Characterization of the inflammatory infiltrates associated
with oral epithelial dysplasia and oral squamous cell
carcinoma
by
Hayder Ali Mahdi
A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Dentistry University of Toronto 2017
© Copyright by Hayder A. Mahdi 2017
Abstract
Oral cancer is a devastating disease which represents a serious public
health challenge. In this study, we evaluated a total of 49 patients’ samples
diagnosed with hyperkeratosis without dysplasia, benign polyps, epithelial
dysplasia and oral squamous cell carcinoma (OSCC). Fluorescent
Immunohistochemistry (FIHC), confocal microscope and multichannel
colocalization (multichannel fluorescent confocal analysis or MFCA) were
used to characterize the inflammatory infiltrate in OSCC and epithelial
dysplasia and compare it to other conditions.
Our results show a distinct profile of inflammatory cells in OSCC when
compared to other lesions. Also, gradual increases in the CD4/CD8 and NLR
ratios were identified when moving from hyperkeratosis to more severe pre-
malignant conditions and SCC.
This is an important line of research that describes a novel method to identify
the population of different inflammatory cells in oral biopsies and our result
supports the implementation of MFCA as a potential diagnostic marker and
predictor of malignant transformation.
ii Acknowledgements
I have a long list of people to thank who helped me directly and indirectly in my research as well as the throughout the 3 years of my academic life. I would like to start with my supervisor, Dr. Marco Magalhaes who offered me a generous support and helped me a lot throughout my thesis. He helped me to understand the principal concepts of immunohistochemistry and shared with me his vast knowledge in this field in order to come with the best experimental design. He was so patient and supportive especially at the beginning of the study when we had to change the analysis methods few times in order to reach to the most representative and reproducible one. I will also never forget the support of my advisory committee Dr.
G. Bradley and Dr. H. Tenenbaum. A special thanks to Denise Eymael at Dr.
Magalhaes lab. She taught me about staining protocols and helped me a lot throughout the different steps of my thesis. Also, I would like to thank all my supervisors in the Oral Radiology department who helped me during my specialty training. A special thanks to Dr. Michael Pharoah who was a great teacher. He also inspired me to understand Radiology as disease processes and encouraged me to continue during tough times. I would like to finish with a big thanks to my family. My wife Rula Shunnar, who encouraged me first to apply for the Oral Radiology program 17 years after finishing my B.D.S degree in Dentistry and took full responsibility of the family to keep me dedicated to my study and research. Of
Course, my 2 lovely daughters Ayah and Jana were big supporters for me. It was funny enough to share common subjects with them about schools and exams. They were always saying: daddy is busy and we should not disturb him.
iii Table of Contents
Page
Acknowledgements iii
Table of Contents iv
List of Figures viii
List of Tables x
List of Supplemental Tables xi
Abbreviations xiii
Chapter 1: Introduction 1
• 1.1 Cancer 2
• 1.2 Oral cancer 3
• 1.3 Inflammation and Cancer 5
o 1.3.1 Inflammation and tumor initiation 7
o 1.3.2 Inflammation and tumor promotion 8
o 1.3.3 Inflammation and invasion and metastasis 9
• 1.4 Tumor associated inflammatory cells 10
o 1.4.1 CD4+ T-lymphocytes 10
o 1.4.2 CD8+ T-lymphocytes 13
o 1.4.3 Natural Killer T-lymphocytes 14
o 1.4.4 B-lymphocytes 15
iv o 1.4.5 Neutrophils 17
o 1.4.6 Eosinophils 18
o 1.4.7 Macrophages 20
o 1.4.8 Plasma cells 21
Chapter 2: Hypothesis and objectives 23
• 2.1 Hypothesis 24
• 2.2 Objectives 24
Chapter 3: Materials and Methods 25
• 3.1 Study Population 26
• 3.2 FIHC staining protocol 28
o 3.2.1 Slides preparation 28
o 3.2.2 Deparaffinization 28
o 3.2.3 Antigen retrieval 28
o 3.2.4 Permeabilization 29
o 3.2.5 Blocking non-specific binding 29
o 3.2.6 Application of primary antibodies 30
o 3.2.7 Application of secondary antibodies 31
o 3.2.8 Application of DAPI staining 33
o 3.2.9 Application of mounting media and cover slip 33
• 3.3 Controls 34
v • 3.4 Imaging with confocal microscope 34
• 3.5 Data analysis 37
o 3.5.1 Colocalization by staining intensity correlation 37
o 3.5.2 Region of interest selection 39
o 3.5.3 Statistical analysis 40
Chapter 4: Results 42
• 4.1 Comparing the infiltrate of different inflammatory cells 43
o 4.1.1 Hyperkeratosis 43
o 4.1.2 Benign polyps 44
o 4.1.3 Mild dysplasia 45
o 4.1.4 Moderate/severe dysplasia 46
o 4.1.5 Squamous cell carcinoma 47
• 4.2 Comparison of inflammatory infiltrate stratified by diagnosis 48
o 4.2.1 T and B lymphocytes and NK cells 48
o 4.2.2 Neutrophils and Eosinophils 50
o 4.2.3 Plasma cells and Macrophages 50
• 4.3 Specific inflammatory cells ratios 51
o 4.3.1 CD4-to-CD8 ratio 51
o 4.3.2 Neutrophils-to-lymphocytes ratio 53
vi Chapter 5: Discussion 54
• 5.1 Distinct profiles of inflammatory infiltrates 55
• 5.2 Inflammatory cells that are significantly increased in carcinoma 56
• 5.3 Important ratios to consider 57
o 5.3.1 CD4-to-CD8 ratio 57
o 5.3.2 Neutrophil-to-lymphocyte ratio (NLR) 58
• 5.4 Inflammatory cells with no change in pattern 59
• 5.5 Challenges and limitations 59
Chapter 6: Conclusions ad Future directions 61
• 6.1 Conclusions 62
• 6.2 Future directions 62
Supplemental Tables 63
References 75
vii List of Figures
Page
Figure 3.1 - Hematoxylin and Eosin slides of oral mucosa lesions 27
Figure 3.2 - Basic concepts of confocal microscope 36
Figure 3.3 - Colocalization by staining intensity correlation with automatic 38 threshold calculation based on Costes et al. 2004
Figure 3.4 - Region of interest selection 39
Figure 3.5 – Colocalization for the detection of CD8+ cells 41 In moderate/severe dysplasia of the tongue
Figure 3.6 – Colocalization for the detection of neutrophils 41 In SCC of the tongue
Figure 4.1 - Scatterplot comparing the relative area of the inflammatory 44 infiltrate for 8 different cells in hyperkeratosis
Figure 4.2 - Scatterplot comparing the relative area of the inflammatory 45 infiltrate for 8 different cells in benign polyps
Figure 4.3 - Scatterplot comparing the relative area of the inflammatory 46 infiltrate for 8 different cells in mild dysplasia
Figure 4.4 - Scatterplot comparing the relative area of the inflammatory 47 infiltrate for 8 different cells in moderate/severe dysplasia
Figure 4.5 - Scatterplot comparing the relative area of the inflammatory 48 infiltrate for 8 different cells in SCC
viii Figure 4.6 - Scatterplots comparing the relative inflammatory infiltrate 49 areas of CD8, CD4, B cells and NK cells in 5 different oral lesions
Figure 4.7 - Scatterplots comparing the relative inflammatory infiltrate 50 areas of neutrophils and eosinophils in 5 different oral lesions
Figure 4.8 - Scatterplots comparing the relative inflammatory infiltrate 51 areas of plasma cells and macrophages in 5 different oral lesions
Figure 4.9 - CD4-to-CD8 ratio 52
Figure 4.10 - Neutrophils-to-lymphocytes ratio 53
ix List of Tables
Page
Table 3.1 – List of primary antibodies with targeted inflammatory cells 31
Table 3.2 – List of secondary antibodies with targeted primary antibodies 32
Table 3.3 – Quorum spinning disk confocal microscope specifications 34
x List of Supplemental Tables
Page
Table 4.1 – Tukey’s multiple comparisons test for hyperkeratosis 63
Table 4.2 – Tukey’s multiple comparisons test for benign polyps 64
Table 4.3 – Tukey’s multiple comparisons test for mild dysplasia 65
Table 4.4 – Tukey’s multiple comparisons test for moderate/severe 66
dysplasia
Table 4.5 – Tukey’s multiple comparisons test for SCC 67
Table 4.6 – Tukey’s multiple comparisons test for CD8+ cells 68
Table 4.7 – Tukey’s multiple comparisons test for CD4+ cells 68
Table 4.8 – Tukey’s multiple comparisons test for B cells 69
Table 4.9 – Tukey’s multiple comparisons test for NK cells 70
Table 4.10 – Tukey’s multiple comparisons test for Neutrophils 71
Table 4.11 – Tukey’s multiple comparisons test for Eosinophils 71
Table 4.12 – Tukey’s multiple comparisons test for Plasma cells 72
Table 4.13 – Tukey’s multiple comparisons test for Macrophages 73
xi
Table 4.14 – Tukey’s multiple comparisons test for CD4/CD8 ratio 73
Table 4.15 – Tukey’s multiple comparisons test for Neutrophils/ 74
Lymphocytes ratio
xii Abbreviations
ANG1 - Angiopoietin 1
A-NK - Adherent NK cells
ANOVA - Analysis Of Variance
APC - Antigen Presenting cells
ASCs - Antibody-secreting Cells
CBDCA - Carboplatin
CDDP - Cisplatin
CTL - Cytotoxic T Lymphocytes
CTX - Chemotherapy Medications
DCs - Dendritic cells
ECP - Eosinophil Cationic Protein
EDN - Eosinophil-derived Neurotoxin
EMT - Epithelial-Mesenchymal Transition
EPO - Eosinophil Peroxidise
xiii
ER - Endoplasmic Reticulum
FASL - FAS Ligand
FGF - Fibroblast Growth Factor
FIHC - Fluorescent Immunohistochemistry
GC - Germinal Centers
GM-CSF - Granulocyte Monocyte Colony-Stimulating Factor
HNSCC - Head and Neck Squamous Cell Carcinoma
IC - Immune Complexes
IDO - Indoleamine 2,3-dioxygenase
IFNs - Interferons
IFN-β - Interferon Beta
IFN-γ - Interferon Gamma
Igs - Immunoglobulins
IL - Interleukins
IL-12Rβ2 - Interleukin-12 Receptor β2-chain
xiv
LAK - Lymphokine-activated Killer cells
LTC4 - Leukotriene C4
M1 - Anti-tumor Phenotype Macrophages
M2 - Pro-tumor Phenotype Macrophages mAb - Monoclonal Antibody
MBP - Major Basic Protein
MDSCs - Myeloid-Derived Suppressor cells
MHC-I - Major Histocompatibility Complex-class 2
MIP-1 α - Macrophage Inflammatory Protein-1 alpha
MMPs - Matrix Metalloproteinases
MV - Membranous Microvesicles
N1 - Anti-tumor Phenotype Neutrophils
N2 - Pro-tumor Phenotype Neutrophils
NF-kB - Nuclear Factor-kappa B
NK cells - Natural Killer cells
xv
NLR - Neutrophils-to-Lymphocytes Ratio
OED - Oral Epithelial Dysplasia
OSCC - Oral Squamous Cell Carcinoma
PAF - Platelet-activating Factor
PBS - Phosphate Buffered Saline
PDM - Product of the Difference from the Mean
PTX - Paclitaxel
RIA - Relative Inflammatory Area
RNI - Reactive Nitrogen Intermediates
ROI - Region of Interest
ROS - Reactive Oxygen Species
SCC - Squamous Cell Carcinoma
SDCM - Spinning Dual-disk Confocal Microscope
SEM - Standard Error of the Mean
STAT3 - Signal Transducer and Activator of Transcription 3
xvi
TAMs - Tumor-associated Macrophages
TATE - Tumor-associated Tissue Eosinophilia
TBS-T - Tris-Buffered Saline-Tween20
TFH - T Follicular Helper
TGF-β - Transforming Growth Factor beta
TNF-α - Tumor Necrosis Factor Alpha
TRAIL - Tumor Necrosis Factor-related apoptosis-inducing ligand
T-reg - T regulatory cells
UPA - Urokinase-type Plasminogen Activator
VEGF - Vascular Endothelial Growth Factor
xvii
Chapter 1 Introduction
1
1.1 Cancer
Cancer is a multifactorial disease that is caused by genetic aberrations that lead to uncontrolled and continuous proliferation of cells (Lobo et al. 2008). The
Canadian cancer statistics (Canadian Cancer Society 2016) show that cancer is the leading cause of death in Canada, being responsible for 30% of all deaths.
Most of cancer morbidity and mortality are caused by invasion and metastasis.
Cancer cells can spread into, and invade adjacent tissues by direct extension or travel to distant places in the body through the blood or the lymph system and form new tumors far from the original tumor through a process called
“metastasis”. As cells progress from normal to neoplastic state, they acquire different biological capabilities that enable them to continuously grow, resist cell death and eventually metastasize. Hanahan et al. (2011) described 6 hallmarks of cancer and subsequently added tumor-promoting inflammation as a consequential characteristic that is important for the acquisition of other cancer hallmarks. It is important to understand that solid tumors are not only a mass of proliferating cells. They rather contain multiple distinct cell types that interact with one another. This tumor-associated stroma is called the “tumor microenvironment” and it is composed of fibroblasts, blood vessels, inflammatory cells and extracellular matrix. This stroma is an active participant in tumorigenesis which keeps evolving during tumor progression and thereby enabling primary growth, invasion and then metastases.
2
Tumor Grading refers to the histopathologic evaluation of the degree of resemblance between the tumor cells and normal tissue of origin. Staging refers to the clinical parameters used to quantify the disease based on tumor size and spread into regional lymph nodes or distant locations. The most popular staging protocol is the Tumor-Node-Metastasis (TNM) Staging system. Pages et al.
(2010) proposed an immune scoring based on the type, density and location of lymphocyte infiltrates as a novel prognostic factor for use in addition to the TNM
Staging system to predict disease-free survival and to aid in decisions regarding adjuvant therapies in early stages of cancers.
Cancer treatment is usually multimodal and depends on several factors including cancer type, stage and grade. The most commonly used treatments include surgery, radiation therapy and chemotherapy. However, within the last few decades, new adjunct lines of treatment including hormonal therapy, immunotherapy and molecular therapy have been developed following new advances in molecular biology and genetics (Borghaei et al. 2009; Urruticoechea et al. 2010)
1.2 Oral Cancer
Oral and pharyngeal cancers represent the sixth most common cancers in the world. In some regions in South Asia, oral cancer is the most common cancer in men, and may contribute up to 25% of all new cases of cancer (Warnakulasuriya et al. 2009). In the United States, there were approximately 45,750 new cases of
3 oral and oropharyngeal cancers in 2015 and an estimated 8,650 deaths from the disease (Siegel et al. 2015). The estimated new cases of oral cancers among
Canadians in 2016 were 4,600 with approximately 1,250 deaths resulting from the disease (Canadian Cancer Statistics 2016).
The relative 5-year overall survival rate for oral cancer is approximately 63%, dropping to 36% in patients with advanced, metastatic disease (Canadian
Cancer Statistics 2016). In addition to high mortality secondary to local destruction and distant metastasis, advanced oral carcinomas are associated with high morbidity due to extensive surgical procedures and concomitant radiotherapy and chemotherapy treatment (Forastiere et al. 2001).
Although OSCC is not linear in its development, it is commonly preceded by a range of tissue and cellular alterations. These conditions are termed oral epithelial dysplasia (OED) and are classified under the umbrella of potentially malignant disorders of the oral mucosa (Van der Waal, I. et al. 2009).
Prediction of malignant transformation and early detection represents a key factor for successful treatment which can control the disease progression, reduce the mortality and morbidity and increase the survival rate.
Studying the inflammatory response that is associated with development of
OSCC may provide insight into malignant transformation of epithelial dysplasia and help in early detection of OSCC.
4
1.3 Inflammation and Cancer:
Inflammation is a physiologic process generated by the body in response to injury, infection, or irritation. In acute stages, the inflammatory process is vital to combat infection and promote healing; however, chronic inflammation can cause tissue damage. Epidemiological studies have shown that chronic inflammation predisposes individuals to various types of cancers and the host response to malignancies shows several similarities to inflammation and wound healing processes (Coussens et al. 2002).
The relationship between inflammation and cancer has been recognized for many decades. In 1863, Virchow hypothesized that the origin of cancer was at sites of chronic inflammation while Dvorak et al. (1986) considered tumors as wounds that fail to heal. Today, the causal relationship between inflammation, innate immunity and cancer is more widely accepted.
The hallmarks of cancer-related inflammation include the presence of inflammatory cells and inflammatory mediators similar to that seen in chronic inflammatory responses, and tissue repair. Immune cells that infiltrate tumors are engaged in an extensive and dynamic crosstalk with different cells within the tumor microenvironment including cancer cells, fibroblasts, endothelial cells, pericytes, and other mesenchymal cells (De Visser et al. 2006) and they play decisive roles at different stages of tumor development (Grivennikov et al. 2010).
The tumor microenvironment contains innate immune cells including:
5 macrophages, neutrophils, mast cells, dendritic cells, and natural killer cells as well as adaptive immune cells including T and B lymphocytes.
Immune cells affect malignant cells through production of cytokines, chemokines, growth factors, prostaglandins, reactive oxygen and nitrogen species. Recent findings in the field of tumor immunology demonstrated that both tumor- promoting inflammation and anti-tumor immunity coexist along the path of tumor progression (Bui et al. 2007) which led to the formulation of the cancer Immuno- editing hypothesis which has three phases including elimination, equilibrium and escape (Dunn et al. 2002; Dunn et al. 2004). It is the expression of various immune mediators and modulators as well as the abundance and activation state of different cell types in the tumor microenvironment that dictate in which direction the balance is tipped and whether tumor-promoting inflammation or antitumor immunity will dominate (Lin and Karin et al. 2007; Smyth et al. 2006).
Coussens et al. (2002), Colotta et al. (2009), Grivennikov et al. (2010) and
Hanahan et al. (2011) demonstrated that tumor-associated inflammation is a key component of the tumor microenvironment, and the immune response besides having anti-tumor activity demonstrates a paradoxical effect of enhancing tumorigenesis.
Inflammatory cells not only contribute to the development and expression of certain cancer hallmarks, but they are also directly involved at different stages of tumorigenesis including initiation, promotion, invasion and metastasis
(Grivennikov et al. 2010). While activation of different classes of oncogenes will initiate an inflammatory response, inflammation may promote cancer
6 development and contribute to proliferation and survival of malignant cells, angiogenesis, invasion and metastasis.
DeNardo et al. (2010); Inoue et al. (2006) and Mantovani et al. (2010) identified the presence of alternatively activated (polarized) immune responses that work as regulators of the inflammatory process, and will eventually lead to pro-tumor immunity. These responses involve both the innate system (including macrophages and neutrophils) as well as the adaptive system (including B and T lymphocytes). The balance between the conflicting inflammatory activities will either lead to tumor regression or progression (Hanahan et al. 2011). Tumor regression is associated with infiltration by mature Dendritic cells (DCs),
Cytotoxic T Lymphocytes (CTL) and type 1 T-helper cells. Contrasting with this, tumor growth is facilitated via immune mediated immunosuppression and neoangiogenesis following infiltration of tumors with immature DCs, Myeloid-
Derived Suppressor cells (MDSCs), M2 macrophages, as well as T regulatory (T- reg) cells (Talmadge et al. 2010; Gabrilovich et al. 1999; Fidler et al. 1976).
1.3.1 Inflammation and tumor initiation:
Tumor initiation is a process in which normal cells acquire the first mutational hits that sends them on the tumorigenic pathway by providing growth and survival advantages over their neighbors. Inflammatory cells and mediators can destabilize the cancer cell genome through a variety of mechanisms that can induce direct DNA damage through the production of Reactive Oxygen Species
7
(ROS) and Reactive Nitrogen Intermediates (RNI). Alternatively, inflammatory cells may be a source of different cytokines and growth factors including Nuclear
Factor-kappa B (NF-kB), Tumor Necrosis Factor Alpha (TNF-α), and Signal
Transducer and Activator of Transcription 3 (STAT3). These cytokines affect
DNA repair systems, altering cell cycle checkpoints and induce expression of anti-apoptotic genes like Bcl2 that can increase cell survival (Colotta et al. 2009;
Nowell et al. 1976; Moore et al. 1999).
1.3.2 Inflammation and tumor promotion:
Tumor promotion is the process of tumor growth from a single initiated cell into a fully developed primary tumor. Inflammatory cells within the tumor microenvironment are powerful tumor promoters, producing an environment that favors tumor growth, facilitating genomic instability and promoting angiogenesis.
A wide array of chemoattractants released by both malignant and stromal cells in tumors recruit a diverse leukocyte population from the tumor vasculature including: neutrophils, dendritic cells, macrophages, eosinophils, mast cells, as well as lymphocytes (Coussens et al. 2002). Following a dynamic crosstalk with cancer cells, the inflammatory cells continue to supply a wide range of cytokines, cytotoxic mediators including ROS, soluble mediators of cell killing, such as,
Interleukins, Interferons (IFNs) and TNF-α (Kuper et al. 2000; Wahl et al. 1998).
Also, the inflammatory cells produce pro-angiogenic growth factors and vascular- modulating enzymes including Vascular Endothelial Growth Factor (VEGF) and
8
Angiopoietin (Ang) factors that help to tip the angiogenic switch to maintain ongoing angiogenesis (Qian et al. 2010; Zumsteg et al. 2009; Murdoch et al.
2008; De Palma et al. 2007). The immune cells infiltrating tumors can regulate tumor growth and progression into the next phase (invasion and metastasis).
1.3.3 Inflammation and invasion and metastasis:
Tumor invasion is defined as the ability of cells to become motile and to navigate through the extracellular matrix within a tissue or to infiltrate the immediately surrounding neighboring tissues. Tumor metastasis is a multistage, sequential and selective process during which malignant cells spread from the primary tumor to discontiguous organs (Talmadge et al. 2010; Fidler et al. 2003). Egeland et al. 2010; Talmadge et al. 2010; Qian et al. 2010). The available evidence suggests that interactions of cancer cells with adjacent tumor-associated stromal cells can induce phenotypic changes leading to loss of cellular adhesion junctions, epithelial to mesenchymal transition, expression of matrix-degrading enzymes and increased motility (Hanahan et al. 2011; Fidler et al. 2003; Karnoub et al. 2007). The transformed epithelial cells will eventually acquire the ability to invade the underlying connective tissue (Peinado et al. 2004). Invasion requires extensive proteolysis of the extracellular matrix at the invasion front. Both macrophages and neutrophils at the tumor periphery can foster local invasion by supplying matrix-degrading enzymes such as Matrix Metalloproteinases (MMPs) and Cysteine Cathepsin proteases (Kessenbrock et al. 2010; Palermo et al.
9
2008). Glogauer et al. (2015) showed how neutrophils increase the invasiveness of OSCC through the activation of invadopodia-dependent mechanism.
The pathogenesis of metastasis is dependent on both the intrinsic properties of the tumor cells and the host response (Fidler et al. 1982). The formation of clinically relevant metastases represents the survival and growth of selected subpopulations of cells that preexist in primary tumors (Talmadge et al. 2010).
The “seed and soil” hypothesis that was first proposed in 1889 by the English surgeon, Stephen Paget (Paget et al. 1889) is now widely accepted although the
“seed” may now be identified as a progenitor cell, initiating cell, cancer stem cell, or metastatic cell, and the “soil” represents host factors, stroma, niche, or organ microenvironment (Langley et al. 2007). Finally, inflammation may promote metastasis through the production of mediators that increase vascular permeability leading to tumor cell extravasation (Murdoch et al. 2008).
1.4 Tumor associated Inflammatory cells:
1.4.1 CD4+ T-lymphocytes:
CD4+ T cells represent a highly heterogeneous population of cells that develop along different functional lineages. The main subtypes of the CD4+ T lymphocyte include Th1 and Th2 lymphocytes. Th1 lymphocytes play an important role in regulating the cellular immune response by up-regulating antigen processing and
10 presentation on major histocompatibility complex [MHC] I and II molecules by professional Antigen Presenting cells (APCs) as well as stimulating growth, differentiation, and survival of antigen-specific cytotoxic T cells. The Th1 cells characteristically express Interleukin-12 Receptor β2-chain (IL-12Rβ2) on their surfaces that allow them to respond to, and get activated upon exposure to IL-12 secreted by activated macrophages (Zhou et al. 2009). Within the tumor microenvironment, Th1 cells can directly kill tumor cells by releasing high levels of Interferon Gamma (IFN-γ), Tumor Necrosis Factor Alpha (TNF-α) and cytolytic granules following strong antigen-specific activation (DeNardo et al. 2010).
Therefore, Th1 responses can directly and indirectly produce anti-tumor programs that restrain cancer development. On the other hand, Th2 lymphocytes characteristically express high levels of IL-4, IL-5, IL-6, IL-10 and IL-13. These cytokines enhance the humoral immune responses directed by B cells (Parker et al. 1993). However, within the tumor microenvironment, Th2 cells can inhibit apoptosis and induce proliferation of some cancer cells (Chin et al. 1991). Also, they interfere with T cell-mediated cytotoxicity through Inducing T cell anergy which is defined as a hyporesponsive state in which the lymphocyte remains alive but it is functionally inactivated (Pollard et al. 2004). It is now clear that a spectrum of CD4+ T cell subtypes are present in human tumors, and the role they play in promoting or inhibiting tumor development likely has to do with the type of CD4+ T cell subtype that is either recruited to, or accumulated within the tumor microenvironment. In fact, a shift from a Th1 to a Th2 cells profile has been reported in progressive cancer cases (Pellegrini et al. 1996) which is
11 thought to be one of the major contributors to the failure of T-cell-mediated immunity against tumors. There is a small but important subset of CD4+ T cells
+ + [CD4 25 ] called regulatory T (Treg) cells (Asano et al. 1996) , distinguished from
CD4+ helper T cells by the expression of high levels of the IL-2R α-chain also known as CD25 (Yu, P. et al. 2006). These cells secrete IL-10 and Transforming
Growth Factor beta (TGF-β), but no secretion of IFN-γ. They have an important role in preventing autoimmune diseases by suppression of self-reactive T cells.
Recent studies showed that the prevalence of Treg were significantly higher in the peripheral blood, tumor-infiltrating lymphocytes, and regional lymph node lymphocytes in patients with breast cancers when compared with controls
(Liyanage et al. 2002). Wieckowski et al. (2009) demonstrated that Tumor- derived Membranous Microvesicles (MV) in the serum of cancer patients
+ + significantly promoted the proliferation of CD4 25 Treg cells. When the Treg was
+ + − co-cultured with activated CD8 cells or CD4 25 cells, Treg significantly suppressed their proliferation and secretion of IFN-γ and therefore, suppressed the cell-mediated adaptive immunity. It is not clear whether the regulatory cells that accumulate in the tumor site are ones that naturally exist in the host, or whether they initially arrive as helper CD4+ T cells, but convert to regulatory cells by encountering the suppressive tumor environment (Yu et al. 2006). Tumor-
+ + induced tolerance mediated by T CD4 25 Treg cells has been demonstrated in a variety of tumor types in mice (North et al. 1984) and it is thought to play a considerable role in regulating tumor immunity by suppressing tumor- specific T- cell responses, and thereby hindering tumor rejection (Yu et al. 2006).
12
1.4.2 CD8+ T-lymphocytes:
CD8+ T cells are a critical subpopulation of T cells and they are important mediators of adaptive immunity. They represent a well-documented effector of immunity by interacting with, and killing infected or abnormal cells through the
Major Histocompatibility Complex-Class 1 (MHC-I) receptor. For the full activation and differentiation of naive CD8+ T cells into Cytotoxic T Lymphocytes
(CTLs), cross-presentation of antigens that have been captured by APCs such as
DCs plays a dominant role (Huang et al. 1994; Spiotto et al. 2002). In inflammatory conditions such as viral infections, direct activation of APCs can subsequently prime antigen-specific CTL responses without the need for mediators (MacDonald et al. 2000). An alternative CD4+ T cells-dependent pathway is also demonstrated to prime CTL, both directly and through stimulation of professional APC (Yu et al. 2006). In many types of cancers, due to unavailability of tumor antigens for cross-presentation, APCs require activation by CD4+ T cells before they can induce CTL response (Spiotto et al. 2002).
Therefore, CD4+ T-cell help has been considered essential for the induction of
CTL responses against tumors in most cases (Yu et al. 2006). After the clearance of antigen, the majority of effector CTLs undergo apoptosis, while a minor portion converts into lymphocytes with a memory phenotype (Yu et al.
2006). It is generally accepted that memory cells persist in circulation subsequent to an effector response.
13
1.4.3 Natural Killer [NK] T lymphocytes:
Natural-killer (NK) cells are important components of the innate immune system and have diverse biological functions including recognition and destruction of certain microbial infections and neoplasms (Cerwenka et al. 2001). Both CTLs and NK cells are the most likely effector cells for an efficient anti-tumor immunity
(Brigati et al. 2002; DeNardo et al. 2008; Abbas et al. 2003; Katou et al. 2007).
NK cells were shown to have the main role in killing MHC class-I-deficient tumor cells in vitro (Ljunggren et al. 1985) and in vivo (Kärre et al. 1986). NK-cell functions are regulated by the balance of inhibiting and activating signals that they receive through their various classes of receptors (Smyth et al. 2002). Resting NK cells circulate in the blood, but, following activation by various stimuli, they are capable of extravasation and infiltration into most tissues that contain pathogen- infected or malignant cells (Wiltrout et al. 1984; Glas et al. 2000). These stimuli include cytokines, such as Interleukins (IL): IL-2, IL-12, IL-15 and type-I IFNs (α and β), which increase NK cytolytic, secretory, proliferative and anti-tumor functions. Once activated, NK cells suppress tumor cells through a variety of effector mechanisms including: direct killing through secretion of cytotoxic granules (such as granzymes) which lyse target cells and direct binding to tumor cell receptors through FAS Ligand (FASL) or TNF-related apoptosis-inducing ligand (TRAIL) with subsequent induction of tumor-cell apoptosis. NK cells can also suppress tumor cells indirectly through the stimulation of adaptive T- and B- cell immune responses, activation of APCs, and the secretion of variety of cytokines such as GM-CSF, IL-3, TNF-α, and IFN-γ. In patients with cancer, NK-
14 cell function has been shown to be impaired, as determined by the reduced proliferation, response to IFNs and cytotoxicity of these cells of cancer patients
(Whiteside et al. 1994; Whiteside et al. 1998). It has been shown that some tumors secrete immunosuppressive cytokines, such as TGF-β or IL-10 that might interfere with the number and function of NK cells (Smyth et al. 2002). Many clinical trials have been proposed to improve the anti-tumor effect of NK cells. These include endogenous activation of the patient’s own NK cells through administration of cytokines (including IL-2), adoptive use of ex vivo, expanded autologous or donor- derived Adherent NK (A-NK) cells or Lymphokine-activated Killer (LAK) cells
(Benyunes et al. 1993; Hayes et al. 1995).
1.4.4 B-lymphocytes:
B-lymphocytes are the effector cells of humoral immunity that terminally differentiate into immunoglobulin-secreting plasma cells. These cells serve multiple functions that either positively or negatively influence cellular immunity.
Their positive role includes serving as APCs and providing co-stimulatory signals to T cells (Crawford et al. 2006; Bouaziz et al. 2007). B cells could induce T-cell anergy. by producing TGF-β (Knoechel et al. 2005; Olkhanud et al. 2011) and IL-
10 (DiLillo et al. 2010; Matsushita et al. 2008; Inoue et al. 2006) that subsequently negatively regulate inflammation. These conflicting roles for B cells in tumor immunity have been reported by several studies (Coughlin et al. 2004;
Houghton et al. 2005; Schreiber et al. 2000). During tumor development, cancer patients often develop antibodies to tumor-associated antigens. However, this
15 autoantibody production leads to the deposition of Immune Complexes (IC) within neoplastic tissue. Signaling of these complexes can activate several pro- tumor activities, including angiogenesis and tissue remodeling. Also, it has been found that in B-cell-deficient mice, enhanced antitumor immunity was associated with increased IFN-γ production and increased T-cell and NK cells activities
(Inoue et al. 2006; Schüler et al. 1999; Perricone et al. 2004, Tadmor et al.
2011). New studies showed that treating patients with CD20 Monoclonal
Antibody (mAb) (Rituximab) reduces the presence of B cells and
Immunoglobulins (Igs). That in turn fosters the development of reprogrammed
Tumor-associated Macrophages (TAMs) which express increased levels of angiostatic molecules (CXCL10 and CXCL 11) and CCR chemokines which enhance CD8+ T cell infiltration of malignant tumors (Affara et al. 2014; Andreu et al. 2010; González-Martín et al. 2011; Hong et al. 2011; Barbera-Guillem et al.
2000). Such therapy prevented neoplastic progression to the dysplastic/carcinoma in situ state when used as a monotherapy (Affara et al.
2014; Braselmann et al. 2006; Silverman et al. 2006; McLaughlin et al.1998), and significantly slowed the Squamous Cell Carcinoma (SCC) growth and reduced tumor vascular density when delivered in combination with Chemotherapy
Medications (CTX) like: Cisplatin (CDDP), Carboplatin (CBDCA), and Paclitaxel
(PTX), an effect that has not been achieved by administration of CTX alone
(Gökbuget et al. 2006; Tarella et al. 2010; Chapoval et al. 1998).
16
1.4.5 Neutrophils:
Neutrophils are key mediators of the innate immune system and they are the most abundant type of leukocytes (Simmons et al. 1974). Neutrophil activation is essential to protect the host system against infections and promote normal healing (Magalhaes et al. 2009; Mayadas et al. 2014). Neutrophils are characterized by an efficient chemotaxis that allows them to migrate from the blood vessels to the site of inflammation. After reaching the site of injury, neutrophils are stimulated by opsonized bacteria/particles, immune complexes, cytokines that bind to different specialized receptors (Magalhaes et al. 2014).
Neutrophils are highly proteolytic and motile cells which allow them to have direct contact with various cells of the tumor microenvironment (Magalhaes et al. 2014).
The polarization of neutrophils into “pro-tumor” and “anti-tumor” subpopulations was suggested by Fridlender et al. (2009). The anti-tumor neutrophils (N1) is characterized by increased ROS formation, reduced MMP-9 expression, and apoptosis after (IFN-β) stimulation. The pro-tumor Neutrophils (N2) are thought to develop after TGF-β stimulation (Lee et al. 2011), and showed increased expression of arginase, MMP-9 and collagenase enzymes. They also promote leukocytes recruitment (Fridlender et al. 2012) which subsequently initiate angiogenesis and supply growth factors. Based on mouse studies, evidence shows a functional modulation within the tumor between pro-tumor and anti- tumor neutrophils (Moses et al. 2016). TGF-β and type I interferon are major inducers of this functional switch in murine models (Fridlender et al. 2009;
Andzinski et al. 2016). In cancer patients, neutrophils use a multitude of
17 mechanisms to promote angiogenesis, tumor initiation, progression, invasion and metastasis (Kuang et al. 2011; Wang et al. 2014; Glogauer et al. 2015). MMPs,
ROS, VEGF, arginase 1 and multiple cytokines have been identified as major mediators in these processes (Uribe-Querol et al. 2015; Tecchio et al. 2013).
Recent studies have shown that neutrophils in the cancer microenvironment may modulate the clinical behavior and increase the invasiveness and metastasis of
OSCC through the activation of invadopodia formation within the tumor cells
(Glogauer et al. 2015) and facilitating EMT-mediated by neutrophil-derived TGF-
β (Moses et al. 2016; Hu et al. 2015). Finally, the Neutrophils-to-Lymphocytes ratio (NLR) is a well-established marker of advanced disease and poor prognosis in various cancers (Szkandera et al. 2013; Guthrie et al. 2013) including Head and Neck Squamous Cell Carcinoma (HNSCC) (He et al. 2012).
1.4.6 Eosinophils:
Eosinophils are granulocytic leukocytes derived from bone marrow hematopoietic progenitors. They are identified by the ability to concentrate acidophilic staining into secretory granules within their cytoplasm. Mature eosinophils enter the blood and migrate to various tissues and organs, where they reside and help maintain homeostasis. These cells are implicated in the pathogenesis of numerous inflammatory processes, such as allergies and parasitic infections (Pereira et al.
2011). The eosinophils express membrane receptors for a wide range of cytokines including: IL-1, IL-3, IL-4, IL-5, IL-8, IL-10, IL-12, IL-13, Granulocyte
Monocyte Colony-Stimulating Factor (GM-CSF), Macrophage Inflammatory
18
Protein-1 alpha (MIP-1 α), IFN-γ and TNF-α (Jacobsen et al. 2007). Under diverse stimuli such as infections and tumors, the eosinophils are able to release vesicular contents, such as: Eosinophil Cationic Protein (ECP), Major Basic
Protein (MBP), Eosinophil Peroxidise (EPO), Eosinophil-derived Neurotoxin
(EDN), IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-13, IL-18, INF-γ, TGF- β, Platelet- activating Factor (PAF), Leukotriene C4 (LTC4), Indoleamine 2,3-dioxygenase
(IDO), Fibroblast Growth Factor (FGF) and VEGF (Kita et al. 1996; Rothenberg et al. 2006; Puxeddu et al. 2005). These substances may cause induction of inflammation with cytotoxic activity (Pereira et al. 2011; Venge et al. 1999; Venge et al. 1998) or contribute to tissue modulation and tumor progression (Mantovani et al. 2010; Puxeddu et al. 2005). Increased number of these cells has been described in many human cancers, including OSCC (Pereira et al. 2011).
However, the exact function of eosinophils still remains unclear, and increased number of eosinophils has been associated with both a good and a poor prognosis depending on the tumor type (Murdoch et al. 2008; Dorta et al. 2002;
Horiuchi et al. 1993). The prognostic value of eosinophils in oral carcinoma still remains unclear and Tumor-associated Tissue Eosinophilia (TATE) in the head and neck region presents controversial results when used as a surrogate marker in prediction of survival and recurrence in OSCC (Martinelli-Kläy et al. 2009;
Alrawi et al. 2005; Goldsmith et al. 1992).
19
1.4.7 Macrophages:
Macrophages are among the first cells to arrive at sites of wounds and infection, where they perform several functions. Macrophages can either originate from monocyte recruitment from the vasculature or embryonically-derived and adult- derived tissue-resident macrophages that are independent of circulating monocytes and are specific for each tissue (Epelman et al. 2014). It is not clear whether resident and newly recruited macrophages possess similar functions
(Epelman et al. 2014). Mantovani et al. (2010) and Quatromoni et al. (2012) demonstrated the presence of phenotypic heterogeneity of macrophages in the tumor microenvironment. In response to diverse signals, macrophages undergo polarized activation including the classically-activated type 1 macrophages (M1) and the alternatively-activated type 2 macrophages (M2). Bacterial surface antigens, certain Toll-like receptor (TLR) activation, and the Th1-derived cytokine interferon-gamma (IFNγ) cause polarization of macrophages along the M1 pathway. Activated M1 macrophages phagocytose and destroy microbes, eliminate tumor cells, present antigen to T cells for an adaptive immune response, and produce high levels of cytokines that are characterized by the production of reactive oxygen and nitrogen radicals (Leek et al. 2002; Brigati et al. 2002; Bingle et al. 2002). Alternatively, exposure to Th2 and tumor-derived cytokines such as IL-4, IL-10, IL-13, Transforming Growth Factor beta (TGF- beta), or certain prostaglandins initiate alternative Pro-tumor Phenotype (M2) activation (Mantovani et al. 2002; Martinez et al. 2009). Activated M2 become oriented to tissue repair and remodeling through digestion of extracellular matrix
20 with matrix metalloproteinases (MMPs), promotion of angiogenesis via VEGF,
Angiopoietin 1 (ANG1) and ANG2 production (Carmeliet et al. 2000; Bando et al.
2000; Leek et al. 2000). Also, M2 are involved in immunoregulation and tumor promotion through the production of Macrophage Colony-Stimulating Factor
(MCSF) which results in inhibition of the maturation of antigen-presenting dendritic cells, suppression of Th1 mediated immunity, promotion of Treg cells and participation in polarized Th2 pro-tumor responses (Mantovani et al. 2002).
Tissue-macrophages recruit other inflammatory cells like neutrophils and mast cells into the tumor microenvironment (Coussens et al. 2002; Balkwill et al. 2001;
Brigati et al. 2002) which subsequently initiate angiogenesis and supply growth factors and thereby may further facilitate neoplastic proliferation. TAMs also promote tumor invasion by producing proteases such as Urokinase-type
Plasminogen Activator (UPA), MMP-9 and Cathepsins (Pollard et al. 2004) that break down the basement membrane and remodel the stromal matrix. It has been proposed that a gradual switching of TAM polarization, from M1 to M2 occurs during different stages of tumor progression (Dunn et al. 2002; Sica et al.
2007).
1.4.8 Plasma cells:
Plasma cells are white blood cells that secrete antibodies which circulate through the blood and the lymphatic system. The generation of antibodies is one of the most important components of the humoral immune response providing immunity during initial exposure to pathogens and mediating the long term protective
21 effects of vaccination (Nutt et al. 2015). Antibodies are usually produced in response to T cell-dependent antigens in a two-step process (Nutt et al. 2015). In the first step, known as the “extrafollicular response”, B cells receive an antigen receptor-dependent signal and differentiate into short-lived plasmablasts that secrete antibodies (MacLennan et al. 2003). The affinity of the resulting antibodies for antigen tends to be moderate. This extrafollicular response accounts for the majority of the early protective antibodies that are produced. The second step, also called “Germinal Centre phase” happens within the lymphoid follicles, and under the influence of specialized T Follicular Helper (TFH) cells.
This will lead to the selection of high-affinity, long-lived plasma cells that are capable of sustaining a high level of antibody secretion (Shlomchik et al. 2012;
Radbruch et al. 2006). Memory B cells are also produced to rapidly differentiate into antibody-secreting cells following re exposure to antigen (Kometani et al.
2013; Rajewsky et al. 1990). The lifespan of plasma cells has been proposed to range from several days to several months and they die as a result of the stress on the Endoplasmic Reticulum (ER) that is caused by massive production of antibody (Radbruch et al. 2006).
Persistent humoral immune responses in tumor microenvironment stimulate recruitment and activation of innate immune cells where they regulate tissue remodeling, activate angiogenesis and enhance tumor progression (Tan et al.
2007).
22
Chapter 2
Hypothesis and Objectives
23
2.1 Hypothesis:
2.1.1 Fluorescent Immunohistochemistry (FIHC) and multichannel
colocalization can be used to characterize the inflammatory cell
infiltrate in formalin fixed-paraffin embedded tissue samples.
2.1.2 Oral epithelial dysplasias and OSCC have a distinct inflammatory
infiltrate compared to non-dysplastic and non-neoplastic lesions of the
oral mucosa.
2.2 Objectives:
2.2.1 To develop a new semi-automated method to detect specific
inflammatory cells in formalin fixed and paraffin embedded tissue
samples using fluorescent immunohistochemistry
2.2.2 To perform a comprehensive analysis of the inflammatory infiltrates
associated with oral epithelial dysplasias and OSCC and compare it to
non-dysplastic and non-neoplastic lesions of the oral mucosa
24
Chapter 3
Material and Methods
25
3.1 Study Population
The study consists of a retrospective analysis of a total of 49 patient samples.
These samples were selected from the archives of the Oral Pathology Diagnostic
Service at the Faculty of Dentistry, University of Toronto and were taken from either the ventral or the lateral surfaces of the tongue, and they were formalin fixed and paraffin embedded. The samples were collected by a lab technician who generated an excel sheet for the samples numbers, patients’ gender and age, and the related information regarding the condition/lesion and the specific site of the biopsy. Information related to medical history and clinical examinations were not collected. The investigator did not have access to the excel sheet and was blinded to the samples’ details during the imaging and data collection stage.
All the results were subsequently stratified according to diagnosis.
The samples consist of the following conditions/lesions:
1) 10 cases of hyperkeratosis without dysplasia.
2) 9 cases of benign polyps (fibromas)
3) 10 cases of mild epithelial dysplasia.
4) 10 cases of moderate and severe epithelial dysplasia.
5) 10 cases of SCC
Hematoxylin and Eosin (H&E) slides of all the selected cases were examined under light microscope to ensure that none of these samples has large areas of epithelial surface defects or ulcers that might influence the inflammatory infiltrate and significantly alter our results.
26
We combined the samples of moderate and severe dysplasia under one group called moderate/severe dysplasia based on:
1. The clinical management of these conditions is the same
2. There are overlapping histopathological features
3. A binary classification of dysplasia is favored by many authors. This classification would provide more reliable criteria for the selection of patient treatment. (Kujan et al. 2006; Van der Waal, I. 2009; Warnakulasuriya et al.
2008).
A B
C D
Figure 3.1 Hematoxylin and Eosin slides of oral mucosa lesions of the tongue. Mild dysplasia lesion A, moderate/severe epithelial dysplasia B &C, squamous cell carcinoma D.
27
3.2 FIHC staining protocol
3.2.1 Slides preparation:
6 µm sections were prepared by cutting paraffin blocks using a Leica RM2125
RT microtome (Leica Biosystems Inc., Canada). The sections were allowed to dry in the oven at 60°C for 30min.
3.2.2 Deparaffinization:
A series of sequential xylene/ethanol washes were performed to remove the wax and rehydrate the tissue for subsequent antibody binding. The following protocol was used:
- Xylene for 3 minutes, repeated 3 times
- Ethanol 100% for 2 minutes, repeated 3 times
- Ethanol 95% for 2 minutes, repeated 2 times
- Ethanol 70% for 2 minutes, repeated 2 times
- Ethanol 50% for 2 minutes, repeated 2 times
The sections were stored in Tris-Buffered Saline, 0.05% Tween20 (TBS-T) solution until use for FIHC.
3.2.3 Antigen retrieval:
Antigen retrieval is used to reverse crosslinks created during the fixation step which could prevent antibody binding by inhibiting access to the antigen. The protocol included high-temperature heating technique using citrate buffer solution. This technique is both pH- and temperature-dependent. The citrate
28 buffer solution was prepared by mixing Sodium citrate tri-basic anhydrous powder with distilled water to give a concentration of 10 mM. The pH of the solution was calibrated to 6. 0.05% of Tween 20 was added to the solution. The
Citrate buffer solution temperature was brought to 98ᴼ C and the samples were immersed in the solution for 40 minutes. After this step, the samples were washed in TBS-T for 1 minute and stored in TBS-T to avoid dehydration.
3.2.4 Permeabilization:
This step was used to improve the penetration of antibodies into cells. Triton X-
100 is the most popular detergent used in IHC staining protocols. 0.05% of Triton
X-100 in Phosphate Buffered Saline (PBS) solution was applied for 5 minutes.
This is a very time sensitive process. Following the Triton X-100 solution application, the slides were washed with TBS-T for 5 minutes and the washing process is repeated 3 times to ensure the complete removal of the detergent solution.
3.2.5 Blocking non-specific binding:
This step is important to prevent non-specific antibody binding in immunoassay techniques which may result from the fact that antibodies are highly charged molecules and may bind non-specifically to tissue components bearing reciprocal charges (e.g. collagen) producing “false-positive” staining. A casein-based blocking solution buffered to pH 7.5 is applied for 2 hours. The solution is prepared by mixing 100 mL 10X blocking buffer with 900 mL of deionized water.
29
The proteins in the blocking solution occupy the charged sites within the tissue
samples and reducing non-specific attachment of antibodies.
3.2.6 Application of primary antibodies:
Monoclonal and polyclonal mouse and rabbit anti-human primary antibodies from
Abcam ® (Abcam, Canada) were used to identify 7 different types of
inflammatory cells, and mouse monoclonal anti-human primary antibodies from
Dako ® (Agilent Technologies, U.S.A) used to identify macrophages (refer to
Table 3.1). These primary antibodies bind to specific antigens on the
inflammatory cells. For each type of inflammatory cells, 2 separate primary
antibodies were used (except for macrophages where a single primary antibody
was used). These antibodies are the most commonly used antibodies in IHC
protocols. Each of the primary antibodies is diluted in the blocking buffer to
achieve higher specific binding owing to the fact that when antibody is present at
a much lower concentration, it favors binding by high-affinity immunologic
reactions. The primary antibodies were incubated for 24 hours at 4°C, then the
slides were washed with TBS-T for 5 minutes and the washing process was
repeated 3 times for complete removal of excess traces of the primary
antibodies.
S/N Targeted Primary antibodies Antibody Origin Dilution inflammatory cells and type ratio
Anti-CD3 (CD3 ab699) Mouse monoclonal 1/50 1 CD 8+ T-lymphocytes Anti-CD8 (CD8 ab4055) Rabbit polyclonal 1/100
30
S/N Targeted Primary antibodies Antibody Origin Dilution inflammatory cells and type ratio
Anti-CD3 (CD3 ab699) Mouse monoclonal 1/50 2 CD 4+ T-lymphocytes Anti-CD4 (ab 133616) Rabbit monoclonal 1/100
Anti-CD45 (ab 8216) Mouse monoclonal 1/1000 3 B-lymphocytes Anti-CD20 (ab78237) Rabbit monoclonal 1/100
Anti-CD45 (ab 8216) Mouse monoclonal 1/1000 4 Neutrophils Anti-CD66b (ab197678) Rabbit polyclonal 1/100
Anti-CD45 (ab 8216) Mouse monoclonal 1/1000 5 Eosinophils Anti-Siglec8 (ab 38578) Rabbit polyclonal 1/100
Anti-CD45 (ab 8216) Mouse monoclonal 1/1000 6 Natural killer (NK) cells Anti-NCAM (ab 75813) Rabbit monoclonal 1/1000
Anti-CD45 (ab 8216) Mouse monoclonal 1/1000 7 Plasma cell Anti-CD38 (ab108403) Rabbit monoclonal 1/1000
8 Macrophages Anti-CD68 (M087629-2) Mouse monoclonal 1/200
Table 3.1 List of targeted inflammatory cells, primary antibodies including the type, animal of origin, and the dilution ratio.
3.2.7 Application of secondary antibodies:
For each primary antibody treatment, secondary antibody treatment was applied.
Polyclonal anti-mouse and anti-rabbit IgG secondary antibodies from Abcam ®
(Abcam, Canada) were used to bind specifically to the primary antibodies that
made contact with the inflammatory cells antigen (refer to Table 3.2). The
31 secondary antibodies were raised in donkey against the IgG of the animal species in which the primary antibody has been raised. Each of these antibodies is labeled with fluorescent tag to allow visualization and identification of the primary/secondary antibodies combination under the confocal microscope. The fluorescent tags were either Alexa Fluor® 555 (for red emission laser channel) or
DyLight® 488 for green emission laser channel. The secondary antibodies were incubated for 1 hour at 4°C, then the slides were washed with TBS-T for 5 minutes and the washing process was repeated 3 times for complete removal of excess traces of the secondary antibodies.
S/N Cat # Targeted Dilution Conjugated Emission Primary ratio fluorescent tag Laser antibody channel
Anti-mouse 1 Anti-CD3 1/800 Alexa Fluor ® 555 555 ab150110 Red Anti-rabbit 2 Anti-CD8 1/800 DyLight ® 488 Green 488 ab 98488 Anti-rabbit 3 Anti-CD4 1/800 DyLight ® 488 Green 488 ab 98488 Anti-mouse 4 Anti-CD 45 1/800 Alexa Fluor ® 555 Red 555 ab150110 Anti-rabbit 5 Anti-CD20 1/800 DyLight ® 488 Green 488 ab 98488 Anti-rabbit 6 Anti-CD66b 1/800 DyLight ® 488 Green 488 ab 98488 Anti-rabbit 7 Anti-Siglec8 1/800 DyLight ® 488 Green 488 ab 98488 Anti-rabbit 8 Anti-NCAM 1/800 DyLight ® 488 Green 488 ab 98488 Anti-rabbit 9 Anti-CD38 1/800 DyLight ® 488 Green 488 ab 98488
32
S/N Cat # Targeted Dilution Conjugated Emission Primary ratio fluorescent tag Laser antibody channel Anti-mouse 10 Anti-CD68 1/800 Alexa Fluor ® 555 Red 555 ab150110
Table 3.2 List of secondary antibodies including the targeted primary antibodies, dilution ratio,
conjugated fluorescent tags and the emission laser channels
3.2.8 Application of DAPI staining:
NucBlue ® Fixed Cell Stain – DAPI: 4,6-Diamidino-2-phenylindole (Thermo
Fisher Scientific, U.S.A) is a fluorescent stain that binds strongly to DNA and allows easy visualization of the cell nucleus. DAPI was diluted in blocking solution
(2 drops per mL) and applied for 1hour. The slides then were washed with TBS-T for 5 minutes and the washing process was repeated 3 times to remove extra traces of the DAPI solution.
3.2.9 Application of mounting media and cover slip:
The final step included the application of ProLong ® Diamond Antifade Mountant
with DAPI mounting media (Thermo Fisher Scientific, U.S.A) as 1 drop onto glass
slides for anti-fade protection of fluorescent dyes followed by covering the slides
by coverslip.
33
3.3 Controls
The following controls were used:
3.3.1 Positive control: Anti-GAPDH primary antibody (ab 8245) and 2 x
secondary antibodies (anti-mouse 555 ab 150110) and (anti-rabbit 488 ab
98488) from Abcam ® (Abcam, Canada) were used to address FIHC
technique-related issues.
3.3.2 Negative non-specific control: 2 x secondary antibodies (anti-mouse 555
ab 150110) and (anti-rabbit 488 ab 98488) from Abcam ® (Abcam,
Canada) without a primary antibody were used to address non-specific
binding.
3.4 Imaging with confocal microscope
Quorum Spinning Disk Confocal microscope (Quorum Technologies Inc.,
Canada) was used to image the samples slides following the staining process.
The microscope specifications are:
1 Microscope Zeiss AxioVert 200M
2 Objective 10x/0.25 (W),
3 Camera Hamamatsu C9100-13 EM-CCD
4 Scanhead Yokogawa CSU10
34
5 Lasers 405 nm (100 mW), 561 (50 mW), 640 (100 mW)
6 Emission Filter Wheel 447/40, 515/40, 515 LP, 594/40, 624/40, 670/40
7 Stage ASI motorized XY
8 Focus Drive Zeiss focus drive
9 Software Perkin Elmer Volocity
10 Environmental Control LCI (temperature/humidity/CO2)
Table 3.3 Quorum spinning disk confocal microscope specifications
For each sample slide, a range of 5-10 images were captured through different areas of the slide.
The confocal microscope offers images with superior contrast and resolution and minimum blur through using a small pinhole aperture in a screen that allows only the light emitted from the desired focal spot to pass through. Any light outside of the focal plane which represents scattered light is blocked by the screen. A photomultiplier tube, on the other side of the pinhole is used to detect the confocal light. This technique allows the specimen to be imaged as one “point” at a time [Confocal Microscopy]. When the excitation laser hits the target tissue, it generates fluorescence beam at a well-defined focal point. Both the excitation beam and the resultant emission fluorescence pass through a Dichroic mirror that reflects the incoming, higher-energy / shorter-wavelength excitation laser light, but allows the lower-energy / higher-wavelength fluorescent light to pass to
35 the pinhole to the light detector (Figure 3.2). Images of the scanned specimen can then be reconstructed point by point.
The Spinning Dual-disk Confocal Microscope (SDCM) uses a special design to scan and obtain rapid, parallel forms of confocal microscopy. A pinhole disk containing approximately 20,000 pinholes arranged in an Archimedean spiral is combined with a second disk having a matching array of microlenses. Each pinhole is associated with a microlens at the same disk coordinate, and the disks are co-mounted onto fast-rotating wheel with a constant velocity with a separation equal to the focal plane.
Pinhole Aperture
Figure 3.2 Basic concepts of confocal microscope
Adapted from: Journal of Investigative Dermatology (2012), Volume 132, page 2.
36
3.5 Data analysis
3.5.1 Colocalization by staining intensity correlation
The identification of each of the inflammatory cells (except for macrophages) was performed by colocalization of pixels of the red [R] and green [G] emission laser channels generated by the primary/secondary antibodies combinations (refer to
Figures 3.5 and 3.6). The colocalization was performed through Volocity 3D
Image Analysis Software (PerkinElmer, U.S.A) using staining intensity correlation and automatically generated thresholds based on Costes et al. (2004) and Li et al. (2004). First, the number of pixels with red [R] and green [G] intensities was plotted by the software as a scatterplot with each axis representing the intensity of each color as the product of the difference of [R] and [G] pixels from its respective mean (PDM). Subsequently, the software applies an algorithm that determines automatically the red and green channels’ thresholds [TR] and [TG] along a line whose slope and intercept (α and b) are obtained by linear least- square fit of the red and green intensities [IR] and [IG] over all pixels in the image
(IG = α x IR + b) as shown in Figure 3.3. This is done based on a simple statistical assumption. If one assumes that a pixel is the summation of a colocalized component and a random component, then there is a higher probability to have colocalization in pixels that are brighter in both channels than in dim pixels.
Therefore, classifying pixels as being colocalized if their red intensities [IR] > the red threshold value [TR] and their green intensity [IG] > the green threshold value
[TG] (i.e > α x TR + b). Starting with the highest intensity value, the algorithm
37 reduces the threshold value [TR] incrementally and computes the correlation coefficient of the image using only pixels with intensities below the threshold. The algorithm continues reducing the threshold until the correlation coefficient of remaining pixel intensities below TR and α x TR + b equals zero.
Macrophages were identified using a single primary/secondary antibodies combination with red emission laser channel. The software generated a red channel threshold [TR] and only the red pixels that are above TR were considered in calculations.
Figure 3.3 Colocalization by staining intensity correlation with automatic threshold calculation based on Costes et al. (2004). The number of pixels with red and green intensities is plotted (right panel) as a scatterplot with each axis representing the intensity of each color.
38
3.5.2 Region of interest (ROI) selection
For benign and epithelial dysplasia lesions, the Region of Interest [ROI] was manually selected to represent the lamina propria of the connective tissue that is located between the epithelium and the submucosa. The submucosa was identified by the presence of relatively large-sized blood vessels, nerve bundles, minor salivary glands and muscle fibers. Carcinoma lesions had islands and cords of epithelial cells invading the adjacent connective tissue. Here, we decided to use the cancer stroma as the ROI. All the subsequent data analysis was restricted to the ROI. The walls and the lumen of the medium-sized blood vessels within the lamina propria were removed from the ROI (see Figure 3.4).
The identification of the epithelium was enhanced with the DAPI blue channel.
V
E
M
Figure 3.4 Region of interest selection in a mild dysplasia case after removing the epithelium (E), medium-sized blood vessels (V) and submucosa (M).
39
3.5.3 Statistical analysis
The quantification of each of the inflammatory cells was based on the combined areas of the colocalized intensities of that inflammatory cell (i.e. inflammatory area) within the ROI. This method overcomes the difficulties encountered by measuring the number of inflammatory cells due to clustering of those cells. The inflammatory area was divided by the ROI to generate a Relative Inflammatory
Area (RIA) to account for the differences in the ROI between different samples.
Subsequently, the RIA was used for comparisons. The inflammatory cells within the walls and lumen of the the medium-sized blood vessels within the ROI were excluded from the calculations by cropping these area from the ROI. For each sample, the RIAs were collected from a range of 5-10 different locations and the average was used in the calculations.
For multiple groups analysis, One-way Analysis Of Variance (ANOVA) with post- hoc Tukey's multiple comparisons test was used to compare the inflammatory infiltrate of the 8 different inflammatory cells in the 5 different oral lesions.
Statistical significance was defined as P<0.05. For all figures *P<0.05, **P<0.01,
***P<0.001 and ****P<0.0001. Error bars represent the Standard Error of the
Mean (SEM).
40
Figure 3.5 Colocalization of CD8 and CD3 antigens for the detection of CD8+ cells in a moderate/severe dysplasia of the tongue. The CD8 antigen is detected by the green channel pixels A, The CD3 antigen is detected by the red channel pixels B, and the colocalization represents the CD8+ infiltrate C.
Figure 3.6 Colocalization of CD66b and CD45 antigens for the detection of neutrophils in a SCC of the tongue. The CD66b antigen is detected by the green channel pixels A, The CD45 antigen is detected by the red channel pixels B, and the colocalization represents the neutrophils infiltrate C.
41
Chapter 4
Results
42
4.1 Comparing the infiltrate of different inflammatory
cells
The inflammatory infiltrate of 5 different oral lesions (hyperkeratosis, benign
polyps, mild dysplasia, moderate/severe dysplasia and SCC) were analyzed
using scatterplots comparing the RIA of the 8 different inflammatory cells.
4.1.1 Hyperkeratosis:
Hyperkeratosis refers to reactive, non-dysplastic conditions that are characterized by thickening of the keratin layer of the surface epithelium with or without a thickened underlying spinous layer. Compared to other oral lesions, these conditions demonstrate a minimum inflammatory infiltrate, and are frequently used as a baseline reference when comparing the inflammatory infiltrate of different oral lesions.
Our study shows a minimal inflammatory cell infiltrate in these lesions with a
predominant CD4+ and CD8+ infiltrate (as seen in Figure 4.1). Both cells were
statistically different from all the other inflammatory cells (ANOVA and Tukey’s
test, P<0.05) (see table 4.1 for details).
43
* *
Figure 4.1 Scatterplot comparing the relative area of the inflammatory infiltrate for each of 8 different inflammatory cells in hyperkeratosis. * refers to statistically significant difference (P<0.05) using One-way ANOVA with post-hoc Tukey's multiple comparisons test.
4.1.2 Benign polyps:
These lesions represent a reactive hyperplasia of the fibrous connective tissue usually in response to local trauma or irritation. Similar to hyperkeratosis, these lesions present with a minimum inflammatory infiltrate (except when they are traumatized and ulcerated) and can also be used as a baseline reference to compare the inflammatory infiltrate of different lesions. Comparable to lesions of hyperkeratosis, fibroepithelial polyps show a predominately CD+4 and CD8+ infiltrate that is statistically different from B cells, neutrophils, eosinophils, NK cells, plasma cells and macrophages (see Figure 4.2 and Table 4.2).
44
* *
Figure 4.2 Scatterplot comparing the relative area of the inflammatory infiltrate for each of 8
different inflammatory cells in benign polyps. * refers to statistically significant difference (P<0.05) using One-way ANOVA with post-hoc Tukey's multiple comparisons test.
4.1.3 Mild dysplasia:
As seen in Figure 4.3, there is a moderate increase in the inflammatory cells infiltrate in mild dysplasia compared to hyperkeratosis. This increase is mainly represented by increase in the numbers of neutrophils, eosinophils, NK cells, plasma cells and macrophages (refer to Table 4.3 for more details).
45
* *
Figure 4.3 Scatterplot comparing the relative area of the inflammatory infiltrate for each of 8 different inflammatory cells in mild dysplasia. * refers to statistically significant difference (P<0.05) using One-way ANOVA with post-hoc Tukey's multiple comparisons test.
4.1.4 Moderate/severe dysplasia:
Similar to mild dysplasia, there is a moderate increase in the inflammatory cells infiltrate in moderate/severe dysplasia compared to non-dysplastic and non- neoplastic specimens (refer to Figure 4.4). The infiltrate of each of the CD8+ cells and CD4+ cells is significantly higher than the other tested cells (except for B cells) as demonstrated by Table 4.4.
46
* *
Figure 4.4 Scatterplot comparing the relative area of the inflammatory infiltrate for each of 8 different inflammatory cells in moderate/severe dysplasia. * refers to statistically significant difference (P<0.05) using One-way ANOVA with post-hoc Tukey's multiple comparisons test.
4.1.5 Squamous cell carcinoma:
A distinct inflammatory infiltrate represented by a marked increase in all the inflammatory cells (except for the plasma cells and macrophages) can be appreciated in SCC when compared to benign and pre-malignant conditions
(refer to Figure 4.5). This is particularly more evident in CD8+ cells, CD4+ cells, neutrophils and eosinophils. Significant differences were also noted between B cells and macrophages, eosinophils and macrophages, and finally between neutrophils and the infiltrate of plasma cells and macrophages (see Table 4.5 for details).
47
* * * *
Figure 4.5 Scatterplot comparing the relative area of the inflammatory infiltrate for each of 8 different inflammatory cells in SCC. * refers to statistically significant difference (P<0.05) using
One-way ANOVA with post-hoc Tukey's multiple comparisons test.
4.2 Comparing the inflammatory infiltrate stratified by
diagnosis
The inflammatory infiltrate of each of the following inflammatory cells (CD8+ cells, CD4+ cells, B cells, neutrophils, eosinophils, NK cells, plasma cells and macrophages) was analyzed using scatterplots comparing the relative inflammatory area (RIA) of each inflammatory cell in 5 different oral lesions.
4.2.1 T and B lymphocytes and NK cells:
There is a significant difference in the infiltrate of these cells in SCC compared to other non-neoplastic lesions (refer to Figure 4.6). Another significant increase is
48 noted in the infiltrate of these cells (except for CD8+ cells) in moderate/severe dysplasia when compared to hyperkeratosis (refer to Tables 4.6, 4.7, 4.8 and 4.9 for more details).
* * *
* * * *
Figure 4.6 Scatterplots comparing the relative inflammatory infiltrate areas of CD8+, CD4+, B cells and NK cells in 5 different oral lesions (non-dysplastic, dysplastic and carcinoma). * refers to statistically significant difference (P<0.05) using One-way ANOVA with post-hoc Tukey's multiple comparisons test.
49
4.2.2 Neutrophils and Eosinophils:
A significant increase in the infiltrate of neutrophils and eosinophils is
demonstrated in SCC when compared to all other lesions (see Figure 4.7, Tables
4.10 and 4.11). Another significant difference is found in neutrophils and
eosinophils infiltrate between moderate /severe dysplasia and hyperkeratosis.
Table 4.11 highlights another difference of the number of eosinophils between
mild dysplasia compared to hyperkeratosis.
* *
* * *
Figure 4.7 Scatterplots comparing the relative inflammatory infiltrate areas of neutrophils and eosinophils in 5 different oral lesions (non-dysplastic, dysplastic and carcinoma). * refers to statistically significant difference (P<0.05) using One-way ANOVA with post-hoc Tukey's multiple comparisons test.
4.2.3 Plasma cells and Macrophages:
The infiltrate of plasma cells and macrophages shows a significant increase
between hyperkeratosis and other dysplastic and neoplastic lesions (refer to
50
Figure 4.8, Tables 4.12 and 4.13). However, there is no significant difference
between the infiltrate of these cells as we move from polyps to more advanced
dysplasias and SCC (except for a significant difference between the number of
macrophages in moderate/severe dysplasia when compared to mild dysplasia as
seen in Table 4.13).
* * *
Figure 4.8 Scatterplots comparing the relative inflammatory infiltrate areas of plasma cells and
macrophages in 5 different oral lesions (non-dysplastic, dysplastic and carcinoma). * refers to
statistically significant difference (P<0.05) using One-way ANOVA with post-hoc Tukey's multiple comparisons test.
4.3 Specific inflammatory cells ratios
4.3.1 CD4-to-CD8 ratio:
Different studies correlated the proportion of CD4-to-CD8 with the behavior of different cancer lesions. Cho et al. (2011) demonstrated an association between
51 increased CD4 to CD8 ratio with advanced stages of HNSCC including larger tumor size and higher incidence of lymph node metastasis. Snyderman et al.
(1989) suggested that a CD4 to CD8 ratio of greater than 1 may be a useful prognostic indicator of the development of cervical metastases in head and neck cancers.
Our results demonstrate a gradual increase in the CD4-to-CD8 ratio when moving from benign lesions to carcinoma as seen in Figure 4.9. Significant differences are shown in Table 4.14.
* *
Ratio ofCD4/CD8
Figure 4.9 CD4-to-CD8 ratio calculated as the ratio between the relative inflammatory infiltrate area of CD4+ divided by CD8+ cells in 4 different oral lesions (hyperkeratosis, mild dysplasia, moderate/severe dysplasia and carcinoma). * refers to statistically significant difference (P<0.05) using One-way ANOVA with post-hoc Tukey's multiple comparisons test.
52
4.3.2 Neutrophil-to-lymphocyte ratio (NLR):
Pretreatment level of neutrophil-to-lymphocyte ratio was suggested by many authors as an independent prognostic factor in patients with head and neck cancers (He et al. 2012; Perisanidis et al. 2013).
Our results show a gradual increase in the neutrophils/lymphocytes ratio as we move from hyperkeratosis to carcinoma (Figure 4.10). Table 4.15 lists all the differences that were statistically significant.
* *
neutrophils to lymphocytes to neutrophils
tio of of tio Ratio of neutrophils to lymphocytes to neutrophils of Ratio
Ra
Figure 4.10 Neutrophils-to-lymphocytes ratio calculated as the ratio between the relative inflammatory infiltrate area of neutrophils divided by lymphocytes in 4 different oral lesions (hyperkeratosis, mild dysplasia, moderate/severe dysplasia and carcinoma). * refers to statistically significant difference (P<0.05) using One-way ANOVA with post-hoc Tukey's multiple comparisons test.
53
Chapter 5
Discussion
54
5.1 Distinct profiles of inflammatory infiltrates
Based on the comparisons of the inflammatory infiltrates among the 5 different oral lesions, three distinct profiles can be appreciated:
1) Benign conditions: including hyperkeratosis and benign polyps
2) Pre-malignant conditions: including mild and moderate/severe epithelial
dysplasia
3) Carcinoma
In the benign conditions profile, there is minimum inflammatory infiltrate in which the CD4+ and the CD8+ cells represent the main infiltrate compared to other inflammatory cells. Within the T lymphocytes population, there are more CD8+ than CD4+ cells. This may suggest a more cytotoxic activity than a regulatory activity of the T lymphocytes. The inflammatory infiltrate in benign polyps is generally higher than hyperkeratosis. This can be justified by the fact that these lesions are more prone to mechanical trauma which can lead to increased inflammatory infiltrate.
In the second, pre-malignant conditions profile, there is an increase in the overall inflammatory cells infiltrate compared to the benign profile. In addition, CD8+ and
CD4+ cells remain to be the dominant cells with a statistically significant difference compared to other cells. These findings are in agreement with
Migliorati et al. (1986) which demonstrated an increase in the percentage of T positive cells in the mild dysplasia lesions compared to hyperkeratosis. Within the
T lymphocytes population, the CD8+ cells infiltrate is almost equal to the CD4+
55 cells in the mild dysplasia lesions and the ratio is slightly tipped to the favor of
CD4+ cells when moving to moderate/severe dysplasia lesions. This suggests an important change in the behavior of the inflammatory infiltrate from a cytotoxic activity that is aimed to eliminate the lesion to a more regulatory and suppression activity which might have an overall pro-lesion progression into carcinoma. On the other hand, plasma cells and macrophages infiltrate appears to be the minimum, and their infiltrate is comparable to the benign profile.
The third profile which represents carcinoma is a very distinct profile. Here, there is an increase in all cells tested except for plasma cells and macrophages compared to benign and pre-malignant conditions. Within this profile, The CD8+ and CD4+ remain to be the dominant cells, and the infiltrate of CD4+ cells compared to CD8+ cells continues to increase.
5.2 Inflammatory cells that are significantly increased
in carcinoma
Looking into the inflammatory infiltrate of neutrophils and eosinophils in carcinoma compared to their infiltrate in all other conditions, there is a significant and sharp increase when moving to carcinoma. This highlights the importance of these cells in cancer initiation and progression. Multiple mechanisms used by neutrophils to promote angiogenesis, tumor development, progression, invasion and metastasis have been described (Magalhaes et al. 2014; Moses et al. 2016).
56
Recently, Glogauer et al. (2015) demonstrated a neutrophils-dependent increase in the invasiveness of the tumor cells through a specific mechanism called invadopodia. As for the significance of eosinophils infiltrate in oral cancers,
Oliveira et al. (2009) correlated TATE with clinical staging and suggested that intense TATE seems to reflect increased invasion of OSCC lesions. Based on these findings, we can propose that high levels of these markers may predict a malignant transformation of pre-malignant lesions.
5.3 Important ratios to consider
5.3.1 CD4-to-CD8 ratio:
The gradual increase in the CD4-to-CD8 ratio when moving from benign lesions to carcinoma (as seen in Figure 4.9) reflects a gradual increase in the infiltrate of the CD4+ cells within the T lymphocytes population. This finding is in agreement with Migliorati et al. (1986) which identified an increase in the percentage of T positive cells in the mild dysplasia lesions compared to hyperkeratosis that was a direct result of an increase in the T4-positive subpopulation whereas the T8- positive subpopulation remained unchanged.
An important change can be appreciated within the premalignant lesions where the CD4-to-CD8 ratio change from more CD8+ cells (CD4/CD8 < 1.0) in mild dysplasia lesions to more CD4+ cells (CD4/CD8 > 1.0) in moderate/severe
57 dysplasia lesions (refer to figure 4.9). This progressive increase in the CD4/CD8 ratio continues as we move to carcinoma. Hirota et al. (1990), also demonstrated an increased number of T-helper cells over T-cytotoxic cells within the T cells subpopulation of OSCC lesions.
The above suggests an overall switching of the immune response in favor of pro- tumor immunity dominated by CD4+ cells population that might help in tumor initiation and progression as suggested by Strauss et al. (2007). Further studies need to be conducted to identify the subpopulations of CD4+ cells that are directly responsible for such increase in the CD4+ population which can be a useful target for anti-tumor molecular therapy.
5.3.2 Neutrophil-to-lymphocyte ratio (NLR):
There is a gradual and progressive increase in the Neutrophils/lymphocytes ratio when moving from benign lesions through increasing grade of dysplasia to carcinoma. This result is in agreement with Kum et al. (2014) which compared the NLR between benign, pre-malignant and SCC lesions of the larynx, and demonstrated a gradual increase in the NLR when moving from benign to carcinoma lesions.
58
5.4 Inflammatory cells with no change in pattern
The subpopulation of plasma cells and macrophages represents a small proportion of the total inflammatory infiltrate of the different oral conditions/lesions. Furthermore, there is no significant difference of the number of these cells when moving from pre-malignant lesions to carcinoma. This finding may suggest a minor role of these cells in tumor associated inflammation and tumor progression. This is in agreement with Gannot et al. (2002) which demonstrated no significant difference in the number of the CD14+ cells
(monocytes and macrophages) when moving from hyperkeratosis to SCC in tongue lesions.
5.5 Challenges and limitations
Some of the challenges and limitations of this study include the limited number of samples for the 5 different conditions of the oral mucosa (9 samples for benign polyps and 10 samples for each of the other conditions). However, we demonstrated statistically significant results that were consistent and reproducible with reasonable variations (as shown by the scatterplots). The only exception was the carcinoma cases in which there was a wide range of variability of the results. This can be explained by the heterogeneity of these lesions that are confirmed clinically and histologically. Another limitation was using a single anatomic location (tongue) for all the samples and this study design was used to
59 maintain a consistent histopathological architecture between samples, facilitating analysis. Another limitation is the possible variation in the ROI area calculations.
The ROI represented the lamina propria in all benign and pre-malignant lesions while the cancer stroma was used for calculations in carcinoma cases since the connective tissue is infiltrated by islands and cords of malignant cells. However, our results show a progressive change in inflammation with increasing degree of epithelial abnormality makes this assumption unlikely. In addition, no correlation was made between the patient history, oral health and the inflammatory infiltrate.
There are different conditions that might influence the inflammatory cells infiltrate of the oral mucosal lesions. Such co-existing conditions can be broadly grouped into systemic conditions (such as Diabetes and systemic inflammatory diseases), oral cavity conditions (including oral hygiene status, associated periodontal diseases, smoking, drinking and other habits) or more localized site-specific conditions (including trauma and ulcer sites). Finally, no adjustments were applied for age, gender and other possible confounders.
60
Chapter 6
Conclusions and Future directions
61
6.1 Conclusions
This novel semi-automated method of using FIHC and multichannel
colocalization is a reliable and consistent method for characterizing inflammatory
cells infiltrate in formalin fixed-paraffin embedded tissue samples. This method
managed to demonstrate a distinct profile of inflammatory infiltrate in carcinoma
lesions which is characterized by intense inflammatory infiltrate (when compared
to benign and pre-malignant lesions). This infiltrate is dominated by T-
lymphocytes (mainly CD4+ cells), neutrophils and eosinophils. Although the
inflammatory infiltrate in dysplastic lesions is not specific, it represents a
transitional stage between non-dysplastic benign conditions and carcinoma when
considering the CD4-to-CD8 ratio.
6.2 Future directions
This method of characterizing inflammatory cells can be further implemented to explore and differentiate the subpopulation of different inflammatory cells for a better understanding of the inflammatory infiltrate of the pre-malignant and carcinoma microenvironment. Of a particular importance is the subpopulation of
CD4+ cells which is demonstrated by different studies to have a major impact in shifting the inflammatory response in favor of tumor progression. Further cohort studies should correlate the inflammatory infiltrate with clinical findings and determine if specific inflammatory patterns can allow us to use inflammation as a prognostic tool and a useful predictor for response to cancer therapy.
62
Supplemental Tables
Table 4.1: Tukey’s multiple comparisons test for hyperkeratosis
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - difference (Alpha= Hyperkeratosis 0.05) CD8 vs. CD4 0.001605 0.009405 to 0.01943 <0.0001 Yes **** CD8 vs. B cells 0.001605 0.01581 to 0.02583 <0.0001 Yes **** CD8 vs. Neutrophils 0.001605 0.01789 to 0.02792 <0.0001 Yes**** CD8 vs. Eosinophils 0.001605 0.01872 to 0.02874 <0.0001 Yes **** CD8 vs. NK cells 0.001605 0.019 to 0.02902 <0.0001 Yes **** CD8 vs. Plasma cells 0.001605 0.01833 to 0.02835 <0.0001 Yes **** CD8 vs. Macrophages 0.001605 0.01903 to 0.02905 <0.0001 Yes **** CD4 vs. B cells 0.001605 0.001395 to 0.01142 0.0037 Yes ** CD4 vs. Neutrophils 0.001605 0.003479 to 0.0135 <0.0001 Yes **** CD4 vs. Eosinophils 0.001605 0.004303 to 0.01432 <0.0001 Yes **** CD4 vs. NK cells 0.001605 0.004584 to 0.01461 <0.0001 Yes **** CD4 vs. Plasma cells 0.001605 0.00391 to 0.01393 <0.0001 Yes **** CD4 vs. Macrophages 0.001605 0.004614 to 0.01464 <0.0001 Yes **** B cells vs. Neutrophils 0.001605 -0.002927 to 0.007095 0.8966 No B cells vs. Eosinophils 0.001605 -0.002103 to 0.007918 0.6145 No B cells vs. NK cells 0.001605 -0.001822 to 0.0082 0.4977 No B cells vs. Plasma cells 0.001605 -0.002496 to 0.007526 0.768 No B cells vs. Macrophages 0.001605 -0.001792 to 0.008229 0.4857 No Neutrophils vs. Eosinophils 0.001605 -0.004187 to 0.005835 0.9996 No Neutrophils vs. NK cells 0.001605 -0.003905 to 0.006116 0.9971 No Neutrophils vs. Plasma cells 0.001605 -0.004579 to 0.005442 >0.9999 No Neutrophils vs. Macrophages 0.001605 -0.003876 to 0.006146 0.9965 No Eosinophils vs. NK cells 0.001605 -0.004729 to 0.005292 >0.9999 No Eosinophils vs. Plasma cells 0.001605 -0.005403 to 0.004618 >0.9999 No Eosinophils vs. Macrophages 0.001605 -0.0047 to 0.005322 >0.9999 No NK cells vs. Plasma cells 0.001605 -0.005685 to 0.004337 0.9999 No NK cells vs. Macrophages 0.001605 -0.004981 to 0.00504 >0.9999 No Plasma cells vs. Macrophages 0.001605 -0.004307 to 0.005714 0.9998 No
Table 4.1 One-way ANOVA with post-hoc Tukey's multiple comparisons test comparing the inflammatory infiltrate of 8 different inflammatory cells in hyperkeratosis. Statistical significance was defined as P<0.05. For all figures *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
63
Table 4.2: Tukey’s multiple comparisons test for benign polyps
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - Benign diff. (Alpha= Polyps 0.05) CD8 vs. CD4 0.002282 -0.00304 to 0.01126 0.6216 No CD8 vs. B cells 0.002282 0.001361 to 0.01566 0.0092 Yes ** CD8 vs. Neutrophils 0.002282 0.003063 to 0.01736 0.0008 Yes *** CD8 vs. Eosinophils 0.002282 0.005643 to 0.01994 <0.0001 Yes **** CD8 vs. NK cells 0.002282 0.008056 to 0.02236 <0.0001 Yes **** CD8 vs. Plasma cells 0.002282 0.004236 to 0.01854 0.0001 Yes *** CD8 vs. Macrophages 0.002282 0.007104 to 0.0214 <0.0001 Yes **** CD4 vs. B cells 0.002282 -0.002749 to 0.01155 0.537 No CD4 vs. Neutrophils 0.002282 -0.001047 to 0.01325 0.1495 No CD4 vs. Eosinophils 0.002282 0.001533 to 0.01583 0.0073 Yes ** CD4 vs. NK cells 0.002282 0.003946 to 0.01825 0.0002 Yes *** CD4 vs. Plasma cells 0.002282 0.0001263 to 0.01443 0.0432 Yes * CD4 vs. Macrophages 0.002282 0.002994 to 0.01729 0.0009 Yes *** B cells vs. Neutrophils 0.002282 -0.005448 to 0.008852 0.9951 No B cells vs. Eosinophils 0.002282 -0.002868 to 0.01143 0.5717 No B cells vs. NK cells 0.002282 -0.0004552 to 0.01385 0.0825 No B cells vs. Plasma cells 0.002282 -0.004275 to 0.01003 0.9099 No B cells vs. Macrophages 0.002282 -0.001407 to 0.01289 0.2073 No Neutrophils vs. Eosinophils 0.002282 -0.00457 to 0.00973 0.9476 No Neutrophils vs. NK cells 0.002282 -0.002157 to 0.01214 0.3726 No Neutrophils vs. Plasma cells 0.002282 -0.005977 to 0.008324 0.9995 No Neutrophils vs. Macrophages 0.002282 -0.003109 to 0.01119 0.6416 No Eosinophils vs. NK cells 0.002282 -0.004737 to 0.009563 0.9631 No Eosinophils vs. Plasma cells 0.002282 -0.008557 to 0.005744 0.9985 No Eosinophils vs. Macrophages 0.002282 -0.005689 to 0.008611 0.9981 No NK cells vs. Plasma cells 0.002282 -0.01097 to 0.003331 0.7036 No NK cells vs. Macrophages 0.002282 -0.008102 to 0.006198 0.9999 No Plasma cells vs. Macrophages 0.002282 -0.004283 to 0.01002 0.9111 No
Table 4.2 One-way ANOVA with post-hoc Tukey's multiple comparisons test comparing the
inflammatory infiltrate of 8 different inflammatory cells in benign polyps. Statistical significance
was defined as P<0.05. For all figures *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
64
Table 4.3: Tukey’s multiple comparisons test for mild dysplasia
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - Mild diff. (Alpha= dysplasia 0.05) CD8 vs. CD4 0.003459 -0.005079 to 0.01657 0.712 No CD8 vs. B cells 0.003459 0.002051 to 0.0237 0.0092 Yes ** CD8 vs. Neutrophils 0.003459 0.005983 to 0.02763 0.0002 Yes *** CD8 vs. Eosinophils 0.003459 0.005846 to 0.02749 0.0002 Yes *** CD8 vs. NK cells 0.003459 0.009362 to 0.03101 <0.0001 Yes **** CD8 vs. Plasma cells 0.003669 0.006439 to 0.0294 0.0002 Yes *** CD8 vs. Macrophages 0.003812 0.01212 to 0.03597 <0.0001 Yes **** CD4 vs. B cells 0.003459 -0.003693 to 0.01795 0.4501 No CD4 vs. Neutrophils 0.003459 0.000239 to 0.02188 0.0416 Yes * CD4 vs. Eosinophils 0.003459 0.0001018 to 0.02175 0.0463 Yes * CD4 vs. NK cells 0.003459 0.003618 to 0.02526 0.0021 Yes ** CD4 vs. Plasma cells 0.003669 0.000695 to 0.02365 0.03 Yes * CD4 vs. Macrophages 0.003812 0.006377 to 0.03023 0.0002 Yes *** B cells vs. Neutrophils 0.003459 -0.006891 to 0.01475 0.9462 No B cells vs. Eosinophils 0.003459 -0.007028 to 0.01462 0.9553 No B cells vs. NK cells 0.003459 -0.003512 to 0.01813 0.4173 No B cells vs. Plasma cells 0.003669 -0.006435 to 0.01652 0.8654 No B cells vs. Macrophages 0.003812 -0.000753 to 0.0231 0.0824 No Neutrophils vs. Eosinophils 0.003459 -0.01096 to 0.01069 >0.9999 No Neutrophils vs. NK cells 0.003459 -0.007444 to 0.0142 0.9762 No Neutrophils vs. Plasma cells 0.003669 -0.01037 to 0.01259 >0.9999 No Neutrophils vs. Macrophages 0.003812 -0.004685 to 0.01917 0.556 No Eosinophils vs. NK cells 0.003459 -0.007307 to 0.01434 0.9704 No Eosinophils vs. Plasma cells 0.003669 -0.01023 to 0.01273 >0.9999 No Eosinophils vs. Macrophages 0.003812 -0.004547 to 0.0193 0.532 No NK cells vs. Plasma cells 0.003669 -0.01375 to 0.009213 0.9985 No NK cells vs. Macrophages 0.003812 -0.008063 to 0.01579 0.9708 No Plasma cells vs. Macrophages 0.004003 -0.006396 to 0.01865 0.788 No
Table 4.3 One-way ANOVA with post-hoc Tukey's multiple comparisons test comparing the
inflammatory infiltrate of 8 different inflammatory cells in mild dysplasia. Statistical significance
was defined as P<0.05. For all figures *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
65
Table 4.4: Tukey’s multiple comparisons test for moderate/severe dysplasia
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - Moderate diff. (Alpha= /Severe dysplasia 0.05) CD8 vs. CD4 0.0041 -0.01411 to 0.01149 >0.9999 No CD8 vs. B cells 0.0041 -0.004432 to 0.02117 0.4625 No CD8 vs. Neutrophils 0.0041 0.001199 to 0.0268 0.0223 Yes * CD8 vs. Eosinophils 0.0041 0.003397 to 0.029 0.0043 Yes ** CD8 vs. NK cells 0.0041 0.0004341 to 0.02603 0.0376 Yes * CD8 vs. Plasma cells 0.0041 -0.0002404 to 0.02536 0.0583 No CD8 vs. Macrophages 0.0041 0.002696 to 0.0283 0.0074 Yes ** CD4 vs. B cells 0.0041 -0.003122 to 0.02248 0.2767 No CD4 vs. Neutrophils 0.0041 0.002508 to 0.02811 0.0085 Yes ** CD4 vs. Eosinophils 0.0041 0.004707 to 0.03031 0.0015 Yes ** CD4 vs. NK cells 0.0041 0.001744 to 0.02734 0.0151 Yes * CD4 vs. Plasma cells 0.0041 0.001069 to 0.02667 0.0244 Yes * CD4 vs. Macrophages 0.0041 0.004006 to 0.02961 0.0026 Yes ** B cells vs. Neutrophils 0.0041 -0.007169 to 0.01843 0.8663 No B cells vs. Eosinophils 0.0041 -0.004971 to 0.02063 0.5492 No B cells vs. NK cells 0.0041 -0.007934 to 0.01767 0.9332 No B cells vs. Plasma cells 0.0041 -0.008608 to 0.01699 0.9695 No B cells vs. Macrophages 0.0041 -0.005672 to 0.01993 0.6625 No Neutrophils vs. Eosinophils 0.0041 -0.0106 to 0.015 0.9994 No Neutrophils vs. NK cells 0.0041 -0.01356 to 0.01203 >0.9999 No Neutrophils vs. Plasma cells 0.0041 -0.01424 to 0.01136 >0.9999 No Neutrophils vs. Macrophages 0.0041 -0.0113 to 0.0143 >0.9999 No Eosinophils vs. NK cells 0.0041 -0.01576 to 0.009837 0.996 No Eosinophils vs. Plasma cells 0.0041 -0.01644 to 0.009162 0.9863 No Eosinophils vs. Macrophages 0.0041 -0.0135 to 0.0121 >0.9999 No NK cells vs. Plasma cells 0.0041 -0.01347 to 0.01213 >0.9999 No NK cells vs. Macrophages 0.0041 -0.01054 to 0.01506 0.9993 No Plasma cells vs. Macrophages 0.0041 -0.009863 to 0.01574 0.9962 No
Table 4.4 One-way ANOVA with post-hoc Tukey's multiple comparisons test comparing the
inflammatory infiltrate of 8 different inflammatory cells in moderate/severe dysplasia. Statistical
significance was defined as P<0.05. For all figures *P<0.05, **P<0.01, ***P<0.001 and
****P<0.0001.
66
Table 4.5: Tukey’s multiple comparisons test for SCC
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - SCC diff. (Alpha= 0.05) CD8 vs. CD4 0.006331 -0.02742 to 0.01217 0.9279 No CD8 vs. B cells 0.006331 -0.0032 to 0.0364 0.1662 No CD8 vs. Neutrophils 0.006331 -0.009226 to 0.03037 0.7061 No CD8 vs. Eosinophils 0.006331 -0.008583 to 0.03101 0.641 No CD8 vs. NK cells 0.006331 -0.001096 to 0.0385 0.0776 No CD8 vs. Plasma cells 0.006715 0.01075 to 0.05275 0.0003 Yes *** CD8 vs. Macrophages 0.006715 0.0168 to 0.0588 <0.0001 Yes **** CD4 vs. B cells 0.006331 0.004424 to 0.04402 0.0066 Yes ** CD4 vs. Neutrophils 0.006331 -0.001601 to 0.038 0.0942 No CD4 vs. Eosinophils 0.006331 -0.0009589 to 0.03864 0.0736 No CD4 vs. NK cells 0.006331 0.006528 to 0.04613 0.0022 Yes ** CD4 vs. Plasma cells 0.006715 0.01837 to 0.06037 <0.0001 Yes **** CD4 vs. Macrophages 0.006715 0.02443 to 0.06643 <0.0001 Yes **** B cells vs. Neutrophils 0.006331 -0.02582 to 0.01377 0.9795 No B cells vs. Eosinophils 0.006331 -0.02518 to 0.01442 0.9893 No B cells vs. NK cells 0.006331 -0.0177 to 0.0219 >0.9999 No B cells vs. Plasma cells 0.006715 -0.005852 to 0.03615 0.3329 No B cells vs. Macrophages 0.006715 0.0002054 to 0.0422 0.0461 Yes * Neutrophils vs. Eosinophils 0.006331 -0.01916 to 0.02044 >0.9999 No Neutrophils vs. NK cells 0.006331 -0.01167 to 0.02793 0.9016 No Neutrophils vs. Plasma cells 0.006715 0.0001742 to 0.04217 0.0467 Yes * Neutrophils vs. Macrophages 0.006715 0.006231 to 0.04823 0.0031 Yes ** Eosinophils vs. NK cells 0.006331 -0.01231 to 0.02729 0.9342 No Eosinophils vs. Plasma cells 0.006715 -0.0004682 to 0.04153 0.0599 No Eosinophils vs. Macrophages 0.006715 0.005589 to 0.04759 0.0043 Yes ** NK cells vs. Plasma cells 0.006715 -0.007955 to 0.03404 0.5274 No NK cells vs. Macrophages 0.006715 -0.001898 to 0.0401 0.101 No Plasma cells vs. Macrophages 0.007078 -0.01608 to 0.02819 0.9889 No
Table 4.5 One-way ANOVA with post-hoc Tukey's multiple comparisons test comparing the
inflammatory infiltrate of 8 different inflammatory cells in SCC. Statistical significance was defined
as P<0.05. For all figures *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
67
Table 4.6: Tukey’s multiple comparisons test for CD8+ cells
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - CD8+ cells diff. (Alpha = 0.05) Hyperkeratosis vs. Polyps 0.006144 -0.01435 to 0.02059 0.9861 No Hyperkeratosis vs. Mild 0.00598 -0.01939 to 0.01462 0.9944 No dysplasia Hyperkeratosis vs. Moderate / 0.00598 -0.01974 to 0.01427 0.9907 No Severe dysplasia Hyperkeratosis vs. SCC 0.00598 -0.03685 to -0.002838 0.0149 Yes * Polyps vs. Mild dysplasia 0.006144 -0.02298 to 0.01197 0.8967 No Polyps vs. Moderate / Severe 0.006144 -0.02333 to 0.01162 0.8744 No dysplasia Polyps vs. SCC 0.006144 -0.04044 to -0.005492 0.0046 Yes ** Mild dysplasia vs. Moderate / 0.00598 -0.01735 to 0.01666 >0.9999 No Severe dysplasia Mild dysplasia vs. SCC 0.00598 -0.03446 to -0.0004505 0.0417 Yes * Moderate / Severe dysplasia vs. 0.00598 -0.03412 to -0.000104 0.048 Yes * SCC
Table 4.6 One-way ANOVA with post-hoc Tukey's multiple comparisons test comparing the
inflammatory infiltrate of CD8+ cells in 5 different oral lesions. Statistical significance was defined
as P<0.05. For all figures *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
Table 4.7: Tukey’s multiple comparisons test for CD4+ cells
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - CD4+ diff. (Alpha= cells 0.05) Hyperkeratosis vs. Polyps 0.004926 -0.0212 to 0.006826 0.5941 No Hyperkeratosis vs. Mild 0.004795 -0.0247 to 0.002578 0.1622 No dysplasia Hyperkeratosis vs. Moderate 0.004795 -0.0321 to -0.004822 0.0033 Yes ** / Severe dysplasia
68
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - CD4+ diff. (Alpha= cells 0.05) Hyperkeratosis vs. SCC 0.004795 -0.05552 to -0.02825 <0.0001 Yes **** Polyps vs. Mild dysplasia 0.004926 -0.01788 to 0.01014 0.9332 No Polyps vs. Moderate / Severe 0.004926 -0.02528 to 0.002737 0.168 No dysplasia Polyps vs. SCC 0.004926 -0.04871 to -0.02069 <0.0001 Yes **** Mild dysplasia vs. Moderate 0.004795 -0.02104 to 0.006237 0.5408 No / Severe dysplasia Mild dysplasia vs. SCC 0.004795 -0.04446 to -0.01719 <0.0001 Yes **** Moderate / Severe dysplasia 0.004795 -0.03706 to -0.009788 0.0001 Yes *** vs. SCC
Table 4.7 One-way ANOVA with post-hoc Tukey's multiple comparisons test comparing the inflammatory infiltrate of CD4+ cells in 5 different oral lesions. Statistical significance was defined as P<0.05. For all figures *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
Table 4.8: Tukey’s multiple comparisons test for B cells
Tukey's multiple SE of diff. 95.00% CI of diff. P Value Significance comparisons test - B cells (Alpha= 0.05) Hyperkeratosis vs. Polyps 0.004358 -0.02159 to 0.003206 0.2347 No Hyperkeratosis vs. Mild 0.004242 -0.0224 to 0.00173 0.1247 No dysplasia Hyperkeratosis vs. Moderate / 0.004242 -0.02725 to -0.003122 0.0072 Yes ** Severe dysplasia Hyperkeratosis vs. SCC 0.004242 -0.03613 to -0.012 <0.0001 Yes **** Polyps vs. Mild dysplasia 0.004358 -0.01354 to 0.01125 0.9989 No Polyps vs. Moderate / Severe 0.004358 -0.01839 to 0.006398 0.6458 No dysplasia Polyps vs. SCC 0.004358 -0.02727 to -0.002482 0.0115 Yes * Mild dysplasia vs. Moderate / 0.004242 -0.01692 to 0.007212 0.7824 No Severe dysplasia Mild dysplasia vs. SCC 0.004242 -0.0258 to -0.001668 0.0185 Yes *
69
Tukey's multiple SE of diff. 95.00% CI of diff. P Value Significance comparisons test - B cells (Alpha= 0.05) Moderate / Severe dysplasia vs. SCC 0.004242 -0.02095 to 0.003184 0.2411 No
Table 4.8 One-way ANOVA with post-hoc Tukey's multiple comparisons test comparing the inflammatory infiltrate of B cells in 5 different oral lesions. Statistical significance was defined as P<0.05. For all figures *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
Table 4.9: Tukey’s multiple comparisons test for NK cells
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - NK cells diff. (Alpha= 0.05) Hyperkeratosis vs. Polyps 0.003177 -0.01472 to 0.003351 0.3925 No Hyperkeratosis vs. Mild 0.003092 -0.01501 to 0.00258 0.2784 No dysplasia Hyperkeratosis vs. Moderate 0.003092 -0.0223 to -0.004717 0.0007 Yes *** / Severe dysplasia Hyperkeratosis vs. SCC 0.003092 -0.03395 to -0.01636 <0.0001 Yes **** Polyps vs. Mild dysplasia 0.003177 -0.009564 to 0.008505 0.9998 No Polyps vs. Moderate / Severe 0.003177 -0.01686 to 0.001208 0.1178 No dysplasia Polyps vs. SCC 0.003177 -0.0285 to -0.01043 <0.0001 Yes **** Mild dysplasia vs. Moderate 0.003092 -0.01609 to 0.001496 0.1458 No / Severe dysplasia Mild dysplasia vs. SCC 0.003092 -0.02773 to -0.01015 <0.0001 Yes **** Moderate / Severe dysplasia 0.003092 -0.02044 to -0.002849 0.0042 Yes ** vs. SCC
Table 4.9 One-way ANOVA with post-hoc Tukey's multiple comparisons test comparing the inflammatory infiltrate of NK cells in 5 different oral lesions. Statistical significance was defined as P<0.05. For all figures *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
70
Table 4.10: Tukey’s multiple comparisons test for Neutrophils
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - diff. (Alpha= Neutrophils 0.05) Hyperkeratosis vs. Polyps 0.003712 -0.02013 to 0.0009861 0.0921 No Hyperkeratosis vs. Mild 0.003613 -0.01876 to 0.001789 0.1491 No dysplasia Hyperkeratosis vs. Moderate 0.003613 -0.02192 to -0.001365 0.0193 Yes * / Severe dysplasia Hyperkeratosis vs. SCC 0.003613 -0.04245 to -0.0219 <0.0001 Yes **** Polyps vs. Mild dysplasia 0.003712 -0.009473 to 0.01164 0.9983 No Polyps vs. Moderate / Severe 0.003712 -0.01263 to 0.008488 0.9804 No dysplasia Polyps vs. SCC 0.003712 -0.03316 to -0.01205 <0.0001 Yes **** Mild dysplasia vs. Moderate 0.003613 -0.01343 to 0.007122 0.9053 No / Severe dysplasia Mild dysplasia vs. SCC 0.003613 -0.03397 to -0.01341 <0.0001 Yes **** Moderate / Severe dysplasia 0.003613 -0.03081 to -0.01026 <0.0001 Yes **** vs. SCC
Table 4.10 One-way ANOVA with post-hoc Tukey's multiple comparisons test comparing the inflammatory infiltrate of neutrophils in 5 different oral lesions. Statistical significance was defined as P<0.05. For all figures *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
Table 4.11: Tukey’s multiple comparisons test for Eosinophils
Tukey's multiple SE of diff. 95.00% CI of diff. P Value Significance comparisons test - (Alpha= Eosinophils 0.05) Hyperkeratosis vs. Polyps -0.007816 -0.01709 to 0.00146 0.1354 No Hyperkeratosis vs. Mild -0.01848 to - -0.009448 0.0362 Yes * dysplasia 0.0004199 Hyperkeratosis vs. Moderate -0.01027 -0.01929 to -0.001238 0.0187 Yes * / Severe dysplasia Hyperkeratosis vs. SCC -0.03236 -0.04139 to -0.02333 <0.0001 Yes **** Polyps vs. Mild dysplasia -0.001632 -0.01091 to 0.007643 0.9869 No
71
Tukey's multiple SE of diff. 95.00% CI of diff. P Value Significance comparisons test - (Alpha= Eosinophils 0.05) Polyps vs. Moderate / Severe -0.002451 -0.01173 to 0.006825 0.9429 No dysplasia Polyps vs. SCC -0.02454 -0.03382 to -0.01527 <0.0001 Yes **** Mild dysplasia vs. Moderate -0.0008183 -0.009846 to 0.00821 0.999 No / Severe dysplasia Mild dysplasia vs. SCC -0.02291 -0.03194 to -0.01388 <0.0001 Yes **** Moderate / Severe dysplasia -0.02209 -0.03112 to -0.01306 <0.0001 Yes **** vs. SCC
Table 4.11 One-way ANOVA with post-hoc Tukey's multiple comparisons test comparing the inflammatory infiltrate of eosinophils in 5 different oral lesions. Statistical significance was defined as P<0.05. For all figures *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
Table 4.12: Tukey’s multiple comparisons test for Plasma cells
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - Plasma diff. (Alpha= cells 0.05) Hyperkeratosis vs. Polyps 0.002524 -0.01604 to -0.001621 0.0097 Yes ** Hyperkeratosis vs. Mild 0.002606 -0.01525 to -0.0003635 0.0357 Yes * dysplasia Hyperkeratosis vs. Moderate 0.002457 -0.02053 to -0.006494 <0.0001 Yes **** / Severe dysplasia Hyperkeratosis vs. SCC 0.002606 -0.01888 to -0.003992 0.0007 Yes *** Polyps vs. Mild dysplasia 0.002669 -0.0066 to 0.008648 0.9952 No Polyps vs. Moderate / Severe 0.002524 -0.01189 to 0.002527 0.3578 No dysplasia Polyps vs. SCC 0.002669 -0.01023 to 0.005019 0.8644 No Mild dysplasia vs. Moderate 0.002606 -0.01315 to 0.001737 0.2045 No / Severe dysplasia Mild dysplasia vs. SCC 0.002747 -0.01147 to 0.004216 0.6799 No Moderate / Severe dysplasia 0.002606 -0.005366 to 0.009519 0.93 No vs. SCC
Table 4.12 One-way ANOVA with post-hoc Tukey's multiple comparisons test comparing the inflammatory infiltrate of plasma cells in 5 different oral lesions. Statistical significance was defined as P<0.05. For all figures *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
72
Table 4.13: Tukey’s multiple comparisons test for Macrophages
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - diff. (Alpha= Macrophages 0.05) Hyperkeratosis vs. Polyps 0.002123 -0.01274 to -0.0005944 0.0252 Yes * Hyperkeratosis vs. Mild 0.002277 -0.008892 to 0.004131 0.8327 No dysplasia Hyperkeratosis vs. Moderate 0.002067 -0.01719 to -0.005369 <0.0001 Yes **** / Severe dysplasia Hyperkeratosis vs. SCC 0.002192 -0.01235 to 0.0001867 0.061 No Polyps vs. Mild dysplasia 0.002329 -0.002374 to 0.01094 0.366 No Polyps vs. Moderate / Severe 0.002123 -0.01068 to 0.001459 0.2116 No dysplasia Polyps vs. SCC 0.002245 -0.005836 to 0.007005 0.9989 No Mild dysplasia vs. Moderate 0.002277 -0.01541 to -0.002386 0.0031 Yes ** / Severe dysplasia Mild dysplasia vs. SCC 0.002392 -0.01054 to 0.003138 0.539 No Moderate / Severe dysplasia 0.002192 -0.001071 to 0.01146 0.145 No vs. SCC
Table 4.13 One-way ANOVA with post-hoc Tukey's multiple comparisons test comparing the inflammatory infiltrate of macrophages in 5 different oral lesions. Statistical significance was defined as P<0.05. For all figures *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
Table 4.14: Tukey’s multiple comparisons test for CD4/CD8 ratio
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - diff. (Alpha= CD4/CD8 ratio 0.05) Hyperkeratosis vs. Mild 0.1657 -0.7784 to 0.114 0.205 No dysplasia Hyperkeratosis vs. Moderate / 0.1657 -1.086 to -0.1933 0.0024 Yes ** Severe dysplasia Hyperkeratosis vs. SCC 0.1657 -1.312 to -0.4199 <0.0001 Yes **** Mild dysplasia vs. Moderate / 0.1657 -0.7535 to 0.1389 0.2654 No Severe dysplasia Mild dysplasia vs. SCC 0.1657 -0.9801 to -0.08771 0.0137 Yes *
73
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - CD4-to- diff. (Alpha= CD8 ratio 0.05) Moderate /Severe dysplasia 0.1657 -0.6729 to 0.2196 0.5271 No vs. SCC
Table 4.14 One-way ANOVA with post-hoc Tukey's multiple comparisons test of the CD4-to-CD8
ratio in 4 different oral lesions. Statistical significance was defined as P<0.05. For all figures
*P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
Table 4.15: Tukey’s multiple comparisons test for neutrophils/lymphocytes ratio
Tukey's multiple SE of 95.00% CI of diff. P Value Significance comparisons test - diff. (Alpha= Neutrophils-to- 0.05) lymphocytes ratio Hyperkeratosis vs. Mild 0.04231 -0.2411 to -0.01321 0.0238 Yes * dysplasia Hyperkeratosis vs. Moderate 0.04231 -0.296 to -0.06805 0.0007 Yes *** /Severe dysplasia Hyperkeratosis vs. SCC 0.04231 -0.378 to -0.1501 <0.0001 Yes **** Mild dysplasia vs. Moderate 0.04231 -0.1688 to 0.05911 0.5713 No /Severe dysplasia Mild dysplasia vs. SCC 0.04231 -0.2508 to -0.0229 0.0133 Yes * Moderate / Severe dysplasia 0.04231 -0.196 to 0.03194 0.2305 No vs. SCC
Table 4.15 One-way ANOVA with post-hoc Tukey's multiple comparisons test of the Neutrophils-
to-Lymphocytes ratio in 4 different oral lesions. Statistical significance was defined as P<0.05.
For all figures *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
74
References
1. Abbas, A. K., Lichtman, A. H., & Pillai, S. (2003). Immunity to tumors. Cellular and molecular immunology, 4, 382-405.
2. Affara, N. I., Ruffell, B., Medler, T. R., Gunderson, A. J., Johansson, M., Bornstein, S. & Ma, Y. (2014). B cells regulate macrophage phenotype and response to chemotherapy in squamous carcinomas. Cancer cell, 25(6), 809-821.
3. Alrawi, S. J., Tan, D., Staler, D. L., Dayton, M., Anderson, G. R., Mojica, P., ... & Loree, T. (2005). Tissue eosinophilic infiltration: a useful marker for assessing stromal invasion, survival and locoregional recurrence in head and neck squamous neoplasia. The Cancer Journal, 11(3), 217- 225.
4. Andreu, P., Johansson, M., Affara, N. I., Pucci, F., Tan, T., Junankar, S., ... & Naldini, L. (2010). FcRγ activation regulates inflammation-associated squamous carcinogenesis. Cancer cell, 17(2), 121-134.
5. Andzinski, L., Kasnitz, N., Stahnke, S., Wu, C. F., Gereke, M., von Köckritz‐Blickwede, M., ... & Jablonska, J. (2016). Type I IFNs induce anti‐tumor polarization of tumor associated neutrophils in mice and human. International journal of cancer, 138(8), 1982-1993.
6. Asano, M., Toda, M., Sakaguchi, N., & Sakaguchi, S. (1996). Autoimmune disease as a consequence of developmental abnormality of a T cell subpopulation. Journal of Experimental Medicine, 184, 387-396.
7. Balkwill, F., & Mantovani, A. (2001). Inflammation and cancer: back to Virchow?. The lancet, 357(9255), 539-545.
8. Bando, H., & Toi, M. (2000). Tumor angiogenesis, macrophages, and cytokines. In Angiogenesis (pp. 267-284). Springer US.
9. Barbera-Guillem, E., Nelson, M. B., Barr, B., Nyhus, J. K., May Jr, K. F., Feng, L., & Sampsel, J. W. (2000). B lymphocyte pathology in human colorectal cancer. Experimental and clinical therapeutic effects of partial B cell depletion. Cancer Immunology, Immunotherapy, 48(10), 541- 549.
10. Benyunes, M. C., Massumoto, C., York, A., Higuchi, C. M., Buckner, C. D., Thompson, J. A., ... & Fefer, A. (1993). Interleukin-2 with or without lymphokine-activated killer cells as consolidative immunotherapy after autologous bone marrow transplantation for acute myelogenous leukemia. Bone marrow transplantation, 12(2), 159-163.
75
11. Bingle, L., Brown, N. J., & Lewis, C. E. (2002). The role of tumor‐associated macrophages in tumor progression: implications for new anticancer therapies. The Journal of pathology, 196(3), 254-265.
12. Borghaei, H., Smith, M. R., & Campbell, K. S. (2009). Immunotherapy of cancer. European journal of pharmacology, 625(1), 41-54.
13. Bouaziz, J. D., Yanaba, K., Venturi, G. M., Wang, Y., Tisch, R. M., Poe, J. C., & Tedder, T. F. (2007). Therapeutic B cell depletion impairs adaptive and autoreactive CD4+ T cell activation in mice. Proceedings of the National Academy of Sciences, 104(52), 20878-20883.
14. Braselmann, S., Taylor, V., Zhao, H., Wang, S., Sylvain, C., Baluom, M. & Wong, B. R. (2006). R406, an orally available spleen tyrosine kinase inhibitor blocks fc receptor signaling and reduces immune complex-mediated inflammation. Journal of Pharmacology and Experimental Therapeutics, 319(3), 998-1008.
15. Brigati, C., Noonan, D. M., Albini, A., & Benelli, R. (2002). Tumors and inflammatory infiltrates: friends or foes? Clinical & experimental metastasis, 19(3), 247-258.
16. Bui, J. D., & Schreiber, R. D. (2007). Cancer immunosurveillance, immunoediting and inflammation: independent or interdependent processes? Current opinion in immunology, 19(2), 203-208.
17. Canadian Cancer Society’s Advisory Committee on Cancer Statistics (2016). Canadian Cancer Statistics 2016. Toronto, ON: Canadian Cancer Society; 2016, ISSN 0835-2976.
18. Carmeliet, P., & Jain, R. K. (2000). Angiogenesis in cancer and other diseases. nature, 407(6801), 249-257.
19. Cerwenka, A., & Lanier, L. L. (2001). Natural killer cells, viruses and cancer. Nature Reviews Immunology, 1(1), 41-49.
20. Chapoval, A. I., Fuller, J. A., Kremlev, S. G., Kamdar, S. J., & Evans, R. (1998). Combination chemotherapy and IL-15 administration induce permanent tumor regression in a mouse lung tumor model: NK and T cell-mediated effects antagonized by B cells. The Journal of Immunology, 161(12), 6977-6984.
21. Chin, Y. I. N. G., Janseens, J., Vandepitte, J., Vandenbrande, J., Opdebeek, L., & Raus, J. (1991). Phenotypic analysis of tumor-infiltrating lymphocytes from human breast cancer. Anticancer research, 12(5), 1463-1466.
22. Colombo, N., Peccatori, F., Paganin, C., Bini, S., Brandely, M., Mangioni, C., ... & Allavena, P. (1992). Anti‐tumor and immunomodulatory activity of intraperitoneal IFN‐γ in ovarian carcinoma patients with minimal residual tumor after chemotherapy. International journal of cancer, 51(1), 42-46.
76
23. Colotta, F., Allavena, P., Sica, A., Garlanda, C., & Mantovani, A. (2009). Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability. Carcinogenesis, 30(7), 1073-1081.
24. Coughlin, C. M., Vance, B. A., Grupp, S. A., & Vonderheide, R. H. (2004). RNA-transfected CD40- activated B cells induce functional T-cell responses against viral and tumor antigen targets: implications for pediatric immunotherapy. Blood, 103(6), 2046-2054.
25. Coussens, L. M., & Werb, Z. (2002). Inflammation and cancer. Nature, 420(6917), 860-867.
26. Crawford, A., MacLeod, M., Schumacher, T., Corlett, L., & Gray, D. (2006). Primary T cell expansion and differentiation in vivo requires antigen presentation by B cells. The Journal of Immunology, 176(6), 3498-3506.
27. De Palma, M., Murdoch, C., Venneri, M. A., Naldini, L., & Lewis, C. E. (2007). Tie2-expressing monocytes: regulation of tumor angiogenesis and therapeutic implications. Trends in immunology, 28(12), 519-524.
28. De Visser, K. E., Eichten, A., & Coussens, L. M. (2006). Paradoxical roles of the immune system during cancer development. Nature reviews cancer, 6(1), 24-37.
29. De Visser, K. E., Korets, L. V., & Coussens, L. M. (2005). De novo carcinogenesis promoted by chronic inflammation is B lymphocyte dependent. Cancer cell, 7(5), 411-423.
30. DeNardo, D. G., Andreu, P., & Coussens, L. M. (2010). Interactions between lymphocytes and myeloid cells regulate pro-versus anti-tumor immunity. Cancer and Metastasis Reviews, 29(2), 309-316.
31. DeNardo, D. G., Barreto, J. B., Andreu, P., Vasquez, L., Tawfik, D., Kolhatkar, N., & Coussens, L. M. (2009). CD4+ T cells regulate pulmonary metastasis of mammary carcinomas by enhancing protumor properties of macrophages. Cancer cell, 16(2), 91-102.
32. DeNardo, D. G., Johansson, M., & Coussens, L. M. (2008). Immune cells as mediators of solid tumor metastasis. Cancer and Metastasis Reviews, 27(1), 11-18.
33. DiLillo, David J., Takashi Matsushita, and Thomas F. Tedder. "B10 cells and regulatory B cells balance immune responses during inflammation, autoimmunity, and cancer." Annals of the New York Academy of Sciences 1183.1 (2010): 38-57.
34. Dorta, R. G., Landman, G., Kowalski, L. P., Lauris, J. R. P., Latorre, M. R. D. O., & Oliveira, D. T. (2002). Tumor‐associated tissue eosinophilia as a prognostic factor in oral squamous cell carcinomas. Histopathology, 41(2), 152-157.
35. Dunn, G. P., Bruce, A. T., Ikeda, H., Old, L. J., & Schreiber, R. D. (2002). Cancer immunoediting: from immunosurveillance to tumor escape. Nature immunology, 3(11), 991-998.
77
36. Dunn, G. P., Old, L. J., & Schreiber, R. D. (2004). The immunobiology of cancer immunosurveillance and immunoediting. Immunity, 21(2), 137-148.
37. Dunn, G. P., Old, L. J., & Schreiber, R. D. (2004). The three Es of cancer immunoediting. Annu. Rev. Immunol., 22, 329-360.
38. Dvorak, H. F. (1986). Tumors: wounds that do not heal. New England Journal of Medicine, 315(26), 1650-1659.
39. Egeblad, M., Nakasone, E. S., & Werb, Z. (2010). Tumors as organs: complex tissues that interface with the entire organism. Developmental cell, 18(6), 884-901.
40. Epelman, S., Lavine, K. J., & Randolph, G. J. (2014). Origin and functions of tissue macrophages. Immunity, 41(1), 21-35.
41. Frieri, M., & Metcalfe, D. D. (1983). Analysis of the effect of mast cell granules on lymphocyte blastogenesis in the absence and presence of mitogens: identification of heparin as a granule- associated suppressor factor. The Journal of Immunology, 131(4), 1942-1948
42. Fidler, I. J. (2003). The pathogenesis of cancer metastasis: the'seed and soil'hypothesis revisited. Nature Reviews Cancer, 3(6), 453-458.
43. Fidler, I. J., & Poste, G. (1982). Macrophage-mediated destruction of malignant tumor cells and new strategies for the therapy of metastatic disease. In Springer Seminars in immunopathology (Vol. 5, No. 2, pp. 161-174). Springer Berlin/Heidelberg.
44. Fidler, I. J., Gersten, D. M., & Budmen, M. B. (1976). Characterization in vivo and in vitro of tumor cells selected for resistance to syngeneic lymphocyte-mediated cytotoxicity. Cancer research, 36(9 Part 1), 3160-3165.
45. Forastiere, A., Koch, W., Trotti, A., & Sidransky, D. (2001). Head and neck cancer. New England Journal of Medicine, 345(26), 1890-1900.
46. Fridlender, Z. G., & Albelda, S. M. (2012). Tumor-associated neutrophils: friend or foe? Carcinogenesis, 33(5), 949-955.
47. Fridlender, Z. G., Sun, J., Kim, S., Kapoor, V., Cheng, G., Ling, L. & Albelda, S. M. (2009). Polarization of tumor-associated neutrophil phenotype by TGF-β: “N1” versus “N2” TAN. Cancer cell, 16(3), 183-194.
48. Gabrilovich, D. I., Ishida, T., Nadaf, S., Ohm, J. E., & Carbone, D. P. (1999). Antibodies to vascular endothelial growth factor enhance the efficacy of cancer immunotherapy by improving endogenous dendritic cell function. Clinical Cancer Research, 5(10), 2963-2970.
78
49. Gannot, G., Gannot, I., Vered, H., Buchner, A., & Keisari, Y. (2002). Increase in immune cell infiltration with progression of oral epithelium from hyperkeratosis to dysplasia and carcinoma. British journal of cancer, 86(9), 1444-1448.
50. Glas, R., Franksson, L., Une, C., Eloranta, M. L., Öhlén, C., Örn, A., & Kärre, K. (2000). Recruitment and Activation of Natural Killer (Nk) Cells in Vivo Determined by the Target Cell Phenotype An Adaptive Component of Nk Cell–Mediated Responses. The Journal of experimental medicine, 191(1), 129-138.
51. Glogauer, J. E., Sun, C. X., Bradley, G., & Magalhaes, M. A. (2015). Neutrophils increase oral squamous cell carcinoma invasion through an invadopodia-dependent pathway. Cancer immunology research, 3(11), 1218-1226.
52. Gökbuget, N., & Hoelzer, D. (2006). Novel antibody-based therapy for acute lymphoblastic leukaemia. Best practice & research Clinical haematology, 19(4), 701-713.
53. Goldsmith, M. M., Belchis, D. A., Cresson, D. H., Merritt III, W. D., & Askin, F. B. (1992). The importance of the eosinophil in head and neck cancer. Otolaryngology—Head and Neck Surgery, 106(1), 27-33.
54. González-Martín, A., Gómez, L., Lustgarten, J., Mira, E., & Mañes, S. (2011). Maximal T cell– mediated antitumor responses rely upon CCR5 expression in both CD4+ and CD8+ T cells. Cancer research, 71(16), 5455-5466.
55. Gouon-Evans, V., Rothenberg, M. E., & Pollard, J. W. (2000). Postnatal mammary gland development requires macrophages and eosinophils. Development, 127(11), 2269-2282.
56. Grivennikov, S. I., Greten, F. R., & Karin, M. (2010). Immunity, inflammation, and cancer. Cell, 140(6), 883-899.
57. Guthrie, G. J., Charles, K. A., Roxburgh, C. S., Horgan, P. G., McMillan, D. C., & Clarke, S. J. (2013). The systemic inflammation-based neutrophil–lymphocyte ratio: experience in patients with cancer. Critical reviews in oncology/hematology, 88(1), 218-230.
58. Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: the next generation. cell, 144(5), 646-674.
59. Hayes, R. L., Koslow, M., Hiesiger, E. M., Hymes, K. B., Moore, E. J., Pierz, D. M., ... & Ransohoff, J. (1995). Improved long term survival after intracavitary interleukin‐2 and lymphokine‐activated killer cells for adults with recurrent malignant glioma. Cancer, 76(5), 840-852
60. He, J. R., Shen, G. P., Ren, Z. F., Qin, H., Cui, C., Zhang, Y., ... & Jia, W. H. (2012). Pretreatment levels of peripheral neutrophils and lymphocytes as independent prognostic factors in patients with nasopharyngeal carcinoma. Head & neck, 34(12), 1769-1776.
79
61. Hirota, J., Ueta, E., Osaki, T., & Ogawa, Y. (1990). Immunohistologic study of mononuclear cell infiltrates in oral squamous cell carcinomas. Head & neck, 12(2), 118-125.
62. Hong, M., Puaux, A. L., Huang, C., Loumagne, L., Tow, C., Mackay, C. & Abastado, J. P. (2011). Chemotherapy induces intratumoral expression of chemokines in cutaneous melanoma, favoring T-cell infiltration and tumor control. Cancer research, 71(22), 6997-7009.
63. Horiuchi, K., Mishima, K., Ohsawa, M., Sugimura, M., & Aozasa, K. (1993). Prognostic factors for well‐differentiated squamous cell carcinoma in the oral cavity with emphasis on immunohistochemical evaluation. Journal of surgical oncology, 53(2), 92-96.
64. Houghton, A. N., Uchi, H., & Wolchok, J. D. (2005). The role of the immune system in early epithelial carcinogenesis: B-ware the double-edged sword. Cancer cell, 7(5), 403-405.
65. Hu, P., Shen, M., Zhang, P., Zheng, C., Pang, Z., Zhu, L., & Du, J. (2015). Intratumoral neutrophil granulocytes contribute to epithelial-mesenchymal transition in lung adenocarcinoma cells. Tumor Biology, 36(10), 7789-7796.
66. Huang, A. Y., Golumbek, P., Ahmadzadeh, M., Jaffee, E., Pardoll, D., & Levitsky, H. (1994). Role of bone marrow-derived cells in presenting MHC class I-restricted tumor antigens. Science, 264(5161), 961-965.
67. Inoue, S., Leitner, W. W., Golding, B., & Scott, D. (2006). Inhibitory effects of B cells on antitumor immunity. Cancer research, 66(15), 7741-7747.
68. Jacobsen, E. A., Taranova, A. G., Lee, N. A., & Lee, J. J. (2007). Eosinophils: singularly destructive effector cells or purveyors of immunoregulation? Journal of Allergy and Clinical Immunology, 119(6), 1313-1320.
69. Karnoub, A. E., & Weinberg, R. A. (2007). Chemokine networks and breast cancer metastasis. Breast disease, 26(1), 75-85.
70. Kärre, K., Ljunggren, H. G., Piontek, G., & Kiessling, R. (1986). Selective rejection of H–2-deficient lymphoma variants suggests alternative immune defence strategy.
71. Katou, F., Ohtani, H., Watanabe, Y., Nakayama, T., Yoshie, O., & Hashimoto, K. (2007). Differing phenotypes between intraepithelial and stromal lymphocytes in early-stage tongue cancer. Cancer research, 67(23), 11195-11201.
72. Kessenbrock, K., Plaks, V., & Werb, Z. (2010). Matrix metalloproteinases: regulators of the tumor microenvironment. Cell, 141(1), 52-67.
73. Kita, H. (1996). The eosinophil: a cytokine-producing cell? Journal of allergy and clinical immunology, 97(4), 889-892.
80
74. Knoechel, B., Lohr, J., Kahn, E., & Abbas, A. K. (2005). Cutting edge: the link between lymphocyte deficiency and autoimmunity: roles of endogenous T and B lymphocytes in tolerance. The Journal of Immunology, 175(1), 21-26.
75. Kometani, K., Nakagawa, R., Shinnakasu, R., Kaji, T., Rybouchkin, A., Moriyama, S., ... & Kurosaki, T. (2013). Repression of the transcription factor Bach2 contributes to predisposition of IgG1 memory B cells toward plasma cell differentiation. Immunity, 39(1), 136-147.
76. Kuang, D. M., Zhao, Q., Wu, Y., Peng, C., Wang, J., Xu, Z., ... & Zheng, L. (2011). Peritumoral neutrophils link inflammatory response to disease progression by fostering angiogenesis in hepatocellular carcinoma. Journal of hepatology, 54(5), 948-955.
77. Kujan, O., Oliver, R. J., Khattab, A., Roberts, S. A., Thakker, N., & Sloan, P. (2006). Evaluation of a new binary system of grading oral epithelial dysplasia for prediction of malignant transformation. Oral oncology, 42(10), 987-993.
78. Kum, R. O., Ozcan, M., Baklaci, D., Kum, N. Y., Yilmaz, Y. F., Gungor, V., & Unal, A. (2014). Elevated neutrophil-to-lymphocyte ratio in squamous cell carcinoma of larynx compared to benign and precancerous laryngeal lesions. Asian Pacific Journal of Cancer Prevention, 15(17), 7351-7355.
79. Kuper, H., Adami, H. O., & Trichopoulos, D. (2000). Infections as a major preventable cause of human cancer. Journal of internal medicine, 248(3), 171-183.
80. Langley, R. R., & Fidler, I. J. (2007). Tumor cell-organ microenvironment interactions in the pathogenesis of cancer metastasis. Endocrine reviews, 28(3), 297-321.
81. Lee, J. J., Chang, Y. L., Lai, W. L., Ko, J. Y., Kuo, M. Y. P., Chiang, C. P. & Chia, J. S. (2011). Increased prevalence of interleukin‐17–producing CD4+ tumor infiltrating lymphocytes in human oral squamous cell carcinoma. Head & neck, 33(9), 1301-1308.
82. Leek, R. D., & Harris, A. L. (2002). Tumor-associated macrophages in breast cancer. Journal of mammary gland biology and neoplasia, 7(2), 177-189.
83. Leek, R. D., Hunt, N. C., Landers, R. J., Lewis, C. E., Royds, J. A., & Harris, A. L. (2000). Macrophage infiltration is associated with VEGF and EGFR expression in breast cancer. The Journal of pathology, 190(4), 430-436.
84. Lin, W. W., & Karin, M. (2007). A cytokine-mediated link between innate immunity, inflammation, and cancer. The Journal of clinical investigation, 117(5), 1175-1183.
85. Liyanage, U. K., Moore, T. T., Joo, H. G., Tanaka, Y., Herrmann, V., Doherty, G. & Linehan, D. C. (2002). Prevalence of regulatory T cells is increased in peripheral blood and tumor microenvironment of patients with pancreas or breast adenocarcinoma. The Journal of Immunology, 169(5), 2756-2761.
81
86. Ljunggren, H. G., & Kärre, K. (1985). Host resistance directed selectively against H-2-deficient lymphoma variants. Analysis of the mechanism. Journal of Experimental Medicine, 162(6), 1745- 1759.
87. Lobo, I. (2008). Chromosome abnormalities and cancer cytogenetics. Nature Education, 1(1), 35- 43.
88. MacDonald, G. H., & Johnston, R. E. (2000). Role of dendritic cell targeting in Venezuelan equine encephalitis virus pathogenesis. Journal of virology, 74(2), 914-922.
89. MacLennan, I., Toellner, K. M., Cunningham, A. F., Serre, K., Sze, D. M. Y., Zúñiga, E., ... & Vinuesa, C. G. (2003). Extrafollicular antibody responses. Immunological reviews, 194(1), 8-18.
90. Magalhaes, M. A. (2009). Effective neutrophil activation during innate immunity: understanding the specific roles of Rac1 and Rac2 (Doctoral dissertation, University of Toronto).
91. Magalhaes, M. A., Glogauer, J. E., & Glogauer, M. (2014). Neutrophils and oral squamous cell carcinoma: lessons learned and future directions. Journal of leukocyte biology, 96(5), 695-702.
92. Mantovani, A., & Sica, A. (2010). Macrophages, innate immunity and cancer: balance, tolerance, and diversity. Current opinion in immunology, 22(2), 231-237.
93. Mantovani, A., Sozzani, S., Locati, M., Allavena, P., & Sica, A. (2002). Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. Trends in immunology, 23(11), 549-555.
94. Martinelli-Kläy, C. P., Mendis, B. R. R. N., & Lombardi, T. (2009). Eosinophils and oral squamous cell carcinoma: a short review. Journal of oncology, 2009.
95. Martinez, F. O., Helming, L., & Gordon, S. (2009). Alternative activation of macrophages: an immunologic functional perspective. Annual review of immunology, 27, 451-483.
96. Matsushita, T., Yanaba, K., Bouaziz, J. D., Fujimoto, M., & Tedder, T. F. (2008). Regulatory B cells inhibit EAE initiation in mice while other B cells promote disease progression. The Journal of clinical investigation, 118(10), 3420-3430.
97. Mayadas, T. N., Cullere, X., & Lowell, C. A. (2014). The multifaceted functions of neutrophils. Annual Review of Pathology: Mechanisms of Disease, 9, 181-218.
98. McLaughlin, P., White, C. A., Grillo-López, A. J., & Maloney, D. G. (1998). Clinical status and optimal use of rituximab for B-cell lymphomas. Oncology (Williston Park, NY), 12(12), 1763-9.
99. Migliorati, C. A., Migliorati, E. K. J., Silverman, S., Greenspan, D., & Greenspan, J. S. (1986). Phenotypic identification of mononuclear cells in oral premalignant lesions and cancer by monoclonal antibodies. Journal of Oral Pathology & Medicine, 15(6), 352-358.
82
100. Moore, R. J., Owens, D. M., Stamp, G., Arnott, C., Burke, F., East, N. & Kollias, G. (1999). Mice deficient in tumor necrosis factor-α are resistant to skin carcinogenesis. Nature medicine, 5(7).
101. Moses, K., & Brandau, S. (2016). Human neutrophils: Their role in cancer and relation to myeloid-derived suppressor cells. In Seminars in immunology (Vol. 28, No. 2, pp. 187-196). Academic Press.
102. Murdoch, C., Muthana, M., Coffelt, S. B., & Lewis, C. E. (2008). The role of myeloid cells in the promotion of tumor angiogenesis. Nature Reviews Cancer, 8(8), 618-631.
103. North, R. J. (1984). The murine antitumor immune response and its therapeutic manipulation. Advances in immunology, 35, 89-155.
104. Nossal, G. J. (1961). Genetic control of lymphopoiesis, plasma cell formation, and antibody production. International review of experimental pathology, 1, 1-72.
105. Nowell, P. C. (1976). The clonal evolution of tumor cell populations. Science, 194(4260), 23-28.
106. Nutt, S. L., Hodgkin, P. D., Tarlinton, D. M., & Corcoran, L. M. (2015). The generation of antibody- secreting plasma cells. Nature Reviews Immunology, 15(3), 160-171.
107. Olkhanud, P. B., Damdinsuren, B., Bodogai, M., Gress, R. E., Sen, R., Wejksza, K., ... & Biragyn, A. (2011). Tumor-evoked regulatory B cells promote breast cancer metastasis by converting resting CD4+ T cells to T-regulatory cells. Cancer research, 71(10), 3505-3515.
108. Urruticoechea, A., Alemany, R., Balart, J., Villanueva, A., Vinals, F., & Capella, G. (2010). Recent advances in cancer therapy: an overview. Current pharmaceutical design, 16(1), 3-10.
109. Pages, F., Galon, J., Dieu-Nosjean, M. C., Tartour, E., Sautes-Fridman, C., & Fridman, W. H. (2010). Immune infiltration in human tumors: a prognostic factor that should not be ignored. Oncogene, 29(8), 1093.
110. Paget, S. (1889). The distribution of secondary growths in cancer of the breast. The Lancet, 133(3421), 571-573.
111. Palermo, C., & Joyce, J. A. (2008). Cysteine cathepsin proteases as pharmacological targets in cancer. Trends in pharmacological sciences, 29(1), 22-28.
112. Parker, D. C. (1993). T cell-dependent B cell activation. Annual review of immunology, 11(1), 331-360.
113. Peinado, H., Marin, F., Cubillo, E., Stark, H. J., Fusenig, N., Nieto, M. A., & Cano, A. (2004). Snail and E47 repressors of E-cadherin induce distinct invasive and angiogenic properties in vivo. Journal of cell science, 117(13), 2827-2839.
83
114. Pellegrini, P., Berghella, A. M., Beato, T. D., Cicia, S., Adorno, D., & Casciani, C. U. (1996). Disregulation in TH1 and TH2 subsets of CD4+ T cells in peripheral blood of colorectal cancer patients and involvement in cancer establishment and progression. Cancer immunology, immunotherapy, 42(1), 1-8.
115. Pereira, M. C., Oliveira, D. T., & Kowalski, L. P. (2011). The role of eosinophils and eosinophil cationic protein in oral cancer: a review. archives of oral biology, 56(4), 353-358.
116. Perisanidis, C., Kornek, G., Pöschl, P. W., Holzinger, D., Pirklbauer, K., Schopper, C., & Ewers, R. (2013). High neutrophil-to-lymphocyte ratio is an independent marker of poor disease-specific survival in patients with oral cancer. Medical Oncology, 30(1), 334.
117. Perricone, M. A., Smith, K. A., Claussen, K. A., Plog, M. S., Hempel, D. M., Roberts, B. L. & Kaplan, J. M. (2004). Enhanced efficacy of melanoma vaccines in the absence of B lymphocytes. Journal of immunotherapy, 27(4), 273-281.
118. Pollard, J. W. (1997). Role of colony‐stimulating factor‐1 in reproduction and development. Molecular reproduction and development, 46(1), 54-61.
119. Pollard, J. W. (2004). Tumor-educated macrophages promote tumor progression and metastasis. Nature Reviews Cancer, 4(1), 71-78.
120. Pollard, J. W., & Stanley, E. R. (1996). Pleiotropic roles for CSF-1 in development defined by the mouse mutation osteopetrotic. Advances in developmental biochemistry, 4, 153-193.
121. Puxeddu, I., Ribatti, D., Crivellato, E., & Levi-Schaffer, F. (2005). Mast cells and eosinophils: a novel link between inflammation and angiogenesis in allergic diseases. Journal of Allergy and Clinical Immunology, 116(3), 531-536.
122. Quatromoni, J. G., & Eruslanov, E. (2012). Tumor-associated macrophages: function, phenotype, and link to prognosis in human lung cancer. Am J Transl Res, 4(4), 376-389.
123. Qian, B. Z., & Pollard, J. W. (2010). Macrophage diversity enhances tumor progression and metastasis. Cell, 141(1), 39-51.
124. Radbruch, A., Muehlinghaus, G., Luger, E. O., Inamine, A., Smith, K. G., Dörner, T., & Hiepe, F. (2006). Competence and competition: the challenge of becoming a long-lived plasma cell. Nature Reviews Immunology, 6(10), 741-750.
125. Rajewsky, K., & Schittek, B. (1990). Maintenance of B-cell memory by long-lived cells generated from proliferating precursors. Nature, 346(6286), 749.
126. Rothenberg, M. E., & Hogan, S. P. (2006). The eosinophil. Annu. Rev. Immunol., 24, 147-174.
84
127. Saccani, A., Schioppa, T., Porta, C., Biswas, S. K., Nebuloni, M., Vago, L., ... & Sica, A. (2006). p50 nuclear factor-κB overexpression in tumor-associated macrophages inhibits M1 inflammatory responses and antitumor resistance. Cancer research, 66(23), 11432-11440.
128. Sarode, S. C., & Sarode, G. S. (2014). Neutrophil‐tumor cell cannibalism in oral squamous cell carcinoma. Journal of Oral Pathology & Medicine, 43(6), 454-458.
129. Schreiber, H., Wu, T. H., Nachman, J., & Rowley, D. A. (2000). Immunological enhancement of primary tumor development and its prevention. In Seminars in cancer biology (Vol. 10, No. 5, pp. 351-357). Academic Press.
130. Schüler, T., Qin, Z., Ibe, S., Noben-Trauth, N., & Blankenstein, T. (1999). T helper cell type 1– associated and cytotoxic T lymphocyte–mediated tumor immunity is impaired in interleukin 4– deficient mice. The Journal of experimental medicine, 189(5), 803-810.
131. Shen, M., Hu, P., Donskov, F., Wang, G., Liu, Q., & Du, J. (2014). Tumor-associated neutrophils as a new prognostic factor in cancer: a systematic review and meta-analysis. PLoS One, 9(6), e98259.
132. Shlomchik, M. J., & Weisel, F. (2012). Germinal center selection and the development of memory B and plasma cells. Immunological reviews, 247(1), 52-63.
133. Sica, A., & Bronte, V. (2007). Altered macrophage differentiation and immune dysfunction in tumor development. The Journal of clinical investigation, 117(5), 1155-1166.
134. Siegel, R. L., Miller, K. D., & Jemal, A. (2015). Cancer statistics, 2015. CA: a cancer journal for clinicians, 65(1), 5-29.
135. Silverman, G. J. (2006). Therapeutic B cell depletion and regeneration in rheumatoid arthritis: emerging patterns and paradigms. Arthritis & Rheumatology, 54(8), 2356-2367.
136. Simmons, A., Leaverton, P., & Elbert, G. (1974). Normal laboratory values for differential white cell counts established by manual and automated cytochemical methods (Hemalog DTM). Journal of clinical pathology, 27(1), 55-58.
137. Smyth, M. J., Dunn, G. P., & Schreiber, R. D. (2006). Cancer immunosurveillance and immunoediting: the roles of immunity in suppressing tumor development and shaping tumor immunogenicity. Advances in immunology, 90, 1-50.
138. Smyth, M. J., Hayakawa, Y., Takeda, K., & Yagita, H. (2002). New aspects of natural-killer-cell surveillance and therapy of cancer. Nature Reviews Cancer, 2(11), 850-861.
139. Spiotto, M. T., Yu, P., Rowley, D. A., Nishimura, M. I., Meredith, S. C., Gajewski, T. F. & Schreiber, H. (2002). Increasing tumor antigen expression overcomes “ignorance” to solid tumors via crosspresentation by bone marrow-derived stromal cells. Immunity, 17(6), 737-747.
85
140. Strauss, L., Bergmann, C., Szczepanski, M., Gooding, W., Johnson, J. T., & Whiteside, T. L. (2007). A unique subset of CD4+ CD25highFoxp3+ T cells secreting interleukin-10 and transforming growth factor-β1 mediates suppression in the tumor microenvironment. Clinical Cancer Research, 13(15), 4345-4354.
141. Szkandera, J., Stotz, M., Eisner, F., Absenger, G., Stojakovic, T., Samonigg, H., ... & Seggewies, F. S. (2013). External validation of the derived neutrophil to lymphocyte ratio as a prognostic marker on a large cohort of pancreatic cancer patients. PloS one, 8(11), e78225.
142. Tadmor, T., Zhang, Y., Cho, H. M., Podack, E. R., & Rosenblatt, J. D. (2011). The absence of B lymphocytes reduces the number and function of T-regulatory cells and enhances the anti- tumor response in a murine tumor model. Cancer Immunology, Immunotherapy, 60(5), 609-619.
143. Talmadge, J. E., & Fidler, I. J. (2010). AACR centennial series: the biology of cancer metastasis: historical perspective. Cancer research, 70(14), 5649-5669.
144. Tan, T. T., & Coussens, L. M. (2007). Humoral immunity, inflammation and cancer. Current opinion in immunology, 19(2), 209-216.
145. Tarella, C., Passera, R., Magni, M., Benedetti, F., Rossi, A., Gueli, A. & Cortelazzo, S. (2010). Risk factors for the development of secondary malignancy after high-dose chemotherapy and autograft, with or without rituximab: a 20-year retrospective follow-up study in patients with lymphoma. Journal of Clinical Oncology, 29(7), 814-824.
146. Tecchio, C., Scapini, P., Pizzolo, G., & Cassatella, M. A. (2013). On the cytokines produced by human neutrophils in tumors. In Seminars in cancer biology (Vol. 23, No. 3, pp. 159-170). Academic Press.
147. Uribe-Querol, E., & Rosales, C. (2015). Neutrophils in cancer: two sides of the same coin. Journal of immunology research, 2015.
148. Van der Waal, I. (2009). Potentially malignant disorders of the oral and oropharyngeal mucosa; terminology, classification and present concepts of management. Oral oncology, 45(4), 317-323
149. Venge, P., & Byström, J. (1998). Molecules in focus eosinophil cationic protein (ECP). The international journal of biochemistry & cell biology, 30(4), 433-437.
150. Venge, R., Byström, J., Carlson, M., Håkansson, L., Karawacjzyk, M., Peterson, C., ... & Trulson, A. (1999). Eosinophil cationic protein (ECP): molecular and biological properties and the use of ECP as a marker of eosinophil activation in disease. Clinical and experimental allergy, 29, 1172-1186.
151. Virchow, R. (1863). Cellular Pathology as Based Upon Physiological and Pathological Histology... JB Lippincott.
152. Wahl, L. M., & Kleinman, H. K. (1998). Tumor-associated macrophages as targets for cancer therapy.
86
153. Wang, J., Jia, Y., Wang, N., Zhang, X., Tan, B., Zhang, G., & Cheng, Y. (2014). The clinical significance of tumor-infiltrating neutrophils and neutrophil-to-CD8+ lymphocyte ratio in patients with resectable esophageal squamous cell carcinoma. Journal of translational medicine, 12(1), 7.
154. Warnakulasuriya, S., Reibel, J., Bouquot, J., & Dabelsteen, E. (2008). Oral epithelial dysplasia classification systems: predictive value, utility, weaknesses and scope for improvement. Journal of Oral Pathology & Medicine, 37(3), 127-133.
155. Warnakulasuriya, S. (2009). Global epidemiology of oral and oropharyngeal cancer. Oral oncology, 45(4), 309-316.
156. Whiteside, T. L., & Herberman, R. B. (1994). Role of human natural killer cells in health and disease. Clinical and diagnostic laboratory immunology, 1(2), 125-133.
157. Whiteside, T. L., Vujanovic, N. L., & Herberman, R. B. (1998). Natural killer cells and tumor therapy. In Specificity, Function, and Development of NK Cells (pp. 221-244). Springer Berlin Heidelberg.
158. Wiltrout, R. H., Mathieson, B. J., Talmadge, J. E., Reynolds, C. W., Zhang, S. R., Herberman, R. B., & Ortaldo, J. R. (1984). Augmentation of organ-associated natural killer activity by biological response modifiers. J Exp Med, 160, 1431-1449.
159. Young, M. R. I., Wright, M. A., Lozano, Y., Matthews, J. P., Benefield, J., & Prechel, M. M. (1996). Mechanisms of immune suppression in patients with head and neck cancer: influence on the immune infiltrate of the cancer. International journal of cancer, 67(3), 333-338.
160. Yu, P., & Fu, Y. X. (2006). Tumor-infiltrating T lymphocytes: friends or foes? Laboratory investigation, 86(3), 231-245.
161. Zhou, L., Chong, M. M., & Littman, D. R. (2009). Plasticity of CD4+ T cell lineage differentiation. Immunity, 30(5), 646-655.
162. Zumsteg, A., & Christofori, G. (2009). Corrupt policemen: inflammatory cells promote tumor angiogenesis. Current opinion in oncology, 21(1), 60-70.
87