Targeting the Tumor Microenvironment to Enhance the Efficacy of Tumor-Targeted Antibody Therapy

TARGETING THE TUMOR MICROENVIRONMENT TO ENHANCE THE EFFICACY OF TUMOR-TARGETED ANTIBODY THERAPY

A Dissertation submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Tumor Biology

Rishi Surana, B.S.

Washington, DC June 16, 2014

TARGETING THE TUMOR MICROEVIRONMENT TO ENHANCE THE EFFICACY OF TUMOR-TARGETED ANTIBODY THERAPY

Rishi Surana, B.S.

Thesis Advisor: Louis M. Weiner , M.D.

ABSTRACT

Tumor-targeted antibody therapy has had a major impact on reducing morbidity and mortality in a wide range of cancers. Antibodies mediate their anti-tumor activity in part by activating immune effector cells; however, the tumor microenvironment is enriched with cellular and soluble mediators that actively suppress generation of anti-tumor immunity. Here, we investigate the potential of prospectively identifying and neutralizing an immunomodulatory soluble mediator within the tumor microenvironment to enhance therapeutic efficacy of the

HER2-directed antibody trastuzumab. Using the D5-HER2 cell line and an immunocompetent human HER2 transgenic animal (hmHER2Tg) in which human HER2 is seen as a self-antigen, we determined that IL-4 was present in the tumor microenvironment and produced by both tumor and stromal cells. Furthermore, IL-4 neutralization using the anti-IL-4 antibody 11B11 enhanced the efficacy of trastuzumab and modulated the tumor microenvironment. For example, IL-4 neutralization resulted in reduced levels of myeloid chemoattractants CCL2, CCL11, and

CXCL5 in the tumor microenvironment. Combination therapy with 11B11 and trastuzumab resulted in a reduction of tumor-infiltrating CD11b+CD206+ myeloid cells compared to monotherapy.

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Additionally, we sought to identify the molecular mechanisms by which D5-HER2 cells regulate

IL-4 in vitro . Using a siRNA-based screening approach, we identified STAT5A as a novel negative regulator of IL-4 expression in D5-HER2 cells. Collectively, these data suggest that IL-

4 neutralization enhances the efficacy of trastuzumab by influencing the phenotype of myeloid cells within the tumor microenvironment and provides further rationale for combining tumor- targeted antibody therapy with agents that neutralize factors in the tumor microenvironment that suppress generation of productive anti-tumor immune responses.

Acknowledgements

First and foremost I would like to thank my mentor, Dr. Louis Weiner, for his unwavering support, for

constantly challenging me to become a better scientist, and for teaching me how to think like a

physician-scientist.

I would like to thank members of the Weiner lab, particularly Dr. Shangzi Wang who developed and

characterized the model system used in this work. Drs. Yong Tang and Yongwei Zhang for scientific discussions. Dr. Sandra Jablonski for her assistance with siRNA screens and for her day-to-day support.

Joe Murray for engaging conversation on topics both scientific and otherwise.

I would like to thank Dr. Karen Creswell for technical assistance with flow cytometry and her willingness

to help no matter what the hour.

I would like to thank Dr. Anna Riegel, Director of the Tumor Biology Training Program, for always

having the best interests of students in mind.

I would like to thank my Thesis Committee for their support:

Drs. Priscilla Furth, Anton Wellstein, Carolyn Hurley and Michael Atkins.

I would like to thank Dr. Todd Waldman, Director of the MD/PhD program at

Georgetown University, for promoting and supporting the training of physician-scientists.

DEDICATION

This work is dedicated to my parents, Karan and Abha Surana, for their unconditional love and support,

and to my brother, Deepak, for always looking out for me.

Without your support, this work would not have been completed.

“Some sciences are exciting because of their generality and some because of

their predictive power. Immunology is particularly exciting, however, because

it provokes unusual ideas, some of which are not easily come upon through

other fields of study. Indeed, many immunologists believe that for this reason,

immunology will have a great impact on other branches of biology and medicine.”

--- Gerald M. Edelman, Nobel Lecture, 19721

1 "Gerald M. Edelman - Nobel Lecture: Antibody Structure and Molecular Immunology". Nobelprize.org. Nobel Media AB 2013. Web. 2 Jun 2014. vi

TABLE OF CONTENTS

Chapter 1: Introduction ...... 1

1.1: Antibody Structure ...... 1 1.2: Generation and Mass Production of Monoclonal Antibodies ...... 4 1.3: Mechanisms of Anti-Tumor Activity ...... 5 1.4: Fc-Receptors ...... 6 1.5: Fc γR-Dependent Mechanisms of Action ...... 8 1.6: Adaptive Immunity and Cancer ...... 12 1.7: Enhancing Adaptive Immunity in Cancer: Targeting T-Cell Checkpoints ...13 1.8: The Tumor Microenvironment Limits Generation of an Effective Anti-Tumor Immune Response ...... 16 1.9: A Model System to Study Tumor-Targeted Antibody Therapy ...... 19

Chapter 2: IL-4 Limits the Efficacy of Tumor-Targeted Antibody Therapy ...... 22

2.1: Objective and Hypothesis ...... 22 2.2: Aims ...... 22 2.3: Materials and Methods ...... 23 2.4: Results ...... 28 2.5: Discussion ...... 38 2.6: Supplemental Figures ...... 42

Chapter 3: A siRNA Based Screening Approach Identifies STAT5A as a Negative Regulator of IL-4 Expression in D5-HER2 Cells ...... 50

3.1: Objective and Hypothesis ...... 50 3.2: Aims ...... 50 3.3: Materials and Methods ...... 51 3.4: Results ...... 55 3.5: Discussion ...... 60 3.6: Supplemental Table ...... 62

Chapter 4: Conclusions and Future Directions ...... 68

4.1: Enhancing the Efficacy of Trastuzumab by Targeting the Tumor Microenvironment ...... 68 4.2: Identification of STAT5A as a Negative Regulator of IL-4 Expression in D5-HER2 cells ...... 71 4.3: Concluding Remarks ...... 72

Bibliography ...... 74

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LIST OF FIGURES

1.1 Structure of IgG ...... 3 1.2 Anti-tumor mechanisms mediated by IgG ...... 11 1.3 Potential targets to enhance T-cell activation ...... 15

2.1 Characterization of cytokines/chemokines expressed by D5-HER2 in vitro and in vivo ...... 29 2.2 Evaluation of IL-4 production by tumors grown in hmHER2Tg:IL4+/+ or hmHER2Tg:IL4-/- animals ...... 30 2.3 The effect of IL-4 neutralization on the efficacy of trastuzumab during primary challenge...... 31 2.4 The impact of trastuzumab and 11B11 therapy on the infiltration of CD11b+ cells into the tumor microenvironment ...... 32 2.5 The impact of trastuzumab and 11B11 therapy on cytokine and chemokine expression in the tumor microenvironment ...... 34 2.6 The impact of 11B11 on generation of trastuzumab-initiated, protective adaptive immunity ...... 35 2.7 The role of host versus tumor-derived IL-4 in growth of D5-HER2 tumors ...... 37 S2.1 HER2 expression on EO771-HER2 ...... 42 S2.2 Strategy for identifying and immunophenotyping tumor-infiltrating leukocytes ...... 43 S2.3 The impact of trastuzumab and 11B11 therapy on cytokine and chemokine expression in the tumor microenvironment ...... 45 S2.4 The impact of host-derived IL-4 on expression of cytokines and chemokines in the tumor microenvironment ...... 46 S2.5 The impact of host-derived IL-4 on expression of cytokines and chemokines in the tumor microenvironment of animals treated with trastuzumab ...... 48

3.1 Plot of the ELISA:crystal violet (CV) ratios for all genes included in the screen ...... 56 3.2 Identification of positive and negative regulators of IL-4 production ...... 56 3.3 The impact of Stat5a knockdown on IL-4 protein production ...... 57 3.4 The impact of Stat5a knockdown on abundance of IL-4 mRNA ...... 58 3.5 The impact of STAT5A overexpression on expression of IL-4 ...... 59

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LIST OF TABLES

S1.1: List of genes used to create siRNA library ...... 62

CHAPTER 1

INTRODUCTION

Monoclonal antibody therapy has revolutionized treatment of both hematological malignancies and solid tumors (1). The mechanisms by which antibodies exert their anti-cancer activity are based on unique structural characteristics which allow for both extraordinary diversity and common effector mechanisms. In the setting of cancer, these effector mechanisms are often subverted by tumor cells and serve to limit clinical efficacy of antibody therapy. Here, we begin by discussing important structural elements of antibodies that make them unique anti-cancer agents, how antibodies exert their anti-cancer activity, and mechanisms by which tumor cells blunt the efficacy of tumor-targeted antibody therapy.

1.1 ANTIBODY STRUCTURE

Antibodies are globular, multi-chain proteins that are members of the immunoglobulin (Ig) superfamily. Antibodies are composed of two heavy chains and two light chains that are connected via disulfide bridges (2). Each light chain contains one variable region (V L) and one constant region (C L) while each heavy chain contains one variable region (V H) and 3-4 constant regions (C H1-4) (2, 3). Digestion with the enzyme papain revealed that a single antibody has two fragments of antigen binding (F ab ) and one fragment of crystallization (F c) (4). Subsequent work revealed that each F ab was composed of a complete light chain and the V H and C H1 domains from the heavy chain (Figure 1.1).

A unique characteristic of antibodies is their diversity and specificity with regard to antigen binding. This diversity is contained within the V H and V L domains, specifically within six

hypervariable regions called complementarity determining regions (CDRs). The site of antigen binding (also called the paratope ) is formed from the juxtaposition of three CDRs on the V L domain with three CDRs on the V H domain. The paratope binds a region of 5-6 amino acids on the antigen termed the epitope. The molecular mechanism responsible for diversity within the variable domains is a complex series of genetic rearrangements that results in the generation of

10 10 -10 11 distinct paratopes (5).

The Fc region of antibodies is composed of either two or three C H domains (C H2-4) and serves as a key mediator of immune effector mechanisms such as complement fixation, antibody- dependent cell mediated cytotoxicity (ADCC), and antibody-dependent cell mediated phagocytosis (ADCP)—all of which are discussed below. The number and organization of C H domains defines the isotype of the antibody, of which there are five: IgM, IgD, IgG, IgA and

IgM. The most common isotype used in therapeutic applications is IgG and will serve as the basis for further discussion.

Figure 1.1: Structure of IgG. IgG molecules are composed of two heavy chain and two light chains molecules connected by disulfide bridges. The fragment of antigen binding (Fab) is composed of variable domains from both heavy and light chains (V H and V L) in addition to the constant domain of the light chain (C L) and a constant domain of the heavy chain (C H1). The hypervariable regions conferring antigen specificity within the Fab are called complementarity determining regions (CDRs). The fragment of crystallization (Fc) is composed of C H2 and C H3 domains and is responsible for mediating effector functions of IgG. Adapted from The Molecular Basis of Cancer, Ed.4, Rishi Surana, Louis M. Weiner, Monoclonal Antibodies for the Treatment of Cancer, Page 684 Fig. 50-1., Copyright (2015,2008,2001,1995), with permission.

1.2 GENERATION AND MASS PRODUCTION OF MONOCLONAL ANTIBODIES

Early work on the structure and function of IgG relied upon immunization and subsequent harvest of partially purified serum fractions as sources for polyclonal IgG (6). Although effective, this method was not amenable to mass production of antibodies nor was it effective in identifying antibodies with optimal paratopes and binding kinetics for therapeutic utility. In

1975, Kohler and Milstein revolutionized antibody research by developing a method to generate and isolate a monoclonal population of antibodies (7). This method involved fusing a murine, antibody-secreting B-cell with an immortalized plasma cell (later termed a hybridoma) and sorting out single cells using limiting dilution. As putative tumor antigens were identified, monoclonal antibodies against these antigens were created using hybridoma technology. These fully murine antibodies elicited a xenogenic immune response in humans, resulting in formation of human anti-murine antibodies (HAMA) which severely reduced circulating half-life and subsequent therapeutic utility (8, 9). Advances in molecular genetics paved the way for generation of chimeric antibodies, which contain murine variable regions and human constant regions, and humanized antibodies, which contain murine CDRs grafted upon an otherwise human antibody (10-12). Both chimeric and humanized antibodies show reduced immunogenicity compared to their fully murine counterpart and are currently used to treat patients with both hematological malignancies and solid tumors. More recently, fully human antibodies are being produced using transgenic animals expressing human immunoglobulin genes (13). Fully human antibodies are optimal for therapeutic applications since they are less immunogenic compared to antibodies with murine components.

1.3 MECHANISMS OF ANTI-TUMOR ACTIVITY

1.3.1 Perturbation OF CELL SIGNALING

Tumor cells often overexpress growth factor receptors, including members of the epidermal growth factor receptor (EGFR) family (EGFR, ERBB2/HER2, HER3 and HER4) which function to drive tumor cell growth and invasion. Thus, abrogating signaling through growth factor receptors using antibodies has proven to reduce tumor burden and reduce morbidity and mortality across a wide range of malignancies. Antibodies are capable of inhibiting signaling via blockade of ligand binding and, in the case of EGFR family members, inhibition of subsequent receptor dimerization and activation. The EGFR-targeted antibody cetuximab binds to domain

III of the EGFR receptor and prevents the receptor from adopting an activated confirmation that is required for receptor dimerization (14). Alternatively, the HER2-directed antibody pertuzumab does not inhibit ligand binding, but instead binds to domain II of the HER2 receptor and prevents receptor heterodimerization (15).

1.3.2 ACTIVATION OF THE COMPLEMENT CASCADE

The complement system is an evolutionary conserved, potent innate immune defense against invading pathogens that involves sequential, regulated activation of a cascade of proteins. This cascade can be triggered by various stimuli, including binding of IgG to antigen (classical complement pathway). Binding of multiple IgG molecules to a pathogen, or tumor cell, triggers activation of the C1-complex, which is composed of C1q, C1r and C1s proteins (it is important to note that not all IgG subclasses are capable of binding complement). C1q binds the C H2 domain of IgG and leads to activation of the serine protease C1r. C1r cleaves C1s, which is also a serine protease, leading to activation of intermediate proteases, and culminating in formation of

the membrane attack complex (MAC). The MAC forms a transmembrane channel on the target cell leading to cell lysis.

Several tumor targeted antibodies rely upon activation of the classical complement pathway for full anti-tumor activity. A pre-clinical model utilizing the B-cell non-Hodgkins lymphoma cell line EL4 demonstrated that the anti-tumor activity of rituximab was abolished in C1q knockout animals (16). Patients with follicular lymphoma harboring an A allele at the C1q [257A/G] polymorphism have increased protein levels of C1q and a longer duration of response to rituximab (17). The next generation CD20-targeted antibody ofatumumab binds to a distinct epitope from rituximab and is a more powerful inducer of complement-dependent cytotoxicity

(CDC) in vitro compared to rituximab (18). Clinically, ofatumumab showed only modest activity in patients with rituximab-refractory follicular lymphoma and more head-to-head clinical trials are needed to determine if ofatumumab is superior to rituximab in management of B-cell malignancies (19). Currently, ofatumumab is approved treatment of fludarabine-resistant chronic lymphocytic leukemia (CLL) (20).

1.4 Fc RECEPTORS

The Fc region of IgG molecules binds Fc gamma receptors (Fc γRs) on immune effector cells and leads to transduction of an activating or inhibitory signal, depending on which Fc γR is engaged

(21). In humans, there are three types of activating Fc γR: Fc γRI (CD64), Fc γRIIA (CD32A) and

Fc γRIII (CD16) and one inhibitory receptor, Fc γRIIB (CD32B). In mice, a fourth Fc γR exits, aptly named Fc γRIV, which transduces an activating signal and contains significant homology to human CD16 (22). Notably, mice do not express CD32A. When engaged, activating Fc γRs

transduce signals through immunoreceptor tyrosine-based activation motifs (ITAMs) while inhibitory receptors signal through immunoreceptor tyrosine-based inhibitor motifs (ITIMs).

Activating receptors are expressed on all myeloid cells; however, CD16 is the dominant activating Fc γR for IgG-mediated immune effector functions and is expressed on natural killer

(NK) cells, macrophages, mast cells, basophils, macrophages, and dendritic cells (DCs). Fc γRs are critical in mediating the optimal anti-tumor activity of antibodies. Clynes et al. demonstrated that the full anti-tumor activity of the HER2-targeted antibody trastuzumab and rituximab were enhanced in Fc γrIIb -/- animals and dependent upon activating Fc γRs (23).

IgG are also capable of binding neonatal Fc receptors (FcRn), which are expressed by the vascular endothelium, professional antigen presenting cells (APCs), intestinal epithelial cells, podocytes, and epithelial cells of the kidney and lung (24). Originally described as being important for the transfer of maternal IgG to the fetus, FcRn-IgG interactions also serve to enhance the half –life of circulating IgG. Circulating IgG is internalized by endothelial cells and bind FcRn within the acidic environment of the endosome. IgG bound to FcRn is then returned to back to the circulation, while unbound serum proteins are degraded in the lysosome.

Circulating levels of both endogenous and exogenous IgG in FcRn-/- animals are significantly lower than that of wild-type animals, suggesting that FcRn play a critical role in maintaining IgG in the circulation (25).

1.5 Fc γγγR-DEPENDENT MECHANISMS OF ACTION

1.5.1 ADCC

Antibody-bound cells are vulnerable to immune attack by engagement of Fc domains with Fc γR on immune effector cells. Crosslinking activating Fc γR leads to signaling through ITAMs and can result in release of perforin and granzyme from effector cells in a process called antibody- dependent cell mediated cytotoxicity (ADCC). Perforin released from effector cells forms pores within the plasma membrane of target cells, allowing entry of granzymes and resulting in cell death via apoptosis. The primary immune effectors responsible for mediating ADCC are NK cells, which express Fc γRIII (the primary Fc γR involved in triggering ADCC) and lack expression of the inhibitory Fc γRIIB. In addition to NK cells, macrophages, monocytes and neutrophils have all been reported to mediate ADCC. Indeed, Clynes et al. demonstrated that trastuzumab and rituximab engaged FcγR on myeloid cells and their capacity to mediate ADCC was greatly enhanced in the absence of Fc γRIIB (23). Clinically, patients harboring the

Fc γRIIIA-158V/V polymorphism bound more IgG1 and a separate study demonstrated that these patients had a higher response rate to rituximab compared with patients with the Fc γRIIIA-

158F/F polymorphism (26). Similarly, patients with refractory metastatic colorectal cancer harboring a Fc γRIIIA-158V/V polymorphism or Fc γRIIA-131H/H polymorphism had a higher response rate to cetuximab, even in the setting of activating Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations (27). Similar findings were observed in breast cancer patients treated with trastuzumab, although these observations were based on retrospective studies with limited numbers of patients (28, 29). Collectively, these data suggest a role for ADCC in the in vivo efficacy of anti-tumor monoclonal antibodies, although the relative role of ADCC versus other mechanisms of action (most notably signaling perturbation) has yet to be determined.

1.5.2 ADCP AND GENERATION OF ADAPTIVE IMMUNITY

An important function of antibodies is their ability to act as opsonins , or agents that facilitate phagocytosis. Antibody bound tumor cells crosslink Fc γR on antigen presenting cells (APC), such as macrophages and dendritic cells (DCs), leading to phagocytosis and eventual degradation of the target via lysosomal processing. This process is called antibody-dependent cell mediated phagocytosis (ADCP) and serves as a delivery mechanism of tumor antigens to APC 2.

Antibody-induced tumor cell death (either by signaling perturbation, CDC, or ADCC) generates tumor cell fragments that are important sources of tumor antigens. These antigens are processed by APC into peptides that are loaded on to major histocompatibility complex (MHC) class II

(MHCII) molecules and shuttled to the cell surface where they are available to bind to cognate T- cell receptors (TCRs) expressed by CD4+ T-helper (Th) cells. Binding of TCR to peptide bound

MHCII is highly specific and results in the activation and clonal expansion of tumor-antigen specific CD4+ T-cells.

Interestingly, exogenous tumor antigens can also be presented on MHC class I (MHCI) molecules and lead to priming of CD8+ T-cells (also known as cytotoxic T-lymphocytes—

CTLs) in a process termed cross presentation . Peptides destined for loading onto MHCI molecules are usually derived from endogenous, cytosolic proteins that are processed by the proteasome. However, during cross presentation, tumor antigens exit the endosome and enter the cytosol to be processed by the MHCI machinery (30). How these antigens exit the endosome is still a matter of debate. There is growing evidence that tumor-targeted antibodies are capable of generating tumor-directed CD8+ T-cells through cross presentation (31-33). Antibodies directed

2 It is important to note that B-cells are also capable of ADCP, but do so in a highly inefficient manner. 9

against the myeloma tumor antigen syndecan-1 lead to priming of CD8+ cells that were specific for the cancer-testes antigen NY-Eso-1 (33). These NY-Eso-1 specific CD8+ T-cells were capable of killing parental myeloma targets and cross presentation depended on expression of functional Fc γRs on primed dendritic cells (33). Furthermore, blockade of Fc γRIIB enhanced the ability of dendritic cells to cross present tumor antigens from antibody-coated tumor cells

(34). Similarly, human DC pulsed with cetuximab-coated colorectal cancer cells were capable of eliciting potent tumor directed CTL responses (35). In preclinical models, an intact host adaptive immune system was required for the optimal anti-tumor efficacy of HER2-directed antibodies, suggesting that antibody-initiated adaptive immunity was important for maximal therapeutic efficacy (36, 37).

Figure 1.2: Anti-tumor mechanisms mediated by IgG. 1) Signaling perturbation: IgG is capable of binding and inhibiting signal transduction mediated by growth factor receptors expressed on tumor cells. 2) Activation of the classical complement pathway: The C H2 domains of IgG are capable of binding complement proteins leading to formation of the membrane attack complex (MAC) resulting in tumor cell death. 3) Induction of ADCC: Tumor-bound IgG is capable of engaging Fc receptors on immune effectors leading to targeted release of perforin and granzyme in a process called antibody dependent cell mediated cytotoxicity (ADCC). 4) Phagocytosis and induction of adaptive immunity: Antibody bound tumor cells or tumor cell fragments engage Fc receptors on macrophages and dendritic cells leading to phagocytosis and processing of tumor antigens. These antigens can be loaded on to MHCII molecules and induce CD4+ T cell responses, or they can be cross-presented on MHCI molecules and induce CD8+ T cell responses. Adapted from The Molecular Basis of Cancer, Ed.4, Rishi Surana, Louis M. Weiner, Monoclonal Antibodies for the Treatment of Cancer, Page 686 Fig.50-3, Copyright (2015,2008,2001,1995), with permission.

1.6 ADAPTIVE IMMUNITY AND CANCER

The phenotype of intra-tumoral CD4+ T-helper cells is critical in determining whether the anti- tumor immune response will promote or inhibit tumor growth. Antigen experienced CD4+ T- cells can be grouped based upon the cytokines they secrete: Th1 cells produce copious amounts of IL-2 and interferon-gamma (IFN-γ) which act to stimulate the tumoricidal activity of CTLs,

NK cells and macrophages. Th2 cells secrete IL-4, IL-5, IL-10 and IL-13 and primarily act to promote humoral immunity by stimulating antibody production and class switching by B-cells.

Th17 cells are pro-inflammatory and express IL-17A, IL-17F, and IL-22. The critical determinant of T-cell polarization is the composition of the local cytokine milieu. For example, local concentrations of IFN-γ and IL-4 are important in determining whether a T-cell will adopt a

Th1 or Th2 phenotype, respectively. IFN-γ directly suppresses transcription of the Il4 locus and induces expression of the transcription factor T-bet, which leads to transcription of Th1- associated genes (38, 39). Conversely, IL-4 suppresses expression of IFN-γ and leads to expression of the transcription factor GATA3, which antagonizes Th1 polarization (40, 41).

Appreciating the plasticity of T-helper cells is critical because T-helper polarization has powerful prognostic value in the setting of solid tumors. Colorectal cancer patients with a higher degree of Th1 polarization within the tumor microenvironment survive longer than patients without Th1 polarization (42). Originally described in the setting of colorectal cancer, the beneficial prognostic value of a Th1 polarized tumor microenvironment has extended to a wide range of solid tumors including breast, lung and ovarian cancers (43-45). In addition to Th1 polarization, patients with a high degree of tumor infiltrating CTLs and memory T-cells (defined as

CD45RO+) within the core or invasive margin of the tumor have a more favorable prognosis

compared to patients with a low density of tumor infiltrating T-cells (42, 46). Based upon these findings, Mlecnik et al. developed an “immune score” based on polarization and infiltration of T- cells within the core and invasive margin of tumors (47). They determined that the immune score could accurate predict disease-free, disease-specific and overall survival in patients with colorectal cancer. Furthermore, the immune score was superior to that of classical histopathological tumor, lymph nodes, metastasis (TNM) staging with regard to prognostic value in the setting of colorectal cancer (47).

In contrast to the powerful predictive value of Th1 polarization in the setting of solid tumors,

Th2 and Th17 polarization have been associated with variable outcomes, with a majority of studies reporting an association with a poor prognosis (48).

1.7 ENHANCING ADAPTIVE IMMUNITY IN CANCER: TARGETING T-CELL

CHECKPOINTS

During the course of an immune response, activated T-cells upregulate expression of inhibitory receptors, termed checkpoints, which collective serve to help resolve an active immune attack.

The absence of these checkpoints results in sustained T-cell activation and development of autoimmunity (49). Recently, antibodies have been generated that block signaling through checkpoints in an attempt to sustain activation of tumor-specific T-cells. Although multiple checkpoint proteins exist and are putative targets for inhibition (Figure 1.3), antibodies generated against cytotoxic T-lymphocyte antigen 4 (CTLA-4) and programmed cell death 1(PD-1) have shown the greatest anti-tumor efficacy in vivo .

CTLA-4 competes with CD28 for binding to CD80 and CD86 on APC. Signaling through CD28 is an important co-stimulatory signal for T-cells and thus competition for binding to CD80/86 results in suboptimal T-cell activation (50). Engagement of CD80/86 also results in upregulation of the tryptophan catabolizing enzyme indoleamine 2,3-dioxygenase (IDO) by APC, most notably DC (51). IDO-mediated degradation of tryptophan results in production of the metabolite kynurenine, which inhibits the activity of T-cells (Figure 1.3) (52). Clinical inhibition of CTLA-4 using the monoclonal antibody ipilimumab (Yervoy®) improved survival in patients with metastatic melanoma leading to FDA approval for this indication in 2011 (53).

PD-1 binds to PD-L1 expressed on the surface of tumor cells and APC, resulting in suppression of T-cell activation. PD-1, like CTLA-4, is important for the maintenance of peripheral tolerance to self-antigens. Blockade of the PD-1/PD-L1 axis results in activation of T-cell mediated anti-tumor immunity (54, 55). Clinically, antibodies targeting PD-1 and PD-L1 are in early phase clinical trials and have demonstrated impressive anti-tumor activity in patients with melanoma, renal cancer, and non-small cell lung cancer (NSCLC) (56-58).

Figure 1.3: Potential targets to enhance T-cell activation 1) Inhibiting signaling through checkpoint molecules and other receptors that lead to T-cell inactivation (Red). 2) Targeting the tryptophan catabolizing enzyme indoleamine 2,3-dioxygenase (IDO) : Byproducts of tryptophan catabolism, such as kynurenine, suppress T-cell activation and proliferation. 3) Targeting soluble mediators: cytokines from APC or tumor cells can inhibit CTL activity and promote infiltration of regulatory T-cells (Treg). Adapted from The Molecular Basis of Cancer, Ed.4, Rishi Surana, Louis M. Weiner, Monoclonal Antibodies for the Treatment of Cancer, Page 686 Fig.50-3, Copyright (2015,2008,2001,1995), with permission.

1.8 THE TUMOR MICROENVIRONMENT LIMITS GENERATION OF AN

EFFECTIVE ANTI-TUMOR IMMUNE RESPONSE

Tumor cells are capable of actively suppressing generation of productive anti-tumor immune responses and co-opting tumor infiltrating immune cells to promote tumor growth and invasion.

Here, we discuss some of the major cell subsets and soluble mediators in the tumor microenvironment that contribute to immunosubversion by tumor cells.

1.8.1 REGULATORY T-CELLS

Regulatory T-cells (Treg) are a subset of CD4+ T-cells that suppress T-cell activation and are important for the maintenance of peripheral tolerance against self-antigens. Treg are identified by surface expression of the alpha-chain of the IL-2 receptor (CD25) and the transcription factor

FOXP3 (59). Treg exert their suppressive activity using a variety of modalities: Treg constitutively express CTLA-4 leading to induction of IDO, which is previously described. In addition, Treg are capable of releasing perforin and granzymes, which can result in apoptosis of

APC and effector T-cells (60). Treg are also potent sources of the IL-10 and TGF-β which lead to NK cell, CTL and APC dysfunction (Figure 1.3) (61-63). Identifying and therapeutically targeting Treg in human tumors has proven to be difficult as CD25 and FOXP3 are also expressed by activated, non-suppressor T-cells (48). Nonetheless, in most settings, Treg accumulation in the tumor microenvironment is associated with a poor prognosis, especially in setting of ovarian cancer (48, 64).

1.8.2 MYELOID-DERIVED SUPPRESSOR CELLS

Myeloid derived suppressor cells (MDSC) are a heterogeneous group of immature macrophages, neutrophils, and dendritic cells which collective act to suppress T-cell function and promote tumor growth. In mice, they are identified by dual expression of CD11b and Gr-1 antigens, while in humans they are identified as being CD14- HLA-DR- CD11b+CD33+ (65). MDSC accumulate in the tumor microenvironment and express high levels of arginase, which acts to deplete arginine from the microenvironment and suppress T-cell function by down-regulating expression components of the TCR (66). Expression of inducible nitric oxide synthase (iNOS) is high in tumor-infiltrating MDSC, leading to accumulation of nitric oxide and resultant decrease in antigen presentation (67). Additionally, MDSC have been reported to inhibit generation of

Th1 polarization, promote accumulation of Treg within the tumor microenvironment, and are capable of differentiating into tumor associated macrophages, which collectively inhibit T-cell activity and promote tumor growth (68-70). Several therapeutic strategies have shown success in reducing MDSC accumulation and inhibiting tumor growth, including cyclooxygenase-2 inhibitors, the phosphodiesterase-5 inhibitor sildenafil, and the nucleoside analog gemcitabine

(71-73). However, it is still unclear whether the anti-tumor activity seen with these agents is dependent upon MDSC depletion.

1.8.3 TUMOR ASSOCIATED MACROPHAGES AND SOLUBLE MEDIATORS

Macrophages and monocytes are abundant in the microenvironment of many solid tumors.

Similar to T-cells, the cytokine milieu within the tumor microenvironment is critical in determining the phenotype and activity of tumor-infiltrating macrophages. In the presence of

IFN-γ, macrophages express high levels of MHCII, iNOS, pro-inflammatory cytokines IL-1, IL-

6 and TNF α, and the chemokine CXCL10. These macrophages are said to be “classically” activated and adopt a “M1” phenotype, while any macrophage not classically activated is said to be “alternatively” activated. M1 macrophages are thought to be tumoricidal, promote induction of anti-tumor immunity and are associated with a good prognosis in some tumor types (74). In addition, CXCL10 from M1 macrophages preferentially recruits Th1 and memory T-cells, inhibits angiogenesis, and is associated with a favorable prognosis in patients with colorectal cancer (75, 76).

Conversely, in the presence of IL-4 or IL-13, macrophages adopt a M2 phenotype characterized by expression of arginase, mannose receptor (CD206), and the chemokines CCL22 and CCL17, which are chemotactic for Th2 cells (77). M2 macrophages are thought to inhibit generation of protective Th1 polarization and drive tumor growth and invasion. Thus, there is an intimate link between T-cell polarization and macrophages polarization: Th1 conditions promote development of M1 macrophages and Th2 conditions promote development of M2 macrophages. Through differential expression of cytokines and chemokines, the opposite is also true: macrophage phenotype promotes cognate polarization of T-cells.

IL-10, produced by Th2 cells, Treg, macrophages and some tumor cells, has an impact on macrophage polarization that is distinct from that of IL-4 and IL-13. Unlike IL-4 and IL-13, IL-

10 decreases expression of MHCII, decreases iNOS-mediated respiratory burst, and induces expression of TGF-β as well as autocrine and paracrine production of IL-10. As previously mentioned, IL-10 has a deleterious effect on the anti-tumor immune response due in part to its suppression of macrophage activation, induction of Treg and production of TGF-β.

TGF-β, produced by Treg, macrophages, MDSC and tumor cells, is similar to IL-10 in its impact on macrophage polarization. TGF-β promotes development of Th2 responses and has a dramatic negative impact on CTLs by reducing their expression of perforin and granzyme (78). With regard to NK cells, TGF-β reduces expression of activating receptors and blockade of TGF-β promotes accumulation of IFN-γ producing NK cells, which in turn promotes Th1 polarization

(79, 80). In preclinical models, therapeutic neutralization of TGF-β synergizes with immunotherapy and can reverse the suppressive effect of Treg (81, 82).

1.9 A MODEL SYSTEM TO STUDY TUMOR-TARGETED ANTIBODY THERAPY

1.9.1 TRASTUZUMAB: A HER2 TARGETED ANTIBODY

HER2, which is overexpressed and gene amplified in approximately 30% of invasive breast cancers, confers a poor prognosis and is the target of the monoclonal antibody trastuzumab

(Herceptin®) (83). Trastuzumab showed activity alone and in combination with chemotherapy as a first or second line agent in patients with HER2+ breast cancer and is currently FDA approved for use in the adjuvant setting in patients with node positive, HER2 overexpressing breast cancer (84, 85). In 2010, trastuzumab was also approved for use in patients with HER2+ metastatic gastric or gastroesophageal junction tumors based on data showing an approximately

2.7 month improvement in survival when trastuzumab was added to a regimen of chemotherapy

(86).

As previously stated, the full therapeutic impact of trastuzumab and other HER2 targeted antibodies is dependent on the presence of adaptive immune cells and FcR+ cells (23, 36). Work by Smyth and colleagues demonstrated that strategies aimed at enhancing the anti-tumor immune

response synergize with anti-Erbb2 antibodies (87, 88). Clinically, tumors from patients treated with trastuzumab showed an increased recruitment of NK cells, granzyme expression, and a greater capacity to mediate ADCC, suggesting a role for ADCC in the clinical activity of trastuzumab (89, 90). Collectively, these data demonstrate that modulating the anti-cancer immune response is a rational approach to enhance the efficacy of trastuzumab.

1.9.2 AN IMMUNOCOMPETENT MODEL SYSTEM TO STUDY THE IN VIVO

ACTIVITY OF TRASTUZUMAB

We previously reported results using a unique model system to study and optimize tumor- targeted antibody therapy. This model employs a human HER2 transgenic (hmHER2Tg) mouse that ubiquitously expresses a kinase inactivated human HER2, and the murine melanoma cell line D5, which has been engineered to overexpress full length human HER2 (D5-HER2). hmHER2Tg animals are immunologically tolerant to human HER2 and therefore do not mount a xenogenic immune response to D5-HER2; therefore, this model permits direct study of the impact of trastuzumab on the induction of T cell based anti-HER2 immune responses. Using this model, trastuzumab monotherapy prevented tumor growth in approximately 30% of animals, and protected those animals against tumor re-challenge (37).

1.9.3 A RATIONAL APPROACH FOR COMBINATION THERAPY

It is clear that cytokines and chemokines are key regulators of immune cell function within the tumor microenvironment. Modulating the levels of these soluble mediators can have a dramatic impact on the overall composition of the tumor microenvironment and could shift the balance between a tumor-promoting environment to an environment favoring tumor regression. To this

end, we utilized the hmHER2Tg/D5-HER2 model system to investigate the potential of enhancing trastuzumab’s efficacy by targeting and neutralizing immunomodulatory soluble mediators in the tumor microenvironment in order to skew polarization of immune effectors and ultimately drive productive anti-tumor immunity (Chapter 2). We conclude by studying the molecular mechanisms by which D5-HER2 cells express one particular immuno-modulator, IL-4

(Chapter 3).

CHAPTER 2

IL-4 LIMITS THE EFFICACY OF TUMOR-TARGETED ANTIBODY THERAPY

2.1 OBJECTIVE AND HYPOTHESIS

The objective of this study is to investigate the utility of targeting an immunomodulatory soluble mediator in the tumor microenvironment to enhance the efficacy of tumor-targeted antibody therapy. We hypothesize that prospectively identifying and neutralizing soluble mediators in the tumor microenvironment will enhance the anti-tumor efficacy of trastuzumab and ultimately promote induction of protective anti-tumor immunity.

2.2 AIMS

1) Identify soluble mediators within the tumor microenvironment of D5-HER2 tumors

grown in hmHER2Tg animals and determine a relevant target for therapeutic

neutralization.

2) Determine if neutralization of a soluble immunomodulator within the tumor

microenvironment enhances the anti-tumor efficacy of trastuzumab.

3) Determine if combination therapy enhances induction of trastuzumab-initiated, protective

adaptive immunity.

4) Determine how combination therapy alters the cellular composition and the

cytokine/chemokine expression profile within the tumor microenvironment.

2.3 MATERIALS AND METHODS

2.3.1 CELL LINES

Generation of D5-HER2 has previously been described (37). Briefly, D5 cells (a subclone of the poorly immunogenic B16/BL6 murine melanoma) were transfected with full length human

HER2 cDNA expressed under control of the CMV promoter. D5-HER2 cells were grown and maintained in RPMI supplemented with 10% fetal bovine serum (FBS) and 2mM L-glutamine.

HEK293T, EO771 and EO771-HER2 cell lines were maintained in DMEM supplemented with

10% FBS and 2mM L-glutamine.

2.3.2 GENERATION OF EO771-HER2 CELL LINE

Human ERBB2 cDNA (DNASU Plasmid repository (91); plasmid: HsCD0002235 (92)) was cloned into the entry vector pENTR4 (Life Technologies; Carlsbad, CA). LR Clonase enzyme mix (Life Technologies) was used to facilitate recombination of HER2 cDNA from pENTR4-

ERBB2 into the destination vector pLENTI CMV Blast DEST (Addgene plasmid 17451 submitted by Dr. Eric Campeau (93)). To generate functional virions, HEK293T cells were transfected at approximately 70% confluence with 12g pLENTI-ERBB2, 20 g psPAX2

(Addgene plasmid 12260 submitted by Dr. Didier Trono) and 3 g VSV-G (gift from Dr. Todd

Waldman; Georgetown University) plasmids using 18 L of FuGENE6 transfection reagent

(Promega; Madison, WI). Media was replaced with normal growth media 24 hours after transfection. Virus containing media was harvested 48 hours post-transfection, centrifuged, filtered using a 0.45 micron syringe filter and stored at -80°C until use. To generate EO771-

HER2 cells, EO771 cells (gift from Dr. Peter Goedegeburre; Washington University in St. Louis) were transduced with 3mL virus containing media, 1ml normal growth media and 3.2 g

polybrene (Sigma; St.Louis, MO). Media was replaced 24 hours post transduction with normal growth media and at 48 hours with media containing blasticidin (Life Technologies). After 7 days of selection, single clones were identified by limiting dilution assay. Surface expression of human HER2 was assessed on individual clones using flow cytometry and the clone with highest

HER2 expression was used for further study (Supplemental Figure S2.1).

2.3.3 MICE

Generation of human HER2 transgenic mice (hmHER2Tg) was previously described (37).

Abrogation of HER2 kinase activity was accomplished by replacement of a lysine residue at position 753 with a methionine residue. The resultant HER2 transgene was transferred to FVB donor zygotes and animals were backcrossed to C57Bl/6 animals for 14 generations. IL4-/- animals (C57BL/6-Il4tm1Nnt/J) were purchased from Jackson Laboratories (Bar Harbor, Maine) and crossed to hmHER2Tg animals to create hmHER2Tg:IL4-/- animals.

2.3.4 SUBCUTANEOUS INOCULATION, TREAMENT AND MONITORING OF D5-

HER2 AND EO771-HER2 TUMORS

Cohorts of 8-12 week old female hmHER2Tg or hmHER2Tg:IL-4-/- mice were injected in the flank with 3x10 3 D5-HER2 cells or 1x10 6 EO771-HER2 cells subcutaneously on day 0. Animals were randomized on day 1 to receive intraperitoneal injections (i.p.) of either of PBS, 200 g trastuzumab (Herceptin®; Genentech, South San Francisco, CA), 1mg anti-IL-4 (clone 11B11;

BioXcell; West Lebanon, NH), 1mg GL113 (rat IgG1 isotype control), or trastuzumab + 11B11

(or GL113) combination therapy. The GL113 antibody was produced from a hybridoma (gift from Dr. Fred Finkelman; University of Cincinnati) and purified using Protein G agarose beads

(Thermo Scientific; Waltham, MA). Trastuzumab was dosed twice a week for four weeks and

11B11/GL113 was dosed every five days for a total of six doses. Tumor growth was followed every 2-3 days and animals were sacrificed when tumors reached 2cm in the largest diameter.

All animal experiments were performed in accordance with the Georgetown University

Institutional Animal Care and use Committee.

2.3.5 LUMINEX-BASED CYTOKINE AND CHEMOKINE ANALYSIS

Tumors were harvested when they reached approximately 10-15mm in diameter and homogenized in five volumes of phosphate-buffered saline (PBS)+0.5% Tween-20 with protease inhibitors (Roche; Penzberg, Bavaria, Germany). Homogenates were centrifuged and the supernatant was immediately stored at -80°C. Protein concentration was determined using the

BCA assay (Bio-Rad; Hercules, CA). For cell culture analysis, supernatant was harvested from

D5-HER2 cells grown in 96-well plates for 72 hours (80% confluence). Media was centrifuged and stored at -80°C. Samples were sent to Eve Technologies (Calgary, Alberta, Canada) and analyzed in duplicate using the mouse 32-plex cytokine/chemokine Discovery Assay.

2.3.6 ENZYME-LINKED IMMUNOSORBENT ASSAY (ELISA)

Cell culture supernatants and tumor homogenates were prepared as described above. IL-4 protein concentration was determined using the standard mouse IL-4 ELISA MAX kit

(Biolegend; San Diego, CA) according to the manufacturer’s recommendations.

2.3.7 ANIMAL RE-CHALLENGE EXPERIMENTS

Approximately 120 days after the initiation of primary challenge experiments, tumor free animals were re-challenged with 1.5x10 4 D5-HER2 tumor cells subcutaneously in the opposite flank. No additional treatments were administered. Tumors were measured every 2-3 days and animals were sacrificed when tumors reached 2cm in the largest diameter.

2.3.8 FLOW CYTOMETRY

Tumors were harvested when they reached approximately 10-15mm in largest diameter and were mechanically disrupted and subjected to enzymatic digest using a 1mg/mL collagenase D solution (Roche). Red blood cells were lysed using a 0.84% NH 4Cl solution. Cells were washed twice with RPMI+10%FBS and passed through a 70-m cell strainer. The resultant cell suspension was washed twice with ice cold PBS and stained with LIVE/DEAD-Violet dye according to manufacturer’s recommendation (Life Technologies). Fc-receptors were blocked using 1 g anti-CD16/CD32 (Biolegend; TruStain FcX). Staining for surface antigens was done in PBS+1% bovine serum albumin (BSA) using the following antibodies: CD45 (Biolegend; clone 30-F11), F4/80 (Biolegend; clone BM8), MHCII (Biolegend; clone M5/114.15.2), CD80

(BD Biosciences; San Jose, CA; clone 16-10A1), CD86 (BD Biosciences; clone GL1), Gr-1

(Biolegend; clone RB6-8C5), CD11b (Biolegend; clone M1/70), NK1.1 (eBioscience; San

Diego, CA; clone PK136), CD3 (Biolegend; clone 145-2C11), CD4 (eBioscience; clone RM4-5),

CD8a (Biolegend; clone 53-6.7), CD69 (Biolegend; clone H1.2F3), CD206 (Biolegend; clone

C068C2), FoxP3 (eBioscience; clone NRRF-30), and HER2 (BD Bioscience; clone Neu 24.7).

Intracellular staining for Foxp3 was performed using fixation and permeablization buffers

according to the manufacturer’s recommended protocol (eBioscience). The gating strategy to identify and phenotype tumor-infiltrating leukocytes is described in Supplemental Figure S2.2.

2.3.9 STATISTICAL ANALYSIS

The following statistical analysis was performed using GraphPad Prism version 6 (GraphPad

Software; La Jolla, CA): log-rank test, Tukey’s test, and Sidak’s multiple comparison analysis.

2.4 RESULTS

2.4.1 IL-4 IS PRESENT IN THE TUMOR MICROENVIRONMENT AND IS EXPRESSED

BY D5-HER2 CELLS IN-VITRO AND IN-VIVO

In order to determine which immunomodulatory soluble mediators may be relevant targets in our model system, a Luminex assay was performed to determine the concentration of 32 cytokines and chemokines in the D5-HER2 tumor microenvironment (Figure 2.1A). Various cytokines and chemokines, including CCL2, CXCL10, CCL11 and IL-4, were expressed in the microenvironment of D5-HER2 tumors. D5-HER2 cells grown in vitro also expressed high levels of CXCL10 in addition to CCL5, EGF and IL-4 (Figure 2.1B). This analysis revealed several putative targets for therapeutic neutralization; we ultimately chose IL-4 for further study due to the pleiotropic effects of this cytokine on the phenotype and activation of both myeloid and lymphoid cells, and the fact that it was both present in the tumor microenvironment and produced by D5-HER2 cells in vitro (Figure 2.1A-B).

Figure 2.1: Characterization of cytokines/chemokines expressed by D5-HER2 in vitro and in vivo . A. Expression of 32 cytokines/chemokines in the microenvironment of D5-HER2 tumors was analyzed using a Luminex-based approach. 3x10 3 D5-HER2 cells were inoculated subcutaneously in the flank of hmHER2Tg animals. Tumors were harvested when they reached 10-15mm in diameter and were homogenized in PBS+0.5%Tween-20. n=5 animals. Error bars represent standard error of the mean (SEM). B. A representative characterization of the cytokine/chemokine expression profile of D5-HER2 cells grown in vitro. Cell supernatant was harvested after 72 hours of growth and analyzed using a Luminex-based approach.

To determine if IL-4 is produced by D5-HER2 tumor cells in vivo or is the product of the

stromal cells surrounding the tumor¸ we assessed the concentration of IL-4 in the tumor

microenvironment of D5-HER2 tumors grown in hmHER2Tg:IL4-/- and IL4+/+ animals.

Tumors grown in hmHER2Tg:IL4-/- animals had a similar concentration of IL-4 compared to

tumors grown in hmHER2Tg:IL4+/+ animals, suggesting that D5-HER2 cells do indeed express

IL-4 in vivo (Figure 2.2A). In contrast, the medullary breast cancer cell line EO771-HER2, which does not express IL-4 in vitro (data not shown), contained low levels of IL-4 in tumors grown in hmHER2Tg:IL4+/+ and no IL-4 in tumors grown in hmHER2Tg:IL4-/- animals, suggesting that the IL-4 from hmHER2Tg:IL4+/+ animals is derived exclusively from the stromal compartment (Figure 2.2B).

Figure 2.2: Evaluation of IL-4 production by tumors grown in hmHER2Tg:IL4+/+ or hmHER2Tg:IL4-/- animals. 3x103 D5-HER2 ( A) or 1x106 EO771-HER2 ( B) cells were inoculated subcutaneously in the flank of indicated animals. Tumors were harvested, homogenized and IL-4 was quantitated using ELISA. Solid line represents the limit of detection of the assay. n=5 animals per group. Error bars represent standard error of the mean (SEM).

2.4.2 NEUTRALIZATION OF IL-4 ENHANCES THE EFFICACY OF TRASTUZUMAB

DURING PRIMARY CHALLENGE

To determine if neutralizing IL-4 could augment the therapeutic benefit of trastuzumab in our model system, we used the anti-IL-4 antibody 11B11 alone or in combination with trastuzumab, which we have previously shown to be an effective therapeutic agent in the D5-

HER2/hmHER2Tg model (37). Combination treatment with 11B11 and trastuzumab significantly enhanced survival of hmHER2Tg animals compared to either agent alone (Figure

2.3A). Treatment with GL113, the isotype control for 11B11, did not impact survival when used as monotherapy and performed similarly to trastuzumab monotherapy when combined with trastuzumab (Figure 2.3B).

Figure 2.3: The effect of IL-4 neutralization on the efficacy of trastuzumab during primary challenge. hmHER2Tg animals were subcutaneously inoculated with 3000 D5-HER2 cells on day 0 and randomized to receive PBS, 200 g trastuzumab b.i.w., 1mg anti-IL-4 (11B11) or isotype control every five days, or combination therapy beginning on day 1 A. Kaplan-Meier survival curve of animals treated with PBS (n=10), 11B11 (n=9), trastuzumab (n=37), or 11B11+trastuzumab (n=38). B. Kaplan-Meier survival curve of animals treated with GL113 (n=9) or GL113+trastuzumab (n=10). Statistical significance was determined by the log-rank test. **p<0.01, ***p<0.001.

2.4.3 NEUTRALIZATION OF IL-4 CHANGES THE COMPOSITION OF THE TUMOR

MICROENVIRONMENT

To elucidate a potential mechanism by which 11B11 enhanced trastuzumab efficacy, we investigated the impact of IL-4 neutralization on the composition of the tumor microenvironment. Tumor-infiltrating immune cells were characterized using flow cytometry and the cytokine/chemokine profile of tumors from different treatment groups was determined.

While no difference in recruitment of CD11b+ myeloid cells was observed, monotherapy with

11B11 or trastuzumab significantly reduced the recruitment of CD11b+CD206+ myeloid cells to the tumor microenvironment (Figure 2.4). This difference in recruitment was enhanced with combination therapy suggesting a synergistic effect of trastuzumab and 11B11 in reducing recruitment of CD11b+CD206+ myeloid cells to the microenvironment (Figure 2.4). No differences in recruitment of T-cells, Treg, NK cells, MDSC or F4/80 macrophages were observed between the treatment groups (not shown).

Figure 2.4 (previous page): The impact of trastuzumab and 11B11 therapy on the infiltration of CD11b+ cells into the tumor microenvironment. Tumors roughly 10-15mm in diameter were harvested from animals from each treatment group, mechanically disrupted and subjected to a collagenase digest. Tumor-infiltrating cells were quantitated using flow cytometry (Supplemental Figure S2.2). n=5 animals per group. Statistical significance was determined by one-way ANOVA using Tukey’s test. *p ≤0.05.

IL-4 neutralization reduced the concentration of potent myeloid chemoattractants CCL2, CCL11 and CXCL5 and the differentiation factor GM-CSF in the tumor microenvironment (Figure 2.5A and Supplemental Figure S2.3). Combination treatment reduced the concentration of IL-1β, IL-6 and IL-3 compared to monotherapy (Supplemental Figure S2.3). Surprisingly, IL-4 neutralization seemed to reduce the concentrations of cytokines important for T-cell activation and proliferation: treatment with 11B11 reduced levels of IFN-γ and combination treatment reduced levels of IL-12(p40) (Figure 2.5B).

Figure 2.5: The impact of trastuzumab and 11B11 therapy on cytokine and chemokine expression in the tumor microenvironment. Tumors from each treatment group were homogenized in PBS+0.5% Tween-20 and homogenates were analyzed using a Luminex-based approach. A. Analysis of chemokines important for myeloid chemotaxis and (B) cytokines important for T-cell function. n=5 animals per group. Error bars indicate SEM. Statistical significance determined by one-way ANOVA using Tukey’s test. *p<0.05.

2.4.4 IL-4 NEUTRALIZATION DOES NOT ENHANCE TRASTUZUMAB-INITIATED

PROTECTIVE ADAPTIVE IMMUNITY

To determine if IL-4 neutralization enhanced generation of trastuzumab-initiated adaptive immunity during primary challenge, tumor-free animals were re-challenged with D5-HER2 with no additional treatments. Approximately 30% of animals initially treated with trastuzumab remained tumor-free following re-challenge, which is consistent with our previous findings (37).

However, despite changes observed in the microenvironment during primary challenge, addition of 11B11 to trastuzumab therapy during primary challenge did not enhance survival during re- challenge and thus did not potentiate generation of trastuzumab-initiated protective adaptive immunity (Figure 2.6).

Figure 2.6: The impact of 11B11 on generation of trastuzumab-initiated, protective adaptive immunity. Kaplan-Meier plot of tumor free animals from the primary challenge that were re-challenged with 1.5x10 4 D5-HER2 cells with no additional treatment. n=10 for naïve animals, n=4 for trastuzumab treated and n=14 for trastuzumab+11B11. Statistical significance was determined by the log-rank test. N.S., not significant.

2.4.5 HOST-DERIVED IL-4 CONTRIBUTES TO TUMOR CONTROL IN THE D5-HER2

MODEL BUT DOES NOT IMPACT EFFICACY OF TRASTUZUMAB

Since IL-4 is produced by both D5-HER2 tumor cells and by the stroma, we sought to understand the relative roles of tumor-derived and stromal-derived IL-4 in modulating the therapeutic activity of trastuzumab and the composition of the tumor microenvironment.

Unexpectedly, D5-HER2 tumors grown in hmHER2Tg:IL4-/- animals grew more quickly compared to tumors grown in hmHER2Tg:IL4+/+ animals (data not shown) and tumor bearing hmHER2Tg:IL4-/- animals displayed reduced survival compared to tumor bearing hmHER2Tg:IL4+/+ animals (Figure 2.7A). Tumors grown in hmHER2Tg:IL4-/- animals showed a marginally reduced level of CCL2 and contained more tumor-infiltrating CD11b+Gr-

1+ MDSC compared to tumors in hmHER2Tg:IL4+/+ animals (Supplemental Figure S2.4 and

Figure 2.7B). Despite enhanced growth of tumors in hmHER2Tg:IL4-/- animals, knockout of host derived IL-4 did not impact the therapeutic efficacy of trastuzumab (Figure 2.7A).

Cytokine/chemokine analysis of animals treated with trastuzumab revealed a reduction of CCL2 in the microenvironment of tumors grown in hmHER2Tg:IL4-/- compared to hmHER2Tg:IL4+/+ animals, which is consistent with data from untreated animals

(Supplemental Figure S2.5). Collectively, these data show that modulation of host-derived IL-4 does not impact the anti-tumor activity of trastuzumab, but may aid in control of D5-HER2 tumor growth.

Figure 2.7: The role of host versus tumor-derived IL-4 in growth of D5-HER2 tumors. A. Kaplan- Meier plots from cohorts of hmHER2Tg:IL4+/+ or hmHER2Tg:IL4-/- animals that were challenged with 3x10 3 D5-HER2 tumor cells on day 0 and randomized to receive either PBS (n=5) or 200 g trastuzumab (n=10) b.i.w. on day 1. Statistical significance determine by the log-rank test. N.S, not significant. B. Tumor infiltrating cells were analyzed using flow cytometry (Supplemental Figure S2.2). n=5 animals per group. Statistical significance was determined using one-way ANOVA using Tukey’s test. *p<0.05.

2.5 DISCUSSION

A growing body of clinical and preclinical data suggests that activation of immune effectors is important for the maximal therapeutic efficacy of tumor targeted antibodies. However, the tumor microenvironment is composed of cells and soluble mediators that collectively serve to limit generation of anti-tumor immunity and drive tumor growth and invasion. Thus, we sought to investigate the therapeutic potential of identifying and neutralizing a soluble factor in the microenvironment to enhance trastuzumab-initiated activation of immune effectors and subsequently enhance efficacy of trastuzumab. We identified many potential soluble mediators in the D5-HER2 tumor microenvironment that might suppress trastuzumab-initiated activation of anti-tumor immunity and decided to focus on IL-4. We demonstrated that IL-4 is expressed by both tumor and stromal cells in vitro and that neutralization of IL-4 enhanced the therapeutic efficacy of trastuzumab in vivo . Furthermore, IL-4 had a profound impact on the tumor microenvironment, resulting in decreased recruitment of CD11b+CD206+ myeloid cells and decreased expression of chemokines important for myeloid chemotaxis. Finally, we found that modulation of tumor-derived IL-4 does not impact the efficacy of trastuzumab; however, it may play a role in control of tumor growth in the absence of trastuzumab. These data collectively demonstrate that neutralizing IL-4 enhances the efficacy of trastuzumab by modulating the myeloid cell compartment.

We chose to study IL-4 because it is important for development of Th2 responses and limits production of IFN-γ and subsequent Th1 polarization, which is of relevance because patients with a more Th1 polarized tumor microenvironment have a more favorable prognosis (42, 43).

IL-4 also promotes the alternative activation of macrophages, leading to suppression of T-cell

function via increased arginase activity, and suppresses generation of tumoricidal, classically activated M1 macrophages (75, 94). IL-4 directly influences activity of monocytes and macrophages by downregulating expression of activating Fc γR and upregulating expression of inhibitory Fc γR, suggesting that IL-4 can modulate the capacity of effectors to mediate ADCC

(95, 96).

We sought to determine whether neutralization of IL-4 could improve the in vivo efficacy of trastuzumab. Combination therapy with trastuzumab and 11B11 resulted in enhanced survival compared to either agent alone. IL-4 neutralization changed the composition of the tumor microenvironment by decreasing expression of the myeloid chemoattractants CCL2, CCL11, and

CXCL5. The dramatic reduction in CXCL5 levels is of note as this chemokine is associated with recruitment of tumor-promoting neutrophils and is associated with a poor prognosis (97, 98).

Similarly, CCL2-mediated recruitment of pro-tumorigenic myeloid cells to the tumor microenvironment is well documented and treatment with CCL2 neutralizing antibodies has been shown to reduce growth of established tumors (99). Treatment with 11B11 and trastuzumab combination therapy also resulted in a decreased recruitment of CD11b+CD206+ myeloid cells to the microenvironment. CD206 (mannose receptor) is upregulated on M2 alternatively activated macrophages and is associated with a poor prognosis in patients with renal cell carcinoma (100). Accumulation of CD206+ cells within tumors and draining lymph nodes may also be partially responsible for failure of vaccine therapy following surgery (101). In addition, depletion of macrophages using liposomal-encapsulated clodronate reduced tumor growth of

B16.F10 melanoma providing evidence that modulation of macrophages has therapeutic benefit in treating B16-family tumors, of which D5-HER2 is a member (102). Collectively, these data

provide evidence that treatment with 11B11 can alter the composition of the tumor microenvironment and limit generation of alternatively activated macrophages to enhance the efficacy of trastuzumab.

Despite changes induced in the myeloid cell compartment, addition of 11B11 did not enhance generation of trastuzumab-initiated adaptive immunity. Tumor free animals originally treated with combination therapy during primary challenge did not receive any further protection from re-challenge compared to animals originally receiving trastuzumab. This can partially be explained by the fact that combination therapy resulted in decreased IFN-γ and IL-12p40 in the tumor microenvironment during re-challenge. The decrease was modest, but could explain the failure of combination therapy to enhance generation of HER2-specific T-cell immunity. These data reveal an important role of IL-4 in the context of tumor-targeted antibody therapy: neutralization of IL-4 primarily affects the myeloid cell compartment without significantly altering the T-cell compartment. Evidence of a similar finding was observed in the setting of a

CD40 agonist antibody for the treatment of pancreatic cancer. Beatty et al demonstrated that the anti-tumor activity of a CD40 agonist antibody was T-cell independent and instead dependent on the re-programming of tumor infiltrating macrophages from tumor promoting to tumoricidal

(103). Therefore, combining strategies primarily aimed at modulating the myeloid cell compartment, like IL-4 neutralization, with strategies that target T-cell activation, like the checkpoint inhibitors anti-CTLA4 and anti-PD1, may be a rational approach to maximally liberate and activate the anti-tumor immune response.

Finally, we sought insight into the relative role between tumor-derived IL-4 and stromal derived

IL-4. Surprisingly, D5-HER2 tumors grew more rapidly in hmHER2Tg:IL4-/- animals compared to hmHER2Tg:IL4+/+ animals and removal of host-derived IL-4 did not impact the efficacy of trastuzumab. These data suggest an intriguing duality of IL-4 function; host-derived

IL-4 seems to contribute to tumor control, while tumor-derived IL-4 contributes to tumor growth.

Other groups have investigated the role of tumor-derived IL-4 in shaping the tumor microenvironment and impacting tumor growth, however, these models involved expression of

IL-4 under control of viral promoters that results in supraphysiological levels of IL-4 (104, 105).

In contrast, this is the first reported insight into the relative contribution of host versus tumor- derived IL-4 using a model system that expresses endogenous levels of IL-4. We also observed an increased recruitment of MDSC in tumors from hmHER2Tg:IL4-/- animals which may help explain why these tumors grew more rapidly.

In summary, we utilized a unique model system to provide evidence that IL-4 neutralization enhances the therapeutic efficacy of trastuzumab via modulation of the myeloid cell compartment. Our results collectively demonstrate that prospectively identifying and targeting soluble factors in the tumor microenvironment is a rational strategy to improve the efficacy of tumor targeted antibody therapy.

2.5 SUPPLEMENTAL FIGURES

Supplemental Figure S2.1: HER2 expression on EO771-HER2. EO771-HER2 cells were grown in vitro and surface expression of HER2 was assessed using flow cytometry. A representative figure from 5 separate experiments is shown.

Supplemental Figure S2.2 (previous page): Strategy for identifying and immunophenotyping tumor-infiltrating leukocytes. A. LIVE/DEAD fixable dye was used to identify living cells. This dye binds dead cells to a higher degree than living cells, so live cells were identified as being LIVE/DEAD low. B. The pan-leukocyte antigen CD45 was used to distinguish immune cells from tumor and other stromal cells. C. Immune subsets within the CD45+ population were identified using antibodies binding to subset-selective antigens (i.e., F4/80 to identify macrophages). An antibody that binds human HER2 was used to identify tumor cells, which comprised a majority of the CD45- population.

Supplemental Figure S2.3: The impact of trastuzumab and 11B11 therapy on cytokine and chemokine expression in the tumor microenvironment. Tumors from each treatment group were homogenized in PBS+0.5% Tween-20 and homogenates were analyzed using a Luminex-based approach. n=5 animals per group. Error bars indicate SEM. Statistical significance determined by one-way ANOVA using Tukey’s test. *p ≤0.05.

Supplemental Figure S2.4 (previous page): The impact of host-derived IL-4 on expression of cytokines and chemokines in the tumor microenvironment. Tumors were grown in hmHER2Tg:IL4+/+ or hmHER2Tg:IL4-/- animals and treated with PBS (as a control). n=5 animal per group. Homogenized tumors were subjected to cytokine and chemokine analysis using a Luminex-based approach. Error bars indicate SEM. Statistical significance was determined by two-way ANOVA using Sidak’s multiple comparisons test. *p<0.05.

Supplemental Figure S2.5 (previous page): The impact of host-derived IL-4 on expression of cytokines and chemokines in the tumor microenvironment of animals treated with trastuzumab. Tumors were grown in hmHER2Tg:IL4+/+ or hmHER2Tg:IL4-/- animals and treated with trastuzumab (200mg b.i.w.). n=10 animals per group. Homogenized tumors were subjected to cytokine and chemokine analysis using a Luminex-based approach. Error bars indicate SEM. Statistical significance was determined by two-way ANOVA using Sidak’s multiple comparisons test. *p<0.05.

CHAPTER 3

A siRNA-BASED SCREENING APPROACH IDENTIFIES STAT5A AS A NEGATIVE REGULATOR OF IL-4 EXPRESSION IN D5-HER2 CELLS

3.1 OBJECTIVE AND HYPOTHESIS

The objective of this study is to investigate the mechanism by which D5-HER2 cells regulate expression of IL-4. We hypothesize that known regulators of IL-4 expression in leukocytes also contribute to IL-4 expression in tumor cells.

3.2 AIMS

1) Create a gene list of known proteins that positively regulate IL-4 expression in

leukocytes.

2) Use the gene list to construct a siRNA library and screen D5-HER2 cells for genes

whose knockdown results in loss of IL-4 production.

3) Validate hits from primary screen.

3.3 MATERIALS AND METHODS

3.3.1 CELL LINE AND CULTURE CONDITIONS

Generation of D5-HER2 cells was described in Chapter 2. D5-HER2 cells were grown and maintained in RPMI supplemented with 10% fetal bovine serum (FBS) and 2mM L-glutamine.

3.3.2 CREATION A OF IL-4 SIRNA LIBRARY

Pathway Studio (Elsevier; Amsterdam, Netherlands) was used to search the literature and curate a list of proteins that positively regulate IL-4 expression (Supplemental Table S3.1). Search criteria included proteins that bound the IL-4 promoter and/or positively regulated IL-4 transcription. The resultant gene list was used to create a custom siRNA library in 96-well plate format (Qiagen; Venlo, Netherlands). Each well of the plate contained two pooled siRNAs targeting a single gene.

3.3.3 PRIMARY SIRNA SCREEN AND IDENTIFICATION OF HITS

Library plates containing siRNAs were resuspended in siRNA suspension buffer (Qiagen) and used in conjunction with RNAiMax transfection reagent (Life Technologies; Carlsbad, CA) to create siRNA/lipid complexes. D5-HER2 cells were harvested at 75-80% confluence and reverse transfected with siRNA/lipid complexes in a total volume of 110 L (15nM siRNA). A non-targeting control siRNA (siNEG) and a murine death control siRNA (siDEATH) were used to verify the fidelity of transfection for each screen. Supernatant was harvested at 72 hours post- transfection to determine IL-4 protein concentration and cells were stained with crystal violet

(CV) as a surrogate measurement of viability. Genes whose knockdown resulted in greater than a 50% reduction in viability were excluded from analysis. ELISA and CV data were normalized

to values obtained from transfection with siNEG and ELISA:CV ratios were generated. These ratios were log transformed and a hit was identified as having a z-score of at least +/-1.20. All screens were performed in duplicate and repeated three times.

3.3.4 VALIDATION OF HITS OBTAINED FROM PRIMARY SIRNA SCREEN

For each hit identified in the primary screen, four unique siRNAs targeting each gene were used in validation studies (two siRNAs used in primary screen and two additional siRNAs). Each siRNA was placed in a single well of a 96-well pate and used to transfect D5-HER2 cells as previously described. A hit was considered validated if two or more siRNAs targeting the gene recapitulated the phenotype observed in the primary screen. All screens were performed in duplicate and repeated three times. The target sequence for Stat5a siRNAs used Figures 3.3 and

3.4 are as follows:

siStat5a#5: 5’-TACATAGAACTCAACATTTAT-3’

siStat5a#7: 5’-TCCAATGGCCATTTCAGTGAA-3’

3.3.5 CRYSTAL VIOLET STAINING

72 hours post-transfection, cells were incubated for 15 minutes in 50 L of a 0.52% solution of crystal violet (Sigma; St. Louis, MO) dissolved in 25% methanol. Cells were then washed thoroughly with de-ionized water and allowed to dry overnight. 100 L of a 100mM sodium citrate solution (dissolved in 50% ethanol) was added to each well and allowed to incubate for 20 minutes. Plates were then agitated, and absorbance at 570nm was read using a Perkin Elmer

Envision plate reader.

3.3.7 ELISA

Cell culture supernatants were prepared as described above. IL-4 protein concentration was determined using the standard mouse IL-4 ELISA MAX kit (Biolegend; San Diego, CA) according to the manufacturer’s recommendations.

3.3.8 REAL-TIME PCR

RNA was extracted from cell pellets using the PureLink RNA Mini Kit (Life Technologies).

RNA concentration was determined spectrophotometrically using the NanoDrop 1000 (Thermo

Scientific; Waltham, MA). 1 g of RNA was reverse-transcribed using Omniscript RT (Qiagen) in a total volume of 25 L. Quantitative PCR was performed using 2 L of cDNA, Quantitect

SYBR Green (Qiagen) and pre-designed primer pairs to IL-4 and GAPDH (Quantitect Primer

Assay; Qiagen) in a total volume of 25 L according to manufacturer’s recommendation.

Reactions were performed using the 7900HT Real-Time PCR System (Life Technologies) and

analyzed using the CT method using Gapdh as the endogenous control.

3.3.9 WESTERN BLOTTING

D5-HER2 cells were lysed using radio-immunoprecipitation assay (RIPA) buffer (Boston

Bioproducts; Ashland, MA) and protein concentration was determined by the BCA assay (Bio-

Rad; Hercules, CA). Approximately 16-20 g of protein was run on tris-glycine gels under reducing conditions. Protein was transferred to a nitrocellulose membrane and blocked for two hours using non-fat milk (Bio-Rad). The following primary antibody dilutions were used:

STAT5A (ab32403; 1:1000; Abcam; Cambridge, England, United Kingdom), anti-phospho

Y694 STAT5A (ab30648; 1:500; Abcam), GAPDH (D16H11; 1:1000; Cell Signaling; Danvers,

MA). Primary antibodies were diluted in PBS-T +5% non-fat milk and incubated overnight at

4°C. Membranes were incubated for one hour at room temperature with secondary antibody

(HRP labeled anti-rabbit IgG; 1:10,000; GE Healthcare; Little Chalfont, Buckinghamshire,

United Kingdom) diluted in PBS-T. For STAT5A and phospho-STAT5A westerns, Supersignal

West Femto high sensitively substrate (Thermo Scientific) was used for visualization while

Supersignal West Pico (Thermo Scientific) was used for GAPDH westerns.

3.3.10 STAT5A OVEREXPRESSION

STAT5A cDNA from pMXc-neo-STAT5AWT-FLAG and pMXc-neo-STAT5A1*6-FLAG constructs (gift from Dr. Toshio Kitamura; University of Tokyo) were cloned into pCMV-

SPORT6 mammalian expression vectors (Life Technologies). STAT5A1*6 is a constitutively active mutant of STAT5A which contains a H229R point mutation in the N-terminal domain and a S711F point mutation in the transactivation domain (106). For transient transfections, 3000

D5-HER2 cells were plated on day 0 and transfected on day 1 with either pCMV-SPORT5-

STAT5AWT or pCMV-SPORT6-STAT5A1*6 using Lipofectamine 2000 transfection reagent

(Life Technologies). Media was collected 72hrs post transfection for ELISA and cells were collected and prepared for western blotting.

3.3.11 STATISTICAL ANALYSIS

Statistical analysis was performed using GraphPad Prism version 6 (GraphPad Software; La

Jolla, CA). P-values were calculated by one-way ANOVA using Dunnett’s multiple comparison analysis.

3.4 RESULTS

3.4.1 siRNA-BASED SCREEN REVEALS POSITIVE AND NEGATIVE REGULATORS

OF IL-4 IN D5-HER2 CELLS

Given the importance of tumor cell derived IL-4 on the growth and survival of cancer cells (107,

108); we sought to determine the mechanism by which tumor cells regulate expression of IL-4.

D5-HER2 cells serve as an appropriate model to address this question since these cells, unlike many murine and human epithelial cancer cell lines, express significant quantities of IL-4 protein in vitro. Pathway Studio was used to curate the literature and identify 193 genes that positively regulate IL-4 expression (Supplementary Table S3.1). Based on this gene list, a siRNA library was constructed and used to screen D5-HER2 cells to identify genes that positively regulate IL-4 expression without a significant impact on viability. A number of genes from the primary screen were candidate positive regulators (genes whose knockdown results in a ELISA:crystal violet

(CV) ratio ≤1) (Figure 3.1). Surprisingly, the knockdown of many of these genes led to

ELISA:CV values higher than 1, suggesting that these genes were in fact negative regulators of

IL-4 production (Figure 3.1).

Figure 3.1: Plot of the ELISA: crystal violet (CV) ratios for all genes included in the screen. ELISA and CV values from each screen were normalized to siNEG. Mean ELISA and CV values from three independent screens were used to generate ELISA:CV ratios

Applying z-score cutoffs of +/-1.2, 17 genes were identified in the primary screen as positive regulators and 10 genes were identified as negative regulators (Figure 3.2).

Figure 3.2 (previous page): Identification of positive and negative regulators of IL-4 production . ELISA: crystal violet (CV) ratios were log-transformed, and positive ( A) and negative regulators ( B) were identifying using a z-score cutoff of -1.2 and +1.2, respectfully. All screens were performed in duplicate and repeated three times. Validation experiments revealed STAT5A as a negative regulator of IL-4 production (grey box).

3.4.2 STAT5A IS A NEGATIVE REGULATOR OF IL-4 EXPRESSION IN D5-HER2

CELLS.

Further validation of hits identified in the primary screen (described in Materials and Methods)

revealed STAT5A as a negative regulator of IL-4 production. Knockdown of Stat5a resulted in

an approximately 2-fold increase in IL-4 protein production (Figure 3.3A-B) and a 1.5 fold

increase in IL-4 mRNA (Figure 3.4) suggesting that STAT5A regulates IL-4 at the

transcriptional level.

Figure 3.3: A. The impact of Stat5a knockdown on IL-4 protein production. D5-HER2 cells were reverse transfected with indicated siRNAs and incubated for 72 hours. A representative western blot is shown. GAPDH was used as a loading control. B. D5-HER2 cells were transfected with indicated siRNAs and the concentration of IL-4 in the cell culture supernatant was determined by ELISA 72 hours post-transfection. Each experiment was repeated three times. Error bars represent SEM. Statistical significance was determined by one way ANOVA using Dunnett’s multiple comparison analysis. *p<0.05.

Figure 3.4: The impact of Stat5a knockdown on abundance of IL-4 mRNA . D5-HER2 cells were reverse transfected with indicated siRNAs. Cells were lysed 72 hours post-transfected and RNA was isolated and reverse transcribed. Error bars represent SEM. Statistical significance was determined by one way ANOVA using Dunnett’s multiple comparison analysis. *p<0.05.

Overexpression of a constitutively active mutant of STAT5A (STAT5A1*6) resulted in hyper- phosphorylation of STAT5A and reduced IL-4 protein expression compared to overexpression of wild type STAT5A in D5-HER2 cells (Figure 3.4A-B). Modulation of STAT5A is not sufficient to drive expression of IL-4 in an IL-4 negative cell line as knockdown of Stat5a in EO771-HER2 does not result in production of IL-4 mRNA or protein (data not shown). Collectively, these data reveal a previously undescribed function of STAT5A as a negative regulator of IL-4 expression in D5-HER2 tumor cells.

Figure 3.5: The impact of STAT5A overexpression on expression of IL-4. A. D5-HER2 cells were transfected with either WT STAT5A or a constitutively active mutant (STAT5A1*6) for 72 hours and phosphorylation of STAT5A was determined using western blotting. GAPDH was used a loading control. B. The concentration of IL-4 in the supernatant from transfected cells was determined by ELISA. Data were normalized to the crystal violet values from each transfection condition. Each experiment was performed at least three times. Error bars represent SEM. Statistical significance was determined by one way ANOVA using Dunnett’s multiple comparison analysis. *p<0.05.

3.5 DISCUSSION

Our previous data revealed that D5-HER2 cells express significant quantities of IL-4 both in vitro and in vivo (Chapter 2). Thus, in this chapter we sought to investigate the mechanisms by which D5-HER2 cells regulate expression of IL-4. We curated the literature and assembled a list of genes whose function was associated with driving expression of IL-4. Using this gene list, we screened D5-HER2 cells for genes whose knockdown resulted in decrease IL-4 protein expression. To our surprise, many genes when knocked down seem to enhance IL-4 expression, suggesting that these genes function to inhibit expression of IL-4. Upon subsequent validation studies, we identified STAT5A as a novel negative regulator of IL-4 expression in D5-HER2 cells.

Expression of IL-4 is most commonly associated with Th2 cells, mast cells, basophils and eosinophils (109). However, our finding that D5-HER2 tumor cells express IL-4 is consistent with published data demonstrating that a majority of primary human colorectal, breast and lung tumor cells express IL-4 (108). This same study demonstrated that tumor-derived IL-4 acts as an autocrine/paracrine growth factor and helps protect tumor cells against the effects of chemotherapy by inducing expression of anti-apoptotic proteins (108). Thus, tumor-derived IL-4 serves a functional purpose to promote growth and protect cells from apoptosis. Similarly, other groups have demonstrated that endogenous, tumor-derived IL-4 from human pancreatic cancer and prostate cancer cell lines serves as an autocrine growth factor and neutralization of IL-4 resulted in diminished proliferation (107, 110).

Given the importance of tumor-derived IL-4 on tumor cell biology and its potential to modulate the phenotype of immune effectors in response to antibody therapy, we sought to understand how IL-4 is regulated in D5-HER2 tumor cells. Using a gene library of known positive regulators of IL-4 expression, we conducted a siRNA screen against D5-HER2 which revealed

STAT5A to be a negative regulator of IL-4 production. Using an RNAi-based approach to study regulation of cytokine production is itself not a novel concept. Astier and colleagues used a lentiviral-based RNAi approach to understand cytokine regulation in primary CD4+ T-cells and revealed a novel role of FLT3 in regulating expression of IL-10 (111). However, to our knowledge, this is the first attempt to understand how tumor cells regulate expression of IL-4.

In D5-HER2 cells, knockdown of Stat5a resulted in increased IL-4 protein production and overexpression of a constitutively active STAT5A mutant resulted in decreased IL-4 production.

We also observed that Stat5a knockdown resulted in increased expression of IL-4 mRNA, suggesting that STAT5A negatively regulates IL-4 transcription, either directly or indirectly.

This finding is contrary to published data, which reported that STAT5A maintains the Il4 locus in a de-methylated state, rendering it accessible to the transcriptional machinery (112). The Il4 gene has an intronic STAT5 binding site that serves as an enhancer element to maintain Il4 locus accessibility in mast cells (113). Similarly, a constitutively active STAT5A enhances IL-4 expression in CD4+ T-cells (112). Therefore, this work raises the possibility of differential regulation of gene expression in tumor versus normal cells. Whether STAT5A directly regulates

IL-4 gene expression in D5-HER2, or indirectly via modulation of another transcription factor is unknown at this time.

3.6 SUPPLEMENTAL TABLE

Table S1.1: List of genes used to create siRNA library

Gene Name Gene ID Gene Annotation Adora2a 11540 adenosine A2a receptor Adora2b 11541 adenosine A2b receptor Aif1 11629 allograft inflammatory factor 1 Apex1 11792 APEX nuclease (multifunctional DNA repair enzyme) 1 Apoh 11818 apolipoprotein H (beta-2-glycoprotein I) Atf3 11910 activating transcription factor 3 Bhlhe41 79362 basic helix-loop-helix family, member e41 C3 12266 complement component 3 C5 15139 complement component 5 C5ar1 12273 complement component 5a receptor 1 Cacna1s 12292 calcium channel, voltage-dependent, L type, alpha 1S subunit Calca 12310 calcitonin-related polypeptide alpha Camk4 12326 calcium/calmodulin-dependent protein kinase IV Cck 12424 cholecystokinin Ccl2 20296 chemokine (C-C motif) ligand 2 Ccr2 12772 chemokine (C-C motif) receptor 2 Cd2 12481 CD2 molecule; LFA-2 Cd27 21940 CD27 molecule Cd274 60533 CD274 molecule Cd28 12487 CD28 molecule Cd3e 12501 CD3e molecule, epsilon (CD3-TCR complex) Cd4 12504 CD4 molecule Cd40 21939 CD40 molecule, TNF receptor superfamily member 5 Cd40lg 21947 CD40 ligand Cd80 12519 CD80 molecule Cd81 12520 CD81 molecule Cd86 12524 CD86 molecule Cebpa 12606 CCAAT/enhancer binding protein (C/EBP), alpha Cebpb 12608 CCAAT/enhancer binding protein (C/EBP), beta Cmip 74440 c-Maf-inducing protein Col2a1 12824 collagen, type II, alpha 1 Cpb2 56373 carboxypeptidase B2 (plasma) Cr1l 12946 complement component (3b/4b) receptor 1-like Crebbp 12914 CREB binding protein Crem 12916 cAMP responsive element modulator Crip1 12925 cysteine-rich protein 1 (intestinal) Csf2 12981 colony stimulating factor 2 (granulocyte-macrophage)

Gene Name Gene ID Gene Annotation Csf3 12985 colony stimulating factor 3 (granulocyte) Ctcf 13018 CCCTC-binding factor (zinc finger protein) Ctsb 13030 cathepsin B Cxadr 13052 coxsackie virus and adenovirus receptor Cxcr6 80901 chemokine (C-X-C motif) receptor 6 Cycs 13063 cytochrome c, somatic Dll1 13388 delta-like 1 (Drosophila) Eed 13626 embryonic ectoderm development Egr1 13653 early growth response 1 Eif2s1 13665 eukaryotic translation initiation factor 2, subunit 1 alpha, 35kDa Enpep 13809 glutamyl aminopeptidase (aminopeptidase A) Enpp3 209558 ectonucleotide pyrophosphatase/phosphodiesterase 3 Ep300 328572 E1A binding protein p300 Esr1 13982 estrogen receptor 1 Esr2 13983 estrogen receptor 2 (ER beta) Ezh2 14056 enhancer of zeste homolog 2 (Drosophila) Fabp2 14079 fatty acid binding protein 2, intestinal Fgfr1 14182 fibroblast growth factor receptor 1 Fos 14281 FBJ murine osteosarcoma viral oncogene homolog Fyn 14360 FYN oncogene related to SRC, FGR, YES Gab2 14389 GRB2-associated binding protein 2 Gata1 14460 GATA binding protein 1 (globin transcription factor 1) Gata2 14461 GATA binding protein 2 Gata3 14462 GATA binding protein 3 Gata4 14463 GATA binding protein 4 Gcg 14526 glucagon Gh1 14599 growth hormone 1 Ghrl 58991 ghrelin/obestatin prepropeptide Gp49a 14727 leukocyte immunoglobulin-like receptor, subfamily B, member 4 Gpr44 14764 G protein-coupled receptor 44 Gpr84 80910 G protein-coupled receptor 84 hypoxia inducible factor 1, alpha subunit (basic helix-loop-helix Hif1a 15251 transcription factor) Hmgb1 15289 high mobility group box 1

Hnf1a 21405 HNF1 homeobox A Hpse 15442 heparanase Hrh4 225192 histamine receptor H4 Hspd1 15510 heat shock 60kDa protein 1 (chaperonin) Hspe1 15528 heat shock 10kDa protein 1 (chaperonin 10) Icos 54167 inducible T-cell co-stimulator

Gene Name Gene ID Gene Annotation Icoslg 50723 inducible T-cell co-stimulator ligand Ikzf1 22778 IKAROS family zinc finger 1 (Ikaros) Il10 16153 interleukin 10 Il10ra 16154 interleukin 10 receptor, alpha Il11 16156 interleukin 11 Il15 16168 interleukin 15 Il18 16173 interleukin 18 (interferon-gamma-inducing factor) Il19 329244 interleukin 19 Il1f5 54450 interleukin 1 family, member 5 (delta) Il2 16183 interleukin 2 Il25 140806 interleukin 25 Il3 16187 interleukin 3 (colony-stimulating factor, multiple) Il33 77125 interleukin 33 Il4 16189 interleukin 4 Il6 16193 interleukin 6 (interferon, beta 2) Il7 16196 interleukin 7 Ins 16334 insulin Ints6 18130 integrator complex subunit 6 Irf4 16364 interferon regulatory factor 4 integrin, alpha 4 (antigen CD49D, alpha 4 subunit of VLA-4 Itga4 16401 receptor) Jag1 16449 jagged 1

Jag2 16450 jagged 2

Jun 16476 jun proto-oncogene Junb 16477 jun B proto-oncogene Kitlg 17311 KIT ligand Lat 16797 linker for activation of T cells Lck 16818 lymphocyte-specific protein tyrosine kinase lymphocyte cytosolic protein 2 (SH2 domain containing Lcp2 16822 leukocyte protein of 76kDa) Lgals1 16852 lectin, galactoside-binding, soluble, 1

Lgals9 16859 lectin, galactoside-binding, soluble, 9

Ltb4r 16995 leukotriene B4 receptor Ly6a 110454 lymphocyte antigen 6 complex, locus A Lyz1 17110 lysozyme1 v-maf musculoaponeurotic fibrosarcoma oncogene homolog Maf 17132 (avian) Maf1 68877 MAF1 homolog (S. cerevisiae) v-maf musculoaponeurotic fibrosarcoma oncogene homolog B Mafb 16658 (avian) 64

Gene Name Gene ID Gene Annotation Map2k5 23938 mitogen-activated protein kinase kinase 5 Map3k1 26401 mitogen-activated protein kinase kinase kinase 1 Map3k11 26403 mitogen-activated protein kinase kinase kinase 11 Map3k2 26405 mitogen-activated protein kinase kinase kinase 2 Mapk1 26413 mitogen-activated protein kinase 1 Mapk14 26416 mitogen-activated protein kinase 14 Mapk7 23939 mitogen-activated protein kinase 7 Mb 17189 myoglobin Mbp 17196 myelin basic protein macrophage migration inhibitory factor (glycosylation-inhibiting Mif 17319 factor) Mta2 23942 metastasis associated 1 family, member 2

Muc1 17829 mucin 1, cell surface associated

Myd88 17874 myeloid differentiation primary response gene (88) Ncoa1 17977 nuclear receptor coactivator 1 Neu1 18010 sialidase 1 (lysosomal sialidase) nuclear factor of activated T-cells, cytoplasmic, calcineurin- Nfatc1 18018 dependent 1 nuclear factor of activated T-cells, cytoplasmic, calcineurin- Nfatc2 18019 dependent 2 nuclear factor of activated T-cells, cytoplasmic, calcineurin- Nfatc2ip 18020 dependent 2 interacting protein

Nfkb1 18033 nuclear factor kappa-light-chain-enhancer of activated B cells

Ngf 18049 nerve growth factor (beta polypeptide)

Notch1 18128 notch 1 Notch3 18131 notch 3 Npy 109648 neuropeptide Y Osgep 66246 O-sialoglycoprotein endopeptidase Parp1 11545 poly (ADP-ribose) polymerase 1 Pcyt1b 236899 phosphate cytidylyltransferase 1, choline, beta Pdpk1 18607 3-phosphoinositide dependent protein kinase-1 Penk 18619 proenkephalin Pggt1b 225467 protein geranylgeranyltransferase type I, beta subunit Pias1 56469 protein inhibitor of activated STAT, 1 Pibf1 52023 progesterone immunomodulatory binding factor 1 Pik3ca 18706 phosphoinositide-3-kinase, catalytic, alpha polypeptide Plcg1 18803 phospholipase C, gamma 1 Ppara 19013 peroxisome proliferator-activated receptor alpha Prkcq 18761 protein kinase C, theta

Gene Name Gene ID Gene Annotation Prmt1 15469 protein arginine methyltransferase 1 Procr 19124 protein C receptor, endothelial ras-related C3 botulinum toxin substrate 1 (rho family, small Rac1 19353 GTP binding protein Rac1) Rbp3 19661 retinol binding protein 3, interstitial recombination signal binding protein for immunoglobulin kappa Rbpj 19664 J region Rel 19696 v-rel reticuloendotheliosis viral oncogene homolog (avian)

Rela 19697 v-rel reticuloendotheliosis viral oncogene homolog A (avian)

Relb 19698 v-rel reticuloendotheliosis viral oncogene homolog B Rock1 19877 Rho-associated, coiled-coil containing protein kinase 1 Rxra 20181 retinoid X receptor, alpha Satb1 20230 SATB homeobox 1 sema domain, immunoglobulin domain (Ig), transmembrane Sema4a 20351 domain (TM) and short cytoplasmic domain, (semaphorin) 4A Sftpd 20390 surfactant protein D

Sh2d1a 20400 SH2 domain containing 1A

Slamf1 27218 signaling lymphocytic activation molecule family member 1 Slamf6 30925 SLAM family member 6 Sleb1 (Tlr5) 53791 systemic lupus erythematosus susceptibility 1 (TLR5) SWI/SNF related, matrix associated, actin dependent regulator of Smarca4 20586 chromatin, subfamily a, member 4 Spn 20737 sialophorin

Spp1 20750 secreted phosphoprotein 1 serum response factor (c-fos serum response element-binding Srf 20807 transcription factor) Stat5a 20850 signal transducer and activator of transcription 5A signal transducer and activator of transcription 6, interleukin-4 Stat6 20852 induced Stat3 20848 signal transducer and activator of transcription 3

Tac1 21333 tachykinin, precursor 1

Tbx21 57765 T-box 21 Tfcp2 21422 transcription factor CP2 Timd4 276891 T-cell immunoglobulin and mucin domain containing 4 Tlr7 170743 toll-like receptor 7 Tlr9 81897 toll-like receptor 9 Tnfrsf18 21936 tumor necrosis factor receptor superfamily, member 18 Tnfrsf21 94185 tumor necrosis factor receptor superfamily, member 21

Gene Name Gene ID Gene Annotation Tnfrsf4 22163 tumor necrosis factor receptor superfamily, member 4 Tnfrsf9 21942 tumor necrosis factor receptor superfamily, member 9 Tnfsf13b 24099 tumor necrosis factor (ligand) superfamily, member 13b Tnfsf14 50930 tumor necrosis factor (ligand) superfamily, member 14 Tnfsf15 326623 tumor necrosis factor (ligand) superfamily, member 15 Tnfsf4 22164 tumor necrosis factor (ligand) superfamily, member 4 Tnfsf8 21949 tumor necrosis factor (ligand) superfamily, member 8 Tnfsf9 21950 tumor necrosis factor (ligand) superfamily, member 9 Trerf1 224829 transcriptional regulating factor 1 Tslp 53603 thymic stromal lymphopoietin Vav1 22324 vav 1 guanine nucleotide exchange factor Vdr 22337 vitamin D (1,25- dihydroxyvitamin D3) receptor Vip 22353 vasoactive intestinal peptide Ythdc1 231386 YTH domain containing 1 Yy1 22632 YY1 transcription factor

CHAPTER 4

CONCLUSIONS AND FUTURE DIRECTIONS

4.1 ENHANCING THE EFFICACY OF TRASTUZUMAB BY TARGETING THE

TUMOR MICROENVIRONMENT

We systematically characterized the cytokines and chemokines expressed in the microenvironment of D5-HER2 tumors grown in hmHER2tg animals. While a number of putative targets were revealed, we ultimately chose to target IL-4. Neutralizing IL-4, using the anti-IL-4 antibody 11B11, enhanced the therapeutic efficacy of trastuzumab during primary challenge and changed the composition of the tumor microenvironment. Treatment with 11B11 reduced infiltration of CD11b+CD206+ myeloid cells to the tumor microenvironment and this reduction was enhanced with 11B11 and trastuzumab combination therapy. Similarly, IL-4 neutralization reduced expression of the myeloid chemo attractants CCL2, CCL11 and CXCL5, but also reduced expression of IFN-γ and IL-12(p40). Despite enhancing the efficacy of trastuzumab during primary challenge and affecting change within the tumor microenvironment,

IL-4 neutralization did not enhance generation of trastuzumab-initiated, protective adaptive immunity. Finally, we demonstrated that knockout of host-derived IL-4 did not impact efficacy of trastuzumab, however, it did enhance growth of untreated tumors, suggesting that host-derived

IL-4 may play a role in control of tumor growth.

The mechanism by which IL-4 neutralization enhances efficacy of trastuzumab during primary challenge is still unclear. While we see a decline in infiltrating CD11b+CD206+ cells, further work is needed to validate that these are indeed alternatively activated macrophages and that reduction in these cells within the tumor microenvironment is important for the enhanced anti-

tumor efficacy seen with combination therapy. The F4/80 antigen is more macrophage-specific than the pan-myeloid CD11b+, thus it will be important to determine if we also see a reduction of infiltrating F4/80+CD206+ cells with combination treatment.

Another critical question that needs to be addressed is the role of T-cells in the therapeutic benefit seen with combination therapy. We have previously demonstrated that CD4+ and CD8+

T-cells are important for the full therapeutic efficacy of trastuzumab in the D5-

HER2/hmHER2Tg model system (37). Therefore, we will pre-treat animals with antibodies that deplete CD4+ and CD8+ cells and perform the therapy experiment with 11B11 and trastuzumab.

Given the fact that 11B11 did not provide any additional protection from re-challenge (which is dependent on generation of tumor-specific, memory T-cells during primary challenge), we expect T-cell depletion will not impact the enhanced therapeutic efficacy we observe with combination therapy.

We will also generalize the observations made in the D5-HER2/hmHER2Tg model to other model systems. A prime candidate for an alternative model is utilizing the EO771-HER2 cell line with hmHER2Tg animals. Since EO771 cells are derived from a murine medullary breast tumor, this model system will be more physiologically relevant compared to D5-HER2, since

HER2 is primary overexpressed in the setting of breast cancer. EO771-HER2 cells do not produce IL-4 in vitro , so we can assess the impact of IL-4 on the efficacy of trastuzumab without use of an anti-IL-4 antibody. We can simply compare the efficacy of trastuzumab in tumors grown in hmHER2Tg:IL4+/+ with tumors grown in hmHER2Tg:IL4-/- animals. However,

EO771-HER2 tumors grown in hmHER2Tg:IL4+/+ animals have lower levels of IL-4 in the

tumor microenvironment compared to D5-HER2 tumors, suggesting that IL-4 may not be a key modulator of anti-tumor immune responses in this model.

It will also be imperative to investigate the mechanism by which knockout of host-derived IL-4 enhances growth of D5-HER2 tumors. An important initial question is whether this enhanced growth is dependent on tumor-associated macrophages or T-cells. We can assess the role of macrophages by depleting macrophages using liposomal-encapsulated clodronate, which causes macrophage apoptosis (114). Since IL-4 also plays a role in inducing dendritic cell maturation, it is possible that deletion of host-derived IL-4 results in suboptimal generation of T-cell responses.

To assess this possibility, we will isolate tumor-infiltrating T-cells from tumors grown in hmHER2Tg:IL4-/- animals and tumors grown in hmHER2Tg:IL4+/+ animals and compare their ability to activate and produce IFN-γ ex vivo in response to D5-HER2 tumor antigens.

Finally, several chemokines and cytokines are highly expressed in the D5-HER2 tumor microenvironment, raising the question of whether targeting these factors may provide more of a therapeutic benefit than targeting IL-4 in the context of trastuzumab therapy. For example,

CCL2 is expressed by D5-HER2 cells in vitro and is highly expressed in the tumor microenvironment. CCL2 is important for trafficking of macrophages as well as Th2 cells.

Thus, CCL2 is a logical therapeutic target for enhancing efficacy of trastuzumab (115).

4.2 IDENTIFICATION OF STAT5A AS A NEGATIVE REGULATOR OF IL-4

EXPRESSION BY D5-HER2 CELLS.

Based on the observation that D5-HER2 cells express copious amounts of IL-4 both in vitro and in vivo (chapter 2), we sought to understand the mechanism(s) by which D5-HER2 cells regulate expression of IL-4 in vitro . To this end, we curated the literature and assembled a list of genes that positively regulate IL-4 expression. We used this gene list to construct a siRNA library and screened D5-HER2 cells with the expected outcome of identifying genes that positively regulate

IL-4 expression. To our surprise, we found a number of putative negative regulators. After validations studies, we uncovered a novel function of STAT5A as a negative regulator of IL-4 production in D5-HER2 cells. Knockdown of Stat5a resulted in enhanced IL-4 protein production and increased IL-4 mRNA in D5-HER2 cells. Overexpression of a constitutively active mutant of STAT5A resulted in decreased IL-4 production, further validating the role of

STAT5A as a negative regulator of IL-4 production.

The mechanism by which STAT5A impacts IL-4 expression in D5-HER2 cells is currently unknown. Specifically, it is unclear whether STAT5A directly acts upon the Il4 gene to promote transcription, or whether STAT5A is affecting expression of another transcription factor that is responsible for modulating IL-4 transcription. Since STAT5a has been reported to bind to an intronic enhancer element in mast cells, we will first determine if STAT5a binds to the Il4 gene in D5-HER2 cells using chromatin immunoprecipitation-sequencing (ChIP-seq) (113). This experiment will allow us to determine if STAT5A may be acting directly upon the Il4 locus in

D5-HER2 cells.

Knockdown of Stat5a may result in increased expression of transcription factors that drive IL-4 expression. In T-cells, IL-4 expression is highly regulated and controlled by a number of transcription factors. Of these transcription factors, GATA3, STAT6 and nuclear factor of activated T-cells (NFAT) family members (primarily NFAT1/NFATc2 and NFAT2/NFATc) are important for facilitating and driving expression of the Il4 locus (116-118). Similarly, STAT5A could be functioning to enhance expression of a factor that inhibits IL-4 expression. For example, suppressor of cytokine signaling-3 (SOCS3) inhibits expression of IL-4 in mast cells and STAT5A has been reported to drive expression of SOCS3 in T-cells (119, 120). Therefore, we plan on determining how knockdown of Stat5a in D5-HER2 cells affects expression of

GATA3, STAT6, NFAT-family members, and SOCS3.

Finally, we plan to generalize our finding regarding STAT5A regulation of IL-4 expression in other cell lines. Identification of murine cell lines that express IL-4 has been challenging, however, several human cancer cell lines have been reported to express IL-4 in vitro (107, 108).

We will first assess expression of STAT5A in these cell lines. Then, using cell lines that express

STAT5A, we will assess the impact of Stat5a knockdown on expression of IL-4.

4.3 CONCLUDING REMARKS

Collectively, these data demonstrate that targeting and disabling soluble mediators in the tumor microenvironment that suppress the anti-tumor immune response is a rational and effective approach to enhance the efficacy of tumor-targeted antibody therapy. Furthermore, tumor cells are capable of producing cytokines and chemokines that serve as growth and survival factors in addition to manipulating the phenotype of immune effectors within the tumor microenvironment.

Understanding the molecular mechanisms by which tumor cells regulate expression of these factors, such as IL-4, can lead to therapeutic interventions that that target these pathways and result in reorganization of the tumor microenvironment, direct inhibition of tumor cell growth, and sensitization to chemotherapeutic agents or antibody therapy.

BIBLIOGRAPHY

1. Weiner LM, Surana R, Wang S. Monoclonal antibodies: versatile platforms for cancer

immunotherapy. Nat Rev Immunol. 2010;10:317-27.

2. Edelman GM, Cunningham BA, Gall WE, Gottlieb PD, Rutishauser U, Waxdal MJ. The

covalent structure of an entire gammaG immunoglobulin molecule. Proceedings of the

National Academy of Sciences of the United States of America. 1969;63:78-85.

3. Fleischman JB, Porter RR, Press EM. The Arrangement of the Peptide Chains in Gamma-

Globulin. The Biochemical journal. 1963;88:220-8.

4. Porter RR. Separation and isolation of fractions of rabbit gamma-globulin containing the

antibody and antigenic combining sites. Nature. 1958;182:670-1.

5. Glanville J, Zhai W, Berka J, Telman D, Huerta G, Mehta GR, et al. Precise

determination of the diversity of a combinatorial antibody library gives insight into the

human immunoglobulin repertoire. Proceedings of the National Academy of Sciences of

the United States of America. 2009;106:20216-21.

6. Tiselius A, Kabat EA. An Electrophoretic Study of Immune Sera and Purified Antibody

Preparations. J Exp Med. 1939;69:119-31.

7. Kohler G, Milstein C. Continuous cultures of fused cells secreting antibody of predefined

specificity. Nature. 1975;256:495-7.

8. Courtenay-Luck NS, Epenetos AA, Moore R, Larche M, Pectasides D, Dhokia B, et al.

Development of primary and secondary immune responses to mouse monoclonal

antibodies used in the diagnosis and therapy of malignant neoplasms. Cancer research.

1986;46:6489-93.

9. Tjandra JJ, Ramadi L, McKenzie IF. Development of human anti-murine antibody

(HAMA) response in patients. Immunology and cell biology. 1990;68 ( Pt 6):367-76.

10. Morrison SL, Johnson MJ, Herzenberg LA, Oi VT. Chimeric human antibody molecules:

mouse antigen-binding domains with human constant region domains. Proceedings of the

National Academy of Sciences of the United States of America. 1984;81:6851-5.

11. LoBuglio AF, Wheeler RH, Trang J, Haynes A, Rogers K, Harvey EB, et al.

Mouse/human chimeric monoclonal antibody in man: kinetics and immune response.

Proceedings of the National Academy of Sciences of the United States of America.

1989;86:4220-4.

12. Jones PT, Dear PH, Foote J, Neuberger MS, Winter G. Replacing the complementarity-

determining regions in a human antibody with those from a mouse. Nature.

1986;321:522-5.

13. Lonberg N. Human antibodies from transgenic animals. Nature biotechnology.

2005;23:1117-25.

14. Li S, Schmitz KR, Jeffrey PD, Wiltzius JJ, Kussie P, Ferguson KM. Structural basis for

inhibition of the epidermal growth factor receptor by cetuximab. Cancer cell. 2005;7:301-

11.

15. Franklin MC, Carey KD, Vajdos FF, Leahy DJ, de Vos AM, Sliwkowski MX. Insights

into ErbB signaling from the structure of the ErbB2-pertuzumab complex. Cancer cell.

2004;5:317-28.

16. Di Gaetano N, Cittera E, Nota R, Vecchi A, Grieco V, Scanziani E, et al. Complement

activation determines the therapeutic activity of rituximab in vivo. J Immunol.

2003;171:1581-7.

17. Racila E, Link BK, Weng WK, Witzig TE, Ansell S, Maurer MJ, et al. A polymorphism

in the complement component C1qA correlates with prolonged response following

rituximab therapy of follicular lymphoma. Clin Cancer Res. 2008;14:6697-703.

18. Teeling JL, French RR, Cragg MS, van den Brakel J, Pluyter M, Huang H, et al.

Characterization of new human CD20 monoclonal antibodies with potent cytolytic

activity against non-Hodgkin lymphomas. Blood. 2004;104:1793-800.

19. Czuczman MS, Fayad L, Delwail V, Cartron G, Jacobsen E, Kuliczkowski K, et al.

Ofatumumab monotherapy in rituximab-refractory follicular lymphoma: results from a

multicenter study. Blood. 2012;119:3698-704.

20. Wierda WG, Kipps TJ, Mayer J, Stilgenbauer S, Williams CD, Hellmann A, et al.

Ofatumumab as single-agent CD20 immunotherapy in fludarabine-refractory chronic

lymphocytic leukemia. J Clin Oncol. 2010;28:1749-55.

21. Nimmerjahn F, Ravetch JV. Divergent immunoglobulin g subclass activity through

selective Fc receptor binding. Science. 2005;310:1510-2.

22. Nimmerjahn F, Bruhns P, Horiuchi K, Ravetch JV. FcgammaRIV: a novel FcR with

distinct IgG subclass specificity. Immunity. 2005;23:41-51.

23. Clynes RA, Towers TL, Presta LG, Ravetch JV. Inhibitory Fc receptors modulate in vivo

cytoxicity against tumor targets. Nature medicine. 2000;6:443-6.

24. Roopenian DC, Akilesh S. FcRn: the neonatal Fc receptor comes of age. Nat Rev

Immunol. 2007;7:715-25.

25. Roopenian DC, Christianson GJ, Sproule TJ, Brown AC, Akilesh S, Jung N, et al. The

MHC class I-like IgG receptor controls perinatal IgG transport, IgG homeostasis, and fate

of IgG-Fc-coupled drugs. J Immunol. 2003;170:3528-33.

26. Cartron G, Dacheux L, Salles G, Solal-Celigny P, Bardos P, Colombat P, et al.

Therapeutic activity of humanized anti-CD20 monoclonal antibody and polymorphism in

IgG Fc receptor FcgammaRIIIa gene. Blood. 2002;99:754-8.

27. Bibeau F, Lopez-Crapez E, Di Fiore F, Thezenas S, Ychou M, Blanchard F, et al. Impact

of Fc{gamma}RIIa-Fc{gamma}RIIIa polymorphisms and KRAS mutations on the

clinical outcome of patients with metastatic colorectal cancer treated with cetuximab plus

irinotecan. J Clin Oncol. 2009;27:1122-9.

28. Musolino A, Naldi N, Bortesi B, Pezzuolo D, Capelletti M, Missale G, et al.

Immunoglobulin G fragment C receptor polymorphisms and clinical efficacy of

trastuzumab-based therapy in patients with HER-2/neu-positive metastatic breast cancer.

J Clin Oncol. 2008;26:1789-96.

29. Tamura K, Shimizu C, Hojo T, Akashi-Tanaka S, Kinoshita T, Yonemori K, et al.

FcgammaR2A and 3A polymorphisms predict clinical outcome of trastuzumab in both

neoadjuvant and metastatic settings in patients with HER2-positive breast cancer. Annals

of oncology : official journal of the European Society for Medical Oncology / ESMO.

2011;22:1302-7.

30. Albert ML, Sauter B, Bhardwaj N. Dendritic cells acquire antigen from apoptotic cells

and induce class I-restricted CTLs. Nature. 1998;392:86-9.

31. Selenko N, Majdic O, Jager U, Sillaber C, Stockl J, Knapp W. Cross-priming of cytotoxic

T cells promoted by apoptosis-inducing tumor cell reactive antibodies? Journal of clinical

immunology. 2002;22:124-30.

32. Cioca DP, Deak E, Cioca F, Paunescu V. Monoclonal antibodies targeted against

melanoma and ovarian tumors enhance dendritic cell-mediated cross-presentation of

tumor-associated antigens and efficiently cross-prime CD8+ T cells. Journal of

immunotherapy. 2006;29:41-52.

33. Dhodapkar KM, Krasovsky J, Williamson B, Dhodapkar MV. Antitumor monoclonal

antibodies enhance cross-presentation of cellular antigens and the generation of

myeloma-specific killer T cells by dendritic cells. J Exp Med. 2002;195:125-33.

34. Dhodapkar KM, Kaufman JL, Ehlers M, Banerjee DK, Bonvini E, Koenig S, et al.

Selective blockade of inhibitory Fcgamma receptor enables human dendritic cell

maturation with IL-12p70 production and immunity to antibody-coated tumor cells.

Proceedings of the National Academy of Sciences of the United States of America.

2005;102:2910-5.

35. Correale P, Botta C, Cusi MG, Del Vecchio MT, De Santi MM, Gori Savellini G, et al.

Cetuximab +/- chemotherapy enhances dendritic cell-mediated phagocytosis of colon

cancer cells and ignites a highly efficient colon cancer antigen-specific cytotoxic T-cell

response in vitro. International journal of cancer Journal international du cancer.

2012;130:1577-89.

36. Park S, Jiang Z, Mortenson ED, Deng L, Radkevich-Brown O, Yang X, et al. The

therapeutic effect of anti-HER2/neu antibody depends on both innate and adaptive

immunity. Cancer cell. 2010;18:160-70.

37. Wang S, Astsaturov IA, Bingham CA, McCarthy KM, von Mehren M, Xu W, et al.

Effective antibody therapy induces host-protective antitumor immunity that is augmented

by TLR4 agonist treatment. Cancer immunology, immunotherapy : CII. 2012;61:49-61.

38. Nishida J, Li Y, Zhuang Y, Huang Z, Huang H. IFN-gamma suppresses permissive

chromatin remodeling in the regulatory region of the Il4 gene. Cytokine. 2013;62:91-5.

39. Lighvani AA, Frucht DM, Jankovic D, Yamane H, Aliberti J, Hissong BD, et al. T-bet is

rapidly induced by interferon-gamma in lymphoid and myeloid cells. Proceedings of the

National Academy of Sciences of the United States of America. 2001;98:15137-42.

40. Zhang Y, Apilado R, Coleman J, Ben-Sasson S, Tsang S, Hu-Li J, et al. Interferon

gamma stabilizes the T helper cell type 1 phenotype. J Exp Med. 2001;194:165-72.

41. Ouyang W, Ranganath SH, Weindel K, Bhattacharya D, Murphy TL, Sha WC, et al.

Inhibition of Th1 development mediated by GATA-3 through an IL-4-independent

mechanism. Immunity. 1998;9:745-55.

42. Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pages C, et al.

Type, density, and location of immune cells within human colorectal tumors predict

clinical outcome. Science. 2006;313:1960-4.

43. Oldford SA, Robb JD, Codner D, Gadag V, Watson PH, Drover S. Tumor cell expression

of HLA-DM associates with a Th1 profile and predicts improved survival in breast

carcinoma patients. International immunology. 2006;18:1591-602.

44. Dieu-Nosjean MC, Antoine M, Danel C, Heudes D, Wislez M, Poulot V, et al. Long-term

survival for patients with non-small-cell lung cancer with intratumoral lymphoid

structures. J Clin Oncol. 2008;26:4410-7.

45. Kusuda T, Shigemasa K, Arihiro K, Fujii T, Nagai N, Ohama K. Relative expression

levels of Th1 and Th2 cytokine mRNA are independent prognostic factors in patients

with ovarian cancer. Oncology reports. 2005;13:1153-8.

46. Pages F, Kirilovsky A, Mlecnik B, Asslaber M, Tosolini M, Bindea G, et al. In situ

cytotoxic and memory T cells predict outcome in patients with early-stage colorectal

cancer. J Clin Oncol. 2009;27:5944-51.

47. Mlecnik B, Tosolini M, Kirilovsky A, Berger A, Bindea G, Meatchi T, et al.

Histopathologic-based prognostic factors of colorectal cancers are associated with the

state of the local immune reaction. J Clin Oncol. 2011;29:610-8.

48. Fridman WH, Pages F, Sautes-Fridman C, Galon J. The immune contexture in human

tumours: impact on clinical outcome. Nature reviews Cancer. 2012;12:298-306.

49. Waterhouse P, Penninger JM, Timms E, Wakeham A, Shahinian A, Lee KP, et al.

Lymphoproliferative disorders with early lethality in mice deficient in Ctla-4. Science.

1995;270:985-8.

50. Leach DR, Krummel MF, Allison JP. Enhancement of antitumor immunity by CTLA-4

blockade. Science. 1996;271:1734-6.

51. Holmgaard RB, Zamarin D, Munn DH, Wolchok JD, Allison JP. Indoleamine 2,3-

dioxygenase is a critical resistance mechanism in antitumor T cell immunotherapy

targeting CTLA-4. J Exp Med. 2013;210:1389-402.

52. Terness P, Bauer TM, Rose L, Dufter C, Watzlik A, Simon H, et al. Inhibition of

allogeneic T cell proliferation by indoleamine 2,3-dioxygenase-expressing dendritic cells:

mediation of suppression by tryptophan metabolites. J Exp Med. 2002;196:447-57.

53. Hodi FS, O'Day SJ, McDermott DF, Weber RW, Sosman JA, Haanen JB, et al. Improved

survival with ipilimumab in patients with metastatic melanoma. N Engl J Med.

2010;363:711-23.

54. Dong H, Strome SE, Salomao DR, Tamura H, Hirano F, Flies DB, et al. Tumor-

associated B7-H1 promotes T-cell apoptosis: a potential mechanism of immune evasion.

Nature medicine. 2002;8:793-800.

55. Iwai Y, Ishida M, Tanaka Y, Okazaki T, Honjo T, Minato N. Involvement of PD-L1 on

tumor cells in the escape from host immune system and tumor immunotherapy by PD-L1

blockade. Proceedings of the National Academy of Sciences of the United States of

America. 2002;99:12293-7.

56. Hamid O, Robert C, Daud A, Hodi FS, Hwu WJ, Kefford R, et al. Safety and tumor

responses with lambrolizumab (anti-PD-1) in melanoma. N Engl J Med. 2013;369:134-

44.

57. Brahmer JR, Tykodi SS, Chow LQ, Hwu WJ, Topalian SL, Hwu P, et al. Safety and

activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl J Med.

2012;366:2455-65.

58. Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, et al.

Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med.

2012;366:2443-54.

59. Khattri R, Cox T, Yasayko SA, Ramsdell F. An essential role for Scurfin in CD4+CD25+

T regulatory cells. Nature immunology. 2003;4:337-42.

60. Grossman WJ, Verbsky JW, Barchet W, Colonna M, Atkinson JP, Ley TJ. Human T

regulatory cells can use the perforin pathway to cause autologous target cell death.

Immunity. 2004;21:589-601.

61. Ghiringhelli F, Menard C, Terme M, Flament C, Taieb J, Chaput N, et al. CD4+CD25+

regulatory T cells inhibit natural killer cell functions in a transforming growth factor-

beta-dependent manner. J Exp Med. 2005;202:1075-85.

62. Chen ML, Pittet MJ, Gorelik L, Flavell RA, Weissleder R, von Boehmer H, et al.

Regulatory T cells suppress tumor-specific CD8 T cell cytotoxicity through TGF-beta

signals in vivo. Proceedings of the National Academy of Sciences of the United States of

America. 2005;102:419-24.

63. Kryczek I, Wei S, Zou L, Zhu G, Mottram P, Xu H, et al. Cutting edge: induction of B7-

H4 on APCs through IL-10: novel suppressive mode for regulatory T cells. J Immunol.

2006;177:40-4.

64. Curiel TJ, Coukos G, Zou L, Alvarez X, Cheng P, Mottram P, et al. Specific recruitment

of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced

survival. Nature medicine. 2004;10:942-9.

65. Almand B, Clark JI, Nikitina E, van Beynen J, English NR, Knight SC, et al. Increased

production of immature myeloid cells in cancer patients: a mechanism of

immunosuppression in cancer. J Immunol. 2001;166:678-89.

66. Rodriguez PC, Zea AH, Culotta KS, Zabaleta J, Ochoa JB, Ochoa AC. Regulation of T

cell receptor CD3zeta chain expression by L-arginine. The Journal of biological

chemistry. 2002;277:21123-9.

67. Harari O, Liao JK. Inhibition of MHC II gene transcription by nitric oxide and

antioxidants. Current pharmaceutical design. 2004;10:893-8.

68. Tsukamoto H, Nishikata R, Senju S, Nishimura Y. Myeloid-Derived Suppressor Cells

Attenuate TH1 Development through IL-6 Production to Promote Tumor Progression.

Cancer immunology research. 2013;1:64-76.

69. Huang B, Pan PY, Li Q, Sato AI, Levy DE, Bromberg J, 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 research. 2006;66:1123-31.

70. Kusmartsev S, Gabrilovich DI. STAT1 signaling regulates tumor-associated macrophage-

mediated T cell deletion. J Immunol. 2005;174:4880-91.

71. Talmadge JE, Hood KC, Zobel LC, Shafer LR, Coles M, Toth B. Chemoprevention by

cyclooxygenase-2 inhibition reduces immature myeloid suppressor cell expansion.

International immunopharmacology. 2007;7:140-51.

72. Serafini P, Meckel K, Kelso M, Noonan K, Califano J, Koch W, et al. Phosphodiesterase-

5 inhibition augments endogenous antitumor immunity by reducing myeloid-derived

suppressor cell function. J Exp Med. 2006;203:2691-702.

73. Suzuki E, Kapoor V, Jassar AS, Kaiser LR, Albelda SM. Gemcitabine selectively

eliminates splenic Gr-1+/CD11b+ myeloid suppressor cells in tumor-bearing animals and

enhances antitumor immune activity. Clin Cancer Res. 2005;11:6713-21.

74. Ma J, Liu L, Che G, Yu N, Dai F, You Z. The M1 form of tumor-associated macrophages

in non-small cell lung cancer is positively associated with survival time. BMC cancer.

2010;10:112.

75. Gordon S. Alternative activation of macrophages. Nat Rev Immunol. 2003;3:23-35.

76. Mlecnik B, Tosolini M, Charoentong P, Kirilovsky A, Bindea G, Berger A, et al.

Biomolecular network reconstruction identifies T-cell homing factors associated with

survival in colorectal cancer. Gastroenterology. 2010;138:1429-40.

77. Imai T, Nagira M, Takagi S, Kakizaki M, Nishimura M, Wang J, et al. Selective

recruitment of CCR4-bearing Th2 cells toward antigen-presenting cells by the CC

chemokines thymus and activation-regulated chemokine and macrophage-derived

chemokine. International immunology. 1999;11:81-8.

78. Maeda H, Shiraishi A. TGF-beta contributes to the shift toward Th2-type responses

through direct and IL-10-mediated pathways in tumor-bearing mice. J Immunol.

1996;156:73-8.

79. Castriconi R, Cantoni C, Della Chiesa M, Vitale M, Marcenaro E, Conte R, et al.

Transforming growth factor beta 1 inhibits expression of NKp30 and NKG2D receptors:

consequences for the NK-mediated killing of dendritic cells. Proceedings of the National

Academy of Sciences of the United States of America. 2003;100:4120-5.

80. Laouar Y, Sutterwala FS, Gorelik L, Flavell RA. Transforming growth factor-beta

controls T helper type 1 cell development through regulation of natural killer cell

interferon-gamma. Nature immunology. 2005;6:600-7.

81. Terabe M, Ambrosino E, Takaku S, O'Konek JJ, Venzon D, Lonning S, et al. Synergistic

enhancement of CD8+ T cell-mediated tumor vaccine efficacy by an anti-transforming

growth factor-beta monoclonal antibody. Clin Cancer Res. 2009;15:6560-9.

82. Nakamura K, Kitani A, Strober W. Cell contact-dependent immunosuppression by

CD4(+)CD25(+) regulatory T cells is mediated by cell surface-bound transforming

growth factor beta. J Exp Med. 2001;194:629-44.

83. Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL. Human breast

cancer: correlation of relapse and survival with amplification of the HER-2/neu

oncogene. Science. 1987;235:177-82.

84. Cobleigh MA, Vogel CL, Tripathy D, Robert NJ, Scholl S, Fehrenbacher L, et al.

Multinational study of the efficacy and safety of humanized anti-HER2 monoclonal

antibody in women who have HER2-overexpressing metastatic breast cancer that has

progressed after chemotherapy for metastatic disease. J Clin Oncol. 1999;17:2639-48.

85. Slamon DJ, Leyland-Jones B, Shak S, Fuchs H, Paton V, Bajamonde A, et al. Use of

chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that

overexpresses HER2. N Engl J Med. 2001;344:783-92.

86. Bang YJ, Van Cutsem E, Feyereislova A, Chung HC, Shen L, Sawaki A, et al.

Trastuzumab in combination with chemotherapy versus chemotherapy alone for

treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer

(ToGA): a phase 3, open-label, randomised controlled trial. Lancet. 2010;376:687-97.

87. Stagg J, Loi S, Divisekera U, Ngiow SF, Duret H, Yagita H, et al. Anti-ErbB-2 mAb

therapy requires type I and II interferons and synergizes with anti-PD-1 or anti-CD137

mAb therapy. Proceedings of the National Academy of Sciences of the United States of

America. 2011;108:7142-7.

88. Stagg J, Sharkey J, Pommey S, Young R, Takeda K, Yagita H, et al. Antibodies targeted

to TRAIL receptor-2 and ErbB-2 synergize in vivo and induce an antitumor immune

response. Proceedings of the National Academy of Sciences of the United States of

America. 2008;105:16254-9.

89. Arnould L, Gelly M, Penault-Llorca F, Benoit L, Bonnetain F, Migeon C, et al.

Trastuzumab-based treatment of HER2-positive breast cancer: an antibody-dependent

cellular cytotoxicity mechanism? British journal of cancer. 2006;94:259-67.

90. Gennari R, Menard S, Fagnoni F, Ponchio L, Scelsi M, Tagliabue E, et al. Pilot study of

the mechanism of action of preoperative trastuzumab in patients with primary operable

breast tumors overexpressing HER2. Clin Cancer Res. 2004;10:5650-5.

91. Seiler CY, Park JG, Sharma A, Hunter P, Surapaneni P, Sedillo C, et al. DNASU plasmid

and PSI:Biology-Materials repositories: resources to accelerate biological research.

Nucleic acids research. 2014;42:D1253-60.

92. Witt AE, Hines LM, Collins NL, Hu Y, Gunawardane RN, Moreira D, et al. Functional

proteomics approach to investigate the biological activities of cDNAs implicated in

breast cancer. Journal of proteome research. 2006;5:599-610.

93. Campeau E, Ruhl VE, Rodier F, Smith CL, Rahmberg BL, Fuss JO, et al. A versatile

viral system for expression and depletion of proteins in mammalian cells. PloS one.

2009;4:e6529.

94. Liu Q, Zhang C, Sun A, Zheng Y, Wang L, Cao X. Tumor-educated CD11bhighIalow

regulatory dendritic cells suppress T cell response through arginase I. J Immunol.

2009;182:6207-16.

95. Tridandapani S, Siefker K, Teillaud JL, Carter JE, Wewers MD, Anderson CL. Regulated

expression and inhibitory function of Fcgamma RIIb in human monocytic cells. The

Journal of biological chemistry. 2002;277:5082-9.

96. te Velde AA, Huijbens RJ, de Vries JE, Figdor CG. IL-4 decreases Fc gamma R

membrane expression and Fc gamma R-mediated cytotoxic activity of human monocytes.

J Immunol. 1990;144:3046-51.

97. Zhou SL, Dai Z, Zhou ZJ, Chen Q, Wang Z, Xiao YS, et al. CXCL5 contributes to tumor

metastasis and recurrence of intrahepatic cholangiocarcinoma by recruiting infiltrative

intratumoral neutrophils. Carcinogenesis. 2014;35:597-605.

98. Kawamura M, Toiyama Y, Tanaka K, Saigusa S, Okugawa Y, Hiro J, et al. CXCL5, a

promoter of cell proliferation, migration and invasion, is a novel serum prognostic marker

in patients with colorectal cancer. European journal of cancer. 2012;48:2244-51.

99. Li M, Knight DA, L AS, Smyth MJ, Stewart TJ. A role for CCL2 in both tumor

progression and immunosurveillance. Oncoimmunology. 2013;2:e25474.

100. Xu L, Zhu Y, Chen L, An H, Zhang W, Wang G, et al. Prognostic Value of Diametrically

Polarized Tumor-Associated Macrophages in Renal Cell Carcinoma. Annals of surgical

oncology. 2014.

101. Predina J, Eruslanov E, Judy B, Kapoor V, Cheng G, Wang LC, et al. Changes in the

local tumor microenvironment in recurrent cancers may explain the failure of vaccines

after surgery. Proceedings of the National Academy of Sciences of the United States of

America. 2013;110:E415-24.

102. Banciu M, Schiffelers RM, Storm G. Investigation into the role of tumor-associated

macrophages in the antitumor activity of Doxil. Pharmaceutical research. 2008;25:1948-

55.

103. Beatty GL, Chiorean EG, Fishman MP, Saboury B, Teitelbaum UR, Sun W, et al. CD40

agonists alter tumor stroma and show efficacy against pancreatic carcinoma in mice and

humans. Science. 2011;331:1612-6.

104. Jiang J, Wang Z, Li Z, Zhang J, Wang C, Xu X, et al. Early exposure of high-dose

interleukin-4 to tumor stroma reverses myeloid cell-mediated T-cell suppression. Gene

therapy. 2010;17:991-9.

105. Kajiwara A, Doi H, Eguchi J, Ishii S, Hiraide-Sasagawa A, Sakaki M, et al. Interleukin-4

and CpG oligonucleotide therapy suppresses the outgrowth of tumors by activating

tumor-specific Th1-type immune responses. Oncology reports. 2012;27:1765-71.

106. Onishi M, Nosaka T, Misawa K, Mui AL, Gorman D, McMahon M, et al. Identification

and characterization of a constitutively active STAT5 mutant that promotes cell

proliferation. Molecular and cellular biology. 1998;18:3871-9.

107. Prokopchuk O, Liu Y, Henne-Bruns D, Kornmann M. Interleukin-4 enhances

proliferation of human pancreatic cancer cells: evidence for autocrine and paracrine

actions. British journal of cancer. 2005;92:921-8.

108. Todaro M, Lombardo Y, Francipane MG, Alea MP, Cammareri P, Iovino F, et al.

Apoptosis resistance in epithelial tumors is mediated by tumor-cell-derived interleukin-4.

Cell death and differentiation. 2008;15:762-72.

109. Gessner A, Mohrs K, Mohrs M. Mast cells, basophils, and eosinophils acquire

constitutive IL-4 and IL-13 transcripts during lineage differentiation that are sufficient for

rapid cytokine production. J Immunol. 2005;174:1063-72.

110. Lee SO, Pinder E, Chun JY, Lou W, Sun M, Gao AC. Interleukin-4 stimulates androgen-

independent growth in LNCaP human prostate cancer cells. The Prostate. 2008;68:85-91.

111. Astier AL, Beriou G, Eisenhaure TM, Anderton SM, Hafler DA, Hacohen N. RNA

interference screen in primary human T cells reveals FLT3 as a modulator of IL-10

levels. J Immunol. 2010;184:685-93.

112. Zhu J, Cote-Sierra J, Guo L, Paul WE. Stat5 activation plays a critical role in Th2

differentiation. Immunity. 2003;19:739-48.

\113. Hural JA, Kwan M, Henkel G, Hock MB, Brown MA. An intron transcriptional enhancer

element regulates IL-4 gene locus accessibility in mast cells. J Immunol. 2000;165:3239-

49.

114. van Rooijen N, Sanders A, van den Berg TK. Apoptosis of macrophages induced by

liposome-mediated intracellular delivery of clodronate and propamidine. Journal of

immunological methods. 1996;193:93-9.

115. Gu L, Tseng S, Horner RM, Tam C, Loda M, Rollins BJ. Control of TH2 polarization by

the chemokine monocyte chemoattractant protein-1. Nature. 2000;404:407-11.

116. Lee HJ, Takemoto N, Kurata H, Kamogawa Y, Miyatake S, O'Garra A, et al. GATA-3

induces T helper cell type 2 (Th2) cytokine expression and chromatin remodeling in

committed Th1 cells. J Exp Med. 2000;192:105-15.

117. Takeda K, Tanaka T, Shi W, Matsumoto M, Minami M, Kashiwamura S, et al. Essential

role of Stat6 in IL-4 signalling. Nature. 1996;380:627-30.

118. Agarwal S, Avni O, Rao A. Cell-type-restricted binding of the transcription factor NFAT

to a distal IL-4 enhancer in vivo. Immunity. 2000;12:643-52.

119. Kim D, Kim SH, Cho SH, Shin K, Kim S. SOCS3 suppresses the expression of IL-4

cytokine by inhibiting the phosphorylation of c-Jun through the ERK signaling pathway

in rat mast cell line RBL-2H3. Molecular immunology. 2011;48:776-81.

120. Takatori H, Nakajima H, Kagami S, Hirose K, Suto A, Suzuki K, et al. Stat5a inhibits IL-

12-induced Th1 cell differentiation through the induction of suppressor of cytokine

signaling 3 expression. J Immunol. 2005;174:4105-12.