with Surveyor Nuclease (Surveyor® Mutation Detection Kit, Integrated DNA Technologies), which recognizes and cleaves any DNA mismatches. To visualize the mutation, the Surveyor Nuclease-treated products were separated by DNA agarose gel electrophoresis.

T synthase assay Cell lysates were obtained using 0.5% Triton X-100 and subsequently used for the T synthase assay as described by Ju and Cummings17. Shortly, T synthase present in cell lysates utilizes the commercially available acceptor-substrate GalNAcα-4MU (Sigma Aldrich) and the donor-substrate UDP-Gal (Sigma Aldrich) to generate Galβ1-3GalNAcα-4MU structure. This product is hydrolyzed by O-glycosidase (New England Biolabs), leading to free 4-MU that is highly fluorescent and can be measured at an excitation of 355 nm and emission of 460 nm. The protein concentration in the cell lysate was determined with PierceTM BCA protein assay kit (ThermoFisher Scientific) to determine T synthase enzyme activity per µg protein.

Determination of the cellular glycosylation profile MC38 cells were incubated with 5 µg/mL of the HPA lectin, anti-Tn antigen antibodies (5F4, kindly provided by H. Clausen and H. Wandall18) or 10 µg/mL of mouse MGL-2-Fc for 30 minutes at 37°C. Cells were washed and incubated with streptavidin-APC (BD Biosciences), goat anti-mouse IgM-A488 (ThermoFisher Scientific) or goat anti-human IgG-FITC (Jackson ImmunoResearch) respectively for 30 minutes at 37°C. Cells were washed and acquired on the Beckman Coulter Cyan flow cytometer. Data was analyzed with FlowJo v10.

CellTiter-Blue® Cell Viability assay The CellTiter-Blue® Cell Viability assay (Promega) was used according to the manufacture’s protocol to measure the metabolic activity of MC38 cells. Briefly, 30.000 MC38 cells were cultured in a 1:6 dilution of the

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CellTiter-Blue® Reagent. The metabolic activity was measured during the first 24 hours of culture using a FLUOstar Galaxy (MTX Lab systems) with an excitation and emission of 560 nm and 590 nm, respectively.

In vivo tumor experiments C57BL/6 mice were used at 8-12 week of age and bred at the animal facilities of Amsterdam UMC/VU University Medical Center (VUmc). An equal distribution of female and male mouse was used for all in vivo experiments,. Experiments were performed in accordance with national and international guidelines and regulations. Each mouse received 2 x 105 tumor cells in 100 µl PBS, which was subcutaneously injected in the flanks. Tumor measurements were performed three times per week in a double blinded manner. The total tumor volume was calculated according the formula 4/3 x π x abc (a = the radius of the width, b = the radius of the length and c = the average radius of width and length). Mice were sacrificed at day 13 or when the tumor reached a size of 2000 mm3. The tumor, tumor draining lymph nodes and spleens were isolated and used for further experiments.

Tumor and organ dissociation First, tumors were finely minced and enzymatically digested for 25 min at 37°C in RPMI containing 1 mg/mL Collagenase type 4 (Worthington), 30 units/mL DNase I type II (Sigma-Aldrich) and 100 µg/mL hyaluronidase type V (Sigma-Aldrich) . Lymph nodes and spleens were finely minced and digested for 10 min at 37°C with 2 U/mL Liberase TM (Roche) containing 30 units/mL DNase I type II. Subsequently, cell suspensions were passed through a 70 µm cell strainer and washed once with RPMI supplemented with 10% FCS, 1% penicillin, 1% streptomycin and 1% glutamax. Spleen digestions were subjected to ACK lysis and, together with the lymph node digestions, washed twice before use in subsequent experiments. To enrich for the lymphocytes and to remove dead cell debris from tumor digestions, the tumor digestion mixture was loaded on a Ficoll gradient. The interface was collected, washed twice and used for subsequent flow cytometric analysis.

Flow cytometric analysis To block Fc receptors, cells were pretreated with 2.4G2 (anti-CD16/32) for 10 min at RT. Cell viability was measured using a fixable viability dye (FVD, Zombie NIR, Biolegend). Cells were stained for 20 min at RT using the following cell surface markers: anti-CD45-PerCp (30-F11), anti-CD3-BV510 (17A2), anti-CD4-A700 (GK1.5), anti-CD8b-FITC (YTS156.7.7), anti-CD11b-BV605 (M1/70), anti-Gr-1-PE-Cy7 (RB6-8C5), anti-CD11c- BV785 (N418), anti-MHC-II-A700 (M5/114.15.2) (all Biolegend). To stain for Foxp3, cells were fixed and permeabilized (Foxp3 Transcription Factor Staining buffer set, eBioscience) and incubated with anti-Foxp3- PE antibody (150D, Biolegend) for 20 min at RT. Samples were acquired on BD LSRFortessaTM X-20. Data was analyzed with FlowJo v10.

Statistical analysis Statistical significance was assessed using the GraphPad Prism software by performing an unpaired nonparametric t-test (*p < 0.05, **p < 0.01, ***p < 0.001; ns, non-significant).

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Results

Loss of C1GalT1C1 increases Tn antigen expression In this study we aimed to dissect the effect of high Tn antigen expression on colorectal cancer (CRC) tumor growth and the immune cell composition at the tumor site. Therefore, we first analyzed the steady state levels of Tn antigen on the mouse CRC cell line MC38 (MC38 wild type, MC38-WT), using the Tn- specific snail lectin Helix Pomatia agglutinin (HPA), and observed that our MC38-WT cells displayed only intermediate Tn antigen levels (Fig. 1A). Normally, the T-synthase enzyme and its chaperone COSMC further elongate the Tn antigen with galactose, thus forming the T antigen epitope19 (see Fig. 1B for a schematic representation of the O-glycosylation pathway, adapted from20). Indeed, CRISPR/Cas9-mediated knock out of the COSMC gene (C1GalT1C1) abolished T-synthase enzymatic activity (Fig. 1C). Furthermore, C1GalT1C1 gene mutations were validated using the Surveyor assay (Supplementary Fig. 1A). Consistently, Tn antigen levels were elevated after C1GalT1C1 gene knock out (MC38-Tnhigh), as confirmed by the increased staining of HPA (Fig. 1D) and an anti-Tn antibody (5F4, Fig. 1E), as well as the stronger binding of the mouse macrophage galactose-type lectin 2 (mMGL-2-Fc, Fig. 1F). In contrast, mouse MGL-1 (mMGL-1-Fc), recognizing the Lewis X and A structures21, failed to interact with MC38-MOCK and MC38-Tnhigh cells (Supplementary Fig. 1B). Mutating the C1GalT1C1 gene did not alter the viability or metabolic activity of MC38-Tnhigh cells (Supplementary Fig. 1C). Thus, CRISPR/Cas9-mediated gene knock out of C1GalT1C1 results in elevated expression of the Tn antigen epitope in MC38 without affecting its intrinsic proliferative capacity.

Tn antigen drives tumor growth specifically at later stages of tumor development Next, we explored the impact of high Tn antigen expression on tumor growth through subcutaneous injection of the MC38-MOCK and MC38-Tnhigh cell lines in C57Bl/6 mice. Interestingly, at day 13 of tumor development the in vivo growth rates were similar between MC38-MOCK and MC38-Tnhigh tumors (Fig. 2A and B). However, from day 23 onwards MC38-Tnhigh tumors started to display significantly faster growth compared to the MC38-MOCK tumors (Fig. 2C and D). Thus, high Tn antigen expression seems to drive tumor growth particularly at later stages of colorectal cancer development.

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Figure 1. C1GalT1C1 knock out in MC38 cells results in high expression of Tn antigen. A, Expression of Tn antigen on MC38 wild type (MC38-WT) cells analyzed by flow cytometry using the lectin HPA. B, Simplified representation of the O-glycosylation pathway, adapted from20. C, T synthase enzyme activity measured using the T synthase assay17 in MC38 cells. D, CRISPR/Cas9-mediated knock out of C1GalT1C1 resulted in increased Tn antigen expression (MC38-Tnhigh) as measured with HPA. As a negative control, MOCK-transfected cells (MC38-MOCK) were used. E and F, Tn antigen expression measured with an anti-Tn antigen antibody (5F4) (E) or mouse macrophage galactose-type lectin (MGL) 2-Fc protein (mMGL-2-Fc) (F) on MC38 cells. Mean ± SD; ***, p<0.001.

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Figure 2. MC38-Tnhigh tumors display enhanced tumor growth at later phases of tumor development. A-D, MC38-MOCK or MC38- Tnhigh cells were injected subcutaneously into C57Bl/6 mice and sacrificed at day 13 of tumor development (n>10 mice/group) (A) or when tumor reached a size of 2000 mm3 (n=14 mice/group) (C). Tumor growth is depicted as mean tumor size (A and C) or individual tumor growth curves (B and D). Mean ± SD; ***, p<0.001; ns, not significant.

MC38-Tnhigh tumors are characterized by lower immune cell infiltration and higher levels of myeloid derived suppressor cells Next, we examined the influence of tumor-associated Tn antigen on the immune cell composition within the tumor microenvironment. Strikingly, compared to MC38-MOCK tumors, MC38-Tnhigh tumors displayed

96 reduced infiltration of viable immune cells (FVD-CD45+) (Fig. 3A). As carbohydrate binding lectin receptors are mainly expressed by antigen presenting cells22, we first analyzed the myeloid immune cell compartment, including professional antigen presenting cells (APCs), such as dendritic cells (DCs) and macrophages, and myeloid derived suppressor cells (MDSCs). APCs are known to internalize and present antigens to T cells, while MDSCs contribute to an immune suppressive tumor microenvironment23. APC (MHC-II+CD11c+) frequencies were equal between MC38-Tnhigh and MC38-MOCK tumors (Fig. 3B), but reduced in the tumor draining lymph node (TDLN, Fig. 3D) and the spleen (Fig. 3E). Interestingly, MDSC frequencies (CD11b+Gr- 1+)24 were significantly higher in MC38-Tnhigh tumors compared to MC38-MOCK tumors (Fig. 3C). Together, increased expression of Tn antigen on MC38 cells negatively influences the overall tumor immune infiltration and more specifically, the myeloid immune compartment, reflected by increased MDSC frequencies at the tumor site and reduced systemic APC frequencies.

MC38-Tnhigh tumors display reduced cytotoxic CD8+ T cell frequencies As MDSCs are known to inhibit T cell activation and proliferation24, we next analyzed the lymphoid compartment in our MC38-MOCK and MC38-Tnhigh tumors. High CD8+ T cell frequencies within the tumor microenvironment strongly correlate to good prognosis in many cancer types25, including CRC, supporting the substantial role of CD8+ T cells in combatting cancer. Indeed, compared to MC38-MOCK tumors, CD8+ T cell frequencies were decreased in MC38-Tnhigh tumors (Fig. 4A). Moreover, CD4+ T cell frequencies tended to be lower in MC38-Tnhigh tumors, however, due to high variation within both groups, the differences observed were not significant (Fig. 4B). Although reduced APC frequencies were found in TDLN of mice bearing MC38-Tnhigh tumors (Fig. 3D), T cell frequencies in TDLN were indistinguishable in the MC38-MOCK and MC38-Tnhigh groups (Supplementary Fig. 2). MDSCs are known to promote regulatory T cells expansion within the tumor microenvironment26,27, yet no increase in regulatory T cell frequencies was observed in MC38-Tnhigh tumors (Fig. 4C) or tumor-draining lymph nodes and spleen (Supplementary Fig. 2). In conclusion, MC38-Tnhigh tumors displayed increased tumor growth at later stages of tumor development, which was characterized by enhanced levels of MDSCs and decreased levels of CD8+ T cell infiltration. Together these results suggest that MC38-Tnhigh tumors are able to establish an immune suppressive tumor microenvironment, which could account for enhanced tumor growth in vivo.

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Figure 3. Diminished contribution of myeloid immune cells in Tnhigh tumors. A-C, Flow cytometric analysis of viable immune cells (FVD- CD45+) (A), antigen presenting cells (APCs, MCH-II+CD11c+) (B) and myeloid derived suppressor cells (MDSCs, CD11b+Gr-1+) (C) at the tumor site. Tumors were harvested when they reached a size of 2000 mm3. D and E, Flow cytometric analysis of APCs present in tumor draining lymph nodes (TDLN) (D) or spleen (E). Data represents pooled data of n=2 in vivo experiments with n>5 and n>8 mice/group (A-D) or one in vivo experiment with n=13 mice/group (E). Mean ± SD; *, p<0.05; FVD, fixable viability dye, ns, not significant.

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Figure 4. MC38-Tnhigh tumors contain reduced numbers of cytotoxic CD8+ T cells. A-C, Flow cytometric analysis of CD8+ T cells (FVD- CD45+CD3+CD8b+) (A), CD4+ T cells (FVD-CD45+CD3+CD4+) (B) and regulatory T cells (Foxp3+ of CD45+CD3+CD4+) (C) at the tumor site. Tumors were analyzed when they reached a size of 2000 mm3. Data are representatives of n=2 in vivo experiments. Mean ± SD; *, p<0.05; ns, not significant.

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Discussion

An aberrant glycosylation profile, including high Tn antigen expression, is a key feature of epithelial cancer types. Tumor-associated Tn antigen expression has been correlated to bad prognosis and increased metastasis6,7. While the receptor recognizing Tn antigen (MGL) is solely expressed on immune cells, the effect of Tn antigen on the anti-tumor immune response has not been investigated. In this study, we explored in a colorectal cancer mouse model how Tn antigen influences tumor growth and the immune cell composition in the tumor microenvironment. Our data suggests that Tn antigen expression drives tumor growth in vivo through increased recruitment of MDSCs. MDSCs can support tumor growth by affecting both CD4+ and CD8+ T cells function. MDSCs consume L-arginine through increased arginase enzyme activity with reduced levels of L-arginine in the environment as a consequence. Reduced levels of L-arginase negatively influence CD3 cell surface expression on T cells and thereby T cell activation and proliferation28. Indeed, MC38-Tnhigh tumors were infiltrated with decreased number of CD8+ T cells, thereby reducing the potential of the immune system to combat MC38-Tnhigh tumors within the tumor microenvironment. In addition, slightly lower CD4+ T cell frequencies were observed in MC38-Tnhigh tumors, however, this was not significantly different from MC38- MOCK tumors due to high variation within the two groups. Whether altered chemokine profiles underlie this enhanced recruitment of MDSCs towards the tumor microenvironment is a subject for future studies. The lectin binding Tn antigen, MGL, is expressed by dendritic cells (DCs) and macrophages29,30. Moreover, upon ligand binding, MGL signaling converges with Toll-like receptor-induced pathways, which increases the secretion of the anti-inflammatory cytokine IL-10 by monocyte-derived DCs31, indicating that MGL ligands might dampen immune responses to support tumor growth. Indeed, MGL ligand expression has been associated with poor survival of later stage CRC32. Remarkably, this was solely observed in stage III CRC and not in stage II CRC, which fits with our findings that Tn antigen augments tumor growth solely from day 23 onwards. This also implies that Tn antigens support tumor progression and dampen immune responses specifically during late stages of tumor development. Tn antigen is the initial step of the O-glycosylation pathway and it can be further elongated in three distinct ways; through the addition of α2-6 sialic acid (sialyl-Tn (sTn) antigen), with a galactose (forming the T antigen) or a N–acetylglucosamine (core 3) (Fig. 1B). By knocking out the COSMC gene, we solely blocked the synthesis of the T antigen glycan structure. sTn antigen has been described to support tumor progression as well33 and could thus be accountable for the enhanced tumor growth of MC38-Tnhigh cells in vivo. However, flow cytometric analysis of α2-6 sialic acid or sTn expression, ruled out the presence of α2-6 sialic acid in any of the cell lines used (data not shown), indicating that de novo Tn antigen expression was not sialylated in our MC38-Tnhigh cells. Core 3 O-glycan structures are essential for intestinal mucus barrier function34 and are responsible for colonic disease resistance35. Also, the glycotransferase responsible for core 3 synthesis is downregulated during CRC progression, which actually accelerates CRC development35,36. Together, this implies an antagonizing role of core 3 in tumor growth, hence it is not likely that core 3 is responsible for the observed increased MC38-Tnhigh tumor growth. Consistent with previous studies, overexpression of Tn antigen promoted in vivo tumor growth11,12. However, enhanced T antigen (core 1) expression has also been described to correlate with bad prognosis and oncogenic features in breast cancer37. As T antigen masks Tn antigen glycan structures, these results contradict with the present study. The discrepancy might be explained by the tumor type investigated (breast cancer vs CRC). Indeed, mice with an deficiency for T-synthase specifically in the mammary gland displayed a delayed onset of tumor development and progression in a murine spontaneous breast cancer model38.

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Moreover, the colorectal cancer cell line HTC116 exhibits enhanced tumor growth in vivo once COSMC was overexpressed39, but also when T synthase was knocked out11, introducing T antigens and Tn antigens respectively. Clearly, both Tn antigen and T antigen affect tumor progression, but, whether they work synergistically or whether one dominates over the other, remains to be investigated and is most likely to be tumor type dependent. In conclusion, we here report for the first time that tumor-associated Tn antigen drives tumor growth in CRC and contributes to an immune suppressive tumor microenvironment through hampered CD8+ T cell infiltration as well as enhanced recruitment of MDSCs.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Authors’ Contributions

L.A.M.C designed and performed experiments, analyzed data and wrote paper; A.B, J.C.H and A.Z. performed experiments; L.K performed subcutaneous cell injections and mouse experiments; T.O. performed cell sorting experiments; Y.v.K and S.v.V conceived and coordinated the study; S.v.V. designed and supervised the study and wrote paper. All authors contributed to reviewing, and/or revising the manuscript.

Acknowledgements

We thank our animal facility for caretaking of the animals. We thank Henrik Clausen and Hans Wandall (Copenhagen Center for Glycomics) for providing the anti-Tn antibody.

Financial Support

This work was supported by a grant from the Amsterdam UMC (LC), the European Union (Marie Curie European Training Network) GlyCoCan project grant number 676421 (AB), project grant VU 2014-6779 from the Dutch Cancer Society (KWF, SvV), ERC advanced grant (Glycotreat, grant number 339977, LK)

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References 1 in Essentials of Glycobiology (eds rd et al.) (2015). 2 Varki, A. & Gagneux, P. in Essentials of Glycobiology (eds rd et al.) 77-88 (2015). 3 Zhou, J. Y., Oswald, D. M., Oliva, K. D., Kreisman, L. S. C. & Cobb, B. A. The Glycoscience of Immunity. Trends Immunol 39, 523-535, doi:10.1016/j.it.2018.04.004 (2018). 4 Pinho, S. S. & Reis, C. A. Glycosylation in cancer: mechanisms and clinical implications. Nat Rev Cancer 15, 540- 555, doi:10.1038/nrc3982 (2015). 5 Parameswaran, R. et al. Binding of aberrant glycoproteins recognizable by Helix pomatia agglutinin in adrenal cancers. BJS Open 2, 353-359, doi:10.1002/bjs5.70 (2018). 6 Laack, E. et al. Lectin histochemistry of resected adenocarcinoma of the lung: helix pomatia agglutinin binding is an independent prognostic factor. Am J Pathol 160, 1001-1008, doi:10.1016/S0002-9440(10)64921-8 (2002). 7 Konno, A., Hoshino, Y., Terashima, S., Motoki, R. & Kawaguchi, T. Carbohydrate expression profile of colorectal cancer cells is relevant to metastatic pattern and prognosis. Clin Exp Metastasis 19, 61-70 (2002). 8 Bennett, E. P. et al. Control of mucin-type O-glycosylation: a classification of the polypeptide GalNAc- transferase gene family. Glycobiology 22, 736-756, doi:10.1093/glycob/cwr182 (2012). 9 Gill, D. J. et al. Initiation of GalNAc-type O-glycosylation in the endoplasmic reticulum promotes cancer cell invasiveness. Proc Natl Acad Sci U S A 110, E3152-3161, doi:10.1073/pnas.1305269110 (2013). 10 Jiang, Y. et al. Aberrant O-glycosylation contributes to tumorigenesis in human colorectal cancer. J Cell Mol Med, doi:10.1111/jcmm.13752 (2018). 11 Dong, X. et al. T-Synthase Deficiency Enhances Oncogenic Features in Human Colorectal Cancer Cells via Activation of Epithelial-Mesenchymal Transition. Biomed Res Int 2018, 9532389, doi:10.1155/2018/9532389 (2018). 12 Radhakrishnan, P. et al. Immature truncated O-glycophenotype of cancer directly induces oncogenic features. Proc Natl Acad Sci U S A 111, E4066-4075, doi:10.1073/pnas.1406619111 (2014). 13 Hofmann, B. T. et al. COSMC knockdown mediated aberrant O-glycosylation promotes oncogenic properties in pancreatic cancer. Mol Cancer 14, 109, doi:10.1186/s12943-015-0386-1 (2015). 14 RodrIguez, E., Schetters, S. T. T. & van Kooyk, Y. The tumour glyco-code as a novel immune checkpoint for immunotherapy. Nat Rev Immunol 18, 204-211, doi:10.1038/nri.2018.3 (2018). 15 Napoletano, C. et al. Tumor-associated Tn-MUC1 glycoform is internalized through the macrophage galactose- type C-type lectin and delivered to the HLA class I and II compartments in dendritic cells. Cancer Res 67, 8358- 8367, doi:10.1158/0008-5472.CAN-07-1035 (2007). 16 Ran, F. A. et al. Genome engineering using the CRISPR-Cas9 system. Nat Protoc 8, 2281-2308, doi:10.1038/nprot.2013.143 (2013). 17 Ju, T. & Cummings, R. D. A fluorescence-based assay for Core 1 beta3galactosyltransferase (T-synthase) activity. Methods Mol Biol 1022, 15-28, doi:10.1007/978-1-62703-465-4_2 (2013). 18 Thurnher, M., Clausen, H., Sharon, N. & Berger, E. G. Use of O-glycosylation-defective human lymphoid cell lines and flow cytometry to delineate the specificity of Moluccella laevis lectin and monoclonal antibody 5F4 for the Tn antigen (GalNAc alpha 1-O-Ser/Thr). Immunol Lett 36, 239-243 (1993). 19 Wang, Y. et al. Cosmc is an essential chaperone for correct protein O-glycosylation. Proc Natl Acad Sci U S A 107, 9228-9233, doi:10.1073/pnas.0914004107 (2010). 20 Cornelissen, L. A. & Van Vliet, S. J. A Bitter Sweet Symphony: Immune Responses to Altered O-glycan Epitopes in Cancer. Biomolecules 6, doi:10.3390/biom6020026 (2016). 21 Singh, S. K. et al. Characterization of murine MGL1 and MGL2 C-type lectins: distinct glycan specificities and tumor binding properties. Mol Immunol 46, 1240-1249, doi:10.1016/j.molimm.2008.11.021 (2009). 22 Figdor, C. G., van Kooyk, Y. & Adema, G. J. C-type lectin receptors on dendritic cells and Langerhans cells. Nat Rev Immunol 2, 77-84, doi:10.1038/nri723 (2002). 23 Veglia, F., Perego, M. & Gabrilovich, D. Myeloid-derived suppressor cells coming of age. Nat Immunol 19, 108- 119, doi:10.1038/s41590-017-0022-x (2018). 24 Gabrilovich, D. I. & Nagaraj, S. Myeloid-derived suppressor cells as regulators of the immune system. Nat Rev Immunol 9, 162-174, doi:10.1038/nri2506 (2009). 25 Fridman, W. H., Pages, F., Sautes-Fridman, C. & Galon, J. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer 12, 298-306, doi:10.1038/nrc3245 (2012). 26 Huang, B. et al. Gr-1+CD115+ immature myeloid suppressor cells mediate the development of tumor-induced T regulatory cells and T-cell anergy in tumor-bearing host. Cancer Res 66, 1123-1131, doi:10.1158/0008- 5472.CAN-05-1299 (2006).

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27 Pan, P. Y. et al. Immune stimulatory receptor CD40 is required for T-cell suppression and T regulatory cell activation mediated by myeloid-derived suppressor cells in cancer. Cancer Res 70, 99-108, doi:10.1158/0008- 5472.CAN-09-1882 (2010). 28 Rodriguez, P. C. et al. Regulation of T cell receptor CD3zeta chain expression by L-arginine. J Biol Chem 277, 21123-21129, doi:10.1074/jbc.M110675200 (2002). 29 Higashi, N. et al. The macrophage C-type lectin specific for galactose/N-acetylgalactosamine is an endocytic receptor expressed on monocyte-derived immature dendritic cells. J Biol Chem 277, 20686-20693, doi:10.1074/jbc.M202104200 (2002). 30 van Vliet, S. J., Gringhuis, S. I., Geijtenbeek, T. B. & van Kooyk, Y. Regulation of effector T cells by antigen- presenting cells via interaction of the C-type lectin MGL with CD45. Nat Immunol 7, 1200-1208, doi:10.1038/ni1390 (2006). 31 van Vliet, S. J. et al. MGL signaling augments TLR2-mediated responses for enhanced IL-10 and TNF-alpha secretion. J Leukoc Biol 94, 315-323, doi:10.1189/jlb.1012520 (2013). 32 Lenos, K. et al. MGL ligand expression is correlated to BRAF mutation and associated with poor survival of stage III colon cancer patients. Oncotarget 6, 26278-26290, doi:10.18632/oncotarget.4495 (2015). 33 Munkley, J. The Role of Sialyl-Tn in Cancer. Int J Mol Sci 17, 275, doi:10.3390/ijms17030275 (2016). 34 Xia, L. Core 3-derived O-glycans are essential for intestinal mucus barrier function. Methods Enzymol 479, 123- 141, doi:10.1016/S0076-6879(10)79007-8 (2010). 35 An, G. et al. Increased susceptibility to colitis and colorectal tumors in mice lacking core 3-derived O-glycans. J Exp Med 204, 1417-1429, doi:10.1084/jem.20061929 (2007). 36 Iwai, T. et al. Core 3 synthase is down-regulated in colon carcinoma and profoundly suppresses the metastatic potential of carcinoma cells. Proc Natl Acad Sci U S A 102, 4572-4577, doi:10.1073/pnas.0407983102 (2005). 37 Chou, C. H. et al. Up-regulation of C1GALT1 promotes breast cancer cell growth through MUC1-C signaling pathway. Oncotarget 6, 6123-6135, doi:10.18632/oncotarget.3045 (2015). 38 Song, K. et al. Loss of Core 1-derived O-Glycans Decreases Breast Cancer Development in Mice. J Biol Chem 290, 20159-20166, doi:10.1074/jbc.M115.654483 (2015). 39 Huang, J. et al. The molecular chaperone Cosmc enhances malignant behaviors of colon cancer cells via activation of Akt and ERK. Mol Carcinog 53 Suppl 1, E62-71, doi:10.1002/mc.22011 (2014).

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Supplementary information

Supplementary Figure 1. Metabolic activity and mMGL-1 binding are unaffected in MC38-Tnhigh cells. A, The COSMC gene, C1GalT1C1, was amplified with qPCR (product length 416 bp, top arrow) and tested for mutations using the Surveyor assay. The Surveyor nucleases recognizes and cleaves DNA mismatches. In the presence of a C1GalT1C1 mutation the qPCR product is cleaved into two fragments of 275 bp (middle arrow) and 141 bp (bottom arrow). B, Flow cytometric analysis of mouse macrophage galactose-type lectin 1 (mMGL- 1-Fc) binding to MC38-MOCK and MC38-Tnhigh cells. C, Metabolic cell activity assessed with the CellTiter-Blue® Cell Viability assay of MC38-MOCK and MC38-Tnhigh cells after the first 24 hours of culture. Mean ± SD.

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Supplementary Figure 2. T cells frequencies were unaffected in MC38-MOCK or MC38-Tnhigh tumor-draining lymph nodes and spleen. A and B, Flow cytometric analysis of CD8+ T cells (FVD-CD45+CD3+CD8b+), CD4+ T cells (FVD-CD45+CD3+CD4+) and regulatory T cells (Foxp3+ of CD45+CD3+CD4+) in the tumor-draining lymph nodes (TDLN) (A) or spleen (B). T cell frequencies were analyzed when the tumor reached a size of 2000 mm3. Data are representatives of n=2 in vivo experiments. Mean ± SD.

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Chapter 5

Tumor sialylation impedes T cell mediated anti- tumor responses while promoting tumor

associated-regulatory T cells

Maurizio Perdicchio Lenneke A.M. Cornelissen Ingeborg Streng-Ouwehand Steef Engels Marleen I. Verstege Louis Boon Dirk Geerts Yvette van Kooyk Wendy W. J. Unger

Oncotarget 7, 8771-8782, doi:10.18632/oncotarget.6822 (2016).

Abstract

The increased presence of sialylated glycans on the tumor surface has been linked to poor prognosis, yet the effects on tumor-specific T cell immunity are hardly studied. We here show that hypersialylation of B16 melanoma substantially influences tumor growth by preventing the formation of effector T cells and facilitating the presence of high regulatory T cell (Treg) frequencies. Knock-down of the sialic acid transporter created “sialic acid low” tumors, that grew slower in-vivo than hypersialylated tumors, altered the Treg/Teffector balance, favoring immunological tumor control. The enhanced effector T cell response in developing “sialic acid low” tumors was preceded by and dependent on an increased influx and activity of Natural Killer (NK) cells. Thus, tumor hypersialylation orchestrates immune escape at the level of NK and Teff/Treg balance within the tumor microenvironment, herewith dampening tumorspecific T cell control. Reducing sialylation provides a therapeutic option to render tumors permissive to immune attack.

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Introduction

Innate immune cells play a crucial role in the immune response against tumors. Particularly, dendritic cells (DCs), macrophages and NK cells are involved in the first phase of the process known as cancer immunoediting, where these cells detect the presence of a developing tumor and coordinate as well as co- operate with the adaptive immune system to eradicate the tumor1. Specific activation of naive tumor-specific T lymphocytes by DCs is required to generate an army of effector CD8+ and CD4+ T cells that can effectively eliminate tumor cells. Activated CD8+ T cells acquire effector functions such as cytolytic activity and/or production of interferon (IFN)-γ and tumor necrosis factor (TNF)2. Tumor-specific CD4+ effector T cells contribute to tumor eradication either via direct tumor cell killing3 or via the activation of macrophages at tumor sites4. Moreover, activated CD4+ T cells aid induction of memory CD8+ T cells, which can maintain long-term tumor control. Next to cytotoxic T cells, also NK cells have the capacity to lyse tumor cells. Yet, in contrast to T cells, NK cells represent the first line of defense against transformed cells. Low or absent expression of MHC-I triggers NK cell effector functions, which include release of IFN-γ and cytotoxic granules and induction of apoptosis of target cells. High frequencies of intra-tumoral NK cells have been associated with good prognosis of patients5. Hence, alterations in frequency and function of innate and T cells aid tumor immune escape. Indeed, DCs and macrophages in the tumor microenvironment are characterized by an anti-inflammatory or tolerogenic phenotype promoting the induction of anergic and/or regulatory T cells (Treg)6. Furthermore, high numbers of both natural as well as induced regulatory T cells (nTregs and iTregs) infiltrate into the tumor site7,8 and together with tolerogenic DCs and macrophages they act in concert to hamper the influx and function of effector T cells7-9. Tumor cells have been shown to display aberrant glycosylation10,11. In particular, increased levels of sialic acids, driven by enhanced expression and activity of beta-galactoside α2,6-sialyltransferase 1 (ST6Gal-1) and/or α-N-acetylgalactosaminide α2,6-sialyltransferase 1 (ST6GalNAc-I), have been detected, which often correlate with tumor invasion and poor prognosis of malignancies12-14. Sialic acids are the outermost monosaccharides on glycan chains of glycoproteins and glycolipids, attached to the underlying glycans with α2,3-, α2,6- or α2,8-linkage13 and as such the recognition elements for selectins and sialic acid-binding Ig-like lectins (siglecs)13-15. The negative charge of highly abundant sialic acids causes dissociation from the primary tumor and spreading of tumor cells. Binding to selectins expressed on endothelial cells allows tumor cells to disseminate16-18, whereas binding to siglecs allows interaction with the immune system. We here investigated whether the increased presence of sialylated glycans on the surface of tumors promotes cellular immune evasion by interfering with the T effector/Treg balance regulated by innate immune cells.

Materials and methods

Cells and lentiviral transduction B16-OVA (murine melanoma; kindly provided by Dr. T. Schumacher, National Cancer Institute, Amsterdam, The Netherlands) were cultured in DMEM supplemented with 10% FCS, 50 U/ml penicillin and 50 µg/ml streptomycin (BioWhittaker, Walkersville, MD). Sialic acid low B16-OVA (hereafter named B16SLC35A1) were generated by transducing B16 with the pLKO.1 lentiviral vector containing SLC35A1 shRNA. As a control, B16-OVA cells were transduced with pLKO.1 containing non-target shRNA (designated B16 scrambled). Lentiviral vectors were produced as described earlier19. In short, HEK293T cells were co- transfected with pLKO.1-shRNA and the packaging plasmids (pVSV-G, pMDL and pRev-RRE) in the presence of calcium phosphate. One day later, medium was replaced by serum-free medium and culture supernatant

109 was harvested another day later. B16-OVA were seeded at 105 cells/well and transduced with a fixed amount of lentiviral vector when 80% confluent. The day following infection, target cells were selected with 1 μg/ml puromycin.

Tumor experiments For in vivo studies, 1 × 105 tumor cells were inoculated subcutaneously into the flank of C57BL/6 mice (8-12 weeks old; Charles River Laboratories) . Mice were sacrificed when tumors reached a diameter of 1.5 cm and tumors, spleens and lymph nodes were removed for analysis by flow cytometry. To deplete NK cells, mice were injected i.p. with anti-NK1.1 antibodies (PK136; mouse IgG2a; 0.2 mg) on days -3, 0, 4, 8 from the start of tumor challenge. From day 8 on, anti-NK1.1 antibodies were injected once a week. All experiments were approved by the Animal Experiments Committee of the VUmc, Amsterdam.

Tumor-infiltrating lymphocytes, spleens and tumor-draining lymph nodes For isolation of tumor-infiltrating lymphocytes, tumors were removed from C57BL/6 mice, cut into grain size pieces and incubated in HE medium (RPMI medium with 10% FCS, 10 mM EDTA, 20 mM HEPES, 50 U/ml penicillin, 50 µg/ml streptomycin) supplemented with 1 WU/ml Liberase-TL (Roche Diagnostics GmbH, Manheim, Germany) and 50 μg/ml DNase I (Roche) for 30 min at 37°C. Erythrocytes were lysed with ACK lysis buffer and tumor-infiltrating lymphocytes were purified by ficoll gradient with Lymphoprep (Axis-Shield, UK). Tumor-draining lymph nodes and spleens were passed through a 100 µm cell strainer (BD Falcon, NJ, USA) in Iscove’s Modified Dulbecco’s Medium (IMDM; Gibco, CA, USA) to generate single cell suspensions. Erythrocytes were lysed with ACK lysis buffer. Subsequently, the presence of different immune cells was analyzed upon staining with specific antibodies and flow cytometry. Alternatively, cells were re-stimulated with 30 μg/ml phorbol 12-myristate 13-acetate (PMA) and 500 ng/ml ionomycin (Sigma-Aldrich) for detection of IFN-γ in cell supernatants by ELISA (eBiosciences, CA, USA).

Co-cultures of tumor cells and NK cells To examine effects of sialylated tumorantigens on NK cell function, either total splenocytes or NK- enriched splenocytes derived from naive C57BL/6 mice were co-cultured at indicated E:T ratio with B16SLC35A1 or B16scrambled tumor cells. Four hours after co-culture, the amount of IFN-γ in the supernatant was analysed by ELISA. To enrich for NK cells, splenocytes from naive mice were incubated with (rat-anti-mouse) antibodies directed against MHC-II, CD3, CD4, CD8, F4/80, MAC-1, followed by incubation with sheep-anti- rat antibody coated magnetic beads. To determine effects of NK-derived IFN-γ on tumor-expressed chemokines and cytokines, B16SLC35A1 and B16scrambled were cultured in the presence or absence of 250 pg/ml IFN-γ for 6 hours and expression of chemokines and cytokines was measured by qRT-PCR.

Analysis of cell cycle, adhesion and motility For cell cycle analysis by DNA content, tumor cells were collected and fixed in 70% ethanol o/n at 4°C. After washing, tumor cells were resuspended in PBS containing 10 μg/ml Propidium Iodide and 200 μg/ml RNase A and analyzed by flow cytometry. For cell adhesion analysis, Nunc-immunoMaxisorp plates (Sigma- Aldrich) were coated o/n with 50 ulmatrigel (BD Biosciences) 1:25 in PBS buffer. After washing with TSM buffer (20 mMTris-HCl, pH 8, 150 mMNaCl, 1 mM CaCl2 , 2 mM MgCl2 ), non-specific binding was blocked with TSM/0,5% BSA . CFSE-labeled tumor cells were allowed to adhere for 30 min at 37°C, and non- adherent cells were removed by gently washing plates three times with pre-warmed TSM/0.5% BSA.

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Subsequently, bound tumor cells were lysed using a 50 mMTris/0,1% SDS buffer and fluorescence was detected using a CytoFluor (Applied Biosystems, CA, USA). A motility or scratch assay was performed as previously described20.

Flow cytometry The following monoclonal antibodies (derived either from BD biosciences or eBioscience) were used: FITC-labeled anti-CD3 (145-2C11), -CD4 (RM4-5), -NK1.1 (PK136 ebio), -MHC-I (H-2Kb; AF688.5BD),-CD8b (eBIOH35); PE-labeled anti-CD8b (eBioH35-17.2), -MHC-II (M5/114.15.2) and –NK1.1 (PK136); and APC- labeled anti-FoxP3 (FJK-169). Biotin-labeled plant derived lectin Sambucus Nigra (SNA) was obtained from Vector Labs, CA, USA, and binding was detected with PE-labeled Streptavidin (Jackson Immunoresearch, UK). qRT-PCR Total RNA was extracted using the RNeasy kit (Qiagen, Valencia, CA) combined with a DNase treatment to remove contaminating DNA (Qiagen). cDNA was synthesized using the Reverse Transcription System kit (Promega, WI, USA) following manufacturer’s guidelines. Real time PCR reactions were performed using the SYBR Green method in an ABI 7900HT sequence detection system (Applied Biosystems), with GAPDH as internal control. Samples were analyzed in triplicate and normalized to GAPDH. Primers were obtained from Invitrogen (Carlsbad, California) and sequences were as follows: GAPDH-FWD:GACAACT CATCAAGATTGTCAGCA, GAPDH-REV:TTCATGAG CCCTTCCACAATG, IL-10- FWD:GGACAACATACTGCTAACCG, IL-10-REV:GGGGCATCACTTCTACCAG, TGFβ-1- FWD:GCTGAACCAAGGAGACGGAATA, TGFβ-1-REV:GGGCTGATCCCGTTGATTT, IP-10- FWD:GACGGTCCGCTGAACTG, IP-10-REV:GCTTCCCTATATGGCCTCATT, IFNg-R-FWD:GCTTTGACGAG CACTGAGGA, IFNg-R-REV:CCAGCATACGACAGGG TTCA, SLC35A1-FWD:CATGGCCTTCCTGGCTCTC, SLC35A1-REV:GGTCACCTGGTACACTGCTGC

Statistical analysis Prism software (GraphPad 5.0) was used for statistical analysis. Student’s t test was to determine statistical significance. Statistical significance for all the tests, assessed by calculating the P values, was defined as P < 0.05.

Results

Reduction of sialic acids on B16 surface antigens empowers anti-tumor immunity To investigate the influence of tumor-expressed sialic acids on anti-tumor immunity we reduced their presence on the hyper-sialylated cell surface of murine B16 melanoma by knockdown of the SLC35A1 gene encoding the Golgi-based CMP-sialic acid transporter21. Using this strategy, B16 cells with sustained reduction of surface sialylation were created, which in contrast to other methods such as sialidase treatment, permits long-term immune monitoring in the tumor. Importantly, the sialic acid content of the B16 remains unaffected, only the addition of the sialic acid residues to growing glycan chains on glycoproteins and glycolipids is impeded. SLC35A1 knockdown was confirmed using qRT-PCR and specifically decreased the quantity of α2,6-linked sialic acids on the B16 surface compared to B16 cells treated with a non-targeting shRNA (hereafter called B16SLC35A1 and B16scrambled, respectively) as shown using the plant lectins Sambucus Nigra and Maackia Amurensis (SNA and MAA-II, Figure 1A, 1B). B16scrambled tumors were already visible on

111 day seven after injection into immunocompetent C57BL/6 mice and grew substantially faster and larger than sialic acid low B16SLC35A1 tumors, which became detectable around day 15 and remained much smaller in size for a prolonged period (Figure 1C). Since aberrant sialylation has been correlated with the invasive properties of tumors, we evaluated whether the reduction of α2,6- sialic acids on B16 altered these characteristics in vitro. However, no difference between B16SLC35A1 and B16scrambled tumor cells in binding to plates coated with tumor-derived extra-cellular matrix components could be observed (Figure 1D). Additionally, the proliferative capacity was not affected as indicated by cell-cycle analysis (Figure 1E). Using the scratch assay we ascertained that the B16SLC35A1 and B16scrambled tumors migrated at the same rate and closed the gap within 28 hours (Figure 1F). B16scrambled tumors showed comparable characteristics to unmodified WT B16 tumor cells (Supplementary Figure 1A–1D). As a reduction of α2,6-sialic acids on B16 surfaces did not alter tumor intrinsic characteristics in vitro, we hypothesized that the reduced growth of B16SLC35A1 tumors in vivo arose from changes in the host’s anti- tumor immune response. At time of sacrifice, significant higher CD4+ T cell numbers were detected within the tumor infiltrating lymphocytes (TILs) and tumor-draining lymph nodes (TDLN) of B16SLC35A1 tumors (Figure 1G). Notably, in the B16SLC35A1 microenvironment the fraction of Foxp3+ within the CD4+ T cell population was strongly reduced. Together with the elevated IFN-γ levels secreted by B16SLC35A1-infiltrating lymphocytes upon ex vivo re-stimulation (Figure 1H), these findings suggest that the CD4+ and CD8+ T cells in B16SLC35A1 tumors are effector rather than tolerogenic T cells. Despite the strong reduction in Foxp3+ T cells within the CD4+ T cell population at this stage of tumor growth, the CTL/ Treg ratio in the B16SLC35A1 tumor was not different from that in B16scrambled tumors (Figure 1G). In contrast to our observation on TILs, no reduction in Foxp3+ CD4+ T cells was found in TDLNs of B16SLC35A1 tumors (Figure 1G). Furthermore, no significant differences in T cell numbers and phenotype were observed in the spleens of tumor bearing mice, indicating that anti-tumor immunity is confined to the tumor-microenvironment (Supplementary Figure 1E). Furthermore, the comparable low MHC-I and MHC-II expression levels on the two tumor cell lines rules out improved recognition of B16SLC35A1 by tumor-reactive CD8+ and CD4+ T cells (Figure 1I). Together, these findings clearly show that a reduction of sialic acids on tumor surfaces evokes a switch in the type of immunity in the tumor area: instead of immunosuppressive Treg IFN-γ-producing TILs cells are present that may control tumor growth.

Sialic acids on tumors dampen activity of NK cells To analyze whether the delayed growth of B16SLC35A1 tumors in vivo was paralleled by variations in innate cell subsets, which are predominantly involved in the first phases of cancer immunoediting, mice were sacrificed 18 days after tumor implantation, coinciding with early phases of B16SLC35A1 tumor development (Figure 2A). Analysis of the T cells within B16SLC35A1 tumors revealed the same increase in total CD4+ T cells found at a later stage (i.e. 40 days after implantation; Figures 2B and 1G) as well as a markedly lower Foxp3+ fraction within the CD4+ T cell compartment than in B16scrambled tumors (Figure 2B). No significant changes in tumor-infiltrating CD8+ T cells were detected at this early stage of tumor development (Figure 2B). Similar as before the reduced frequency of CD4+ Tregs in B16SLC35A1 tumors was concomitant with increased IFN-γ production by infiltrating lymphocytes, indicating a shift from T cell

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Figure 1. Reduced sialic acid levels on B16 melanoma decline Treg frequencies and increase effector T cell numbers and tumor control. (A) SLC35A1 gene expression was analyzed in B16SLC35A1 and B16scrambled cells by qRT-PCR and normalized to GAPDH; the mean ± s.e.m of duplicate measurements is shown (n=4 independent analyses; ***P < 0.001). (B) Detection of α2-6- and α2-3-linked sialic acids using the plant lectins SNA and MAA-II on B16SLC35A1 (black line) and B16scrambled (dashed line) tumors by flow cytometry. Grey filled histograms represent conjugate control. n=3 independent experiments. (C) Tumor growth in B16SLC35A1 and B16scrambled tumor-bearing mice (n=7/group), indicated as mm2 (mean ± s.e.m). Shown is one of two independent experiments. (D) Tumor cell adhesion to matrigel coated plates. Results shown as percentage of adhering cells (mean ± s.e.m) and represent 3 independent experiments. ns., not significant. (E) Cell cycle analysis of B16SLC35A1 and B16scrambled tumors by DNA content. Percentage of cells in G0/G1 interphase and G2/M mitotic phase are indicated. Results represent 3 experiments. (F) Scratch assay to assess migratory capacity of B16SLC35A1 and B16scrambled tumors. Left, Bright-field images (200 ×) of confluent tumor cells showing re-growth following in vitro scratch. Right,

113 quantification of distance between edges of linear scratch. Data represent 3 independent experiments. ns, not significant. (G) Percentage of total CD4+ , CD8+ and Foxp3+ CD4+ T-cells as well as CTL/Treg ratio in tumor and TDLN from tumor-bearing mice as detected by flow cytometry at time of sacrifice. Dots represent individual mice (n=11) of 2 independent experiments. Bars indicate median/group, n.s. = not significant; *P < 0.05; **P < 0.01. (H) IFN-γ levels secreted by TILs from B16SLC35A1 and B16scrambled tumors. Data represent 2 independent experiments, *P < 0.05. (I) MHC-I and MHC-II expression on B16SLC35A1 and B16scrambled tumors before injection into mice. Plots represent two independent measurements. tolerance towards T cell immunity that sets stage early after tumor implantation (Figure 2C). This is also underlined by the significant higher CTL/Treg ratio in the B16SLC35A1 tumors compared to that in B16scrambled tumors at this early stage of B16SLC35A1 tumor growth (Figure 2B). Interestingly, B16SLC35A1 tumors contained 25% of NK1.1+ CD3− NK cells, while in B16scrambled tumors only 10% was detected (Figure 3A). NK cells are amongst the first responders during tumor development, where they actively kill the transformed cells by secreting IFN-γ and/or release of cytotoxic granules. This function may be inhibited in hypersialylated tumors as indicated by recent in vitro studies22,23. Indeed, the IFN-γ levels in the supernatants of in vitro NK and tumor cell co-cultures clearly demonstrate that a reduction in sialic acids increased the IFN-γ production by NK cells, at each E:T ratio tested (Figure 3B).

Figure 2. SLC35A1 knockdown in tumors alters intra-tumoral CD4+ Tregs frequencies at early stages of tumor development. Mice (n=6/group) were inoculated with either B16SLC35A1 and B16scrambled tumors and (A) tumor growth represented in mm2 (mean ± s.e.m) was measured every 2–3 days until day 18. (B) Mice were sacrificed 18 days after tumor injection and percentages of total CD4+ T cells, CD8+ T cells, and of Foxp3+ CD4+ T cells in the tumor and TDLNs from tumor-bearing mice as detected by flow cytometry. CTL/Treg

114 ratios were calculated for each mouse. Dots represent individual mice (n=6 mice/group; n.s. = not significant; *P < 0.05). Bars indicate median of each group. Statistical significance is indicated (Student’s t test). (C) Levels of IFN-γ secreted by TILs from B16SLC35A1 and B16scrambled tumors upon PMA/ionomycin activation (mean ± s.e.m.; **P < 0.01). Data are representative of 2 independent experiments.

In addition to its cytolytic effects, IFN-γ can also induce tumor cells to produce chemokines that attract immune effector cells, such as CXCL10 (IP-10)24,25. The presence of IFN-γ increased the levels of IP-10 mRNA in B16SLC35A1 but not in B16scrambled (Figure 3C). Of note, both tumor types expressed equal levels of IFN-γ receptor mRNA (Supplementary Figure 1F). Furthermore, we found that in contrast to B16scrambled, B16SLC35A1 did not increase IL-10 and TGF-β mRNA levels in the presence of IFN-γ, thus making the tumor micro-milieu of the sialic acid low tumor less tolerogenic (Figure 3C). IFN-γ also resulted in an significantly increased expression of MHC-I on B16 cells, however, no differences between B16scrambled and B16SLC35A1 were observed (Figure 3D). Similar observations were made for MHC-II expression levels, although these were only moderately increased by IFN-γ. Thus, hyper-sialylated tumor cells paralyze the inflammatory program of NK cells and herewith maintain a tolerogenic environment.

Figure 3. Increased numbers of intra-tumoral NK cells ignite an immunogenic milieu in B16SLC35A1 tumors via IFN-γ. (A) Percentage of NK cells, distinguished as NK1.1+CD3- cells, within B16SLC35A1 and B16scrambled tumors (n=6 mice/group) using flow cytometry. Bars indicate the median of each group; **P < 0.05. (B) NK cells, enriched from naive splenocytes, were incubated at indicated E:T ratio’s from B16SLC35A1 and B16scrambled tumors and with IFN-γ was determined by ELISA from the supernatant 4h later. Data are representative of 3 experiments; *P < 0.05. (C) IP-10, IL-10 and TGF-β mRNA levels expressed by B16SLC35A1 and B16scrambled tumors in the presence or absence of rm-IFN-γ (n=4 independent experiments; ***P < 0.001). (D) Flow cytometric analysis of MHC-I and MHC-II expression on B16SLC35A1 and B16scrambled cell surface 24h after culture in the presence of 250 pg/ml rm-IFN-γ. Plots represent two independent measurements.

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Figure 4. Depletion of NK cells in sialic acidlow tumor bearing mice abolishes induction of anti-tumor immunity. (A) NK-depleted mice were challenged with B16SLC35A1 tumor cells; WT mice were challenged with B16SLC35A1 or B16scrambled tumor cells. Tumor growth was assessed at different days after tumor inoculation, indicated as mean mm2 ± s.e.m; n=7–9 mice/group. Data represent results from two independent experiments. (B) TILs and TDLNs were analyzed by flow cytometry to determine the frequency of CD4+ and CD8+ T cells and of Foxp3+ of CD4+ T cells. CTL/Treg ratio was calculated for each mouse. Dots represent individual mice. Bars indicate median of each group (n=7–9 mice/group). (C) IFN-γ production by activated CD8+ and CD4+ T cells in TDLN was determined by intracellular staining after PMA/ionomycin restimulation ex vivo. Dots represent individual mice; n=5 mice/group. *P < 0.05, **P < 0.01, ***P < 0.001. Graphs shown are representative of two independent experiments.

Presence of NK cells is required to evoke the switch in T cell response in sialic acidlow tumors The presence of higher numbers of IFN-γ-secreting NK cells in B16SLC35A1 tumors (Figure 3A) as well as the immunogenic effect of IFN-γ on the tumor milieu, suggests that NK cells play a relevant role in the control of B16SLC35A1 growth. To test this, mice challenged with B16SLC35A1 tumors were depleted of NK cells

116 by treatment with anti-NK1.1 antibodies (Supplementary Figure 1G). As observed previously, B16SLC35A1 tumors grew slower and were smaller in size when implanted in WT NK-competent mice, leading to survival of mice 35 days after tumor transplantation (Figure 4A). However, NK depletion drove B16SLC35A1 tumors to grow at a similar rate as B16scrambled tumors (Figure 4A). Of note, growth of B16scrambled tumors was only slightly enhanced when NK cells were depleted (Supplementary Figure 1G). Furthermore, the increased infiltration of CD4+ and CD8+ T cells into B16SLC35A1 tumors was absent when NK cells were depleted (Figure 4B). This was mirrored by the absence of effector T cells in these mice: IFN-γ-producing CD4+ and CD8+ T cells were only detected in B16SLC35A1 tumors (Figure 4C). Although depletion of NK cells did not alter the proportion of Foxp3+ CD4+ T cells within B16SLC35A1 tumors, the CTL/ Treg ratio was only in favor of tumor control in NK replete B16SLC35A1. In contrast to the tumor environment, Treg numbers were significantly higher in the TDLN of NK cell depleted B16SLC35A1 tumor bearing mice (Figure 4B). In summary, these data unveil a clear and versatile role for the levels of sialic acids on tumors regulating NK cell mediated initiation of potent anti-tumor immunity.

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Discussion

Here we show that reducing sialic acids on the surface of melanoma cells evokes the generation of large numbers of effector T cells that infiltrate the tumor area and simultaneously dampens the intra-tumoral presence of Foxp3+ Tregs, together curtailing tumor growth. Furthermore, we provide evidence that this switch in T cell response from tolerogenic to immunogenic in sialic acidlow melanomas is ignited by NK activation as their depletion abolishes effector T cell induction. In this study we reduced the amounts of sialic acids on the murine B16 melanoma surface to assess the impact of alterations in sialic acids on the anti-tumor immune response. SLC35A1 knock down significantly reduced the presence of α2,6-linked sialic acids on the B16 surface. Besides melanoma, also other invasive and metastatic tumors (e.g. colon and breast carcinoma) present an aberrant expression of α2,6-sialic acids mainly because of the overexpression of ST6Gal1 sialyltransferase, which adds terminal sialic acid moieties on N-linked glycans14. It has been shown that enhanced expression of α2,6- sialic acid on tumors promotes cell detachment from the tumor mass as well as their invasiveness by facilitating interactions with matrix proteins26,27. We observed that in vivo B16SLC35A1 tumors grew significantly slower and remained much smaller in size than B16scrambled tumors for a long period, resulting in better survival of mice bearing B16SLC35A1 tumors. Since in vitro intrinsic properties of B16-OVA such as migratory and adhesion abilities were not affected by the reduction of α2,6-sialic acids, the delayed growth of B16SLC35A1 tumors in vivo is suggested to be the outcome of improved immune control. This is also underlined by our findings that CD4+ T cell numbers were increased in the tumor and TDLNs as well as the ability of both CD4+ and CD8+ T cells to produce IFN-γ. At the same time, frequencies of tumor- associated Tregs were strongly diminished in the B16SLC35A1 tumors. Thus, reducing sialic acids on the tumor surface switches the function of tumor-specific T cells from tolerogenic to effector. Notably, both in the early as well as the later stages of tumor growth, the effects of sialic acidlow tumors on T cell frequency and function were mainly evident in the tumor and not in the TDLN, which points to strong intra-tumor immune regulation that is sufficient to control tumor growth. This study is the first to show the immunological consequences of tumor cell hyper-sialylation. We demonstrated that the presence of NK cells, Tregs, effector T cells, and probably DCs, is affected and that these immune cells cooperate in their mode of action, herewith linking hypersialylation with tumor induced immune tolerance. The sialic acid binding receptors or siglecs are predominantly found on innate immune cells, such as antigen presenting cells (APCs) and NK cells15. On APCs cells, siglecs function as endocytic receptors and regulate their activation and cytokine secretion. The CD33/ Siglec-3-related siglec (hCD33rSiglecs) subgroup of siglecs possess immunoreceptor tyrosine-based inhibitory motifs (ITIMs) in their intracellular portion, which can counteract the activating signals triggered by receptors containing immunoreceptor tyrosine-based activatory motifs (ITAMs)15. This suggests that engagement of hCD33rSiglecs on innate cells by sialylated antigens may result in suppressive effects on the innate immune response. Since on the engineered B16SLC35A1 tumors mostly α2,6-linked sialic acids were reduced, together with our observation that the immune response is affected at multiple cellular levels underscores the possibility that different siglec receptors expressed on NK, Tregs and DCs may mediate sialic acid-tumor immune evasion. Analysis of the immune composition in the early stage of tumor growth revealed increased frequencies of NK cells in B16SLC35A1 tumors compared to B16scrambled tumors. Moreover, NK cells engaging B16SLC35A1 tumor cells secreted elevated amounts of IFN-γ compared to those engaging B16scrambled cells. A similar enhanced IFNγ-secretion by NK cells was reported in a study in which sialylation of the chemically induced fibrosarcomas was reduced via sialidase treatment or when the sialylation status of tumor cell surfaces was remodeled using synthetic glycopolymers22,23. Murine NK cells express siglec receptors such as

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Siglec-E and Siglec-G28,29, which bear specificity for α2-6-linked sialylated branches30. Thus, it is possible that the reduction of α2,6-sialic acids on B16 tumor cells diminished siglec-mediated inhibition of NK cell functions. Indeed, recent studies showed that in-vitro the susceptibility of hypersialyated tumor cells to NK cytotoxicity could be blocked by siglec-blocking antibodies or de-sialylation of the tumors22,31. The absence of this inhibitory interaction paves the way for the NK activating receptor NKG2D with its ligands on tumors, enhancing IFN-γ secretion and killing capacity of NK cells23. The augmented numbers of NK1.1+ NK cells present in sialic acidlow tumors during the early stages of tumor development could be a result of initial activated NK cells producing IFN-γ since IFN-γ has been shown to mediate recruitment of NK cells to sites of infection32. This NK cell influx is likely further enhanced by the chemokine IP-10, which was highly induced by IFN-γ in sialic acidlow tumors. Furthermore, the increased numbers of NK cells could also be due to reduced frequency of Tregs in the tumor area, as it has been shown that Tregs limit expansion of NK cells by restraining IL-2 from effector T cells33. The absence of CD4+ and CD8+ effector T cells in the B16SLC35A1 tumor microenvironment when NK cells were depleted elucidates that the IFN-γ released by activated NK cells likely also functions to activate DCs34, which obviously are instrumental in the subsequent induction of large amounts of effector T cells. Additionally, the released IFN-γ makes the tumor micro milieu susceptible for immune attack as we showed that IFN-γ reduced production of anti-inflammatory cytokines by tumor cells. Next to the IFN-γ released by activated NK cells, DC activation may also be the result of diminished siglec triggering on DCs. Similar to NK cells, DCs also express siglecs, and predominantly Siglec-E. We recently showed that Siglec- E-mediated internalization of sialylated antigens by DCs provides them with a tolerogenic phenotype: these DCs promote the induction of Treg. Additionally, the production of pro-inflammatory cytokines and ability to induce effector T cell generation is significantly reduced in these DCs35. Furthermore, by secreting high levels of sialylated gangliosides tumor cells inhibit the expression of costimulatory molecules on DCs and herewith reduce T cell proliferation36. Also, binding of sialylated pathogens to siglecs on DCs has been described to dampen secretion of pro-inflammatory cytokines37. DC binding of sialic acid structures on C. jejuni modulates the signals DCs provide to naive T cells and that mediate T cell activation and polarization. Thus, it is likely that enhanced tumor sialylation adds to the generation of tolerogenic tumor-associated DCs, which in the tumor microenvironment convert naive T cells into Tregs. This is also suggested by the finding that sialic acidlow tumors were inhabited by lower amounts of Tregs than hypersialylated tumors. Unexpectedly, in sialic acidlow tumors that are depleted of NK cells the frequency of Foxp3+ Tregs did not return to the levels observed in sialic acidhigh tumors. We hypothesized that Tregs expressing NK1.1 could potentially be co-depleted by the anti-NK1.1 Ab treatment. However, this appeared not to be the case as we found that the Foxp3+ T cells present in the tumors and TDLN lack expression of NK1.1 (Supplementary Figure 1I). Moreover, Treg frequencies were increased in the TDLN of NK cell depleted mice bearing sialic acidlow tumors (Figure 4B). A decreased amount of Tregs in sialic acidlow tumors may alternatively result from impaired local expansion of natural Tregs or impaired recruitment due to reduced expression of Treg- attracting chemokines8,38. Which of these scenarios is involved in creating the tolerogenic tumor milieu is subject of current research. Our data strongly indicate that reducing sialylation may provide a therapeutic option to render tumors permissive to immune attack. This could for example be accomplished by targeted delivery of an sialic acid blocking glyco-mimetic, which was recently shown to delay metastasis of B16 melanoma when used as adjuvant therapy39. Additionally, the use of synthetic small molecules to inhibit sialyltransferase or sialyltransporter activity would also allow to tackle tumors that express α2,3- or α2,8-

119 linked sialic acids or sialyl-Lex 13. However, whether α2,3- or α2,8-linked sialic acids or sialyl-Lex on tumors have similar effects on the immune composition in the tumor environment remains to be examined. In conclusion, we showed a correlation between melanoma hyper-sialylation and the induction of a tolerogenic tumor microenvironment marked by high amounts of Tregs and tolerized NK cells. Moreover, our studies unveiled a crucial role for activated NK cells igniting different pathways that result in the generation of tumor-specific effector T-cells. Hence, the reduction of sialic acids on tumors or blocking siglecs on NK cells and/or DCs might have therapeutic implications in immunotherapy of tumors.

Authors’ contributions

M.P. designed and performed experiments, analyzed and interpreted data, and wrote paper; L.A.M.C., I.S.O. performed experiments, analyzed and interpreted data; S.E. and M.I.V. performed experiments; L.B. produced depleting antibodies and D.G. produced shRNA constructs; L.B. and D.G. interpreted data; W.W.J.U. and Y.v.K. designed experiments, interpreted data, wrote paper and supervised the study.

Acknowledgements and funding

We thank our biotechnicians for excellent caretaking of the animals. This work was supported by the seventh framework program of Marie Curie actions (7th Framework Programme for Research and Technological Development-Carmusys); KWF (VU2009-2598); NanoNextInitiative (3D01) and European Research Council (ERCAdvanced339977).

Conflicts of interest

All authors concur with the submission. This work has not been published elsewhere, either completely, in part or in another form. The manuscript has not been submitted to another journal and will not be published elsewhere within one year after its publication in this journal. All experiments performed were done with the permission of local authorities.

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Supplementary information

Supplementary Figure 1. B16scrambled behaves similar to WT B16 in-vitro and in-vivo. (A) Detection of α2,6-sialic acids using SNA on B16WT, B16scrambled and B16SLC35A1 tumors (black lines) by flow cytometry. Gray filled histograms represent conjugate control. Analysis of sialylation was performed at least three times. (B) Tumor growth in B16WT and B16scrambled tumor-bearing mice (n=7 mice/group). Tumor growth assessed at different days after tumor inoculation is indicated as mm2 of tumor volume (mean ± s.e.m). (C) Analysis of intra-tumoral Foxp3+ CD4+ T cells in B16WT and B16scrambled tumors by flow cytometry. Percentage of Foxp3+ of CD4+ T cells in a representative mouse of each group is shown. (D) Cell cycle analysis of B16WT and B16scrambled tumors by DNA content. Percentage of cells in G0/G1 interphase and G2/M mitotic phase are indicated. Results are representative of 3 experiments. (E) Spleens of mice challenged with either B16SLC35A1 or B16scrambled tumor cells were analyzed by flow cytometry to determine the

123 frequency of CD4+ and CD8+ T cells and of Foxp3+ CD4+ T cells. CTL/Treg ratios were calculated for each mouse. Dots represent individual mice (n=7). Bars indicate median of each group. Plots represent 2 experiments. (F) Expression of IFN-y receptor (IFNGR)in B16SLC35A1 and B16scrambled tumors as determined by qRT-qPCR. Gene expression was normalized and presented as relative expression to GAPDH. Values shown are the mean ± s.e.m of two experiments. (G) Detection of NK cells, distinguished as NK1.1+ CD3− cells, in spleens from untreated and anti-NK1.1-treated tumor-bearing mice (n=7/8 per group) by flow cytometry; representative plots are shown (left). The percentage of NK1.1+ CD3− cells (median) is represented in a graph (right). (H) WT and NK-depleted mice were challenged with B16SLC35A1 or B16scrambled tumor cells. Tumor growth, assessed every 2–3 days after tumor inoculation, is indicated as mean mm2 ± s.e.m; n=7−9 mice/group. (I) Analysis of NK1.1 expression by Foxp3+ T cells in TILs of B16scrambled tumors (n=6 mice). CD45+ cells were gated and expression of NK1.1 and Foxp3 is shown. A representative example is shown.

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Chapter 6

Disruption of sialic acid metabolism drives tumor + growth by augmenting CD8 T cell apoptosis

Lenneke A.M. Cornelissen Athanasios Blanas Joost C. van der Horst Laura Kruijssen Anouk Zaal Tom O’Toole Lieke Wiercx Yvette van Kooyk Sandra J. van Vliet

International Journal of Cancer 144, 2290-2302, doi:10.1002/ijc.32084 (2019).

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Abstract

Sialylated glycan structures are known for their immunomodulatory capacities and their contribution to tumor immune evasion. However, the role of aberrant sialylation in colorectal cancer and the consequences of complete tumor desialylation on anti-tumor immunity remain unstudied. Here, we report that CIRSPR/Cas9-mediated knock out of the CMAS gene, encoding a key enzyme in the sialylation pathway, in the mouse colorectal cancer MC38 cell line completely abrogated cell surface expression of sialic acids (MC38-Sianull) and, unexpectedly, significantly increased in vivo tumor growth compared to the control MC38-MOCK cells. This enhanced tumor growth of MC38-Sianull cells could be attributed to decreased CD8+ T cell frequencies in the tumor microenvironment only, as immune cell frequencies in tumor-draining lymph nodes remained unaffected. In addition, MC38-Sianull cells were able to induce CD8+ T cell apoptosis in an antigen-independent manner. Moreover, low CMAS gene expression correlated with reduced recurrence- free survival in a human colorectal cancer cohort, supporting the clinical relevance of our work. Together, these results demonstrate for the first time a detrimental effect of complete tumor desialylation on colorectal cancer tumor growth, which greatly impacts the design of novel cancer therapeutics aimed at altering the tumor glycosylation profile.

Keywords: Glycosylation, colorectal cancer, sialic acid, CD8+ T cell, apoptosis

Abbreviations

CRC: colorectal cancer MAL-II: Maackia Amurensis Lectin II MC38-MOCK: transfection control MC38 cell line MC38-Sianull: CMAS gene knock out MC38 cell line MC38-WT: wild type MC38 cell line Neu5Ac: N-acetylneuraminic acid NK: natural killer cell NSG: NOD.Cg-Prkdcscid Il2rgrm1Wjl/SzJ OVA: ovalbumin Siglecs: Sialic acid-binding immunoglobulin-type I lectins SNA: Sambucus Nigra agglutinin ST6Gal1: β-galactoside α2-6 sialyltransferase 1 TME: tumor microenvironment

128

Introduction

A major focus of current cancer research is the elucidation of immune evasion strategies employed by malignant cells within the tumor microenvironment (TME). The TME contains a wide variety of cells, including stromal, endothelial and immune cell subsets. The type, density and localization of immune cells within the TME are defined as the Immunoscore, which can be used to predict clinical outcome1,2. As such, cytotoxic CD8+ T cells are capable of eliminating tumor cells and are thus strongly associated with an improved prognosis in many cancer types3. Nevertheless, as cancers develop, tumor cells acquire the capacity to escape CD8+ T cell-mediated cytotoxicity through down-regulation of major histocompatibility complex class I (MHC-I) molecules and expression of immune checkpoint molecules. The transition of immune surveillance to immune escape has been described as the cancer immunoediting hypothesis4. Although cancer immunoediting has been widely studied, the contribution of cancer-specific glycan structures on immune evasion still remains undefined. Glycosylation is an enzymatic post-translational process that mediates the attachment of carbohydrate structures to proteins and lipids and functions in a wide range of biological processes including protein folding, cell adhesion and cell signaling5. Compared to their non-malignant counterparts, tumor cells generally harbor an aberrant glycosylation profile of N-glycans, O-glycans and glycolipids structures that are all known to influence cancer development6,7. The cancer related glycosylation profile is often characterized by enhanced expression levels of sialylated glycan structures8. Sialic acids (Sias) are a family of nine-carbon monosaccharides with N-acetylneuraminic acid (Neu5Ac) as the most common member. Sialylated glycan structures can be recognized by Sialic acid-binding immunoglobulin-type lectins (Siglecs), which are mainly expressed on granulocytes, monocytes, antigen presenting cells and natural killer (NK) cells9. It is generally accepted that Sias are able to skew immune responses towards immune suppression, since most Siglec receptors contain an immunoreceptor tyrosine-based inhibitory motif (ITIM) in their cytoplasmic domain. As Sias, although in lower levels, are also expressed on non-malignant cells, the current dogma states that Sias act as a shield to prevent inappropriate immune reactions against self-antigens. This dampening of immunity by Sias, therefore, led to the concept that glycans represent a self-associated molecular pattern (SAMP) recognized by self-pattern recognition receptors10. Indeed, numerous studies have evaluated the negative immunoregulatory function of Sias. Targeting of mouse dendritic cells with Sias conjugated to ovalbumin (OVA) augments the differentiation of regulatory T cells both in vitro and in vivo11. Also, Siglec-7 present on NK inhibits NK cell cytotoxic activity when stimulated with natural sialylated ligands12,13. Since tumor cells express enhanced levels of Sias, these cells might exploit the sialylation machinery to suppress anti-tumor immune responses. Reduced tumor growth as well as enhanced effector T cell responses and diminished regulatory T cell frequencies were found in a mouse model of B16 melanoma wherein the tumor harbored decreased levels of sialylation14-16. Moreover, a subset of tumor-infiltrating T cells upregulates Siglec-9, which dampens anti-cancer immunity, thus facilitating immune escape by the tumor17. The involvement of Sias in suppression of subsequent immune responses prompted new therapeutic strategies aimed at reducing tumor-associated Sia expression to enhance anti-tumor immunity. Xiao et al. developed an anti-HER2 antibody and sialidase conjugate that simultaneously cleaves Sias from the cell surface and targets tumor cells for antibody-dependent cell-mediated cytotoxicity by NK cells17,18. More recently, intracellular Sia blockade was shown to be sufficient to enhance CD8+ T cytotoxicity, resulting in reduced tumor burdens16. While these new therapeutic approaches lower the Sia expression of tumors, the effect of complete tumor desialylation on the anti-tumor immune response has never been investigated.

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Moreover, glycosylation patterns differ substantially between and within tumor types19, urging the need to increase our understanding on how tumor-associated glycan structures influence the immune system in tumors of different origins. With this study we are the first to explore the effect of complete tumor desialylation in colorectal cancer (CRC). The adult gastrointestinal tract is a heavily glycosylated area with distinct regional differences, whereby sialic acid levels are higher in the colon compared to the ileum20-22. Also, recurrence of CRC tumors is correlated with a distinct glycosylation profile, illustrating the relevance of tumor glycosylation in CRC specifically23. We assessed tumor growth and the anti-tumor immune response in vivo using CRISPR/Cas9 glyco-engineered MC38 CRC cells. Unexpectedly, desialylated MC38 (MC38-Sianull) cells grew significantly faster in vivo, which we attribute to decreased CD8+ frequencies present within the tumor microenvironment. In addition, we found that MC38-Sianull cells were capable of inducing CD8+ T cell apoptosis, likely explaining the reduced CD8+ T cells frequencies in vivo. Collectively, these results strongly argue that complete tumor desialylation may have detrimental effects on anti-tumor immunity, thus greatly impacting the design of novel cancer therapeutics aimed at targeting the tumor sialylation profile.

Materials and Methods

CRISPR/Cas9 constructs CRISPR/Cas9 constructs were made as previously described24. The following gRNA strands for murine CMAS were used: CMAS #1: top strand CACCGAATGTGGCCAAACAGTT; bottom strand CTTACACCGGTTTGTCAACAAA; and CMAS #2: top strand CACCGTTTCAGAACTTCTTCGA; bottom strand: CAAAGTCTTGAAGAAGCTCAAA. The gRNA strands were phosphorylated and annealed prior to cloning in the pSpCas9(BB)-2A-Puro plasmid, a gift from Feng Zhang (Addgene #62988). Cloning mixtures were treated with PlasmidSafe exonuclease (Epicentre) to digest residual linearized DNA and used for transformation in XL1-Blue Sublconing-Grade competent bacteria (Stratagene). The Nucleobond Xtra Midi kit (Macherey- Nagel) was used to purify the plasmids. Purified constructs were stored at -20°C until further use.

Generation of the MC38-Sianull cell line MC38 cells were cultured in DMEM supplemented with 10% heat inactivated fetal calf serum (FCS, Biowest), 1% penicillin and 1% streptomycin. Cells (70-90% confluency) were transfected with CRISPR/Cas9 constructs (3 µg per 6-well) targeting the CMAS gene (MC38-Sianull) or an empty CRISPR/Cas9 construct (MC38-MOCK). Lipofectamine LTX with PLUSTM reagent (ThermoFisher Scientific) was used for transfection and applied according manufacturer’s protocol. After 24 h, fresh medium containing 3 µg/mL puromycin was added for selection. Transfected MC38-Sianull cells were incubated with 5 µg/mL biotinylated Sia-specific Maackia Amurensis Lectin II (MAL-II, Vector Laboratories) for 1 h on ice, washed with medium and incubated with streptavidin- PE (Jackson ImmunoResearch) on ice. After 1 h cells were washed and sorted in bulk on MAL-IIlow cells. To obtain supernatant samples, MC38-MOCK or MC38-Sianull cells were cultured overnight in 96-wells plates at a cell density of 3x104 cells/200 µL per well. For heat inactivation, supernatant samples were incubated at 65°C for 5 min.

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Surveyor assay Genomic DNA was isolated with the Quick-DNATM kit (Zymo research) according to manufacturer’s protocol. PCR products (732 bp) for the CMAS gene (forward primer: GGCCTGGGATTCAGGAACAT, reverse primer: TGCAGCTGTACCCCAAGCA) were hybridized and treated with Surveyor Nuclease (Surveyor® Mutation Detection Kit, Integrated DNA Technologies) and loaded on a DNA agarose gel to visualize presence or absence of the mutation.

Lectin flow cytometry The biotinylated plant lectins Sambucus Nigra agglutinin (SNA, Vector Laboratories) and Maackia Amurensis Lectin II (MAL-II, Vector Laboratories) were used to detect α2-6 and α2-3 Sias, respectively. MC38 cells were incubated with 5 µg/mL of the lectin for 30 min at 37°C. Cells were washed and incubated with streptavidin-APC (BD Biosciences) for 30 min at 37°C. Cells were washed and acquired on the Beckman Coulter Cyan flow cytometer. Data was analyzed with FlowJo v10.

N-glycan profiling using Liquid Chromatography Mass-Spectrometry (LC-MS) MC38-MOCK and MC38-Sianull cells were washed three times with PBS and resuspended in 100 μL pure water, before homogenization for 45 min in a sonication bath. Samples were centrifuged (500xg, 15 min) and 17.5 µL of pure water was added. 5 µL of reaction buffer (250 mM sodium phosphate buffer; pH 7.5) and 1.25 µL of denaturation buffer (2% SDS in 1 M β-mercaptoethanol) were added. Samples were incubated for 10 min at 100°C to denature the proteins, afterwards 1.25 µL of Triton X-100 and 500 units of PNGase F (QABio, California, USA) were added. Samples were vortexed and incubated overnight at 37°C. The released glycans were then converted to aldoses with 0.1% formic acid, filtered through a protein-binding membrane and dried. Released N-glycans were fluorescently labelled by reductive amination with procainamide as described previously25 using LudgerTagTM Procainamide Glycan Labelling Kit (LT-KPROC-24). Procainamide labelled samples were analyzed by HILIC-(U)HPLC-ESI-MS with fluorescence detection. Samples were injected in 24% pure water/76% acetonitrile onto an ACQUITY UPLC® BEH-Glycan 1.7 μm, 2.1x150 mm column at 40°C on a Ultimate 3000 UHPLC instrument (Thermo Scientific, Massachusetts, USA) with a fluoresence detector (λex = 310nm, λem = 370nm). A glucose homopolymer ladder labelled with procainamide (Ludger) was used as a standard. ESI-MS and MS/MS data analysis was performed using Bruker Compass DataAnalysis V4.1 software and GlycoWorkbench software26.

CellTiter-Blue® Cell Viability assay The metabolic activity of MC38 cells was analyzed using the CellTiter-Blue® Cell Viability assay (Promega) and measured on the FLUOstar Galaxy (MTX Lab systems, excitation 560 nm , emission 590 nm).

In vivo tumor experiments C57BL/6, NOD.Cg-Prkdcscid Il2rgrm1Wjl/SzJ (NSG, Charles River) and OT-I mice were used at 8-12 weeks of age and bred at the animal facilities of Amsterdam UMC. For all in vivo experiments, an equal distribution of female and male mice was used. Experiments were performed in accordance with national and international guidelines and regulations. Tumor cells (2x105 per mouse) were injected subcutaneously in the flanks. Tumors were measured three times per week in a double-blind manner. Total tumor volume was calculated using the formula 4/3 x π x

131 abc (a = width of the tumor/2, b = length/2 and c = the average of a and b). Mice were sacrificed at day 13 after inoculation or when the tumor reached a size of 2000 mm3. Tumor and tumor-draining lymph nodes were isolated and used for further experiments.

Tumor and lymph node dissociation Tumors were finely minced and enzymatically digested in RPMI containing 1 mg/mL Collagenase type 4 (Worthington), 30 units/mL DNase I type II (Sigma-Aldrich) and 100 µg/mL hyaluronidase type V (Sigma- Aldrich) for 25 min at 37°C. Lymph nodes were digested with 2 U/mL Liberase TM (Roche) enriched with 30 units/mL DNase I type II for 10 min at 37°C. All cell suspensions were passed through a 70 µm cell strainer and washed with RPMI supplemented with 10% FCS, 1% penicillin, 1% streptomycin and 1% glutamax. Lymph node digestions were washed twice and directly used for subsequent experiments. Tumor digestion mixtures were loaded on a Ficoll gradient to enrich for lymphocytes and to remove dead cell debris.

Immune cell profiling Cells were pretreated with 2.4G2 (anti-CD16/32) for 10 min at RT and cell viability was measured using a fixable viability dye (Zombie NIR, Biolegend), 2 µg/mL DAPI or 7-AAD (Invitrogen). Cells were stained for 20 min at RT using the following markers: anti-CD45-PerCp (30-F11), anti-CD31-FITC (390), anti-CD3-BV510 (17A2), anti-CD4-A700 (GK1.5), anti-CD8b-FITC (YTS156.7.7), anti-PD-1-BV785 (29F.1A12), anti-NK.1.1- PE/Dazzle 594 (PK136), anti-NKp46-BV421 (29A1.4) (all from Biolegend) and anti-CD8b-APC (eBioH35-17.2, eBioscience). To stain for Foxp3, cells were fixed and permeabilized according to manufacturer’s protocol (Foxp3 Transcription Factor Staining buffer set, eBioscience) and incubated with anti-Foxp3-PE antibodies (150D, Biolegend) for 20 min at RT. For tetramer staining, fluorophore-conjugated MHC-I peptide complexes were added to the cells prior cell surface staining. The Dpagt1-H-2Kb, Reps1-H-2Db and Adpgk-H-2Db tetramers were obtained through the NIH Tetramer Core Facility (Emory University, Atlanta) and labeled with PE, APC and BV421, respectively. Samples were acquired on BD LSRFortessaTM X-20. Data was analyzed with FlowJo v10.

Generation of activated OT-I CD8+ T cells To obtain activated CD8+ OT-I T cells, an OT-I spleen was minced and meshed through a 70 µm cell strainer. Cells were cultured in RPMI supplemented with 10% heat inactivated FCS, 1% penicillin, 1% streptomycin, 1% glutamax and 50 µM 2-Mercaptoethanol at a concentration of 3.5x106 splenocytes/mL in presence of 0.75 µg/mL OVA257-264 peptide. After three days, 2 mL medium containing 10 U/mL rmIL-2 was added. At day 5 of culture, CD8+ T cells were purified using Lympholyte separation medium (Cedarlane) and checked for purity by staining for CD8b (>90%).

T cell cytotoxicity assay MC38-MOCK and MC38-Sianull cells were labeled with CFSE (Thermofisher) and CellVue® Claret (Sigma) respectively, according to manufacturer’s protocol. After labeling, cells were pulsed with the OVA257-264 peptide for 2 h at 37°C. MC38-MOCK and MC38-Sianull cells were plated with or without activated OT-I CD8+ T cells and after 5 h of incubation, cells were stained with anti-CD45 and DAPI and acquired on BD LSRFortessaTM X-20. Data was analyzed with FlowJo v10. Tumor cell viability was calculated by dividing the number of viable MC38 cells incubated with CD8+ T cells by the number of viable MC38 cells cultured without CD8+ T cells x 100%.

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CD8+ T cell apoptosis assays Fresh C57BL/6 splenocytes or activated OT-I CD8+ T cells were co-cultured with MC38-MOCK or MC38- Sianull cells or supernatants as indicated. 2 ug/mL PHA-L (Vector Laboratories) was added to the C57BL/6 splenoctyes for activation. Cells were stained with anti-CD8b and 7-AAD and subsequently with Annexin V- FITC (in Annexin V buffer, BD Bioscience). Non-apoptotic CD8+ T cells were measured on the BD LSRFortessaTM X-20 and analyzed with FlowJo v10.

Colorectal cancer cohort Dataset GSE39582 was obtained from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/gds)27. The median of CMAS gene expression from 566 CRC patients was ranked from low to high and divided into CMAShigh (median gene expression >300) and CMASlow (<150) groups. Both groups were used to analyze the recurrence-free survival.

Statistical analysis Statistical significance was tested using GraphPad Prism by performing an unpaired nonparametric t-test. Recurrence free survival was displayed in Kaplan-Meier curves and statistical significance was calculated by Log-rank Mantel-Cox test using GraphPad Prism. (*p < 0.05, **p < 0.01, ***p < 0.001; ns, not significant).

Results

Lack of cell surface Sia expression after CMAS gene knockout We first analyzed the mouse colorectal cancer cell line (MC38 wild type, MC38-WT) for the presence of sialic acids (Sias). Sias, linked in an α2-6 or α2-3 configuration to the underlying galactose residue, can be visualized using the plant lectins Sambucus Nigra agglutinin (SNA) and Maackia Amurensis lectin II (MAL- II), respectively. MC38-WT cells displayed high binding of MAL-II, while SNA binding was negative (Fig. 1A), indicating that MC38-WT cells only expressed α2-3 linked Sias. In order to reduce Sia expression on the tumor cell surface, we previously targeted the SLC35A1 Sia transporter using shRNA, which resulted in decreased, but still detectable levels of Sia15 (see Fig. 1B for an overview of the sialylation pathway). However, SLC35A1 was recently shown to be involved in the O-mannosylation pathway as well28. Hence, for the present study we mutated the N-acylneuraminate cytidylyltransferase (CMAS) gene, which catalyzes the activation of Neu5Ac to 5’-monophosphate N-acetylneuraminic acid (CMP-Neu5Ac), the substrate required for the addition of Sia to the growing glycan chain (Fig. 1B). We designed two different CRISPR/Cas9 guideRNAs, targeting distinct regions in the CMAS gene. Independent of the guideRNA used, both CMAS knockouts (MC38-Sianull) displayed a complete lack of cell surface Sia (Fig. 1C). The CMAS gene mutation was confirmed using the Surveyor assay (Supplementary Fig. 1A) and by mass spectrometric analysis (Fig. 1D and Supplementary Table S1). We could only detect one minor sialylated glycan structure on MC38- Sianull cells, which we assume is a contamination from the MC38 culture medium.

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Figure 1. Cell surface sialic acid expression is abrogated in the CMAS gene knockout. A, The presence of α2-6 and α2-3 Sia in MC38- WT cells was assessed by flow cytometry using the plant lectins SNA and MAL-II, respectively. B, Schematic representation of the sialylation pathway. C, Two CRISPR/Cas9 guideRNAs targeting distinct regions in the CMAS gene both completely abrogate MC38 sialylation (MC38-Sianull) as measured with plant lectins SNA and MAL-II. MOCK-transfected MC38 cells (MC38-MOCK) were used as a negative control. Relative mean fluorescence intensities (MFI) were calculated relative to MFI of the 2nd antibody only. Data are representative of three independent experiments (A and C). Mean ± SD; ***, p<0.001. D, Liquid Chromatography-Mass spectrometry (LC-MS) analysis of MC38-MOCK (upper) and MC38-Sianull (bottom) sialylation. Only representative examples of the identified sialylated glycans structures are depicted.

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Manipulation of the glycosylation machinery may influence the proliferation capacity of cells. Nevertheless, MC38-MOCK and MC38-Sianull cells displayed identical growth rates under in vitro culture conditions as observed by manual counting and by measuring their metabolic activity (Supplementary Fig. 1B and C). Thus, we obtained a completely desialyated non-clonal MC38 cell line through CRISPR/Cas9 genome engineering of the CMAS gene.

MC38-Sianull cells display enhanced tumor growth in vivo Next, we investigated the effect of complete tumor desialylation on in vivo tumor growth by injecting MC38-MOCK and MC38-Sianull cells into C57Bl/6 mice (Fig. 2A). Unexpectedly, the MC38-Sianull tumors grew significantly faster than MC38-MOCK tumors (Fig. 2B and C). The tumor mass, weighed directly after isolation, correlated to tumor size (Supplementary Fig. 2A), confirming the tumor measurements were accurately performed. Moreover, the MC38-Sianull cells retained their Sia negative phenotype after in vivo passage (Supplementary Fig. 2B).

Figure 2. MC38-Sianull tumors display enhanced tumor growth in vivo. A, MC38-MOCK or MC38-Sianull cells were injected into C57Bl/6 mice (n=14) and immunodeficient NOD.Cg-Prkdcscid Il2rgrm1Wjl/SzJ (NSG) mice (n=9). B-D, Tumor growth was monitored and depicted as mean tumor size (B) or individual tumor growth curves (C and D). Data are representative of one (D) or three (C) independent experiments. Mean ± SEM; ***, p<0.001; ns, not significant.

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To ascertain that the differences observed in tumor growth were due to alterations in anti-tumor immunity, we inoculated the MC38-MOCK and MC38-Sianull cells into immunodeficient mice (NOD.Cg- Prkdcscid Il2rgrm1Wjl/SzJ, NSG mice). Compared to immunocompetent mice, MC38-MOCK tumors grew faster in immunodeficient mice, providing evidence for the existence of immune surveillance towards the MC38- MOCK tumors (Fig. 2B and D). In contrast, the MC38-Sianull only displayed a small increase in tumor growth in the immunodeficient mice compared to the immunocompetent mice (Fig. 2B and D), suggesting in the case of MC38-Sianull, the immune system is less able to recognize or can less efficiently combat MC38-Sianull tumor outgrowth. Together, our data imply that a complete CRC desialylation actually augments tumor growth, which might be attributed to a lack of activation or a heightened suppression of anti-tumor immunity.

MC38-Sianull tumors harbor less CD8+ T cells To explore whether tumor desialylation influences anti-tumor immunity, we analyzed the immune cell composition of the tumor and tumor-draining lymph nodes at day 13 of tumor development (Fig. 3A). Compared to MC38-MOCK, the MC38-Sianull tumors harbored less cytotoxic CD8+ T cells and NK cells, while CD4+ T cells tended to be lower in MC38-Sianull tumors (Fig. 3B). Strikingly, frequencies of Foxp3+CD4+ regulatory T cells were equal in MC38-MOCK and MC38-Sianull tumors (Fig. 3B). Moreover, the reduced levels of CD8+ T cells and NK cells were only observed locally at the tumor site, as the tumor-draining lymph nodes contained equal frequencies of all subsets investigated (Supplementary Fig. 3A). As MC38 tumors develop, CD8+ T cell frequencies seem to drop29. Thus, the decreased CD8+ T cell infiltration in MC38-Sianull tumors might be explained by tumor size, which at day 13 is significantly larger for MC38-Sianull, and not by the desialylated phenotype (Supplementary Fig. 4A). Therefore, we also checked the immune composition when tumors had an identical size of 2000 mm3 (Fig. 3C and D). Interestingly, the reduced CD8+ T frequencies in the MC38-Sianull were maintained in equally sized tumors (Fig. 3E). In contrast to day 13 of tumor development, tumor-infiltrated NK cell frequencies were similar in both MC38-MOCK and MC38-Sianull tumors (Fig. 3E). Again, the levels of Foxp3+CD4+ regulatory T cells in the tumor (Fig. 3E) and the levels of CD8+, CD4+, regulatory T and NK cells in the tumor-draining lymph nodes remained unaffected (Supplementary Fig. 3B). Remarkably, also lower frequencies of PD-1+CD8+ T cells were found in the MC38-Sianull tumors compared to the MC38-MOCK tumors, both at day 13 of tumor development (Fig. 4A) as well as when the tumors had an equal size (Fig. 4B). This effect was solely observed at the tumor site and not in the tumor draining lymph nodes (Fig. 4A and B). PD-1 is well-known for its crucial role as a checkpoint molecule in down-regulating immune responses30. Nevertheless, PD-1 expression is primarily induced upon TCR stimulation and lost when T cells fail to receive sustained TCR signaling31. Therefore, we checked whether the MC38-Sianull tumors also contained less antigen-experienced tumor-specific CD8+ T cells. Yadav et al29 identified three MC38-specific neoantigens with a strong immunogenic potential. We tested the CD8+ T cell reactivity towards these neoantigens by generating tetramers specific for these three identified neoantigens. Isolated CD8+ T cells obtained from MC38-MOCK tumors indeed displayed higher reactivity towards two of the neoantigens, while the response to the third antigen showed a trend towards higher recognition (Fig. 4C). The specificity of CD8+ T cells in the tumor draining lymph node was indistinguishable between MC38-MOCK and MC38- Sianull groups (Supplementary Fig. 4B). Together, these results indicate that the MC38-MOCK tumors were infiltrated with more tumor-specific CD8+ T cells than MC38-Sianull tumors, thus uncovering a predominant effect of tumor desialylation on the CD8+ T cell compartment exclusively in the tumor microenvironment.

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Figure 3. MC38-Sianull tumors harbor less CD8+ T cells. MC38-MOCK or MC38-Sianull cells were injected in C57Bl/6 mice and sacrificed at day 13 of tumor development (A-B, n=5) or when the tumor reached a size of 2000 mm3 (C-E, n=9). B and E, Flow cytometric analysis of viable, CD45+ CD8+ T cells (CD3+CD8b+), CD4+ T cells (CD3+CD4+), Regulatory T cells (CD3+CD4+Foxp3+) and NK cells (CD3-NK1.1+) at tumor site after 13 days of tumor development (B) or when the tumor reached a size of 2000 mm3 (E). Data are representative of two independent experiments (A-E). Mean ± SEM; *, p<0.05; **, p<0.01.

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Figure 4. Less PD-1 positive and MC38 neoantigen-specifc CD8+ T cells at the MC38-Sianull tumor site. A and B, Percentage of PD- 1+CD8+ T cells was analyzed at the tumor location or in tumor-draining lymph nodes with flow cytometry at an early stage of tumor development (A) or when the tumors reached an equal size (B). C, Tumor infiltrated CD8+ T cells (viable CD45+CD3+CD8b+) were stained with tetramers specific for three different MC38 neoantigens29 and analyzed by flow cytometry. Data are representative of two independent experiments. Mean ± SEM; *, p<0.05; **, p<0.01.

MC38-Sianull tumors induce CD8+ T cell apoptosis As the presence of CD8+ T cells in the tumor microenvironment correlates with good prognosis3, we hypothesized that the enhanced growth of the MC38-Sianull tumors was due to hampered elimination by cytotoxic CD8+ T cells. Moreover, this appeared to be a local effect, as tumor-specific CD8+ T cells frequencies were unaffected in tumor-draining lymph nodes. To explore whether MC38-Sianull cells might be able to suppress the cytotoxic function of CD8+ T-cells directly, we performed a cytotoxicity assay using OT-I T cells + and OVA-loaded tumor cells as a model system. Activated OT-I CD8 T cells were co-cultured with OVA257-

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null 264 (SIINFEKL)-pulsed MC38-MOCK and MC38-Sia cells and assesed for tumor cell eradication. To track the MC38 cells, MC38-MOCK cells were labeled with CFSE and MC38-Sianull with CellVue© Claret (Fig. 5A). After null + 5 h, OVA257-264-pulsed MC38-Sia cells were less efficiently killed by the OT-I CD8 T cells than the MC38- MOCK cells (Fig. 5B), suggesting that upon encounter of desialylated tumor cells, CD8+ T cells are functionally inhibited in their cytotoxic response. We next investigated whether MC38-Sianull cells are able to influence CD8+ T cell function by provoking CD8+ T cell death. Interestingly, fewer viable (7-AAD-Annexin V-) CD8+ T cells could be recovered from 48 h co-cultures of wild type C57Bl/6 splenocytes with MC38-Sianull cells, than from splenocytes and MC38-MOCK co-cultures (Fig. 5C). This effect seemed antigen-independent, as it occurred both under medium conditions or when T cells were further stimulated with PHA-L (Fig. 5C). To mimic activated tumor-infiltrating CD8+ T cells, we employed antigen-experienced OT-I T cells and investigated whether MC38-Sianull also induced apoptosis of these CD8+ T cells without accompanying antigen-specific stimulation. Indeed, at low T cell- tumor ratios, MC38-Sianull cells induced 40% more CD8+ T cell apoptosis than MC38-MOCK cells (Fig. 5D). We next assessed whether the MC38-Sianull-induced apoptosis was mediated by cell-intrinsic factors or via the secretion of a soluble factor. Thererfore, activated OT-I T cells were co-cultured with MC38-MOCK cells in the presence or absence of supernatants derived from MC38-Sianull cells. Indeed, in the presence of MC38- Sianull supernatant more CD8+ T cell apoptosis was measured, indicating that MC38-Sianull cells secrete a soluble pro-apoptotic mediator (Fig. 5E). As the apoptosis induction appeared to be antigen-independent and mediated by a soluble factor in the MC38-Sianull supernatant, we questioned whether MC38-Sianull supernatant alone might be sufficient to cause CD8+ T cell apoptosis. To address this, we cultured CD8+ T cells only in the presence of supernatants obtained from MC38-MOCK or MC38-Sianull cells or plain medium as a negative control. As expected, CD8+ T cell apoptosis was increased by 30% when cells were cultured in the presence of MC38-Sianull supernatant instead of plain medium or MC38-MOCK supernatant (Fig. 5F). To further characterize the apoptosis inducing factor secreted by MC38-Sianull cells, supernatants were heat inactivated prior to addition to the CD8+ T cells. Heat inactivation of the MC38-Sianull supernatant completely abbrogated CD8+ T cell apoptosis (Fig. 5G), suggesting that the MC38-Sianull-specific apoptosis mediator is a heat-sensitive protein or peptide, which acts in an antigen-independent manner. Moreover, the augmented CD8+ T cell apoptosis could provide an explanation for both the reduced CD8+ T cell frequences present in MC38-Sianull tumors as well as the diminished CD8+ T cell cytotoxicity towards MC38-Sianull cells in vitro.

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null + null Figure 5.MC38-Sia cells induce CD8 T cell apoptosis. A and B, MC38-MOCK and MC38-Sia cells were pulsed with OVA257-264 and labeled with CFSE or Cellvue Claret respectively (A). After 5 h of incubation with activated OT-I CD8+ T cells, viability of MC38 cells was analyzed by flow cytometry and calculated by dividing the number of viable (7-AAD-Annexin V-) MC38 cells present in the CD8+ T cell co-culture by the viable MC38 cells cultured without T cells and multiplied by 100% (B). C-F, Viable CD8+ T cells were analyzed with flow cytometry and gated as CD8b+7-AAD-Annexin V-. C, C57Bl/6 splenocytes were co-cultured with MC38-MOCK or MC38-Sianull cells in presence of medium or PHA-L for 48 h. D, Activated OT-I CD8+ T cells were co-cultured with MC38-MOCK or MC38-Sianull cells for 24 h. E, Activated OT-I CD8+ T cells were co-cultured with MC38-MOCK in presence of medium or MC38-Sianull supernatant for 24 h. F and G, Activated OT-I CD8+ T cells were cultured with control (F) or heat inactivated (G) medium, MC38-MOCK or MC38-Sianull supernatants for 24 h. Data are representative of one (G), two (C, E and F) or three (B and D) independent experiments. Bar, mean ± SD;*, p<0.05; **, p<0.01; ***, p<0.001; ns, not significant.

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Low CMAS gene expression is associated with lower recurrence-free survival So far, our results indicate that loss of sialic acids might actually be detrimental in CRC and thereby contradict other studies investigating the role of tumor sialylation on the anti-tumor immune response15,16,32. To translate our findings to human CRC patients, we analyzed CMAS gene expression in a human colorectal cancer cohort (GSE39582) in relation to disease progression and survival. We stratified the CRC patients according to their CMAS gene expression into two groups, CMAS low (median <150, n=74) or CMAS high (median >300, n=81) patients, and evaluated their recurrence-free survival. Interestingly, low CMAS gene expression indeed correlated to a lower recurrence-free survival in CRC patients (Fig. 6A), reflecting the results obtained in our in vivo mouse studies. Thus, also in human CRC patients, a low sialylation status of the tumor is correlated to poor prognosis.

Figure 6. CMAS gene expression levels are correlated to recurrence-free survival in human colorectal cancer and a partial CMAS knock out is sufficient to drive tumor growth in vivo. A, CMAS gene expression data was analyzed in the GSE39582 colorectal cancer cohort27. Patients were stratified in two groups with either low CMAS gene expression (median <150) or high CMAS gene expression (median >300) and evaluated for their recurrence-free survival. B, MC38-MOCK, MC38-Sianull or MC38-MOCK cells (30%) mixed with MC38- Sianull cells (70%) were injected into C57Bl/6 mice (n=9) and sacrificed when the tumor reached a size of 2000 mm3. Tumor growth was measured and survival curves of the mice are displayed. C, Flow cytometric analysis of viable CD8+ T cells (CD3+CD8b+) at the tumor site when the tumor reached a size of 2000 mm3. Mean ± SD; *, p<0.05; **, p<0.01; ***, p<0.001; ns, not significant.

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Partial tumor desialylation is sufficient to drive tumor growth in CRC Since low CMAS expression correlated to a lower recurrence-free survival in CRC patients, we tried to mimic this in vivo by investigating whether partial tumor desialylation would be enough to sustain the enhanced tumor growth. Therefore, we inoculated mice with MC38-MOCK and MC38-Sianull cells in a 30:70 ratio (MC38-MOCK/Sianull). Interestingly, the survival curve of the mice injected with the mixed tumors followed the survival curve of mice that received MC38-Sianull cells only (Fig. 6B). Moreover, CD8+ T cell frequencies were decreased to an equal level as in the MC38-Sianull tumors (Fig. 6C). Together, these data imply that in CRC tumor growth is promoted, even when only a proportion of the malignant cells in the tumor microenvironment is completely desialylated.

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Discussion

The upregulation of sialylated glycan structures on the cell surface is a key feature of tumor cells, independent of the tumor type. Sialylated structures are known to regulate immune responses, whereby the current dogma states that tumor cells exploit tumor-associated Sias to dampen anti-tumor immunity14-16. These findings have led to the development of new therapeutic strategies aimed at reducing tumor- associated Sia expression to dismantle the Sia-induced tolerance. However, these novel therapeutic strategies may result in a complete loss of tumor sialylation, yet the effect of a total absence of sialylated structures on tumor growth and anti-tumor immunity has not been investigated before. Here, we report for the first time that complete abrogation of Sias on the murine CRC cell line MC38 (MC38-Sianull) significantly increased tumor growth in vivo. Noteworthy, the MC38 CMAS mutated cells were selected in bulk and sorted based on cell surface expression of Sias. In this way tumor heterogeneity was maintained in our cell lines, allowing us to exclude potential clonal effects. Intestinal mucins are known to be both sialylated and/or sulphated20, yet in the present study we solely affected the sialylation pathway. Therefore, we cannot exclude that sulphated glycans contribute to tumor development in our model. The increased tumor growth of MC38-Sianull cells could be attributed to reduced CD8+ T cell frequencies present within the tumor. Reduced migration of CD8+ T cells towards the tumor core has been correlated with bad prognosis2,33,34. As we did not address the localization of the T cells within the tumor microenvironment, we cannot rule out that CD8+ T cells were hampered in reaching the tumor core. Nevertheless, we did observe that CD8+ T cells displayed diminished cytotoxicity and underwent apoptosis upon encounter of MC38-Sianull cells. Thus, our study highlights that complete tumor desialylation might be detrimental to the anti-tumor immune response in CRC, which greatly complicates the design of novel cancer therapeutics aimed at targeting the tumor glycosylation profile. To abrogate cell surface sialylation we knocked out the CMAS gene, a catalyzing enzyme involved in the sialylation pathway, whereas other studies focused on the CMP-sialic acid transporter, the SLC35A1 gene. Although both interrupt the sialylation pathway, the consequences of this interruption might significantly differ, since SLC35A1 has been described to be involved in the O-mannosylation pathway as well28. E- cadherin, a protein well known for its role in epithelial-mesenchymal transition and cancer metastasis35, is O-mannosylated, which is also crucial for the E-cadherin-mediated cell adhesion functions. However, the choice of the target gene cannot fully explain the observed discrepancies. Teoh et al32 recently showed decreased lung metastasis upon CMAS gene knock out in a highly metastatic mammary cell line injected in BALB/C mice. We, on the other hand, employed a non-metastatic CRC model that was inoculated in C57Bl/6 mice. The different mouse strains, with their different predisposition towards T helper 2 (Th2) and Th1 responses36,37, as well as the different tumor model used, may have impacted the outcome of the respective studies. Discrepancies due to tumor type-specific effects is further supported by the cohort data presented in both studies32 (Fig. 6A). Our CRC cohort analysis revealed that CMAS gene expression was positively correlated with prognosis. Strikingly, only a proportion of MC38-Sianull cells was sufficient to sustain the enhanced tumor growth in vivo. The analysis of sialoglycans in CRC human tissues and their link to CD8+ T cell tumor infiltration are subjects for future studies. Also in the B16 melanoma model, a reduction in tumor sialylation (B16-Sialow) had a positive effect on the anti-tumor immune response, resulting in decreased tumor growth15,16. Interestingly, the B16 cell line expresses both α2-3 and α2-6-linked Sias15, while the MC38 cell line only carries α2-3-linked Sias. This raises the possibility that our findings might be related to the unique presence of α2-3-linked Sias or conversely the absence of α2-6-linked Sias. Indeed, humans generally express higher levels of α2-6-linked Sias than

143 mice38. Unfortunately, the different functions and roles of α2-3 and α2-6-linked Sias and their cognate Siglec receptors in tumor biology are hardly described in literature. In hepatocellular carcinoma, Siglec-1+ macrophages negatively associate with progression and predict favorable survival39. Siglec-1 prefers binding of α2-3-linked Sias over α2-6-linked Sias40 and lacks the cytoplasmic ITIM domain, common in the majority of Siglec receptors41. Intriguingly, Siglec-1+ macrophages are able to scavenge Sia+-particles from apoptotic tumor cells for subsequent cross-presentation to tumor-specific CD8+ T cells42,43. Thus, Siglec-1+ macrophages may actually have the potential to propagate anti-tumor immunity. In light of the lack of α2-6-linked Sias on our MC38 cells, low and high levels of α2-6-linked Sias have indeed been correlated to tumor regression or progression, respectively44. Especially, high expression of ST6Gal1, the enzyme responsible for α2-6-sialylation, has been associated with metastasis and therapeutic failure in colorectal cancer specifically45,46. ST6Gal1 modulates tumor differentiation in vivo via an enhanced β1-integrin function that stimulates cell motility46. These findings suggest that α2-6-linked Sias may be an important instigator of Sia-mediated immune evasion in CRC. However, conclusive data is lacking to support this hypothesis and should thus be further explored. One soluble protein known to induce CD8+ T cell apoptosis is Galectin-1. Produced by tumor cells, Galectin-1 can bind the Galβ1-4GlcNAc (LacNAc) epitope47, which is elevated in our MC38-Sianull cells after removal of the Sia. Galectin-1 supports tumor progression through cell-cell and cell-matrix interactions as well as tumor-induced angiogenesis and an elevated CD8+ T cell apoptosis48. MC38-Sianull cells indeed showed higher mRNA levels of the LGALS1 (Galectin-1) gene, however, compared to MC38-MOCK, Galectin- 1 protein concentrations in MC38-Sianull supernatants were not increased (data not shown). Therefore, more research is required to identify the soluble factor present in the MC38-Sianull supernatant.

In conclusion, we have demonstrated that complete tumor desialylation of the MC38 mouse CRC cell line augments tumor growth and enhances CD8+ T cell apoptosis. Clearly, discrepancies still exist regarding the expression levels or the linkage type of α2-3 or α2-6 of Sias and the cellular origins of the tumor. Therefore, the specific impact of α2-3 and α2-6-linked Sias and their relative amounts on the anti-tumor immune responses requires further research to fully understand how various tumor types exploit the sialylation machinery to evade anti-tumor immunity. Nevertheless, we argue that in certain tumor types, for instance CRC, tumor sialylation may actually have a protective effect and we suggest that therapies aimed at abrogating tumor-associated Sias should be undertaken with caution.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Authors’ Contributions

L.A.M.C designed and performed experiments, analyzed data and wrote paper; A.B performed experiments and analyzed data; J.C.H performed experiments; L.K performed subcutaneous cell injections and mouse experiments; A.Z performed mouse experiments; T.O. performed cell sorting experiments; L.W. performed cell culture experiments; Y.v.K and S.v.V conceived and coordinated the study; S.v.V. designed and supervised the study and wrote paper. All authors contributed to reviewing, and/or revising of the manuscript.

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Acknowledgements

We thank our animal facility for caretaking of the animals and Joke den Haan (Amsterdam UMC) for her advice regarding mouse experiments. We thank Daniel Spencer, Rad Kozak and Max Kotsias (Ludger) for their support regarding the mass spectrometry experiments. We thank the NIH Tetramer Core Facility for providing the MHC-peptide complexes.

Financial Support

This work was supported by a grant from the Amsterdam UMC (LC), the European Union (Marie Curie European Training Network) GlyCoCan project grant number 676421 (AB), project grant VU 2014-6779 from the Dutch Cancer Society (KWF, JvdH & SvV), research grant CCA2016-5-29 from the Cancer Center Amsterdam (AZ) and ERC advanced grant (Glycotreat, grant number 339977, LK).

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Supplementary information

Supplementary Table 1. List of possible N-linked glycans identified in MC38-MOCK and MC38-Sianull cells using HILIC-(U)HPLC-FLR- ESI-MS. Information about the composition, registered or calculated mass [m/z]+ and charge [H+] is provided for the three most abundant N-glycans detected under each peak of the FLR chromatograms (Fig 1D). For each cell line, the possible N-linked glycans were categorized into four groups, including neutral (non-fucosylated/non-sialylated), fucosylated (mono-, di- or tri-fucosylated), sialylated (mono-, di- or tri-sialylated) and mixed (fucosylated and sialylated) compositions.

MC38-MOCK Possible Registered Calculated Charge composition Mass [m/z]+ Mass [m/z]+ [H]+ Neutral H3N2 565.77 565.76 2 H3N3 667.31 667.30 2 H3N4 768.82 768.84 2 H4N2 646.78 646.78 2 H4N3 748.21 748.32 2 H4N4 566.90 849.86 2 H5N2 727.79 727.81 2 H5N3 829.32 829.35 2 H5N4 930.89 930.89 2 H6N2 808.78 808.84 2 H6N3 910.60 910.37 2 H6N4 674.94 674.95 3 H7N2 889.80 889.86 2 H7N5 796.63 796.66 3 H7N6 864.31 864.35 3 H8N2 970.81 970.89 2 H9N2 1051.85 1051.92 2 H9N8 831.08 831.08 4 Fucosylated H3N2F1 638.80 638.79 2 H3N3F1 740.32 740.33 2 H3N4F1 841.83 841.87 2 H3N5F1 943.35 943.41 2 H4N3F1 821.32 821.35 2 H4N4F1 615.58 615.59 3 H4N5F1 683.30 683.29 3 H5N2F1 800.81 800.84 2 H5N3F1 902.31 902.38 2 H5N4F1 669.61 669.61 3 H5N5F1 737.31 737.31 3 H6N4F1 723.63 723.63 3 H6N5F1 791.31 791.33 3 H7N4F1 777.64 777.65 3 H7N5F1 845.31 845.34 3 H7N6F1 912.64 913.04 3 H7N7F1 736.25 736.80 4 H8N5F1 899.30 899.36 3 H8N6F1 725.50 725.54 4 H8N7F1 776.30 776.31 4 H9N5F1 953.32 953.38 3 H9N6F1 1021.01 1021.07 3 H9N8F1 867.56 867.59 4

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H4N6F2 799.34 799.67 3 Sialylated H4N4S1 663.89 663.94 3 H5N4S1 718.28 717.96 3 H5N5S1 785.69 785.65 3 H6N4S1 771.96 771.98 3 H6N5S1 839.65 839.67 3 Mixed H4N3F1S1 967.00 966.89 2 H4N4F1S1 712.63 712.63 3 H4N4F2S1 761.29 761.31 3 H4N5F1S1 585.59 585.49 4 H5N4F1S1 766.64 766.65 3 H6N4F1S1 820.30 820.66 3 H6N5F1S1 888.31 888.36 3 H7N6F1S1 1010.01 1010.07 3 H5N4F2S2 912.51 912.36 3 H5N5F2S2 979.65 980.06 3 H6N5F1S2 739.57 739.29 4 H5N4F3S2 960.63 961.05 3

MC38-Sianull Possible Registered Calculated Charge composition Mass [m/z]+ Mass [m/z]+ [H]+ Neutral H3N2 565.77 565.76 2 H3N3 667.31 667.30 2 H3N4 768.82 768.84 2 H4N2 646.78 646.78 2 H4N3 748.21 748.32 2 H4N4 566.90 566.91 3 H5N2 727.79 727.81 2 H5N3 829.32 829.35 2 H5N4 930.89 930.89 2 H6N2 808.78 808.84 2 H6N3 910.60 910.38 2 H6N4 674.94 674.95 3 H7N2 889.80 889.86 2 H8N2 970.84 970.89 2 H9N2 1051.89 1051.92 2 H10N2 755.62 755.63 3 Fucosylated H3N2F1 638.80 638.79 2 H3N3F1 740.32 740.33 2 H3N4F1 841.83 841.87 2 H3N5F1 943.35 943.41 2 H3N6F1 696.98 696.97 3 H4N3F1 821.32 821.35 2 H4N4F1 615.58 615.59 3 H4N5F1 683.30 683.29 3 H4N6F1 750.89 750.98 3 H5N2F1 800.81 800.84 2 H5N3F1 902.31 902.38 2 H5N4F1 669.61 669.61 3

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H5N5F1 737.31 737.31 3 H5N6F1 804.98 805.00 3 H6N3F1 983.36 983.41 2 H6N4F1 723.63 723.63 3 H6N5F1 791.31 791.33 3 H6N7F1 926.34 926.71 3 H7N4F1 777.64 777.65 3 H7N5F1 845.31 845.34 3 H7N6F1 912.64 913.04 3 H8N5F1 899.30 899.36 3 H8N6F1 725.50 725.54 4 H8N7F1 776.30 776.31 4 H9N5F1 953.32 953.38 3 H9N6F1 1021.01 1021.07 3 H10N6F1 1075.36 1075.09 3 H5N4F2 717.97 718.30 3 Mixed H4N4F2S1 761.33 761.31 3

Supplementary Figure 1. Mutating the CMAS gene in the MC38-Sianull cell line has no effect on cell proliferation rates in vitro. A, The CMAS gene from MC38-MOCK and MC38-Sianull cells was amplified with qPCR (742 bp, top arrow) and subsequently tested for the presence of mutations using the Surveyor assay. The Surveyor nuclease enzyme recognizes and cleaves DNA mismatches present in the qPCR product. The CMAS mutation was located after 260 bp of the qPCR product, resulting in a band of 260 bp and 482 bp when cleaved (middle and bottom arrow). B, MC38-MOCK and MC38-Sianull were cultured, harvested at day 1, 2, 3 or 4 days and cell numbers were counted manually. C, The metabolic activity of MC38-MOCK and MC38-Sianull cells was measured after 4.5 h of culture in a cell concentration-dependent manner (left) or after the first 24 h (30.000 cells plated, right) using the CellTiter-Blue® Cell Viability assay. Data are representative of one (A and B) or two (C) independent experiments. Mean ± SD.

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Supplementary Figure 2. MC38-Sianull tumors retain their desialylated phenotype in vivo. A, The manually measured tumor size (mm3) significantly correlates to the tumor mass (mg) weighed directly after tumor resection. B, The sialaylation phenotype of ex vivo MC38- Sianull tumor cells (CD31-CD45-) was analyzed with the α2-3-specific plant lectin MAL-II. Data are representative of pooled data of two experiments (A) or one experiment (B). Mean ± SEM; *, p<0.05.

Supplementary Figure 3. Equal immune cell frequencies in the tumor-draining lymph nodes of MC38-MOCK and MC38-Sianull tumors. MC38-MOCK or MC38-Sianull cells were injected in C57Bl/6 mice and sacrificed after 13 days of tumor development (A, n=5) or when the tumor reached a size of 2000 mm3 (B, n=9). A and B, Flow cytometric analysis of viable, CD45+ CD8+ T cells (CD3+CD8b+), CD4+ T cells (CD3+CD4+), Regulatory T cells (CD3+CD4+Foxp3+) and NK cells (CD3-NK1.1+) in the tumor-draining lymph node. Data are representative of two independent experiments (A and B). Mean ± SEM.

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Supplementary Figure 4. Similar frequencies of neoantigen-specific CD8+ T cells in tumor-draining lymph nodes of MC38-MOCK and MC38-Sianull tumors. A, MC38-MOCK or MC38-Sianull cells were injected in C57Bl/6 mice (n=5). Tumor growth was monitored and mice were sacrificed 13 days after tumor inoculation. B, Tumor-draining lymph node cells from MC38-MOCK and MC38-Sianull tumors were stained with tetramers specific for three different MC38 neoantigens. The frequencies of tetramer positive CD8+ T cells was indistinguishable between MC38-MOCK or MC38-Sianull groups. Data are representative of two independent experiments (A and B). Mean ± SEM; ***, p<0.01.

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Chapter 7

Discussion

Lenneke A.M. Cornelissen

Sialic acid: friend or foe of tumor immunity?

Sialic acids are generally accepted as immune suppressors since the majority of the receptors recognizing sialic acids, the Sialic acid-binding immunoglobulin-type lectins (Siglecs), contain an immunoreceptor tyrosine-based inhibitory (ITIM) motif in their cytoplasmic domains. Based on the immune suppressive activities of sialic acids, the current dogma states that tumor hypersialylation provides a selective advantage for tumor cells to escape from immune destruction. However, in Chapter 6 of the present thesis, we are the first to show a detrimental effect of complete tumor desialylation on tumor progression and the immune cell composition within the tumor micro-environment. Therefore, the effect of tumor-sialylation on tumor immunity holds a higher complexity than initially believed.

Level of sialylation is crucial for immune effector function

In the present thesis, we investigated the role of tumor-associated sialic acids in two different murine tumor models, the B16 melanoma model (Chapter 5) and the MC38 colorectal cancer model (Chapter 6). Although in both models the level of tumor-associated sialic acids was lowered, opposite effects on in vivo tumor growth were observed. Next to the fact that the two cell lines represent tumors of two different origins, the cell lines also differ in their overall sialylation patterns at steady state (MOCK cells), which could be classified as intermediate for MC38 and high for B16 cells. Knockout of the CMAS gene in MC38 cells (MC38-Sianull) completely abrogated the expression of sialic acids and enhanced in vivo tumor growth (Chapter 6). In contrast, a reduction in sialic acids on the B16 cells (B16SLC35A1) resulted in a cell line that displayed diminished in vivo tumor growth (Chapter 5). Moreover, a hampered tumor growth upon reduction of B16 sialylation has also been proven by others1,2. These results combined, led to a new hypothesis that the level of sialylation matters for the overall effect on anti-tumor immunity (Figure 1). This hypothesis states that both hyper- and a complete lack of sialylation negatively affect tumor immunity, while intermediate and low levels of sialylation actually promote anti-tumor immunity. Therefore, in addition to tumor-type dependent effects, the absolute amount of sialic acids present on a particular tumor cell may play a crucial role in determining not only tumor growth, but also the extent of the anti-tumor immune response. Besides sialylation quantity, the type of sialic acids as well as the type of conjugate, glycoprotein or glycolipid, may be crucial for eliciting an anti-tumor immune response. Both subjects will be discussed in more detail in the next two sections.

Figure 1. Schematic representation how tumor sialylation levels influence the anti-tumor immune response. Abbreviations used: mouse colorectal cancer cell line (MC38), steady state (MC38-MOCK), CMAS knock out (MC38-Sianull), mouse melanoma cancer cell line (B16), steady state (B16-MOCK), SLC35A1 knock down (B16SLC35A1).

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Sialic acid is not alone, it is a family

Sialic acid is a generic term for any acidic monosaccharide with a nine-carbon backbone. Reasonably, there is not just one structure that meets these requirements, hence, sialic acid is a family name for more than forty different neuraminic acid derivatives. The first level of diversity is created by the sialic acid linkage type to the underlying glycan structure. Sialic acids can be linked to the C3 or C6 positions of a galactose, the C6 position of a N-acetylgalactosamine (GalNAc) or to the C8 position of another sialic acid, generating polysialic acid glycan structures3. The second level of diversity is determined by the chemical modifications of the individual carbon groups of the sialic acid. Examples include acetylation, hydroxylation and methylation. The most common sialic acid type contains a 5-N-acetyl group, creating N-acetylneuraminic acid (Neu5Ac)3. A third level of complexity includes the type and arrangements of the underlying glycans as well as the spatial organization of sialoglycans within the cell membrane4. Importantly, the linkage as well as the chemical diversity of sialic acids determines the recognition and/or binding affinity of sialic acid binding lectins, including Siglecs5. Therefore, the exact composition of the sialome on a tumor cell is likely crucial for the outcome of the elucidated immune response. For instance, human Siglec-1 preferentially binds α2-3 linked sialic acids over α2-6 linkages6, whereas Siglec-2 (CD22) prefers α2-6 over α2-3 linkages7. Although the majority of Siglecs contain an ITIM motif in their cytoplasmic domain, Siglec-1 is an exception. Siglec-1 lacks tyrosine-based motifs in its cytoplasmic domain and thereby a signaling capacity5. Macrophages use Siglec-1 to scavenge sialic acid-expressing particles or pathogens, and subsequently, cross-present the engulfed antigens to enhance CTL activation8-10. On the contrary, Siglec-2 is specifically expressed by B cells and the cytoplasmic domain of the Siglec-2 receptor does carry ITIM motifs5. Hence, Siglec-2 is an essential inhibitory receptor for B cell activation, thus preventing hyper- activation of humoral immune responses11,12. The immune inhibitory features of Siglec-2 have led to the development of antigen-containing liposomes displaying Siglec-2 ligands to induce antigen-specific B cell tolerance in order to dampen auto-immune responses that are present in diseases such as rheumatoid arthritis13. The most prominent chemical modifications of sialic acids present in mammals are the addition of a N- acetyl or N-glycoyl group at the C5 position, leading to Neu5Ac and Neu5Gc respectively. The N-glycoyl modification is synthesized from Neu5Ac by the enzyme CMP-Neu5Ac hydroxylase (CMAH). In humans the CMAH gene has been mutated during evolution, rendering it dysfunctional, therefore humans are no longer able to hydroxylate Neu5Ac and lack Neu5Gc expression. However, human cells can still extract Neu5Gc from dietary sources, which subsequently undergoes metabolic incorporation into the sialylation pathway resulting in Neu5Gc expression on the cell surface. Interestingly, anti-Neu5Gc antibodies can be detected in healthy individuals, which are able to induce inflammatory responses14. Moreover, as chronic inflammation supports tumor development, Neu5Gc has been suggested to promote tumorigenesis. Indeed, Neu5Gc is detected in high levels in human tumors14 and CMAH knock out mice that were injected subcutaneously with Neu5Gc-expressing B16F1 cells, established antibodies against Neu5Gc and remarkably, displayed enhanced tumor growth curves15. Conclusive evidence that dietary Neu5Gc accumulation is responsible for the tumor-promoting inflammation has been provided by Samraj et al16. In their studies, CMAH knock out mice that were fed with Neu5Gc expressing mucins and vaccinated with Neu5Gc-containing glycans, had an increased incidence of hepatocellular carcinoma compared to control mice16. Since Neu5Gc antibodies are not only present, but also elevated in serum derived from cancer patients17, Neu5Gc antibodies have been proposed as a new biomarker for various types of cancer18.

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Another sialic acid modification present in cancer is the hydroxyl modification at C5, generating 2-keto- 3-deoxy-D-glycerol-D-galacto-nononic acid, also termed KDN. A hypoxic environment promotes uptake of KDN precursors and leads to an enhanced expression of KDN19. KDN has, at least, been associated with pancreatic cancers20 and head and neck cancers21. Additionally, , free KDN levels are predictive for the metastatic potential of head and neck cancer, hence valuable for the prognosis of the patient21. O-acetylation of sialic acids has been shown to be reduced on colorectal cancer mucins22. In contrast, 9- O-acetylation of the ganglioside GD3 and O-acetylation of the 7-hydroxyl group are identified as melanoma specific antigens23,24. Moreover, 9-O-acetylation seems to have an important role in controlling immune homeostasis, since individuals that lack sialic acid 9-O-acetylation, due to a defect in the sialic acid acetylesterase, have a higher risk to develop a broad range of auto-immune disorders. Together, next to differences in α2-3 and α2-6 linkages, chemical modifications of the individual sialic acids can also have a crucial impact on tumor development.

When comparing the sialome of B16 with the MC38 cell line (Table 1), the main difference is the lack α2-6 sialic acids on MC38 cells. Whereas MC38-MOCK cells solely express α2-3 sialic acids, B16-MOCK cells exhibit both α2-3 and α2-6 sialic acids. Hence, desialylation of the MC38 cells resulted in complete abrogation of α2-3 sialic acids specifically (Chapter 6). Unlike MC38, knock down of the sialic acid transporter (SLC35A1) in B16 cells resulted in a reduction of α2-6 sialic acids, while expression of α2-3 sialic acids remained unaffected (Chapter 5). Altogether, it is very tempting to speculate that a certain level of α2-3 sialic acids might be crucial for immune-mediated tumor destruction. Indeed, abrogation of both α2- 3 and α2-6 sialylation on B16 cells through CRISPR/Cas9-mediated SLC35A1 gene knock out had no effect on in vivo B16 tumor growth (preliminary results S. T. T. Schetters et al, data not shown). Noteworthy, different sialylation pathway genes were targeted in the two discussed models, CMAS for MC38 and SLC35A1 for B16. This may have contributed to the observed discrepancies also and will be discussed in more detail later on.

Table 1. Effects of MC38 and B16 sialylation on in vivo tumor growth. Abbreviations used: sialic acids (Sia), knock out (KO), knock down (KD).

Cell lines Gene target Results on tumor growth α2-3 sialylation α2-6 sialylation

MC38-Sianull vs MC38-MOCK CMAS KO Enhanced (Chapter 6) Abrogated Not present

B16-SLC35A1 vs B16-MOCK SLC35A1 KD Hampered (Chapter 5) Not affected Reduced

B16-Sianull vs B16-MOCK SLC35A1 KO Not affected (data not shown) Abrogated Abrogated

Given that the linkage of the sialic acid to its underlying glycan structure determines the binding affinity of the individual Siglec receptors, it is likely that the sialic acid repertoire as whole, the sialome4, influences the type of immune response elucidated. The ratio between α2-3 and α2-6 sialic acids in association with tumor growth and the immune cell composition present at the tumor microenvironment are interesting subjects for future research. In the MC38 model, overexpression of the sialyltransferase responsible for α2- 6 sialylation (beta-galactoside alpha-2,6-sialyltransferase, ST6Gal1) alone or together with specific knock out of α2-3 sialyltransferases in the MC38 cell line are examples of potential follow up experiments to perform. Moreover, improvements in mass spectrometric analysis that were made in the last few years, make the analysis of various types of sialic acids more feasible25-28. Also, sialic acids often target not just one but

158 multiple Siglec receptors at the same time. In order to explore the impact of each Siglec receptor on the outcome of an elucidated immune response, a sialic acid library that binds and subsequently activates each individual Siglec receptor is needed. Chemical modification of sialic acid can modulate its recognition and binding affinity by Siglecs, allowing the opportunity to generate a sialic acid that selectively targets individual Siglecs. One possible approach to apply chemical modifications to sialic acids is click chemistry. The first attempts have been made, whereby positively charged modifications of Neu5Ac resulted in enhanced binding towards Siglec-5, but importantly, the modified sialic acid was not recognized anymore by the other Siglecs29.

The role of gangliosides on tumorigenesis

Although research into tumor-associated sialic acids often concentrates on glycoproteins, sialylated glycolipids (gangliosides) also belong to the sialome presented by cells, and therefore, also contribute to sialic acid-mediated tumor-promoting functions. Gangliosides are sialic acid containing glycosphingolipids. Glycospingolipids are a group of lipids that consists of a lipid anchor, the ceramide, and carbohydrate groups. Gangliosides are abundant in the nervous system, as stated by the name the “Ganglionzellen” (neurons)30. The large variability in the carbohydrate structures as well as ceramide variations underlie the more than one hundred gangliosides types identified to date31 (See Figure 2 for a schematic representation of the glycosphingolipid pathway). Positioned in the plasma membrane, gangliosides influence a various set of cellular events, including cell growth, signaling and differentiation. Reasonably, they are also known to be involved in cancer malignancies and metastasis. For instance, GD3 expression is correlated with the metastatic potential of melanoma cells in human32 and exogenous GD3 stimulation of glioma cells augments vascular endothelial growth factor (VEGF) secretion33. Likewise, many tumor types are enriched with gangliosides, whereof GM3 is an example expressed in colorectal cancer34 and GM3, GM2, GD3 and GD2 are known to be present in melanoma35. Even though research on the immunomodulatory effects of gangliosides is limited or outdated, Chung et al. have recently proven that GM1 induces arginase-1 expression in macrophages by binding the mannose receptor (CD206). As a consequence, GM1 stimulated macrophages promote angiogenesis in vivo36. In addition to macrophages, ganglioside treatment of DCs hampers co-stimulatory marker expression and production of IL-12 and IL-6. Subsequently, ganglioside- exposed DCs prime CD4+ T cells normally, but promote regulatory T cell activity within the CD4+ T cell population37. Siglec-dependent effects of gangliosides have been identified for GD3, whereby GD3 dampens NK cell cytotoxicity via Siglec-738,39. Also T cells can be affected by gangliosides. Tumor-associated gangliosides induce apoptosis in peripheral blood T cells, which could be partially blocked with an antibody specific for GM240. Moreover, very recently, GD3 has been identified to be responsible for the rapid and reversible T cell arrest mediated by exosomes derived from human ovarian cancer41. T cell arrest was inhibited by the removal of GD3 from the exosomes, but very interestingly, removal of sialic acids from the exosomes was already enough to abrogate the inhibitory capacity41. CD8+ CTLs can eliminate cancer cells through the release of granules including perforin and granzymes that perfuse the target cell membrane, eventually leading to apoptosis. Interestingly, tumor-shed gangliosides inhibit granule trafficking and exocytosis of these granules by CD8+ T cells, thereby suppressing the lytic activity of CD8+ T cells42. This might be another mechanism of tumor cells to evade CTL-mediated tumor destruction. Therefore, analysis of the gangliosides present on the non- and glycoengineerd B16 and MC38 cell lines might provide more inside into how the sialome affects the in vivo tumor growth in both models (Chapter 5 and Chapter 6). Glycosphingolipids with known tumor-promoting effects in colorectal cancer are the non-

159 sialylated glycosphingolipids Gb3 and Gb443,44, while in melanoma the gangliosides GD2, GD3 and GM3 promote tumor progression45-48. Intriguingly, sialylation of glycosphingolipdis depends on the activity of the sialyltransferase ST3Gal5 that adds α2-3 sialic acid (Figure 2). In this thesis we report that MC38-MOCK cells solely display α2-3 sialylation and that sialic acid abrogation enhances the in vivo tumor growth (Chapter 6). Desialylation of the colorectal cancer cell line MC38 may have initiated the synthesis of the tumor- promoting glycosphingolipids Gb3 and/or Gb4. In line with this, neuramidase 3 (Neu3) is increased in human colorectal cancer tissues compared to non-malignant counterparts49,50. Since Neu3 specifically desialylates gangliosides structures, these data suggest that desialyation of gangliosides in CRC is advantageous for cancer development. Regarding the B16 model, these cells may expose sialylated tumor-promoting gangliosides, such as GM3 and GD3. Upon SLC35A1 knock down, the immune suppressive functions of these gangliosides may have been abrogated, resulting in hampered tumor growth. Taken together, gangliosides shed from tumor cells into their microenvironment can support tumorigenesis by multiple mechanisms, including the induction of immune evasion and by promoting metastases. In 2009, a prioritization of cancer vaccine targets was generated by the National Cancer Institute. Interestingly, not only sialylated Tn antigen and polysialic acid, but also 4 gangliosides (Fucosyl GM1, GM3, GD2 and GD3) were chosen among 69 other antigens51. Hence, the specific contribution of gangliosides on immune evasion of cancer cells cannot be neglected and should be included in studies investigating the role of tumor-associated glycans on tumor immunity.

Figure 2. Glycosphingolipid synthesis pathway, adapted from Boccuto et al.52

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How the choice of target gene to inhibit sialic acid metabolism influences the outcome

The sialylation pathway involves various key enzymes and crucial steps (Figure 3). The synthesis of Neu5Ac from UDP-N-acetylglucosamine (UPD-GlcNAc) occurs in the cytosol and involves four different steps, catalyzed by UDP-N-acetylglucosamine 2-epimerase/N-acetylmannosamine kinase (GNE), N- acetylneuraminate synthase (NANS) and Neu5Ac-9-phosphate phosphatase (NANP). After relocalization of Neu5Ac to the nucleus, Neu5Ac is activated by the CMP-sialic acid synthetase (CMAS) enzyme resulting in CMP-Neu5Ac. Accumulation of CMP-Neu5Ac in the nucleus provides a negative feedback loop for GNE, preventing overproduction of sialic acid53,54. The sialyltransferases that are responsible for the addition of CMP-Neu5Ac to the underlying glycan structures are located in the Golgi. The transport of CMP-Neu5Ac from the nucleus towards the Golgi is facilitated by the CMP-sialic acid transporter SLC35A1. After incorporation into glycan structures, sialoglycans are shuttled towards and expressed on the cell surface3. In Chapter 5, we targeted the sialic acid transporter SLC35A1 via shRNA knock down to downregulate cell surface sialylation of B16 cells. Nevertheless, in the meantime Riemersma et al. published that cells deficient for SLC35A1 lacked, as expected, the incorporation of sialic acids, but additionally, also O- mannosylation55. This suggests that SLC35A1 is involved in the O-mannosylation pathway next to the sialylation pathway. This is a crucial observation for oncological studies, since the degree of E-cadherin O- mannosylation determines its tumor suppressive functions56,57. E-cadherin is essential for the formation of epithelial tissues by promoting cell-cell adhesion58. In cancer, E-cadherin is downregulated and therefore responsible for dysfunctional cell-cell contacts and subsequently abnormal tissue architecture, leading to an increased risk of local invasion59. Moreover, E-cadherin is well known for its role in epithelial-mesenchymal transition and cancer metastasis60. To ascertain that we solely abrogate the sialylation pathway, we targeted the CMAS gene instead of the SLC35A1 gene in the second tumor model studied, the MC38 cell line (Chapter 6). Now, a possibility arises that the discrepancies between the two tumor models, hampered tumor growth of the B16SLC35A1 and enhanced growth of the MC38-Sianull tumors, can be attributed to the choice of target gene. To explore this, we also generated the MC38 SLC35A1 knockout (MC38-SLC35A1 KO) and subsequently injected this cell line next to MC38-MOCK in mice. Fascinatingly, compared to MC38- MOCK tumors, the MC38-SLC35A1 KO tumors, like the MC38-Sianull (CMAS KO), grew significantly faster in vivo (Figure 4). Thus, we are confident that disruption of the sialylation pathway in MC38 cells, in contrast to B16, drives tumor growth in vivo.

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Figure 3. Schematic representation of the sialylation pathway, adapted from Chapter 6. Accumulation of CMP-Neu5Ac in the nucleus provides a negative feedback loop for the rate-limitng step via GNE (red line).

Although both the SLC35A1 and CMAS knock out MC38 cells display enhanced tumor growth in vivo, the tumor-promoting effect of the CMAS knock out was much more pronounced (Chapter 6 vs Figure 4). Remarkably, one third of the mice bearing MC38-SLC35A1 knock out tumors had to be sacrificed prematurely during the second week of the in vivo tumor experiment due to the development of an open, ulcerative tumor. Hence, the tumor growth curves are mainly based on the tumor growth curves of mice with non-ulcerated tumors, which might have influenced the end results. In contrast to the enhanced tumor growth of the MC38-Sianull and MC38-SLC35A1 KO observed by us, Stanczak et al.61 recently reported hampered in vivo tumor growth of the GNE gene knock out in the MC38 cell line. The GNE gene was initially discovered as the mutated gene in recessive hereditary inclusion body myopathy (HIBM) patients62. Whether hyposialylation causes this neuromuscular disease is not clear and controversial results exist. Both hyposialylation as well as an unaltered overall sialylation has been reported in HIBM63-65. Moreover, only mutations in the GNE gene, and not in other genes involved in the sialylation pathway, are known to cause HIBM pathogenesis. In addition to HIBM, Sialura is characterized by excessive synthesis and urinary excretion of free sialic acid whereby mutation in the GNE gene is the only known gene to cause the disease66. Clinical features of Sialuria include jaundice, anemia, frequent upper respiratory infections, dehydration and equivocal or mild hepatomegaly. Besides, Sialuria has been described as a risk factor for the development of intrahepatic cholangiocarcinoma in adulthood67. Next to GNE related diseases, blocking GNE results in UPD-GlcNAc accumulation, which functions as a sugar substrate for the O-linked N- acetylglucosamine transferase to initiate O-GlcNAcylation. O-GlcNAc is involved in cellular signaling pathways in a manner analogous to protein phosphorylation68. Hence, increased levels of O-GlcNAc may disrupt cell signaling features, affecting protein degradation, stability and transcription69. Thus, GNE may have other possible roles besides sialic acid synthesis, which complicates the comparison of the different knock outs with respect to tumor growth and the anti-tumor immune response.

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Figure 4. SLC35A1 knock out drives tumor growth in vivo. MC38-MOCK or MC38-SLC35A1 KO cells were injected into C57BL/6 mice (n=14) Tumor growth was monitored and depicted as individual tumor growth curves and mean tumor size. Data are representative of one experiment. Mean ± SEM; *, p<0.05; **, p<0.01; ns, not significant.

Therapeutic options targeting the sialic acid-Siglec axis The strong immunomodulatory capacity of sialic acids has prompted new therapeutic strategies to interfere with the sialic acid-Siglec axis in order to release the brake on anti-tumor immunity. While we have investigated the role of tumor-associated glycan structures on tumor immunity with the use of genetic engineering, others have explored alternative approaches to modify the level of sialylation in tumor cells. Xiao et al70 has developed an antibody-sialidase conjugate to precisely edit the glycocalyx of tumor cells. The antibody part of the conjugate specifically binds tumor antigens, in this case HER2, thereby delivering the sialidase specifically to breast cancer cells. Subsequently, the antibody directs immune cells to eliminate the desialylated tumor cell via antibody-dependent cell-mediated cytotoxicity (ADCC). The antibody- sialidase conjugate improved tumor killing compared to treatment with the antibody alone70, implying that the antibody-sialidase conjugate represents a promising approach for cancer immunotherapy. In 2014, 71 Rillahan et al. developed a global sialyltransferase inhibitor (Ac53FaxNeu5Ac) that can be used to abrogate 72 OVA sialic acid expression in vitro and likewise in vivo . Treatment of B16-F10 with Ac53FaxNeu5Ac prior to inoculation in the mice effectively prevented cancer metastasis73. Furthermore, Büll et al73 showed that it is possible to target Ac53FaxNeu5Ac specifically to melanoma cells with the use of nanoparticles coated with anti-tyrosinase-related protein-1 antibodies (anti-TRP-1), resulting in long-term sialic acid blockade. Similar to treating cancer cells with Ac53FaxNeu5Ac prior to injection, the in vivo melanoma targeting of Ac- 73 53FaxNeu5Ac nanoparticles also prevented metastasis formation in a murine lung metastasis model . Next OVA to specifically targeting Ac53FaxNeu5Ac to melanoma cells, both the murine immunogenic B16-F10 melanoma and 9464D neuroblastoma cells displayed a hampered in vivo tumor growth after Ac53FaxNeu5Ac 1 injections into the palpable tumor . Compared to the non-treated tumors, Ac53FaxNeu5Ac treatment enhanced both effector CD4+ and CD8+ T cell frequencies within the tumor microenvironment, while

163 regulatory T cell and myeloid cell numbers were diminished1, collectively contributing to immune-mediated tumor destruction. Moreover, The main difference between a global sialyltransferase inhibitor and a genetically engineered tumor cell, is that the sialyltransferase inhibitor might not specifically act on tumor cells only. The tumor microenvironment holds many different cell types, including immune cells, that all express sialoglycans. Consequently, treating the tumor microenvironment with Ac53FaxNeu5A is likely to result in diminished sialic acid expression on all cells, including immune cells, and not just the tumor cells.

Interestingly, Ac53FaxNeu5Ac treatment of human monocyte-derived dendritic cells (moDCs) lowers the 74 activation threshold of moDCs for TLR stimulation . Next to Ac53FaxNeu5A treatment, also neuraminidase- treated moDCs exhibit a higher capacity to activate CD4+ and CD8+ T cells75 and a deficiency in α2-6 sialic acids on DCs improves their phagocytic capacity76. DCs derived from cancer patients are used in several clinical trials for DC vaccination studies. In these studies, DCs are activated and loaded with tumor antigens ex vivo and subsequently injected back into the patient77. Desialylation of these DCs might be a future direction to further enhance their effectivity in vivo, thereby augmenting CD4+ and CD8+ anti-tumor immune responses in the patient. As removal of sialic acids from DCs can potentiate their activity, the hampered in vivo tumor growth after intratumoral injections with global sialyltransferase inhibitors might not solely depend on tumor desialylation only, but could also be due to enhanced immune cell or more specifically DC function. Nevertheless, treating the tumor with sialyltransferase inhibitors has a higher clinical feasibility than genetic engineering of the tumor cell, though administration of high doses of Ac53FaxNeu5Ac into the tumor, as well as systemically, may lead to kidney dysfunction and failure72,73. Therefore, specific targeting of the compound to tumor cells is required before it can be employed in the clinic. In addition to interrupting the sialylation pathway, another approach to prevent tumor immune evasion could be inhibiting the sialic acid-Siglec axis via the Siglec receptors. Recently, Stanczak et al.61 were the first to discover that CD4+ and CD8+ T cells present in the tumor microenvironment express the Siglec-5, Siglec- 7, Siglec-9 and Siglec-10 receptors, with Siglec-9 being the most consistently expressed Siglec among patients. Siglec-9 positive CD8+ T cells (Sig-9+CD8+) within the tumor microenvironment co-express higher levels of several inhibitory immune receptors than Siglec-9 negative CD8+ T cells. That these Sig-9+CD8+ cells support tumor growth was further validated using mice that express a chimeric Siglec receptor, consisting of the extracellular domains of the murine Siglec-E coupled to the cytoplamic domains of the activating human Siglec-16 (SigE16). Compared to wild type littermates, mice expressing SigE16 displayed reduced subcutaneous MC38 tumor growth. Moreover, this effect was completely abrogated upon depletion of CD4+ or CD8+ T cells61. The discovery of Siglec expression on T cells reveals new potential targets for immune checkpoint blockade therapy. Although very encouraging, future studies blocking Siglec receptors in preclinical tumor models are warranted to gain more insight in the effectiveness of inhibiting Siglec receptors on T cells. In addition to blocking the sialic acid-Siglec interaction, Siglecs can also be harnessed to target the tumor in case the cancer is of hematopoietic or lymphoid origin. For instance, CD22 (Siglec-2) ligands coupled to immunotoxins are actively bound and endocytosed by CD22, resulting in the specific elimination of all CD22- positive B-cells, including the B cell lymphoma cells78,79. The CD22 ligand can also be coupled to an antigen to suppress antigen-specific undesired antibody responses in autoimmune diseases, such as rheumatoid arthritis13. Thus, sialic acids can function astherapeutic compounds to target CD22 on B cells, both to treat B cell lymphoma as well as B cell mediated autoimmune diseases. Overall, research over the last decade into the sialic acid-Siglec axis has prompted new promising therapeutic strategies to prevent cancer immune evasion. However, as also illustrated by the present thesis, more pre-clinical research is required to create a comprehensive and complete understanding of the role

164 sialic acids play in the tumor microenvironment, including their tumor type specific effects, before the sialic acid-Siglec axis can actually be exploited in the clinic.

Concluding remarks and future directions

Every cell exposes a dense layer of glycoproteins and glycolipids on its membrane. The appreciation that cancer cells display an aberrant glycosylation profile, sparked renewed scientific interest in the relationship between these glycan structures and tumor development. In the present thesis we demonstrate that, indeed, these tumor-associated glycans have a crucial impact on tumor growth via the modulation of anti-tumor immunity. In accordance with the current dogma that tumor hypersialylation provides a selective advantage for tumor cells to evade tumor immunity, we show that a reduction in sialylation decreased in vivo tumor growth in a murine melanoma model. This hampered tumor growth could be attributed to a less immunosuppressive tumor microenvironment. However, unlike the melanoma tumors, complete desialylation of murine colorectal cancer cells enhanced in vivo tumor growth due to diminished CD8+ T cell frequencies and increased apoptosis of these cells. It is clear that sialic acids, based on their well-established immunomodulatory properties, hold great promise for future clinical applications in the treatment of cancer. Yet, as this present thesis illustrates, the effect of tumor-sialylation on tumor immunity is far more complex than initially believed. Some of the outstanding questions that need to be resolved are:

• Can we explain the effect on tumor immunity by the tumor sialylation level and is this dependent on the tumor type? • What is the effect of each individual sialic acid modification, including the α2-3 vs α2-6 sialic acid linkages, on Siglec receptor binding and subsequently tumor immunity? • What is the effect of the whole sialome, including gangliosides, on tumor immunity?

We started this discussion with the question whether sialic acids are the friends or foes of tumor cells. The thesis provides evidence that it can be both, depending on the tumor type, the approach in order to desialylate the tumor cell and the type of sialic acid still expressed by the tumor cells. The observation that sialic acid can be a friend of tumor immunity is a completely new. The work described in this thesis argues that it is crucial to remain a certain level of sialic acid to support (tumor) immunity. Sialic acid are wide spread among cells, among tissues and even within species. Therefore, from an evolutionary perspective, sialic acids must have essential functions for survival. The generally believed dogma states that sialic acid protect the body from unwanted immune reactions against self–antigens80. A diverse set of receptors binding sialic acid exist, and although mostly known for their immune inhibitory capacities, some of these receptors actually activate the immune system. Thus, sialic acid receptors may function, similar to the activating and inhibitory receptors on NK cells, to maintain immune homeostasis, whereby sialic acids dampen unwanted immune reactions, while at the same time facilitate the detection of unwanted cell growth and activate the immune system accordingly. However, we currently do not have enough proof exist to state which types of sialic acids and/or which expression levels are necessary for immune promoting and immune inhibitory functions in (tumor) immunity. Future research will determine whether and under which conditions sialic acids can be regarded as a friend of tumor immunity and once we understand, we might be able to exploit the different actions of sialic acids on (tumor) immunity.

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Sialic Acid-Dependent Inhibition of T Cells by Exosomal Ganglioside GD3 in Ovarian Tumor Microenvironments. J Immunol 201, 3750-3758, doi:10.4049/jimmunol.1801041 (2018). 42 Lee, H. C. et al. Ganglioside inhibition of CD8+ T cell cytotoxicity: interference with lytic granule trafficking and exocytosis. J Immunol 189, 3521-3527, doi:10.4049/jimmunol.1201256 (2012). 43 Park, S. Y., Kwak, C. Y., Shayman, J. A. & Kim, J. H. Globoside promotes activation of ERK by interaction with the epidermal growth factor receptor. Biochim Biophys Acta 1820, 1141-1148, doi:10.1016/j.bbagen.2012.04.008 (2012). 44 Distler, U. et al. Shiga toxin receptor Gb3Cer/CD77: tumor-association and promising therapeutic target in pancreas and colon cancer. PLoS One 4, e6813, doi:10.1371/journal.pone.0006813 (2009). 45 Zhuo, D., Li, X. & Guan, F. Biological Roles of Aberrantly Expressed Glycosphingolipids and Related Enzymes in Human Cancer Development and Progression. 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Sialidase NEU3 contributes neoplastic potential on colon cancer cells as a key modulator of gangliosides by regulating Wnt signaling. Int J Cancer 137, 1560-1573, doi:10.1002/ijc.29527 (2015). 51 Cheever, M. A. et al. The prioritization of cancer antigens: a national cancer institute pilot project for the acceleration of translational research. Clin Cancer Res 15, 5323-5337, doi:10.1158/1078-0432.CCR-09-0737 (2009).

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78 Schweizer, A., Wohner, M., Prescher, H., Brossmer, R. & Nitschke, L. Targeting of CD22-positive B-cell lymphoma cells by synthetic divalent sialic acid analogues. Eur J Immunol 42, 2792-2802, doi:10.1002/eji.201242574 (2012). 79 Chen, W. C. et al. In vivo targeting of B-cell lymphoma with glycan ligands of CD22. Blood 115, 4778-4786, doi:10.1182/blood-2009-12-257386 (2010). 80 Varki, A. Since there are PAMPs and DAMPs, there must be SAMPs? Glycan "self-associated molecular patterns" dampen innate immunity, but pathogens can mimic them. Glycobiology 21, 1121-1124 (2011).

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Chapter 8

Appendices

Summary Acknowledgements Curriculum Vitae List of publications, grants and awards

Portfolio

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Summary

Cancer is a disease characterized by abnormal cell growth and an ability to spread to other locations in the body (metastasis). A well-functioning immune system is crucial to prevent outgrowth of malignant cells into a bulky tumor. As such, individuals with a compromised immune system, have an increased risk to develop cancer1,2. Immune control requires immune activation by antigens. Prior to cancer development, DNA mutations occur resulting in expression of neo-antigens. The immune system can recognize these neo-antigens as foreign, leading to immune activation and elimination of the cancer cell. However, during cancer development tumor cells acquire several mechanisms to escape from immune attack. More specifically, cancer cells can actively shut down immune responses by disguising themselves as non- dangerous or by recruiting immune suppressive cells that inhibit other immune cells in their ability to eliminate cancer cells3. Together, these actions executed by the cancer to evade anti-tumor immunity form the rationale behind cancer immunotherapy. Principally, cancer immunotherapy aims to stimulate the immune system’s natural ability to eradicate cancer cells. During the last decade, cancer immunotherapy has shown impressive successes, curing cancer patients that were not curable with the standard treatments of care, such as surgery, radiotherapy and chemotherapy4. Therefore, the scientific journal Science awarded cancer immunotherapy as the Breakthrough of the Year in 20135 and more recently, in 2018 the scientists James P. Allison and Tasuku Honjo, who stood at the basis of cancer immunotherapy, were awarded the Nobel prize in Physiology or Medicine. The obtained successes with cancer immunotherapy demonstrate a proof of concept that the patients’ own immune system is a powerful tool to fight cancer and thereby, opens up many new opportunities to treat cancer. However, despite these successes, not every cancer patient and not every tumor type responds well to immunotherapy, hence, immunotherapy requires dedicated improvements to increase its applicability among patients. To do this, a better understanding on how tumor cells evade anti-tumor immunity is needed. In the present thesis, we investigated the role of tumor glycosylation in cancer development by exploring how tumor-associated glycan structures modulate the capacity of the immune system to eliminate cancer cells.

A sweet decoration of cells Glycosylation is a post-translational modification of protein and lipids that occurs in every single cell in the body6. A cell membrane is typically illustrated as a cartoon representing a phospholipid bilayer containing glycoproteins and glycolipids sticking out of the membrane (Figure 1A). However, in real life, cells show a much more denser layer of glycan structures (the glycocalyx) on their cell surface (Figure 1B). The appreciation of the glycosylation machinery has increased over time after it became clear that glycans play crucial roles in multiple cellular functions and are actively involved in disease pathogenesis7. The diversity in glycan structures is enormous, varying in length, monosaccharide type, linkage between monosaccharides, chemical modifications of the monosaccharides and their peptide or lipid backbone structure8,9. Additionally, the glycosylation profile is strongly affected by the physiological status of a particular cell . For instance, activation of murine T cells leads to remodeling of their cell surface glycosylation profile, which has implications for differential recognition of activated and naïve T cells by other immune cells subsets10. Likewise, tumorigenesis alters the glycosylation profile, resulting in an aberrant glycan expression on tumor cells compared to the healthy tissue11,12.

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Figure 1. The cell membrane. A, Detailed schematic representation of a cell membrane, adapted from Wikipedia (www.wikipedia.org/wiki/cell_membrane) B, Microscopic image of an endothelial cell, adapted from Rutledge et al13.

Glycan-based modulation of anti-tumor immune responses A wide variety of receptors, named lectins, is able to recognize and bind tumor-associated glycan structures. Interestingly, lectins are primarily found on immune cells and upon glycan binding, the majority of these receptors release intracellular signals that can either activate or inhibit immune responses. Therefore, it has been postulated that tumor-associated glycan structures support tumor progression via their immune modulatory capacities. Hence, the aim of this thesis is to investigate whether tumor cells exploit the glycosylation machinery to disarm the anti-tumor immune response.

Implementation of CRISPR/Cas9 to study glycan immunomodulation Studying the role of tumor-associated glycan structures on immunity is challenging. As the tumor glyco- code comprises, amongst others, overexpression of fucosylated structures, truncated O-glycans and hypersialylation14, our approach was to generate sophisticated tumor cell lines, using CRISPR/Cas9 technology, that lack or overexpress one particular tumor-associated glycan (the tumor glyco-code) and subsequently assess these cell lines for their ability to modulate the anti-tumor immune response. CRISPR/Cas9 is a new genetic engineering tool developed during the last years and it has become one of the most popular methods to edit genomes for scientific purposes15. To study fucosylation as part of the glyco-code, we employed the murine colorectal cancer cell line MC38. As MC38 cells do not endogenously express fucosylated glycan structures, we enhanced MC38 fucosylation via activation of genes responsible for adding fucose moieties: e.g. the fucosyltransferases. In Chapter 2 we

173 were the first to explore the possibilities of CRISPR-VPR-dCas9 to enhance MC38 fucosylation. CRISPR-VPR- dCas9 targeting of both fucosyltransferase 4 (FUT4) or 9 (FUT9) resulted in enhanced expression of fucosylated glycan structures by MC38 cells. For FUT9, the fucosylation phenotype was stable, allowing us to use this cell line for translational studies investigating the role of MC38 fucosylation on in vivo tumor growth and anti-tumor immunity. Truncated O-glycans are a second feature of the tumor glyco-code. Normally, after addition of the first monosaccharide to Serine or Threonine in the protein backbone, the O-glycan chain is further elongated to create complex O-glycans. However, in malignant cells, the O-glycosylation pathway is prematurely interrupted, leading to truncated O-glycan expression. We employed CRISPR/Cas9 in MC38 cells to knock out the COSMC gene, thereby preventing elongation of the O-glycan chains. As shown in Chapter 4, the COSMC knockout cell lines indeed display high levels of truncated O-glycan structures. Finally, we studied the role of hypersialylation. As MC38 cells already display high levels of sialylated glycan structures, we needed to abrogate the MC38 sialylation pathway. Therefore, we used CRISPR/Cas9 to knock out the N-aceylneuraminate cytidylyltransferase gene (CMAS), generating a cell line that is unable to sialylate its glycans and is thus completely devoid of sialylated glycan structures (Chapter 6). Collectively, we successfully implemented the genetic engineering tool CRISPR/Cas9, which provided us with the glyco-engineered cell lines needed to conduct our research. The translational in vivo results obtained from these glyco-engineered cell lines will be discussed in more detail in the next two sections.

Truncated O-glycosylation drives tumor growth As mentioned earlier, cancer cells often express high levels of truncated O-glycans. A detailed overview of the current knowledge on how tumor-associated O-glycans affect anti-tumor immune responses has been summarized in Chapter 3. Although a vast body of evidence exists that truncated O-glycans support tumor growth, the in vivo role of O-glycans on the anti-tumor immunity has scarcely been studied. In Chapter 4 we show that increased levels of truncated O-glycans on MC38 cells enhanced tumor growth in vivo and modulated the immune cell composition within the tumor microenvironment. Tumors expressing elevated levels of truncated O-glycans were infiltrated with less immune cells. In more detail, the immune cell composition within tumors with truncated O-glycosylation had shifted from tumor-eliminating immune cells (CD8+ T cells) towards more suppressive immune subsets (CD11b+GR-1+, myeloid derived suppressor cells) that dampen on going anti-tumor immune responses. In conclusion, our data provides the first evidence that truncated O-glycans support tumor development through modulation of the immune cell composition within the tumor microenvironment.

Tumor hypersialylation: friend or foe in tumor immunity? Compared to their non-malignant counterparts, cancer cells frequently express higher level of sialic acids, independent of the tumor type16. The receptors that are able to recognize and bind sialic acids are the Sialic acid-binding immunoglobulin type lectins (Siglecs). Upon sialic acid binding, the majority of Siglec receptors recruits intracellular signaling molecules that have an inhibitory effect on the immune cell they are expressed in17. Therefore, it is generally accepted that sialic acids are able to dampen immunity. Given the immune suppressive characteristics of sialic acids, the current dogma states that tumor cells increase their sialylation to actively prevent anti-tumor immunity. Indeed, in Chapter 5, we demonstrate that a reduction in sialylation on murine B16 melanoma cells augments immune-mediated tumor destruction with decreased tumor growth in vivo as a result. In more detail, B16 tumors expressing low levels of sialic acids (B16SLC35A1) were

174 infiltrated with lower regulatory T cell frequencies, while CD4+ T cell and natural killer (NK) cell numbers were increased. Further analysis showed that CD3+ T cells isolated from the sialic acid low B16SLC35A1 tumors produced higher levels of IFNγ, indicative of cell-mediated immune activation. Moreover, the hampered in vivo tumor growth of the B16SLC35A1 tumors could be completely abrogated upon NK cell depletion, suggesting that NK cells play a crucial role in controlling tumor growth in sialic acid low environments. To investigate whether suppression of anti-tumor immunity by sialic acids is a broad phenomenon or melanoma-specific, we followed up the work in the murine colorectal cancer model MC38. In Chapter 6, we interrupted the sialylation pathway of MC38 cells, resulting in complete desialylation of the cells (MC38- Sianull). Unexpectedly, MC38-Sianull tumor grew significantly faster in vivo than wild type or mock control tumors. Analysis of the tumor microenvironment revealed decreased CD8+ T cell infiltration, both when mice were sacrificed at day 13, representing early tumor development, or when tumors reached an equal size of 2000 mm3. As CD8+ T cells are the key players in eliminating cancer cells, CD8+ T cells frequencies in human tumors are correlated to better prognosis18. Hence, the decreased frequencies of CD8+ T cells at the tumor site might explain the aggressive and increased tumor growth observed in vivo. This hypothesis has been further validated through the observation that supernatants derived from MC38-Sianull cultures induced CD8+ T cell apoptosis, leading to hampered tumor killing capacities. Which factor, present in the supernatant, is responsible for the CD8+ T cell apoptosis has not been elucidated yet and is subject of future studies. Thus, a reduction in sialic acid levels in the B16 melanoma hampered tumor growth in vivo while the opposite was observed in the colorectal cancer MC38 model, where sialic acids were completely absent. These contradictory findings are further discussed in Chapter 7. First of all, our studies comprise two different tumor types, with B16 representing melanoma and MC38 colorectal cancer. These models also differ in the relative levels of sialic acids, which were reduced on B16 cells and completely absent on MC38. Our data suggest that a certain level of tumor sialylation might be needed for an effective anti-tumor immune response. Secondly, sialic acids is a generic term for any acidic monosaccharide with a nine-carbon backbone. Hence, sialic acid is a family name containing more than forty different neuraminic acid derivates. For example, sialic acids can be bound to the underlying glycan in an α2-3, α2-6 or α2-8 linkage. While B16 displays α2-3 and α2-6 linked sialic acids and upon sialic acid reduction only α2-3, MC38 cells only expressed α2-3 linked sialic acids, which were totally absent after sialylation pathway disruption. Given that the linkage of the sialic acid to its underlying glycan structure determines the binding affinity for Siglec receptors, the ratio between α2-3 and α2-6-linked sialic acids on tumor cells and their relation with tumor growth and anti- tumor immunity are interesting research topics for future research. Finally, sialylated glycans are conjugated to proteins as well as lipids, also known as gangliosides. While glycoproteins are widely studied, the immunomodulatory capacities of gangliosides are less well explored. Nevertheless, melanoma tumor cells are known to shed gangliosides, which have been associated with a bad prognosis19-21. In contrast to the melanoma, the non-sialylated glycolipids in colorectal cancer are especially able of promoting tumor growth22,23. Thus, complete abrogation of sialic acids on MC38 might have actually introduced tumor- promoting glycolipids, which are lacking on the B16 cells. It is therefore interesting to study the different glycolipid repertoires of both B16 and MC38, including the gangliosides. Taken together, the present thesis revisits how tumor sialylation influences anti-tumor immunity and highlights the necessity to further investigate tumor type-dependent effects, sialic acid linkages and the involvement of gangliosides next to sialylated glycoproteins.

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Conclusion This thesis focusses on the immune modulatory capacities of tumor-associated glycan structures. To study glycosylation, we have implemented the CRISPR/Cas9 gene editing technology in our laboratory, which allowed us to glyco-engineer our murine tumor models. We are the first to show that overexpression of truncated O-glycans alters the immune cell composition within the tumor microenvironment, leading to accelerated tumor development. We also demonstrate that a reduction in sialic acids in murine B16 melanoma cells hampers tumor growth in vivo and enhances tumor immune cell infiltration. In contrast to the B16 model, we identified detrimental effects of complete tumor desialylation in the murine MC38 model for colorectal cancer. The challenge will be to determine whether and in which context sialic acids should be regarded as a foe for tumor immunity or whether we should perceive low levels of sialic acids as an indispensable friend in helping anti-tumor immune responses. Overall, the thesis clearly shows that tumor glycans should be considered as a powerful immune evasion strategy elicited by the tumor.

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References 1 Goedert, J. J. et al. Spectrum of AIDS-associated malignant disorders. Lancet 351, 1833-1839 (1998). 2 Kaplan, D. H. et al. Demonstration of an interferon gamma-dependent tumor surveillance system in immunocompetent mice. Proc Natl Acad Sci U S A 95, 7556-7561 (1998). 3 Mittal, D., Gubin, M. M., Schreiber, R. D. & Smyth, M. J. New insights into cancer immunoediting and its three component phases--elimination, equilibrium and escape. Curr Opin Immunol 27, 16-25, doi:10.1016/j.coi.2014.01.004 (2014). 4 Lugowska, I., Teterycz, P. & Rutkowski, P. Immunotherapy of melanoma. Contemp Oncol (Pozn) 22, 61-67, doi:10.5114/wo.2018.73889 (2018). 5 Couzin-Frankel, J. Breakthrough of the year 2013. Cancer immunotherapy. Science 342, 1432-1433, doi:10.1126/science.342.6165.1432 (2013). 6 Opdenakker, G., Rudd, P. M., Ponting, C. P. & Dwek, R. A. Concepts and principles of glycobiology. FASEB J 7, 1330-1337 (1993). 7 Ohtsubo, K. & Marth, J. D. Glycosylation in cellular mechanisms of health and disease. Cell 126, 855-867, doi:10.1016/j.cell.2006.08.019 (2006). 8 Cohen, M. & Varki, A. The sialome--far more than the sum of its parts. OMICS 14, 455-464, doi:10.1089/omi.2009.0148 (2010). 9 Bertozzi, C. R. & Rabuka, D. in Essentials of Glycobiology (eds nd et al.) (2009). 10 Comelli, E. M. et al. Activation of murine CD4+ and CD8+ T lymphocytes leads to dramatic remodeling of N- linked glycans. J Immunol 177, 2431-2440 (2006). 11 Stowell, S. R., Ju, T. & Cummings, R. D. Protein glycosylation in cancer. Annu Rev Pathol 10, 473-510, doi:10.1146/annurev-pathol-012414-040438 (2015). 12 Pinho, S. S. & Reis, C. A. Glycosylation in cancer: mechanisms and clinical implications. Nat Rev Cancer 15, 540- 555, doi:10.1038/nrc3982 (2015). 13 Rutledge, J. C., Ng, K. F., Aung, H. H. & Wilson, D. W. Role of triglyceride-rich lipoproteins in diabetic nephropathy. Nat Rev Nephrol 6, 361-370, doi:10.1038/nrneph.2010.59 (2010). 14 RodrIguez, E., Schetters, S. T. T. & van Kooyk, Y. The tumour glyco-code as a novel immune checkpoint for immunotherapy. Nat Rev Immunol 18, 204-211, doi:10.1038/nri.2018.3 (2018). 15 Adli, M. The CRISPR tool kit for genome editing and beyond. Nat Commun 9, 1911, doi:10.1038/s41467-018- 04252-2 (2018). 16 Pearce, O. M. & Laubli, H. Sialic acids in cancer biology and immunity. Glycobiology 26, 111-128, doi:10.1093/glycob/cwv097 (2016). 17 Macauley, M. S., Crocker, P. R. & Paulson, J. C. Siglec-mediated regulation of immune cell function in disease. Nat Rev Immunol 14, 653-666, doi:10.1038/nri3737 (2014). 18 Fridman, W. H., Pages, F., Sautes-Fridman, C. & Galon, J. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer 12, 298-306, doi:10.1038/nrc3245 (2012). 19 Hersey, P., Jamal, O., Henderson, C., Zardawi, I. & D'Alessandro, G. Expression of the gangliosides GM3, GD3 and GD2 in tissue sections of normal skin, naevi, primary and metastatic melanoma. Int J Cancer 41, 336-343 (1988). 20 Furukawa, K. et al. Ganglioside GD3 induces convergence and synergism of adhesion and hepatocyte growth factor/Met signals in melanomas. Cancer Sci 105, 52-63, doi:10.1111/cas.12310 (2014). 21 Zhuo, D., Li, X. & Guan, F. Biological Roles of Aberrantly Expressed Glycosphingolipids and Related Enzymes in Human Cancer Development and Progression. Front Physiol 9, 466, doi:10.3389/fphys.2018.00466 (2018). 22 Distler, U. et al. Shiga toxin receptor Gb3Cer/CD77: tumor-association and promising therapeutic target in pancreas and colon cancer. PLoS One 4, e6813, doi:10.1371/journal.pone.0006813 (2009). 23 Park, S. Y., Kwak, C. Y., Shayman, J. A. & Kim, J. H. Globoside promotes activation of ERK by interaction with the epidermal growth factor receptor. Biochim Biophys Acta 1820, 1141-1148, doi:10.1016/j.bbagen.2012.04.008 (2012).

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Acknowledgements

A quick google search on ‘how to conduct scientific research’ results in various steps starting from determining the problem, formulating hypotheses, towards the design of experiments, interpreting the data, and drawing conclusions. In my opinion, the first step should be to find colleagues willing to help you with your research, as it is impossible to conduct research on your own. Without the help and support of many colleagues, friends and family, I could not have completed this thesis.

Yvette, Sandra and Wendy, the glycosylation adventure started almost five years ago. At that time, sialic acids, Tn antigen, sialyltransferases, the glycocalyx, mucins, plant lectins, MGL, and Siglec receptors were all new terms for me, whereas I use them on a daily basis today. There was so much to be learned, but you have supported and encouraged me to do so. Yvette, thank you for your constructive criticism, and your enthusiasm for science. Sandra, thank you for your excellent supervision, endless knowledge about everything, and your creative ideas. It is surprising how often we had the same thoughts: being ‘on the same page’ within 10 sec was no exception. Thank you.

Thanos, Joost, Anouk, Lieke and Neha, thank you for your support and nice discussions during our weekly meetings. You are all hard-working individuals that share a passion for science and made my time as a PhD candidate more fun. Thanos, thank you for your contribution to the papers. You have fixed many mice for me and added many antibodies to the cells for me. I am really grateful to have had you as a colleague. Joost, thank you for your help and input and for always being critical about experimental work. Your humor is unique and made it fun to work with you. Working with mice was new and somewhat unnerving for you, but your focus on the things at hand made you an indispensable colleague.

Tom, Laura and all the members of Group Yvette/Tumor Immunology, thank you for being critical during the work meetings, the many nice discussions, and your suggestions. I appreciate that such a large group manages to find the time to review each other’s data critically and on a regular basis. Besides science, the many ‘borrels’ we had were always nice and vibrant, which made it fun to get together besides work. Tom, a special thanks for all the time you have spent behind the sorter to obtain the genetically engineered cell lines. Your patience and persistence in completing the sort resulted in a research tool, the glycovariant cell line, which was indispensable to the present thesis. Laura, a special thanks for your collegiality, enthusiasm and willingness to help me with the mouse experiments. Basically, all the work started with your help: you have injected every single mouse with tumor cells in every in vivo experiment that I have conducted during my PhD. It would have been much less fun without you at the animal facility. Thank you very much for your contribution during these years.

Sanne and Dieke, my lovely roommates, thank you for the distractions that everyone needs to function well. Okay, maybe it was too much and too often, but it was good fun! Dieke, thank you for listening to me, helping me out regularly with mouse stuff and for your sincere feedback on my work. Thumps up for being who you are! Sanne, I still remember the Friday evening you left your home in order to help me in the lab, knowing that I needed some help but would not dare to ask. I can always count on you. After having known you for one month, we had so much fun together that we were asked whether we were sisters or if we had known each other for a long time already. Although we did not work on the same project, we were almost

179 inseparable during my last two years at the department. Thank you for the nice days with you and your family at Texel, many coffee breaks and for your endless distractions that made my day.

To my friends and family, I am grateful for having such a lovely family. Although you do not understand everything that I have been doing during the last couple of years, you have always been very supportive. Especially to my parents and Martijn, thank you for the opportunities you have given me. Without you, I wouldn’t be where I am today. Kasper, no words can describe your role and support in my life. Thank you for always being there for me.

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Curriculum Vitea

Lenneke A.M. Cornelissen was born on the 27th of February 1988 in Boxmeer, the Netherlands. After completing her secondary education in 2007, she studied Animal Sciences at Wageningen University and Research. She performed two internships, one at the department Adaptation Physiology under supervision of Dr. Aart Lammers and Dr. Ir. Henk Parmentier and a second internship at the department Cell Biology and Immunology under supervision of Prof. dr. Ir. Geert Wiegertjes and Prof. dr. Ir. Huub F.J. Savelkoul. In her first internship she researched the effect of an early antibiotic treatment on immune development in poultry. The research during her second internship was focused on the expression, function and regulation of innate immune receptors of carp, Cyprinus carpio. In addition, as part of the Research Master cluster, Lenneke wrote a PhD proposal which was awarded the Tjeerd de Jong award. The Tjeerd de Jong contest is a contest in the field of research of Animal livestock and economics and should encourage outstanding students of the Master of Science at the Wageningen University and Research for conducting or deepening special research projects or ideas. The €2000,- cash prize obtained with the award supported Lenneke financially to go to Auckland, New Zealand, for an internship at the department Nutrition at the University of Auckland. Here she studied the effect of food on IL-10 gene expression in inflammatory bowel disease patients. Her internship was supervised by Prof. dr. Ir. Huub FJ Savelkoul and Dr. Maria Forlenza. This internship made Lenneke eager to pursue her research in human immunology. After receiving her Master’s degree (cum laude) in 2013, she started her PhD research at the department Molecular Cell Biology and Immunology at Amsterdam UMC, location VUmc, in Amsterdam under supervision of Dr. Sandra J. van Vliet and Prof. dr. Yvette van Kooyk where she performed the research described in this thesis. As of September 2019, she works as a postdoctoral researcher in the group of Prof. dr. Gosse J. Adema at the Radiotherapy and OncoImmunology Laboratory at Radboudumc, where she continues her studies on the role of tumor-associated glycan structures in immunity.

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List of publications, grants and awards

Publications 1. Sophie A. Dusoswa, Erik Abels, Jan Verhoeff ,Valerie M.C.J. Wouters, Myron G. Best, Ernesto Rodriguez, Lenneke A.M. Cornelissen, Sandra J. van Vliet, Pieter Wesseling, Xandra O. Breakefield, David P. Noske, Thomas Würdinger, Marike L.D. Broekman, Yvette van Kooyk, Juan J. Garcia-Vallejo. Glioblastomas exploit cell surface O-linked glycans for local and distance immune modulation. Manuscript submitted.

2. Sjoerd T.T. Schetters, Ernesto Rodriguez, Laura Kruijssen, Kelly Boelaars, Kari Brown, Thijs Crommentuijn, Lenneke A.M. Cornelissen, Sandra J. van Vliet, Yvette van Kooyk. Immune modulatory effect of sialic acid in pancreatic cancer. Manuscript in preparation.

3. Lenneke A.M. Cornelissen, Athanasios Blanas, Joost C. van der Horst, Laura Kruijsen, Anouk Zaal, Tom O’ Toole, Yvette van Kooyk, Sandra J. van Vliet. Tn antigen expression contributes to an immune suppressive microenvironment and drives tumor growth in colorectal cancer. Manuscript submitted.

4. Lenneke A.M. Cornelissen, Athanasios Blanas, Joost C. van der Horst, Laura Kruijsen, Anouk Zaal, Tom O’ Toole, Lieke Wiercx, Yvette van Kooyk, Sandra J. van Vliet. Disruption of sialic acid metabolism drives tumor growth by augmenting CD8+ T cell apoptosis. International Journal of Cancer. 2019, 2290-2302, doi: 10.1002/ijc.32084

5. Athanasios Blanas, Lenneke A.M. Cornelissen, Maximilianos Kotsias, Joost C. van der Horst, Henri van de Vrugt, Hakan Kalay, Daniel I.R Spencer, Radoslaw P. Kozak, Sandra J. van Vliet. Transcriptional activation of fucosyltransferase (FUT) genes using the CRISPR-dCas9-VPR technology reveals potent N-glycome alterations in colorectal cancer cells. Glycobiology 2019, 137-150, doi: 10.1093/glycob/cwy096

6. Lenneke A.M. Cornelissen, Sandra J. van Vliet. A Bitter sweet symphony: Immune responses to altered o- glycan epitopes in cancer. Biomolecules. 2016, 6, 26, doi: 10.3390/biom6020026

7. Maurizio Perdicchio, Lenneke A.M. Cornelissen, Ingeborg Streng-Ouwehand, Steef Engels Marleen I. Verstege, Louis Boon, Dirk Geerts, Yvette van Kooyk, Wendy W. Unger. Tumor sialylation impedes T cell mediated anti-tumor responses while promoting tumor associated-regulatory T cells. Oncotarget. 2016, 8771-8772, doi: 10.18632/oncotarget.6822

8. Maurizio Perdicchio, Juan M. Ilarregui, Marleen I. Verstege, Lenneke A.M. Cornelissen, Sjoerd T.T. Schetters, Steef Engels, Martino Ambrosini, Hakan Kalay, Henrike Veninga, Joke M. den Haan, Lisette A. Van Berkel, Janneke N. Samson, Paul R. Crocker, Tim Sparwasser, Luciana Berod, Juan J. Garcia-Vallejo, Yvette van Kooyk, Wendy W. Unger. Sialic acid-modified antigens impose tolerance via inhibition of T cell proliferation and de novo induction of regulatory T cells. Proceedings of the National Academy of Sciences (PNAS). 2016, 3329-3334, doi: 10.1073/pnas.1507706113

9. Ingeborg Streng-Ouwehand, Nataschja I. Ho, Manja Litjens, Hakan Kalay, Martine A. Boks, Lenneke A.M. Cornelissen, Satwinder Kaur Singh, Eirikur Saeland, Juan J. Garcia-Vallejo, Ferry A. Ossendorp, Wendy W.

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Unger, Yvette van Kooyk. Glycan modification of antigen alters its intracellular routing in dendritic cells, promoting priming of T cells. Elife. 2016, 1-21, doi: 10.7554/eLife.11765

Grants and Awards 1. 1st prize Tjeerd de Jong Award 2012. €2000,- 2. Travel grant, Maag Lever Darm stichting 2014. €1500,- 3. Travel grant, Amsterdam Infection, Inflammation and Immunity Institute 2017. €500,- 4. Travel grant, Nederlandse vereniging voor Immunologie 2017. €500,-

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Portfolio Name: Lenneke A.M. Cornelissen PhD period: June 2014 – November 2018

Courses & Workshops Year Workload OOA PhD day: Your PhD as a Stepping Stone to Success 2017 0.2 ECTS OOA PhD day: Improving presentation skills 2017 0.2 ECTS Instantie van Dierwelzijn nascholingsdag: Pijn in de praktijk, Fysiologie, 2016 0.2 ECTS herkenning en bestrijding Training in Scientific Data Visualization: Getting your message across 2016 0.2 ECTS Advanced Immunology Summer school, Sardinië 2015 2.0 ECTS Advanced Immunology (OOA), Amsterdam, NL 2015 3.0 ECTS

Seminars and masterclasses Seminar CCA: Jerome Galon 2018 0.2 ECTS Masterclass: Laurance Zitvogel (with oral presentation) 2017 0.2 ECTS Masterclass: Adrian Liston 2016 0.2 ECTS Masterclass: Robbert Spaaben 2016 0.2 ECTS Masterclass: Leo Koenderman 2016 0.2 ECTS Masterclass: Thorbald van Hall 2015 0.2 ECTS Masterclass: Gerd Bouma 2015 0.2 ECTS Masterclass: Jeroen den Dunnen 2015 0.2 ECTS Seminar Sanquin: Peter Cresswell 2015 0.2 ECTS Masterclass: Paul Coffer 2014 0.2 ECTS Seminar Center for Gynaecological Oncology: Yvette van Kooyk + Katja 2014 0.2 ECTS Jordanova Seminar MCBI + Pathology: Elga de Vries + Hetty Bontkes 2014 0.2 ECTS

General skills Weekly department lectures 2014-2019 4.0 ECTS Weekly Tumor Immunology group data discussion 2014-2019 4.0 ECTS Weekly Gr. Sandra research meeting 2015-2019 4.0 ECTS Biweekly Journal clubs 2014-2019 2.0 ECTS

Oral presentations 12th Jenner Glycobiology and Medicine symposium, Croatia 2017 1.0 ECTS NVVI Winterschool, Noordwijkerhout, NL 2015 1.0 ECTS Advanced Immunology Summer school, Sardinië 2015 1.0 ECTS

Poster presentations ECI, Amsterdam, NL 2018 1.0 ECTS AACR Tumor Immunology and Immunotherapy, Boston, USA 2017 1.0 ECTS Cancer Centrum Amsterdam Next, Amsterdam, NL 2017 0.2 ECTS EMDS, Amsterdam, NL 2016 1.0 ECTS Frontiers in Sialic Acid, Bad Lauterberg, Germany 2016 1.0 ECTS Cancer Vaccine Conference, Amsterdam, NL 2015 0.25 ECTS NVVI Winterschool, Efteling, NL 2014 1.0 ECTS OOA retreat, Renesse, NL 2014 1.0 ECTS

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Attendance conferences, symposia and others Trippenhuis meeting (NVvM/DScB/NVBMB) “Sticky fingers”, NL 2018 0.25 ECTS NvvI Lunteren, NL 2018 0.5 ECTS NvvI Lunteren, NL 2017 0.5 ECTS Maatschappelijk Oncologisch debat, 50th VUmc Anniversary (CCA) 2016 0.25 ECTS NvvI Lunteren, NL 2016 0.5 ECTS Cancer Centrum Amsterdam (CCA) Next 2016, 2017 0.5 ECTS National Cancer Immunotherapy Symposium, NKI, Amsterdam, NL 2016 0.25 ECTS NvvI Lunteren, NL 2015 0.5 ECTS BD Horizon tour 2.0, Amsterdam 2015 0.25 ECTS

Teaching Supervision Master student (2x) 2016, 2018 2.0 ECTS Supervision HLO student 2017 1.0 ECTS Practical course “Glycoworkbench” 2016, 2017 2.0 ECTS Practical course “Blood groups” 2014-2018 4.0 ECTS Practical course “Fagocytose” 2014 1.0 ECTS

Extracurricular Activities TEDx talk Breda 2016 0.5 ECTS Committee PhD UPC council 2014, 2015 0.5 ECTS

Total: 46.15 ECTS

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