α blockade reduces immune suppression in the …

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The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access)

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Biomedical Sciences

12-2018

IL-1α BLOCKADE REDUCES IMMUNE SUPPRESSION IN THE IL-1 BLOCKADE REDUCES IMMUNE SUPPRESSION IN THE

EARLY TUMOR MICRO-ENVIRONMENT EARLY TUMOR MICRO-ENVIRONMENT

Brenda Melendez

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i

IL-1α BLOCKADE REDUCES IMMUNE SUPPRESSION IN THE EARLY TUMOR

MICROENVIRONMENT

By Brenda Melendez, B.S.

Approved:

_____________________________

Gregory Lizee, PhD.

Advisory Professor

_____________________________

Jagannadha Sastry, PhD.

_____________________________

Michael Curran, PhD.

_____________________________

Kwong Wong, PhD.

_____________________________

Chantale Bernatchez, PhD.

APPROVED

_____________________________

Dean, The University of Texas

MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences

ii

IL-1α BLOCKADE REDUCES IMMUNE SUPPRESSION IN THE EARLY TUMOR MICRO-

ENVIRONMENT

A Dissertation

Presented to the faculty of

The University of Texas

MD Anderson Cancer Center UTHealth

Graduate School of Biomedical Sciences

in Partial Fulfillment

of the Requirements

for the Degree of

DOCTOR OF PHILOSOPHY

By

Brenda Melendez, B.S.

Houston, Texas

December 2018

iii

Acknowledgements

First, I would like to thank my mentor, Dr. Lizee, for his support and mentorship. It

has been a long process and his help was invaluable to get to this point. Thank you for your

advice in and outside the lab.

I would also like to thank my present and past committee members: Dr. Jagannadha

Sastry, Dr. Michael Curran, Dr. Kwong Wong, Dr. Willem Overwijk, Dr. Stephen Ullrich, and

my newest member Dr. Chantale Bernatchez. Presentations have always been extremely

stressful for me. Thank for your feedback, which helped move my project forward and

helped improve my presentations skills.

In the lab, I worked with great members inside my group. In my early lab days, my

labmate, Dr. Amjad Talukder and Sherille Bradely, were fundamental in improving my

microbiology skills. Amjad and Sherille taught me the basics in bacterial work and fancier

techniques like site mutagenesis. This knowledge allowed me to design and make my own

vectors to study any type of gene. Dr. Lizee, helped me improve my cell culture technique

and also taught me about viral work. I would also like to thank Kyle Jackson, who helped me

throughout the years in viral work and other experiments.

Outside my lab I would like to thank Dr. Weiyi Peng, Dr. Yared Hailemichael, Rina

Mbofung, and Hiep Khong. Their mentorship in animal studies and immune populations was

invaluable to my project. Rina and Hiep taught me animal techniques and were patient

enough with me in the beginning when I couldn’t hold a black mouse properly. I still say they

are more aggressive than white mice. Weiyi and Yared helped me analyze data and plan

animal experiments.

iv

Leila Williams shared her human T cell knowledge with me and helped me with the

trickier experiments that she and her lab spent years optimizing. She is also an amazing

friend!

Finally, I would like to thank my friends who kept me happy, even in the most

stressful times. A huge thank you to Leila, Rene, Mayra, Rina, OJ, Shawne, Esteban, Ben,

Arehly, Brittany, and Nicolle.

v

IL-1α Blockade Reduces Immune Suppression in the Early Tumor Micro-Environment

Brenda Melendez, B.S.

Advisory Professor: Gregory Lizee, PhD.

Immunotherapy against melanoma has shown great promise in the clinic for treating

advanced-stage patients. However, a major barrier against effective T cell mediated

cytotoxicity is immunosuppression in the tumor micro-environment. It has been described

that tumors secrete pro-inflammatory cytokines capable of modulating immune responses

that favors the growth of tumor cells. Specifically, IL-1 plays a critical role in myeloid cell

recruitment and activation, which can in turn inhibit T cell activity in vivo. Moreover, IL-1 is

also known to up-regulate immune inhibitory molecules in the tumor micro-environment. To

further investigate the effects of IL-1 in melanoma progression, IL-1α was blocked in a highly

aggressive pre-clinical B16 melanoma tumor model in three different treatment settings: as a

monotherapy, in combination with checkpoint blockade, and in combination with adoptive T

cell therapy. In all three settings, IL-1α blockade resulted in tumor reduction and increase in

murine survival. This was accompanied by a decrease in myeloid cell tumor infiltration. At

early time points following IL-1 α blockade, these myeloid cells also demonstrated partial

loss of their immunosuppressive abilities, as supported by a decrease in arginase

production and inhibitory molecule expression. Moreover, monocytes demonstrated an

increase in co-stimulatory molecules following IL-1 α blockade. In vitro, the myeloid cells’

ability to inhibit T cell cytotoxicity was significantly compromised. These results collectively

provide evidence in support of IL-1 α contributing to melanoma immune suppression.

Antibody-mediated blockade of IL-1 α improved antitumor responses, suggesting that this

modality may improve outcomes of patients undergoing treatment with T-cell based

immunotherapies.

vi

Table of Contents

Approvals ............................................................................................................................. i

Title page ............................................................................................................................. ii

Acknowledgements ............................................................................................................ iii

Abstract ............................................................................................................................... v

Table of Contents ............................................................................................................... vi

List of Figures .................................................................................................................... ix

List of Tables ..................................................................................................................... xii

Chapter 1: Introduction and Background .......................................................................... 1

I. Immune Response ............................................................................................. 2

a. Neutrophils ............................................................................................. 2

b. Macrophages .......................................................................................... 3

c. Myeloid-derived suppressor cells ......................................................... 4

d. Adaptive Immune Response .................................................................. 5

e. T cells ...................................................................................................... 6

f. T cell activation ...................................................................................... 7

II. Melanoma ......................................................................................................... 10

a. Melanoma treatments ........................................................................... 11

vii

b. Checkpoint blockade ........................................................................... 11

c. Peptide vaccines .................................................................................. 13

III. Interleukin-1 .................................................................................................... 13

a. IL-1α biogenesis ................................................................................... 14

b. IL-1β biogenesis ................................................................................... 15

c. IL-1 signaling ........................................................................................ 19

d. IL-1α function........................................................................................ 20

e. IL-1β function........................................................................................ 21

f. IL-1 in cancer ........................................................................................ 21

Chapter 2: Specific Aims ................................................................................................. 24

Chapter 3: IL-1α blockade ................................................................................................. 27

I. Rationale .......................................................................................................... 28

II. Results .............................................................................................................. 28

III. Discussion ........................................................................................................ 65

IV. Material and Methods ...................................................................................... 67

Chapter 4: Anti-IL-1α in combination with checkpoint blockade and peptide vaccine

............................................................................................................................................ 70

I. Rationale .......................................................................................................... 71

viii

II. Results .............................................................................................................. 72

III. Discussion ........................................................................................................ 98

IV. Material and Methods .................................................................................... 100

Chapter 5: Discussion .................................................................................................... 104

References ....................................................................................................................... 113

Vita ................................................................................................................................... 121

ix

List of Figures

Figure 1: Activation of naïve CD8 T cells requires three signals ........................................... 9

Figure 2: IL-1β activation .................................................................................................... 17

Figure 3: Anti-IL-1α reduces tumor growth in B16-F10 murine model .................................. 30

Figure 4: Flow cytometric analysis of the immune component of the tumor micro-

environment after one week of IL-1α blockade .................................................................... 33

Figure 5: cyTOF analysis of the tumor micro-environment after IL-1α inhibition on day 7 .... 37

Figure 6: Pseudocolor density map of monocyte subsets .................................................... 40

Figure 7: Percentage of monocyte subsets in melanoma tumors on day 7 ......................... 41

Figure 8: Marker expression intensity heat map of monocyte subsets ................................ 42

Figure 9: Histogram of the expression profile of monocyte subsets .................................... 44

Figure 10: Macrophage subsets in melanoma tumors on day 7 ........................................... 46

Figure 11: Marker expression intensity heat map of macrophage subsets .......................... 47

Figure 12: Histograms of the phenotypic profile of macrophage subsets ............................ 49

Figure 13: IL-1α blockade decreases CD8+ T cell infiltrates in melanoma tumors on day 7 53

Figure 14: Neutralization of IL-1α modulates B cell subset composition on day 7 ............... 56

Figure 15: IL-1α inhibition decreases Arg-1 and NO in myeloid cells. ................................ 58

Figure 16: Gating strategy for isolating myeloid cells in the tumor ...................................... 59

Figure 17: Tumor killing assay experimental procedure ...................................................... 60

x

Figure 18: Tumor killing assay shows myeloid cells isolated from tumor treated with anti-IL-

1α have less effect on tumor killing ..................................................................................... 61

Figure 19: Percentage of monocytes and granulocytes out of total myeloid cells at day 7 and

termination day ................................................................................................................... 63

Figure 20: Percentage of macrophages out of total myeloid cells on day 7 ........................ 64

Figure 21: Arg-1 and NO production are similar at termination day in IgG and anti-IL-1α ... 65

Figure 22: Anti-IL-1α and anti-PD-1 delay tumor growth ..................................................... 75

Figure 23: Anti-IL-1α and anti-PD1 treatment modulates immune infiltration on day 7 ........ 76

Figure 24: Anti-IL-1α and anti-PD-1 modulate macrophage function on day 7 ..................... 79

Figure 25: Pseudocolor map of macrophage subsets on day 7 ........................................... 80

Figure 26: Macrophage subset percentages in anti-IL-1α and anti-PD-1 treated tumors .... 81

Figure 27: Marker expression intensity heat map of macrophage subsets .......................... 82

Figure 28: Marker expression histograms of macrophage subsets found in anti-IL-1α and

anti-PD-1 group .................................................................................................................. 83

Figure 29: IL-1α and PD-1 blockade decreases non-activated monocytes on day 7 ........... 87

Figure 30: IL-1α and PD-1 blockade promotes CD8+LY6C- T cells on day 7 ..................... 90

Figure 31: Anti-IL-1α and anti-PD-1 reduce CD4+ TIGIT+ T cells on day 7. ........................ 93

Figure 32: IL-1α blockade and peptide vaccine with naïve CD8+ T cells extends mice

survival to over 3 months .................................................................................................... 96

Figure 33: Flow cytometric analysis of the tumor micro-environment showing reduced

myeloid infiltration .............................................................................................................. 97

xi

Figure 34: Immune response of anti-IL-1α and peptide vaccine consists of predominantly

TNF-α producing CD8+ T cells ........................................................................................... 98

Figure 35: Myeloid cells infiltrate the tumor after two weeks of anti-IL-1α and vaccine

treatment. ........................................................................................................................... 99

xii

List of Tables

Table 1: cyTOF markers ..................................................................................................... 35

1

CHAPTER 1:

INTRODUCTION AND BACKGROUND

2

I. Immune response

The study of evolution in a variety of organisms has taught us that several biological

mechanisms are conserved and improved through the passage of time. The most basic

mechanism of defense is the innate immune system. The innate immune system represents

the pinnacle of evolutionary biology, in which generation after generation, we selected the

genes and machinery necessary to protect us from pathogens. This response is so versatile

and effective that it is conserved across plants, invertebrates, and mammals (1). The innate

immune system detects foreign bodies by using receptors encoded in our germline. These

receptors activate cell-dependent responses, soluble factors, complement factors, alarmins,

cytokines and chemokines, and other molecules responsible for instigating inflammation (2).

The innate immune response is triggered by the activation of pattern recognition receptors

(PRRs). A subtype of PRRs, toll-like receptors (TLRs), expressed on the surface of resident

epithelial cells and recruited hematopoietic cells, sense pathogen-associated molecular

patters (PAMPs) or microbe-associated molecular patterns (MAMPs) (3). Other important

PRRs are NOD-like receptors, complement receptors and scavenger receptors (4). Once

any of these receptors are activated, they start a downstream signal that leads to the

secretion of cytokines and chemokines involved in the recruitment of immune cells. Innate

immune cells, then, travel to the area of infection (5). Innate cells encompass an array of

myeloid and lymphoid cells. They tend to originate from hematopoiesis without antigen

receptors. The majority of cells do not develop memory (6). Although, new studies in natural

killer cells (NK cells) have introduced the concept of trained immunity based on memory

responses by NK cells (7).

a. Neutrophils

Neutrophils are the first cells recruited (2). They were considered as short-lived and

terminally differentiated phagocytic cells, without any role in the regulation of adaptive

3

immunity. However, these notions were challenged with newer studies. Observation of

circulatory neutrophils in healthy patients showed the average span of neutrophils to be

almost a week, more than 10 fold longer than previous estimates (8). Neutrophils were

found to be somewhat fluid, acting as myeloid derived suppressor cells (MDSCs) by

suppressing T cell expansion and cytokine production (9) ; therefore, having a role in

adaptive immunity.

The main role of neutrophils is as phagocytes. Phagocytosis is the process in which

small bacteria are engulfed by neutrophils. It usually occurs within minutes of infection.

Large organisms cannot be phagocytosed. Instead, neutrophils release granules that induce

death by degrading bacterial and fungal proteins (10). After prolonged exposure to

pathogens, neutrophils undergo cell death by apoptosis, necrosis or neutrophil extracellular

trap (NET) formation (10). Their death continues the inflammatory response by releasing

inflammatory cytokines.

b. Macrophages

Elie Metchnikoff, categorized macrophages as “the phagocytic component of the

immune system”. Since the discovery of macrophages, their function has expanded to the

phagocytosis of foreign bodies, the clearance of dead cells, production of inflammatory

cytokines and participation in homeostasis. Macrophages express an extensive variety of

PRRs capable of sensing microorganisms, danger signals, and changes in pH and oxygen

concentration (11). After activation, macrophages use several mechanisms to fight infection

and eventually stop the immune response. Macrophages have the ability to cross-talk with

other neighboring cells by releasing cytokines, chemokines and growth factors. Directly,

macrophages can have cell-to-cell interactions via their receptors and gap junctions (12). It

has been estimated that humans have approximately 200 billion macrophages (13). Tissue-

resident macrophages have a slow turn-over rate under homeostasis. They are established

4

during embryonic development. They are capable of self-renewal. Blood-circulating

monocytes can also differentiate into tissue-resident macrophages (14). Their self-renewal

abilities are not shared across all macrophage subtypes. Macrophages derived from the

intestine, pancreas, dermis, and heart tend to be replaced by circulating monocytes (Ly6CHi)

in a CCR2-dependent way (15). Macrophages have been classically separated into 2 unique

categories. M1 (classically activated macrophages) are activated by Th1 cytokines such as

IFNγ and bacterial factors. M2 (alternatively activated macrophages) are activated by Th2

cytokines like IL-4 and IL-13. M1 macrophages fight against bacterial function, while, M2

macrophages are part of anti-inflammatory, allergic and tissue repair processes (2). This

categorical separation does not hold in vivo due to the presence of both Th1 and Th2

priming factors. Therefore, the newest understanding is that macrophages exist across a

spectrum of phenotypes rather than as separate distinct subtypes (16). Macrophages can

easily go from one end to the other end of the spectrum with the appropriate stimulating

molecules. They also have an epigenetic plasticity (17). Macrophages exposed to

pathogens undergo epigenetic reprograming that establishes “innate immune memory” (18).

Their plasticity allows them to be an effective arm of the innate immune response.

c. Myeloid-derived suppressor cells (MDSCs)

As their name states, MDSCs are of myeloid origin. Their main function is to

suppress T cell responses. They can be categorized into polymorphonuclear (PMN-MDSC)

and monocytic MDSCs. In mice, they can be identified by their Ly6C (monocytic MDSCs)

and Ly6G (PMN-MDSCs) expression. In humans, the Ly6C and Ly6G markers do not

translate and, thus, the identification of these populations is more complicated. PMN-

MDSCs and monocytic MDSCs express CD11b and CD33. Monocytic MDSCs, also,

express CD14 and have low levels of MHC-II. PMN-MDSCs, on the other hand, have low

CD14, express CD15 and CD66b (19). Since their markers are found in other immune cells,

5

functional assays are necessary to ascertain their suppressive function. Moreover,

neutrophils tend to have similar markers as PMN-MDSCs, so their closeness has been

debated.

MDSCs have been heavily studied in mice due to their T cell suppression

capabilities. Several mechanism of suppression have been discovered. Bronte and Yang

described the ability of MDSCs to modulate angiogenesis and tumor cell motility by the

production of metalloproteinases (MMP) and vascular endothelial growth factor (VEGF) (20).

Corzo, found that this process is regulated by the transcription factor hypoxia inducible

factor (HIF)1α (21). Moreover, MDSCs polarize tissue macrophages into an M2 phenotype,

which promotes angiogenesis (22). MDSCs have also been found to suppress NK cell-

mediated lysis (23). There are several ways in which MDSCs inhibit T cells. They inhibit

antigen-dependent cytokine secretion in T cells (24), induce apoptosis in activated CD8+ T

cells via TNF and nitric oxide (NO) (25), secrete immunomodulatory molecules such as

TNF-α, H2O2, and TGF-β, and release enzymes involved in amino acid metabolism that are

used for T cell activation (arginine, tryptophan, and cysteine) (26-28). MDSCs can also

induce T regulatory cells (Tregs) (29). Hanson et al, showed how MDSCs disrupted T cell

homing to lymph nodes via L-selectin (30).

MDSCs stifle the activation of the adaptive and innate immune response.

d. Adaptive Immune Response

Even though the innate response is effective in eliminating a variety of organisms,

the number of molecular patterns it can recognize is limited (31). The ability of pathogens to

mutate and evolve forced the development of the adaptive immune response (32). The main

feature of the adaptive immune system is the wide receptor repertoire specificity created by

somatic recombination of gene segments (31). This mechanism evolved from gene

6

duplication in early vertebrates to generate highly specific and flexible responses (31).

Another key feature, is the ability to gain memory. Cells with antigen-specific receptors can

persist for life. T cells and B cells are the two main types of cells in the adaptive immune

response.

e. T cells

T cells develop in the thymus from common lymphoid progenitor cells (33). Common

lymphoid progenitor cells travel from the bone marrow or fetal liver to the thymus. In the

thymus, they begin to expand via IL-7 induction. Mutations in the IL-7 receptor, lead to

deficiency in T cells. Expansion of common lymphoid progenitor cells activate Notch-1 and

other transcription factors involved in T-cell lineage commitment and up-regulation of the

expression of genes responsible for T-cell receptor (TCR) assembly (34). T cells undergo an

antigen-independent differentiation process, in which genetic rearrangements create

functional genes that encode the α/β chains or the γ/δ chains of the TCR. The TCR loci has

arrays of V (variable), D (diversity), and J (joining) segments. β and δ TCR loci contain V, D,

and J segments. The rest only have V and J segments. In a serial process, one V, one D

(for β, δ), and one J segment are randomly spliced. The spliced recombination is mediated

by the V(D)J recombinase, which consists of 2 proteins encoded by the recombinase-

activating genes 1 and 2 (RAG1 and RAG2). RAG1 and RAG2 bind to the recombinase

signal sequences flanking the edges of V-D-J segments. The structure of chromatin

regulates the accessibility of the signal sequence (35). The V(D)J recombinase cuts the

DNA at the signal sequences to get hairpin structures. Artemis then cleaves these

structures. The DNA breaks are repaired in a process called nonhomologous end-joining,

creating a variety of V(D)J combinations. The resulting V(D)J cassette dictates the amino

acid sequence and binding specificity of the TCR (35). This is called combinatorial diversity.

Junctional diversity, is obtained when bases are added or removed during the repair of

7

DNA. Created junctional areas encode the region of the antigen-binding pocket of the TCR

(35).TCR rearrangements are successful when no stop codons are introduced and a TCR

protein can be translated. This turns a pre-T cell to a double-positive T cell. Double positive

T cells express both CD4 and CD8. Transition from a double positive T cell to a single

positive T cells requires positive and negative selection. Positive selection happens when

the TCR binds with low avidity to self-MHC-Peptide complex. Cells that do not bind to self-

MHC are eliminated. In negative selection, cells that bind with high avidity to self-MHC are

eliminated. Cells that pass either selection differentiate to CD8+ or CD4+ T cells depending

on their interaction with MHC class I (CD8) or MHC class II (CD4). Single positive cells exit

the thymus and enter into circulation as naïve T cells.

f. T cell activation

T cells are activated after successful TCR-peptide-MHC engagement. CD8+ T cells

can recognize peptides between 9-11 amino acids in length bound to MHC class I (HLA-A,

HLA-B, and HLA-C). MHC-class I peptides are produced from endogenous protein encoded

by either the host or pathogen. CD4+ T cells recognize MHC-II restricted peptides (HLA-DR,

HLA-DQ, and HLA-DP). MHC class II molecules are expressed on the surface of antigen

presenting cells (APCs). APCs roam in the host sampling environmental antigens and

danger signals (35). They phagocytose or endocytose exogenous proteins and then present

them on their surface via MHC class II molecules. After APC activation, these cells travel to

regional lymph nodes, where they present antigen to T cells. An immunological synapse is

formed when the TCR associates with the peptide-MHC complex. Several TCR-associated

molecules start clustering at the boundary between T cell and APC (immunological synapse)

(36). The CD4/CD8 molecules stabilize the CD3-TCR-MHC complex by binding it to

nonpolymorphic regions of MHC. Integrins stabilize the immune synapse.

8

T cell activation is a three-signal process. Signal one consists of the recognition of

the peptide-MHC complex on APCs by the TCR (37). This is an antigen-dependent process

that starts the tyrosine phosphorylation of ITAMS located on the cytoplasmic tails of the

TCR-CD3 complex, which leads to the initiation of a signaling cascade that activates NFAT

and NF-κB pathways, involved in T cell effector function (38). Signal one, alone, may cause

T cells to become anergic and apoptotic (39). Signal two is co-stimulation. The most studied

co-stimulatory axis is between CD80/CD86 on APCs with CD28 receptor on T cells (40).

CD80/CD86-CD28 interactions modulate the scale of T cell response, triggering clonal

expansion, differentiation and up-regulation of anti-apoptotic genes (BCL-2 and Bcl-XL) (41).

Finally, signal three is cytokine dependent. In 2010, Curtsinger and Mescher, suggested that

signals 1 and 2 were not sufficient to sustain clonal expansion, effector function and the

creation of memory populations (Figure 1) (42). Cytokines such as IL-12 and Type I IFN,

sustain the transcription of factors and regulation of genes involved in differentiation and

function. Altogether, all three signals are necessary for optimal T cell response.

9

Figure 1: Activation of naïve CD8 T cells requires three signals. Ag, co-stimulation, and

either IL-12 or IFNα/β. Stimulation with Ag and B7-1 results in extensive proliferation, but

survival is compromised and development of effector functions is suboptimal. The small

numbers of cells that survive long term are anergic. When either IL-12 or IFNα/β is present,

proliferation is comparable but survival is increased, the cells develop strong effector

functions, and a protective memory population is formed. Reused by permission from

Elsevier Publisher Ltd. Curtsinger, J.M. and M.F. Mescher, Inflammatory cytokines as a third

signal for T cell activation. Curr Opin Immunol. Ref 23. Copyright © 2010.

10

II. Melanoma

Cutaneous melanoma is the deadliest type of skin cancer. It accounts for only 1% of

all cases observed. In the United States, it has been estimated that almost 100,000 patients

will be diagnosed with melanoma, and 10,000 will succumb to the disease (43). Melanoma

occurrence differs greatly among countries, but certain risk factors have been linked to the

disease. Racial skin features, sun exposure, gender and age are the most common linking

factors in patients. Patients with low levels of melanin in their skin are more sensitive to UV

rays, which cause inflammation in the skin. Excessive UV exposure can lead to DNA

damage and genetic mutations. The most affected pathways are cell proliferation (BRAF,

NRAS, and NF1), growth and metabolism (PTEN, and KIT), and apoptotic resistance (TP53,

CDKN2A, and TERT) in melanocytes (44). Current statistics show that incidence rate

increases in younger patients (median age of 57 years) compared to other cancers (44).

These patients are associated with intermittent sun exposure, BRAF mutation and a low

mutation load. Chronical sun exposure is most commonly associated with older patients.

These melanomas are driven by BRAF, NF1 and NRAS mutations and have a high

mutational load (45). Gender also plays a role, female patients comprise the majority of

cases seen in the younger age group. Meanwhile, male patients are more prominent in the

older than 55 group (46). Other lesser factors associated with melanoma are pre-existing

nevi (commonly known as moles) and family history. Bevona et al showed that 26% of

melanoma cases appeared on a pre-existing nevi (47). Only 5-12% of melanomas are

inherited (48). The two genes related to inherited melanoma are cyclin-dependent kinase

inhibitor 2A (CDKN2A) and cyclin-dependent kinase 4 (CDK4). CDKN2A is a tumor

suppressor gene that is involved in the production of p16 and p14 (49). P16 and p14 are

both involved in cell cycle regulation (50). CDK4 mutation inhibits cell cycle regulation by

p16 (51).

11

a. Melanoma treatments

If caught during early stage, most melanomas are curable with surgery. Melanoma is

highly metastatic and in late stages it’s no longer operable and, thus, deadly. The earliest

treatment against advanced-stage melanoma was chemotherapy. Dacarbazine was the first

chemotherapy drug approved by the FDA to treat metastatic melanoma (52). Dacarbazine

induces apoptosis in proliferating cells. Dacarbazine alone achieved less than 5% complete

response (CR) rate. For at least 20 years, chemotherapy was the standard of care for late

stage melanoma. During the 90’s, studies connected immunology and cancer. Researchers

observed that inflammation was greatly increased at the tumor site and there was a

consistent presence of infiltrating immune cells (53). Specifically, T cells were linked to anti-

tumor response. T cells are immune cells capable of recognizing tumor-specific antigen.

After recognition, T cells are activated and are capable of killing tumor cells associated with

the corresponding antigen. This discovery led to the design of drugs that stimulated the

immune system in patients. In 1995, Interferon α-2b was approved as an adjuvant therapy

for treating resected stage II/III melanoma (54). In melanoma, systemic IFNα has an

immunomodulatory effect. It can activate T cells, B cells, NK cells, while, inhibiting Tregs and

MDSCs (55). It reduces angiogenesis by limiting VEGF expression (56). Also, it increases

MHC Class I levels on melanoma cells, leading to increased recognition (57). Alone, IFNα

moderately decreases reoccurrence of disease (58). The other cytokine-mediated

immunotherapy is Interleukin-2 (IL-2). IL-2 directly increases proliferation of T cells. High-

dose IL-2 was FDA approved in 1998 for metastatic melanomas (59). IL-2 has a CR rate of

4% (59). These two immunotherapies paved the way to the development of current

immunotherapies. New treatments are capable of producing long lasting responses in

metastatic patients.

b. Checkpoint blockade

12

Checkpoint blockade is based on the concept of improving immune response against

tumor cells by inhibiting the mechanism responsible for inhibiting T cell activation. Prolonged

T cell activation is tightly regulated by several factors most importantly, CTLA-4 and PD-1.

Rampant and unchecked T cell activation can cause autoimmunity.

During the 80s, Brunet et al, identified a molecule currently known as CTLA-4. It took

almost a decade for its function to be described by Allison’s group in 1995. CTLA-4 is a

member of the immunoglobulin superfamily and is solely expressed on T cells (60). CTLA-4

expression is induced upon TCR engagement. Initially, CTLA-4 is expressed intracellularly.

After TCR activation and co-stimulation by CD28, CTLA-4 translocates to the surface of T

cells, where it competes with its homologous receptor, CD28, for binding against CD80 and

CD86 on the surface of APCs (61). CTLA-4 has a higher avidity and affinity with CD80 or

CD86 than CD28 leading CTLA-4 to outcompete CD28 (62). Attenuation of downstream

CD28 signaling decreases T cell activation and proliferation (63). Mechanistically, CTLA-4

ligation reduces IL-2 production and arrests cell cycle progression (64).

Tregs constitutively express CTLA-4. Loss of CTLA-4 in Tregs, leads to an abnormal

activation of conventional T cells, stressing the role of CTLA-4 in Treg-mediated tolerance.

The other prominent T cell inhibitor is programmed cell death protein 1 (PD-1). PD-1

is homologous to CD28 and is involved in inhibiting immune signaling (65) T cells

constitutively express PD-1, except naïve T cells. B cells, myeloid dendritic cells, mast cells

and Langerhans cells also express PD-1. PD-1 has 2 ligands, PD-L1 and PD-L2. PD-1

ligands are often found on the surface of APCs (66). IFNγ is the main inducer of PD-L1 and

PD-L2 (67). PD-1 has an immunoreceptor tyrosine-based inhibition motif (ITIM) and in

imunoreceptor tyrosine-based switch motif (ITSM) on its cytoplasmic tail (68). Upon PD-1

activation, ITSM becomes phosphorylated and induces the recruitment of the Src homoly

region 2 domain-containing phosphatase 2 (SHP-2). The PD-1-SHP-2 complex can

13

dephosphorylate CD28, thus inhibiting CD28-CD80/CD86 signaling, resulting in reduced T

cell proliferation and cytokine production. PD-1 activity is only relevant during simultaneous

T cell activation (68). PD-1 works in maintaining tolerance.

Both anti-CTLA-4 and anti-PD-1 have shown increased patient survival in a variety of

cancers. A phase III clinical study showed that anti-PD-1 resulted in better response (44%)

compared to CTLA-4 (19%) in melanoma patients. Combination of anti-CTLA-4 and anti-PD-

1 has a response of 58% (69).

c. Peptide vaccines

Vaccination has proven effective in fighting pathogens (70). Vaccines consists of

injecting inactivated forms of microbes to induce an antigen-specific response to protect

against future infections. These types of vaccines contain hundreds of proteins; however,

only a few offer immunity. Peptide vaccines are a specialized form of vaccine, in which

known antigenic-peptides are used to trigger an immune response. To date several peptide

vaccines have been designed to stimulate CD8+ T cell cytotoxicity against pathogens.

Peptides alone are not sufficient to trigger a robust immune response so adjuvants were

developed to improve the immune response to peptide vaccines. The most common

adjuvant in the clinic is Freunds complete adjuvant (CFA). CFA contains inactivated and

dried mycobacteria. In this manner we can increase humoral and cellular immunity.

Melanoma’s high mutation load makes it easier to find potential candidates to use as a

peptide vaccine. For instance, gp100 is a peptide found on a majority of melanoma cells and

is currently being used as a peptide vaccine.

III. Interleukin-1

The discovery of the first interleukin started from studies trying to isolate endogenous

factors responsible for causing fever in patients. Elisha Atkins reported that a protein

14

appeared in circulation during endotoxin fever and coined the term “endogenous pyrogen”

(71). Atkins, Murphy and Wood, went on to study the molecular properties of pyrogen

isolated from rabbit neutrophils (72). Eventually, Atkins and Bodel moved to human PBMCs

and reported that unlike in rabbit peritoneal cells, pyrogens in human PBMCs were

synthesized de novo, and therefore were not present during homeostasis (73). Bodel also

showed that pyrogen could also be secreted by monocytic leukemia cells and Hodgkin’s and

lymphoma cells (74). In parallel, Dinarello, was, also, trying to isolate the soluble pyrogen. In

1977, his lab isolated pyrogen and named it, human leukocytic pyrogen (LP) (75).

Endogenous pyrogen and human leukocytic pyrogen are different labels to describe the

properties of what we now call Interleukin-1. Later findings demonstrated the potent ability of

IL-1 to induce a fever with levels as low as 1-10ng/kg (76).

Klampschmidt et al, were the first to propose that individual factors in the

supernatant of leukocytes had more than one function (77). Dinarello, showed that human

IL-1 could stimulate the production of serum amyloid A (78). Thus, IL-1 became a pleotropic

molecule and started cytokine biology.

With the invention of cDNA cloning, scientists were able to identify two distinct

molecules with pyrogenic properties. They both had similar molecular weights, but had

different isoelectric points. One of the molecules had a pI of 7, which was the recorded pI of

IL-1 (79). The other had a pI of 5 (79). We now call pI5 IL-1α and pI7 IL-1β.

a. IL-1α biogenesis

ProIL-1α is the 31Kda precursor to IL-1α. ProIL-1α is customarily found intracellularly

and has been described to be active. The precursor is synthesized in association with

microtubules (80). ProIL-1α is released when cells undergo necrosis and can then be

cleaved by extracellular proteases. In the absence of necrosis, calpain, a calcium-

15

dependent cysteine protease is also capable of cleaving ProIL-1α into its 17kDa mature

form (81).

b. IL-1β biogenesis

Contrary to IL-1α, the precursor of IL-1β is not active and requires further processing

for optimal function. ProIL-1β is cleaved by caspase-1. Since both IL-1 precursors lack a

signaling peptide, they cannot go through the endoplasmic reticulum-golgi pathway for

secretion. During infection or cellular stress, the inflammasome, a 700kDa multi-protein

complex is formed to help mediate the cleavage of ProIL-1β. Several types of

inflammasomes have been discovered. They all have a distinctive NOD-like receptor (NLR).

These are soluble intracellular proteins that survey for foreign bodies. NLRs are multidomain

proteins with a tripartite architecture which have a C-terminal region containing a series of

leucine-rich repeats, a central region known as the NACHT domain, and an N-terminal

effector domain (82). The N-terminal effector domain is responsible for transmitting the

signal downstream which prompts caspase activation. NLRs are categorized into several

subfamilies. NALP1, NALP2, and NALP3 are known to be the part of caspase-1-related

inflammasomes (83). Inflammasomes are triggered by danger signals. IL-1β synthesis and

processing requires two signals. The first signal can be initiated by Toll-like receptor (TLR)

activation, which results in the production of ProIL-1β. Common activators are LPS, CpG,

and resiquimod (84). The second signal can be obtained by the activation of P2X7 receptor

by ATP (85). P2X7 activation leads to potassium efflux, plasma-membrane depolarization,

cell swelling and disaggregation of the cytoskeletal network (84). Studies have shown that

loss of potassium is necessary to trigger caspase-1 (86). Reduction in potassium levels

activates calcium-independent phospholipase A2 (iPLA2) (figure 2) (87). Walev, noted that

inhibiting iPLA2 stopped IL-1β processing (87). The two signal requirement for IL-1β

16

synthesis guarantees that inflammation is only induced during infection or tissue injury (84).

Dysregulation of this process leads to autoimmune diseases.

Mature IL-1β is released by two separate mechanisms. Andrei et al, illustrates how

IL-1β is loaded into secretory lysosomes and eventually released in a phospholipase-

dependent way (88). Otherwise, IL-1β is externalized by the budding of microvesicles (89).

17

Figure 2: IL-1β activation. In signal 1, stimulation of innate immune cells (such as

macrophages) with any of the various Toll-like receptor (TLR) agonists (such as

lipopolysaccharide (LPS), CpG dinucleotides and the lipopeptides Pam3CysSerLys4

(Pam3CSK4) and R848) induces the synthesis of pro-interleukin-1β (pro-IL-1β) and

certain inducible components of the inflammasome, such as caspase-11. This signal

'primes' the cells encountering a secondary trigger that stimulates caspase-1 activation,

pro-IL-1β and pro-IL-18 cleavage and subsequent mature cytokine release. As such, TLR

priming alone is not sufficient for caspase-1 activation. In signal 2, this trigger is in the

form of agents that can cause ionic perturbations, specifically potassium efflux. Activating

the purinergic P2X7 receptor by ATP is one such example. Other agents that cause

18

membrane blebbing and pore formation similar to those elicited by P2X7 receptor

stimulation include nigericin (a potassium ionophore), maitotoxin (a potent marine toxin

that is derived from dinoflagellates) and aerolysin (a pore-forming toxin from Aeromonas

hydrophila). Potassium depletion mediates IL-1β processing through the activation of

calcium-independent phospholipase A2 (iPLA2). TLR priming (for example, by LPS pre-

stimulation) of macrophages 'accelerates' caspase-1 processing. In addition, LPS priming

results in IL-1β release, which is due to de novo synthesis of pro-IL-1β. However, ASC

(apoptosis-associated speck-like protein containing a CARD), pro-caspase-1 and pro-IL-

18 are constitutively present in large quantities in macrophages and do not require LPS-

mediated upregulation. IκB, inhibitor of NF-κB; NF-κB, nuclear factor-κB; NLR, NOD-like

receptor. Reused by permission from Springer Nature. Mariathasan, S. and D.M. Monack,

Inflammasome adaptors and sensors: intracellular regulators of infection and inflammation.

Nat Rev Immunol, 2007. 7(1): p. 31-40. Copyright © 2007.

19

c. IL-1 signaling

Due to the potent nature of IL-1, there are three main regulatory checkpoints. The

first is the control of synthesis and secretion of IL-1 covered in the earlier section (82). The

second is the expression levels of the IL-1 family receptors on the surface of cells (90). The

last checkpoint, involves the regulation of the downstream signaling after receptor activation

(90). Both IL-1α and IL-1β bind to the same receptor, type I IL-1 receptor (IL-1R1).

Successful IL-1 signaling, requires the IL-1/IL-1R1 complex to bind to a second receptor, IL-

1 receptor accessory protein (IL-1RAcP). At the receptor level, there exists two inhibitory

molecules. IL-1 receptor antagonist (IL-1RA) is a ligand of IL-1R1 and has a similar affinity

to IL-1. Binding of IL-1RA to IL-1R1 does not trigger the activation of the receptor. The other

is the type II IL-1 receptor (IL-1R2). IL-1R2 lacks the cytoplasmic signaling machinery, so

binding of IL-1 with IL-1R2, does not induce the activation of the IL-1 pathway (90).

When IL-1 binds to IL-1R1, IL-1R1 undergoes a conformational change in the first

extracellular domain to recruit IL-1RAcP (91). The IL-1R1/IL-1RAcP complex contains

conserved cytosolic regions called Toll- and IL-1R1-Like (TIR) domains (92). After activation,

myeloid differentiation primary response gene 88 (MYD88) and interleukin-1 receptor-

activated protein kinase (IRAK) 4 bind to the TIR domains on the complex (93). IRAK4, then,

auto phosphorylates leading to the phosphorylation of IRAK1 and IRAK2 (94). Tumor

necrosis factor-associated factor (TRAF) 6 is recruited and oligomerizes with the complex

(95). IRAK1, IRAK2 and TRAF6 detach from the receptor complex and are released in the

cytoplasm. TRAF6 mediates the attachment of K-63 linked polyubiquitin chains to several

IL-1 signaling factors, including IRAK1, transforming growth factor-β (TGF-β)-activated

protein kinase-binding protein (TAB2), TAB3 and TGF-β-activated protein kinase (TAK1)

(96) (97, 98). Ubiquitination of TAK1 helps it associate with TRAF6 and MEKK3 (99). Pellino

homolog (PELI) 1-3 are ubiquitin E3 ligases that bind to IRAK1, 4 and are then

20

phosphorylated by the IRAKs. The combination of these proteins activate the NF-κB, c-Jun

N-terminal kinase (JNK), and p38 MAPK pathways (100, 101). The order of events is still not

well understood. NF-κB binds to a conserved motif in several IL-1 responsive genes such as

IL-6 (102), IL-8, monocyte chemoattractant protein 1 (MCP1) (103), and cyclooxygenase-2

(COX-2) (104).

The IL-1 signaling pathway is short-lived. IL-1R binds to the adaptor toll-interacting

protein (TOLLIP). TOLLIP, then, stops IRAK1 and facilitates the internalization of IL-1R1 to

endosomes (105). There are also negative feedback loops in IL-1 signaling. For instance,

phosphorylation of TAB1 inhibits TAK1 (106), synthesis of IκBα turns off the NFκB pathway

(107), MAPK phosphatase I (MKP1) dephosphorylates MAPKs.

d. IL-1α function

As established earlier, ProIL-1α is biologically functional in the cytosol. During

necrosis it acts as an alarmin triggering an immune response. This response can be

modulated by the sequestration of ProIL-1α by IL-1R2 (108). This is important in order to

differentiate between necrosis and apoptosis. Under normal apoptotic conditions, ProIL-1α

is sequestered, to avoid inflammation (109).

IL-1α can start an inflammation loop, wherein IL-1α can start the production of more

IL-1α and IL-1β. Membrane-bound IL-1α can induce IL-1 signaling pathway in nearby

immune cells (110). IL-1α can induce the expression of other cytokines needed for the

recruitment of immune cells to the inflammation site. The cells, in turn, make more IL-1. The

IL-1 signaling continues until it’s inhibited (110). The paracrine signaling does not only affect

immune cells. It has been demonstrated that in systemic sclerosis, fibroblasts produce high

levels of IL-1α, which in turn upregulates the expression of IL-6, PDGF-α, IL1R1, and

collagen (111).

21

IL-1α is known to induce other pro-inflammatory cytokines such as COX-2, inducible

nitric oxide synthase (iNOS), IL-6, IL-8, and MMPs. Moreover, IL-1α can increase the

expression of adhesion molecules on endothelial cells, stromal cells and leukocytes, in order

to facilitate cell migration (112).

e. IL-1β function

IL-1β has similar effects as IL-1α in the proliferation and differentiation of innate

immune cells. However, studies in the past decade have been able to elucidate different

functions between the two molecules. Paul et al, has shown that IL-1β can induce the

proliferation of naïve and memory CD4+ T cells after antigen recognition (113). For the

expansion to take place, T cells must express IL-1R1 on their surface. Members of the IL-1

family can interact with individual STATs to promote a specific phenotype (114). IL-1β in

association with STAT3 induces Th17 cells. IL-33 and STAT5 can start a Th2 response,

while, IL-18 and STAT4 a Th1 response. Ben-Sasson et al, published the effects of IL-1 in

the activation of antigen-specific CD8+ T cells, demonstrating its function as an adjuvant

(115).

IL-1β, also, plays a role in hematopoiesis. IL-1β can up-regulate the expression of

receptors for colony-stimulating factors (CSFs) on precursor cells (116). In this manner, IL-

1β can promote the differentiation and expansion of myeloid-derived suppressor cells

(MDSCs), during inflammation (117). MDSCs differentiate into a variety of cells including,

macrophages, granulocytes (112).

f. IL-1 in cancer

In cancer, IL-1α has several functions. It can induce fever, fenestrations in the

vasculature, prostaglandins, pituitary hormones, and collagenases (118). IL-1 can also

boost the immune system by increasing the infiltration of leukocytes to the tumor. Due to the

22

varied functions of IL-1, IL-1 can be either beneficial or disadvantageous in a cancer setting.

The ability to increase inflammation can be used by tumor cells to promote malignancy.

Cancer cells can produce IL-1 (autocrine) and can also induce IL-1 in other cells in a

paracrine fashion. IL-1β has been found in a variety of cancers such as breast, head and

neck, colon, melanoma, etc (119). Patients with IL-1β+ tumors have a decreased survival

rate. The exact mechanism of how IL-1 promotes malignancy has still not been fully

elucidated, although, the consensus is that IL-1 acts indirectly. For example, IL-1 can

promote metastases by up-regulating MMPs, VEGF, IL-8, IL-6, TNF-α and TGF-β. In IL-1

knockout mice, melanoma tumors tend to not establish subcutaneously after inoculation. If

tumors grow, survival is increased due to less lung metastasis. These studies highlight the

importance of IL-1 in angiogenesis, and the extravasation of tumor cells. Saijo et al,

demonstrated that Lewis lung carcinoma cells transduced with IL-1β had no significant

increase in proliferation in vitro (120). Nevertheless, in vivo, these cells, had a higher tumor

growth rate. IHC staining proved that these tumors had more microvessels compared to the

control. The increase in angiogenesis was explained by the elevated expression of VEGF,

and macrophage-inflammatory protein-2 (MIP-2) (120). In other studies, supernatants from

melanoma cell lines (high vs low IL-1) were used to study the effect of IL-1 in endothelial cell

permeability (121). Supernatant from the high IL-1 expression cell line, could increase cell

permeability and the effect was reversed when using IL-1Ra, the IL-1 agonist. IL-1Ra has

also been used to inhibit VEGF production in a colon carcinoma animal model. Another

point to note is the importance of tumor-derived IL-1 in the effectivity of IL-1Ra. SMEL (High

IL-1) and PMEL (low IL-1) melanoma cells were transduced with IL-1Ra and injected into

mice. Only SMEL tumors had a significant reduction in the tumor growth rate. Another

animal study, evaluated systemic administration of IL-1Ra to mice injected with human

cancer xenografts (122). Similar to the previous study, only tumors that had significant levels

of IL-1 responded to the IL-1Ra treatment. These studies suggest the indirect role IL-1 plays

23

in tumorigenesis and the importance of cross-talk between the tumor and the

microenvironment. Lizee et al, described how oncogenes can up-regulate the expression of

IL-1 in melanocytes. Our lab showed that mutant B-RAF significantly increased pro-

inflammatory cytokines including IL-1α/β, IL-6, and IL-8 (123). Co-culture of recombinant IL-

1 with human fibroblasts demonstrated the ability of IL-1 to induce inhibitory molecules on

stromal cells, such as PD-L1, PD-L2 and COX-2. Thus, revealing another function of IL-1 on

the tumor micro-environment.

In some cancers IL-1α, has anti-tumor properties. Fibrosarcoma cells transduced

with IL-1α rarely grow in immune-competent mice. Tumors that grow regress in a mostly

CD8+ T cell-mediated fashion. In this scenario, IL-1α acts as an adjuvant for CD8+ T cells

(124). Furthermore, increase of adhesion molecules, allows for better cell to cell interactions,

improving cytotoxicity.

The dual ability of IL-1 to induce pro- and anti-tumor effects makes it an interesting

target to study and modulate. Proper regulation of the expression of IL-1 could tilt the tumor-

microenvironment.

24

CHAPTER 2:

SPECIFIC AIMS

25

Rationale

Research from our lab demonstrated that melanoma cells up-regulate IL-1

expression via the aberrant activation of the MAPK pathway by mutated B-raf. Culturing IL-1

with tumor-associated fibroblasts resulted in an increase in inhibitory molecules, PD-L1, PD-

L2, and COX-2. Therefore, we suggested a potential mechanism on how tumor cells can

modulate immunosuppressive factors on neighboring cells via IL-1 induction. Moreover,

research has shown that IL-1 is one of the main drivers of innate response, recruiting

neutrophils and other immune cells into sites of inflammation. Therefore, we were interested

in understanding how IL-1α affected melanoma tumor development. We hypothesized that

IL-1α increases immunosuppression in the tumor by promoting the presence of suppressive

cells and factors. We tested our hypothesis by answering the following aims:

Aim 1: Investigate the effect of IL-1α in the melanoma tumor micro-environment during early

tumor development.

Aim 2: Evaluate the clinical potential of anti-IL-1α treatment in combination with other

immunotherapies in melanoma.

We used an anti-IL-1α antibody to demonstrate that blocking IL-1α delays tumor

growth. Moreover, we used mass cytometry to illustrate that IL-1α inhibition decreases anti-

inflammatory myeloid cells during early tumor progression. Analysis of these cells ex-vivo

showed that they had a decreased capacity to inhibit T cell activity via Arg-1 and NO.

Furthermore, we showed that anti-IL-1α effect is not sustained and eventually the tumor

activates alternates pathways to increase myeloid-derived suppressor cells. Results from

our first aim indicated that anti-IL-1α could potentially synergize with T-cell-mediated

immunotherapies. We hypothesized that the decrease of anti-inflammatory cells in the tumor

micro-environment would improve the function of CD8+ T cells in conjunction with checkpoint

26

blockade or peptide vaccine and T cell therapy. Furthermore, we demonstrated that the

combination of anti-IL-1α and anti-PD-1 slowed down tumor growth. Similarly as in anti-IL-1α

monotherapy, in the combination group the major changes observed in the tumor micro-

environment were in the myeloid cells. Our most promising anti-tumor response, was

obtained from combining anti-IL-1α and peptide vaccine with T cell therapy. This

combination increased survival of mice by over 3 months. The effect was obtained due to

the high CD8+ T cell infiltrate and reduced myeloid cell density during the first week after

vaccine treatment.

27

CHAPTER 3:

IL-1α BLOCKADE

28

I. Rationale

To evaluate the effects of IL-1α during tumor progression, we contacted the

company, XBIOTECH, to use the murine equivalent, Flo1-2a, of their human IL-1α

neutralizing antibody. MABp1, is the first true human antibody that targets IL-1α. In a phase

1 clinical trial, MABp1, was found to be well tolerated by patients with no significant side

effects. The trial resulted in metastatic cancer patients having stable disease and some

partial response after treatment (125).

We treated C57BL/6 mice that had been inoculated with B1- F10 melanoma cells

and tracked the changes in the tumor micro-environment, specifically in immune cells. We

hypothesized, that in the B16 melanoma mouse model, IL-1α contributed to

immunosuppression and inhibition of IL-1α would lead to an increase in the immune

response by restructuring the tumor-microenvironment. Understanding how IL-1α

orchestrates the infiltration of immune cells into the tumor will help us understand how

tumors use IL-1α to evade immune responses. Moreover, the insight gained will help us

develop improved therapies for cancer.

II. Results

a. Anti-IL-1α reduces tumor growth in the B16-F10 murine model

As mentioned in the introduction, Weinreich et al, published studies where they

determined that inhibition of IL-1 in vivo was only effective against tumors that expressed IL-

1. We, determined that our B16 F10 (melanoma) cell line expressed significant levels of IL-

1α by western blot (Figure 3A). Based on previous animal studies by Overwijk et al (126),

we optimized the number of cells and day of treatment to model a significant tumor growth

response. We compared treatment regimens targeting other cytokines such as TNF-α and

IFN-γ, and tailored ours as follows: Day 0: tumor inoculation day, Day 3: Start of anti-IL-1α

29

treatment (100 μg intraperitoneally), mice received treatment every three days until the end

of the experiment (Figure 3B). Mice treated at day 3 after tumor inoculation showed signs of

tumor delay compared to the control (Figure 3C). Since the B16 model is quite aggressive,

we euthanized the mice when the tumor burden reached 200 mm2. The spider plots show

that in the control, tumors reached the maximum tumor size limit by day 12 (Figure 3C). The

majority of mice were euthanized on day 15 and only 20% of the mice survived until day 17

(Figure 3D, E). The treated group, reached its maximum tumor burden by day 15. The

majority of mice were sacrificed after day 21. Only 20% of the mice reached day 27. None of

the mice were disease free by the end of the experiment.

30

Figure 3: Anti-IL-1α reduces tumor growth in B16-F10 murine model. (A) Western blot

showing Pro-IL-1α and IL-1α expression in METR, MC38, BJAB, and B16 cells. (B) Anti IL-

1α (Flo1-2a) treatment schedule (C) Spider plots of tumor measurements per treatment

group. (D) Tumor growth curve during the first two weeks of treatment. Data represents

mean ± SEM, N=10 (E) Kaplan-Meier tumor survival curve. Data represents at least 3

independent experiments with 8-10 mice per group.

31

b. Inhibition of IL-1α decreased the myeloid cell infiltrates after one week of

treatment

Even though IL-1α blockade did not result in disease free mice, we were encouraged

by the moderate anti-tumor response. We believed that studying the tumor micro-

environment would give us an insight into how to improve anti-IL-1α therapy in melanoma.

Analysis of our growth curves showed tumor size separation after day 6 (Figure 3D). We

hypothesized that the most significant changes in immune response occurred in that time

frame. We isolated tumors at day 7, after mice received 2 anti-IL-1α treatments (Figure 4A).

Immune cells were enriched by gradient density centrifugation and then analyzed by flow

cytometry. Our analysis focused on the following immune subsets, T cells (CD45+, CD3+), B

cells (CD45+, CD19+), myeloid cells (CD45+, CD11b+), macrophages (CD11b+, F4/80+),

monocytes (CD11b+, Ly6CHi), and granulocytes (CD11b+, Ly6CLo, Ly6GHi) (Figure 4C).

Tumors on day 7, exhibited a weight difference that we took into consideration for our

absolute number calculations (Figure 4B). Compared to the control, IgG, anti-IL-1α tumors

consistently had less immune cell infiltrates with an average drop of 30% (Figure 4D). The

immune cell decrease was mostly due to significantly less monocyte infiltrates in the first

week of tumor development (Figure 4D). T cells increased by at least an average of 35%.

Analysis of CD8/CD4 ratios, showed that the spike in numbers was due to increased CD4+ T

cells (Figure 4E). Macrophages decreased by an average of 20%. B cells were not

significantly affected by loss of IL-1α. Our findings show that blocking IL-α alters immune cell

infiltration in the tumor, as early as the first week of treatment. The main effect was observed

in myeloid subtypes congruent with published data on the effects of IL-1α.

33

Figure 4: Flow cytometric analysis of the immune component of the tumor micro-

environment after one week of IL-1α blockade. (A) Treatment schedule of anti-IL-1α. (B)

Weight (mg) of collected tumors on day 7. (C) Gating strategy for immune populations in the

34

tumor micro-environment. (D) Absolute number of immune infiltrates normalized to tumor

weight. (E) Absolute number of CD8+ T cells and CD4+ T cells normalized to tumor weight.

N=5. Unpaired T-test. Data represents at least 2 independent studies.

35

c. Inhibition of IL-α promotes pro-inflammatory features in myeloid cells

The first round of animal experiments showed the ability and potential of anti-IL-1α in

reconstructing the tumor-microenvironment. To further show the immunosuppressive effects

of IL-1α on immune cells, we used mass spectrometry to categorize the immune infiltrates.

Enriched immune cells, from day 7, were stained with 30 markers (Table 1) and analyzed

using the viSNE algorithm. viSNE allow us to visualize all the immune cell infiltrates in one

graph (Figure 5A). cyTOF analysis was performed in FlowJo using the tSNE pluggin.

Individual group data was concatenated into a single file to create a visual representation of

immune cells across all groups. Additionally, using the concatenated file, we can compare

individual groups since the algorithm creates the same map for all conditions. To identify

changes in immune subsets, we used the density tool to create density heat maps to

highlight clustered populations. viSNE clusters cells according to their similarity in their

marker expression profile, thus cells that have similar markers will be clustered together in

sub-populations. The further the clusters are from each other, the less they have in

common. After the clusters were determined by the algorithm, we manually analyzed each

cluster to confirm they were in fact a different subset. We set the cluster threshold at 70

events, meaning clusters that contained 70 cells or less were considered an artifact.

At first glance, we observed that the three main cell types were monocytes,

macrophages, and B cells. Percentage comparison of the main populations did not show a

major difference between the groups (Figure 5C). However, density plots of each group

showed a significant change in subsets of these populations (Figure 5B), specifically in

macrophages and monocytes.

36

Table 1: cyTOF markers.

38

Figure 5: cyTOF analysis of the tumor micro-environment after IL-1α inhibition on day

7. (A) Color coded composite graph of IgG and anti-IL-1α showing major populations

identified by viSNE algorithm and manual gating. (B) Pseudocolor density maps of IgG and

anti-IL-1α showing differences in subsets of main populations (C) Table showing percent

averages ± SEM. Data represents at least 2 independent experiments.

39

Detailed study of the monocytes, showed 12 unique subset populations. We named

them Mo_1 to Mo_12 (Figure 6). After IL-1α inhibition, Mo_1 and Mo_8 subsets increased

by 5% and 4%, respectively (Figure 7). Mo_1 was the least differentiated subset, mainly

expressing CD11b, Ly6C, and F4/80lo. They express low or no levels of co-stimulatory

molecules (CD80, CD86) or MHC-II (Figure 8, 9). Compared to Mo_1, Mo_8 is more active.

They have increased expression of co-stimulatory molecules, CD80, CD86 and MHC-II. The

Mo_3 subset decreased by 5%. Mo_3 cells express CD11b, Ly6C, CD80, CD86, MHC-II,

and produce moderate levels of IL-α and TNF-α. Important to note was that the most active

subset, Mo_6, was not impacted by IL-α blockade. Mo_6 was the only subset that

expressed Arg-1, IFNγ, IL-1α, IL-6, and TNFα. Moreover, they expressed the proliferation

marker, ki67. Neutralization of IL-1α seems to promote the infiltration of non-activated and

pro-inflammatory monocytes.

40

Figure 6: Pseudocolor density map of monocyte subsets in melanoma tumors on day

7. Mo-1 to Mo-12 represent 12 unique monocyte subpopulations identified by viSNE

analysis on day 7.

41

Figure 7: Percentage of monocyte subsets in melanoma tumors on day 7. Percent of

individual subset out of CD11b, Ly6CHi cells (monocytes). N=3. Data represents at least 2

independent experiments. Unpaired T-test.

42

Figure 8: Marker expression intensity heat map of monocyte subsets. Marker

expression levels of monocyte subsets in IgG (grey column) and anti-IL-1α (red column)

groups. Expression is normalized to the min and max expression per marker. Blue

represents the subset with the lowest expression level and red represents the subset with

the highest expression per row.

44

Figure 9: Histogram of the expression profile of monocyte subsets. Marker profile

of Mo_1 (least differentiated), Mo_3 (intermediate differentiation), and Mo_6 (highly

differentiated). Values represent the min and max MFI per marker ± SEM. Data

represents at least 2 independent experiments.

45

Evaluation of the macrophage compartment led to the identification of 7 subsets

(Figure 10A). Three subsets responded to IL-1α inhibition. The Mac_3 subset decreased by

an average of 10% compared to the IgG group (Figure 10B). The Mac_3 subset expressed

moderate levels of CD11b and F4/80. The Mac_4 subset was reduced by 5%. The Mac_4

cells had the lowest F4/80 expression of all the subsets. They more closely resembled the

“M2” phenotype by expressing known M2 markers, CSF-1R, and PD-L2 (Figure 11). The

most significant change observed was in the Mac_7 subset, which increased by 15% in the

anti-IL-1α group (Figure 10B). Mac_7 cells resemble both M1 and M2 macrophages,

signifying that these cells are in the middle of the macrophage differentiation spectrum. They

are the most activated cells expressing high levels of M1 markers: CD11b, F4/80, CD11c,

CD80, CD86, IFNα, MHC-II, OX-40L, and TNF-α. Moreover, they also express canonical M2

markers such as CD206, Arg-1, and IL-6. It is the only macrophage subset that is actively

proliferating at the tumor site.

46

Figure 10: Macrophage subsets in melanoma tumors on day 7. (A) Pseudocolor density

graph of macrophage subsets in IgG and anti-IL-1α groups. (B) Percentage of subsets out of

47

total macrophages (CD11b, F4/80). N=3, Unpaired T-test. Data represents at least 2

independent experiments.

Figure 11: Marker expression intensity heat map of macrophage subsets. Marker

expression of macrophage subsets in IgG (grey column) and anti-IL-1α (red column) groups.

Expression is normalized to the min and max expression per marker. Blue represents the

subset with the lowest expression level and red represents the subset with the highest

expression per row.

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Figure 12: Histograms of the phenotypic profile of macrophage subsets. Marker profile

of Mac_1 (least differentiated), Mac_3 (int. differentiation), and Mac_7 (highly differentiated).

Values represent the min and max MFI per marker ± SEM. Data represents at least 2


51

d. IL-1α blockade decreases CD8+T cell infiltrates

Tumors treated with anti-IL-α had reduced CD8+ T cell infiltrates. cyTOF analysis

identified 2 CD8+ sub-populations (Figure 13A) differentiated by their Ly6C expression

(Figure 13C). We did not see an increase in TH1 response by IL-1α neutralization based on

the CD8+ infiltrates’ lack of IFNγ, TNF-α, and Granzyme B (Figure 13D). Contrary to CD8+ T

cells, CD4+ T cells increased with lack of IL-1α. Similarly to CD8+ T cells, CD4+ T cells were

separated into 2 subtypes, CD4_1 and CD4_2. CD4_2 cells have a slight increase in CD4

and TIGIT expression compared to CD4_1 (Figure 13E). TIGIT has been shown to act an

inhibitory molecule in CD4 effector T cells. Neither subset had intracellular FoxP3. Blocking

IL-1α does not induce IFN-γ and TNF-α production in CD4_1 and CD4_2 cells (Figure 13F).

Overall, the total T cell infiltrate increases with IL-1α inhibition, compromised by a majority of

CD4+ T cells.

54

Figure 13: IL-1α blockade decreases CD8+ T cell infiltrates in melanoma tumors on

day 7. (A) Pseudocolor density map of T cell subsets. (B) Percentage of CD8 and CD4

subsets. (C) Ly6C expression in CD8_1 (blue) and CD8_2 (red). (D) Expression of CD8+ T

cell activating markers in subsets ± SEM. N=3. Unpaired T-test. Data represents at least 2


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e. Neutralization of IL-1α modulates B cell subsets

Analysis of the B cell subsets showed that 5 distinct populations were present at the

tumor site (Figure 14A). These subsets were differentiated by their level of CD19, B220 and,

MHC-II. Subset B_1, increased by at least 10% in tumors treated with anti-IL-1α. B_1, is the

least activated B cell, having the lowest expression of CD19, B220 and MHC-II. It has no co-

stimulatory molecules, and production of cytokines such as TNF-α, IFNγ and IL-α. The most

activated B cell subtype was the one of two subpopulations that decreased in the treated

group. B_5 has the highest expression of CD19, B220, and MHC-II, and shows signs of

slight up-regulation of OX-40L, TNF-α, and IL-α. Likewise, the other reduced population was

B_3, which has a similar expression profile as B_5. More markers are needed to elucidate

the function of B cells in the tumor micro-environment after IL-1α inhibition.

57

Figure 14: Neutralization of IL-1α modulates B cell subset composition on day 7. (A)

Pseudocolor density map of B cell subsets. (B) Percentage of B cell subsets. (C) Marker

expression histograms of B cell subsets. Values represent the lowest (blue) and highest

(purple) MFI ± SEM. N=3. Unpaired T-test. Data represents at least 2 independent

experiments.

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f. Myeloid cells isolated from tumors treated with anti-IL-1α are inefficient in

suppressing T cells

Flow cytometry and cyTOF analysis showed that lack of IL-1α predominantly affects

myeloid cells. Specifically, IL-1α blockade increased non- or low-differentiated myeloid cells

and decreased M2-like macrophages. M2 macrophages and MDSCs inhibit T cell function.

Therefore we wanted to asses if myeloid cells (CD11b+) treated with anti-IL-1α were less

capable in reducing T cell activation. Since we cannot measure their ability to suppress T

cells in vivo, we isolated myeloid cells from both control and treated groups and measured

their arginase and NO activity, by using an arginase colorimetric assay and the griess assay,

respectively. Inhibition of IL-1α significantly decreased the production of both arginase and

NO in isolated myeloid cells (Figure 15). To further prove their decreased T cell inhibiting

ability, we co-cultured isolated myeloid cells with p-mel T cells for three days and then used

those T cells in a tumor cell killing assay (Figure 17). The tumor cell killing assay measures

caspase 3 expression. Caspase 3 cleavage is activated during cell apoptosis, so increase in

caspase 3 is directly proportional to increase in tumor cell apoptosis. The assay

demonstrated that myeloid cells isolated from tumors treated with anti-IL-1α affected T cell

activity to a lesser degree which led to an increase in tumor killing. Thus, from our data we

can infer that IL-1α increases immunosuppression in B16 tumors by restructuring the tumor-

microenvironment, specifically myeloid cells.

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Figure 15: IL-1α inhibition decreases Arg-1 and NO in myeloid cells isolated from day

7 tumors. CD11b+ positive cells were sorted from tumors. Cell lysates and supernatants

were collected to measure Arg-1 and NO using an arginase colorimetric assay (arginase)

and the griess reaction assay (NO). N=5. Unpaired T-test. Data represents 2 independent

experiments.

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Figure 16: Gating strategy for isolating myeloid cells in the tumor. Immune cells were

isolated from tumors at day 7. Enriched immune cells were sorted on CD45 and CD11b

positive cells.

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Figure 17: Tumor killing assay experimental procedure. Enriched immune cells from

tumors were sorted on CD11b positive cells. After sorting cells were cultured with p-mel T

cells for 3 days. On day 2, DDAO stained B16 cells were plated on a 96 well plate. On day

3, p-mel T cell/myeloid cell mixture was added to B16 plate. After 3 hours, cells were stained

for caspase 3 and analyzed by flow cytometry.

62

Figure 18: Tumor killing assay shows myeloid cells isolated from tumor treated with

anti-IL-1α have less effect on tumor killing. Enriched immune cells from tumors were

sorted on CD11b positive cells. After sorting cells were cultured with p-mel T cells for 3

days. On day 2, DDAO stained B16 cells were plated on a 96 well plate. On day 3, p-mel T

cell/myeloid cell mixture was added to B16 plate. After 3 hours, cells were stained for

caspase 3 and analyzed by flow cytometry. N=3. Unpaired T-test. Data represents at least 2


63

e. Anti-IL-1α effect is not sustainable

Our animal experiments show that inhibition of IL-1α modulates anti-tumor response

by decreasing the infiltration of monocytes into the tumor micro-environment during the first

week of treatment. Since, we did not see any disease free mice, we inferred that IL-1α effect

was short-lived and, eventually, the tumor microenvironment reverts to an

immunosuppressive state, even though anti-IL-1α treatment is administered until the end.

We were able to show this by analyzing mice at the end stage of the experiment. When

comparing the myeloid population at day 7 and at termination day, we can see that the

positive effect seen at day 7 is gone (Figure 19). At termination, the myeloid composition

looks similar in both groups. There is no difference in monocytes and granulocytes.

Likewise, we see a similar effect in macrophages at termination day (Figure 20).

Furthermore, on termination day, Arg-1 and NO expression returns to similar levels seen in

the control during day 7 (Figure 21). At some point, tumors activate alternative pathways

that induce immunosuppression in the tumor micro-environment.

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Figure 19: Percentage of monocytes and granulocytes out of total myeloid cells at

day 7 and termination day. Pseudocolor plot shows that at day 7 there’s a decrease in

monocytes and increase in granulocytes due to anti-IL-1α. The effect is eradicated at

termination day. Percent ± SEM. Data represents at least 2 independent experiments.

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Figure 20: Percentage of macrophages out of total myeloid cells on day 7. Anti-IL-1α

promotes a decrease in macrophages. The effect is gone by termination day. Percent ±

SEM. Data represents at least 2 independent experiments.

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Figure 21: Arg-1 and NO production are similar at termination day in IgG and anti-IL-

1α. Myeloid cells isolated from IgG and anti-IL-1α have similar production of Arg-1 and NO

at termination day. N=3. Unpaired t-test. Data represents at least 2 independent

experiments.

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III. Discussion

Current FDA approved immunotherapies have been shown to have success in

metastatic melanoma patients. We know that tumor-mediated immunosuppression

decreases the effectivity of these treatments in patients. Studies have shown that melanoma

tumors express IL-1. We know that IL-1 coordinates a variety of pathways that leads to an

increase in immunosuppression in the tumor-microenvironment. Thus, we wanted to study

how IL-1α altered immune cells in the tumor. We used, Flo1-2a, the murine equivalent of

MapB1 to treat B16 tumors in C57BL/6 mice. Tumors were treated on day 3 and thereafter

every 3 days until the end of the experiment. As a monotherapy, anti-IL-1α, reduced tumor

growth rate and increased mice survival by 10 days compared to IgG. Tumor growth is

controlled by a variety of mechanism including, increased tumor cell proliferation,

angiogenesis, and reduced immune response. In this project we focus on the immune

response. Anti-IL-1α decreased immune infiltrates at the tumor site and changed immune

cell percentages. The most evident change was observed in monocytes. Monocyte infiltrates

decreased by half compared to IgG group. When we analyzed the different monocyte

subsets in the treated group, we observed an increase in non-activated monocytes, Mo_1.

Non-activated monocytes expressed CD11b and Ly6C. They had no antigen presentation

co-stimulatory molecules and did not produce pro-inflammatory cytokines. The most

differentiated monocyte subsets, Mo_5, Mo_6, and Mo_7 were not affected by anti-IL-1α.

Unlike monocytes, highly active macrophages were the most affected by lack of IL-1α.

Mac_7, expressed markers inducing both pro-inflammatory and anti-inflammatory

responses. We deduced that Mac_7 subset was in the middle of the macrophage

differentiation spectrum thus expressing markers from both extremes. Finally, we saw a

decrease in Mac_4. Based on the marker expression, we can classify Mac_4 as an “M2”

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macrophage involved in anti-inflammatory responses. Mac_4 cells expressed PD-L2, which

is involved in the activation of PD-1 in T cells, resulting in T cell inhibition. Anti-IL-1α

increased T cell infiltrates at day 7 via increase of CD4+ T cells. These T cells did not exhibit

TH1-associated cytokines such as IFNγ, TNF-α, and Granzyme β production. Important to

note, was that CD8+ T cell percentage decreased. Their function was also not changed by

anti-IL-1α. The last immune population we analyzed was B cells. Anti-IL-1α did not alter the

number of B cells but did affect their composition. TNF-α producing B cells were reduced in

the treated group. At day 7, there were no B cell subsets producing co-stimulatory

molecules CD80, and CD86. Unfortunately, we needed more markers to explain the function

of B cells during early tumor progression.

Our data illustrates how blocking IL-1α induces an increase in non-activated cells

and decrease in anti-inflammatory cells. Thus, we can conclude that the tumor micro-

environment favors an anti-tumor response during the first week of tumor growth.

Furthermore, we wanted to show that blocking IL-1α in the tumor micro-environment

reduced the immunosuppressive function in myeloid cells. To do this, we isolated myeloid

cells from tumors and measured their production of Arg-1 and NO. Studies have shown that

Arg-1 inhibits T cell function by diminishing arginine, an amino acid necessary for T cell

activation. NO is involved in T cell apoptosis. Quantification of Arg-1 and NO showed that

myeloid cells isolated from IgG tumors, had higher levels of these molecules compared to

tumors treated with anti-IL-1α. We can infer that IgG myeloid cells have a higher capacity to

inhibit T cell activation. We tested this by using the tumor killing assay. We co-cultured

myeloid cells, isolated from both groups, and p-mel T cells for 3 days in the presence of

gp100 peptide, necessary for T cell activation. We, then, used this mixture to induce B16

killing, which was measured by the increase of caspase 3 cleavage in tumor cells. We noted

that myeloid cells isolated from tumors treated with anti-IL-1α, had a lesser ability to inhibit T

69

cell activation. Therefore, one possible mechanism of action of anti-IL-1α is the reduction of

immunosuppressive cells, allowing proper T cell activation and expansion.

The reduction of myeloid cells by inhibiting IL-α is temporary. When we analyzed

tumors at the termination day, we observed no differences in monocytes and macrophages,

even though we administered anti-IL-1α until the end of the experiment. This could be

attributed to tumors over-expressing IL-1α so the antibody dosage is not enough to

neutralize all available IL-1α. Also, cancer cells could be activating alternative pathways to

drive immunosuppression, which are IL-1α independent.

IV. Material and Methods

a. Mice and tumor cells

Animal experiments performed in this study were approved by the Institutional Animal

Care and Use Committee (IACUC) of the University of Texas MD Anderson Cancer Center.

6 to 12 wk old female C57BL/6 mice were purchased from Charles River Frederick research

model facility (Bethesda, MD). B16.F10 gp100+ is a spontaneous C57BL/6 melanoma

obtained from the National Cancer Institute tumor repository and maintained in RPMI 1640

with 10% FBS, 100 μg ml-1 streptomycin and 100 μg ml-1 penicillin (Invitrogen).

b. Animal Experiments

Antibody against the secreted form of IL-1 α, Flo1-2a, was provided by XBioTech. Rat

IgG2a, κ was purchased from BioXcell. C57BL/6 mice were inoculated with 3 x 105 B16.F10

gp100+ cells s.c. on the belly. Three days later mice were treated with 100 μg of Flo1-2a

(i.p), or 100 μg IgG2a and, thereafter, 3x a week until end of experiment. Tumors were

measured using calipers and collected at determined time points for tumor micro-

environment analysis. For survival experiments, mice were sacrificed when tumors reached

200 mm2 or became too ulcerated.

c. Flow cytometry and CyTOF analysis

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Single cell suspensions were prepared by carefully smashing tumors on a 70 μm strainer

using a plunger from a 3 ml syringe. Cells were kept at 4°C in PBS with 2% FBS until

needed. Intracellular staining was performed using the cytofix/cytoperm kit (BD Bioscience)

according to the manufacturer’s protocol. The following antibodies were used during flow

cytometry analysis, CD45, CD11b, CD3, CD19, CD11c, CD4, CD8, Ly6C, and Ly6G.

Alternatively, cells were stained for CyTOF analysis. Briefly, surface markers were stained in

0.5% BSA-PBS for 1 hr at RT. After washing, cells were stained with 25 μM cisplatin for 1

min. Cells were fixed, permeabilized and stained with intracellular markers for 1 hr at RT,

washed and then stained with 250nM IR-intercalator. Cells analyzed by Helios mass

cytometer.

d. Arginase and NO assays

CD11b+ cells were isolated from tumor and spleen. Arginase activity was measured

using the BioVision Arginase Activity Colorimetric assay kit. The kit indirectly measures

arginase by first reacting arginine with arginase producing an intermediate that reacts with

OxiRed probe. We measure the absorbance of the samples at 570nm and then use an

equation to calculate arginase activity. Sample arginase activity = H2O2 amount from

standard curve /(reaction time X sample volume added into the reaction well) X Dilution

factor. NO was measured using the Griess reagent system from promega. NO is measured

by quantifying one of its products, nitrate. The Griess Reagent system uses sulfanilamide

and N-1-napthylethylenediamine dihydrochloride (NED) under acidic conditions.

Sulfanilamide and NED compete for nitrite. Absorbance was measured at 520nm and

concentration was calculated using a nitrate standard curve.

e. Tumor killing assay

CD11b+ cells were isolated from tumors and spleen. CD11b+ cells were quickly co-

cultured with p-mel T cells isolated from P-Mel-1 TCR/Thy1.1 mice at a 1:1 ratio with 1ng/ml

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of gp100 peptide to activate the T cells. After 2 days, B16 cells were stained with DDAO and

plated on a 96 well plate. On day 3, CD11b/Tcell co-culture was added to B16 cells for 3

hours and then cells were stained for caspase 3. Cells were analyzed by flow cytometry.

f. Statistical Analysis

All results are expressed as (mean ± SEM). Animal group size was (n=10) unless

otherwise indicated. All experiments were repeated at least twice with comparable results.

Data was analyzed using paired and non-paired t-test where appropriate and differences

were determined significant at (P<0.05). Difference in tumor size among several treatments

was evaluated using variance ANOVA. To compare survival, we used the Kaplan-Meier

method and log-rank test. Graphs were created using GraphPad Prism 6 software.

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CHAPTER 4: ANTI-IL-1α IN COMBINATION WITH CHECKPOINT BLOCKADE AND

PEPTIDE VACCINE

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I. Rationale

Data from our first experiments show that Flo1-2a reduces immunosuppression in the

tumor micro-environment. This effect is seen during early tumor progression and dissipates

as the tumor grows. Therefore, we hypothesized that Flo1-2a can be used as a pre-

treatment to improve immunotherapy. We believe that reduction of myeloid cells by anti-IL-

1α will improve the efficacy of T cell-mediated immunotherapies. We picked two different T-

cell mediated immunotherapies. The first, anti-PD-1, has proven moderately effective in

treating metastatic melanoma patients. The second one is a peptide vaccine plus T cell

therapy. This peptide vaccine has been extensively studied by Dr. Overwijk’s lab in B16

melanoma models. The peptide vaccine consists of a combination of naïve p-mel T cells, IL-

2, gp100, anti-CD40, and imiquimod (TLR-7 agonist) (127). From now on we will call this

combination, covax. In this model, IL-2 induces the proliferation of T cells. gp100 activates

naïve p-mel T cells. Anti-CD40, activates APCs. Lastly, imiquimod activates the innate

immune response via TLR pathways. Together, the treatment activates both the adaptive an

innate immune response, producing a strong anti-tumor effect. In the B16 model, both

immunotherapies do not result in disease free mice. We believe, that adding Flo1-2a will

increase mice survival.

II. Results

a. Combination of Anti-IL-1α and anti-PD1 increases survival in B16 tumor

bearing mice.

Similarly to our monotherapy experiments, we started anti-IL-1α and anti-PD1

treatment at day 3 and continued every 3 days until the termination of the experiment

(Figure 22A). We noted that anti-IL-1α alone and anti-PD-1α alone produced similar results

(Figure 22B). We saw no difference in mice survival between both monotherapies (Figure

22C). The combination increased survival by 10 days. Compared to the IgG group, the

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combination of anti-IL-1α and anti-PD-1 increased survival by 2 weeks. Moreover, 20% of

the mice survived over 1 month. Analysis of the tumor micro-environment, at day 7, in the

combination group showed similar infiltration pattern of myeloid cells as seen in the anti-IL-

1α monotherapy group. For instance, the absolute number of monocytes decreased by half

when we added anti-IL-1α to the anti-PD-1 group (Figure 23). We also noted an increase in

granulocytes in the combination group. On the other hand, we saw a decrease in total T

cells in the combination group. Our data shows that, as expected, Flo1-2a decreased

monocytes even in the presence of anti-PD-1.

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Figure 22: Anti-IL-1α and anti-PD-1 delay tumor growth. (A) Treatment schedule. Flo1-

2a and anti-PD-1 treatment started at day 3 via (IP) injection. (B) Spider plots of tumor size

from each animal. (C) Kaplan-Meier tumor survival curve. Data represents at least 2


77

Figure 23: Anti-IL-1α and anti-PD1 treatment modulates immune infiltration on day 7.

Monocyte and granulocyte infiltration retains the same pattern seen as anti-IL-1α

monotherapy. N=3. Unpaired T-test. Data represents at least 2 independent experiments.

78

b. Combining anti-IL-1α and anti-PD-1 alters macrophage subsets.

Analysis of macrophages, show 11 distinct sub-populations (Figure 25). In the

combination group, we observe a decrease in non-activated macrophages represented by

Mac_2 (Figure 26). Moreover, there was a decrease in Mac_4, which are macrophages that

have high levels of MHC-II, but do not have co-stimulatory molecules on their surface

(Figure 27, 28). We also noted a decrease in Mac_10, which most closely resembles “M2”

macrophages by expressing PD-L2, CD206 and Arg-1. Mac_7 and Mac_11 represent

subsets that are in between “M1” and “M2” phenotypes. They, both express, varying levels

of antigen presentation-associated molecules and anti-inflammatory molecules. These two

subsets increase in the combination group. IL-1α and PD-1 blockade promotes the increase

in pro-inflammatory macrophages and decreases of anti-inflammatory macrophages.

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Figure 24: Anti-IL-1α and anti-PD-1 modulate macrophage function on day 7. (A)

Composite viSNE map of IgG, anti-IL-1α, anti-PD1, and anti-IL-1α and anti-PD-1 groups

showing the distribution of immune cells in the tumor microenvironment.

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Figure 25: Pseudocolor map of macrophage subsets on day 7. viSNE analysis identified

11 distinct macrophage populations.

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Figure 26: Macrophage subset percentages in anti-IL-1α and anti-PD-1 treated tumors

on day 7. N=3. Unpaired T-test. Data represents at least 2 independent experiments.

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Figure 27: Marker expression intensity heat map of macrophage subsets. Marker

expression intensity of macrophage subsets in IgG (grey column), anti-IL-1α (red column),

anti-PD-1 (green column), and anti-IL-1α + anti-PD-1 (blue column) groups. Expression is

normalized to the min and max expression per marker. Blue represents the subset with the

lowest expression level and red represents the subset with the highest expression per row.

84

Figure 28: Marker expression histograms of macrophage subsets found in anti-IL-1α

and anti-PD-1 group. Blockade of IL-1α and PD-1 promotes pro-inflammatory

85

macrophages, while reducing anti-inflammatory macrophages. Values show min and max

values ± SEM. Data represents at least 2 independent experiments.

86

c. Combining anti-IL-1α and anti-PD-1 decreases non-activated monocytes.

In anti-IL-1α treated tumors, we observed the increase of non-activated monocytes.

However, in tumors treated with the anti-IL-1α and checkpoint blockade, we observe a

preference for activated monocytes instead of non-activated. viSNE analysis separated

monocytes into 7 subsets (Figure 29A). Mo_1 and Mo_6 were the two subsets that were

reduced in the combination group (Figure 29B). Mo_1 are non-activated monocytes, while

Mo_6 are monocytes with MHC-II expression, only. The Mo_3 subset increased in the

combination group. They are pro-inflammatory cells that produce TNF-α and IL-1α.

88

Figure 29: IL-1α and PD-1 blockade decreases non-activated monocytes on day 7. (A)

Pseudocolor density map showing 7 monocyte subsets. (B) Monocyte subset percentage

(C) Marker expression of monocyte subsets found in tumors treated with anti-IL-1α and anti-

PD-1. Values show the min and max MFI ± SEM. N=3. Unpaired T-test. Data represents at

least 2 independent experiments.

89

I. Combining anti-IL-1α and anti-PD-1 decreases CD8+ Ly6C+ T cells.

viSNE analysis segregated CD8+ T cells into 3 subtypes (Figure 30A). CD8_1 and

CD8_2 subsets expressed moderate levels of Ly6C (Figure 30C). These two subpopulations

were decreased in the combination group (Figure 30B). CD8_1 and CD8_2 are naïve T cells

that do not express IFNγ and TNF-α. Anti-IL-1α and anti-PD-1 increased the CD8_3 subset

in the tumor micro-environment. CD8_3 is the only subset that does not express Ly6C. The

function of Ly6C on T cells is still not completely clear. It has been associated with T cell

homing to the lymph nodes (128). Crosslinking of Ly6C, decreases IL-2 production by T

cells. Therefore, we can infer that reduction of Ly6C+ T cells increases IL-2 availability,

which in turn promotes proliferation in CD8+ T cells.

91

Figure 30: IL-1α and PD-1 blockade promotes CD8+LY6C- T cells on day 7. (A)

Pseudocolor density map showing 3 CD8+ T cell subsets. (B) CD8+ T cell subset

percentages. (C) Marker expression histograms of CD8+ T cell subsets. Values represent

the low (blue) and highest (red) value ± SEM. N=3. Unpaired T-test. Data represents at least

2 independent experiments.

92

II. Combining anti-IL-1α and anti-PD-1 decreases CD4+ TIGIT+ T cells on day 7.

viSNE analysis separated CD4+ T cells into 5 different sub-populations (Figure 31A).

Only two subpopulations changed due to the blockade. Inhibition of IL-1α and PD-1

increased TNF-α producing CD4+ T cells (Figure 31C). The most significant change was in

CD4_3 cells. CD4_3 cells express inhibitory receptor, TIGIT. TIGIT signaling blocks NF-κB,

PI3K and MAPK pathways (129). Studies have shown that presence of CD4+ TIGIT+ cells

correlates with poor clinical outcome in melanoma patients due to their potent

immunosuppressive ability (130). In our model, checkpoint blockade with anti-IL-1α induces

CD4+ T cells.

94

Figure 31: Anti-IL-1α and anti-PD-1 reduce CD4+ TIGIT+ T cells. (A) Pseudocolor density

map showing 5 CD4+ T cell subpopulations. (B) CD4 subtype percentage out of total T cells.

(C) Marker expression histograms. Values show low (blue) and max (red) MFI per marker ±

SEM. N=3. Unpaired T-test. Data represents at least 2 independent experiments.

95

III. Combining anti-IL-1α with peptide vaccine and T cell therapy.

In our previous animal experiments, we were able to show that decreasing myeloid

cells via IL-1α inhibition improved the anti-tumor response of anti-PD-1. In that model, we

activated endogenous T cells by blocking, inhibitory receptor, PD-1. We then wanted to

combine anti-IL-1α with a peptide vaccine treatment, optimized by Overwijk’s lab. Dr.

Overwijk has specialized in using peptide vaccines with naïve T cells to treat B16 tumor

cells. We decided to first pre-treat with anti-IL-1α before starting vaccine protocol (Figure

32A). We wanted to reduce myeloid cells first before adding exogenous T cells into the

mice. We hypothesized that pre-treatment would significantly improve T cell activation. We

injected tumor cells on day 0 and started anti-IL-1α treatment on day 3 and continued every

3 days for 21 days (Figure 32A). Vaccine treatment started on day 6. The vaccine consisted

of naïve p-mel T cells, IL-2, for T cell proliferation, gp100, for T cell activation, anti-CD40, to

activate APCs, and imiquimod, to activate the innate response. The combination of IL-2,

anti-CD40, and imiquimod will be called covax. Survival experiments showed a significant

increase in mice survival compared to peptide vaccine with P-mel T cells (Figure 32B). Mice

survived for more than three months in the combination group. The most startling effect of

the combined treatment was the significant tumor growth delay. Figure 32C shows pictures

of the tumors from p-mel + gp100 + covax and anti-IL-1α + p-mel + gp100 + covax groups. It

is evident that on day 40 the combination group has barely palpable tumors. Contrary to the

peptide vaccine alone group, there is no sign of ulceration in the anti-IL-1α and peptide

vaccine group. Due to the small size of the tumors cyTOF analysis was not feasible. We

switched to 18 color flow to analyze the tumor micro-environment after one week of

treatment (day 14). After one week of vaccination, analysis of the tumor micro-environment

showed that 80% of immune cells are CD8+T cells in the combination group (Figure 34A).

Myeloid cells barely infiltrate the tumor. 70% of the CD8+ T cells produce TNF-α (Figure

34B). The dramatic reduction in myeloid cells does not last. Analysis, of the tumor

96

microenvironment on day 21 shows the re-population of myeloid cells (Figure 35). At this

stage, we do not observe large percentages of monocytes. But as we have seen in the

monotherapy, as the tumors become non-responsive to treatment they start increasing

myeloid infiltrates in the tumor, specifically monocytes.

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Figure 32: IL-1α blockade and peptide vaccine with naïve CD8+ T cells extends mice

survival to over 3 months. (A) Treatment schedule (B) Kaplan-Meier curve showing

increased survival in the P-mel + covax + anti-IL-1α group compared to the P-mel + covax

group. (C) Pictures of tumors on day 40. Combination group shows no ulceration at this time

point.

98

Figure 33: Flow cytometric analysis of the tumor micro-environment showing reduced

myeloid infiltration. Tumor isolated on day 14 were stained and analyzed using flow

cytometry. Data represents at least 2 independent experiments.

99

Figure 34: Immune response of anti-IL-1α and peptide vaccine consists of

predominantly TNF-α producing CD8+ T cells. (A) Percent of CD8+ T cells present in the

tumor on day 14 in each group (B) Plots showing TNF-α and Ly6C expression in CD8+ T

100

cells isolated from each group. Values show MFI ± SEM. Data represents at least 2


101

Figure 35: Myeloid cells infiltrate the tumor after two weeks of anti-IL-1α and peptide

vaccine treatment. Tumors were isolated on day 21 and analyzed by flow cytometry to

identify monocytes and granulocytes. Values show MFI ± SEM. Data represents at least 2


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III. Discussion

Chapter 3 of this dissertation, shows the potential of IL-1α blockade as a treatment in

metastatic melanoma. In chapter 4, we further prove the advantages of inhibiting IL-1α

before treating with T-cell mediated immunotherapies. Our studies with checkpoint blockade

and anti-IL-1α demonstrated the combined effect in increased survival of mice compared to

the control group. Analysis of the tumor micro-environment showed a decrease in non-

activated monocytes and an increase of pro-inflammatory monocytes. This is the opposite

effect seen in chapter 3. We believe this change is due to anti-PD-1-mediated responses.

Pro-inflammatory monocytes are major sources of TNF-α. TNF-α has dual roles in tumor

progression. TNF-α can activate the NFκB pathway leading to increased proliferation in

cells. Thus, TNF-α can increase the expansion of immune cells and tumor cells, equally. A

proper balance of TNF-α production is required to maintain an anti-tumor response.

Blockade of IL-1α and PD-1, also increases pro-inflammatory macrophages. These cell

secrete IFNγ, TNF-α, IL-1α and IL-6. These cytokines induce Th1 and Th2 responses in T

cells. Moreover, we were able to study macrophages in hybrid states. These macrophages

do not conform to the classical M1 vs M2 phenotype. They expressed both pro and anti-

inflammatory molecules. Inhibition of PD-1 and IL-1α decreased CD8+ T cell infiltration while

increasing CD4+ T cells. Specifically, CD8+, Ly6C+ subsets were reduced in the tumor micro-

environment. Furthermore, blockade promoted the expansion of CD4+ TNF-α+ T cells, while

decreasing an immunosuppressive subset identified by their TIGIT expression. Taken

together, we can see that inhibiting IL-α even in the presence of anti-PD1 skews the tumor

micro-environment to induce an anti-tumor response. In both models, we see a decrease in

monocytes cells. Our data suggests that monocytes are a key driver of immunosuppression

in our model.

Our last model, anti-IL-1α and peptide vaccine with naïve T cells, illustrates why we

studied the effects of IL-1α on the tumor micro-environment. Analysis of immune filtration

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showed that in the combination group mostly CD8+T infiltrated the tumor. The effect was

temporary but enough to increase survival by a month.

IV. Material and Methods

Animal Experiments

Anti PD-1 antibody (clone 29F.1A12) and Rat IgG2a, κ were purchased from

BioXcell. The synthetic H-2Db restricted gp10025-33 peptide (KVPRNQDWL) was a gift from

Dr. Willem Overwijk’s lab. C57BL/6 mice were inoculated with 3 x 105 B16.F10 gp100+ cells

s.c. on the belly. Three d later mice were treated with 100 μg of Flo1-2a (i.p), 200 μg Anti

PD-1 (i.p) or 100 μg IgG2a and, thereafter, 3x a week until end of experiment. For the

peptide vaccine experiment, on d 6 after tumor inoculation, mice were injected with 1000

naïve P-mel 1 T cells (i.v) and were vaccinated with one s.c injection in each flank of PBS

containing 100 μg of hgp100 and 50 μg of anti CD40. 5% Imiquimod cream was applied

topically on the vaccination site. 100,000 UI of hrIL-2 was injected (i.p) on d 0 and then 2x a

d for 2 d for a total of 500,000 UI. Tumors were measured using calipers and collected at

determined time points for tumor micro-environment analysis. For survival experiments,

mice were sacrificed when tumors reached 200 mm2 or became too ulcerated.

18 color flow cytometry

Single cell suspensions were prepared by carefully smashing tumors on a 70 μm

strainer using a plunger from a 3 ml syringe. Cells were kept at 4°C in PBS with 2% FBS

until needed. The following antibodies were used during flow cytometry analysis CD45,

CD3, CD8, CD11b, CD11c, CD19, CD25, CD49b, CD103, Arg-1, CD206, FoxP3, F4/80,

Ly6C, and Ly6G. Surface markers were stained in 2% FBS-PBS for 1 hr at RT. After

washing, cells were fixed and permeabilized using the cytofix/cytoperm kit (BD Bioscience)

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according to the manufacturer’s protocol. Intracellular markers were stained for 1 hr at RT,

washed and then fixed with 1.6% paraformaldehyde. Cells were analyzed by X-20 fortessa.

Statistical Analysis

All results are expressed as (mean ± SEM). Animal group size was (n=10) unless

otherwise indicated. All experiments were repeated at least twice with comparable results.

Data was analyzed using non-paired t-test where appropriate and differences were

determined significant at (P<0.05). To compare survival, we used the Kaplan-Meier method

and log-rank test. Graphs were created using GraphPad Prism 6 software.

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CHAPTER 5: DISCUSSION

Current advancements in technology and science have facilitated the design of

improved therapies to treat metastatic melanoma. Clinical trials have demonstrated the

effectiveness of immunotherapies in treating a wide range of cancer patients, especially

treatments that boost the activity of endogenous CD8+ T cells. Nevertheless, a major barrier

against effective T-cell mediated treatments is the rise of immunosuppressive cells and factors

in the tumor micro-environment mediated by the tumor. Our lab, published a tumor-dependent

mechanism in which abnormal activation of the MAPK pathway, via the induction of mutated

B-raf, led to the increase of NFκB, a modulator of IL-1 (123). The tumor-derived IL-1 can then

increase inhibitory molecules on neighboring cells in a paracrine fashion, potentially inhibiting

T cell cytotoxicity. The purpose of my project was to understand how IL-1α altered the

architecture of the tumor micro-environment. We hypothesized that blocking IL-1α would lead

to a decrease in immunosuppressive factors, improving T cell response. To gain insight into

IL-1α function in tumor development, we contacted XBioTech. They currently synthesize an

anti-human and anti-mouse IL-1α neutralizing antibody. Animal studies in aim 1,

demonstrated that blocking IL-1α led to a delay in tumor development. Due to the malignancy

of B16-F10, we did not expect to see any disease free mice. IL-1α blockade prolonged mouse

survival by ten days. The increase in survival and reduced growth rate can be attributed to

several functions of IL-1α in melanoma. First, IL-1α up-regulates VEGF, which promotes

tumor angiogenesis (131). Increase in vasculature leads to an increase in oxygen and

metabolic factors necessary for optimal cell growth and proliferation. Moreover, IL-1 directly

impacts the expression of adhesion molecules and metalloproteinases involved in the process

of tumor invasion (132). Increased expression of integrins on the surface of tumor cells, allow

cancer cells to more efficiently enter into circulation and seed in other areas. Lastly, IL-1α

increases other growth factors that can activate cell growth and proliferation pathways, such

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as IL-8, IL-6, TNF-α and TGF-β (132). Thus reducing available IL-1α limits the tumor’s ability

to grow and expand, as demonstrated by our animal studies. However, due to an innate

redundancy in growth and proliferation pathways, tumors can activate other pathways to

overcome the effects of inhibiting IL-1α. Therefore, it is possible for some tumors to be more

sensitive to IL-1α blockade. Survival experiments showed that after six days of treatment,

tumor sizes started diverging in the anti-IL-α group. Certain tumors responded to anti-IL-1α

treatment and displayed a slower tumor growth rate, while others continued growing at a rapid

rate. The control group, which was injected with IgG, rarely survived past two weeks after

initial tumor inoculation. Aside from the effects of IL-1α on cancer growth and proliferation, we

were interested on the effects of IL-1α on infiltrating immune cells during early tumor

development. Of most interest were the changes in infiltrating myeloid cells, since IL-1

expression has been associated with the increase of MDSCs in the tumor (133). We used

flow cytometry and mass cytometry to phenotype all immune cells on day seven. As stated

earlier, size discrepancies were apparent after day 6, so we chose to analyze the tumor micro-

environment after one week of treatment to try to understand how immune cells respond to

lack of IL-1α, while the tumor is still developing. Mass cytometry or cyTOF allows the use of

35 surface and intracellular antibodies simultaneously. Contrary to flow cytometry, the use of

metal conjugates in lieu of fluorochromes, leads to a significant reduction in background. Our

findings demonstrated that IL-1α neutralization, results in a decrease in immune cell infiltrates,

identified by their expression of CD45. Further analysis, showed that mainly myeloid cell

trafficking was affected, consistent with published data. This effect on myeloid cells may be

explained by a decreased expression of known chemokines involved in myeloid cell

recruitment, such as, CXCL1 (neutrophils), CXCL14 (monocytes), and CXCL13

(macrophages) (134). Focusing on specific myeloid subsets, monocyte infiltration is drastically

reduced. The monocyte composition changed favoring non-activated monocytes and

monocytes expressing antigen presentation molecules: CD80, CD86 and MHC-II. This shift

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may be due to a loss of anti-inflammatory cytokines needed for suppressive monocyte

polarization, like IL-6, CCL2, CXCL1, and CXCL10 (134). Moreover, we found an increase in

TNF-α expression across several cells when treated with anti-IL-1α. TNF-α induces the

differentiation of myeloid cells into pro-inflammatory cells (135). Thus, reduced IL-1α could

directly induce TNF-α production in immune cells which in turn can promote a pro-

inflammatory response. Similarly, macrophages displayed a propensity to express and

produce factors involved in a TH1 response. Specifically certain subsets had an increase

production of IL-1α and TNF-α, which are known cytokines linked to M1 macrophages.

Interestingly, there were macrophage subsets that exhibited both M1 and M2 markers. This

can be explained by the plasticity of macrophages. When encountering TH1 and TH2 signals,

macrophages can quickly move across the differentiation spectrum (16). On day 7, the Mac_7

subset expressed M2-associated molecules such as Arg-1, and CD206, while also,

expressing M1-associated markers MHC-II, CD80, CD86 and TNF-α. Finally, the last

observed changed in myeloid cells was the increase of granulocytes. Research has shown

that in most cancers, granulocyte presence correlates with a poor prognosis. Specifically, an

increase in the neutrophil to lymphocyte ratio is associated with worse outcomes in solid

tumors (136). Early decrease in neutrophil to lymphocyte ratio due to targeted treatment leads

to favorable outcomes. Thus, reduction in granulocytes would be an advantageous result.

However, our data shows the opposite result. Tumors treated with anti-IL-1α have an increase

in granulocytes. We observed no difference, in their surface marker profile, between

granulocytes in the control group versus the treated group. Anti-IL-1α granulocytes exhibited

higher TNF-α levels, hinting at the possibility of N1 neutrophils. Neutrophils and PMN-MDSCs

have similar phenotypes and can only be distinguished by extensive functional studies in vitro.

Analogous to macrophages, neutrophils can be commonly categorized into N1 and N2

neutrophils. N1 neutrophils express elevated levels of TNF-α and ICAM1a and have tumor

killing abilities (137). Alternatively, N2 neutrophils produce IL-6, CCL2, CXCL1 and CXCL10,

108

iNOS, and Arg-1. N2 neutrophils have been linked to help in tumor initiation, growth,

proliferating and metastatic spread (137). Based on the elevated levels of TNF-α in myeloid

cells treated with anti-IL-1α, we can speculate that the infiltrating granulocytes can be N1

neutrophils. Ex-vivo analysis of immunosuppressive factors in myeloid cells, identified by

CD11b, showed a decrease in arginase-1 and nitric oxide production, suggesting that the

majority of anti-IL-1α myeloid cells have reduced immunosuppressive abilities. Moreover, we

directly demonstrated that these cells have a decreased capacity in inhibiting T cell function

and activity, in vitro. T cell analysis shows that on day 7, IL-1α inhibition increases T cell

infiltration, specifically CD4+ T cells. Neither CD8+ nor CD4+ T cells expressed TH1-related

factors such as IFN-γ, Granzyme B, and TNF-α. Thus, on day 7, IL-1α blockade does not elicit

a TH1 response in T cells. It could be possible that at a later time point, we can observe the

induction of these factors, or that in our model T cell response is not TH1-dependent. Based

on our studies IL-1α blockade is not sufficient to mount a robust immune response. It is clear,

that suppressive myeloid cell reduction is not enough to obtain a lasting anti-tumor effect.

Finally, analysis of terminal day tumors, showed that the changes observed in the tumor

micro-environment by inhibiting IL-1α are not sustained. Termination day tumors in both

control and treated groups have similar myeloid cell compositions, even though anti-IL-1α is

administered until the end of the experiment. It’s possible that tumors over-express IL-1α so

the antibody dosage is not enough to neutralize all available IL-1α. In the future, testing

progressive escalating doses of anti-IL-1α could be beneficial in prolonging the anti-tumor

response. Cancer cells could be activating alternative pathways to drive immunosuppression,

which are IL-1α independent. This immune evasion mechanism is commonly seen in patients

that have undergone targeted therapy. For instance, patients treated with B-raf inhibitor exhibit

abnormal up-regulation of other genes in the MAPK pathway which leads to increased

proliferation and survival. Similarly, tumors could be up-regulating other cytokines including

IL-6, IL-8, and IL-10 which are also involved in the recruitment of myeloid cells into the tumor

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(138). Taking into consideration all these factors, to induce long lasting anti-tumor effects, it

is necessary to combine IL-1α blockade with other therapies. In aim 2, we test the synergy of

IL-1α blockade with T-cell mediated immunotherapies.

The second animal model tested the joint effect of anti-IL-1α with anti-PD-1. The

decrease in myeloid cells, due to IL-1α inhibition combined with the improved T cell activity,

due to anti-PD-1, furthered delayed tumor growth, compared to anti-IL-1α monotherapy alone.

Twenty percent of mice survived for a month. Analysis of the tumor micro-environment painted

a similar picture as seen in the monotherapy setting. Overall myeloid cells decreased in the

combination group, as expected. This change was due to a decrease in the monocyte

population. The remaining monocytic cells presented pro-inflammatory molecules on their

surface. The combination of anti-IL-1α and anti-PD-1 increased the differentiation of

monocytes, based on their increased number of markers displayed. This could be partially

due to the unintended activation of monocytes by anti-PD-1. Studies have shown that certain

macrophages and monocytes express PD-1 on their surface (139). Ligation of the PD-1

receptor may have started an activation cascade that converted monocytes into activated pro-

inflammatory monocytes or macrophages. This could also explain the unexpected rise of

macrophages in the combination group. Joint activity of anti-IL-1α and anti-PD-1 produced an

environment that favored M1 macrophage activation and proliferation. Startlingly, there was

no strong TH1 T cell response in tumors treated with the combination. Anti-PD-1 works by

preventing T cell activity inhibition, so we were expecting the presence of activated TH1 T

cells. However, at this time point both CD8+ and CD4+ T cells lacked IFN-γ, and Granzyme B

production. Based on this and data from our previous model, it would be valuable to test T cell

activity at different time points to understand the kinetics of T cell activation in the absence of

IL-1α. From our findings, it is evident that additional factors are needed to prime and activate

T cells. Therefore, in our last model we combined anti-IL-1α with a peptide vaccine and naïve

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CD8+ p-mel T cells. The peptide vaccine contained the peptide, gp100, which can prime p-

mel+ T cells, anti-CD40, increases T cell priming by activating APCs, IL-2, induces T cell

proliferation and imiquimod, a TLR agonist that increases APC activity. Contrary to our anti-

PD-1 and anti-IL-1α model, we started anti-IL-1α treatment three days before the vaccine day,

this resulted in a significant decrease of myeloid cell infiltration after one week of vaccine

treatment. With this combination, mice survival was extended for a month compared to the

peptide vaccine alone. Tumor growth was significantly delayed. Pictures of tumors on day 40

show barely palpable tumors with no ulceration. Analysis of the tumor micro-environment after

one week of vaccine, showed that almost 80% of immune cells were CD8+ T cells. Consistent

with a Th1 response, CD8+ T cells expressed high levels of TNF-α and IFN-γ. We saw no

significant difference in IFN-γ levels, but saw an increase in TNF-α, which was the most

common up-regulated molecule across all cells. As in the earlier models, TNF-α plays an

important role in the conversion of the tumor micro-environment into a less

immunosuppressive surrounding. Studies have shown that low levels of IL-1 can induce local

inflammation which leads to an immune response; however, high levels of IL-1 can instead

induce anti-inflammatory responses to attenuate the increased immune response (110). So,

low levels of IL-1α can directly increase TNF-α production in cells in a paracrine fashion

resulting in a favorable anti-tumor response. This point seems critical in patient care since

depending on the expression of IL-1 in tumors, the anti-IL-1 dosage might have to be

optimized in a patient to patient basis to obtain satisfactory results.

Our data suggests that anti-IL-1α has potential in delaying tumor growth when

combined with the appropriate therapy in patients. Nevertheless, there a few things to

consider. In our most promising model, we obtained almost complete inhibition of myeloid cell

trafficking into the tumor, which greatly increased T cell presence, but most importantly TNF-

α and other TH1 cytokines. Unregulated synthesis of these cytokines could induce

111

autoimmune diseases in patients. Therefore, in patients it would be appropriate to combine

anti-IL-1α with CAR T cell therapy, to avoid having to add the extra immune modulating

factors. In some cases, patients would have to be administered steroids to decrease the

cytokine storm. Moreover reduction of anti-inflammatory myeloid cells potentially reduce

factors involved in angiogenesis, tumor growth and survival. Due to this, IL-1α blockade is

more pertinent when tumors are just starting to grow to limit several pro-tumor mechanisms

simultaneously. Anti-IL-1α could be administered to patients that have just undergone tumor

resection, to avoid possible recurrence and further metastases. For advanced solid tumors,

IL-1α monotherapy is not sufficient and would have to be combined with other T cell-mediated

immunotherapies. Patients undergoing ACT could be administered with anti-IL-1α after whole-

body irradiation to prevent the trafficking of suppressive cells to the tumor site before T cell

infusion.

This project started by trying to understand how IL-1α modulates immune response

during tumor development. In this manuscript, we suggest that the anti-tumor effect is mostly

myeloid cell driven. Lack of IL-1α promoted the differentiation of pro-inflammatory monocytes

and macrophages, which are involved in T cell priming and direct cancer cell killing. The

reduction in suppressive myeloid cells led to an increase in anti-tumor response. Most

importantly, we were able to wield the capabilities of anti-IL-1α to limit immunosuppression to

improve checkpoint blockade and peptide vaccine immunotherapies in mice. Further studies

with other immunotherapies would give us a better insight into which treatments synergize

with anti-IL-1α blockade. For instance, we have only explored anti-PD-1, future studies could

focus on treating mice with anti-IL-1α and anti-CTLA-4. We would expect an even better anti-

tumor response since anti-CTLA-4 also blocks the activation of Tregs. Another possible

treatment route is targeted therapy. Melanoma cells over express oncogenic B-raf and MEK.

Combining anti-IL-1α with B-raf and MEK inhibitors could potentially increase the activity of T

112

cells due to the increase in available stimulating peptides. Finally, to improve our phenotypic

profile, we could combine genetic profiling based on single cell RNA-seq. In this manner, we

could identify which genes are up- or down- regulated with IL-1α blockade. This could give us

a hint into other possible targets to combine with anti-IL-1α.

113

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Brenda Melendez was born in Lima, Peru, the daughter of Yanet Terrazas and Felix

Melendez. After completing her work at Clements High School, Houston, Texas, in 2006,

she entered the University of Houston in Houston, Texas. She received the degree of

Bachelor of Science in biomedical engineering from U of H in May, 2011. In September of

2011 she entered The University of Texas MD Anderson Cancer Center UTHealth Graduate

School of Biomedical Sciences.

α blockade reduces immune suppression in the …

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