i

Effect of donor KIR Genotype On the Outcome of

Bone Marrow Transplantation

By: Lee Jia-Hui Jane Bachelor of Science in Biomedical Science and Molecular Biology

This thesis is presented for the Honours degree in Biomedical Science at Murdoch University, Western Australia.

May 2013

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DECLARATION

I declare this thesis is my own account of research and contains as its main content, work that has not been previously submitted for a degree at any tertiary educational institution.

. Lee Jia-Hui Jane

(Student)

. A Prof. Campbell S. Witt

(RPH Supervisor)

.

Dr. Dianne De Santis (RPH Supervisor)

. A Prof. Wayne Greene (Murdoch Supervisor)

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LIST OF ABBREVIATIONS A/A Haplotype ADCC aKIR ALL AML APC ATG B/B Haplotype BCR BMT Bp Bu B/x Haplotype CAMP CML CMV Cy DNA Flu GvHD GvL HLA HSCT IFNγ Ig iKIR ITIM KIR KLR LILR Mel MHC NK NCR PBSC TBI TCR TNFα WBC

Homozygous A Haplotype Antibody Dependent Cell-mediate Cytotoxicity Activating Killer Immunoglobulin-Like Receptor Acute Lymphoid Leukaemia Acute Myeloid Leukaemia Antigen Presenting Cell Anti-Thymocyte Globulin Homozygous B Haplotype B-cell Receptor Bone Marrow Transplant Basepair Busulphan Heterozygous B/x Haplotype Campath Chronic Myeloid Leukaemia Cytomegalovirus Cyclophosphamide Deoxyribonucleic Acid Fludarabine Graft-versus-Host Disease Graft-versus Leukaemia Human Leukocyte Antigen Haematopoietic Stem Cell Transplant Inferon Gamma Immunoglobulin Inhibitory Killer Immunoglobulin-Like Receptor Immunoreceptor Tyrosine-base Inhibitory Motif Killer Immunoglobulin-Like Receptor Killer cell Lectin-like Receptor Leukocyte Immunoglobulin-Like Receptor Melphalan Major Histocompatibility Complex Natural Killer Natural Cytotoxicity Receptor Peripheral Blood Stem Cell Total Body Irradiation T-cell Receptor Tumour Necrosis Factor Alpha White Blood Cell

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ACKNOWLEDGEMENTS

First and foremost I wish to express my sincere gratitude to my two Royal

Perth Hospital supervisors, A.Prof Campbell Witt and Dr. Dianne De Santis for

the opportunity to work under them for my honours project. They have

supported and guided me through it, patiently teaching me and not to mention

correcting my grammar on countless occasions. As I am writing this I know

that they are mentally re-formatting and editing my flowery non-scientific

writing. It has been a truly enjoyable journey with both of you! Not forgetting,

A.Prof Wayne Greene who was always there to offer his advice and guidance

through the fundamentals of the Murdoch Honours degree.

Secondly, I would like to thank the RPH Clinical Immunology routine staff and

Conexio staff for lending me a hand on multiple occasions and teaching me

how to use the various equipments in the lab. I have met some genuinely

awesome people who are just a pleasure to work along side; they make long

tedious experiment-filled days a little less tedious and a little more enjoyable!

Thirdly, I would like to thank my parents, without whom I’d have no meals, no

clothes, no roof over my head and no paid school fees. Thanks Mum and Dad

for your unconditional love and encouraging words of support! You are the

best parents any child could ask for; you have supported my ambition to study

abroad and never stopped believing in me, which means more than my

vocabulary can do justice for. To my two beloved elder brothers who never

stopped poking fun at me and my dyslexia, this is for you! Your baby sister

finished her honours!

Fourthly, I would like to give a shout out to my best friend, Tricia. Who has

been in my corner with encouraging from day 1 and she never stopped

believing. And to my tight bunch old close friends in Perth and Singapore who

I have neglected but still stood by me, you made my journey less lonely.

Lastly, I would like to thank the honours committee for giving me a chance

and my examiners who will be taking time out of their busy schedules to read

this thesis.

Thank you, everyone. This is for you.

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ABSTRACT

Haematopoietic stem cell transplantation is the only curative treatment for

some forms of haematologcial malignancies and bone marrow failure. The

role of donor Natural Killer (NK) cells that accompany the donor stem cells is

under investigation. In particular, there is interest in the role of the killer

immunoglobulin-like receptors (KIR) family of receptors expressed on the

surface receptors of NK cells. In this study, we focused on the donor KIR

genes and the possibility that the KIR receptors interact with other transplant

variables to influence survival. We analyzed a cohort of 140 unrelated donors

from bone marrow transplants carried out at Royal Perth Hospital and

Princess Margaret Hospital. The variables that were analyzed for interactions

with KIR were: cytomegalovirus (CMV) status, transplant graft source,

conditioning agents. A number of significant interactions between KIR and

transplant variables were identified, the strongest being the interaction

between KIR2DS2 and the use of cyclophosphamide as a conditioning agent.

Kaplan-Meier analyses showed that the presence of KIR2DS2 in a

cyclophosphamide positive transplant resulted in a significantly improved

survival (p=0.002) whereas the presence of KIR2DS2 in a cyclophosphamide

negative transplant resulted in a poorer survival (p=0.032). Hence the

presence of KIR2DS2 could be beneficial or deleterious depending on the

presence or absence of cyclophosphamide. As this was an exploratory study,

observations of the interactions discovered need to be confirmed in additional

studies.

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TABLE OF CONTENTS

TITLE

PAGE

Chapter 1 Literature Review 1.1 Immune System 1.1.1 Adaptive Immunity 1.1.2 Innate Immunity 1.2 Natural Killer Cells 1.3 “Missing self” Hypothesis in NK cell Recognition 1.4 Natural Killer Cell Functions and Pathways 1.5 Natural Killer Cell Receptors 1.5.1C-type Lectin Receptors 1.5.1.1 CD94/NKG2 1.5.1.2 Ly49 1.5.2 Immunoglobulin Super-family Receptors 1.5.2.1 Killer Cell Immunoglobulin-like Receptors (KIR) 1.5.3 KIR Receptor Structure and Nomenclature 1.5.4 KIR Genomics and Diversity 1.5.4.1 Allelic Polymorphism of KIR Genes 1.5.5 KIR Haplotypes 1.5.5.1 KIR Haplotype Frequencies 1.5.6 Ligands for KIR Receptors 1.5.7 KIR Expression 1.6 Haematopoietic Stem Cell Transplantation (HSCT) 1.7 Factors Affecting the Outcome of Allogeneic HSCT 1.7.1 NK Cell Alloreactivity due to Ligand-Ligand Incompatibility 1.7.2 KIR Repertoire on the Outcome of HSCT 1.7.3 Preparative Regimen Variables 1.7.3.1 Total Body Irradiation 1.7.3.2 Cytomegalovirus (CMV) Prophylaxis 1.8 Cytomegalovirus (CMV) 1.8.1 KIR Repertoire With Associationg to CMV Protection Chapter 2. Materials and Methods 2.1 DNA Samples and Preparation 2.1.1 DNA Source 2.1.2 Calculations for the Preparation of DNA Samples 2.2 Polymerase Chain Reaction Sequence Specific Priming (PCR-SSP) Assay for KIR Genotyping 2.2.1 Oligoneucleotide Primers

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2.2.2 Preparation of PCR Reagents (Reaction mix components) 2.2.2.1 10x TDMH PCR Buffer (100ml) 2.2.2.2 40mM dNTP 2.2.2.3 Other PCR Reagents 2.2.3Preparation of Gel Electrophoresis Reagents 2.2.3.1 10x TBE Buffer (2 litre batch) 2.2.3.2 0.5x TBE Buffer (20litre batch) 2.2.3.3 3% and 3.5% Agarose Gel 2.2.3.4 Gel Electrophoresis Loading Buffer 2.2.3.5 Gel Electrophoresis Kb Plus DNA Lambda Ladder 2.3 KIR Multiplex PCR-SSP Genotyping Assay Optimization 2.3.1 Polymerase Chain Reaction (PCR) Runs 2.3.1.1 Reaction Mix (Mastermix) Volumes 2.3.1.2 KIR PCR-SSP Gene Groupings Optimized Recipes 2.3.1.3 Thermocycler Run Conditions 2.3.1.4 Gel Electrophoresis 2.4 Statistical Analysis 2.4.1 Survival Analyses 2.4.2 Pearson Chi-Square Analysis of Acute Graft-versus-Host Disease (aGvHD) 2.4.3 Multivariate Analysis on Survival Chapter 3. Results 3.1 Multiplex PCR-SSP KIR Genotyping Assay Optimizations 3.1.1 Optimization of PCR-SSP Group 1 3.1.2 Optimization of PCR-SSP Group 2 3.1.3 Optimization of PCR-SSP Group 3 3.1.4 Optimization of PCR-SSP Group 4 3.2 KIR Genotyping of the 146 Donors 3.3 Transplant Characteristics and Statistics 3.3.1 Year of Bone Marrow Transplants 3.3.2 Transplant Centre and Number of Transplants 3.3.3 Transplant Source of Graft 3.3.4 Donors’ Ages and Genders 3.3.5 Patient Diagnosis 3.3.6 Cytomegalovirus (CMV) Status 3.3.7 Conditioning Regimens 3.3.8 Acute Graft-versus-Host Disease (GvHD) 3.3.9 KIR Gene Frequencies of the Entire Cohort 3.4 Analysis of Acute Graft-versus-Host Disease (aGvHD) and KIR genes 3.4.1 Effect of KIR Genotype on Prevalence of Acute GVHD 3.4.2 Effect of interactions between KIR Genotype and other Transplant Variables on the Prevalence of Acute GVHD 3.5 Analysis of KIR Genes on Survival

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3.5.1 Univariate Kaplan-Meier Analysis of KIR genes on Survival 3.5.2 Univariate Kaplan-Meier Analysis of KIR Genes on Survival in patients with Myelogenous leukaemias 3.6 Effect of Interactions between KIR Genes and other Transplant Variables on Survival 3.7 Multivariate Cox Regression Analysis Chapter 4. Discussion 4.1 Optimization of the Multiplex PCR-SSP KIR Genotyping Assay 4.1.1 Unexpected PCR bands Migration 4.1.2 Validation of the PCR-SSP KIR Genotyping Assay 4.2 Overview of the Data Analyzed in this Study 4.2.1 Interactions between KIR2DS2 and Conditioning Agents 4.2.2 Interactions between KIR2DS1, KIR2DS5, KIR3DS1, KIR2DL5 and CMV and Graft Source 4.3 KIR Repertoire and Acute Graft-versus-Host Disease (aGvHD) 4.4 The Effect of KIR Repertoire on Survival 4.4.1 Mechanism of KIR Interaction Effect on Survival 4.4.2 Effect of KIR Genotype in Myeloid and Lymphocytic Leukaemia 4.5 Statistical Analysis Errors 4.6 Conclusions REFERENCES APPENDIX A APPENDIX B

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LIST OF ILLUSTRATIONS

TITLE PAGE FIGURES Figure 1. NK cell’s response (receptor-ligand models) with association to a healthy cell and a tumor cell. Figure 2. KIR protein domains and region lengths. Figure 3. Map of the Leukocyte Receptor Complex (LRC). Figure 4. Centromeric and telomeric region separation of KIR genes which includes a few different A/B haplotypes. Figure 5. Various KIR receptors and their HLA class I ligands. Figure 6. shows the 10mM and 40mM dNTP concentrations for selected cell lines with Group 1 primers. Figure 7. Shows the gels of the optimized PCR-SSP Group 1 primers on 20-cell line panel. Figure 8. Gel picture of the two different dNTP concentrations from Group 2. Figure 9. Optimized PCR-SSP Group 2 on the 20-cell line panel. Figure 10. Gel picture of the PCR products produced using 10mM and 40mM dNTP concentrations from Group 3. Figure 11. The initial Group 3 (before the swapping of KIR primers). Figure 12. The new group 3 (after the swapping of KIR genes). Figure 13. The preliminary test for the new group 3 after the removal of KIR2DS1 sequencing primer tags. Figure 14. The optimized new group 3 primers on the validated panel. Figure 15. PCR products produced using 10mM and 40mM dNTP concentrations for group 4 primers. Figure 16. The preliminary PCR run test on the new group 4 primers on selected cell lines from the validated panel. Figure 17. The optimized PCR-SSP Group 4 on selected cell lines. Figure 18. The frequency of haematopoietic stem cell transplants performed in each year. Figure 19a. (Left) The presence of KIR3DS1 in peripheral blood transplant was associated with a poorer survival while there was no observable difference in bone marrow transplants Figure 19b. (Right) There was no difference in the presence or absence of KIR3DS1 in bone marrow transplants. Figure 20a. (Left) Donors without KIR2DS5 in CMV negative transplants were associated with an improved survival while donors with KIR2DS5 were associated with a worse survival. Figure 20b. (Left) There was no difference in survival for CMV positive transplants, in the presence or absence of KIR2DS5. Figure 21a. (Left) KIR2DS1 was associated with a poorer survival in CMV negative transplants. Figure 21b. (Right) There was no difference in the presence or absence of KIR2DS1 in CMV positive transplants. Figure 22a. (Left) KIR3DS1 in CMV negative transplants was associated with a poorer survival. Figure 22b. (Left) There was no difference in the presence or absence

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of KIR3DS1 in CMV positive transplants. Figure 23a. (Left) KIR2DL5 in CMV negative transplants was associated with a poorer survival. Figure 23b. (Left) There was no difference in the presence or absence of KIR2DL5 in CMV positive transplants Figure 24a. (Left) Donors with high number of KIR genes were associated with a poorer survival in CMV negative. Figure 24b. (Right) No significant difference in survival was observed in transplants with donor with a high number of KIR genes. Figure 25a. (Top left) Donors with KIR2DS2 had poorer survival in TBI negative transplants. Figure 25b. (Top right) Donors with KIR2DS2 had better survival in TBI+ transplants. Figure 25c. (Bottom left) Donors with KIR2DL2 had poorer survival in TBI negative transplants. Figure 25d. (Bottom right) Donors with KIR2DL2 had better survival in TBI+ transplants. Figure 26a. (Top left) KIR2DS2 was associated with a poorer survival in cyclophosphamide negative transplants. Figure 26b. (Top right) KIR2DS2 was associated with an improved survival in cyclophosphamide positive transplants. Figure 26c. (Bottom left) KIR2DL2 was associated with a poorer survival in cyclophosphamide negative transplants. Figure 26d. (Bottom right) KIR2DL2 was associated with an improved survival in cyclophosphamide positive transplants. Figure 27. KIR2DS2 was associated with an improved survival in cyclophosphamide positive transplants in the ALL cohort. Figure 28a. (Left) Presence of KIR2DS2 in cyclophosphamide negative transplants was associated with a worse survival in the MYO cohort. Figure 28b. (Right) KIR2DS2 was associated with an improved survival in cyclophosphamide positive transplants in the MYO cohort. Figure 29a. (Left) The absence of KIR2DS2 in melphalan negative transplant was associated with a poorer survival. Figure 29b. (Right) The absence of KIR2DS2 in melphalan positive transplants was associated with better survival. Figure 30a. (Left) The absence of KIR2DS2 in fludarabine negative transplant was associated with poorer survival. Figure 30b. (Right) The absence of KIR2DS2 in fludarabine positive transplants was associated with better survival. TABLES Table 1. PCR reaction mix volumes for different amounts of sample. Table 2. Transplant numbers performed at the two transplant centres. Table 3. Frequency of the different transplant graft source. Table 4. Age range of the donors amongst the different transplants. Table 5. Gender of patients and donors of the transplants analyzed in this study. Table 6. Frequency of the different diagnoses in the entire cohort of patients.

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Table 7. Frequency of patient, donor CMV status and Transplant CMV Status. Table 8. Frequency of the different conditioning regimens used. Table 9. Prevalence of different grades of aGvHD. Table 10. Frequency of the individual KIR genes. Table 11. Frequency of donors with different numbers of activating, inhibitory and total number of KIR genes. Table 12. P values for Pearson chi-square analysis of contingency tables relating KIR genotype, or KIR genotype in different transplant subgroups, to grade of acute GVHD. Table 13. Kaplan-Meier p-values for the association of individual donor KIR genes on survival. Table 14. P. values of individual KIR genes on the survival rate of the myelogenous and non-mylogenous cohort Table 15. P-values of all the conditioning variables with individual KIR genes on survival rate. Table 16. Variables initially entered into the multivariate Cox Regression model Table 17. Variables left in the final equation in the multivariate Cox Regression model. Table 18. The genotypes of the validated 20-cell line panel Table 19. Conditioning agents used in the different diagnoses cohorts.

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1. Literature Review

1.1 Immune System

The human immune system can be divided into two broad branches, the

adaptive immune system and the innate immune system. These two branches

work hand in hand to combat invading infections and foreign pathogens that

causes harm the human body (Janeway et al. 2001).

1.1.1 Adaptive Immunity

Adaptive immunity is a part of the immune system, which learns and adapts to

the pathogen. The cells involved in the adaptive immune system are T and B

cells. These cells require a sensitizing event to a pathogen and the response

is improved with subsequent exposure to the same pathogen. A key feature of

the adaptive immune system is therefore memory. There are three types of T

cells: CD4+ T helper cells which aid in the signaling of B cell activation and

growth, CD8+ T cytotoxic cells which recognize and destroy virally infected

cells when they are presented to by the T helper cells, and T regulatory cells

which maintain balance by modulating tolerance to self-antigens, thus

preventing autoimmune diseases (Haribhai et al. 2011 and Holaday et al.

1993). T cell receptors (TCR) are molecules on the surface of T cells, which

recognize antigens on the major hiscompatibility (MHC) class I molecules. B

cells differentiate into plasma cells and large volumes of antibodies are

secreted to combat the foreign pathogens. Both T and B cells have highly

specific antigen receptors on their surface. The B cell antigen receptors (BCR)

are membrane bound immunoglobulins, which activate the cell when a

specific antigen binds to the receptor. Antigen presentation is a process in the

immune system, employed by macrophages, dendritic cells and other cells to

2

activate T cytotoxic cells. T cell receptors are restricted to the recognition of

antigenic peptides when they are bound to major histocompatibility complex

(MHC), which is also known as human leukocyte antigen (HLA). The foreign

antigen is taken up by the antigen presenting cell (APC) and processed, after

which a peptide fragment is bound to an MHC class II molecule which is

necessary for the T helper cells to recognize it (Rolland and O’Hehir, 1999).

Peptide fragments bound to MHC molecules - MHC class I molecules interact

with immature CD8+ T cells to stimulate maturation into mature CD8+ T

cytotoxic cells, while peptide fragments bound to MHC class II molecules

interact with immature CD4+ T cells to become mature CD4+ T helper cells

(Milstein et al. 2011).

1.1.2 Innate Immunity

The innate immune system is described as the first line of defense in

response to foreign infections until the adaptive immunity takes over. The

innate immune system does not discriminate between pathogens and has no

immunological memory (Janeway et al. 2001). The innate immune system

provides protection in the form of proteins and white blood cells (WBC) in the

blood.

The innate immune system consists of particular subsets of WBC in the

bloods, which come into play when the physical barriers fail to stop the

pathogens from entering the body. The cells of the innate immune system

include natural killer cells, neutrophils, macrophages, monocytes, dendritic

cells and mast cells (Robinson and Babcock, 1998). These cells are present

in the blood and are fully functional without the need for prior sensitization as

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required by the adaptive immune system (Alberts et al. 2002). Macrophages

and monocytes have different methods of combating pathogens; they vary

from engulfing the pathogen to secreting anti-microbial substances and

lysozymes. Natural killer (NK) cells detect mismatches between ‘self’ and

‘non-self’ and proceed to signal the target cell for apoptosis (programmed cell

death). In addition to the white blood cells of the innate immune system, the

blood also contains a variety of proteins some of which serve to recruit the

white cells of the adaptive immune response (Janeway et al. 2001).

1.2 Natural Killer Cells (NK Cells)

NK cells are derived from bone marrow and appear morphologically as large

granular lymphocytes (Roitt et al. 2001). NK cells do not require prior

sensitization to the foreign antigen in order to carry out their effector function.

This is the intrinsic difference between the innate and adaptive immune

system. NK cells recognize and lyse target cells either by (i) natural

cytotoxicity, (ii) cytolytic granule mediated cell apoptosis or (iii) antibody-

dependent cell mediated cytotoxicity (ADCC) (Rajalingam, 2012). NK

cytotoxicity does not require antibodies but is controlled by a balance of

inhibitory and activating signals resulting from the interaction of receptors on a

NK cell’s surface with specific corresponding ligands on a target cell. NK cells

possess multiple surface receptors that help distinguish ‘self’ from ‘non-self’.

Natural killer cell receptors include: killer cell lectin-like receptors (KLR), killer

immunoglobulin-like receptors (KIR), leukocyte immunoglubin-like receptor

(LILR) and natural cytotoxicity receptors (NCR). In this study, the KIR

receptors are of interest and their interactions with transplant variables.

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1.3 “Missing Self” Hypothesis in NK Cell Recognition

NK cells possess activating and inhibitory receptors which produce

cytoplasmic signals corresponding their function and the balance of these

signals determine the NK cell’s response to the target cell. The “missing self”

hypothesis is based on the foundational understanding that if a target cell

lacks the human lymphocyte antigen (HLA) class I ligand to the inhibitory

receptor on the NK cell, it leads to the activation of NK cell cytotoxicity and

lysis of target cell (Kroger et al. 2006). The “missing self” theory was

introduced by Ljunggren and Kärre (1990) in a series of experiments, which

used lymphoma cells and transplantation into mice. The study demonstrated

the importance of the expression of MHC class I molecules, by analyzing

murine MHC class I (H-2) molecule expression in malignant tumours and

linking it to NK cell reactivity (Ljunggren & Karre, 1985). It was observed that

lymphoma cells with the loss of H-2 expressions were less malignant than the

wild types, which resulted in decreased tumourigenicity. The down regulation

of MHC class I molecules, as seen in tumour cells and virus infected cells,

results in NK cell mediated lysis of a target cell (Rajalingam, 2012). It was

suggested that tumour cells could be killed at low or reduced expression

levels of MHC class I molecules, due to NK cell interactions with MHC class I

molecules (Karre et al. 1986). So it was hypothesized that NK cells were able

to recognize and lyse target cells that lack the expression of MHC class I

molecules (Ljunggren & Karre, 1990).

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1.4 Natural Killer (NK) Cell Functions and Pathways

NK cells play an important role in tumour surveillance, eradication of

pathogens and pregnancy. NK cells mediate the recognition function though

natural cytotoxicity receptors and antibody-dependent cell-mediated

cytotoxicity (ADCC), while mediating killing functions through cytolytic granule

mediated cell apoptosis, cytokine production and natural cytotoxicity (Smyth

et al. 2002 and Smyth et al. 2005).

Antibody-dependent cell-mediated cytotoxicity (ADCC) involves the activating

receptor CD16. The infected cell is opsonized (binding of antibodies to

enhance effector molecules) with antibodies that are recognized by CD16

receptors on the NK cell. This triggers activation and the release of cytolytic

granules and cell apoptosis (Tschopp et al. 1986).

Cytolytic granule mediated cell apoptosis is the utilization of perforin, a pore-

forming protein and proteases known as granzymes. Upon degranulation of

the target cell’s membrane, perforins are inserted into the membrane creating

a pore (Tschopp et al. 1986). The synergistic effect of perforins and

granzymes trigger an endogenous pathway of programmed cell death through

the activation of apoptotic cysteine proteases (caspases). However it is said

that apoptosis can occur even in the absence of these activated caspases

(Trapani, 1995). Tumour cell surveillance in NK cells has many modes of

effector pathways, but most of the NK cell responses lead to apoptosis.

Activated NK cells can release cytokines such as: tumor necrosis factor α

(TNFα) and interfon gamma (IFNγ) – both are pro-inflammatory, while

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interleukin (IL-10) is immuno-suppressive. NK cells are able to mediate

tumour cell recognition through various receptors: NKG2D, KNp44, NKp46,

NKp30 and DNAM (Terunuma et al. 2008). For instance, irradiation was

reported to up-regulate ligands for the activating NK cell receptor NKG2D,

which in turn increased NK cell cytotoxicity towards tumour cells (Kim et al,

2006).

Adapted from Elsevier Science, 2002 (USA).

Figure 1. NK cell’s response (receptor-ligand models) to a healthy cell and a tumor cell.

Cancers have been shown to down-regulate MHC class I molecules, thereby

preventing presentation of tumour antigens to T cells. However, such cells are

susceptible to NK cell mediated lysis.

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1.5 Natural Killer Cell Receptors

NK receptors can be divided two families; the C-type lectin-like family and the

immunoglobulin superfamily, which include the killer immunoglobulin-like

receptors (KIR), leukocyte immunoglubin-like receptor (LILR) and the natural

cytotoxicity receptors (NCR). The C-type lectin-like family includes the

homodimer NKG2D and CD94/NKG2-A,B,C,F heterodimers in humans and in

the mouse, the Ly49 family (equivalent to human KIR receptors). Both families

of receptors include inhibitory receptors and activating receptors. The NCR

group consists of three receptors, NKp46, NKp44 and NKp30. All three

receptors share the same crystal structure and are important activating

receptors, however, their ligands are still poorly defined (Rajalingam, 2012).

1.5.1 C-type Lectin Receptors

1.5.1.1 CD94/NKG2

The CD94/NKG2 heterodimers are found in rodents and primates, but also in

humans. CD94/NKG2 interact with non-classical MHC class I molecules like

HLA-E. HLA-E has a very specific role in NK cell recognition. The peptide

binding groove of HLA-E binds signal peptides of classical MHC class I

molecules such as; HLA-A, -B, -C and –G. HLA-E expression on a cell’s

surface is not stable unless it is bound to the signal peptides. Hence the

CD94/NKG2 receptors recognition of HLA-E is dependent on the production

of the other MHC class I molecules. Though it is an indirect method of

surveillance, it is able to monitor the “average” expression level of MHC class

I molecules (Braud et al. 1997).

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1.5.1.2 Ly49

The Ly49 receptor family is a family of activating and inhibitory receptors

found only in mice that interact with H-2 (murine MHC class I) molecules as

their ligands. They are part of the C-type lectin family, which is found on

murine chromosome 6 (Yokoyama and Seaman, 1993). It is thought that

humans probably evolved from an ancestral species containing Ly49 genes

because a single Ly49 pseudogene was found in the human natural killer

complex (NKC) (Hsu et al. 2002). The Ly49 homodimers are found in mice

and despite being members of the C type lectin family, are the functional

equivalent of the human KIR receptors that are found in primates including

man (Moretta et al. 2002). In relation to murine recognition of MHC class I

ligands on target cells, there are inhibitory Ly49 receptors that trigger an

inhibitory signal, thus preventing NK cell mediated cytotoxicity. However, like

killer cell immunoglobulin-like receptors (KIR), some members of the Ly49

receptor family also are activating receptors (Yokoyama et al. 1989).

1.5.2 Immunoglobulin super-family Receptors

The other major sub-family of NK cell receptors are the immunoglobulin

superfamily which includes the KIR receptors encoded on the human

chromosome 19 in the leukocyte receptor complex (LRC) (Vilches and

Parham, 2002). Besides KIR, the other set of receptors is the natural

cytotoxicity receptors (NCR) comprising: NKp46, NKp44 and NKp30. Upon

stimulation, these receptors mediate NK cytotoxicity through the release of

IFNγ (Terunuma et al. 2008).

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1.5.2.1 Killer Cell Immunoglobulin-like Receptor (KIR)

KIR receptors are a large family of receptors that are expressed by NK cells

and a small subset of T cells. KIR receptors are considered to be important

receptors in the development and function of human NK cells. KIR receptors

are encoded in a highly polymorphic gene family that results in a vast

diversity, in that different individuals have different sets of KIR genes. Genes

encoding KIR receptors and HLA class I ligands are located on different

chromosomes. This allows for different KIR-HLA interactions in different

individuals and thus genetic diversity of the immune response. Consequently,

certain KIR-HLA combinations are associated with various autoimmune

diseases, viral infections and cancers (Khakoo & Carrington. 2006 and

Bashirova et al. 2006).

1.5.3 KIR Receptor Structure and Nomenclature

KIR receptors are type I transmembrane proteins and have two or three Ig-like

domains. The Ig-like domains in their extracellular regions, enable recognition

of classical MHC class I molecules, which are the ligands for the KIR

receptors. Ligand binding results in either activating or inhibitory signals in the

cytoplasm of the NK cell, depending on the KIR receptor it is bound to (Garcia

et al. 2003).

KIR receptor nomenclature can be broken down into three parts. The first is

the number of Ig-like domains that are present in the receptor protein; “2D”

represents two Ig-like domains while “3D” represents three Ig-like domains.

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The second part of the nomenclature specifies the length of the cytoplasmic

tail; an “S” represents a short tail while an “L” represents a long tail. The long

cytoplasmic tails contains immune-receptor tyrosine-based inhibitory motifs

(ITIMs) that are responsible for triggering inhibitory signals. The short

cytoplasmic tails lack ITIMs but they possess positively charged lysine residue

in their transmembrane region. This is association with the DAP12 signaling

molecule that is capable of generating activation signals (Lanier. 2009). The

third and final part is the number that comes at the end, which differentiates

members having the same structure but different amino acid sequence. An

example would be KIR2DS1 and KIR2DS2.

Adapted from “KIR Proteins” by Ebi.ac.uk

Figure 2. KIR protein domains and region lengths.

KIR receptors can be divided into three groups based on the configuration of

their Ig-like domains. Type I KIR receptors are KIR2D proteins (KIR2DL1, -

2DL2, -2DL3, -2DS1, -2DS2, -2DS3, -2DS4 and -2DS5) with the exception of

KIR2DL4 and 2DL5 which have a membrane-distal Ig-like domain similar in

structure to KIR3D receptors (Garcia et al. 2003). Type II receptors are the

11

KIR2D proteins: KIR2DL4 and KIR2DL5 which have D0 and D2 domains but

not the D1 (middle) domain. Type III receptors the KIR3DL and KIR3DS and

they use all three Ig-like domains (Vilches et al. 2000).

1.5.4 KIR Genomics and Diversity

The human KIR gene complex is located on chromosome 19q13.4 in the

Leukocyte Receptor Complex (LRC) and is approximately 150kb long (Wilson

et al. 2000 and Trowsdale, 2001). The region itself is highly variable in terms

of gene content and up to 14 KIR genes are packed closely. Each KIR gene is

separated from the next KIR gene by a 2.4kb intergenic region. The only

exception to this pattern is KIR3DP1 (a pseudogene) and KIR2DL4 because it

is the center of KIR complex where multiple reciprocal recombination events

happen in that region (Yawata et al. 2010).

12

Adapted from “The KIR Gene Cluster” by Carrington M and Norma P. (2003)

Figure 3. Map of the Leukocyte Receptor Complex (LRC)

Genomic diversity of KIR genes can be achieved on several levels. There are

four framework genes that are present in all haplotypes. They are: KIR3DL3,

KIR2DL4, KIR3DL2 and the pseudogene KIR3DP1. Apart from these

framework genes, diversity arises from a combination of gene content and

allelic polymorphism, which together results in genetically diverse human KIR

genotypes. That is, the KIR gene receptor repertoire differs between different

individuals. Individuals’ genotypes differ but there are distinct sets of genes

that form common haplotypes. KIR haplotypes are divided into two groups:

13

KIR-A and KIR-B haplotypes. Each haplotype consists of between 8 KIR to 14

KIR genes (Hsu et al. 2002).

1.5.4.1 Allelic Polymorphism of KIR Genes

Allelic polymorphism exists in all the KIR genes and allelic polymorphism is a

significant contributor to the diversity of KIR genes. Allelic polymorphism

arises mainly from point mutation and homologous recombination.

(Rajalingam, 2012) There is a similarity between allelic polymorphism in KIR

genes and HLA class I genes in that they follow a shared pattern of

homologous recombination.

1.5.5 KIR Haplotypes

Studies show that there are about 30 distinct KIR haplotypes differing in gene

content. This was established by sequencing genomic clones and haplotype

segregation analysis (Uhrberg et al. 2002, Yawata et al. 2006 and Pyo et al.

2010). The concept of KIR haplotypes was first introduced by Uhrberg et al

(1997), who documented gene repertoire variation among individuals. This

was later confirmed by studies showing that the number of genes in a

haplotype varies. The 30 haplotypes can be divided into two groups: KIR-A

haplotypes and KIR-B haplotypes.

14

Figure 4. Centromeric and telomeric region separation of KIR genes which includes a few different A/B haplotypes. The most commonly occurring haplotype is termed the “A haplotype”, which

consist of a fixed set of KIR genes: KIR3DL3 (framework gene (FWG)), -

KIR2DL3, KIR2DP1, KIR2DL1, KIR3DP1, KIR2DL4 (FWG), KIR3DL1,

KIR2DS4 and –KIR3DL2 (FWG). The remaining haplotypes are collectively

termed “group-B haplotypes”. Unlike the A haplotype, the genetic content of

the group-B haplotype differs amongst different individuals, and includes

genes that are not present in the A haplotype. KIR2DS1, KIR2DS2, KIR2DS3,

KIR2DS5, KIR2DL2, KIR2DL5 and KIR3DS1, are KIR genes that are only

encoded on group-B haplotypes. The group-B haplotypes have more

activating receptors than the A haplotype, which has only one activating

receptor, KIR2DS4 (Wilson et al. 2000, Middleton et al. 2007 and Pyo et al.

2010).

An individual derives his/her haplotypes from paternal and maternal

inheritance. This results in diversity of KIR gene repertoire, even amongst

siblings. Individuals may be homozygous for the A-haplotype (A/A),

15

homozygous for the B-haplotype (B/B) or heterozygous (A/B). Homozygous

A/A individual have a maximum of 7 functional KIR genes whilst a

heterozygous A/B individual could have all 14 functional KIR genes (Yawata

et al. 2002 and Shiling et al. 2002).

KIR haplotypes have centromeric and telomeric halves. The halves are

divided by a 14kb region enriched with L1 repeats upstream of KIR2DL4. (Pyo

et al. 2010) The centromeric half encodes KIR3DL3, KIR2DS2, KIR2DL2 or

KIR2DL3, KIR2DL5B, KIR2DS3, KIR2DP1, KIR2DL1 and KIR3DP1, while the

telomeric half encodes KIR2DL4, KIR3DL1, KIR2DL5A, KIR2DS3 or

KIR2DS5, KIR2DS1, KIR2DS4 and KIR3DL2. The framework genes sit on the

ends of each half, with KIR3DL3 situated at the 5’ end and KIR3DP1 at the

3’end of the centromeric half. At the telomeric half, KIR2DL4 is situated at the

5’ end and KIR3DL2 at the 3’ end.

The centromeric half of the KIR haplotypes encode the inhibitory receptors

KIR2DL2 on B-haplotypes and KIR2DL3 on A-haplotypes. Although originally

given distinct gene names, they segregate as different alleles of the same

locus. Hence a single centromeric region has either a KIR2DL2 or KIR2DL3

gene. Similarly in the telomeric half, the same phenomenon occurs between

KIR3DL1 and KIR3DS1. Nearly all haplotypes contain these two loci, so it is

expected that nearly everyone has either KIR2DL2 or KIR2DL3 and KIR3DL1

or KIR3DS1 within their genome. In addition, there are three KIR genes –

2DL5, 2DS3 and 2DS5, which can be encoded in either the centromeric or

telomeric region (Middleton et al. 2007 and Shiling et al. 2002).

16

There is a phenomenon in KIR genomics known as linkage disequilibrium

(LD) whereby some genes are almost always found together. These genes

are located close together on the chromosome. Hence if one is present, the

other is almost always present. A study of LD by Hsu et al. (2002) found that

KIR2DS2 and KIR2DL2 are in strong linkage disequilibrium with each other:

also KIR3DL1 and KIR3DS1 shared the same linkage (even though they were

in different haplotypes). Another pair is KIR2DL1 and KIR2DL3, which often

occur together and are in linkage disequilibrium with KIR3DL1, which are

strongly linked to KIR2DS4 (as both KIR3DL1 and KIR2DS4 are A haplotype

genes).

1.5.6.1 KIR Haplotype Frequencies

Within the human population, haplotype frequencies differ among the races.

Individuals with A and B haplotype are commonly found in all races (Uhrberg

et al. 1997 and Yawata et al. 2002). Individuals who are homozygous for the

A haplotype are more frequent in northeastern Asians – Chinese, Japanese

and Koreans but also represent 25% of Caucasians (Yawata et al. 2002). On

the other hand, individuals with at least one B haplotype are common in

Native Americans (Ewerton et al. 2007), Australian Aborigines (Toneva et al.

2001) and Indians (Rajalingam et al. 2002).

NK cells from homozygous A/A individuals can express a maximum of four

inhibitory KIR receptors (KIR2DL1, -2DL3, -3DL1 and -3DL2) and one

activating KIR gene (KIR2DS4). In contrast, heterozygous A/B or B/B

individuals can express a maximum of six inhibitory KIR genes (KIR2DL1-3,

17

KIR2DL5, KIR3DL1 and KIR3DL2) and two to six activating KIR genes

(KIR3DS1, KIR2DS1-5). Hence, NK cells of A/B and B/B genotype have more

activating KIR receptors compared to A/A genotypes. This suggests that they

might respond more vigorously to foreign pathogens although at this time,

there is very little information concerning the ligands for the activating KIR

receptors (Rajalingam et al. 2008).

1.5.7 Ligands for KIR Receptors

KIR receptors recognize allelic motifs on HLA class I molecules that are

encoded on chromosome 6. KIR recognition is not only locus-specific but also

specific for certain allotypes that share a common epitope. KIR receptors

recognize and bind to the orthogonal orientation across the α1 and α2 helices

of the HLA class I molecule (Rajalingam, 2012). Inhibitory KIR receptors

interact with the classical HLA class I molecules (HLA-A, -B and –C) resulting

in the inhibition of NK cell mediated lysis.

HLA-C alleles are ligands for several KIR receptors. HLA-C alleles have one

of two possible amino acid residues at position 80 that determine KIR binding

specificity. All HLA-C allotypes at position 80 have a dimorphism of either

asparigine (N) or lysine (K) (Colonna et al. 1993, Wagtmann et al. 1995 and

Winter et al. 1995).

18

Adapted from “KIR genes” By Saikiran Sedimbi.

Figure 5. Various KIR receptors and their HLA class I ligands.

The KIR2DL1 inhibitory receptor binds to HLA-C alleles (Cw2, Cw4, Cw5,

Cw6, Cw15, Cw17 and Cw18) that carry a lysine residue at position 80. They

are said to have the C2 epitope. KIR2DL2 and KIR2DL3 inhibitory receptors

bind to the remaining HLA-C allelles (Cw1, Cw3, Cw7, Cw8, Cw13 and

Cw14), which have an asparagine residue at position 80. These allotypes are

said to have the C1 epitope. In addition to C1 epitope binding, KIR2DL2/3

also interacts weakly with C2 epitopes. However the KIR2DL2/3-C2

interactions are comparatively weaker to KIR2DL1-C2 interactions, thus the

inhibitory signals triggered are weaker (Colonna et al. 1993 and Winter et al.

1995).

HLA-B alleles can be divided into two groups based on the presence of either

a Bw4 or Bw6 motif in the α1 domain at residues 77-83 of the molecule.

KIR3DL1 inhibitory receptor binds to a subset of HLA-A (HLA-A23, A24, A25

and A32) and HLA-B alleles that have the Bw4 epitope on their a-helix

19

(approximately 40% of B allotypes have the Bw4 epitope). The KIR3DL2

inhibitory receptor binds to only HLA-A3 and A11 allotypes. The strength of

the interaction is highly sensitive to the sequence of the peptide bound in the

HLA-A peptide-binding groove (Cook et al. 2006).

The ligands specificities of KIR2DS2, KIR2DS5, KIR3DS1 and KIR2DL5 have

remained elusive.

1.5.8 KIR Expression

The expression of KIR genes in NK cells influence the behavior and

interactions of these cells. However the mechanisms controlling expression

are barely understood (McErlean et al. 2010). KIR expression in NK cells of

siblings shows that the expression repertoire is mostly dependent on the KIR

genotype (Davies et al. 2002). There is also evidence that allelic variation in

the KIR gene may have a profound effect on expression (Buckland, 2004) and

transcription control (Johnson et al. 2005).

Each NK cell expresses only one or a few KIR receptors. Selection of KIR

receptor expression occurs during NK cell development resulting in NK cells

that are only cytotoxic when they have inhibitory receptors to self-HLA class I

ligands. This prevents auto-agression/auto-immunity (Uhrberg et al. 1997).

1.6 Haematopoietic Stem Cell Transplantation (HSCT)

HLA-matched allogeneic haematopoietic stem cell transplantation (HSCT) is

used to treat individuals suffering from haematological malignancies (eg.

20

leukaemia, lymphomas), bone marrow failure syndromes and inborn

biochemical deficiencies (Appelbaum, 2003). The donor maybe a HLA-

identical sibling (patient and donor have the same HLA type) or a HLA

matched unrelated donor (MUD). MUD donors may have a small number of

HLA mismatches with the patient. Often there is a mismatch at HLA-C. The

mismatch of HLA-C in the donor’s genotype can result in NK alloreactivity due

to incompatibility of KIR ligands (Witt, 2009). This will be explained in detail in

the next chapter. Patients are prepared for HSCT by high dose chemotherapy

and/or irradiation which is intended to destroy the malignant cells but also

destroys the patient’s bone marrow to make room for the transplanted donor’s

stem cells. The donor’s stem cells can be collected from the bone marrow or

peripheral blood. After preparation of the patient, the stem cells are infused

and usually engraft but rejection occurs in a few percent of transplants.

(Proquest, 2011). Successfully eliminating leukaemia by HSCT is not only

attributed to pre-transplant chemoradiotherapy but also due to an anti-tumour

effect provided by the infused donor lymphocytes that accompany the stem

cells. This effect is termed “graft-versus-leukaemia” effect (Barnes et al.

1957).

Despite advances in medical science, HSCTs are still plagued with

immunological complications due to: graft rejection, graft-versus-host disease

(GvHD), CMV and other infections, and leukaemia relapse.

21

1.7 Factors Affecting the Outcome of Allogeneic HSCT

1.7.1 NK Alloreactivity due to Ligand-Ligand Incompatibility

HLA antigens are transplantation antigens that are involved in the interactions

between patient and donor lymphocytes. Hence to get a successful transplant

it’s best to use an HLA identical donor. Unlike B and T cells allorecognition

that involves recognition of foreign HLA antigens. NK cells allorecognition

mostly involves the recognition of missing self-HLA antigens. KIR ligand

incompatibilities refer to presence or absence of specific HLA ligands (in the

patient) for specific inhibitory KIR receptors (in the donor). As mentioned in

the previous chapter, an NK cell engages a potential target cell with activating

and inhibitory receptors. If the target cell does not have the relevant inhibitory

ligands to engage the NK cells’ inhibitory receptors, the NK cell will proceed to

lyse the target (Class, 2010 and Witt & Christiansen. 2006). NK alloreactivity

may play a part in the outcome of HSCT.

Many studies on the effect of NK alloreactivity on the outcome HSCT have

been contradictory. Some studies find a beneficial effect, whilst others find a

detrimental effect. The reason for these conflicting reports is unclear. Two

studies in particular demonstrate the inconsistency of findings in relation to

NK alloreactivity. Davies et al. (2002) studied 175 pediatrics and adult patients

with differing malignancies receiving a transplant with at least one HLA allele

mismatch. The results showed a poorer survival in transplants with KIR ligand

incompatibility and no significant effects on relapse rates. The two biggest

factors that affect survival were relapse and GvHD. ATG was used in the

preparative regimens for T cell depletion. The results were supported by

22

Schaffer et al. (2004) who reported reduced survival rate, no effects on

relapse rates and the use of ATG. In contrast to these studies, Giebel et al.

(2003) studied 121 pediatric and adult patients with differing malignancies and

preparative regimens in which no ATG was used. The results reported

improved survival in transplants with KIR ligand incompatibility. Giebel’s study

supports Ruggeri et al. (2002), findings of an increased survival rate and

reduced relapse rates in transplants with ligand incompatibility. It was

suggested that the difference between the studies findings of the deleterious

and beneficial effects of KIR ligands incompatibility were due to the use of

ATG, as a result, more T cells were depleted (Schaffer et al. 2004).

KIR ligand incompatibility has also been studied in relation to GvHD. GvHD is

thought to be initiated when donor T cells interact with recipient APC. In a

mouse model, NK cells have been shown to prevent GvHD by destroying

recipient APC and preventing activation of T cells (Ruggeri et al. 2002).

Morishima et al (2007) studied 1790 patients receiving T cell repleted grafts

and a relatively uniform transplant procedure. KIR ligand incompatibility in

acute myloid leukaemia (AML), chronic myeloid leukaemia (CML) and acute

lymphoid leukaemia (ALL) patients resulted in an increase in grade III-IV

GvHD and mortality. But in similar transplants in which anti-thymocyte globulin

(ATG) was used to deplete donor T cells in vivo, KIR ligand incompatibilities

protected against GvHD. These two observations suggest that the effect of

KIR ligand incompatibility on GvHD may be detrimental or beneficial

depending on whether donor T cells are present or not. Other reports have

also made similar observations (Franco, 2002).

23

When KIR ligand incompatibility was studied in relation to relapse, a beneficial

effect on relapse rate was observed. Giebel et al. (2003) studied 130

unrelated patient-donors transplants reporting an association between KIR

ligand incompatibilities and decreased relapse rates. When the myeloid

leukaemia group was analyzed, the effects were more prominent, leading to a

suggestion that myeloid malignancies were more responsive to ligand

incompatibility compared to lymphoid leukaemias. However, NK cell-mediated

effects have been reported to have an impact on childhood ALL (Pende et al.

2009). Childhood leukaemia blasts express high levels of adhesion

molecules, which aid the NK cell-mediated lysis of target cells (Mengarelli et

al. 2001). Unfortunately, in Giebel et al. (2003) study, the ALL patients were

not categorized into children or adult transplants. Hence, if the studies on

childhood ALL are confirmed then not only would patients with myeloid

leukaemias benefit from NK cell mediated responses but also childhood ALL

patients.

In support of Giebel et al. study, a study conducted by Hsu et al. (2006) stated

that in the absences of certain KIR ligands, there was a decreased risk of

relapse for patients in AML, CML and ALL. There was evidence to support NK

cell mediated graft-versus-leukaemia (GvL) effect in ALL patients but it was

more prominent in AML patients (Willemze et al. 2009).

1.7.2 KIR Repertoire on the Outcome of HSCT

There are many contradictory reports in relation to whether particular KIR

activating receptors in donors, either increase (Kroger et al, 2006) or

24

decrease (Verheyden et al, 2005 and Schellekens J et al, 2008) relapse and

GvHD rates. Consequently, the presence of activating KIR genes either

improve or decrease survival rate. In this chapter, linking of KIR repertoire

(particular set of KIR genes in the individual) to survival rate, GvHD and

relapse rate will be focused on. Studies performed by Kroger et al. (2006) and

Cooley et al. (2008) showed distinctly different results. Kroger et al. (2006)

studied 142 patients with leukaemia who underwent unrelated stem cell

transplantation and ATG was used for T cell depletion. The results showed a

significantly lower survival rate in transplant with KIR haplotype B/x donors

(more activating receptors), while a higher survival rate in transplants with KIR

haplotype A/A (less activating receptors) donors. Giebel et al. (2003) found

similar observations as Kroger et al. (2006). However, this effect was only

seen in AML and less in myeloid leukaemias. In contrast to Kroger et al.’s

(2006) study, Cooley et al. (2008) studied 448 patients; results showed that

donor KIR haplotype A/A (few activating receptors) had a higher treatment

related mortality rate (poorer survival) as compared to donors having a B/x

haplotype (more activating receptors). Following the aforementioned study,

Cooley et al. (2009) continued to study KIR haplotypes in HLA-matched

unrelated HSCT outcome, in patients receiving T cell replete grafts. The

survival rate was significantly higher with homozygous haplotype B (B/B)

donors or at least, heterozygous haplotype B (B/x) donors than with A/A

donors. Donors having at least one B haplotype showed a 30% increase in

relapse-free survival as compared to a homozygous haplotype A donor (A/A).

25

There were contrasting results in the association of the rate of GvHD and KIR

repertoire reported by Kroger et al. (2006) and Cooley et al. (2008). Kroger et

al. (2006) reported no effect on GvHD rates in association to KIR repertoire.

While Cooley et al. (2008) found that increased GvHD rates correlate to

increased number of activating receptors. Cooley et al. (2008) showed an

increased rate of chronic GVHD but not acute GHVD, in patients transplanted

with KIR haplotype B/x or B/B donors.

Likewise, contrasting results were observed with relapse rates and KIR

repertoire. Kroger et al. (2006) hypothesized an increase in relapse rates in

association with activating genes, however they found no effect on relapse

rates. These observations by Kroger et al. (2006) were supported by Schaffer

et al. (2004). However, Cooley et al. (2009) reported that donor KIR haplotype

A/A (few activating receptors) had a higher relapse rate as compared to a B/x

haplotype (more activating receptors).

The conflicting results reported for KIR genotype and HSCT outcome may be

related to the methods used for transplants (preparative regimens) thereby

influencing whether matching for donor KIR genotype is beneficial or not.

section 1.7.3 will look into a few of the more prominent factors that have been

previously reported to play a role in a HSCT outcome.

1.7.3 Preparative Regimens Variables

Preparative regimens include many drugs and other treatments such as total

body irradiation (TBI) and T cell depletion (use of ATG) that may influence NK

26

cell alloreactivity. T cell depletion with the use of ATG was mentioned briefly

in the previous chapters. The two variables that will be focused on in this

section are total body irradiation (TBI) and cytomegalovirus (CMV)

prophylaxis, and their potential effects on HSCT outcomes.

1.7.3.1 Total Body Irradiation (TBI)

TBI is a form of radiotherapy; it is usually part of the preparative regimens in

HSCT. The purpose of TBI is to destroy the recipient’s body’s immune cells,

thus preventing any immune responses (from patient lymphocytes against

donor graft) that would lead to immunological rejection. In addition to

destroying the recipient’s immune cells, it also kills off malignant cells,

hopefully increasing the success rate of the transplant (Soule et al. 2007).

TBI has also been shown to cause an up-regulation of NKG2D ligands and

increased sensitivity of NK cell mediated cytotoxicity of tumour cells (Kim et

al. 2006). NKG2D is an activating receptor that is found on NK cells and

CD8+ T cells (Gasser et al. 2005). Upon stress-induced interaction with

tumour cells, NKG2D ligands are up-regulated, which causes the tumour cell

to be susceptible to NK cell-mediated lyses (Zafirova et al. 2011). As the use

of TBI varies between transplant centres and among the different diagnoses,

this might be one factor that influences the role that NK cells play in HSCT,

particularly with respect to the GvL effect.

27

1.7.3.2 Cytomegalovirus (CMV) Prophylaxis

There are three drugs commonly used to prevent or treat CMV infection;

acyclovir, ganciclovir and valacyclovir. Prophylaxis has been shown to reduce

CMV activation but it is not 100% effective (Syndman et al. 1993). Acyclovir is

known to interfere with viral DNA synthesis and inhibits the herpes simplex

virus from replicating (Balfour et al. 1990). Ganciclovir inhibits the viral DNA

polymerase (McEvoy, 2003). As a result it interferes with DNA synthesis of

the virus. However, problems like poor absorption, resistance of CMV, etc,

led to the development of valacyclovir. Valacyclovir is a different form of

acyclovir that is administered orally and rapidly converted to ganciclovir in the

gastrointestinal tract and liver. The dose given to a particular patient differs

depending on CMV risk status. As particular donor KIR gene repertoires have

been reported to protect against CMV reactivation (see 1.8 below), the use of

CMV prophylaxis might be one factor that influences the effect of donor KIR

gene repertoire on the outcome of HSCT.

1.8 Cytomegalovirus (CMV)

Cytomegalovirus falls under the broad family of the Herpesviridae, better

known as the Herpes virus (Ryan and Ray, 2004). CMV has close relations

with another well-known virus, Epstein-Barr virus (associated to cancers like

Burkitt’s lymphoma, etc) (Maeda et al. 2009). A characteristic that CMV

shares with Herpes virus family is the ability to remain latent in the healthy

human body. However, the problem arises when the body is

immunocompromised from taking immunosuppressuve drugs for organ

transplants (kidney, bone marrow, etc) or in a HIV-infected person. The ability

28

of CMV to remain latent in the body without alarming the immune system is a

result of its genome, which encodes for a few proteins that interferes with viral

antigen presentation. They interfere with antigen presentation by degrading

MHC class I proteins before it reaches the cell surface as well as blocks

translocation of peptides to the endoplasmic reticulum. HSCT in which either

the patient or donor is CMV positive tend to have worse outcomes than CMV

negative HSCT (Ljungman et al. 2003).

1.8.1 KIR Repertoire with Association to CMV Protection

Several studies have reported beneficial effects of KIR activating genes in the

protection against CMV reactivation. Zaia et al. (2010) and Cook et al. (2009)

reported that more donor KIR activating receptors are associated with

protection from CMV infection. The former study focused on individual KIR

genes, while the latter study focused more on the different (A/A, B/x and B/B)

KIR haplotypes as a whole.

In a study conducted by Zaia et al. (2010), involving 211 patients-donors who

had undergone transplant from 2001 to 2006, data provided showed that

there was a prominence of CMV reactivation in recipients, when the donor

KIR genotypes contained less than 5 activating KIR genes. 83% of recipients

with donors that have 0 activating KIR genes developed CMV reactivation

after HSCT, while only 17% were CMV-free. As for recipients of donors with

1-4 activating (aKIR) KIR genes, 72% developed CMV reactivation while 28%

did not. Lastly, last than half (48%) of recipients with donors that have more

than 5 activating KIR genes developed CMV reactivation. The data showed

29

that if the donor KIR genotype had activating (aKIR) KIR2DS2 and/or

KIR2DS4, this resulted in a lower incidence of CMV reactivation. Interestingly,

the KIR2DS4 deletion variant – 2DS4d, which does not express the activating

KIR2DS4 receptor on the surface, is associated with a higher rate of CMV

reactivation. This emphasizes the importance of having activating KIR2DS2

and KIR2DS4 in donor KIR genotypes. Aside from these activating KIR

genes, inhibitory (iKIR) KIR2DL2 are seen more frequently in groups of

patients with no CMV reactivation. But KIR2DS2 and KIR2DL2 are known to

be in strong linkage disequilibrium and they are usually expressed together. In

conclusion, Zaia et al’s data indicated that donor genotypes with KIR2DS2

and KIR2DS4 are associated with reduced CMV activation. In addition to that,

the same association of reduced CMV activation can be applied to donor

genotypes with at least 5 activating KIR genes, regardless of which activating

gene it is. They suggest that the ideal protective donor genotype should be

one with both KIR2DS2 and KIR2DS4 or a genotype with at least 5 activating

KIR genes. However, this should not be mistaken for a genotype that will

result in absolutely no CMV activation. CMV reactivation may occur

regardless of these protective KIR genotypes, but it was concluded that the

“ideal protective” genotypes are associated with lower rates of CMV (Gallex-

Hawkins et al. 2011).

Another study, carried out by Cook et al. (2009) studied 234 patients, 97 with

myeloid malignancy, 87 with lymphoid malignancy and 50 with nonmalignant

disease. In CMV seropositive recipients, there was a 53% CMV reactivation

rate in sibling donor transplants (38 out of 72) and 64% CMV reactivation in

30

unrelated or HLA non-identical donor transplants (22 out of 35). In transplants

involving siblings, when both donor and recipient were seropositive and the

donor KIR haplotype was homozygous A/A, the CMV reactivation rate was

65%. Inversely, donors with a copy of KIR haplotype B, the CMV reactivation

rate 28%. However, the KIR haplotype B’s protective influence was restricted

to myeloablative stem cell transplants. From a multivariate analysis, sibling

donor KIR haplotype B was associated to a significantly reduced rate of CMV

reactivation.

Likewise, in kidney transplants Stern et al. (2008), showed that activating KIR

genes played a role in controlling CMV infection. It was observed that the A

haplotype (which only has one aKIR gene) had an infection rate of 36% while

a genotype (B haplotype, B/B or B/x) with more than one aKIR gene had an

infection rate of 20%. Using a Cox regression analysis, the risk factor of B

haplotype compared to A haplotype was p=0.034. This suggests that

protection against CMV increases with the number of aKIR genes.

In summary, there are many conflicting reports of beneficial or detrimental

effects of KIR repertoire on the outcome of HSCT. This may be due to

transplant variables, such as: TBI, CMV status of patients and donors and

CMV prophylaxis used, which may have an effect on NK cell activity. The

purpose of this thesis is to determine:

(a) Whether donor KIR gene repertoires influence the survival rate in

HSCT performed at Royal Perth Hospital and Princess Margaret

Hospital.

31

(b) Whether transplant variables such as TBI, CMV status and

prophylaxis interact with activating KIR receptors to influence

outcome.

2. Materials and Methods

2.1 DNA Samples and Preparation

2.1.1 DNA Source

DNA samples were from unrelated donors of all the haematopoietic stem cell

transplants performed at RPH since 1990 and PMH. DNA extraction from

whole blood was performed by the staff at the Department of Clinical

Immunology & Immunogenetics, Royal Perth Hospital using a commercial kit

(Qiagen, Valencia, USA).

DNA used in the optimization of KIR PCR-SSP genotyping assay was

extracted from Epstein-Barr Virus (EBV) transformed cells of the 13th

International Histocompatibility Workshop (IHWS) (De Santis et al, 2004).

DNA from 20 IHWS cell lines that had been previously typed by other KIR

genotyping methods was selected. DNA extraction from cell lines was

performed by the staff at the Department of Clinical Immunology &

Immunogenetics, Royal Perth Hospital using a commercial kit (Qiagen,

Valencia, USA) or a salting out method (Miller et al, 1988).

32

2.1.2 Calculations for the Preparation of DNA samples

C1 V1 = C2 V2

(Initial Concentration)(Initial Volume) = (Final Concentration)(Final Volume)

This equation was used to dilute both the primers and DNA samples. All DNA

samples were diluted to 25ng/uL. The optimal DNA concentration for the KIR

PCR-SSP ranged between 25ng/uL to 30ng/uL.

2.2 Polymerase Chain Reaction Sequence Specific Priming (PCR-SSP)

Assay for KIR Genotyping

2.2.1 Oligonucleotide Primers

The primers were purchased from Gene Works (Adelaide, Australia) as

freeze-dried material, which were stored in the -20oC freezer prior to liquid

reconstitution. The new primers were reconstituted to 100pmol/uL with TE

buffer, pH 8.0 (made by RPH routine staff).

An example:

Primer KIR3DL1F_4x542 was initially received at 60.0 nmol per tube. After

reconstitution the final concentration of KIR3DL1F_4x542 primer was

100pmol/uL per tube.

33

C1V1 = C2V2

60nmol = 100pmol/ul x V2

V2 = 60/100

V2 = 0.6 ml

V2 = 0.6 ml x 1000

V2 = 600ul of TE buffer

All primers were reconstituted to 100 pmol/uL. The reconstituted primers were

vortexed for 1 minute then left on a circular rotator for 20 minutes at room

temperature. After rotation, the primers were kept in the -20 oC freezer. The

different primers were then diluted to different concentrations based on

desired optimal band intensities, during the optimization assay.

To avoid freezing and thawing the primers too many times, this would result in

the primers gradually degrading and leading to PCR inaccuracies. Working

sub-aliquots of 50ul were made in a 1.5ml Eppendorf microcentrifuge tubes.

The sub-aliquots were labeled and stored in -20oC freezer for daily usage.

34

Primers Sequence Information

KIR Gene: Primer Sequnce: Product Size

Group 1:

2DL3F

2DL3Ra

2DL3Rb

2DL1F

2DL1R

3DL1F

3DL1R

TCTTCTTTCTCCTTCATCGCTGATGCTG

caggaaacagctatgaccCCTGCAGGCTCTTGGTCCATTACAA

caggaaacagctatgaccCTGCAGGCTCTTGGTCCATTACCG

tgtaaaacgacgccaGTTGTTGGTCAGATGTCATGTTTGAAC

caggaaacagctatgaccAGGTCCCTGCCAGGTCTTGCG

tgtaaaacgacgccaTCCATYGGTCCCATGATGCT

caggaaacagctatgaccCCACGATGTCCAGGGGA

~500bp

185bp

140bp

Group 2:

3DL3F

3DL3R

2DS3Fc

2DS3FT

2DS3R

3DS1F

3DS1R

2DS5F

2DS5R

tgtaaaacgacgccaGTAATGTTGGTCAGATGTCAG

caggaaacagctatgaccGCYGACAACTCATAGGGTA

tgtaaaacgacgccaAGTCTTGTCCTGMAGCTCCC

tgtaaaacgacgccaAGTCTTGTCCTGMAGCTCCT

caggaaacagctatgaccGCATCTGTAGGTTCCTCCT

tgtaaaacgacgccaTTTCTCCATCRGTTCCATGATGCG

caggaaacagctatgaccCCACGATGTCCAGGGGA

tgtaaaacgacgccaCTGCACAGAGAGGGGACGTTTAACC

caggaaacagctatgaccGTCATGCGACCGATGGAGAAGTTGC

222bp

191bp

140bp

128bp

35

Group 3:

2DL5F

2DL5R

2DL2F

2DL2R

2DS1F – No tag

2DS1RC – No tag

2DSIRT – No Tag

2DL4F

2DL4R

tgtaaaacgacgccaATCTATCCAGGGAGGGGAG

caggaaacagctatgaccCGGGTCTGACCACTCATAGGGT

tgtaaaacgacgccaGTAAACCTTCTCTCTCAGCCCA

caggaaacagctatgaccGCCCTGCAGAGAACCTACA

GTTGTTGGTCAGATGTCATGTTTGAAC

TAGGTCCCTGCCAGGTCTTGCC

TAGGTCCCTGCCAGGTCTTGCT

tgtaaaacgacgccaGTATCGCCAGACACCTGCATGCTG

caggaaacagctatgaccCACCAGCGATGAAGGAGAAAGAAGGG

193bp

173bp

140bp

122bp

Group 4:

2DS4delF

2DS4delR

3DL2F

3DL2R

2DS2F

2DS2R

2DS4F

2DS4R

tgtaaaacgacgccaGTCTTGTCCTGCAGCTCCATCTATC

caggaaacagctatgaccGAGTTTGACCACTCGTAGGGAGC

tgtaaaacgacgccaAGGCCCATGAACGTAGGCTCCG

caggaaacagctatgaccGGTCACTTGAGTTTGACCACACGC

tgtaaaacgacgccaCCTTCTGCACAGAGAGGGGAAGTA

caggaaacagctatgaccAGGTCCCTGCAAGGTCTTGCTTGCATC

tgtaaaacgacgccaGTTTCCTGGCCCTCCCAGGTCAC

caggaaacagctatgaccAAGGAAGTGCTCAAACATGACATCC

231bp

159bp

165bp

119bp

(Note: Framework genes are in bold and universal sequencing primer tags are lower case.)

36

The universal sequencing primer tags are: the forward tag was M13F

(tgtaaaacgacgcca) and the reverse tag was M13R (caggaaacagctatgacc).

2.2.2 Preparation of PCR Reagents (Reaction mix components)

2.2.2.1 10 x TDMH PCR Buffer (100ml)

8.114g of Trizma base (Sigma, St Louis, USA) was placed in a sterile 200ml

bottle and dissolved with 80ml of molecular grade water (Bioscience, St Louis,

USA) to make 1 x Tris buffer. The pH was adjusted using a pH meter

(EUTECH Instruments, Singapore) and HCL, to pH 8.8. 2.192g of ammonium

sulphate (Merck, Victoria, Australia) were then dissolved into the Tris buffer,

using a magnetic stirrer. A 0.22um filter (Pall Corporation, Cornwall, UK) was

attached to disposable syringe and the mixture was filtered into a new sterile

200ml bottle. 1ml of Tween20 (Promega, Madison, USA) was added to the

filtered mixture and shaken then transferred to a sterile measuring cylinder.

The mixture was made up to 100ml with molecular grade water and inverted

back and forth gently to mix well.

The buffer was aliquoted into 15ml tubes and stored at -80oC. When needed,

a 15ml tube was then sub-aliquoted into 1.5ml Eppendorf microcentrifuge

tubes and stored in a -20oC freezer for daily uses.

2.2.2.2 40mM dNTP

The routine staff at RPH Clinical Immunology Department prepared the 40mM

dNTP used in the reaction mix.

37

100mM dNTP set (Invitrogen, Carlsbad, USA) was thawed and vortexed.

600ul of each dATP, dCTP, dGTP and dTTP was added into 3600ul of sterile

deionized water in 15CTS tube. The mixture was vortexed to mix well.

Aliquots of 30 x 40ul volumes were prepared into 0.5ml Eppendorf tube and

stored at -20oC.

2.2.2.3 Other PCR Reagents

Other components of the PCR-SSP reaction mix were commercially acquired:

25mM MgCl2 (Roche, Indianapolis, USA), molecular grade water (Sigma-

Aldrich, St Louis, USA) and GOTaq Polymerase (Promega, Madison, USA).

2.2.3 Preparation of Gel Electrophoresis Reagents

2.2.3.1 10 x TBE Buffer (2 litre batch)

The routine staff at RPH Clinical Immunology Department prepared buffer for

gel electrophoresis.

215.6g of Trizma base, 110g of boric acid and 16.4g of EDTA were weighed

and placed into a sterile 2L conical flask and dissolved with 1.2L of Milli-Q

Ultrapure water using a magnetic stirrer. When dissolved, the mixture was

made up to 2L with Milli-Q Ultrapure water then autoclaved.

2.2.3.2 0.5 x TBE Buffer (20 litre) – Gel Electrophoresis Running Buffer

19L of Milli-Q Ultrapure water was added into a 30L dispenser and 1L of 10 x

TBE buffer (made by RPH routine staff) was added to the dispenser. The

buffer was thoroughly mixed.

38

2.2.3.3 3% and 3.5% Agarose Gel

12g (3%) or 14g (3.5%) of UltraPureTM Agarose powder (Invitrogen) was

weighed and added to an autoclaved 500ml bottle. 400ml of TBE buffer was

measured in a measuring cylinder and transferred to the 500ml bottle. The

bottle was placed into a microwave oven, which was set to a 1000w, and

microwaved for 2minutes 30 seconds, after which it was taken out, allowed to

cool slightly and swirled gently to facilitate even mixing. The bottle was placed

back into the microwave oven for 30 seconds then taken out, swirled gently

again and labeled “3% -EB”. Prior to use, 20uL of ethidium bromide was

added to the gel. The bottle was placed into a 70oC incubator. For this KIR

project, all gels were made and used the same day; no molten gels older than

2 days old were used.

(Note: before the molten gel was cast, if the gel looked slightly opaque, it was

microwaved at 1000w for 1 minute and swirled gently, to make sure there

were no small solidified agar lumps.)

2.2.3.4 Gel Electrophoresis Loading Buffer

The routine staff at RPH Clinical Immunology Department prepared the

loading buffer.

8g of sucrose and 0.05g of bromophenol blue were added to 20ml of Milli-Q

Ultrapure water and mixed using a magnetic stirrer. Once dissolved, the

mixture was made up with Milli-Q Ultrapure water to 160ml. The mixture was

aliquoted into 96-well plates and stored –at -20oC freezer (long term) and 4oC

fridge (daily use).

39

2.2.3.5 Gel Electrophoresis 1Kb Plus DNA Lambda Ladder

The routine staff at RPH Clinical Immunology Department prepared the ladder

for use.

1ml of 1Kb Plus DNA ladder (Invitrogen, Carlsbad, USA) was added to 4ml of

molecular grade water. The mixture was mixed well and aliquoted into 1.5ml

Eppendorf centrifuge tubes. The ladder was stored at 4oC for immediate daily

use and at -20oC freezer for long-term storage.

2.3 KIR Multiplex PCR-SSP Genotyping Assay Optimization

The KIR multiplex PCR-SSP genotyping assay included the amplification of

15 KIR genes. Amplification of the 15 KIR genes was divided into 4 groups.

Each multiplex PCR group includes the amplification of a framework gene

(present in all individuals), which acts as an internal PCR control. Each PCR

run included 3 controls – 2 positive controls (JBUSH and CB6B), which

together include the amplification of all the 15 KIR genes, and 1 negative

control (sterile molecular grade water) to check for contamination. Also, as an

added precaution, an internal PCR control, a framework KIR gene (present in

everyone’s DNA) primers were strategically selected for each group based on

product sizes of the other primers, so that all PCR product bands will be clear

and distinct. For the initial experimental optimization stage, the 3 controls

indicated above and another 2 samples from the 20 13th IHWS cell line panel

were used. Once the PCR was shown to be both specific for the KIR genes as

determined by PCR product size on an agarose gel and showed good PCR

40

band intensities, the full 20-cell-line IHWS panel run was tested to further

confirm primer specificity.

To optimize the KIR gene multiplex assay, the following variables were tested,

different: dNTP concentrations (10mM or 40mM), primer concentrations

(5pmol/ul to 30pmol/ul), primer volumes (0.5ul per sample to 1ul per sample),

MgCl2 concentrations (2.0mM to 3.0mM), batch volumes (10 typings, 25

typings, 100 typings and 200 typings) and Taq Polymerases (AmpliGold Taq

and GO Taq).

2.3.1 Polymerase Chain Reaction (PCR) Runs

2.3.1.1 Reaction Mix (Mastermix) Volumes

Number of Samples

1 10 25 100

Primers Varied

Volumes

Varied

Volumes

Varied

Volumes

Varied

Volumes

10x TDMH 2ul 20ul 50ul 200ul

25mM MgCl2 1.6ul 16ul 40ul 160ul

10mM/40mM

dNTP

1ul 10ul 25ul 100ul

Sterile Water Total Volume – Other Reagents = Volume of Sterile Water

Taq Polymerase 0.2ul 2ul 5ul 20ul

Total Volume 18ul 180ul 450ul 1800ul

Table 1. PCR reaction mix volumes for different amounts of sample.

41

The total volume in each well on the 96-well plate was 20ul, which consisted

of 2ul of DNA and 18ul of mastermix. The mastermix was vortexed before

mixing with the DNA sample. The PCR plate was placed on ice while the DNA

samples and mastermixes were pipetted. To make sure there was no liquid on

the walls of the wells, the plate was spun down in the centrifuge then placed

into the thermocycler (refer to 2.3.1.4 for thermocycler conditions).

42

2.3.1.2 KIR PCR-SSP Gene groups Optimized Recipes

Group 1 Optimized Mastermix Recipe

Reaction Mix

Components

Concentration Volume Per

Sample

2DL1F_4x431

2DL1R_4x583

2DL3F_7x782M6

2DL3Ra_8x826

2DL3Rb_8x827

3DL1F_4x542

3DL1R_4x649

5pmol/ul

5pmol/ul

10pmol/ul

10pmol/ul

10pmol/ul

10pmol/ul

10pmol/ul

1ul

1ul

1ul

1ul

1ul

1ul

1ul

dNTP 40mM 1ul

10x TDMH Buffer - 2ul

25mM MgCl2 - 1.6ul

Sterile Water - 6.2

GO Taq Polymerase 5ug/ul 0.2ul

Total Reaction Mix Volume: 20ul

Group 2 Optimized Mastermix Recipe

Reaction Mix

Components

Concentration Volume Per

Sample

3DL3F_4x428

3DL3R_4x623

2DS3F_Fy803_C

2DS3F_Fy803_T

2DS3R_5x576

2DS5F_4x177

2DS5R_4x272

3DS1F_4x542

3DS1R_4x649

25pmol/ul

25pmol/ul

30pmol/ul

30pmol/ul

30pmol/ul

10pmol/ul

10pmol/ul

7.5pmol/ul

7.5pmol/ul

0.5ul

0.5ul

0.5ul

0.5ul

0.5ul

0.75ul

0.75ul

0.75ul

0.75ul

dNTP 10mM 1ul

10x TDMH Buffer - 2ul

25mM MgCl2 - 1.6ul

Sterile Water - 7.7ul

GO Taq Polymerase 5ug/ul 0.2ul

Total Reaction Mix Volume: 20ul

43

Group 3 Optimized Mastermix Recipe

Reaction Mix

Components

Concentration Volume Per

Sample

2DL4F_7x707

2DL4R_7x796

2DL2F_5x383

2DL2R_5x523

2DL1F_4x431 – No Tag

2DS1R_4x541C –No Tag

2DS1R_4x541T –No Tag

2DL5F_5x460

2DL5R_5x621

5pmol/ul

5pmol/ul

20pmol/ul

20pmol/ul

15pmol/ul

15pmol/ul

15pmol/ul

5pmol/ul

5pmol/ul

1ul

1ul

1ul

1ul

1ul

1ul

1ul

1ul

1ul

dNTP 40mM 1ul

10x TDMH Buffer - 2ul

25mM MgCl2 - 1.6ul

Sterile Water - 4.2ul

GO Taq Polymerase 5ug/ul 0.2ul

Total Reaction Mix Volume: 20ul

Group 4 Optimized Mastermix Recipe

Reaction Mix

Components

Concentration Volume Per

Sample

3DL2F_5x778

3DL2R_5x904

2DS4F_4x91

2DS4R_4x177

2DS4dF_5x437

2DS4dR_5x635

2DS2F_4x168

2DS2R_4x297

5pmol/ul

5pmol/ul

10pmol/ul

10pmol/ul

10pmol/ul

10pmol/ul

10pmol/ul

10pmol/ul

1ul

1ul

1ul

1ul

1ul

1ul

1ul

1ul

dNTP 40mM 1ul

10x TDMH Buffer - 2ul

25mM MgCl2 - 1.6ul

Sterile Water - 5.2ul

GO Taq Polymerase 5ug/ul 0.2ul

Total Reaction Mix Volume: 20ul

44

2.3.1.3 Thermocycler Run Conditions

Thermocycler PCR Programme Name: KIR62

Temperature and Time Number of Cycles

96oC for 6minutes 1

96oC for 30 seconds

62oC for 30 seconds

72oC for 2 minutes

32

32

32

72oC for 10 minutes 1

4oC HOLD

After completion of thermocycling, the 96-well plate was centrifuged (to make sure the

condensed liquids on the walls of the wells were not left out) and placed in a 4oC fridge

until loaded onto agarose gels.

2.3.1.4 Gel Electrophoresis

The percentage of agarose gel used was dependent on the KIR genotyping PCR

group; 3% agarose gels were used for Group 1 and Group 2 while 3.5% agarose gels

were used for Group 3 and 4. This was because Group 3 and 4 PCR products were

slightly harder to separate.

The casting of the agarose gel was as follows; the bottle containing the molten agar

gel was removed from the 70oC incubator. 20ul of ethidium bromide was added and

gently but thoroughly swirled. A liberal amount was poured into the gel cast, to create

a deep well so no PCR products would accidentally float out when pipetting into the

well. While still hot, the bubbles at the top and inside the gels were carefully pushed to

45

the ends of the gels to prevent impeding the visualization and migration of PCR

products through the gel. After which the 16-well comb was inserted at the top of the

gel. The gel was left to set for about 30 to 40 minutes depending on the gel size.

5ul of PCR product and 5ul of loading buffer were mixed, pipetted up and down 5

times and added to each gel well. 5ul of 1Kb Plus DNA Lambda ladder (made by RPH

staff) was added to the first well. Gels were subjected to electrophoresis at 150 volts

(V) for 45minutes. An extra 5 to 15minutes was sometimes required to ensure clear

band separation. After electrophoresis, the gel was taken out of the tank and PCR

bands were visualized using a Gel DocTM (BIO-RAD).

2.4 Statistical Analysis

All analyses were performed using the Statistical Package for Social Sciences (SPSS)

Version 21. Survival analyses were performed on patients who only had

haematological malignancies (n = 130), as leukaemia/lymphoma relapse is a major

contributor to death in these patients but not in non-malignant cases. Analyses of

acute graft versus host disease were performed on the entire cohort (n = 140) of

donors.

2.4.1 Survival Analyses

For survival, the univariate analyses were performed by Kaplan-Meier analysis.

Kaplan-Meier analyses were also used to look for interactions between KIR and non-

KIR variables by coding new variables based on the presence or absence of the KIR

gene and non-KIR variable. Variables showing significance at the p < 0.05 level were

then included in a multivariate Cox-regression model.

46

2.4.2 Pearson Chi-Square Analysis of Acute Graft-versus-Host Disease (aGvHD)

Acute GvHD grades 0-IV were collapsed into variables with only two categories:

I. Grades 0-I v II-IV

II. Grades 0-II v III-IV

Univariate analyses were then conducted using the chi-square test for contingency

tables. Those interaction variables found to interact with KIR genes in the survival

analysis were also tested for influence on aGvHD using contingency tables.

2.4.3 Multivariate Cox Regression Analysis on Survival

Cox regression is used to determine if newly identified variables remained significant

after correcting for other variables known to influence survival. The interactions that

were newly identified as significant (from the Kaplan-Meier analyses) were entered

into the initial model. Through a process of elimination, only the most significant

interactions will be retained in the final equation

Chapter 3. Results

3.1 Multiplex PCR-SSP KIR Genotyping Assay Optimizations

The KIR PCR-SSP genotyping assay was used to genotype all unrelated bone marrow

transplant donors. The assay was designed and optimized to produce strong and

distinct PCR bands for each of the 15 KIR genes. Group 1 included primers that

amplified: KIR2DL3, KIR2DL1 and KIR3DL1. Group 2 included primers that amplified:

KIR3DL3, KIR2DS3, KIR2DS5 and KIR3DS1. Group 3 included primers that amplified:

KIR2DL5, KIR2DL2, KIR2DS1 and KIR2DL4. Lastly, Group 4 included primers that

ampilfed: KIR2DS4d (deleted variant of KIR2DS4), KIR3DL2, KIR2DS2 and KIR2DS4.

The PCR-SSP primer groupings were selected so that all PCR products in that one

47

group would have different sizes and would therefore produce a distinct band in an

electrophoresis gel. The second consideration was that each group was to include an

internal positive PCR control, thus the inclusion of primers for the framework genes

(present in everyone): 3DL3 (in Group 2), 2DL4 (in Group 3), and 3DL2 (in Group 4)

were each included in a group. The KIR gene 2DL1 present in all but one individual

out of the entire cohort of donors genotyped (99.3%), was also considered as an

internal PCR control in Group 1. The assay was validated on a 20-cell line panel from

the 13th International HLA and Immunogenetics Workshop (IHIWS). The KIR genotype

of these cell lines had been established in international exchanges (De Santis et al,

2006). At the start of this honours project, none of the PCR conditions for the 4 PCR-

SSP multiplex groups were optimized. However, the scientists in the Department of

Clinical Immunology, Royal Perth Hospital, had demonstrated previously that all

primers amplified the intended KIR gene.

3.1.1 Optimization of PCR-SSP Group 1

The first step of the optimization of Group 1 was to test which dNTP concentration

(10mM or 40mM) was optimal. For Group 1, 40mM dNTP proved to be the ideal

concentration because the 10mM dNTP concentration had missing bands (Figure 2,

top part of the picture).

48

Figure 6. shows the 10mM and 40mM dNTP concentrations for selected cell lines with Group 1 primers.

After determining the optimal dNTP concentration, it was necessary to optimize the

concentration of each primer, as the bands from the 40mM dTNP concentration were

weak (refer to Figure 6). KIR2DL3 primer concentrations were adjusted from 5pmol/ul

to 10pmol/ul, which resulted in a strong PCR band intensity. Fortunately, after this

primer concentration increment, it was not necessary to alter Group 1 primers

concentrations further as they already produced specific and intense PCR bands. For

Group 1 primers, the optimal concentrations were: KIR2DL1 at 5pmol/ul, KIR2DL3 and

KIR3DL1 at 10pmol/ul (Figure 7).

49

Figure 7. Shows the gels of the optimized PCR-SSP Group 1 primers on 20-cell line

panel. (Refer to APPENDIX A, for genotypes of the validated cell line panel)

3.1.2 Optimization of PCR-SSP Group 2

The first step in the optimization of Group 2 was to test which dNTP concentration was

optimal. For Group 2, 10mM dNTP proved to be the ideal concentration, even though

both dNTP concentrations produced intense defined bands, 10mM dNTP produced

stronger band intensities. (Figure 8)

Figure 8. Gel picture of the two different dNTP concentrations from Group 2.

50

Fortunately, the initial Group 2 primer concentrations produced specific and intense

bands. The optimized primer concentrations were: KIR2DS3 at 30pmol/ul, KIR3DL3 at

25pmol/ul, KIR2DS5 at 10pmol/ul and KIR3DS1 at 7.5pmol/ul (Figure 9).

Figure 9. Optimized PCR-SSP Group 2 on the 20-cell line panel. (Refer to APPENDIX A, for genotypes of the validated 20-cell line panel)

3.1.3 Optimization of PCR-SSP Group 3

The first step of the optimization of Group 3 was to test which dNTP concentration was

optimal. For Group 3, 40mM dNTP proved to be the ideal concentration, as 10mM

dNTP produced many non-specific PCR bands (Figure 10).

Figure 10. Gel picture of the PCR products produced using 10mM and 40mM dNTP concentrations from Group 3. The initial optimization of Group 3, which included primers that amplified: KIR2DL4,

KIR2DL2, KIR2DL5 and KIR2DS2 (Figure 11).

51

Figure 11. The initial Group 3 (before the swapping of KIR primers). However, as a result of resolving issues associated with the optimization of Group 4,

where it was necessary to swap KIR2DS2 (from Group 3) with KIR2DS1 (from Group

4), a problem was encountered with the new Group 3. The new Group 3 now included

primers for KIR2DL5, KIR2DL2, KIR2DL4 and KIR2DS1. The PCR products of

KIR2DS1 and KIR2DL2 migrated to the same amplicon size in the electrophoresis gel,

resulting in indistinguishable band separation in samples containing both KIR2DL2

and KIR2DS1 (Figure 12). The PCR product of KIR2DS1 migrated to a larger than

expected size, at an approximate 165bp instead of its predicted amplicon size of

143bp. The theoretical expected product size of KIR2DL2 was 173bp.

Figure 12. The first PCR run for new group 3 primers (after the swapping of KIR genes). The PCR products of this particular gel were run for 60minutes instead of the usual 40minutes in an attempt to better separate the PCR products.

52

To resolve this new problem, the 33bp M13F and M13R sequencing primer tags were

removed from the KIR2DS1 primers in order to reduce the PCR product size. The

initial results following the removal of KIR2DS1 sequencing primer tags were

promising, in that the bands for KIR2DS1 and KIR2DL2 were now distinct (Figure 13).

Figure 13. The first PCR run for the new group 3 after the removal of KIR2DS1 sequencing primer tags.

As the intensity of the bands for KIR2DL5 and KIR2DL2 were much stronger than

those for KIR2DS1 and KIR2DL4, the primer concentrations were readjusted. The

KIR2DL2 primer concentrations were reduced from 20pmol/ul to 15pmol/ul and those

of KIR2DL5 were decreased from 15pmol/ul to 10pmol/ul. This resulted in the final

primer concentrations being: KIR2DL4 at 5pmol/ul, KIR2DL5 at 10pmol/ul, KIR2DL2

and KIR2DS1 at 15pmol/ul (Figure 14).

53

Figure 14. The optimized new group 3 primers on the validated panel. (Refer to APPENDIX A, for genotypes of the validated 20-cell line panel)

3.1.4 Optimization of PCR-SSP Group 4

The first step of the optimization of group 4 was to test which dNTP concentration was

optimal. 40mM dNTP was found to be the ideal concentration, because 10mM dNTP

produced non-specific bands (Figure 15).

Figure 15. PCR products produced using 10mM and 40mM dNTP concentrations for group 4 primers. The problem with the initial group 4 primers (before the primer swap between

KIR2DS1 and KIR2DS2) was that we could not distinguish between KIR3DL2 and

KIR2DS1 in the electrophoresis gels (Figure 15). This was because both KIR3DL2 and

KIR2DS1 PCR products did not migrate as the expected amplicon size. KIR3DL2

migrated at 180bp while KIR2DS2 migrated at about 165bp. We tested the ability of

capillary electrophoresis to distinguish the PCR products but the results were

inconclusive. To resolved this issue, we resorted to the gene swap with Group 3,

swapping KIR2DS1 (from Group 4) with KIR2DS2 (from Group 3). The results were

great, KIR2DS4d (largest band, migrated at 198bp), KIR3DL2 (second largest band,

54

migrated at ~170bp), modified KIR2DS1 (third largest band, migrated at ~140bp) and

KIR2DS4 (smallest band, migrated at 119bp). The preliminary tests with 8 selected

cell lines (from the validated cell line panel) showed clear band separations and strong

band intensities (Figure 16).

Figure 16. The preliminary PCR run test on the new group 4 primers on selected cell lines from the validated panel. As the PCR products for KIR2DS4 were relatively weak in other PCR runs (not

shown), the concentration of KIR2DS4 primers was increased from 5pmol/ul to

10pmol/ul. The optimal primer concentrations for group 4 primers were: KIR3DL2 at

5pmol/ul, KIR2DS4, KIR2DS4d and KIR2DS2 at 10pmol/ul (Figure 17).

Figure 17. The optimized PCR-SSP Group 4 on selected cell lines. (Refer to APPENDIX A for genotypes of the validated 20-cell line panel)

55

3.2 KIR Genotyping of the 146 Donors

PCR reactions were scored based on PCR band intensity as follows:

0= no band

1= weak band.

2=definite band

3=strong band

4=very strong band.

All gels were read independently by two readers – student and supervisor. For all

genes except KIR2DS1, all samples had scores of 0 or > 2. For these genes it was

clear that 0 represented the absence of the gene while scores of > 2 represented the

presence of the gene. For KIR2DS1, 4 samples produced weak bands (score = 1).

Repeating these samples at different DNA concentrations still resulted in weak bands.

It was therefore decided that these 4 samples would not be called positive or negative

for KIR2DS1. They would be left as indeterminate and omitted from any analyses of

the effect of KIR2DS1 on transplant outcome.

3.3 Transplant Characteristics and Statistics

This section of the results describes the transplant cohorts including characteristics of

the donor cohorts, patient diagnoses, conditioning regimens, etc.

56

3.3.1 Year of Bone Marrow Transplants

Figure 18. The frequency of haematopoietic stem cell transplants performed in each year. The haematopoietic stem cell transplant dates ranged from 1994 to 2012 (Figure 18).

3.3.2 Transplant Centre and Number of Transplants

Table 2 shows the number of transplants performed at the two different transplant

centres that were analyzed in this study. The majority (90%) of the transplants were

performed at Royal Perth Hospital.

Transplant Centre Number of

Transplants

Percentage (%)

Royal Perth Hospital (RPH)

Princess Margret Hospital (PMH)

126

14

90

10

Total: 140 100

Table 2. Transplant numbers performed at the two transplant centres.

0

2

4

6

8

10

12

14

16

18

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

Nu

mb

er

of

Tran

spla

nts

Transplant Year

Haematopoietic Stem Cell Transplants

57

3.3.3 Transplant Source of Graft

Table 3 shows the three different graft source analyzed in this study, however only the

bone marrow and peripheral blood transplants were used in the donor KIR genotype

and transplant variables interaction analysis.

Source of Graft Frequency (n) Percentage (%)

Cord Blood

Bone Marrow

Peripheral Blood

4

52

84

2.8

37.1

60

Total: 140 100

Table 3. Frequency of the different transplant graft source.

3.3.4 Donors’ Ages and Genders

Table 4 shows the age range of the donors. The average age of the donors was 36.

Total n = 140 Minimum Maximum Mean

Age 2.42 63.99 36.81

Table 4. Age range of the donors amongst the different transplants.

Table 5 shows the distribution of gender ratios amongst the patient and donor cohorts.

Gender of Patients Frequency

(n = 140)

Percentage (%)

Male

Female

95

45

67.9

32.1

Total: 140 100

Gender of Donors Frequency

(n = 140)

Percentage (%)

Male

Female

91

49

65

35

Total: 140 100

Table 5. Gender of patients and donors of the transplants analyzed in this study.

58

3.3.5 Patient Diagnosis

Table 6 shows the frequency and percentage of patients with different diagnoses. The

largest group was AML (31.1%), followed by ALL (17.6%) patients. In the survival

analyses only malignant diagnoses (n = 130) were analyzed.

Diagnosis Frequency (n) Percentage

(%)

ALL (Acute Lymphoid Leukaemia)

AML (Acute Myeloid Leukaemia)

BMF (Bone Marrow Failure)

CLL (Chronic Lymphoid Leukaemia)

CML (Chronic Myeloid Leukaemia)

HD (Hodgkins Disease)

IMD (Inherited Metabolic Disorder)

MDS (Myelodysplastic Syndrome)

MM (Multiple Myelomas)

NHL (Non-Hodgkins Lymphoma)

OTH (Other)

RCC (Renal Cell Carcinoma)

SAA (Severe Aplastic Anemia)

26

46

1

3

15

8

3

17

2

14

3

1

1

17.6

31.1

0.7

2.0

10.1

5.4

2.0

11.5

1.4

9.5

2.0

0.7

0.7

Total: 140 100

Table 6. Frequency of the different diagnoses in the entire cohort of patients.

59

3.3.6 Cytomegalovirus (CMV) Status

Table 7 shows the patient, donor and overall transplant CMV status. The transplant

(Tx) CMV status: ‘Tx CMV Negative’ represents the transplants in which both donor

and patient were CMV negative, while ‘Tx CMV Positive’ represents transplants in

which either donor, patient or both were CMV positive.

Patient CMV Status Frequency

(n = 140)

Percentage (%)

Negative

Positive

Not Available

78

61

1

55.7

43.6

0.7

Donor CMV Status Frequency

(n = 140)

Percentage (%)

Negative

Positive

Not Valid

64

74

2

45.7

52.9

1.4

Total 140 100

Transplants CMV

Status

Frequency

(n = 140)

Percentage (%)

Tx CMV Negative (0)

Tx CMV Positive (>1)

42

98

30

70

Total 140 100

Table 7. Frequency of patient, donor CMV status and Transplant CMV Status.

3.3.7 Conditioning Regimens

Table 8 shows the distribution of conditioning regimens, the two major conditioning

regimens were: busulphan/melphalan (21.4%) and cyclophosphamide/total body

irradiation (24.3%). (For the conditioning regimens used in patients with different

diagnoses refer to APPENDIX B.)

60

Conditioning Regimens Frequency (n) Percentage (%)

AraC1/Camp2/Cy3/TBI4

ATG5/Bu6/Cy

ATG/Bu/Flu7/Mel8

ATG/Cy/TBI

BEAM

Bu/Camp/Cy

Bu/Camp/Mel

Bu/Cy

Bu/CY

Bu/Flu

Bu/Flu/Mel

Bu/Mel

Camp/Cy/TBI

Cy/Camp

Cy/Etop9/TBI

Cy/Flu/Mel

Cy/Flu/TBI

Cy/TBI

Cy/Thio10/TBI

Flu/Ida/Mel

Flu/Mel

Mel/TBI

Nil

1

3

1

1

1

4

2

7

2

15

3

30

8

1

1

1

1

34

1

1

16

5

1

0.7

2.1

0.7

0.7

0.7

2.9

1.4

5.0

1.4

10.7

2.1

21.4

5.7

0.7

0.7

0.7

0.7

24.3

0.7

0.7

11.4

3.6

0.7

Total: 140 100

1Arabinoside, 2Campath, 3Cyclophosphamide, 4Total Body Irradiation, 5Anti-thymocyte Globulin, 6Busulphan, 7Fludarabine, 8Melphalan, 9Etoposide, 10Thiotepa Table 8. Frequency of the different conditioning regimens used.

3.3.8 Acute Graft-versus-Host Disease (GvHD)

Table 9 shows the frequency of the prevalence of different grades of aGvHD in the

study cohort. Type 0 represents patients that did not have aGvHD, type I represented

patients that had mild aGvHD, type II represented patients that had mild to moderate

61

aGvHD, type III represented patients that had moderate to severe aGvHD and type IV

represented patients that had severe to very severe aGvHD.

Type of GVHD Frequency (n) Percentage (%)

0 (No GvHD)

I (Mild)

II (Moderate)

III (Severe)

IV (Very Severe)

46

17

41

16

20

32.9

12.1

29.3

11.4

14.3

Total: 140 100

Table 9. Prevalence of different grades of aGvHD.

3.3.9 KIR Gene Frequencies of the Entire Cohort

Table 10 shows the frequency of the individual donor KIR genes. The KIR gene

frequencies in this study were similar to the frequencies of the Western Australian

population previously found in Witt et al (2004).

KIR Gene Frequency

(Total n = 140)

Percentage (%)

2DL1

2DL2

2DL3

2DL4 (framework gene)

2DL5

3DL1

3DL2 (framework gene)

3DL3 (framework gene)

2DS1

2DS2

2DS3

2DS4

2DS4d

2DS5

3DS1

A/A Haplotype

B/x Haplotype

N/A

139

71

134

140

70

136

140

140

61

72

36

132

113

43

57

38

100

2

99.3

50.7

95.7

100

50

97.1

100

100

44.9

51.4

25.7

94.3

80.7

30.7

40.7

27.1

71.4

1.4

Table 10. Frequency of the individual KIR genes.

62

The top section of Table 11 shows the frequency of donors with differing numbers of

activating KIR genes. The middle section of Table 11 shows the frequency of donors

with differing numbers of inhibitory KIR genes. The bottom section of Table 11 shows

the frequency of donor with differing total number of KIR genes.

Number of

Activating KIR

Number of

Donors

Percentage (%)

1

2

3

4

5

6

N/A

39

30

11

29

20

7

4

27.9

21.4

7.9

20.7

14.3

5.0

2.9

Total: 140 100

Number of

Inhibitory KIR

Number of

Donors

Percentage (%)

2

3

4

5

N/A

1

43

54

37

5

0.7

30.7

38.6

36.4

3.6

Total: 140 100

Total Number of

KIR

Frequency (n) Percentage (%)

3

4

5

6

7

8

9

10

11

N/A

1

38

2

29

4

26

9

15

7

9

0.7

27.1

1.4

20.7

2.9

18.6

6.4

10.7

5.0

6.4

Total: 140 100

Table 11. Frequency of donors with different numbers of activating, inhibitory and total number of KIR genes.

63

3.4 Analysis of Acute Graft-versus-Host Disease (aGvHD) and KIR genes

Acute graft-versus-host disease (aGvHD) is a complication associated with bone

marrow transplants wherein donor immune lymphocytes in the graft recognize the

patient as foreign and attacks the patient’s tissues. Analysis of the effect of KIR

genotype on the prevalence of aGvHD was performed using Pearson Chi-square

analysis for contingency tables. aGvHD was analysed as two variables. The variable

GvHD2 was created by dividing all transplants into those with aGvHD grade less than

II and those with aGvHD grade > II. The variable GVHD3 was created by dividing all

transplants into those with aGvHD grade less than III and those with aGvHD >= III.

3.4.1 Effect of KIR Genotype on Prevalence of Acute GVHD

Table 12 (second and third columns) shows the relationship between KIR genotype

and prevalence of grade II and grade III aGvHD without considering interaction

variables. There were no significant associations between the prevalence of aGvHD

and the presence of individual KIR genes, the number of KIR genes or KIR-A or –B

haplotypes.

3.4.2 Effect of interactions between KIR Genotype and other Transplant

Variables on the Prevalence of Acute GVHD

The following variables from Table 12 require explanation:

ATG/CAMP: transplants in which either anti-thymocyte globulin (ATG) or Campath

(CAMP) was used (or not). ‘ATG/CAMP-’ refers to transplants that did not use ATG or

Campath, while ‘ATG/CAMP+’ refers to transplants that used either ATG or Campath.

ATG and Campath have similar T cells eradicating effects.

64

Transplant Cytomegalovirus status (Tx CMV): ‘Tx CMV-’ refers to the transplants in

which both donor and patient are CMV negative while ‘Tx CMV+’ refers to the

transplants in which either the donor or patient are CMV positive.

Graft Source: Transplants were divided into those in which the stem cell source was

peripheral blood or bone marrow. The 4 cord blood transplants were excluded from

analyses of graft source.

Total body irradiation (TBI): ‘TBI-’ represented transplants in which TBI was not used

while ‘TBI+’ represented transplants in which TBI was used.

Cyclophosphamide (Cy): ‘Cy-’ represents transplants in which cyclophosphamide was

not used while ‘Cy+’ represents transplants in which cyclophosphamide was used.

The majority of analyses did not show any significant interaction between KIR and

other transplant variables that affected aGvHD prevalence. A modest increase in the

prevalence of grade III aGvHD (p=0.018) was observed in ATG/Campath negative

transplants with donors having KIR2DS1, which was not present in ATG/Campath

positive transplants. This was also true for a higher number of KIR genes (p=0.034).

However, the same trend was not apparent for grade II aGvHD.

65

Pearson Chi-Square Analysis of Acute Graft-versus-Host Disease (aGvHD)

KIR Genes

(Column 1 and 2) Type 2 GvHD Type 3 GvHD Type 2 GvHD Type 3 GvHD

Type 2 GvHD

Type 3

GvHD

ATG/CA

MP-

ATG/CA

MP+

ATG/CA

MP-

ATG/CA

MP+

Tx

CMV-

Tx

CMV+

Tx

CMV-

Tx

CMV+

2DL2 1.000 0.564 0.513 0.168 0.483 0.438 0.530 0.690 1.000 0.650

2DL5 1.000 0.562 0.513 0.488 0.061 0.682 1.000 0.840 0.152 1.000

2DS1 0.728 0.329 0.191 0.302 0.018 0.408 0.757 0.412 0.062 1.000

2DS2 1.000 0.699 0.662 0.168 0.638 0.438 0.530 0.842 1.000 0.819

2DS3 0.441 0.508 1.000 0.682 0.302 0.617 1.000 0.487 1.000 0.600

2DS5 1.000 0.681 0.349 0.734 0.312 0.686 0.742 0.825 0.259 0.806

3DS1 0.607 0.695 0.268 0.494 0.152 0.438 1.000 0.419 0.128 0.650 A/A vs. B/x

3 0.848 0.520 0.631 0.369 0.282 0.328 0.767 1.000 0.735 0.619

HiKIR4 0.484 0.241 0.498 1.000 0.034 1.000 1.000 0.402 0.075 0.820

aKIR15 0.705 0.670 1.000 0.685 0.433 0.652 0.745 1.000 0.270 0.791

aKIR 26 0.494 0.439 0.508 0.732 0.099 1.000 0.758 0.679 0.152 1.000

KIR Genes

Type 2 GvHD Type 3 GvHD Type 2 GvHD Type 3 GvHD Type 2 GvHD Type 3 GvHD

Peri. Blood

Bone Marrow

Peri. Blood

Bone Marrow

TBI - TBI + TBI - TBI + Cy - Cy + Cy - Cy +

2DL2 0.500 0.160 0.482 1.000 1.000 1.000 1.000 0.492 0.487 0.460 0.215 0.764

2DL5 0.649 1.000 0.094 0.319 0.671 1.000 0.642 0.729 0.240 0.325 0.318 1.000

2DS1 0.643 0.773 0.093 0.728 0.828 0.776 0.485 0.483 0.335 0.802 0.305 0.769

2DS2 0.500 0.262 0.482 0.740 1.000 1.000 1.000 0.726 0.487 0.623 0.215 0.558

2DS3 0.314 0.741 0.186 0.420 0.450 1.000 0.785 0.448 0.601 0.775 0.778 0.721

2DS5 0.807 0.762 0.443 1.000 1.000 1.000 0.620 1.000 0.615 0.789 0.585 1.000

3DS1 0.644 0.767 0.337 1.000 0.659 1.000 1.000 0.307 0.471 1.000 1.000 0.547 A/A vs. B/x

1 0.283 0.207 0.401 0.378 0.822 0.777 0.810 1.000 0.116 0.277 0.151 0.507

HiKIR2 0.335 1.000 0.053 0.722 0.506 1.000 0.482 0.475 0.231 0.791 0.315 0.758

aKIR13 0.290 0.565 0.585 1.000 0.476 0.761 1.000 0.702 0.183 0.405 0.263 0.517

aKIR 24 0.353 1.000 0.092 0.489 0.516 1.000 0.644 0.496 0.153 0.617 0.446 1.000

Table 12. P values for Pearson chi-square analysis of contingency tables relating KIR genotype, or KIR genotype in different

transplant subgroups, to grade of acute GVHD.

66

1A/A vs. B/x divides transplants into donors with homozygous A/A KIR haplotypes and donors with heterozygous B/x haplotypes. 2HIKIR divides transplants into those with donors having >7 KIR genes and those having <= 6 KIR genes. 3aKIR1 divides

transplants into donors with at least 1 activating KIR and donors with >1 aKIR genes. 4aKIR2 divides transplants into donors with at

least 2 aKIR genes and donors with >2 aKIR genes.

67

3.5 Selection of KIR Genes for Survival Analysis

All KIR genes except the framework genes were analyzed in the Kaplan-Meier

analysis of the effect of individual KIR genes on survival. However, only KIR2DS1,

KIR2DS2, KIR2DS3, KIR2DS5, KIR3DS1, KIR2DL2 and KIR2DL5 were selected for

analysis of interactions between KIR and other transplant variables. The criterion for

gene selection was that the gene had to have a population frequency between 25%

and 75% so that there would be adequate numbers of transplants with and without

the gene.

3.5.1 Univariate Kaplan-Meier Analysis of KIR genes on Survival

130 transplants were analyzed in this univariate Kaplan-Meier analysis. These 130

transplants only included patients with haematological malignancies so that the

analysis would be based on a more clinically homogenous cohort. In some instances

myelogenous (AML, CML, MDS) and acute lymphocytic leukaemia (ALL) subsets

were also separately analyzed. Table 15 shows that none of the KIR genes was

associated with significantly better or worse survival (on the entire malignant cohort,

n=130).

68

KIR Genes P. Value

(Kaplan-Meier)

2DL1

2DL2

2DL3

2DL4 (Framework Gene)

2DL5

3DL1

3DL2 (Framework Gene)

3DL3 (Framework Gene)

2DS1

2DS2

2DS3

2DS4

2DS5

3DS1

0.394

0.775

0.534

-

0.455

0.448

-

-

0.283

0.780

0.975

0.940

0.077

0.155

Table 13. Kaplan-Meier p-values for the association of individual donor KIR genes on survival. 3.5.2 Univariate Kaplan-Meier Analysis of KIR Genes on Survival in patients with Myelogenous leukaemias. The literature includes several reports showing an effect of KIR genotype on survival

but only in patients with myelogenous leukaemia and not in ALL patients. The

analysis was therefore repeated in the myeloid and non-myeloid patient subsets.

Table 14 shows that there were no significant effects of KIR genotype on survival in

myelogenous and non-myelogenous subsets.

69

KIR Gene MYO1- MYO2+

2DL2

2DL5

3DL1

2DS1

2DS2

2DS3

2DS4

2DS5

3DS1

A/A vs. B/x3

HIKIR4 (>7 KIR)

aKIR15

aKIR26

0.375

0.583

0.268

0.664

0.375

0.855

0.170

0.122

0.500

0.669

0.473

0.998

0.664

0.717

0.933

0.870

0.334

0.804

0.864

0.175

0.507

0.198

0.607

0.631

0.493

0.942 1 Acute/Chronic lymphoid Leukaemia cohort subset, 2 Mylogenous Leukaemia cohort subset, 3 KIR Haplotype A/A vs B/x, 4 High Number of KIR, 5 Subset of donors with at least 1 activating KIR gene, 6 Subset of donors with at least 2 activating KIR genes. Table 14. P. values of individual KIR genes on the survival rate of the myelogenous and non-mylogenous cohort.

70

3.6 Effect of Interactions between KIR Genes and other Transplant Variables on

Survival

Table 15 (below, refer to page 80) shows the p-values from the Kaplan-Meier

analyses of KIR genotype in the presence or absence of other transplant variables.

KIR2DS1, KIR2DS5, KIR3DS1 and KIR2DL5, interacted with stem cell source

(particularly, with peripheral blood transplants), the strongest interaction being with

KIR3DS1. The Kaplan-Meier survival curves (Figure 19) showed that the absence of

KIR3DS1 was associated with better survival in PBSC transplants (p=0.008) but not

in bone marrow transplants. The trend was the same for KIR2DS1, KIR2DS5 and

KIR2DL5. That is, the absence of the gene conferred an advantage. This was also

reflected in transplants in which donors having a high number of KIR genes, denoted

by ‘High KIR’ in Table 15 (p=0.023) and donors having at least two activating KIR

genes denoted by ‘aKIR2’ in Table 15 (p=0.046). The Kaplan-Meier survival graphs

of peripheral blood transplants for KIR2DS1 (p=0.028), KIR2DS5 (p=0.039),

KIR2DL5 (p=0.032), donors having high KIR numbers (‘High KIR’ variable in Table

15) (p=0.023) and donors having at least 2 activating KIR genes (‘aKIR2’ variable in

Table 15) (p=0.046), are not shown but they showed the same trends as the

interactions between KIR3DS1 and stem cell source (Figure 19).

71

Figure 19a. (Left) The presence of KIR3DS1 in peripheral blood transplant was

associated with a poorer survival while there was no observable difference in bone

marrow transplants, Figure 19b. (Right) There was no difference in the presence or

absence of KIR3DS1 in bone marrow transplants.

Table 15 shows that the same KIR genes that interacted with stem cell source also

interacted with the CMV status of the transplant. In this case, the strongest

interaction occurred with KIR2DS5 (p=0.001). In all cases, better survival was

observed when the donor lacked KIR2DS5 in CMV negative transplants (KIR2DS5-

/CMV-) (Figure 20a). This same effect was also reflected in interactions with

KIR2DS1 (p=0.005, Figure 21a), KIR3DS1 (p=0.008, Figure 22a), KIR2DL5

(p=0.025, Figure 23a), donors having a high number of KIR genes denoted by ‘High

KIR’ in Table 14 (p=0.009, Figure 23a) and donors having at least 2 activating KIR

genes denoted by ‘aKIR2’ in Table 15 (p=0.025). The Kaplan-Meier survival graphs

for ‘aKIR2’ is not shown. However, KIR genotype had no significant effect in CMV

positive transplants.

72

Figure 20a. (Left) Donors without KIR2DS5 in CMV negative transplants were

associated with an improved survival while donors with KIR2DS5 results in a worse

survival. Figure 20b. (Right) There was no difference in survival for CMV positive

transplants, in the presence or absence of KIR2DS5.

Figure 21a. (Left) KIR2DS1 was associated with a poorer survival in CMV negative

transplants. Figure 21b. (Right) There was no difference in the presence or absence

of KIR2DS1 in CMV positive transplants.

73

Figure 22a. (Left) KIR3DS1 in CMV negative transplants was associated with a

poorer survival. Figure 22b. (Right) There was no difference in the presence or

absence of KIR3DS1 in CMV positive transplants.

Figure 23a. (Left) KIR2DL5 in CMV negative transplants was associated with a

poorer survival. Figure 23b. (Right) There was no difference in the presence or

absence of KIR2DL5 in CMV positive transplants.

74

Figure 24a. (Left) Donors with high number of KIR genes were associated with a

poorer survival in CMV negative. Figure 24b. (Right) No significant difference in

survival was observed in transplants with donor with a high number of KIR genes.

Table 15 (refer to page 80) shows that KIR2DS2 (p=0.034) and KIR2DL2 (p=0.028)

interacted with total body irradiation (TBI) to influence survival. The presence of

these genes was associated with improved survival in TBI+ transplants (Figure 25b

and 25d) and poorer survival in TBI- transplants (Figure 25a and 25c). These two

genes are in very strong linkage disequilibrium with each other such that only one

donor in this cohort was discordant for KIR2DL2 and KIR2DS2.

75

Figure 25a. (Top left) Donors with KIR2DS2 had poorer survival in TBI negative

transplants. Figure 25b. (Top right) Donors with KIR2DS2 had better survival in TBI+

transplants. Figure 25c. (Bottom left) Donors with KIR2DL2 had poorer survival in

TBI negative transplants. Figure 25d. (Bottom right) Donors with KIR2DL2 had better

survival in TBI+ transplants.

As TBI is invariably combined with cyclophosphamide in transplants for ALL, it was of

interest to determine whether TBI might be acting as a surrogate marker for

cyclophosphamide. Table 15 shows that KIR2DL2 (Cy- transplants p=0.032/ Cy+

transplants p=0.002) and KIR2DS2 (p=0.032/p=0.002) showed stronger interactions

with the use of cyclophosphamide (Cy) than with TBI. As for TBI, in Cy+ transplants

the survival was improved if the donor had KIR2DS2/2DL2 (Figure 26b and 26d)

76

while in Cy- transplants the survival was improved if the donor lacked KIR2DS2/2DL2

(Figure 26a and 26c). KIR3DS1 (p=0.049) only showed a just significant interaction

on survival in Cy- transplants (Kaplan-Meier survival graph not shown).

Figure 26a. (Top left) KIR2DS2 was associated with a poorer survival in

cyclophosphamide negative transplants. Figure 26b. (Top right) KIR2DS2 was

associated with an improved survival in cyclophosphamide positive transplants.

Figure 26c. (Bottom left) KIR2DL2 was associated with a poorer survival in

cyclophosphamide negative transplants. Figure 26d. (Bottom right) KIR2DL2 was

associated with an improved survival in cyclophosphamide positive transplants.

As TBI and cyclophosphamide are used almost invariably together when conditioning

ALL patients, the possibility was considered that these two agents were simply

77

identifying ALL patients. Further analyses were therefore undertaken on ALL

patients’ alone (n= 26) and myelogenous leukaemia patients (AML, CML, MDS)

(n=89) to see if the interaction between KIR2DS2 and Cy was preserved in the

different diagnoses. It was not possible to examine ALL patients who were not

treated with Cy as there were very few of these. Figure 27 shows that as anticipated,

the presence of KIR2DS2 resulted in an improved survival in ALL patients (p=0.08).

Figure 28 shows that the interaction between KIR2DS2 and Cy was also preserved in

patients with myeloid leukaemia. Although the p-values are not quite significant, there

are clear trends that are similar to those seen in the entire cohort (Figure 26). These

analyses supports the observations made in the interaction analysis of

KIR2DS2/KIR2DL2 and Cy on the entire malignant cohort, in that the presence of

KIR2DS2 in Cy positive transplants is beneficial and detrimental in Cy negative

transplants. The interactions between KIR genes and Cy were preserved even in the

different specific diagnoses cohorts (ALL only and Myelogenous (MYO) only cohort).

Figure 27. KIR2DS2 was associated with an improved survival in cyclophosphamide positive transplants in the ALL cohort.

78

Figure 28a. (Left) Presence of KIR2DS2 in cyclophosphamide negative transplants was associated with worse survival in the MYO cohort. Figure 28b. (Right) KIR2DS2 was associated with an improved survival in cyclophosphamide positive transplants in the MYO cohort.

KIR2DL2 (melphalan negative transplants p=0.020/ melphalan positive transplants

p=0.064) and KIR2DS2 (p=0.024/p=0.064) also showed significant interactions with

melphalan (Mel) (Table 15). Interestingly, the effect of KIR2DS2/2DL2 was the

opposite of that seen with cyclophosphamide and TBI. That is, the presence of

KIR2DS2/2DL2 resulted in poorer survival in Mel+ transplants (Figure 29b) and

improved survival in Mel- transplants (Figure 29a). A similar phenomenon was

observed in transplants that used fludarabine (Figure 30a and Figure 30b). The

Kaplan-Meier survival graphs are not shown for KIR2DL2 (for both melphalan and

fludarabine transplants) however, they showed the same trend as KIR2DS2.

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Figure 29a. (Left) The absence of KIR2DS2 in melphalan negative transplant was

associated with poorer survival. Figure 29b. (Right) The absence of KIR2DS2 in

melphalan positive transplants was associated with better survival.

Figure 30a. (Left) The absence of KIR2DS2 in fludarabine negative transplant was associated with poorer survival. Figure 30b. (Right) The absence of KIR2DS2 in fludarabine positive transplants was associated with better survival.

No significant interactions were observed between KIR genotype and the use of

busulphan in transplants (Table 15).

80

Univariate Kaplan-Meier Analysis of KIR Genes with Transplant Variables on Survival

KIR

Genes

Peri.

Blood

Bone

Marrow

Tx

CMV-1

Tx

CMV+2

Cy –

3

Cy +

4

Bu –

5

Bu +

6

Flu –

7

Flu +

8

Mel –

9

Mel +

10

TBI -

11

TBI +

12

2DL2 0.944 0.751 0.837 0.777 0.032 0.002 0.287 0.577 0.196 0.036 0.020 0.064 0.151 0.028

2DL5 0.032 0.386 0.025 0.621 0.123 0.605 0.489 0.659 0.859 0.238 0.847 0.179 0.315 0.943

2DS1 0.028 0.863 0.005 0.592 0.088 0.968 0.450 0.294 0.373 0.467 0.880 0.102 0.174 0.857

2DS2 0.944 0.690 0.837 0.732 0.032 0.002 0.309 0.577 0.198 0.038 0.024 0.064 0.151 0.034

2DS3 0.279 0.238 0.454 0.602 0.170 0.069 0.945 0.945 0.417 0.129 0.184 0.281 0.204 0.122

2DS5 0.039 0.674 0.001 0.937 0.328 0.120 0.100 0.485 0.063 0.878 0.147 0.318 0.481 0.073

3DS1 0.008 0.946 0.008 0.952 0.049 0.718 0.372 0.139 0.284 0.257 0.454 0.083 0.096 0.593

KIR A/A vs B/X 0.302 0.739 0.065 0.430 0.118 0.354 0.684 0.598 0.998 0.215 0.624 0.181 0.289 0.798

High KIR 0.023 0.605 0.009 0.633 0.173 0.815 0.496 0.442 0.591 0.298 0.984 0.211 0.212 0.860

aKIR1 0.376 0.664 0.065 0.435 0.146 0.352 0.707 0.634 0.940 0.215 0.628 0.238 0.320 0.770

aKIR2 0.046 0.460 0.025 0.555 0.238 0.741 0.514 0.721 0.866 0.298 0.908 0.295 0.358 0.893

Table 15. P-values of all the conditioning variables with individual KIR genes on survival rate.

1Transplants with both patient and donor CMV negative, 2Transplants with at 1 patient or donor CMV positive, 3Transplant regimens with no cytophosphamide, 4Transplants with cytophosphamide, 5Transplant regimens with no busulphan, 6Transplant regimens with busulphan, 7Transplant regimens with no fludarabine, 8Transplant regimens with fludarabine, 9Transplant regimens with no melphalan, 10Transplant regimens with melphalan, 11Transplant regimens with no total body irradiation, 12Transplant regimens with total body irradiation. Table 15, shows all the p-values from the univariate Kaplan-Meier analyses of individual KIR genes’ on survival in subsets of

transplants differing for various transplant variables.

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3.7 Multivariate Cox Regression Analysis

Multivariate Cox Regression analyses were used to determine if the three

most significant interactions from the Kaplan-Meier analyses remained

significant after correcting for other variables known to influence survival. Only

the three strongest interactions from the Kaplan-Meier analyses KIR2DS5

with CMV (p=0.001), KIR2DS2 with cyclophosphamide (p=0.002) and

KIR3DS1 with stem cell source (p=0.008) were included in the Cox regression

analysis (Refer to Table 16). Table 16 shows the frequency of patients with

each category of the variables included in the starting model.

Variable: Frequency (n)

CY Negative 71

Positive 48

CMVTX (Transplant CMV Status)

Negative 38

Positive 81

RISK1 0 38

1 81

Age Group < 40years old 31 >40 years old 88

ERA (0)2

ERA (1)3

ERA (2)4

ERA (3)5

Before 2000 9

2000--2004 17

2004-2008 33

After 2008 60

KIR2DS5-/CMV- Negative 89

Positive 26

KIR2DS2-/CY- Negative 83

Positive 36

KIR2DS2+/CY+ Negative 96

Positive 23

KIR3DS1-/PBSC Negative 72

Positive 47

RIC6 Negative 95

Positive 20

Table 16. Variables initially entered into the multivariate Cox Regression

model.

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For Table 16: 1RISK = 0 (acute leukaemias and lymphomas in first complete remission, MDS and CML in chronic phase) and RISK = 1 (acute leukaemia and lymphoma in other than first complete remission, CML in other than chronic phase, multiple myeloma and chronic lymphocytic leukaemia),2Represents transplants before 2000, 3Represents transplants between 2000 to 2004, 4Represents transplants between 2004 to 2008, 5Represents transplants after 2008. 6Represents reduced intensity conditioning.

Table 17 shows the five variables that remained significant in the final

equation. The interactions between KIR2DS5/CMV and between KIR2DS2/Cy

were retained whereas the interaction between KIR3DS1/PBSC was

eliminated. This confirms that the two retained interactions remained

significant after correcting for other variables known to influence outcome.

Variable P. Value Exp (B)

ERA (0)

ERA (1)

ERA (2)

ERA (3)

CMVTx

KIR2DS5-/CMV-

KIR2DS2+/CY+

0.029

0.007

0.053

0.108

0.009

0.000

0.006

-

3.382

2.012

1.628

0.359

7.473

3.109

Table 17. Variables left in the final equation in the multivariate Cox

Regression model. (Exp (B) is the relative risk)

83

Chapter 4. Discussion

4.1 Optimization of the Multiplex PCR-SSP KIR Genotyping Assay

4.1.1 Unexpected PCR bands Migration

In the process of optimizing the multiplex PCR-SSP KIR genotyping assay,

problems arose with the unexpected migration of the PCR products for

KIR3DL2 and KIR2DS1. The PCR amplicons for these KIR genes migrated to

the same position in the 3% agarose gel, which made interpretation difficult.

The PCR products for both KIR3DL2 and KIR2DS1 migrated to 180bp instead

of 159bp for KIR3DL2 and 165bp instead of 143bp for KIR2DS1. Even when

taking into account the added length of the 33bp primer tags (M13F and

M13R) that were incorporated into the primer design and which would have

made the PCR products slightly larger, it was unclear how the PCR products

migrated to the same position. The most likely explanation would be that

some PCR products could adopt a secondary conformation resulting in

anomalous apparent size. To resolve this issue, the KIR2DS1 primers (from

Group 4) were swapped with the KIR2DS2 primer (from Group 3). In doing so,

it would have theoretically provided sufficient product size difference for clear

band separation in both PCR primer groups. Testing of the new Group 4 mix

which included primers for KIR2DS4d, KIR3DL2, KIR2DS2 and KIR2DS4

revealed good PCR product band separation in the agarose gel. However,

testing of the new Group 3 mix, which included primers for KIR2DL5,

KIR2DL2, KIR2DS1, and KIR2DL4, revealed poor PCR product bands

separation, this made interpretation impossible. The KIR2DS1 PCR product

migrated to approximately 165bp, which was too close to the KIR2DL2 PCR

product (173bp). In addition the KIR2DL5 primer concentration was too high

84

making the distinction between KIR2DL5 and KIR2DL2 difficult. For good

band separation, a minimum of a 20bp difference between two different PCR

products is required. Having identified that the KIR2DS1 PCR product was

migrating to approximately 165bp, it was determined that if the product size of

KIR2DS1 was smaller, then theoretically good band separation could be

achieved. Removal of the sequencing primer tags (M13F and M13R) would

result in a theoretical smaller product size. Testing of the modified KIR2DS1

primers revealed good PCR product separation in the agarose gel resulting in

the ability to identify the presence of KIR2DS1 and KIR2DL2 PCR products.

Finally minor readjustments to the KIR2DL5 and KIR2DL2 primer

concentrations were made which resulted in all four PCR products giving

distinct bands in the gel.

4.1.2 Validation of the PCR-SSP KIR Genotyping Assay

The forward and reverse sequencing primer tags were designed to facilitate

sequencing of the PCR products thereby confirming that the correct gene was

being amplified. However, there was insufficient time to sequence the PCR

products in this study. Nevertheless, the specificity of the PCR products were

validated by testing the 20 cell-line panel from 10th International HLA and

Immunogenetics Workshop (IHIWS), the genotypes of which were known

through international exchanges (De Santis et al. 2006). An internal positive

PCR control was included in each of the four KIR PCR-SSP gene groupings

by including primers for one framework gene (present in everyone). Each

framework KIR gene was carefully selected so that the PCR product size was

compatible with the other genes being amplified in that particular PCR group.

85

This ensured that if any gel electrophoresis wells were completely negative it

would likely be the result of poor quality DNA or human error (mis-pipetting)

and the PCR run would be repeated. The final configuration of the assay

produced robust, distinct bands for all genes when they were present and

clear absence of bands when the gene was not present without the presence

of any non-specific PCR products.

4.2 Overview of the Data Analyzed in this Study

The aim of this study was to identify transplant variables that might interact

with donor KIR genotype and influence patient survival. Cytomegalovirus

(CMV) status, total body irradiation (TBI) and transplant graft source were

identified in the aims as potential variables that might display interactions with

KIR genes. These variables were selected as they were likely to influence NK

cell numbers (stem cell source) or NK activity (TBI, CMV status). Both CMV

and graft source were found to interact with particular KIR genes, and to a

lesser degree, TBI.

4.2.1 Interactions between KIR2DS2 and Conditioning Agents

TBI damages DNA and DNA damage is a strong stimulus for up-regulation of

stress ligands in many cell types including leukaemia cells (Kim et al, 2006

and Zafirova et al, 2011). Several other conditioning agents (busulphan,

melphalan, and cyclophosphamide) also damage DNA and might be expected

to induce stress ligands, although the ability of these specific agents to induce

stress ligands has not been tested. Fludarabine is a cell-cycle arrester and

does not directly damage DNA (Tournilhac et al, 2003).

86

Based on the interactions observed with TBI, additional transplant

conditioning variables (cyclophosphamide and different diagnoses cohorts)

were investigated for interactions with donor KIR genotype. Significant

interactions between KIR2DS2 and cyclophosphamide, melphalan and

fludarabine were identified. However, the interactions between KIR2DS2 and

melphalan and fludarabine were the inverse of the interaction between

KIR2DS2 and cyclophosphamide. The presence of KIR2DS2 in the donor was

beneficial in the presence of cyclophosphamide but detrimental in the

presence of melphalan or fludarabine. It would seem unlikely that KIR2DS2

would have so many independent interactions. As melphalan and fludarabine

were generally used in place of cyclophosphamide, it is possible that

melphalan and fludarabine may have simply acted as surrogate markers for

patients who did not receive cyclophosphamide. The interaction between

KIR2DS2 and TBI was similar to that with cyclophosphamide although not as

strong. This may reflect the fact that TBI is invariably coupled with

cyclophosphamide for conditioning of ALL patients. Further analysis with

different diagnoses cohorts was done to see if these previously mentioned

interactions would be preserved. The two other cohorts that were analyzed

were the ALL-only cohort (all patients received Cy) and the myelogenous

cohort (AML, CML and MDS). Results from both cohorts’ analyses support

that the presence of 2DS2 in cyclophosphamide positive transplants is

beneficial while the presence of 2DS2 in cyclophosphamide negative

transplants is deleterious. Given that cyclophosphamide showed the strongest

interaction with KIR2DS2, it would seem likely that cyclophosphamide was the

primary interaction variable and that TBI appeared to interact with KIR2DS2

87

due to its frequent use in combination with cyclophosphamide. Although this

analysis has focused on KIR2DS2, it should be mentioned that KIR2DS2 and

KIR2DL2 are almost always found together in the same people as they are in

positive linkage disequilibrium with each other (Hsu et al, 2002). The genes

for KIR2DS2 and KIR2DL2 are next to each other on chromosome 19 and in

this cohort of 140 donors, only one individual did not have both genes and the

interactions observed with conditioning agents were just as strong with

KIR2DL2 as with KIR2DS2. Therefore, it cannot be determined, which gene

was actually responsible.

This is the first study that has identified an interaction between donor KIR

genes and conditioning agents. In relation this our study, Cooley et al. (2010)

reported reduced relapse and improved survival in patients who received

transplants from KIR B haplotype donors. The study compared the role of

centromeric and telomeric regions in the protective effect of KIR B haplotypes.

They concluded that the donor KIR genes on the centromeric end had a

stronger protective effect than the telomeric end. The results in this study

support the notion of separate influences in the telomeric and centromeric

ends of the KIR B haplotype: in that KIR2DS2/KIR2DL2 (centromeric)

interacted with cyclophosphamide whereas KIR2DS1, KIR2DS5, KIR3DS1

(the three telomeric KIR genes) and KIR2DL5 interacted with stem cell source

and CMV status (Refer to Figure 4, page 14, for KIR genes locations).

88

4.2.2 Interactions between KIR2DS1, KIR2DS5, KIR3DS1, KIR2DL5 and

CMV and Graft Source

Four studies (Chen et al. 2006, Cook et al. 2009, Sobecks et al. 2011 and

Behrendt et al. 2013) have reported that activating KIR genes have a

protective effect against CMV reactivation in haematopoietic stem cell

transplants. The findings in this study show that the presence of: KIR2DS1

(p=0.005), KIR2DS5 (p=0.001), KIR3DS1 (p=0.008) and 2DL5 (p=0.025)

resulted in poorer survival in CMV negative transplants. Upon further analysis

of donors with: high KIR (>7 KIR genes) and low KIR genes (<6 KIR genes),

donors with low number of KIR genes resulted in an improved survival while

donors with a high number of KIR genes resulted in poorer survival in CMV

negative transplants. It is not clear how the findings of the current study relate

to the previous reports of the protective effect of KIR in relation to CMV

reactivation. Regardless of number of KIR genes or individual KIR genes, we

found no effect of KIR genotype in CMV positive transplants.

The same set of donor KIR genes (KIR2DS1, KIR2DS5, KIR3DS1 and

KIR2DL5) that showed significant interactions with CMV, also showed

significant interactions with the source of stem cells. Specifically, the presence

of each of these genes resulted in worse survival in peripheral blood

transplants. The interactions observed would presumably be mediated by

donor NK cells (or a subset of T lymphocytes) in the graft. This effect was also

observable if analyzed as donors with high numbers of KIR genes. In contrast,

the presence or absence of these genes had no significant effect in bone

marrow transplants.

89

In the case of CMV status and stem source, it cannot be concluded that any

particular donor KIR gene was responsible for these adverse effects on

survival. These four KIR genes (KIR2DS1, KIR2DS5, KIR3DS1 are activating

KIR genes in the telomeric half (on chromosome 19) of the KIR B haplotype

and KIR2DL5 (inhibitory KIR gene)) are in positive linkage disequilibrium with

each other. It will therefore be difficult to determine which gene or genes are

responsible for the effects observed. It would be easy to assume that the

gene with the highest p-value is the most likely candidate but this would be

unwise at this time as one or two transplants with the appropriate outcome

can change the p-values. In addition, it may also be the number of genes that

is important, eg. a cumulative or synergistic effect. Certainly, there is evidence

in the mouse that multiple activating Ly49 receptors (murine equivalent of

KIR) are involved in protection against CMV (Pyzik et al, 2011).

4.3 KIR Repertoire and Acute Graft-versus-Host Disease (aGvHD)

There were only a few significant interactions between KIR genes and other

transplant variables on the prevalence of aGvHD. KIR2DS1 (p=0.018) and a

high number of KIR genes (p=0.034) were associated with an increase of

grade II-IV aGvHD in patients not receiving ATG (ATG/CAMP- transplants in

Table 18). As there was no significant effect of these genotypes in relation to

grade II-IV aGvHD, we cannot exclude the possibility that the association with

grade II-IV aGvHD is a type I error. The results obtained in this study contrast

with the results found in a study by De Santis et al. (2005) which showed that

a greater number of donor KIR genes (either inhibitory or activating) were

associated with protection against aGvHD. However, other studies (Giebel et

90

al. 2009) also did not find any effects of donor KIR genes on aGvHD. It is

possible that there are other transplant variables that we have not studied that

determine whether donor KIR affects aGvHD.

4.4 The Effect of KIR Repertoire on Survival

Many studies have looked at the KIR genes’ effect on the outcome of bone

marrow transplants but these studies found contradictory results to each other

(reviewed in Witt and Christiansen, 2009). For example, Kroger et al. (2006)

and Cooley et al. (2009) both showed significantly different results. Kroger et

al. (2006) showed that transplants with donors having B/x haplotypes had a

worse outcome (p=0.05) while Cooley et al. (2009) showed that transplants

with donors having B/x haplotypes had a better outcome (p=0.007). In

contrast to both papers, the results obtained from our Kaplan-Meier analyses

of the KIR genes showed no simple influence on survival. However, given that

the effect of KIR2DS2 (on survival) may be entirely dependent on an

interaction with cyclophosphamide, it is clear that different cohorts of

transplant data may show opposite or no effect of KIR genotype depending on

the proportion of transplants in which cyclophosphamide was used as a

conditioning agent.

4.4.1 Mechanism of KIR Interaction Effect on Survival

The largest influences on survival following haematopoietic stem cell

transplantation are usually aGvHD, relapse and infection. As we were unable

to show any conclusive relationship between donor KIR genotype and

aGvHD, while other studies have shown effects of KIR genotype on relapse, it

91

seems likely that the KIR effect may influence relapse. Unfortunately, we were

unable to obtain sufficient accurate relapse information on the transplants in

this study, software program and expertise required for relapse analysis was

not available.

4.4.2 Effect of KIR Genotype in Myeloid and Lymphocytic Leukaemia

It was expected that donor KIR genes would have shown a stronger influence

in the myelogenous leukaemia transplants than in the lymphoid cohort. Many

studies (Cooley et al. 2010 & 2009, De Santis et al. 2005, Cook et al. 2004)

reported that more KIR genes resulted in better survival in myelogenous

(specifically AML) patients as compared to lymphoid leukaemias. Cooley et al.

(2009) showed that the 3-year disease-free survival for donors having KIR B/x

haplotype were significantly (p=0.007) higher than donors having A/A

haplotypes in AML patients but found no significant effect in ALL patients.

Likewise, Kroger et al. (2006) found the opposite effects of the KIR haplotypes

but again the effects were only seen in AML patients and not in ALL patients.

It was therefore unexpected that we should find a KIR influence on survival in

both lymphoblastic and myeloid leukaemias. It is possible that another

unknown transplant variable determines whether KIR genes influence survival

in ALL patients.

4.5 Statistical Analysis Errors

A large number of univariate Kaplan-Meier analyses were done in this study

thereby raising the probability of a type I error. That is, falsely identifying a

significant interaction. There are statistical methods in dealing with the issue

92

of multiple statistical testing that have been put forward. For example, the

Bonferoni correction required the p-value to be multiplied by the number of

statistical tests performed. However if this was applied, none of the p-values

that was less than p > 0.05 (identified in this study) would have remained

significant after such a correction. Therefore, the interactions identified must

be reproduced in other transplant cohorts before the interactions are to be

accepted.

4.6 Conclusions

The results of interactions obtained in this study show important implications

in donor selection. Firstly, there is no simple relationship between KIR genes

and patient survival. Secondly, in relation to CMV negative and peripheral

blood transplants, the presence of KIR3DS1, KIR2DS1, KIR2DL5, and donors

with a high number of KIR genes results in a poorer survival. This suggests

that if the both patient and donor are CMV negative, that it would be ideal if

the donor lacked the three aforementioned KIR genes. The same rationale

would be applied for the peripheral transplant donors. Thirdly, the presence of

KIR2DS2 and KIR2DL2 in donors could be beneficial or deleterious

depending on the presence or absence of cyclophosphamide. Lastly, from the

results obtain in this study show KIR genotypes may not have a significant

role in the prevalence of aGvHD as there were only two significant

interactions out of the multiple analyses that were undertaken with the

Pearson Chi-Square analysis. However, it cannot be assumed that KIR

genotypes as having absolutely no influence on aGvHD prevalence, as there

may be other transplant variables or factors that we have yet to research on.

93

As this is an exploratory study and being the first study to find interactions

between conditioning agents and KIR genes, the findings in this study could

potentially be practice changing. However they must be regarded as

provisional and await reproduction in other transplant cohorts before they are

to be accepted.

A future area of research in relation to this study would be to look at donor

KIR genotype selection as a frontier in haematopoietic stem cell transplant

donor selection, instead of HLA-matching. If the effect of donor KIR genotype

outweighs HLA matching, this could open up a whole new field of

transplantation, whereby HLA-mismatch transplant would not suffer the full

impact of the adverse effects of HLA-mismatches. By using different variables

that are beneficial to patient survival and a selection for an ideal KIR

genotype, this could potentially increase the 3-year disease-free survival rate

of HSCT patients. However, this further study would require a cohort of a

larger number, maybe a collaboration of all the hospitals in Australia.

94

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APPENDIX A

Table 18. The genotypes of the validated 20-cell line panel

Cell Line Name 2DL1 2DL2 2DL3 2DL4 2DL5 3DL1 3DL2 3DL3 2DS1 2DS2 2DS3 2DS4 2DS4d 2DS5 3DS1

JBUSH

BTB

KAS116

E4181324

PE117

BOLETH

EJ23B

HOR

PITOUT

LBUF +2DL2v

WT100BIS

CF996 +2DL2v

T5727

CB6B 2DL1v

WT47 2DL1v

BSN9402071

BSN9402387

BSN9402455

BSN9400191

BSN9400324

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APPENDIX B

Diagnosis

(Percentage in diagnosis)

Melphalan (Mel)

Fludarabine (Flu)

Busulphan (Bu)

Total Body Irradiation (TBI)

Campath (CAMP)

Cyclophosphamide (CY)

ALL (Acute Lymphoid

Leukaemia)

5 (16.7%)

1 (3%)

3 (10%)

27 (90%)

7 (23.3%)

25 (83.3%)

AML (Acute Myeloid

Leukaemia)

27 (54%)

17 (43%)

30 (80%)

5 (10%)

1 (2%)

14 (28%)

BMF (Bone Marrow Failure)

0 0 0 1 (100%)

0 1 (100%)

CLL (Chronic Lymphoid

Leuakemia)

0 0 0 3 (100%)

0 3 (100%)

CML (Chronic Myeloid

Leuakemia)

5 (29.4%)

2 (11.8%)

6 (35.3%)

9 (52.9%)

4 (23.5%)

11 (64.7%)

HD (Hodgkins Disease)

8 (100%)

8 (100%)

0 0 0 0

IEM

0 0 4 (100%)

0 2 (50%)

4 (100%)

IMD (Inherited Metabolic

Disease)

0 0 4 (100%)

0 4 (100%)

4 (100%)

IMM

0 0 2 (100%)

0 2 (100%)

2 (100%)

MDS (Myelodysplastic

Syndrome)

8 (47.1%)

7 (41.2%)

14 (82.4%)

2 (11.8%)

2 (11.8%)

3 (17.6%)

MM (Multiple Myelomas)

1 (50%)

0 2 (100%)

0 0 1 (50%)

NHL (Non-Hodgkins

Disease)

4 (26.7%)

2 (13.3%)

0 11 (73.3%)

0 9 (60%)

RCC (Renal Cell Carcinoma)

1 (100%)

1 (100%)

0 0 0 1 (100%)

SAA (Severe Aplastic

Anemia)

0 0 0 0 2 (100%)

2 (100%)

Table 19. Conditioning agents used in the different diagnoses cohorts.