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
ii
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)
iii
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
iv
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.
v
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.
vi
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
1
1-2 2-3
3
4
5-6
7 7-8 7-8 8 8 9
9-11 11-13
13 13-15 15-17 17-19
19
19-20
21-23 23-25 25-26
26 27
27-28 28-31
31 32
32-36
vii
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
36
36-37 37 37 37 37 38 38 39
39-40 40
40-41 42-43
44 44-45
45 45 46
46
46-47 47-49 49-50 50-53 53-54
55 55 56 56 57 57 58 59
59-60 60-61 61-62
63
63 63-66
67-68
viii
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
68-69 69-70
70-80
81-82
83 83-84 84-85
85
85-87 88-89
89-90
90 90-91
91
91-92
92-93
94-105
106 107
ix
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
6
10 12 14
18 48
49
49
50 50
51 51 52
52-53 53
54
54 56
71
71
72
72
72
72
73
73
x
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.
73
73
74
74
75
75
75
75
76
76
76
76
77
78
78
79
79
79
79
40 56 57 57 57
58
xi
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.
59
60 61 61 62
65
68
69
80
81
82
106 107
1
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
3
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.
4
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).
5
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
6
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.
7
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).
8
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).
9
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.
10
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.
79
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.
81
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.
82
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
REFERENCES: Alberts B, Johnson A, Lewis J, Raff M, Roberts K, and Walters P (2002) Molecular Biology of the Cell; Fourth Edition. New York and London: Garland Science. ISBN 0-8153-3218-1. Afessa B, Peters SG (2006) ‘Major complications following haematopoietic stem cell transplantation’. Semin Respir Crit Care Med 2006; Volume 27: pp 297-309 Bacigalupo A, Lamparelli T, Bruzzi P, et al. (2001). ‘Antithymocyte globulin for graft-versus-host disease prophylaxis in transplants from unrelated donors: 2 randomized studies from Gruppo Italiano Trapianti Midollo Osseo (GITMO)’. Blood Volume 98 (Issue 10): pp 2942–7. doi:10.1182/blood.V98.10.2942. PMID 11698275 Balfour HH Jr. (1990) Antiviral drugs. Nat Engl J Med. 1999; Volume 340: pp 1255-68 Barnes, D. W. H., Corp, M. J., Loutit, J. F. & Neal, F. E (1956) ‘Treatment of murine leukemia with x-rays and homologous bone marrow.’ Br Med. J Volume 32, pp 626-627.
Bashirova AA et al (2006) ‘The killer immunoglobulin-like receptor gene cluster: tuning the genome for defense’. Annu Rev Genomics Hum Genet Volume 7: pp 277-300 Beelen DW, Ottinger HD, Ferencik S, Elmaagacli AH, Peceny R, et al. (2005) ‘Genotypic inhibitory killer immunoglobulin-like receptor ligand incompatibility enhances the long-term antileukemic effect of unmodified allogeneic hematopoietic stem cell transplantation in patients with myeloid leukemias’. Blood, 2005 Volume 105: pp 2594-2600. doi:10.1182/blood-2004-04-1441. Bleakley M and Riddell S (2004) ‘Molecules and mechaniams of the graft-versus-leukaemia effect.’ Nature Reviews Cancer Volume 4, pp 371-380| doi:10.1038/nrc1365 Billingham RE (1966) ‘The biology of graft-versus-host reactions.’ Harvey Lect. 1966; Volume 62: pp 21-78. Biron CA, Nguyen KB, Pien GC, Cousens LP, Salazar-Mather TP (1999). ‘Natural killer cells in antiviral defense: function and regulation by innate cytokines.’ Annu Rev Immunol Volume 17: pp189-220. Bornhauser M, Schwerdtfeger R, Martin H, Frank KH, Theuser C, Ehninger G (2004) ‘Role of KIR ligand incompatibility in hematopoietic stem cell transplantation using unrelated donors.’ Blood 2004: Volume 103: 2860–1.
95
Bottino CL, Moretta D, Pende M. Vitale, A. Moretta, (2004) ‘Learning how to discriminate between friends and enemies, a lesson from natural killer cells.’ Mol. Immunol. Volume 41: pp 569-575. Braud V, Jones EY, McMichael A (1997). ‘The human major histocompatibility complex class Ib molecule HLA-E binds signal sequence-derived peptides with primary anchor residues at positions 2 and 9.’ Eur. J. Immunol. Volume 27 (Issue 5): pp 1164–9. doi:10.1002/eji.1830270517. PMID 9174606 Behrendt CE, Nakamura R, Forman SJ and Zaia JA. (2013). ‘Donor killer immunoglobulin-like receptor genes and reactivation of cytomegalovirus after HLA-matched hematopoietic stem-cell transplantation: HLA-C allotype is an essential cofactor’. Front Immunol. 2013; Volume 4: pp 36. doi: 10.3389/fimmu.2013.00036. Buckland PR (2004) ‘Allele-specific gene expression differences in humans.’ Hum Mol Genet Volume 13:pp 255– 260 Charoudeh HN, Terszowski G, Czaja K, Gonzalez A, Schmitter K and Stern M. (2012) ‘Modulation of the natural killer cell repertoire by cytomegalovirus infection.’ Eur J Immunol. 2012 Nov 17. doi: 10.1002/eji.201242389. PMID: 23161492 Claas FHJ (2010) ‘Allorecognition.’ In “The HLA Complex in Biology and Medicine, A resource book.” Chapter 27, pp 465-470. Clausen J, Wolf D, Petzer AL, Gunsilius E, Schumacher P, Kircher B, et al. (2007) ‘Impact of natural killer cell dose and donor killer-cell immunoglobulin-like recep- tor (KIR) genotype on outcome following human leucocyte antigen-identical haematopoietic stem cell transplantation’. Clin Exp Immunol 2007; Volume 148 (June (Issue 3)): pp 520–8. Colonna M, Borsellino G, Falco M, et al (1993) ‘HLA-C is the inhibitory ligand that determines dominant resistance to lysis by NK1- and NK2-specific natural killer cells’. Proc Natl Acad Sci USA Volume 90: pp 12000-12004 Cook M, Briggs D, Craddock C, Mahendra P, Milligan D, Fegan C et al. (2006) ‘Donor KIR genotype has a major influence on the rate of cytomegalovirus reactivation following T-cell replete stem cell transplantation’. Blood 2006; Volume 107: pp 1230–1232. Cooley S, Trachtenberg E, Bergemann T, Saeteurn K, Klein J et al. (2009). ‘Donors with group B KIR haplotypes improve relapse-free survival after unrelated hematopoietic cell transplantation for acute myelogenous leukemia’ Blood. 2009 Volume 113: pp 726-732. Cooley S, Weisdorf D, Guethlein L, Klein J, Wang T et al. (2010). ‘Donor selection for natural killer cell receptor genes leads to superior survival after unrelated transplantation for acute myelogenous leukemia’ Blood. 2010 Oct 7; Volume 116(Issue 14): pp 2411-9. doi: 10.1182/blood-2010-05-283051.
96
Davies SM, Ruggeri L, DeFor T et al. (2002) ‘Evaluation of KIR ligand incompatibility in mismatched unrelated donor hematopoietic transplants. Killer immunoglobulin-like receptor’. Blood 2002: Volume 100: pp 3825–7. Deeg HJ, Amylon ID, Harries RE, et al. (2001) ‘Marrow transplants from unrelated donors for patients with aplastic anemia: minimum effective dose of total body irradiation.’ Biol Blood Marrow Transplant 2001; Volume 7: pp 208-215 De Santis D, Bishara A, Witt CS, Nagler A, Brautbar C et al. (2005). ‘Natural killer cell HLA-C epitopes and killer cell immunoglobulin-like receptors both influence outcome of mismatched unrelated donor bone marrow transplants’ Tissue Antigens 2005: Volume 65: pp 519–528. De Santis, D, CS Witt, N Gomez-Lozano, C Vilches, CA Garcia, SGE Marsh, F Williams, D Middleton, Hsu K, B Dupont, and FT Christiansen (2006). ‘A Multi-Laboratory Evaluation of Reference Cells for KIR Typing. Immunobiology of the Human MHC: Proceedings of the 13th International Histocompatability Workshop and Conference’ 1:1233-1236. Ewerton PD, Leitite MM, Magalhaes M, Sena L and Melo dos Santos EJ. (2007) ‘Amazonian Amerindians exhibit high variability of KIR profiles.’ Immunogenetics Volume 59: pp 625-630. Falkenburg JH, Van De Corput L, Marijit EW and Willemze R. (2003). ‘Minor histocompatibility antigens in human stem cell transplantation.’ Exp. Hematol. Volume 31, pp 743-751. Farag SS, Fehniger TA, Ruggeri L, Velardi A, Caligiuri MA (2002). ’Natural killer cell receptors: new biology and insights into the graft-versus-leukemia effect.’ Blood Volume 100: pp 1935-1947. Ferrara JL, Guillen FJ, van Dijken PJ, Marion A, Murphy GF, Burakoff SJ
(1989) ‘Evidence that large granular lymphocytes of donor origin mediate acute graft-versus-host disease’. Transplantation 1989, Volume 47: pp 50-54. Finke J, Bethge WA, Schmoor C, et al. (2009). ‘Standard graft-versus-host disease prophylaxis with or without anti-T-cell globulin in haematopoietic cell transplantation from matched unrelated donors: a randomised, open-label, multicentre phase 3 trial’. The Lancet Oncology Volume 10 Issue 4 pp 855–64. doi:10.1016/S1470-2045(09)70225-6. PMID 19695955 Franco A (2002) ‘Hematopoietic stem cell transplantation from full-haplotype mismatched donors.’ Transfusion and Apheresis Science 27; (2002) pp 175-181. Gagne K, Brizard G , Gueglio B, Milpied N, Herry P, et al. (2002) ‘Relevance of KIR gene polymorphisms in bone marrow transplantation outcome’. Human
97
Immunology, Volume 63, Issue 4, April 2002, p271-280, ISSN 0198-8859, 10.1016/S0198-8859(02)00373-7. Gallez-Hawkins GM, Franck AE, Li X, Thao L, Oki A, Gendzehadze K, et al. (2011) ‘Expression of Activating KIR2DS2 and KIR2DS4 genes following hematopoietic cell transplant (HCT): relevance to cytomegalovirus (CMV) infection.’ Biol Blood Marrow Transplant. 2011; Volume 17 Issue 11: pp 1662–1672. doi:10.1016/j.bbmt.2011.04.008. Garcia CA, Robinson J, Madrigal JA, Marsh SGE (2003) ‘Natural Killer Cell Receptors: Functional Roles’, Immunogenetics. Vol. 22 / N2 2003: 190-202 Gasser S, Orsulic S, Brown EJ and Raulet DH. (2005) ‘The DNA damage pathway regulates innate immune system ligands of the NKG2D receptor.’ Nature Vol 436|25 August 2005|doi:10.1038/nature03884. Giebel S, Lamparelli T, Velardi A, Davies S, Frumento G, et al. (2003) ‘Survival advantages with KIR Ligand incompability in hematopoietic stem cell transplant from unrelated donors.’ Blood 2003; Volume 102(August(Issue 3)): pp 814-9. Giebel S, Nowak I, Dziaczkowska J, Czerm T, Wojnar J, et al. (2009) ‘Activating killer immunoglobulin-like receptor incompatibilities enhance graft-versus host disease and affect survival after allogeneic hematopoietic stem cell transplantation.’ European Journal of Haematology. doi: 10.1111/j.1600-0609.2009.01280.x Haribhai D, Williams JB, Shuang J, Nickerson D, Schmitt EG, Edwards B, et al. (2011). ‘A Requisite Role for Induced Regulatory T cells in Tolerance Based on Expanding Antigen Receptor Diversity’. Immunity Volume 35 Issue 1: pp 109-122. Hickey,M., J.Crewe, J.P.Goodridge, C.S.Witt, I.S.Fraser, D.Doherty, F.T.Christiansen, and L.A.Salamonsen. (2005). ‘Menopausal hormone therapy and irregular endometrial bleeding: A potential role for uterine natural killer cells?’. J Clin.Endocrinol.Metab Volume 90: pp 5528-5535. Holaday B, Pompeu M, Jeronimo S, Texeira M, Sousa A, et al. (1993). ‘Potential Role for Interleukin-10 in Immunosuppression Associated with Kala Azar’. Journal of Clinical Investigation Volume 92: pp 2626-2632. PMID 8254019 Horowitz MM, Gale RP, Sondel PM, Goldman JM, Kersy J, et al. (1990) ‘Graft-versus-leukemia reactions after bone marrow transplantation’. Blood Volume 75, pp 555-562. Hsu KC, Chida S, Geraghty DE, Dupont B (2002) ‘The killer cell immunoglobulin-like receptor (KIR) genomic region: gene-order, haplotypes and allelic polymorphism’. Immunological Reviews Volume 190: pp 40–52
98
Hsu KC, Gooley T, Malkki M, Pinto-Agnello C, Dupont B, Bignon JD, et al. (2006) ‘KIR ligands and prediction of relapse after unrelated donor hematopoietic cell transplantation for hematologic malignancy.’ Biol Blood Marrow Transplant 2006;Volume 12(August Issue 8): pp 828–36. Jaing TH (2011). ‘Complications of haematopoietic stem cell transplantation’. ISBT Science Series (2011) Volume 6, pp 332-336. Janeway CA Jr, Travers P, Walport M, et al (2001). Immunobiology: The Immune System in Health and Disease. 5th edition. New York: Garland Science. The complement system and innate immunity. Johnson AD, Wang D, Sadee W (2005). ‘Polymorphisms affecting gene regulation and mRNA processing: broad implications for pharmacogenetics’. Pharmacol Ther Volume 106: pp 19–38 Karre K, Ljunggren HG, Piontek G, Kiessling R (1986). ‘Selective rejection of H- 2-deficient lymphoma variants suggests alternative immune defence strategy’. Nature Volume 319: pp675-8 Kawase T, Matsuo K, Kashiwase K, Inoko H, Ogawa S, et al. (2009). ‘HLA mismatch combinations associated with decreased risk of relapse: implications for the molecular mechanism’. Blood 2009; Volume 113: pp 2851-8. Khakoo SI, Carrington M (2006). ‘KIR and disease: a model system or system of models?’ Immunol Review Volume 214: pp 186-201 Kim JY, Son YO, Park SW, Bae JH, Chung JS, et al. (2006). ‘Increase of NKG2D ligands and sensitivity to NK cell-mediated cytotoxicity of tumor cells by heat shock and ionizing radiation’. Exp. Mol. Med. Volume 38(Issue 5), pp 474-484 Kroger N, Binder T, Zabelina T, Wolschke C, Schieder H, et al. (2006). ‘Low Number of Donor Activating Killer Immunoglobulin-Like Receptors (KIR) Genes but not KIR-Ligand Mismatch Prevents Relapse and Improves Disease-Free Survival in Leukemia Patients After In Vivo T-Cell Depleted Unrelated Stem Cell Transplantation’. Transplantation. ISSN 0041-1337/06/8208-1024DOI: 10.1097/01.tp.0000235859.24513.43 Lanier LL. (1998). ‘NK cells receptors;. Annu Rev Immunol Volume 16: pp 359-393 Lanier LL. (2009). ‘DAP10- and DAP12- associated receptors in innate immunity’. Immunol Rev Volume 227: pp150-160 Lannello A., Debbeche O., Samarani, S., Ahmad A (2008). ‘Antiviral NK cell responses in HIV infection: I. NK cell receptor genes as determinants of HIV resistance and progression to AIDS’. Journal of Leukocyte Biology Volume 84: pp 1–26. doi:10.1189/jlb.0907650.
99
Ljunggren HG and Karre K. (1985). ‘Host Resistance Directed Selectively Against H-2-Deficient Lymphoma Variants: Analysis of the Mechanism’. J. Exp. Med. Volume 162: pp 1745-1759. Ljunggren HG, Karred K (1990). ‘In search of the `missing self ': MHC molecules and NK recognition’. Immunol. Today 11: 7±10 Ljungman P, Brand R, Einsele H, Frassoni F, Niederwieser D, et al. (2003). ‘Donor CMV serologic status and outcome of CMV-seropositive recipients after unrelated donor stem cell transplantation: an EBMT megafile analysis’. Blood 2003 102: 4255-4260. doi:10.1182/blood-2002-10-3263 Long EO et al. (2001). ‘Inhitbiton of natural killer cell activation signals by killer cell immunoglobulin-like receptors(CD158)’. Immunol Rev Volume 181: pp 223-233 Maeda E, Akahane M, Kiryu S, et al. (2009). ‘Spectrum of Epstein–Barr virus-related diseases: a pictorial review’. Jpn J Radiol Volume 27 (Issue 1): pp 4–19. doi:10.1007/s11604-008-0291-2. PMID 19373526 Mandelboim O, Malik P, Davis DM, Chang HJ, Boyson JE, and Strominger JL
(1999). ‘Human CD16 as a lysis receptor mediating direct natural killer cell cytotoxicity’ Proc Natl Acad Sci U S A. 1999; Volume 96: pp 5640–5644. doi: 10.1073/pnas.96.10.5640 PNAS Marmont AM, Horowitz MM, Gale RP, Sobocinski K, Ash RC, et al. (1991). ‘T-cell depletion of HLA-identical transplants in leukemia’. Blood Volume 78, pp 2120-2130.
McErlean C, Gonzalez AA, Cunningham R, Meenagh A, Shovlin T, Middleton D (2010). ‘Differential RNA expression of KIR alleles’. Immunogenetics Volume 62: pp 431–440. DOI 10.1007/s00251-010-0449-9 McEvoy GK, ed. Cytomegalovirus Immune Globulin IV. In: AHFS Drug Information 2003. Bethesda, MD: American Society of Health-System Pharmacists; 2003:3121-5. McQueen KL and Parhan P (2002). ‘Variable receptors controlling activation and inhibition of NK cells’. Curr Opin Immonol Volume 14: pp 615-621. Mengarelli A, Zarcone D, Caruso R, Tenca C, Rana I, Pinto RM, et al. (2001). ‘Adhesion molecule expression, clinical features and therapy outcome in childhood acute lymphoblastic leukemia’. Leuk Lymphoma 2001; Volume 40(February Issue 5–6): pp 625–30. Middleton D. (2010). ‘The extensive polymorphism of KIR genes’. Immunology Volume 129 (Issue 1), pp 8. DOI: 10.1111/j.1365-2567.2009.03208.x
100
Middleton D, Meenagh A and Gourraud PA. (2007). ‘KIR haplotype conten at the allele level in 77 northen irish families’. Immunogenetics Volume 59: pp 145-158 Milstein O, Hagin D, Lask A, Reich-Zeliger S, Shezen E, Ophir E, Eidelstein Y, Afik R, Antebi YE, Dustin ML, Reisner Y. (2011). ‘CTLs respond with activation and granule secretion when serving as targets for T-cell recognition’. Blood Volume 117 (Issue 3): pp 1042–52. doi:10.1182/blood-2010-05-283770. PMC 3035066. PMID 21045195 Mingari MC, Moretta A and Moretta L. (1998). ‘Regulations of KIR expression in Tcells: a safety mechanism that may repair protective T-cell responses’. Immunol Today. 1998 April, Volume 19 (Issue 4): pp 153-7. Monsivais-Urenda A, Noyola-Cherpitel D, Hernandez-Salianas A, Garcia-Sepulveda C, Romo N, et al. (2010). ‘Influence of human cytomegalovirus infection on the NK cell receptor repertoire in Children’. Eur. J. Immunol. 2010. Volume 40: pp 1418–1427. DOI 10.1002/eji.200939898-8674(00)81054-5. PMID 8625414 Moretta A, Bottino C, Mingari MC, Biassoni R and Moretta L. (2002). ‘What is a natural killer cell?’ Nature Immunology Volume 3, pp 6 – 8. Moretta et al. (2002). ‘Activating receptors and coreceptors involved in human natural killer cell-mediated cytosis’. Annu Rev Immunol Volume 10: pp 197-223 Morishima Y, Yabe T, Matsuo K, Kashiwase K, Inoko H, Saji H, Yamamoto K, Maruya E, Akatsuka Y, Onizuka M et al. (2007). ‘Effects of HLA allele and killer immunoglobulin-like receptor ligand matching on clinical outcome in leukemia patients undergoing transplantation with T-cell-replete marrow from an unrelated donor’. Biol Blood Marrow Transplant 2007, 13:315-328. Neuberger MS, Honjo T, Frederick W. (2004). ‘Molecular biology of B cells’. Amsterdam: Elsevier. pp 189–191. ISBN 0-12-053641-2. Olson JA, Leverson-Gower DB, Gill S, Baker J, Beilhack A and Negrin RS. (2010). ‘NK cells mediate reduction of GVHD by inhibiting activated, alloreactive T cells while retaining GVT effects’. Blood. 2010 May 27; Volume 115(Issue 21): pp 4293-301. doi: 10.1182/blood-2009-05-222190. Panse JP, Heimfeld S, Guthrie KA, Maris MB, Maloney DG, Baril BB, et al. (2005). ‘Allogeneic peripheral blood stem cell graft composition affects early T-cell chimaerism and later clinical outcomes after non-myeloablative conditioning’. Br J Haematol. 2005 Mar; Volume 128(Issue 5): pp 659-67. Parham P. (2005). ‘MHC class I molecules and KIRs in human history, health and survival’. Nat Rev Immunol Volume 5: pp 201-214.
101
Parham P and McQueen KL. (2003). ‘Alloreactive killer cells: hinderance and help for haematopoietic transplants’. Nature Rev. Immunol. Volume 3, pp 108-122. Passweg JR, Tiberghien P, Cahn JY, Vowels MR, Camitta BM, Gale RP. et al. (1998). ‘Graft-versus-leukemia effects in T lineage and B lineage acute lymphoblastic leukemia’. Bone Marrow Transplant. Volume 21 (Issue 2), pp 153-158. Pende D, Marcenaro S, Falco M, Martini S, Bernardo ME, Montagna D, et al. (2009). ‘Anti-leukemia activity of alloreactive NK cells in KIR ligand-mismatched haploidentical HSCT for pediatric patients: evaluation of the functional role of activating KIR and redefinition of inhibitory KIR specificity’. Blood 2009; Volume 113(March Issue 13): pp 3119–29. Pregram HJ, Ritchie DS, Symth MJ, Wiernik A, Prince HM, Darcy PK, Kershaw MH (2010). ‘Alloreactive Natural Killer Cell in hematopoietic stem cell transplantation’. Leukaemia Research Volume 35: pp 14-21. Doi10.1016/j.leukres.2010.07.030. Pyo C-W, Guethlein LA, Vu Q, Wang R, Abi-Rached L, et al. (2010). ‘Different Patterns of Evolution in the Centromeric and Telomeric Regions of Group A and B Haplotypes of the Human Killer Cell Ig-Like Receptor Locus’. PLoS ONE Volume 5(Issue 12): e15115. doi:10.1371/journal.pone.0015115. Pyzik M, Charbonneau B, Gendron-Pontbriand E-M, BaBic M, Krmpotic A, et al. (2011). ‘Distinct MHC class I–dependent NK cell– activating receptors control cytomegalovirus infection in different mouse strains’ J. Exp. Med. Vol. 208 No. 5: pp 1105-1117. Rajalingam R. (2012) ‘Overview of the killer cell immunoglobulin-like receptor system’. Methods Mol Biol. 2012; Volume 882: pp 391-414. doi: 10.1007/978-1-61779-842-9_23. Rajalingam R et al (2002). ‘Distinctive KIR and HLA diversity in a panel of north Indian Hindus’. Immunogenetics. Volume 53: pp1009-1019. Rajalingam R et al. (2008). ‘Distinct diversity of KIR genes in three southern indian populations: comparison with world populations revealed a link between KIR gene content and pre-historic human migrations’. Immunogenetics. Volume 60: pp 207-217. Robinson JP and Babcock GF(1998), ‘Phagocyte Function – A guide for research and clinical evaluation’. Wiley-Liss. p. 187. ISBN 0471123641 Roitt I, Brostoff J and Male D (2001). ‘Immunology (6th ed.)’, pp 480. St. Louis: Mosby, ISBN 0-7234-3189-2. Rolland J and O'Hehir R. (1999). ‘Turning off the T-cells: Peptides for treatment of allergic Diseases’. Today's life science publishing, 1999, pp 32.
102
Ruggeri L, Capanni M, Urbani E, Perruccio K, Shlomchik WD, et al. (2002). ‘Effectiveness of Donor Natural Killer Cell Alloreactivity in Mismatched Hematopoietic Transplants’. Science 15 March 2002: Volume 295 (Issue 5562), pp 2097-2100. DOI:10.1126/science.1068440 Ryan KJ and Ray CG (2004). ‘Sherris Medical Microbiology (4th ed.)’. McGraw Hill. pp566–9. ISBN 0-8385-8529-9. Schaffer M, Malmberg KJ, Ringden O, Ljunggren HG, Remberger M, et al. (2004). ‘Increased infection-related mortality in KIR-ligand mismatched unrelated allogeneic hematopoietic stem-cell transplantation’. Transplantation 2004: Volume 78: pp 1. Schellekens J, Rozemuller EH, Petersen EJ, van den Tweel JG, Verdonck LF, Tilanus MG. (2008). ‘Activating KIRs exert a crucial role on relapse and overall survival after HLA- identical sibling transplantation’. Mol Immunol 2008; Volume 45: pp 2255-2261. Shilling HG et al. (2002). ‘Allelic polymorphismsynergizes with variable gene content of individualize human KIR genotype’. J Immunol Volume 168: pp 2307-2315. Smyth MJ, Cretney E, Kelly JM, Westwood JA, Street SE, et al. (2005). ‘Activation of NK cell cytotoxicity’. Mol Immunol Volume 42(Issue 4): pp 501-10. Smyth MJ, Hayakawa Y and Takeda KY. (2002). ‘New aspects of natural-killer-cell surveillance and therapy of cancer’. Nature Review Volume 2: pp 850-852. Doi:10.1038/nrg928. Snydman DR, Werner BG, Dougherty NN et al. (1993). ‘Cytomegalovirus immune globulin prophylaxis in liver transplantation. A randomized, double-blind, placebo-controlled trial’. The Boston Center for Liver Transplantation CMVIG Study Group. Ann Intern Med. 1993; Volume 119: pp 984-91. Sobecks RM, Askar M, Thomas D, Rybicki L, Kalaycio M et al. (2011). ‘Cytomegalovirus Reactivation After Matched Sibling Donor Reduced-Intensity Conditioning Allogeneic Hematopoietic Stem Cell Transplant Correlates With Donor Killer Immunoglobulin-like Receptor Genotype’. Experimental and Clinical Transplantation (2011) Volume 1: pp 7-13. Soule BP, Simone NL, Savani BN et al. (2007). ‘Pulmonary function following total body irradiation (with or without lung shielding) and allogeneic peripheral blood stem cell transplant’. Bone Marrow Transplant. Volume 40 (Issue 6): pp 573–8. doi:10.1038/sj.bmt.1705771. PMID 17637691. Stem cell research; new half-match bone marrow transplant procedure yields promising outcomes for cancer patients. (2011). NewsRx Health & Science. 143. Retrieved from
103
http://search.proquest.com/docview/889820589?accountid=12629 (on 8 October 2012) Stern M, Elsasser H, Honger G, Steiger J, Schaub S and Hess C. (2008). ‘The number of activating KIR genes inversely correlates with the rate of CMV infection/reactivation in kidney transplant recipients’. Am J Transplant. 2008 Jun;Volume 8(Issue 6): pp 1312-7. doi: 10.1111/j.1600-6143.2008.02242.x. Sullivan, LC; Clements, CS; Beddoe, T; Johnson, D; Hoare, HL; Lin, J; Huyton, T; Hopkins, EJ et al. (2007). ‘The heterodimeric assembly of the CD94-NKG2 receptor family and implications for human leukocyte antigen-E recognition’. Immunity Volume 27 (Issue 6): pp 900–11. doi:10.1016/j.immuni.2007.10.013. PMID 18083576. Sykes M (2011). ‘Transplantation immunology’. In Goldman L, Schafer AI, eds. Cecil Medicine. 24th ed. Philadelphia, Pa: Saunders Elsevier; 2011:chap 48. Terunuma H, Deng X, Dewan Z, Fujimoto S, Yamamoto N. (2008). ‘Potential role of NK cells in the induction of immune responses: implications for NK cell-based immunotherapy for cancers and viral infections’. International Reviews of Immunology Volume 27: pp 93–110. doi:10.1080/08830180801911743 Toneva M, Lepage V, Lafay G, Dulphy N, Busson M, et al (2001). ‘Genomic diversity of natural killer cell receptor genes in three populations’. Tissue Antigens Volume 57: pp 358-362 Tournilhac O, Cazin B, Lepretre S, Divine M, Maloum K et al (2003). ‘Impact of frontline fludarabine and cyclophosphamide combined treatment on peripheral blood stem cell mobilization in B-cell chronic lymphocytic leukemia’. Blood January 1, 2004 volume 103 (Issue 1): pp 363-365. doi: 10.1182/blood-2003-05-1449 Trapani JA (1995). ‘Target cell apoptosis induced by cytotoxic T cells and natural killer cells involves synergy between the pore-forming protein, perforin, and the serine protease, granzyme B’. Aust N Z J Med Volume 25(Issue6): pp 793-9. Trowsdale J (2001) ‘Genetic and functional relationshops between MHC and NK receptor genes’. Immunity Volume 15: pp 363-374. Tschopp J, Masson D, Stanley KK (1986). ‘Structural/functional similarity between proteins involved in complement- and cytotoxic T-lymphocyte-mediated cytolysis’. Nature Volume 322 (Issue 6082): pp 831–4. doi:10.1038/322831a0. PMID 2427956. Uhrberg M et al. (1997). ‘Human diversity in killer cell inhibitory genes’. Immunity Volume 7: pp 753-763.
104
Uhrberg M et al. (2002). ‘Definition of gene content doe nine common group B haplotypes of the Caucasoid population: KIR haplotypes contain between seven and eleven KIR genes’. Immunogenetics Volume 54: pp 221-229. Velardi A. (2008). ‘Role of KIRs and KIR ligands in haematopoietic transplantation’. Curr Opin Immunol. 2008; Volume 20(Issue 5): pp 581-87. Verheyden S, Schots R, Duquet W, Demanet C. (2005). ‘A defined donor activating natural killer cell receptor genotype protects against leukemic relapse after related HLA-identical hematopoietic stem cell transplantation’. Leukemia 2005; Volume 19: pp 1446-1451. Vilches C, Parham P (2002). ‘KIR: diverse, rapidly evolving receptors of innate and adaptive immunity’. Annu Rev Immunol Volume 20: pp 217–51. doi:10.1146/ Annu Rev Immunol 20.092501.134942. PMID 11861603 Vilches C, Rajalingam R, Uhrberg M, Gardiner CM, Young NT, Parham P (2000). ‘KIR2DL5, a novel killer-cell receptor with a D0-D2 configuration of Ig- like domains’. J Immunol Volume164: pp 5797-804. Wagtmann N, Biasonni R, Cantoni C, Verdiana S, Malnati MS, et al. (1995). ‘Molecular clones of the p58 NK cell receptor reveal immunoglobulin-related moelcules with diversity in both the extra- and intracellular domains’. Immunity Volume 2: pp 439-449 Weiden PL, Flournoy N, Thomas ED, Prentice R, Fefer A, et al. (1979) ‘Antileukemic effect of graft-versus-host disease in human recipients of allogeneic-marrow grafts’. N. Engl. J. Med. Volume 300, pp 1068-1073.
Whang DH, Park H, Yoon JA and Park MH. (2005). ‘Haplotype analysis of killer cell immunoglobulin-like receptor genes in 77 korean families’. Hum Immunol Volume 66: pp 146-154 Wiertz EJHJ, Jones TR, Sun L, Bogyo M, Geuze HJ and Ploegh HL. (1996). ‘The Human Cytomegalovirus US11 Gene Product Dislocates MHC Class I Heavy Chains from the Endoplasmic Reticulum to the Cytosol’ Cell Volume 84 (Issue5): pp 769–779. doi:10.1016/S0092 Willemze R, Rodrigues CA, Labopin M, Sanz G, Michel G, Socie G, et al. (2009). ‘KIR-ligand incompatibility in the graft-versus-host direction improves out- comes after umbilical cord blood transplantation for acute leukemia’. Leukemia 2009; Volume 23(March (Issue 3)): pp 492–500. Wilson MJ, Torkar M, Haude A, Milne S, Jones T, et al. (2000). ‘Plasticity in the organization and sequences of human KIR/ILT gene families’. Proc Natl Acad Sci USA Volume 97: pp 4778-4783 Winter CC and Long EO (1997). ‘A single amino acid in the p58 killer cell inhibitory receptor controls the ability of natural killer cells to discriminate
105
between the two groups of HLA-C allotypes’. J Immunol Volume 158: pp 4026-4028 Witt C (2009). ‘The influence of NK alloreactivity on matched unrelated donor and HLA identical sibling haematopoietic stem cell transplantation’. Current Opinion in Immunology 2009, Volume 21: pp 531–537. DOI 10.1016/j.coi.2009.08.004 Witt CS and Christiansen FT. (2006). ‘The relevance of natural killer cell human leucocyte antigen epitopes and killer cell immunoglobulin-like receptors in bone marrow transplantation’. Vox Sanguinis (2006) Volume 90, pp 10-20. DOI: 10.1111/j:1423-0410.2005.00712.x Yawata M et al (2002) Variation within the human killer cell immunoglobulin-like receptor (KIR) gene family. Crit Rev Immunol 22:463-482 Yawata M, Yawata N, Abi-Rached L and Parham P. (2006). ‘Roles of HLA and KIR polymorphisms in natural killer cell reportaire selectionand modulation of effector function’. J Exp Med Volume 203: pp 633-645. Yokoyama WM and Seaman WE (1993). ‘The Ly-49 and NKRP1 gene families encoding lectin-like receptors on natural killer cells: The NK Gene Complex’. Annual Review of Immunolgy Volume 11: pp 613-635. Zafirova B, Wensveen FM, Gulin M and Polic B. (2011). ‘Regulation of immune cell function and differentiation by the NKG2D receptor’. Cell Mol Life Sci Volume 68 (Issue 21): pp 3519–29. doi:10.1007/s00018-011-0797-0. PMC 3192283. PMID 21898152 Zaia JA, Sun JY, Gallez-Hawkins GM, Thao L, Oki A, Lacey SF, et al. (2010) ‘The Effect of Single and Combined Activating Killer Immunoglobulin-like Receptor Genotypes on Cytomegalovirus Infection and Immunity after Hematopoietic Cell Transplant’. Biol Blood Marrow Transplant Volume 15: pp 315-325. doi:10.1016/j.bbmt.2008.11.030
107
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
108
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.