Identification of Myeloid Derived Suppressor Cells in Tumor Bearing Dogs. A Thesis. Matthew G. Sherger

Transcription

1 Identification of Myeloid Derived Suppressor Cells in Tumor Bearing Dogs. A Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Matthew G. Sherger Graduate Program in Comparative and Veterinary Medicine The Ohio State University 2012 Committee: Tracey L. Papenfuss, DVM, MS, PhD, DACVP, Co-Advisor William C. Kisseberth, DVM, PhD, DACVIM (Oncology), Co-Advisor Cheryl A. London, DVM, PhD, DACVIM (Oncology)

2 Copyrighted by Matthew George Sherger 2012

3 Abstract For a majority of the past century, cytotoxic chemotherapy has served as the basis for the treatment of cancer, both in humans as well as in domestic animals. While the strategy of using drugs designed primarily to kill rapidly growing neoplastic cells achieved some initial successes, time has proven that this strategy is not sufficient to maintain long-term remissions or cures for most patients. Immunotherapeutic approaches to cancer treatment, such as cancer vaccines, showed initial success in preclinical studies but generally failed to demonstrate the expected clinical activity in patients when evaluated in clinical trials. The shortcomings of such cancer immunotherapies are believed to primarily involve inadequate recruitment of the cellular components of the immune system by the vaccine allowing for poor tumor recognition and subsequent tumor progression and metastasis. One of the pivotal cell types that has been shown to limit the efficacy of these therapies is a cell of bone marrow origin termed a myeloid derived suppressor cells (MDSCs). MDSCs are immature myeloid cells of bone marrow origin that are increased in both cancer bearing mice and humans and distinguished by their coexpression of CD11b and GR-1. Their primary mechanism of action is inhibition of the innate and adaptive T lymphocyte responses. To date these cells have not been described in the dog. The primary limiting factor preventing the identification of these cells in the ii

4 dog has been a lack of analogous murine, human and canine antibodies. Using commercially available canine antibodies, CD11b and CADO48A, peripheral blood myeloid cells from forty healthy control dogs and forty untreated, tumor-bearing dogs were compared for the differential expression of myeloid cells. Using this technique, a population of CD11b low /CADO48A low cells was found to be up-regulated in tumor bearing dogs, potentially representing a canine MDSC phenotype. The identification of MDSCs in the canine represents another tool that can be used in better understanding both the biology of cancer as well as the therapeutic opportunities in both the canine and humans. iii

5 Acknowledgements I wish to thank my co-advisors, Tracey Papenfuss and William Kisseberth, for their support in the completion of my Master s project. Thank you Dr. Papenfuss for welcoming me into your laboratory. Without your guidance and assistance this project never would have come to fruition. Thank you Dr. Kisseberth for your guidance with this project and your mentorship through the graduate student experience. Without your assistance, this project would have been a much more difficult experience. A final thank you to all of the technicians, veterinarians, pet owners and pets who contributed to this project. There are too many to count, however, without each and every one this project would never have been completed. iv

6 Vita June River Dell Regional High School B.S. Animal Science, Rutgers University D.V.M., The Ohio State University 2008 to Small Animal Rotating Internship, Animal Medical Center, New York City, NY 2009 to present...resident, Department of Veterinary Clinical Sciences, The Ohio State University Fields of Study Major Field: Comparative and Veterinary Medicine v

7 Table of Contents Abstract... ii Acknowledgements.iv Vita... v Fields of Study... v Table of Contents... vi List of Tables... viii List of Figures... ix Chapter 1: Literature Review... 1 The Innate Immune Response... 4 Cancer and the Immune Response... 5 Myeloid Derived Suppressor Cells in Cancer Immunology Mechanisms of MDSC Mediated Immune Suppression Heterogeneity of MDSCs MDSC Targeted Therapeutic Intervention Role of Animal Models in Cancer Biology vi

8 Chapter 2: Methods and Materials Flow Cytometry Staining and Optimization Study Design/ Animals Flow Cytometry Flow Analysis Statistical Analysis Chapter 3: Results Flow Cytometry Optimization Population Characteristics Gate Analysis Chapter 4: Discussion References vii

9 List of Tables Table 1. Patient (Dog) Characteristics Table 2. Percent Dual Postive Staining Cells, Control Versus Tumor-bearing Dogs Table 3. Percent Dual Postive Staining Cells, Comparison Among Tumor Types...42 viii

10 List of Figures Figure 1. Mechanisms of Myeloid Derived Suppressor Cell Development Figure 2. Therapeutic Strategies Against MDSCs Figure 3. Blood Handling Protocol Figure 4. Timing of Flow Cytometry Figure 5. Antibodies used in Flow Cytometry Protocol Figure 6. Gating of Peripheral Blood Cells Figure 7. Comparison of Different Granulocyte Antibodies Figure 8. Determination of Optimal Secondary Antibody Concentrations Figure 9. Effects of Storage Methods on Flow Cytometry Results Figure 10. Effects of Cell Fixation on Flow Cytometry Results ix

11 Chapter 1: Literature Review Introduction to Cancer Immunology For a majority of the past century, cytotoxic chemotherapy has served as the basis for the medical treatment of cancer in both humans and domestic animals. While the strategy of using drugs designed to kill rapidly growing neoplastic cells achieved some initial successes, time has proven that this strategy is not sufficient to maintain long-term remissions or cures for most patients. As scientists began to better understand the shortcomings of chemotherapy, alternative methods of cancer treatment began to emerge. As early as the late 1800s, William Coley and others began experimenting with the use of bacteria injected into tumor-bearing patients in hopes of stimulating the body s response to the invading bacteria would also stimulate the immune system to eradicate cancer [1]. From these modest beginnings, the field of cancer immunology was born. As our understanding of the complex interaction between the immune system and cancer has evolved, new treatments have emerged. Immunotherapeutic approaches to treat cancer, such as, cancer vaccines(e.g. prostate cancer in humans), showed initial success in preclinical studies but generally have failed to achieve the expected clinical activity in patients when evaluated in clinical trials [1]. The shortcomings of such cancer 1

12 immunotherapies are believed to primarily involve inadequate recruitment of the cellular components of the immune system by the vaccine, resulting in poor tumor recognition by the immune system, and subsequent tumor progression and metastasis. A better understanding of the actions and interactions of the immune response with cancer cells is necessary to develop and improve cancer immunotherapies in both humans and animals alike. The interaction between the immune system and cancer was first proposed by Paul Ehrlich in the early 1900s [2]. Although early speculation suggested that the immune system contributes to carcinogenesis, later theories proposed that the adaptive immune response was critical for preventing carcinogenesis, a concept called immunosurveillance [3]. The idea that the immune system could respond against cancer was not fully accepted until the 1990s when a series of publications demonstrated that mice deficient in components of the immune response (IFN-γ, IFN-γ receptor, STAT, RAG) had a higher susceptibility to spontaneous, transplantable and chemically induced tumors [4-8]. Since that time, a greater understanding of the immune system s interaction with cancer has led to the revised current model of cancer immunoediting hypothesis [9]. The cancer immunoediting theory suggests that cancer cells proceed sequentially through three phases. The first phase, elimination, begins with both the innate and adaptive arms of the immune system monitoring for developing tumors (tumor immunosurveillance). The second phase, equilibrium, is the period during which both the cancer cells and the immune systems are reciprocally influenced by each other. During equlibrium 2

13 lymphocytes and soluble factors, such as, interferons, place selection pressure on tumors cells, killing many of them in the process. Reciprocally, tumor cells continue to grow and mutate, selecting for the most resistant clones. This process is likely the longest of the 3 phases and can occur over the course of years. The final phase, escape, then occurs when specific tumor variants develop which are able to escape immune surveillance and the host response and grow uncontrollably. The equilibrium phase represents the most prolonged component of interaction between the cancer cell and the host s immune system and potentially the earliest phase which can be influenced by therapeutic intervention. Unfortunately, many malignancies are not discovered until the escape phase, a point at which tumor cells have adopted novel characteristics that have allowed them to escape the local microenvironment and immune response. While many traditional therapies are less effective at the escape phase, this is the most likely clinically available opportunity to utilize and retrain the immune system to recognize and combat these cancer cells. Patients with advanced disease treated with immunomodulatory therapy (e.g. dogs treated with toceranib phosphate and low-dose cyclophosphamide), have shown reductions in regulatory T (Treg) cells and elevations in IFN-γ, demonstrating the potential utility of cancer immunotherapy during the escape phase [10]. 3

14 The Innate Immune Response The immune system in mammals is complex and divided into three primary means of protection. The first level of protection is a physical barrier (i.e. skin, epithelial/mucosal surfaces, etc.) which protects the host from invasion by pathogenic organisms. After the physical barrier, immune responses are mediated by two separate but interacting arms (innate and adaptive) of immune responses. These two arms of immune responses play specific roles in responding to pathogens, inciting inflammatory responses and maintaining homeostasis. The innate arm of the immune response is typically the first responder against pathogens or other threats. Although potent, the innate immune response is relatively non-specific and may not, by itself, generate precise or memory responses. Innate immune cells include neutrophils, natural killer cells (NK cells), dendritic cells (DCs) and macrophages which utilize a variety of soluble mediators and other mechanisms to neutralize threats. Select innate immune cells, the antigen presenting cells (APCs), play an important role in communicating and instructing the development of adaptive immune responses by processing and presenting both self and foreign antigens. Recognition of self versus foreign antigen, is done, in large part, through the major histocompatibility complex (MHC) molecules. Class I MHC molecules are present on all nucleated cells in the body and allow cytotoxic T lymphocytes (CTLs) to recognize normal self versus foreign antigen and play an important role in cell-mediated immunity. Class II MHC molecules are specifically located on APCs (including macrophages, DCs and B lymphocytes) and present digested 4

15 antigen from external sources to T helper cells. These APC-T cell interactions stimulate the antigen-specific immune response of T and B lymphocytes which leads to a more precise, antigen-specific immune response and immunological memory. The generation of memory cells allows for rapid and specific immune responses to be mounted against specific antigenic stimuli. The ultimate goal of these various innate and adaptive responses is to eliminate the foreign antigen and prevent damage to the host. In the case of cancer, the host s own cells are the foreign invader. While CTLs, NK cells, neutrophils and other cells all have the same ultimate goal of destroying the tumor, a complex network of signaling orchestrates these various cellular interactions at the level of the local microenvironment. Each of these interactions between immune cells and tumor cells allows for an opportunity to tip the balance in favor of either tumor elimination or progression. Cancer and the Immune Response Although tumor cells are derived from the host and should be recognized as self, these cells will often present mutated proteins or inappropriate proteins for the given location of the tumor. This altered protein expression serves as a signal to the cells of the innate immune response that foreign material is present and the elimination phase of the immunoediting process begins. Initially in this setting, tumor associated antigens (TAA) and soluble tumor-derived factors attract both innate (e.g. neutrophils, NK cells, macrophages and DCs) and adaptive (e.g. T and B cells) immune cells with the goal of 5

16 destroying the tumor. If not eliminated, over time the elimination phase transitions into the equilibrium phase where the tumor has reciprocal influence on the immune response. These complex interactions of elimination and equilibrium take place in the tumor microenvironment, a term used to describe the interactions of the tumor cells, immune cells, tissue stroma and vascular network [11]. Neutrophils, NK cells and APCs such as macrophages and DCs are all innate immune cells that, along with subsequent adaptive immune responses, influence cancer progression. Neutrophils are the most abundant innate immune cell circulating in most species but have historically been thought to play a limited role in influencing the interaction between the tumor and the inflammatory response. Typically, neutrophils are rapid responders to inflammatory stimuli such as bacterial infection and tissue damage/necrosis. However, more recent data show that these cells do have a role in regulating tumor progression and inflammation seen in cancer [12]. Initially, neutrophils are attracted to the tumor microenvironment through chemokine expression by tumor cells. Once in the microenvironment, local soluble factors influence the cytotoxicity of these tumor-associated neutrophils (TAN) [12]. High TGF-β expression often present in the tumor microenvironment leads to a TAN with less tumor cytotoxicity. Conversely, low TGF-β levels allow the TANs to acquire a more cytotoxic phenotype that is associated with increased tumor necrosis factor (TNF) production, elevated hydrogen peroxide release and increased CTL activation which all have anti-tumor effects [12]. 6

17 NK cells are important immune cells with anti-tumor effects within the tumor microenvironment. NK cells represent one of the major effectors of tumor cell lysis and are considered to play one of the most important roles in directly killing tumor cells [13]. NK cells are lymphocytes that lack conventional antigen receptors like B and T cells and normally circulate in the body in a resting state. In the presence of IL-12 from macrophages or IFN-γ and other factors, these cells can become activated effector cells. NK cell activation involves several mechanisms. First, NK cells are inhibited from killing normal cells through the recognition of MHC class I molecules normally present on self cells. NK cells have inhibitory receptors on their cell surface, such as killer immunoglobulin-like receptor (KIR) and Ly49, that when stimulated by MHC class I are inhibited from becoming activated [14]. Cells that do not express MHC class I activate signaling that leads to cellular destruction via the release of perforin and granzyme [15]. Tumor cells often lack or have diminished levels of MHC class I expression. A second mechanism of NK activation involves a class of proteins called MHC class I related chain A (MICA) and MICB. MICA is a ligand for NK cells that triggers activation, these molecules are expressed in cells experiencing stress as well as tumor cells. When the NKG2D receptor on the NK cell comes into contact with MICA or MICB, these cells act to kill the target [16]. This cellular pathway overrides typical MHC class I induced inhibition. The final mechanism for activation takes place via antibody binding to the CD 16 receptor on the surface of NK cells. CD16 is an Fc receptor (FcγRIII) present in NK cells that when stimulated upregulates the production of IFNγ, CD25, and TNF. 7

18 Macrophages and DCs are important innate immune cells present within the tumor microenvironment with the majority of studies focusing on the role of macrophages in tumor pathogenesis. Macrophages are present throughout the body and have important roles in the protection against invading pathogens and maintaining homeostasis [17]. Specific macrophage phenotypes have been described with M1 macrophages representing prototypical inflammatory macrophages while M2 macrophages are alternatively activated macrophages able to contribute to regulating immune responses and immune healing. Within the tumor microenvironment, cytokines and other factors control the relative balance between M1 and M2 macrophages. IFN-γ and TNF-α are two cytokines often found within the tumor microenvironment that induce the generation and activation of M1 macrophages. These M1 macrophages are capable of cytotoxic effects via production of substances like nitric oxide (NO) and reactive oxygen species (ROS). These chemicals are directly cytotoxic to tumor cells. In addition, these activated macrophages produce a variety of cytokines, including interleukin (IL- 12, IL-23, IL-1β, IL-6 and TNF-α which aid in the activation of both adaptive cell responses (T and B cell responses) and NK cells. Although M1 macrophages are potentially generated in the tumor microenvironment, M2 macrophages (also known as tumor-associated macrophages; TAMs) typically predominate. Various cytokines present in the local microenvironment (e.g. IL-4, IL-10, TGF- IL-13) and other factors (e.g. glucocorticoids, factors produced by parasites, etc.) have been described to promote M2 macrophages which have a regulatory, or immunosuppressive, role. TAMs are a subset of M2 macrophages which can significantly contribute to tumor-associated 8

19 immunosuppression. Chemokine ligand 2 (CCL2), also known as monocyte chemotactic protein 1 (MCP-1), is produced by TAMs and the tumor itself and serves to recruit immunosuppressive Treg cells and additional TAMs to the tumor site. Once present in the tumor microenvironment, TAMs secrete epidermal growth factor (EGF), neoangiogenic factors (vascular endothelial growth factor) and anti-apoptotic proteins that allows inhibition of the caspase cascade and subsequently, prevents immune clearance of the tumor [18, 19]. The end result of TAM infiltration is local immunosuppression and an increase in tumor growth and metastasis [20, 21]. Like macrophages, DCs are important innate immune cells present within the tumor microenvironment. Similar to other professional APCs, DCs take up and present antigen but DCs have the unique ability to promote the differentiation of specific (e.g. helper T cell) subsets which shapes the nature of the resultant adaptive immune response. Multiple CD4+ helper T cell populations have been described (e.g. Th1, Th2, Th17, Tregs, etc.) which influence the induction and regulation of antigen-specific cellmediated or humoral (antibody)-mediated immunity in the context of cancer. The role of Th1 cells is to promote the cell-mediated immune response. These Th1 cells produce IFN-γ which stimulates the expansion of CD8+ cytotoxic T cells that can then lyse and destroy tumor cells. The ability of CTLs to recognize tumor cells as foreign occurs due to the presence of altered MHC class I by the tumor cell. Additionally, DCs can also present extracellular antigens, normally processed by MHC II pathways, via MHC class I and activate CD8+ T cells in a process called cross-presentation. Another more recently 9

20 described Th17 helper T cell population is also stimulated by the local DCs in the inflammatory tumor microenvironment. Th17 cells add to the inflammatory milieu by producing cytokines such as IL-17 in addition to IFN-γ and IL-4 [22]. While DCs provide a link between the innate and adaptive immune response, they also serve a role in propagating the local inflammatory reaction through the production of IL-4, IL-10. These and other immunoregulatory factors aid in generating and stimulating Th2 cells. These Th2 cells play an important role via production of Il-4, IL-5, IL-10 and IL-13, thus stimulating B cells and the adaptive arm of the immune response. DCs through production of IL-10 and TGF- are also critical in the generation of Treg cells which can potently multiple aspects of an immune response. Myeloid Derived Suppressor Cells in Cancer Immunology In addition to regulatory TAMs and Tregs, recently described myeloid derived suppressor cells (MDSCs) significantly contribute to the immune dysregulation seen in cancer. Importantly, potent immunoregulatory cells are important roadblocks to achieving complete anti-tumor immunity and preventing escape. MDSCs are immature myeloid cells produced by bone marrow precursor cells that are most commonly increased in cancer patients but elevated MDSCs have also been described in other diseases, including sepsis, autoimmunity and graft-versus-host disease [23-26]. These MDSCs represent a myeloid precursor population that, normally, has the potential to differentiate into granulocytes, macrophages or DCs [27] (Figure 1). However, in cancer, these MDSCs 10

21 are arrested and accumulate [28]. MDSCs have been recognized as early as the 1970s when Kirchner et al described a population of cells of the macrophage/monocyte lineage in the spleen of mice that suppressed T cell function; although the definitive terminology of MDSCs was not officially adopted until recently [29, 30]. In 1996, Gabrilovich et al described a DC-like population present in breast cancer patients that inhibited effector T cell responses and there was a renewed interest in these immunosuppressive myeloid cells [31]. Previously termed myeloid suppressor cells or immature myeloid cells, in 2007, the suggestion was made by leaders in the field to term these cells MDSCs [29]. 11

22 Early work in the mouse found that they were a heterogeneous population but were phenotypically identifiable based on their co-expression of CD11b and Gr-1. CD11b is an integrin the represents one subunit of the integrin alpha-m beta-2, also known as macrophage-1 antigen (Mac-1) and complement receptor 3 (CR3). Gr-1 represents a composite of both lymphocyte antigen 6 complex locus C (Ly6C) and Ly6G. Under normal environmental conditions, these cells are produced by the bone marrow during the normal process of hematopoesis (myelopoiesis) of both mice and humans and will mature and differentiate into normal macrophages, granulocytes and DCs. Under pathologic conditions, elevated numbers of these cells are found in both the peripheral circulation and lymphoid organs [32]. The significance of these cells lies in their effect on the host s immune response. Under certain pathologic conditions, most notably cancer, a large body of evidence suggests that these cells induce a state of immune tolerance and may even support the growth of cancer [33]. However, in other pathologic conditions (e.g. sepsis), evidence suggests that these cells have variable effects and may exert a hostprotective effect [34, 35]. Based on the varied effects of these cells, a better understanding of the role they play in achieving health or preventing disease is needed to better guide therapies in a wide array of diseases. While the primary role of MDSCs is direct immunosuppression of cellular immune responses such as NK cell activation and CTL responses, MDSCs achieve immunosuppression through a variety of mechanisms. In addition to their direct 12

23 immunosuppressive effects, MDSCs can promote the expansion of additional immunosuppressive cells, such as Tregs. Tregs are a subset of T lymphocytes that are identified by their expression of forkhead box P3 (foxp3) whose function in normal individuals is to maintain homeostatis and regulate inflammation [36]. Tregs can be increased in cancer and, due to their potent immunoregulatory abilities may aid in tumor growth and progression [37]. Studies in humans have demonstrated elevated levels of Tregs and MDSCs in cancer patients and additional studies show a direct induction of Tregs by MDSCs [38-40]. Thus, MDSCs are not only immunosuppressive themselves but can also induce additional potent immunosuppressive Treg cells. The presence of the immunosuppressive MDSCs in cancer represents a major hurdle for cancer immunotherapies designed to stimulate the immune system to eliminate tumors [41]. Mechanisms of MDSC Mediated Immune Suppression MDSCs are a diverse and heterogeneous population having multiple mechanisms of immune suppression. Direct cytotoxic effects on the adaptive arm of the immune system are mediated through the production of arginase, ROS and NO. The formation of ROS and peroxynitrite (via NO) causes primary effects at the level of the T cell receptor (TCR) of cytotoxic CD8 + T lymphocytes through the nitration of that receptor. When nitrated, the TCR is unable to participate in T cell-peptide-mhc interactions and under some conditions leads to apoptosis of the T cells [42, 43]. Induction of both the arginase gene expression and protein levels of arginase is another directly suppressive mechanism 13

24 MDSCs use to down regulate the host s immune response. Normal CTLs, as well CD4 + lymphocytes, require extracellular ARG, an essential amino acid for T cell activation. mediates a reduction in local extracellular ARG, thereby blunting the function of these T lymphocytes [44]. An additional function of arginase production by MDSCs is the induction of a local upregulation of Tregs [45]. These increased levels of Tregs allows for further abrogation of the CTL response. Other mechanisms that play an active role in the MDSC mediated suppression of CTLs include the production of ROS. ROS are produced by MDSCs through STAT3-mediated upregulation of NADPH oxidase (NOX2) [46]. These ROS function to induce posttranslational modifications to proteins, reduce CD3ζ chain expression induce apoptosis of activated CTLs via down regulating of intracellular proteins (B-cell lymphoma 2) and increase CD95 expression [47]. Elevated NO can also lead directly to T cell apoptosis [48, 49]. Through activation of the nuclear transcription factor STAT1, inducible nitric oxide synthase (inos) and arginase 1 (ARG1) activity are upregulated. inos upregulation leads to increased nitric oxide (NO) in the microenvironment. An additional effect of NO in the presence of ROS is the formation of peroxynitrite which has the ability to induce the nitration of the T cell receptor (TCR) of CD8+ T cells which results in altered TCR/MHC-peptide recognition [42]. A better understanding of the signaling pathways involved in MDSC generation and function will facilitate the development of novel targeted therapies. In addition to affecting the adaptive immune response, MDSCs have more recently been shown to influence the innate immune response through a variety of mechanisms 14

25 including MDSC-mediated production of TGF-β and IL-10 [50]. TGF-β production by MDSCs inhibits the cytotoxic activity and IFN- -producing ability of NK cells [51]. Decreased IFN-γ in the microenvironment limits the recruitment of additional immune effectors. Additionally, Other work has shown that TGF-β secreted from MDSCs allows for epithelial to mesenchymal transition, thought to be critical in metastasis [52]. At an even earlier step in the immune cascade, MDSCs act to inhibit the production of mature DCs thereby decreasing the processing and presentation of antigen to other parts of the immune response [53]. This action of MDSCs is considered to be secondary to the preferential uptake of cystine by these cells. Cysteine is an essential amino acid needed in the metabolic process of generating cystine by normal DC and macrophages for activation [54]. Through depletion of local cysteine concentrations DC, macrophages as well as T cells are unable to activate. MDSCs have also been shown to produce elevated levels of matrix metalloproteinases (MMPs) which may facilitate local extravasation and metastasis and represents another mechanism by which MDSC alters the local tumor microenvironment [55]. Alteration in macrophage differentiation and function is also mediated through the release of the cytokine IL-10 by MDSC. The presence of elevated IL-10 in the local microenvironment stimulates macrophages to down regulate their expression of IL-6, IL-12 and MHC class II, leading to decreased antigen presentation. The altered levels of IL-10 also act to preferentially polarize macrophages into developing an M2 phenotype, similar to that of TAMs [56]. Taken together, the actions of MDSCs represent a significant hurdle for the immune system to overcome in cancer bearing hosts. 15

26 The significant induction and accumulation of MDSCs in cancer is thought to be due to a combination of factors including signals from, or factors released by, the tumor, the tumor microenvironment or activated immune cells [45]. Several factors have been shown to elevate MDSC numbers, including granulocyte-macrophage colony stimulating factor (GM-CSF), IL-1β, IL-6, S100A8/A9, vascular endothelial growth factor (VEGF), cyclooxygenase 2 (COX2), colony stimulating factor-1 (CSF-1) and prostaglandins (PGE2) [45, 57-59]. Factors such as IL-1β and IL-6 have been recognized to specifically elevate the numbers of MDSCs while other factors are known to contribute to either their induction and/or accumulation [55]. Both tumor cells and inflammatory cells within the tumor microenvironment can produce IL-1, IL-6 and GM-CSF. Based on our understanding of factors which have been shown to contribute to MDSC expansion and accumulation, several pharmaceuticals now exist to inhibit pathways responsible for generating MDSCs. Various compounds have been used in both humans and dogs which prevent the formation of MDSCs, reduce MDSC accumulation, affect MDSCs inhibitory abilities or promote MDSC differentiation (Figure 2) [60-62]. Although we are beginning to understand the mechanisms by which MDSCs are generated and how they mediate their immunosuppressive effects, our knowledge of signaling pathways involved in MDSC biology and overall activation/function is still limited. Several signaling pathways have been identified that are responsible for activation of MDSC. Signal transducer and activator of transcription 3 (Stat3) is a 16

27 molecule that plays a role in events governing tumor cell proliferation, survival and invasion. STAT 3 signaling leads to promotion of cellular proliferation and prevention of apoptosis leading to cellular differentiation into mature myeloid cells [57, 63]. The importance of STAT3 signaling in MDSC suppressive function was demonstrated by the fact that when the tyrosine kinase inhibitor sunitinib is administered, STAT3 signaling is inhibited and MDSC accumulation and immune suppression is abrogated [64]. Heterogeneity of MDSCs A final additional factor that adds a layer of complexity to the MDSC story is the inherent heterogeneity of these cells. Subpopulations of MDSC have been described based on phenotypic or morphologic characteristics and there is emerging evidence that MDSCs within the tumor are distinctly different than those located within peripheral immune organs such as the spleen and lymph nodes. In mice, differential expression of Ly6C versus Ly6G is used to identify specific subpopulations with relatively distinct phenotypic and functional characteristics. MDSCs that express CD11b + /Ly6G + /Ly6C low are termed granulocytic (Gr-MDSC) and produce elevated levels of ROS and ARG in addition to acting outside of MHC class I mediated antigen presentation [65]. This differs from monocytic MDSCs (Mo-MDSC) whose phenotype of CD11b + /Ly6C + /Ly6G low morphologically resemble monocytes and these Mo-MDSCs primarily produce NO and arginase [57]. This classification used in mice differs from the morphologic and phenotypic distinction that is made in humans. In humans, MDSC are 17

28 typically classified as CD11b + /CD33 + /HLA-DR low/-. MDSCs expressing CD15 often represent a more neutrophilic (granulocytic) phenotype, whereas those expressing CD14 are of a monocytic phenotype. These different MDSC subpopulations not only have unique phenotypes, they are associated with different tumor types and have distinct mechanisms of immune suppression. Mo-MDSCs can be seen in such malignancies as melanoma. In melanoma, CD14 + MDSC have been shown to decrease DC maturation, impair the ability to take up and present antigen, as well as induce IFN-γ production by T cells. This is of particular concern for patients with melanoma as the vaccines used for the treatment of this malignancy depend on a robust T cell response [66]. Additionally, CD14 + MDSCs in patients with hepatocellular carcinoma are shown to have impaired NK cell function, believed to be due to blockade of the NKp30 receptor on the surface of NK cells [67]. The other subtype of MDSC, CD15 + granulocytic MDSC are more commonly seen in patients with glioblastoma, renal cell carcinomas, pancreatic carcinomas and several other tumor types. This subtype of MDSC relies on mechanisms such as arginase production, which lowers arginine concentrations, a requirement for appropriate T cell function [68]. The common thread between the various subsets of MDSCs is the ability of these cells to inhibit T cell and NK cell function and, although not explored, likely inhibit other immune cells such as DCs. 18

29 While the phenotypic heterogeneity of MDSCs has been shown to be an important factor in terms of the various mechanisms of T cell suppression MDSCs exert, locational heterogeneity is also present among MDSCs. MDSCs arise from hematopoietic precursors in the bone marrow. Once released from the bone marrow, MDSCs will naturally traffic to other body sites, such as the spleen and liver [69, 70]. The MDSCs in these storage depots typically lack significant functional immunosuppressive ability. The activation into functional, immunosuppressive cells typically takes place in the tumor s microenvironment or following exposure to appropriate inflammatory conditions. Chemokines produced in the local microenvironment, such as STAT family members, NF-κB, the calcium binding proteins S100A8/9 and others, act as signals for the release and localization stored MDSCs [71]. Once in the primary tumor microenvironment, MDSCs become activated and lead to immunosuppression; these MDSCs can also travel to local lymph nodes to aid in propagation of T cell dysfunction [70]. MDSC Targeted Therapeutic Intervention As the medical community has sought to determine the mechanisms underlying the failure of effective immunotherapy and ways to enhance effective therapies, a more complex understanding of the relationship between the cancer cell and the host s immune system has evolved. It is now known that cancer cells increase the production of host derived bone marrow cells of the myeloid lineage and these cells subsequently alter the immune system s ability to recognize and eradicate tumor [47]. These cells, now known 19

30 as MDSCs, are potentially immunosuppressive and represent one of the major hurdles in establishing effective immunotherapy [47]. As our knowledge of MDSC biology has evolved over the past two decades, the complexities regarding the interaction of cancer cells with the immune system have become more apparent as have the opportunities for therapeutic intervention. Diminishing the effects of MDSCs is critical for effective cancer immunotherapies and the variety of signaling mechanisms involved in MDSC expansion and activation offers potential therapeutic targets. The mechanisms which can be exploited to reverse the immunosuppressive effects of MDSCs can be divided into 4 basic categories that include: 1) forced differentiation of MDSCs into mature DC, 2) inhibition of MDSC expansion from the precursor stage, 3) decreased MDSC accumulation in peripheral organs and 4) direct inhibition of inhibitory soluble factors [62] (Figure 2). 20

31 Forcing the maturation of MDSCs into mature DC that are no longer immunosuppressive can be accomplished through several mechanisms. Both retinoic acid and vitamin D3 are commercially available drugs which have been shown to force the maturation of MDSCs in patients. Retinoic acid has been shown to decrease the accumulation of ROS in MDSC, a necessity for MDSCs to maintain an immature state [72]. When administered to people at high enough concentrations, retinoic acid was able to not only decrease the number of circulating MDSCs but to improve T cell response [73]. Vitamin D3, has also been shown to reduce the number of MDSCs in humans diagnosed with head and neck carcinaoms as well as increase IFN-γ levels and improve T cell blastogenesis in vitro [74]. Blocking the development of hematopoietic precursors from developing into MDSCs is the second method that can be utilized to prevent or reverse the effects of MDSCs. While numerous soluble factors exist that stimulate the production of MDSCs (some of these include GM-CSF, SCF, VEGF, TGFβ, IL-1β, IL-10, IL-12, IL-13, CCL2) which makes blockade of MDSC development difficult, many of these soluble factors promote MDSC development through common signaling pathways. In particular, the STAT3 pathway has proved to be one of the crucial mechanisms responsible for the activation and immune suppressive effects of MDSC. Previous work has identified over-activation of STAT3 signaling in a variety of human malignancies including breast cancer and hematopoietic malignancies in addition to others [75, 76]. Of particular interest are the 21

32 tyrosine kinase inhibitors (TKIs), a group of compounds that have shown to reduce MDSC levels in renal cell carcinoma [64]. Sunitinib is a TKI that acts to inhibit several signaling pathways including vascular endothelial growth factor receptor (VEGFR), platelet derived growth factor [62] or receptor (PDGFR), and stem cell factor receptor (ckit) [77]. Sunitinib is also known to directly inhibit STAT3 expressed in both cancer cells like renal cell carcinoma, as well as MDSCs [64]. This compound, when used in tumor bearing mice, has been shown to not only decrease the numbers of MDSCs and Treg but to also proportionally reverse T cell inhibition in relation to decreasing MDSC numbers [60]. These findings were incidental and were not the intended initial target of the drug. Now, in addition to the TKIs, several other STAT3 inhibitors exist that are at various stages of drug development [62]. Still another method to combat MDSC effects is to decrease the number of these cells in peripheral organs such as the spleen. Therapies such as monoclonal antibodies (mab) against Gr-1 have been investigated in the rat, however, once the mab is below therapeutic levels, a significant rebound in the number of MDSCs is seen [62]. However, conventional chemotherapeutics such as gemcitabine and 5-fluorouracil have been shown to have unconventional effects on MDSCs. Both drugs have been shown to preferentially deplete the number of MDSCs in the spleen while preserving the number and function of other cell lines including CD8 + T cells, NK cells, macrophages and B cells [62, 78]. 22

33 The final way to inhibit MDSC function is to directly target and inhibit the soluble immunosuppressive factors produced by MDSC. Early attempts at treating MDSC immunosuppression via ROS scavengers and antagonists of arginase and NO had side effects that inhibited other biologically necessary pathways, such as the urea cycle [44, 79]. However, nitroaspirines and phosphodiesterase 5 (PDE5) inhibitors have shown potential benefit in reversing MDSC-mediated immunosuppression without causing significant biologic side effects. Nitroaspirines couple a NO-releasing moiety to aspirin. The effects on MDSC are reduction in ROS production and feedback inhibition of NO catalytic activity [62]. PDE5 has also been exploited in down-regulating arginase and NO activity leading to an improved functional T cell response in these patients [80]. Numerous other avenues of research are ongoing in an attempt to modify MDSC effects on the tumor microenvironment and disease progression. Triozzi et al. have demonstrated the use of an IL-1 receptor antagonist (IL-1ra) for use in uveal melanomas [81]. Not only were the numbers of MDSCs reduced in patients treated with IL-1ra, but tumor size was reduced and the tumor stroma modified. Other methodologies being explored include the use of the anti-vegf monoclonal antibody bevicizumab (Avastin) in mouse models of breast cancer. Mice treated with bevicizumab had reduced MDSC infiltration, as well as, decreased Treg and increased mature DC numbers [82]. Still other examples of therapeutic targets being investigated include the use of bis-phosphonates to reduce the number of MDSCs from the bone marrow and the peripheral circulation through antagonism of MMP9 [83]. While many of these therapies remain under 23

34 investigation in the human medical arena, ongoing research is making advances in our understanding of the complex relationship between the tumor, the immune system and potential therapeutic interventions. Role of Animal Models in Cancer Biology It is becoming increasingly recognized that spontaneous cancers in dogs represent a useful model for understanding carcinogenesis and evaluating new therapies. Chemotherapy has been the primary medical treatment of choice in both veterinary and human oncology. In general, traditional cytotoxic chemotherapies non-specifically kill rapidly dividing cells with the goal of targeting rapidly dividing neoplastic cells. However, toxicity to normal tissues, particularly rapidly dividing hematopoietic and gastrointestinal cells can commonly be seen. Cancer immunotherapy, such as vaccines, antibody treatments, and cell-based therapies, are more targeted, potentially more effective and less toxic than chemotherapy and are actively being explored and applied to cancer patients (canine and human). In veterinary medicine, the best example of immune therapy is the Oncept melanoma vaccine. This vaccine is a xenogeneic DNA vaccine against human tyrosinase [84]. This vaccine has been FDA-approved and represents a significant step forward in both the understanding of the canine immune system and our ability to therapeutically harness the immune system for cancer treatment. Additional therapeutics that have been developed and FDA-approved for the treatment of cancer in animals include the TKI Palladia (toceranib). This TKI has direct antitumor effects by 24

35 inhibiting c-kit but also has indirect effects via inhibition of VEGFR and PDGFR which have been shown in human and mouse models of MDSC to have therapeutic potential [85]. These two therapies have both been FDA approved within the past 2 years and represent not only scientific advances in the understanding of the immune system in cancer but also the desire for more effective, multi-modal approaches to veterinary cancer care. From the translational medicine perspective, the dog is an excellent model for many human cancers. As compared to the mouse, the dog shares the same environments as humans, is a naturally outbred population (as compared to the highly inbred nature of mice) and develops neoplastic disease spontaneously [86]. This is evident in both molecular studies evaluating new biomarkers of disease and in drug development. Preclinical work with drugs such as rapamycin in canine osteosarcoma has been performed in the dog to aid in human drug development given the similarities between canine and human osteosarcoma. Through the use of this drug in the dog, both drug toxicity and optimal dosing schedules could be developed to inform the human drug development effort [87]. Research in the dog has the opportunity to not only benefit man but his best friend as well. To date, only a single recent study has been published describing canine MDSCs or evaluating whether canine patients with cancer have increased numbers of circulating MDSCs [88]. Challenges faced in veterinary oncology, specifically in the arena of 25

36 canine immunology research, include a deficit of appropriate antibodies against cell surface markers. This has made efforts to build on and translate the work being done on MDSC in human oncology challenging. Given that cancer is not only an important disease in dogs, but also that dogs are increasingly becoming a model for studying human cancer biology and cancer therapeutics (comparative oncology), the identification and characterization of MDSCs and, eventually, MDSC subpopulations in specific cancers, in dogs is a first step to determining the role of these cells in canine cancer patients. Until we are able to accurately and reliable identify MDSCs in dogs, our understanding of the biology and impact on cancer therapeutics in both dogs and humans will be limited. The following study investigated the use of commercially available canine antibodies to identify and detect MDSCs in canine cancer patients. 26

37 Chapter 2: Methods and Materials Flow Cytometry Staining and Optimization Prior to enrolling dogs into the clinical phase of the study, initial studies optimizing primary and secondary antibody combinations and concentrations were necessary. Antibodies were first evaluated on whole blood samples. One to four millilters (ml) of whole blood was collected into 7.5% ethylenediaminetetraacetic acid (EDTA) blood collection tubes through peripheral venipuncture from dogs being euthanized as part of a separate study approved by The Ohio State University s Institutional Animal Care and Use Committee (IACUC). Optimization of blood sample handling and staining parameters included the evaluation of 1) staining and immediate analysis, 2) maintenance of blood samples in EDTA for 24 hours prior to erythrocyte lysis and staining and 3) erythrocyte-lysed blood cell samples kept in 5% RPMI culture media for either 24 or 48 hours and washed in FACs buffer immediately prior to staining (Figure 3). Prior to staining cells with antibodies, samples were pelleted and washed with erythrocyte lysis buffer (NH 4 Cl/KHCO 3 /EDTA/distilled water) and incubated for 4 minutes at room temperature. The cells were then centrifuged at 1450 rpm for 7 minutes, lysed and washed/centrifuged twice more with 10mL of 1X phosphate buffered saline (PBS). Cells 27

38 were then re-suspended in fluorescence-activated cell sorting buffer (FACS; phosphate buffered saline/fluoride buffered saline/sodium azide) at a final concentration of 5x10 5 /100uL. Cells were then stained with a panel of antibodies including MHC class II (AbdSerotec, MCA1044F), IgG1 (AbdSerotec, MCA928), CD11b (AbdSerotec, MCA1777S), CD14 (AbdSerotec, MAC1568A647), DH59B (VMRD) and CADO48A (VMRD). All antibodies were primary, non-conjugated with the exception of MHC class II and CD14 which were directly conjugated to fluorescein isothiocyanate (FITC) and Alexa Fluor 647 (BD Biosciences), respectively. The primary antibody CD11b was stained with 5uL phycoerythrin (PE) and the antibodies CADO48A and DH59B were secondarily stained with 1uL of undiluted FITC or 1uL of FITC at dilutions of 1:10, 1:50, and 1:100 for optimization studies. Antibody combinations included: MHCII, IgG1 isotype, CD11b, CD14, DH59B, CADO, CD11b/DH59B and CD11b/CADO48A.. Following incubation, cells were resuspended in excess FACS buffer, spun and resuspended in 10uL of FACS in preparation for secondary staining or 300 ul for running on the flow cytometer. Optimization of various antibodies evaluated sequential and/or simultaneous staining with primary and secondary antibodies. For the tubes containing CD11b and either DH59B or CADO, cells were incubated with CD11b followed by PE and then DH59B or CADO respectively followed by 1uL of FITC diluted at concentrations of 1:10, 1:50 and 1:100 of FITC with FACS buffer. Samples were run on a BD Accuri flow cytometer and analyzed with BD Accuri CFlow analysis software. Samples were run immediately following staining or fixed in 4% formalin for 4, 24 and 48 hours after staining (Figure 4). 28

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40 Study Design/ Animals Eighty client-owned dogs were prospectively enrolled in the study at The Ohio State University s Veterinary Medical Center. Forty dogs presenting to the Community Practice or Blood Bank Services were enrolled into the control group. These animals had no prior history of neoplasia when the owner was questioned and were healthy upon physical examination. Forty dogs presenting to the Medical or Radiation Oncology Services were enrolled into the experimental population. Inclusion criteria for patients in the experimental population were (1) cytological or histopatholgic diagnosis of a mesenchymal or epithelial neoplasm, (2) local or metastatic disease, (3) no prior surgical of chemotherapeutic treatment and (4) no prior history of neoplasia. No patients with round cell tumors were enrolled in the study. Once client consent was obtained, 1-2 ml of whole blood was collected via venipuncture into 7.5% ethylenediaminetetraacetic acid (EDTA) blood collection tubes. For animals in the experimental group, blood was collected prior to treatment for their underlying neoplasia. Animal use was approved by The Ohio State University s Institutional Animal Care and Use Committee and The Ohio State University s Veterinary Medical Center s Clinical Research Advisory Committee. 30

41 Flow Cytometry Once a whole blood EDTA sample was collected from a patient, the sample was refrigerated and processed for flow cytometry within 12 hours of collection. Prior to staining cells with antibodies, samples were pelleted, washed with erythrocyte lysis buffer and incubated for 4-5 minutes at room temperature. The cells were then pelleted, lysed twice and washed in 10mL of 1X PBS. Cells were resuspended in FACS buffer at a final concentration of 5x10 5 /100uL into one of 6 tubes containing the following antibodies 1) cells alone (no antibody), 2) MHC class II, 3) IgG1, 4) CD11b, 5) CADO48A and 6) a combination of CD11b and CADO48A (Figure 5). All antibodies were primary, non-conjugated with the exception of MHC class II which was directly conjugated to FITC. Based on optimization studies performed within the laboratory, the primary antibodies CD11b and CADO were secondarily stained with 5uL PE and 1 ul of a 1:50 dilution of FITC, respectively. In the tubes containing non-conjugated antibodies, cells were labeled by primary and secondary antibodies in sequence with 15 minute incubations for each antibody. Prior to adding secondary antibody, cells were washed with PBS. For cells in the tube containing the combination of CD11b and CADO48A, primary followed by secondary antibodies for CD11b were added, then primary followed by secondary antibodies for CADO48A were added (Figure 5). Following staining and washing, the samples were immediately analyzed with an Accuri flow cytometer. 31

42 Flow Analysis Cells were analyzed in order to determine the percent of all cells staining positive for both CD11b and CADO48A. The data obtained from the cells in the CD11b/CADO48A tube was analyzed. Gates were created to include all cells (both dead and live cells, ungated), all peripheral blood mononuclear cells (PBMC) or R1, and all non-lymphocytes based on the appearance of cells on forward scatter (FSC) and side scatter (SSC) or P1. Additionally, cells were plotted with FL-1 (CADO48A) on the x- axis and FL-2 (CD111b) on the y-axis. Gates were established based on isotype control values and each of 3 cells populations was gated and evaluated for the percent of cells staining positive for both CD11b and CADO48A (Figure 3). The gates assigned were as follows P2 (CD11b hi CADO hi ), P3 (CD11b hi CADO low ) and P4 (CD11b low CADO low ) (Figure 6). For all gates, the percent and total count of all cells staining positive for both antibodies was recorded. 32

43 Statistical Analysis For all statistical analyses, the percentage of cells staining positive for both CADO48A and CD11b were evaluated. Differences between the control and experimental groups were compared using a Wilcoxon rank-sum (Mann-Whitney) test. Additional comparisons between the individual tumor types (sarcoma, carcinoma and melanoma) were made using a Kruskal-Wallis equality-of-populations rank test. For all comparisons made, p-values less than 0.05 were considered to be significant. 33

44 Chapter 3: Results Flow Cytometry Optimization Prior to enrolling patients into the prospective phase of the study a series of optimization experiments were performed to evaluate 1) the most appropriate granulocytic antibody, 2) the most appropriate concentration of secondary antibody (FITC) and 3) the optimal sequence of sample processing steps to ensure consistent results. Two commercially available candidate canine-specific granulocyte markers (CADO48A and DH59B) were initially evaluated to determine their efficacy when used to identify canine granulocytes and commercially available CD11b used as a myeloid cell marker. Simultaneously, secondary indirectly-labeled antibodies and the appropriate fluorochrome labeling was evaluated to determine the optimal concentrations needed to appropriately identify the target cell population staining positive for both anti-canine CD11b and the granulocytic marker (CADO48A and DH59B). When compared using the manufacturer suggested concentrations of CADO48A and DH59B, and the same concentration of secondary antibody (FITC), CADO48A led to an improved distinction of the populations of cells staining positive for both CD11b and the granulocytic marker (Figure 7). These experiments also showed that a FITC concentration of 1:50 led to an improved distinction 34

45 between positively and negatively staining cells (Figure 8). Cells stained with FITC at a concentration of 1:10 (Figure 8a) displayed a linear pattern (i.e. comet tail ) appearance, indicating that compensation issues and a secondary antibody concentration that was too high. This artifact was not seen at FITC concentrations of 1:50 and 1:100. Based on these initial findings, the 1:50 and 1:100 dilutions of FITC were then used in the dual staining process and can be seen in (Figure 8d) and (Figure 8e) respectively. In panel (e), the secondary antibody concentration of 1:100 failed to allow the sufficient distinction of cell populations compared to a 1:50 dilution (d). Based on these findings, a FITC concentration of 1:50 was used in all clinical samples. 35

46 Following the identification of CADO48A as a more useful marker than DH59, a series of experiments was performed to determine the optimal time frame for staining PBMCs once blood was collected into EDTA for analysis. Furthermore, the optimal time frame for performing flow cytometry after cellular staining also was determined. These steps were considered necessary for ensuring efficient and consistent sample handling procedures once the prospective phase of the study began. 36

47 Figure 9 displays the results when cells were stained immediately, kept in EDTA for approximately 24 or 48 hours prior to staining or kept in 5% RPMI culture media for 24 or 48 hours prior to cellular staining with antibodies. Results obtained with PBMCs that were collected immediately upon collection gave the most consistent results as compared to those isolated from blood stored in EDTA for 24 or 48 hours at 4C. The effects of storing PBMCs in 5% RPMI culture media for 24 or 48 hours prior to antibody labeling were then assessed as compared to PBMCs isolated and labeled immediately upon collection. The most consistent results were obtained when cells were either stained immediately or after being kept in culture media for 24 hours. Figure 10 displays the results of PBMCs stained immediately after sample acquisition but evaluated by flow cytometry at different time points following fixation. Based on these experiments, the PBMCs of all clinical samples obtained were processed as soon after blood collection as possible in an effort to avoid altering the characteristic receptor expression or differentiation of the cells. However, these findings do indicate that if peripheral blood samples needed to be stored for some period of time, storing PBMCs in RPMI is superior to cells being kept in EDTA. 37

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49 Population Characteristics A total of 80 patients were enrolled into the study between April 2011 and January The control group was comprised of 40 dogs with a median age of 5.0 years (range 3-15 years). The following breeds were represented, mixed breed (n=17), Greyhound (n=13), Labrador Retriever (n=3), Collie (n=2), Boxers (n=2), Beagle (n=1), Galgo (n=1) and Australian Shepherd (n=1) (Table 1). Nineteen dogs were castrated males and the 39

50 remaining 21 dogs were spayed females. The experimental group was comprised of 40 dogs with a median age of 9.3 years (range 3-14 years). The following breeds were represented Labrador Retriever (n=6), mixed breed (n=6), Greyhound (n=4), Golden Retriever (n=4), Beagle (n=-3), Doberman Pinscher (n=2), Australian Shepherd (n=2), Shih Tzu (n=2), and 1 each of the following breeds: Shar-pei, English Mastiff, American Cocker Spaniel, Airedale Terrier, Chesapeake Bay Retriever, Bichon Frise, Irish Wolfhound, Flat Coat Retriever, Newfoundland, Rottweiler, and Boxer. Twenty-three dogs were male (1 intact and 22 castrated) and the remaining 17 dogs were spayed females. The tumor types represented included sarcomas (n=19), carcinomas (n=18) and oral melanomas (n=3). 40

51 Gate Analysis The mean percentage of cells staining positive for both CADO48A and CD11b were compared between the control and experimental groups. The mean percentage of cells staining positive for both antibodies were also compared amongst tumor types. The percentage of cells staining positive and associated statistical parameters (e.g. mean, median, SD, min, max) for both CADO48A and CD11b for both the control and experimental groups are shown in Table 2. Similarly, the percentage of cells staining positive for individual tumor subtypes are shown in Table 3. 41

52 Tables 2 shows that only the percentage of CD11b low CADO48A low c (i.e. P4 gate; Figure 6) population of cells was significantly higher in the P4 gate of the tumor bearing population (p=0.048) with no statistical difference seen between the control and experimental groups was seen in the R1, P1, P2 or P3 gates. Similar gating parameters were used to compare cell populations between the different tumor types of sarcoma, carcinoma and melanoma patients (Table 3). Statistical analysis showed an increased percentage of cells staining CD11b/CADO48A positive among the melanoma patients in the following gates: R1 (p=0.027) and P1 (p=0.036). The sarcoma and carcinoma patients were compared separately due to the concern that the small number of patients in the melanoma subcategory would significantly skew the statistical analysis across tumor 42