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Kristian Boasman
06036783
Elucidating the molecular mechanisms of CALR and CD47 in myelodysplastic
syndromes, myeloproliferative neoplasms
1st August 2021 Page 2 of 2
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Elucidating the molecular mechanisms of CALR and CD47 in myelodysplastic syndromes, myeloproliferative neoplasms
Kristian Boasman
A thesis submitted in partial fulfilment of the requirements of the University of Lincoln for the degree of Doctor of Philosophy
School of Life Science
March 2021
1
Acknowledgements First of all, I would like to thank the University of Lincoln for giving me the opportunity to conduct
my research and complete this thesis.
Like many PhD students I am thankful to my friends and family who helped me through the stress of writing a thesis. Specifically, my wife Kay Hancock Boasman and my daughter Grace for their
constant support. My parents for whom I would not be the person I am today without them. Also, my brother Adam Boasman and close friend Dr James Henshaw for always listening to my rants
when I need them to and for always supporting me.
Finally, I am indebted to my supervisors, Prof Ciro Rinaldi and Dr Matthew Simmonds. Their constant understanding, insight and encouragement enabled me to keep pushing myself in a drive to produce
the work I was capable of.
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Abstract Myelodysplastic syndromes (MDS) and Myeloproliferative neoplasms (MPN) are myeloid
malignancies arising from clonal disorders of haematopoietic stem cells, with the tendency to
progress into acute myeloid leukaemia (AML). Whilst diagnostic criteria have been established,
treatment options remain relatively limited promoting the search for markers which could be used
to help predicting disease progression or be targeted therapeutically to provide new or enhanced
treatment options. In solid tumours, calreticulin (CALR) overexpression produces a pro-phagocytic
signal and is counteracted by concomitant expression of anti-phagocytic CD47, reflecting an
apoptosis vs survival mechanism. Increases of both CALR and CD47 on the cell membrane have been
observed in response to chemotherapy, however their role in myeloid malignancies is poorly
understood. The main aim of this work is to investigate the expression and cellular localisation of
CALR and CD47 in MDS and MPN, determine any changes in CALR /CD47 as disease progresses and
establish how cellular location of CALR and CD47 alters in response to treatment in MDS and MPN.
Initial investigation of cell line models of MDS and MPN indicate that when treated with disease
specific cyto-toxic drugs, total expression of CALR and CD47 increased. However, CALR was shown to
internalise away from the membrane upon treatment whereas CD47 was externalised, suggesting a
different mode of action for CALR and CD47 in MDS/MPN compared to solid tumours. Further work
investigating whether these changes in CALR and CD47 expression in patient samples found that
CALR and CD47 baseline protein expression was increased in both MDS and MPN patients compared
with controls. CD47 showed higher overall protein expression on MDS and MPN cell membranes
when compared with CALR. As MDS progressed, CD47 expression is seen to increase with the
severity of the disease as classified by IPSS subgroups. Interestingly, when investigating MPN
subtypes, significant increases in CD47 cell membrane expression, in particular, after treatment was
only seen in the MF and PV subtypes, suggesting an anti-phagocytic effect induced by cytotoxic
drugs. In the ET subtype, CD47 expression is reduced after cyto-reductive treatment, suggesting
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instead a prophagocytic effect. To better understand the relationship between CALR and CD47 and
support any future work looking to use these in a more therapeutic setting, knockdown experiments
were undertaken. Knockdown experiments revealed a possible link between CALR and CD47. In fact,
it was observed that a mutual reduction in either CALR or CD47 when one or the other is repressed,
suggesting the influence on each other pathways. Reduction in CD47 expression resulted in higher
rate of cell death when cell were treated with cytotoxic drugs, however due to the concomitant
reduction of CALR the overall therapeutical effect is reduced. Leaving both CALR and CD47 intact on
the myeloid cancer cells but interfering with the CD47/SIRPα interaction instead, could actively
signal cell death messages in these myeloid neoplasms via monoclonal antibodies, and let the pro-
phagocytic message conducted by CALR to prevail. The overexpression of CD47 in MDS, PV and MF
could represent the novel target to block via monoclonal anti-CD47 antibody in monotherapy or in
combination to enhance treatment efficacy in myeloid cancers.
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Contents Acknowledgements ................................................................................................................................. 1
Abstract ................................................................................................................................................... 2
Tables ...................................................................................................................................................... 8
Figures ..................................................................................................................................................... 8
Abbreviations list .................................................................................................................................. 11
Publications ........................................................................................................................................... 14
Conference posters ............................................................................................................................... 14
Chapter 1 – Introduction ................................................................................................................ 15
1.1: Myeloid Malignancies ............................................................................................................... 16
1.2: Characterisation of Different Myeloid Malignancies ................................................................ 16
1.3: Myelodysplastic syndromes (MDS) Prognostic Scoring ............................................................ 18
1.3.1: Initial Prognostic Scoring Generated by The French-American-British leukaemia and the World Health Organisation ................................................................................................................... 18
1.3.2: International Prognostic Scoring System and WHO Prognostic Scoring System .................. 19
1.4: WHO Classification (2001 – 2016)............................................................................................. 20
1.5: Myeloproliferative Neoplasms: WHO Classification (2001 – 2016) .......................................... 23
1.5.1: Essential Thrombocythaemia (ET) ........................................................................................ 24
1.5.2: Polycythaemia Vera (PV) ....................................................................................................... 25
1.5.3: PRE-MF and myelofibrosis (MF) ............................................................................................ 27
1.6: Targeted drugs for treatment ................................................................................................... 29
1.6.1: Azacytidine ............................................................................................................................ 29
1.6.2: Ruxolitinib ............................................................................................................................. 30
1.7: Immunosurveillance of cancer .................................................................................................. 31
1.8: Calreticulin ................................................................................................................................ 32
1.8.1: CALR in antigen presentation ............................................................................................... 36
1.8.2: CALR in cancer signalling ....................................................................................................... 37
1.8.3: Prognostic and predictive value of CALR .............................................................................. 38
1.9: CD47 .......................................................................................................................................... 39
1.9.1: Mechanism of function and intracellular signalling .............................................................. 41
1.9.2: CD47 pathways targeted for therapeutic intervention ........................................................ 43
1.9.3: Anti-CD47 approaches to treat cancer and AML .................................................................. 44
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1.10: Purposed aims of research.................................................................................................... 47
Chapter 2: ............................................................................................................................................ 48
Methods ............................................................................................................................................... 48
2.1: Ethics ......................................................................................................................................... 49
2.2: Cell Line Models of MDS & MPN ............................................................................................... 49
2.3: Cell Culture ................................................................................................................................ 50
2.3.1: Testing cell lines for mycoplasma via qPCR ................................................................................ 51
2.4: Cell counting and seeding ......................................................................................................... 52
2.5: Growth curves ........................................................................................................................... 52
2.6: Drug titrations and drugging of cell lines .................................................................................. 53
2.7: Cell viability assay ..................................................................................................................... 54
2.8: Harvesting of cells to undertake RNA and Protein Analysis ..................................................... 55
2.9: Extraction of RNA from cell lines .............................................................................................. 55
2.10: cDNA synthesis from RNA ..................................................................................................... 56
2.11: Quantitative PCR (qPCR) ....................................................................................................... 57
2.12.1: Western blotting ................................................................................................................... 58
2.12.2: Buffer formulations for SDS-PAGE and Western blot experiments ...................................... 59
2.12.3: Protein isolation and quantification in preparation for SDS-PAGE and Western blotting ... 60
2.12.4: SDS-PAGE .............................................................................................................................. 61
2.12.5: Transfer to PVDF membrane ................................................................................................ 62
2.12.6: PVDF membrane blocking and antibody incubation ............................................................ 62
2.13: Fractionations of protein in cells .......................................................................................... 64
2.14: CALR and CD47 Knockdowns ................................................................................................ 65
2.15: Patient Samples Processing and Analysis ............................................................................. 66
2.15.1: Isolation of Peripheral Blood Mononuclear cells (PBMCs) from human donors .................. 66
2.15.2: Processing and collection of mononuclear cells from patient blood samples ..................... 67
2.15.3: Extraction of RNA using TRIzol® method from PBMCs ......................................................... 68
2.15.4: Clean-up of PBMC RNA obtained using the TRIzol® extraction method .............................. 69
2.16: Data Analysis and Statistics ................................................................................................... 70
2.16.1: Calculating relative gene expression .................................................................................... 70
2.16.2: Calculating relative protein expression ................................................................................ 71
Chapter 3: Establishing basal expression levels and location percentage of CALR and CD47 in MDS and MPN cell line models ..................................................................................... 72
3.1: Introduction .............................................................................................................................. 73
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3.2: Aims........................................................................................................................................... 76
3.3: Methods .................................................................................................................................... 76
3.3.1: Cell culture and drug treatment ........................................................................................... 76
3.3.2: Protein analysis ..................................................................................................................... 77
3.3.3: Gene analysis ........................................................................................................................ 77
3.3.4: Fractionisations ..................................................................................................................... 77
3.4: Results ....................................................................................................................................... 77
3.4.1: CALR and CD47 changes and impact on survival in cell line models .................................... 77
3.4.2: Alterations in CALR and CD47 mRNA expression in untreated & treated cell lines ............. 78
3.4.3: CALR and CD47 protein expression of untreated and treated cell line models ................... 84
3.4.4: CALR and CD47 localization in MDS in response to treatment ............................................. 86
3.4.5: Examining changes in cellular locations of CALR and CD47 in intermediate MDS/MPN ...... 90
3.4.6: Examining changes in cellular locations of CALR and CD47 in MPN ..................................... 92
3.5: Discussion .................................................................................................................................. 95
Chapter 4: ......................................................................................................................................... 100
CALR & CD47 expression in patient with MDS and MPN .................................................... 100
4.1: Introduction ............................................................................................................................ 101
4.2: Aims ......................................................................................................................................... 102
4.3: Methods .................................................................................................................................. 102
4.3.1: Processing of patient samples ............................................................................................. 102
4.3.2: Protein analysis ................................................................................................................... 103
4.3.3: Gene analysis ....................................................................................................................... 103
4.3.4: Fractionisations ................................................................................................................... 103
4.4: Results ..................................................................................................................................... 103
4.4.1: Initial analysis of Total RNA & Protein Expression of CALR and CD47 in MDS and MPN patient samples 103
4.4.2: In-depth look at MDS disease severity sub-groups ............................................................. 105
4.4.4: Examining changes in cellular locations of CALR and CD47 in MDS subgroups ................... 107
4.4.5: In-depth look at MPN untreated Vs treated patient samples ............................................. 108
4.4.6: Examining the cellular locations of CALR and CD47 between different MPN subgroups .... 110
4.5: Discussion ................................................................................................................................ 111
Chapter 5: ......................................................................................................................................... 114
Investigating whether CALR and CD47 work together directly or independently in determining response to therapy within MDS and MPN cell models ............................... 114
5.1: Introduction ............................................................................................................................ 115
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5.2: Aims ......................................................................................................................................... 116
5.3: Methods .................................................................................................................................. 117
5.3.1: Cell culture .......................................................................................................................... 117
5.3.2: Transfections ....................................................................................................................... 117
5.3.4: Gene Expression qPCR Analysis ........................................................................................... 117
5.3.5: Western Blotting Analysis .................................................................................................... 118
5.4: Results ..................................................................................................................................... 119
5.4.1: CALR and CD47 changes and impact on survival in transfected MDS & MPN cell line models . 119
5.4.2: CALR silenced transfection efficiency in cell line models .................................................... 120
5.4.5: CALR and CD47 silenced cells survival rate upon treatment with chemotherapeutics ....... 128
5.5: Discussion ................................................................................................................................ 129
Chapter 6: Conclusions ....................................................................................................... 132
6.1: Conclusions ............................................................................................................................. 133
6.2: Future studies ......................................................................................................................... 136
References .......................................................................................................................................... 137
Appendix ............................................................................................................................................. 152
8
Tables
Table 1: WHO classification of MDS
Table 1: 2016 WHO Diagnostic Criteria for ET. All four major criteria required for diagnosis or first
three major criteria and the minor criteria
Table 2: World Health Organisation’s 2016 PV diagnostic criteria. PV can be diagnosed either by the
presence of all three major criteria or with major criteria 1 and 2 with the minor criteria
Table 3: 2016 WHO Diagnostic Criteria for pre-MF. Diagnosis requires meeting all three major and
one minor criteria. Minor criteria to be confirmed twice on consecutive investigation
Table 5: 2016 WHO Diagnostic Criteria for overt MF. Diagnosis requires meeting all three major and
one minor criteria. Minor criteria to be confirmed twice on consecutive investigations
Table 6: Table of MDS cell line models (MOLM-13/SKM-1), intermediate cell line model (K562) and
MPN cell line models (HEL-92/GDM-1)
Table 7: Table of primer sequence use for GAPDH, CALR and CD47
Table 8: Buffer formulations for SDS-PAGE and Western blot experiments
Table 9: Antibodies and dilutions used in Western blot experiments
Figures
Figure 1.1: DNA methylation of azacytidine
Figure 1.2: The constitutive activity of the JAK-STAT signalling pathway, present within the
hematopoietic stem cells of patients with myelofibrosis
Figure 1.3: A potential model for CALR network signalling.
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Figure 1.4: Linear diagram of human calreticulin indicating its amino-terminal (N-CALR), globular (P-
CALR), and carboxy-terminal (C-CALR) domains and the location of the sequences predicted by
computer modelling to determine an intrinsically disordered structure
Figure 1.5: The impact of the SIRPα–CD47 checkpoint and its blockade
Figure 2.1: Depiction of western blots antibodies on a membrane and the fluorescent image of
proteins
Figure 2.2: Depiction of a Bradford assay standard curve
Figure 3.1: Macrophages CD47/SIRPα interactions
Figure 3.2: Overall percentage survival rate of each cell line model treated with azacytidine and
ruxolitinib
Figure 3.3: Expression of untreated and treated MOLM-13 and SKM-1 cell line models
Figure 3.4: Expression of untreated and treated K562 cell line models
Figure 3.5: Expression of untreated and treated HEL-92 and GDM-1 cell line models
Figure 3.6: Representative example of western blot samples of CALR, CD47 and GAPDH proteins
Figure 3.7: Expression of CALR and CD47 protein in untreated Vs treated cell line models
Figure 3.8: Percentage of cellular protein location in untreated Vs treated MOLM-13 cell line models
Figure 3.9: Percentage of cellular protein location in untreated Vs treated SKM -1 cell line models
Figure 3.10: Percentage of cellular protein location in untreated Vs treated K562 cell line models
Figure 3.11: Percentage of cellular protein location in untreated Vs treated HEL-92 cell line models
Figure 3.12: Percentage of cellular protein location in untreated Vs treated GDM- 1 cell line models
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Figure 4.1: Total RNA and protein fold expression of CALR and CD47 in healthy controls Vs MDS and
MPN patients
Figure 4.2: Total RNA and protein fold expression of CALR and CD47 in healthy controls Vs MDS
subgroups
Figure 4.3: Membrane expression of CALR and CD47 protein in MDS subgroups Vs control
Figure 4.4: Total protein fold expression of CALR and CD47 in untreated Vs treated and treated MPN
patients
Figure 4.5: Total protein fold expression of CALR and CD47 in untreated vs treated MPN subgroups
Figure 4.6: Membrane protein expression of CALR and CD47 in untreated vs treated MPN subgroups
Figure 5.1: Representative example of western blot samples of CALR, CD47 and GAPDH proteins
Figure 5.2: Survival rate percentage in transfected cell lines
Figure 5.3: Overall mRNA CALR expression in each cell line model treated with CALR silencing siRNAs
Figure 5.4: Overall mRNA CD47 expression in each cell line model treated with CALR silencing siRNAs
Figure 5.5: Overall mRNA CD47 expression in each cell line model treated with CD47 silencing siRNAs
Figure 5.6: Overall mRNA CALR expression in each cell line model treated with CD47 silencing siRNAs
Figure 5.7: Cell survival upon treatment with chemotherapeutics in MDS ad MPN cell lines
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Abbreviations list
AML - Acute myeloid leukaemia
BAK1 - BCL2 antagonist/killer 1
BAX - Apoptosis regulator
Ca2+ - Calcium
CALR – Calreticulin
CANX - calnexin
CASP8 - Caspase 8
CBD - C-terminal binding
CMML - Chronic myelomonocytic leukaemia
CMPD - Chronic myeloproliferative disorders
eIF2α - Eukaryotic translation initiation factor 2 subunit alpha
ER - Endoplasmic reticulum
ET - Essential thrombocythemia
FAB - French-American-British
HSP70 - Heat shock protein family A
HSP90B1 - Heat shock protein 90 beta family A
Hu5F9-G4 - Humanised anti-CD47 antibody
Ig - Immunoglobulin
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IPSS - International Prognostic Scoring System
IPSS-R - IPSS ranking system
ISR - Integrated stress response
ITAM - Immunoreceptor tyrosine activation motifs
ITIMs - Immunoreceptor tyrosine inhibitory motifs
JAK inhibitor - Janus kinase inhibitor
JAK/STAT - Janus kinase/signal transducer which is an activator of transcription
LRP - Lipoprotein receptor-related protein
LRP1 - LDL receptor-related protein 1
MBL - Mannose-binding lectin
MDS - Myelodysplastic syndromes
MHC - Major histocompatibility complex
MF - Myelofibrosis
MPL - Thrombopoietin receptor MCD - Mast cell disease
MPN - Myeloproliferative neoplasms
PD-L1 - Anti-programmed death-ligand 1
PDIA3 - Protein disulfide isomerase family A member 3
PERK - Eukaryotic translation initiation factor 2 alpha kinase 3
PLC - Peptide-loading complex
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PTPases - Protein tyrosine phosphatases
PV - Polycythaemia vera
qPCR - Quantitative PCR
RA - Refractory anaemia
RAEB - Refractory anaemia with excess blasts
RAEB-T - Refractory anaemia with excess blasts in transformation
RARS - Refractory anaemia with ring sideroblasts
SIRPs - Signal regulatory proteins
SHPS-1 - Src homology 2 domain-containing protein tyrosine phosphatase substrate 1
SNAP25 - Synaptosome-associated protein 25.
SH2 - Src homology region 2
SHP - domain-containing phosphatase 1
THBS1 - Thrombospondin 1
TSPs – Thrombospondins
w/t - Wild type
WHO - World Health Organization
VAMP1 - Vesicle-associated membrane protein 1
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Publications
1. Lally J, and Boasman K, et al. 2019. GATA-1 a potential novel biomarker for the differentiation of
essential thrombocythaemia and myelofibrosis. J Thromb Haemost. P14433
2. Boasman K, Simmonds MJ, Graham C, Saunthararajah Y, Rinaldi CR. 2018. Using PU.1 and Jun
dimerization protein 2 transcription factor expression in myelodysplastic syndromes to predict
treatment response and leukaemia transformation. Ann Hematol.
3. Boasman K, Simmonds MJ, Rinaldi CR, Rinaldi C. 2018.CALR and CD47: An Insight into their roles in
the disease pogression of MDS and MPN. J Blood Disord Transfus. (10).1.
Conference posters
American Society of Clinical Oncology Asia – 2019 (Bangkok): Expression of CD47 and CALR in
myeloproliferative neoplasms: Potential new therapeutic targets.
European Haematology Association – 2019 (Amsterdam): Expression of CD47 & CALR in
myeloproliferative neoplasms: potential new therapeutical targets.
British Society of Haematology – 2019 (Glasgow): Expression of CD47 and CALR in myeloproliferative
neoplasms: potential new therapeutical targets.
American Society of Clinical Oncology – 2018 (Chicago): PU.1 and JDP2 Expression in Myelodysplastic
Syndromes to Predict Treatment Response and Leukaemia Transformation.
European School of Haematology – 2018 (Dublin): Calreticulin and CD47 expression in a MPN cell
system K562: a model for outcome of therapeutic response.
British Society of Haematology – 2018 (Liverpool): Calreticulin & CD47 expression in a MDS/MPN cell
system K562: a model for outcome of therapeutic response.
British Society of Haematology – 2018 (Liverpool): PU.1 Expression Correlates with patient disease
status In Myelodysplastic Syndromes, A Potential New Prognostic Marker
European Haematology Association – 2017 (Madrid): Role of pro-phagocytic calreticulin and anti-
phagocytic CD47 in MDS and MPN models treated with azacytidine or ruxolitinib.
16
1.1: Myeloid Malignancies Myelodysplastic syndromes (MDS), Myeloproliferative neoplasms (MPN) and acute myeloid leukaemia
(AML) are Myeloid malignancies, which are a group of bone marrow diseases arising from clonal
disorders of haematopoietic stem cells. MDS and MPN generally effect the older populations. MDS
median age of diagnosis is 65-70, with patients under the age of 50 accounting for less than 10% of the
disease. Annually, 4 in 100,000 people are diagnosed with MDS, which increases to 40-50 people per
100,000 in the over 70s (Adès et al, 2014). MPNs have a similar age of diagnosis of between 59-65 years
with 1.6 in 100,000 diagnosed annually (Ayalew Tefferi, 2016; Titmarsh et al., 2014). MDS are
characterised by bone marrow failure and peripheral cytopenia’s in myeloid progenitors (Boasman,
2019), while MPN are characterised by differentiation of mature myeloid cell types (Lally et al., 2019).
MDS and MPN both show tendencies to develop into AML as the diseases progress (Murati et al., 2012).
In the UK AML is the second most prevalent leukaemia. AML is characterised by excessive myeloid blast
cell proliferation within the bone marrow, resulting in abnormal haematopoiesis (Yang et al,, 2018).
Whilst much has been achieved to classify these diseases over the years there are still challenges
associated with disease progression and treatment as laid out below.
1.2: Characterisation of Different Myeloid Malignancies
The formation of the components of blood from hematopoietic stem cells is called haematopoiesis,
with genetic changes and alterations in methylation status known to disrupt haematopoiesis. These
alterations in Haematopoiesis can lead to a variety of haematological neoplasms including
leukaemia and the chronic myeloid disorders (Orkin, 2000). In 2008, the World Health Organisation
re-classified Myelodysplastic syndromes (MDS), Myeloproliferative neoplasms (MPN) and acute
myeloid leukaemia (AML), to try and gain an International recognised criterion to aid diagnosis and
avoid overlap. Often myeloid neoplasms are characterised by dysmyelopoiesis (a hematologic
disorder characterised by the presence of cytopenia’s in the blood and dysplastic cells), abnormal
differentiation of hematopoietic cells, increased apoptosis with ineffective haematopoiesis, cytopenias
17
(lower than normal/expected blood cells) or leucocytosis and in particular MDS and MPN tend to
transform into AML (Korn & Méndez-Ferrer, 2017).
MDSs are classified based on the extent of cytopenia, the level of blast infiltration of the bone marrow,
and abnormal chromosomal structure (Gill et al., 2016). MPNs are characterized by overproduction of
mature blood cells (Park et al., 2015). Essential thrombocythemia (ET), polycythaemia vera (PV) and
myelofibrosis (MF) are the three most common forms of MPN (Vardiman et al., 2009), which often have
overlapping presentations but have been well defined from each other in the WHO revision of myeloid
classification in 2016 (Arber et al., 2016). In MPNs somatic mutations are common. In most cases the
patient harbour a Janus kinase inhibitor 2 (JAK2) mutation, which affects the JAK/STAT pathway
signalling (Campbell et al., 2006). Other mutations are also prevalent in MPN, such as the calreticulin
(CALR) mutations or cMPL mutations (Jonas & Greenberg, 2015).
The type and severity of MDS and MPN heavily effects the prognosis of the disease. Different
classifications have been used to indicate prognosis and clinical features, French-American-British [FAB]
classification and the World Health Organization [WHO] classification together with prognostic scoring
systems, such as the International Prognostic Scoring System (IPSS), have been used for over 25 years
(Jonas & Greenberg, 2015; Vardiman, 2012; Vardiman et al., 2009). Clinical indicators including: signs
and symptoms of anaemia, infection and bleeding complications predominate, with systemic symptoms
or features of autoimmunity being seen in some patients; perhaps indicative of disease pathogenesis
which result from the vast heterogeneity in these disorders (Adès., 2014).
Prognostic scoring systems used in MDS and MPN are mainly based on bone marrow/peripheral blood
features, cytogenetics and some clinical manifestations such as systemic symptoms or splenomegaly.
Although the presence of different mutations has been linked to these conditions, there are currently
no specific genetic markers to highlight specific subtypes or specific stages of these conditions that
predicts transformation and disease progression. As such, the standard IPSS revised system (IPSS-R) is
currently used in MDS, to rank risk factors for the disease into: very low, low, intermediate, high and
18
very high risk scores (Vardiman, 2012). A similar scoring system is used in MPN, in particular the
Dynamic International Prognostic Scoring System (DIPSS) prognostics on MF, describing potential
different stages in its progression (Gangat et al., 2011).
1.3: Myelodysplastic syndromes (MDS) Prognostic Scoring
1.3.1: Initial Prognostic Scoring Generated by The French-American-British leukaemia and the World Health Organisation
The French-American-British (FAB) leukaemia working group provided the first publication classifying
MDS (Bennett et al., 1982). With it taking nearly two decades later for a group of hematopathologists
and hematologists to write the World Health Organization (WHOs) first classification of hematologic
neoplasms including MDS, AML, MPNs and MDS/MPN overlap neoplasms which came out in 2001
(Vardiman et al., 2002).
The FAB group recognized 5 subtypes of MDS, refractory anaemia with excess blasts (RAEB), chronic
myelomonocytic leukaemia (CMML), refractory anaemia (RA), refractory anaemia with ring
sideroblasts (RARS) and refractory anaemia with excess blasts in transformation (RAEB-T) (Bennett et
al., 1982).
The FAB classification showed some issues with its classification system. Firstly, clarification on how
the RA and RARS subtypes were formed was necessary as to support how such patients should be
categorized. RA and RARS were characterized by dyserythropoiesis showing marrow blasts under 5%,
with “almost normal granulocytic and megakaryocytic morphology” (Bennett et al., 1982). However,
some MDS patients with marrow blasts under 5% also displayed multilineage dysplasia and reported
lower survival rates compared to patients with dyserythropoiesis only (Rosati et al., 1996). Secondly,
the FAB categorised that for RAEB the blast range was 5-20% and could be used as an important
prognostic factor. However, some authorities reasoned 5-20% was too broad of a range to define a
prognostically relevant subgroup (Greenberg et al., 1997). Third, RAEB-T survival rate and response to
19
treatment was similar to that of AML patients. It was suggested that RAEB-T should be encompassed
with AML and not MDS (Bernstein et al., 1996). Fourth, there was debate over whether CMML should
be reclassified or subdivided into two subgroups. CMML is clinically and morphologically
heterogeneous with some patients showing myeloproliferative features (marked leukocytosis and
splenomegaly but minimal hematopoietic dysplasia) whereas others have MDS features (modest
leukocytosis, no splenomegaly and marked dysplasia) (Michaux & Martiat, 1993). Fifth, a lack of
minimal diagnostic criteria was raised by some clinicians and pathologists who had patients with
suggested MDS but inconclusive morphologic features (Vardiman et al., 2002).
A number of prognostic scoring systems arose from studies trying to identify prognostic factors
beyond the FAB classification, including the most prominently used International Prognostic Scoring
System (IPSS) (Greenberg et al., 1997; Vardiman, 2012). Inclusion of the IPSS enhances the risk
stratification of patients with the various prognostic significance of the FAB subtypes, through the
scoring of the percentage of bone marrow blasts, number of cytopenia’s, and specific cytogenetic
abnormalities as independent prognostic variables (Vardiman, 2012).
1.3.2: International Prognostic Scoring System and WHO Prognostic Scoring System
For the diagnosis and treatment of MDS the IPSS has been the most widely used prognostic scoring
system. It was first published by FAB in 1997, intended for use with FAB classification system and has
been updated several times. Addition of the IPSS to the FAB classification system has been proven to
have reproducible and prognostic value. IPSS states that the patients risk factor is decided on points
scoring where it is scored on a scale from 0 to 2. Low risk - (0), Intermediate risk – 1 - (0.5-1),
Intermediate risk – 2 – (1.5-2) and High risk - (≥2). The points are scored by the percentage of blasts in
the bone marrow, - Karyotype (both scored from zero to 1) and cytopenia’s (scored as zero or 0.5). A
patient’s risk is classified according to the total score (Greenberg et al., 1997).
20
In 2012, a revised IPSS system (IPSS-R) was released now comprised of five risk groups (very low, low,
intermediate, high and very high). This is a revised tier system which gives greater specific detail into
cytogenetic prognostic categorization and cut-offs for haemoglobin levels, platelet counts and
neutrophil counts. A patient’s risk factor and prognostic scoring is now calculated by the severity of
cytopenia’s along with their bone marrow blast percentage (Greenberg et al., 2012). The IPSS and
IPSS-R are used only at the time of diagnosis, whereas the WHO Prognostic Scoring System (WPSS),
developed in 2005 based on the 2001 WHO classifications for MDS and revised in 2011 (Malcovati et
al., 2011), can constantly adjust allowing for patients to be reassigned categories as their disease
progresses. The WHO scoring system calculates a patients risk factor using a similar points system to
the IPSS-R; very low - (0) , low - (1) , intermediate - (2), high – (3-4) and very high - (≥5). The points are
scored by the WHO disorder classification (0-3), Karyotype (0-2) and presence or absence of severe
anaemia (0-1) (Malcovati et al., 2011).
1.4: WHO Classification (2001 – 2016)
The WHO classification was established to be used as a guide to combine all available information;
clinical, morphologic, cytochemical, immunophenotypic, genetic and other biologically significant
information. To aid in the specific diagnosis of each disease (Harris et al., 1999), the WHO modified
and expanded on the previously published data on the FAB subtypes, with their 2001 classification
(Vardiman et al., 2002). This was then further expanded on in the 2008 reclassification of
hematopoietic tumours, which included; morphology, immunophenotyping, genetics, morphologic
and histologic photomicrographs, and clinical information (Vardiman et al., 2009) (table 1). The WHO
published their latest classification in 2016 updating some key points (Arber et al., 2016).
21
Table 4: WHO classification of MDS (Vardiman, 2012).
Disease entity Blood findings Bone marrow findings
Refractory cytopenias with
unilineage dysplasia (RCUD):
refractory anemia (RA), refractory
neutropenia (RN), refractory
thrombocytopenia (RT)
Unicytopenia or
bicytopeniaa
No or rare
blasts (<1%)b
Unilineage dysplasia: ≥10% of the cells in
one myeloid lineage
<5% blasts
15% of erythroid precursors are ring
sideroblasts
Refractory anemia with ring
sideroblasts (RARS)
Anemia
No blasts
≥15% of the erythroid precursors are ring
sideroblasts
Dyserythropoiesis only
<5% blasts
Refractory anemia with multilineage
dysplasia (RCMD)
Cytopenia(s), no
or rare blastsb
No Auer rods
<1 × 109/L
monocytes
Dysplasia in >10% of cells in 2 or more
lineages
<5% blasts in marrow
No Auer rods
<1 × 109/L monocytes
Refractory anemia with excess
blasts-1 (RAEB-1)
Cytopenia(s)
<5% blasts
No Auer rods
<1 × 109/L
monocytes
Unilineage or multilineage dysplasia
5–9% blasts
No Auer rods
Refractory anemia with excess
blasts-2 (RAEB-2)
Cytopenia(s)
5–19% blasts
Auer rods ±c
Unilineage or multilineage dysplasia
0–19% blasts
Auer rods ±c
22
<1 × 109/L
monocytes
MDS, unclassifiable (MDS-U) Cytopenias
<1% blastsb
Unequivocal dysplasia in less than 10% of
cells in one or more myeloid lines when
accompanied by a cytogenetic
abnormality considered as presumptive
evidence for a diagnosis of MDS
<5% blasts
MDS associated with isolated del(5q) Anemia
Usually normal
to elevated
platelets
No or rare
blasts
Normal to increased megakaryocytes with
hypolobated nuclei
<5% blasts del(5q) is the sole cytogenetic
abnormality
No Auer rods
a= Bicytopenia may be occasionally observed. Cases with pancytopenia should be classified as MDS-U
b = If marrow blast % is less than 5%, but there are 2–4% myeloblasts in blood, the diagnosis is RAEB-
1. Cases of RCUD and RCMD with 1% blasts in blood are classified as MDS-U
c =Cases with Auer rods and <5% myeloblasts in the blood and <10% in the marrow should be
classified as RAEB-2
In the latest 2016 classification WHO update (8 years after the last edition), several aspects of MDS
were updated. Flow cytometry may be used to identify atypical antigen expression on the cell surface,
when assessing whether pancytopenia occurs as a result of MDS. This is completed by checking that
there is no increase in the blast percentage resulting from immature proliferation of cells. At least
23
10% dysplastic erythroid, granulocytic, or megakaryocytic cells should be identified (Goasguen et al.,
2016; Kern et al., 2010).
1.5: Myeloproliferative Neoplasms: WHO Classification (2001 – 2016)
Myeloproliferative neoplasms weren’t classified until the 2008 revision of the 2001 WHO
classification. The WHO originally classified myeloproliferative disorders into four groups, 1- chronic
myeloproliferative disorders (CMPD), 2- myelodysplastic syndromes (MDS), 3- MDS/MPN crossover
diseases and 4- mast cell disease (MCD). In this classification system more recent subclassifications of
MPN, PV, ET & MF, were all classified under CMPD grouping along with chronic myeloid leukaemia
and chronic neutrophilic leukaemia, chronic eosinophilic leukaemia (and the hypereosinophilic
syndrome) and CMPD unclassified (Vardiman et al., 2002). A shared effective clonal
myeloproliferation was the major criteria for classification in the CMPD group, with the grouping
based purely on this feature, with other variability initially not take into account (Tefferi & Vardiman,
2008).
Research into molecular markers of CMPD including, the most prevalent JAK somatic mutation
(JAK2V617F) in 2005, led to a revised WHO classification in 2008. The main changes to the 2001
classification including myeloproliferative diseases being renamed to myeloproliferative neoplasms
(MPNs), and a separate group being created for eosinophilia and abnormalities of PDGFRA, PDGFRB
and FGFR1 for myeloid neoplasms. Due to its formation form clonal stem cells disorder, MCD was
grouped with the MPNs (Vardiman et al., 2009).
Along with the new revision of myeloid classification in 2008, MPNs were also given specific
diagnostic criteria. Mutations such as the JAK2V617F mutation provides additional information to
assist with diagnosis, although these mutations are not specific to any MPN they are used in
conjunction with other diagnostic criteria to reject other diagnoses. MPN is generally split into 3
subgroups: MF, ET and PV. There is debate on the specific determination of the individual MPNs
24
during diagnosis. Sometimes patients’ symptoms overlap making it hard to distinguish between
MPNs, leading to terms of “true ET” or “pre-fibrotic MF” (Campbell et al., 2009). In addition, after the
2008 revision of myeloid classification it was shown that there was a potential under-diagnosis of PV.
Patients with the disease transformation from MF to AML with consistent bone marrow morphology
stated in 2008 classification showed significantly increased risk of disease transformation when
compared to the disease transformation of PV to AML (Barbui et al., 2014). This led to the revised
2016 WHO MPN classification of myeloid neoplasms stating that the diagnostic criteria for PV now has
greater attention to bone marrow morphology and haemoglobin levels have been reduced (Arber et
al., 2016).
The WHO refined this issue in the new 2016 revision of myeloid classification to prevent misdiagnoses
of MF and ET, the updated criteria distinguished pre-MF from “true” ET (Arber et al., 2016). In the
new 2016 classification the diagnostic criteria was updated to recognise the CALR mutation found in
2013 (Klampfl et al., 2013; Nangalia et al., 2013a). Diagnostic criteria and guidance on myelofibrosis
grading was used to distinguish the different ET, pre-MF, and overt-MF subgroups of MPN (Arber et
al., 2016).
From the 2016 revision of myeloid neoplasms MPNs are now split into 4 sub-groups; with diagnosis of
each sub-group requiring elimination of the other myeloproliferative disorders (further details of each
subgroup are detailed below).
1.5.1: Essential Thrombocythaemia (ET)
This MPN subgroup is characterised by increases in platelet count circulating within blood (> 450 x 109
/L) combined with an increase of megakaryocytes - this is the criteria used to diagnose ET (Tefferi &
Vardiman, 2008). ET when compared to other MPNs has a longer overall median survival rate (19.8
years), whereas PV is (13.5 years). Although, ET has a high survival rate it is still much lower than the
age matched control population (Tefferi et al., 2014). Longer survival rates do not exempt ET from the
possibility of myeloid or leukemic disease transformation, with disease transformation seen within 0 -
25
10 years after diagnosis at 9.1% and 1.4%, respectively, increasing during 11-20 years (28.3% and 8.1%
chance of transformation, respectively) and in 21-30 years after disease onset the chance of disease
transformation after diagnosis significantly increases to 58.5% and 24.0%, respectively (Wolanskyj et
al., 2006). Criteria for ET diagnostics is shown in Table 2.
Table 5: 2016 WHO Diagnostic Criteria for ET. All four major criteria or the first three major criteria and the minor criteria
are required for diagnosis (Arber et al., 2016).
ESSENTIAL THROMBOCYTHAEMIA
Major Criteria
1 Platelet count ≥ 450 x 109 /L
2
BM biopsy showing proliferation mainly of the megakaryocyte lineage with
increased numbers of enlarged, mature megakaryocytes with hyperlobulated
nuclei. No significant increase or left shift in neutrophil granulopoiesis or
erythropoiesis and very rarely minor (grade 1) increase in reticulin fibres
3 Not meeting WHO criteria for CML, PV, MF, MDS or other myeloid neoplasm
4 Presence of JAK2, CALR, or MPL mutations
Minor Criteria 1
Presence of a clonal marker or absence of evidence for reactive
thrombocytosis
1.5.2: Polycythaemia Vera (PV)
PV is a subgroup within MPN, characterised by an elevated number of erythrocytes, greater than 25%
red cell mass above normal (Arber et al., 2016). Diagnostic criteria for PV is shown in Table 3. Vascular
and thrombotic complications can arise from the overproduction of red blood cells potentially having
serious implications for the patient’s quality of life (Stein et al., 2014). Each patient shows common
symptoms such as pruritus, fatigue, night sweats, bone pain, thrombosis, and bleeding
26
(Griesshammer et al., 2015). PV patients median age of diagnosis is between 55 and 60 years old
(Harrison & Lee, 2017), with an estimated 0.86 per 100,000 annual incidence rate (Titmarsh et al.,
2014). PV has the potential to transform into either myelofibrosis (MF) or AML. This transformation
can occur over 10-15 years and occurs in 10-15% and 5-10% of patients ,respectively (Harrison & Lee,
2017).
Table 6: World Health Organisation’s 2016 PV diagnostic criteria. PV can be diagnosed either by the presence of all three
major criteria or with major criteria 1 and 2 with the minor criteria (Arber et al., 2016).
POLYCYTHAEMIA VERA
Major Criteria
1
Haemoglobin > 165/160 g/L (men/women)
OR
Haemocrit > 49/48% (men/women)
OR
Elevated red cell mass > 25% above mean normal predicted value
2
BM biopsy showing hypercellularity for age with trilineage growth
(panmyelosis) including prominent erythroid, granulocytic, and
megakaryocytic proliferation with pleomorphic, mature megakaryocytes
(differences in size). This criterion may not be required in cases with sustained
absolute erythrocytosis, haemoglobin levels > 185/165 g/L and haematocrit
> 49.5%
3 Presence of JAK2V617F or similar mutation
Minor Criteria 1 Subnormal serum EPO level
27
1.5.3: PRE-MF and myelofibrosis (MF)
In the 2008 revision of the WHO myeloid classification PRE-MF and MF were classified together under
the integrated group MF. After the 2016 revision of myeloid classification PRE-MF and MF were
separated into different forms (Arber et al., 2016). Diagnostics criteria for PRE-MF is shown in Table 4.
Myelofibrosis can be presented as primary MF (if initial presentation), or secondary if MF is derived
from the progression of ET and PV (Curto-Garcia et al, 2018). Patients with PRE-MF can stay in a PRE-
MF state for many years and mimic the presentation of ET and PV. To distinguish, the presence of
leucoerythroblastosis cannot be present and the histopathological features have particular prominence
in discriminating pre-MF from ET or PV (Curto-Garcia et al., 2018).
Table 7: 2016 WHO Diagnostic Criteria for pre-MF. Diagnosis requires meeting all three major and one minor criteria. Minor
criteria to be confirmed twice on consecutive investigation (Arber et al., 2016).
PRE-MF
Major Criteria
1
Megakaryocytic proliferation and atypia, without reticulin fibrosis > grade 1,
accompanied by increased age-adjusted bone marrow cellularity, granulocytic
proliferation, and often decreased erythropoiesis
2 Not meeting WHO criteria for CML, ET, PV, MF, MDS, or other myeloid
neoplasm
3
Presence of JAK2, CALR, or MPL mutations or in the absence of these
mutations, presence of another clonal marker, or absence of reactive
myelofibrosis
Minor Criteria
1 Anaemia not attributed to a comorbid condition
2 Leucocytosis: ≥ 11 × 109/L
28
PRE-MF
3 LDH increased to above upper normal limit of institutional reference range
4 Palpable splenomegaly
MF is the least common of the three classical non-CML MPNs, as classified in the 2008 revision of
myeloid classification. MF accounts for around 0.47 new cases per 100,000 of diagnosed MPNs
(Titmarsh et al., 2014). Diagnostic criteria for MF are shown in Table 5. The median survival rate for MF
patients is 5.9 years, making MF the most serious MPN, when compared to PV and ET (Tefferi et al.,
2014). MF patients have a 25% potential of blast transformation and progression to AML, lowering the
prognostic outcome (Harrison & Lee, 2017). The physical symptoms of MF include sweating, pruritus,
fever and bone pain these result from a combination of bone marrow failure and/or splenomegaly,
leading to MF and causing a large negative impact on the quality of life for the patient (Harrison & Lee,
2017).
Table 8: 2016 WHO Diagnostic Criteria for overt MF. Diagnosis requires meeting all three major and one minor criteria.
Minor criteria to be confirmed twice on consecutive investigations (Arber et al., 2016).
MF
Major Criteria
1 Presence of megakaryocytic proliferation and atypia, accompanied by either
reticulin and/or collagen fibrosis grades 2 or 3
2 Not meeting WHO criteria for CML, ET, PV, MDS, or other myeloid neoplasm
3 Presence of JAK2, CALR, or MPL mutation or in the absence of these mutations,
presence of another clonal marker, or absence of reactive myelofibrosis
Minor Criteria 1 Anaemia not attributed to a comorbid condition
29
MF
2 Leucocytosis: ≥ 11 × 109/L
3 LDH increased to above upper normal limit of institutional reference range
4 Palpable splenomegaly
5 Leucoerythroblastosis
1.6: Targeted drugs for treatment
Different treatment options are used in the treatment of myeloid malignancies. Initially most
treatments start with chemotherapy/ targeted drugs before moving onto more extreme therapies if
the patients are able, e.g. stem cell transplants.
1.6.1: Azacytidine
A main drug used in the treatment of MDS is azacytidine. Azacytidines acts via multiple mechanisms
including DNA hypomethylation and cytotoxic effects on abnormal haematopoietic cells in the bone
marrow (Leone, Voso, Teofili, & Lübbert, 2003) (Figure 1.1). Azacytidine is a pyrimidine nucleoside
analogue of cytidine which is phosphorylated intracellularly to become its active form, azacytidine
triphosphate (Jones et al., 1983; Leone et al., 2003). Initial phosphorylation of azacytidine depends on
uridine cytidine kinase which contributes to a lack of resistance seen in certain cancer cell lines (Qin,
Jelinek, Si, Shu, & Issa, 2009). Upon administration of azacytidine it is incorporated into the RNA,
which affects the synthesis of nucleic acids and proteins resulting in cytotoxicity, it has also been seen
to be incorporated into DNA but to a lesser extent (Keating, 2009). Due to its incorporation into RNA
and DNA azacytidine mainly affects rapidly dividing cells like cancerous cells (Santini et al, 2001), while
slow/ non-proliferating cells are relatively unresponsive to the drug (Keating, 2009).
30
Figure 1.1: DNA methylation of azacytidine (Leone et al., 2003)
1.6.2: Ruxolitinib
Ruxolitinib is a janus kinase inhibitor (JAK inhibitor) with selectivity for subtypes JAK1 and JAK2
(Pardanani & Tefferi, 2011) (Figure 1.2). Approved for the treatment of patients with MPN, usually it is
used to treat disease-related splenomegaly or symptoms in adults with myelofibrosis, post PV and
post ET (Gotlib, 2013), which are types of myeloproliferative disorder that affects the bone marrow
(Mesa et al., 2012). PV patients previously treated with hydroxyurea as a first line of treatment may
be eligible for ruxolitinib when there has been an inadequate response to or intolerance of
hydroxyurea (Vannucchi et al., 2015). Patients with PV and ET have been shown to go into complete
molecular remission upon treatment with ruxolitinib after 5 years (Pieri et al, 2015). It dysregulates
activation of Janus kinase/signal transducer which is an activator of transcription in (JAK/STAT)
signalling, which controls the production of blood cells. Stopping the uncontrolled proliferation of
hematopoietic progenitors and cancer cells and stops the generation of a proinflammatory reaction
responsible for symptomatic burden (Cazzola & Kralovics, 2014).
31
Figure 1.2: The constitutive activity of the JAK-STAT signalling pathway, present within the hematopoietic stem cells of
patients with myelofibrosis (Mascarenhas & Hoffman, 2012)
1.7: Immunosurveillance of cancer
The hallmarks of cancer states that, cancers were thought to be initiated from genetic or epigenetic
defects within neoplastic precursor cells. These defects within the cell allow the cell to resist cell death,
over-proliferate and invade local as well as distant sites to generate metastases (Hanahan et al., 2011).
Work around the early 2000s indicated that cancer and its progression are also affected by other factors
such as the hosts immune system (Kaplan et al., 1998; Shankaran et al., 2001). Collected literature
amassed, stating that the immune system eliminates (pre)neoplastic cells as they form, while in some
cases (epi)genetic alterations can allow immunoevasion leading to growth of the (epi)genetic altered
cells (Dunn et al., 2004).
Oncogenesis and disease progression rely on intrinsic alterations and changes in expression of genes
and proteins in cancer cells, allowing a proliferative advantage and evasion of recognition from the
immune system that would signal them for death (Fucikova, Spisek, Kroemer, & Galluzzi, 2020). Current
models in the hallmarks of cancer state that oncogenesis and disease progression occur when increases
32
in genetic expression induces proliferative potential and amass a resistance to cell death. Also, the cells
react in response to disease progression and/or respond to the cancer therapy, resulting in the diseased
cell escaping the immunosurveillance of the hosts immune system (Hanahan et al., 2011; Galluzzi et al.,
2015).
Newly emerging targets of immune system escape include CALR and CD47, and their relationship
regarding one another. Overexpression of calreticulin (CALR) has been documented to produce a pro-
phagocytic signal in solid tumours and it is often counteracted by a concomitant expression of the anti-
phagocytic CD47, reflecting an apoptosis vs survival mechanism in response to chemotherapy which
will be discussed further(Boasman et al., 2019).
1.8: Calreticulin
Calreticulin (CALR) (Figure 1.3) is a calcium (Ca2+)-binding endoplasmic reticulum (ER) protein, which
is involved in many different functions including supporting the movement of secreted and inserted
proteins in the plasma membrane, maintaining homeostasis of proteins that accumulate in the ER,
aiding in the initial reaction to provide anticancer immunity relating to immunogenic cell death and
playing a role in regulated cell death by signalling an antigen-specific immune response to
damaged/irregular cells (Galluzzi et al., 2020; Matsuo et al., 1997; Ren et al., 2019; Saito et al., 1999;
Salvagno & Cubillos-Ruiz, 2019).
33
Figure 1.3: A potential model for CALR network signalling. (Left panel) CALR binds to calcium, actively involved in the
folding of newly synthesized glycoproteins, interacts with other molecular chaperones in the endoplasmic reticulum.
Promotes proliferation, cell cycle progression, migration, invasion and anoikic resistance by CALR regulating; STAT3, STAT5,
AKT, MAPK and FAK cell signalling. Under stress conditions, CALR translocates onto the plasma membrane by Golgi
apparatus-mediated exocytosis, which participates in immunogenic cell death. (Right panel) depicts a myeloproliferative
neoplasm cell with mutated-CALR (exon 9 indel mutations), which induces activation of MPL and JAK2/STAT signalling,
promoting cell proliferation and survival (Machado-Neto et al., 2016).
CALR is 46 kDa Ca2+-binding chaperone protein comprised of 417 amino acids that is principally
located in the lumen of the ER (Michalak, Groenendyk, Szabo, Gold, & Opas, 2009). CALR has a crystal
structure comprised of an extended proline-rich P-domain inserted between N- and C-terminal
domains (Boasman et al., 2019), which has 3 domains each with a specific function (Figure 1.4).
34
Figure 1.4: Linear diagram of human calreticulin indicating its amino-terminal (N-CALR), globular (P-CALR), and carboxy-
terminal (C-CALR) domains and the location of the sequences predicted by computer modelling to determine an
intrinsically disordered structure (indicated by the straight lines). (A) Normal human CALR and factors which may
potentially affect its tertiary structure. (B) Diagram of the structure of representative Type 1 (deletions) and Type 2
(insertions) CALR mutations found in Philadelphia-negative myeloproliferative disorders. The mutations found in these
maladies are all localized in exon 9 encoding the terminal C-region of the protein and encode a truncated (Type 1, top
diagram) or elongated (Type 2, bottom diagram) form of C-CALR (Varricchio et al., 2017).
The highly acidic C-domain found predominantly within the ER is responsible for calcium regulation
and binding, due to its low-affinity and high-capacity for Ca2+ binding. The globular N- terminal lectin-
like globular domain and extended P-domain are primarily responsible for the protein’s chaperone
function due to their high-affinity and low-capacity for Ca2+ binding (Chao & Gotlib, 2014; Michalak et
al., 2009). The C-domain contains the KDEL amino acid sequence which prevents CALR from being
secreted from the ER and ensures retrieval from the Golgi apparatus. Whilst the KDEL signal resides it
35
to the ER it can however be translocated by protein folding to other parts of the cell, including the
plasma membrane, when needed but normally CALR is found in the ER (Guglielmelli et al., 2014).
KDEL-containing proteins bind to KDEL receptors on the ER that affect its translocation. If the KDEL
receptor protein is missing or has a mutated KDEL region cellular mis-localisation can occur.
Localisation studies show that in myeloid cells CALR is upregulated, however when mutated (see
Figure 1.4), CALR stays located in the ER (Guglielmelli et al., 2014).
CALR is closely linked to another chaperone protein calnexin (CALR/CANX). Together these proteins
are a part of a multiple additional quality control system within cells that is responsible for
chaperoning newly synthesised proteins and glycoproteins within the ER. CALR/CANX supports
refolding and is responsible for preventing premature export of misfolded proteins from the ER. Other
chaperones include but are not limited to; heat shock protein family A (HSP70), heat shock protein 90
beta family A (HSP90B1) and protein disulfide isomerase family A member 3 (PDIA3) (Rutkevich &
Williams, 2011). Integrin and Ca2+ signalling in both excitable (neurones, skeletal muscle) and non-
excitable cells (red blood cells) are controlled by CALR (Coppolino et al., 1997). In addition to its ER
chaperone functions, CALR has also been found expressed at the surface of tumour and apoptotic
cells where it is believed to promote phagocytosis (Gardai et al., 2005; Obeid et al., 2007). Klampfl
(Klampfl et al., 2013) and Nangalia (Nangalia et al., 2013b), are the two groups who discovered the
mutation in the CALR gene, located in exon 9. Over 50 different mutations have been identified to
date, the most common mutations are type 1 mutation (52-bp deletion) and type 2 mutation (5-bp
insertion) (Cazzola & Kralovics, 2014). Ligand-independent signalling through JAK-STAT, PI3K and
MAPK pathways have been seen to be activated by CALR mutants via the thrombopoietin receptor
(MPL) (Chachoua et al., 2016). CALR mutations are suspected to have a contributory role in the
pathogenesis of MPNs (Luo & Yu, 2015).
36
Translocation of CALR from the ER lumen to the plasma membrane in cells acts in response
(survive/succumb to stress) to a specific change to the cells integrated stress response (ISR) (Pakos‐
Zebrucka et al., 2016). There are three functional models that trigger the translocation of CALR
throughout the cell. The first for CALR translation of the plasma membrane is driven by anthracyclines
and completed by inactivating the phosphorylation of the eukaryotic translation initiation factor 2
subunit alpha (eIF2α), through eukaryotic translocation initiation factor 2 alpha kinase 3 (PERK)
(Panaretakis et al., 2009). The second involves the pro-apoptotic Bcl-2 family and associated genes
involved in apoptotic signalling, including BCL2 associated X, apoptosis regulator (BAX) and BCL2
antagonist/killer 1 (BAK1), caspase 8 (CASP8) triggering CALR translocation (Panaretakis et al., 2009).
The third module is transport from ER to the Golgi apparatus, where CALR exposure driven by
anthracyclines is inhibited via downregulation of vesicle-associated membrane protein 1 (VAMP1) or
synaptosome-associated protein 25 (SNAP25) (Brandizzi & Barlowe, 2013; Panaretakis et al., 2008).
1.8.1: CALR in antigen presentation
Major histocompatibility complex (MHC) class I molecules are found on the cell surface of all
nucleated cells and present peptide fragments derived from intracellular proteins. The MHC class 1
pathway has an important role of alerting the immune system to damaged and infected cells (Hewitt,
2003). Loading of cellular antigens onto MHC Class I molecules is mediated by the peptide-loading
complex (PLC) of which CALR contributes to two main functions (Blees et al., 2017). Firstly, CALR
preserves steady-state levels of MHC Class I heavy chains within the ER, through binding of PDIA3 in a
glycan-dependent manner (Wearscha et al., 2011). Secondly, suboptimal assembled MHC Class I
molecules are retrieved by CALR from post ER compartments (ER-Golgi intermediate compartment
and cis-Golgi) (Howe et al., 2009). Cells with low expression or lacking CALR expression have a 50%–
80% reduction in properly loaded MHC class I on the cell surface (Gao et al., 2002). Increased
expression of CALR is seen in cancers with mutant and wild type (w/t) CALR, which avoid
immunodeletion due to upregulation of CD47 (Chao et al., 2012) and the inappropriate loading of the
37
MHC class I molecules with peptide (Galluzzi etal., 2018). Increased overall expression of CALR with
low surface expression has been seen in bladder and breast solid tumours, which result in a poor
disease prognosis (Cathro et al., 2010; Noblejas-López et al., 2019).
Specifically within MPNs, it has been suggested that expression of CALR creates a spontaneous
immune response in patients by creating shared tumour neoantigens (Holmström et al., 2018), under
the strict regulation of cytotoxic T lymphocyte-associated protein 4 and programmed cell death 1
signalling (Bozkus et al., 2019).
1.8.2: CALR in cancer signalling
CALR has been seen in different cancers to influence malignant transformation, disease progression
and response to therapy marking it as a prominent factor in multiple oncological settings. CALR plays
a major role in signalling cellular adjuvanticity, allowing the stressed or dying cell to deliver a
stimulatory signal rather than an inhibitory signal to immune cells. These stimulatory signals do not
originate from the internal CALR within the ER of the cell, they instead initiate a potent pro-
phagocytic signal from the CALR exposed on the surface of a stressed cell stimulatory (rather than co-
inhibitory) signals to immune cells (Obeid et al., 2007). This allows for the signalling cell to be targeted
by phagocytes (Gardai et al., 2005). LDL receptor-related protein 1 (LRP1) on the surface of
macrophages are commonly known to bind to extracellular CALR (Garg et al., 2012). However,
additional proteins including thrombospondin 1 (THBS1)(Goicoechea et al., 2002), C1q and mannose-
binding lectin (MBL) family (Stuartet al., 1997) have been shown to bind to extracellular CALR
resulting in CALR-dependent phagocytosis of apoptotic bodies (Ogden et al., 2001).
The pro-phagocytic properties of CALR are counteracted on the cell surface by two main mechanisms
and effected by others. Firstly, phosphatidylserine, a phospholipid found on the inner side of the
plasma membrane, is seen to be over produced on the cell surface (Suzuki et al., 2013). This binds to
arginine demethylase and lysine hydroxylase leading to the immunologically silent clearance of
38
resulting cell debris by macrophages (Fucikova et al., 2020). Therefore CALR must be overexpressed
on the cell surface to initiate phagocytosis prior to phosphatidylserine (Panaretakis et al., 2009).
Secondly phagocytosis of dying cells due to CALR can be inhibited by CD47, which binds the signal
regulatory protein α (SIRPα) on the surface of antigen presenting cells (Oldenborg et al., 2001).
Interestingly, upregulation of CALR on the surface of cancer cells is usually followed by a concomitant
upregulation in cell surface expression of the anti-phagocytic CD47, in a survival response to keep the
cell alive and evade immunosurveillance (as detailed later) (Feng et al., 2015). Cells being able to
evade immunosurveillance is a key hallmark for cancer (Hanahan and Weinberg, 2011).
1.8.3: Prognostic and predictive value of CALR
Preclinical models of CALR in prostate, colorectal cancers, urothelial carcinomas and AML suggest that
increased proliferation of CALR expression is lower in progressive/malignant stages and increases in
expression as the disease outcome improves (Alur et al., 2009; Cathro et al., 2010; Peng et al., 2010;
Schardt et al., 2009). In many cases upregulated levels of CALR correlate with the different markers of
cancer immunity activity. High levels of CALR on the surface of the cell in AML correspond with
increased levels of T cells specific for tumour-associated antigens (Fucikova et al., 2016) also with
limiting CD47 expression (Wemeau et al., 2010). These studies show AML blasts have the potential to
spontaneously expose CALR on their surface in a cellular response to changes in the
microenvironment of the bone marrow or in response to oncogenic stresses (Méndez-Ferrer et al.,
2020).
To apparently contradict the above, high expression of CALR has been seen in different cancers
including; breast (Erić et al., 2009), gastric (Chen et al., 2009), pancreatic (Sheng et al., 2017) and
neuroblastomas (Chao et al., 2010). Increased CALR expression in these cancers is associated with the
concomitant expression of the anti-phagocytic CD47 (Boasman et al., 2019), resulting in rapid tumour
progression and poor disease outcome. Indicating that along with being a key regulator of
39
Ca2+ homeostasis and integrin-dependent signalling in healthy cells it may also be involved in the
progression of cancers (Fucikova et al., 2020). Alternatively, some myeloid malignancies showed
overexpression of CD47 which could be independent of the CALR expression causing independent
negative prognostic impact in cancers (Chao et al., 2010).
Somatic frameshift mutations in CALR exon 9 have been identified in different cancers but are largely
associated with MPN. Over 50 different mutations have been identified to date, with type 1 mutation
(a 52-bp deletion) and type 2 (a 5-bp insertion) seen most frequently (Cazzola & Kralovics, 2014). A
protein secreted by the mutated CALR binds to the CALR receptors on the cell surface acting as a
decoy subverting CALR’s immunosurveillance activity in favour of tumour progression (P. Liu et al.,
2020). Patients with the CALR mutation tend to be younger than those with other non-mutated
variants and experience improved disease outcome. MF and ET patients bearing CALR mutations
display lower incidence of anaemia and leucocytosis, lower Dynamic International Prognostic Scoring
System (DIPSS) score and reduced frequency of spliceosome mutations, as well as higher platelet
counts than patients with wild-type CALR (Rotunno et al., 2014; Tefferi et al., 2014). CALR mutants
have been shown to activate the thrombopoietin receptor (MPL) resulting in ligand-independent
signalling through JAK-STAT, PI3K and MAPK pathways (Chachoua et al., 2016). Evidence suggests that
CALR mutation (as opposed to a JAK2 or MPL mutation) in ET patients have a lower risk of thrombosis
(Rotunno et al., 2014). Leukemic transformation is lower, in people with CALR mutations versus those
with JAK2 mutations (2.5% Vs 4.3%, respectively), with survival rates for CALR-mutated MF being
greater when compared to JAK2 mutated or triple-negative patients with a median survival of 17.7
years for CALR versus 9.2 years for JAK2 and 9.1 years for MPL (Rumi et al., 2014). This shows the
potential for changes in CALR to impact upon disease progression/prognosis.
1.9: CD47
CD47 is known as the “don’t eat me signal” and thus prevents phagocytosis by macrophages
(Boasman et al., 2019). It has a variety of key roles in immune regulation, atherogenesis, stress
40
resistance and tumour development (Eladl et al., 2020). CD47 is comprised of an extracellular
immunoglobulin (Ig) variable domain, a five times transmembrane-spanning domain, and a short
alternately spliced cytoplasmic tail (Oldenborg & Per-Arne, 2013). It is a 47 kDa protein that appears
70 kDa when resolved via SDS-PAGE due to its hyperglycosylated structure (Mushegian, 2002).
Different CD47 tissue expression patterns can be seen, which has been proposed to be due to
different isoforms of CD47 determined by alterations in length of the cytoplasmic tail from between
4-36 amino acid long (Jaiswal et al., 2009). The most abundantly expressed isoform is isoform 2, while
isoform 4 is the second most abundant one (Reinhold et al., 1995). Expression of CD47 is seen in
virtually all cells and affects cellular proliferation, apoptosis, adhesion, migration and immune system
homeostasis (Oldenborg & Per-Arne, 2013). Seen in different hematopoietic and non-hematopoietic
healthy cells CD47 plays a crucial role in the homeostatic system by being down regulated to allow for
phagocytosis (Oldenborg et al., 2000).
The most important role of CD47 is to relay the “do not eat me” signal to the immune system, it does
this via binding to the SIRPα on the macrophage surface membrane. The “do not eat me” signal is
seen in healthy cells and younger red blood cells thereby preventing cell phagocytosis. Older, stress
damaged cells which should be eliminated from the circulation, express CD47 in much lower amount
(or not at all for mature red blood cells) on their surface membranes, allowing for their phagocytosis
by macrophages (Oldenborg et al., 2000; Jaiswal., et al., 2009). CD47 has gained interest and is
considered as a promising target for the treatment of cancers. Studies into CD47 in cancer have
shown many cancers express high levels of CD47 on their surface, allowing cancer cells to evade
immunosurveillance by protecting them from phagocytosis by macrophages (Boasman et al., 2019).
CD47 is commonly seen and overexpressed in cancer cell, in the attempt to protect themselves from
phagocytosis (Eladl et al., 2020). Studies within MDS have specifically shown that blocking the CD47
signal leads to selective phagocytosis of the MDS cells by reducing the “don’t eat me” signal (Pang et
al., 2013).
41
1.9.1: Mechanism of function and intracellular signalling
CD47 plays an essential role in various cellular functions, these functions can occur through cell-cell as
well as cell-extracellular matrix interactions (Sick et al., 2012). Interactions of CD47 are mediated
through binding to extracellular ligands; signal regulatory proteins (SIRPs) and thrombospondins
(TSPs), membrane ligands and internal ligands (Eladl et al., 2020) (Figure 1.5).
SIRPα is also known as CD172a, Src homology 2 domain-containing protein tyrosine phosphatase
substrate-1 (SHPS-1) (Hatherley et al., 2008). It is expressed on haematopoietic stem cells and their
derived myeloid cells; granulocytes, monocytes, macrophages and dendritic cells (van Beek,
Cochrane, Barclay, & van den Berg, 2005). SIRPα is a transmembrane glycoprotein consisting of an
extracellular N-terminal domain composed of three immunoglobulin-like and a cytoplasmic domain,
two tyrosine phosphorylation sites and four immunoreceptor tyrosine inhibitory motifs (ITIMs)
(Hatherley et al., 2008).
42
Figure 1.5: The impact of the SIRPα–CD47 checkpoint and its blockade is depicted, the regulation of phagocytosis by SIRPα–
CD47 Immune Checkpoint. (A) On tumour cells surface SIRPα interacts with its ligand CD47, SIRPα undergoes tyrosine
phosphorylation and recruits the protein tyrosine phosphatases SHP-1 and SHP-2. These phosphatases inhibit the ability of
prophagocytic receptors to trigger phagocytosis when ligands are present on tumour cells. (B) prevention of the interaction
between CD47 and SIRPα , stops the prophagocytic receptors being able to induce productive activating signals, which lead
to phagocytosis (Veillette & Chen, 2018).
A phosphorylation reaction of the ITIM is induced by CD47 binding to the N-terminal of SIRPα on the
phagocytic cells, activating the protein tyrosine phosphatases (PTPases); Src homology region 2 (SH2)
domain-containing phosphatase1(SHP-1) and 2(SHP-2) (Tsai & Discher, 2008). Dephosphorylation of
immunoreceptor tyrosine activation motifs (ITAM) gives the “don't eat me” signal to the immune
system preventing the macrophage from engulfing the cancer cell, leading to the immunoevasion and
survival of the cancer cell (Murata et al., 2014).
43
CD47 also interacts with thrombospondins, extracellular matrix calcium-binding glycoproteins, that
are secreted by vascular inflammatory cells which regulate cellular functions including motility,
proliferation, and differentiation (Broom et al., 2009). The extracellular IgV section of CD47 binds to
the C-terminal binding domain (CBD) peptide 4N1K of TSPs. Disruption of CD47-TSP-1 interaction by
TSP-1-blocking antibodies has had positive outcomes in cancer therapies (Oaks et al., 2018), and has
shown a down-regulation of CD47 on tumour cells in multiple myeloma (Kukreja et al., 2009).
1.9.2: CD47 pathways targeted for therapeutic intervention
One of the overarching aims of CD47 targeted therapies is inhibition of the CD47-SIRPα axis. By
inhibiting CD47-SIRPα binding, anti-CD47 antibodies and anti-SIRPα antibodies stop the “do not eat
me” signalling in macrophages, allowing phagocytosis of the cancer cells by the macrophages.
Macrophage-mediated phagocytosis of cancer cells has been shown to be promoted when CD47-
SIRPα binding is targeted. As a result, APCs presents cancer cell antigens to T cells via MHC class I,
activating CD8+ T cells which can also target cancer cells, resulting in their destruction. Inhibition of
the CD47-SIRPα signalling also can increase cancer cell phagocytosis when dendritic cells migrate to T
cell zones in lymph nodes which present the cancer cell antigens to the T cells in turn activating the
CD8+ T cells (Hayat et al., 2020). Anti-CD47 treatments target the CD47-SIRPα signalling by; enhancing
the phagocytosis of cancer cells via macrophages, activation of apoptosis in cancer cells and inhibiting
the maturation of dendritic cells causing phagocytosis.
Mimicry peptides and gene silencing (siRNAs and miRNAs) have been used to inhibit CD47-SIRPα
binding to enhance the phagocytosis of cancer cell via macrophages, preventing the inhibitory signals
being received by the macrophages, resulting in increased phagocytosis (Willingham et al., 2012).
Activation of apoptosis in cancer cells can be activated by several anti-CD47 antibodies, initiating
independently of caspase-mediated cell death pathways (Manna & Frazier, 2004). The rate of the
44
induction of apoptosis of cancer cells differs with each anti-CD47 antibody, depending on the CD47-
targeting domain of the antibodies and the IgG isotype (Majeti et al., 2009).
In addition, anti-CD47 and SIRPα targeted therapies can also increase cancer cell phagocytosis by
promoting dendritic cell maturation and function. Binding of SIRPα on the surface of dendritic cells to
CD47 present on cancer cells, has been shown to be key in supressing dendritic cell maturation and
cytokine production, in turn decreasing phagocytosis of cancer cells (Matozaki et al., 2009). When the
interactions between CD47 and SIRPα are inhibited by anti-CD47, it leads to the dendritic cells being
able to mature, which in turn will force the production of anti-tumour cytokines produced by
dendritic cells, which in turn can promote cancer cell phagocytosis (Matozaki et al., 2009).
1.9.3: Anti-CD47 approaches to treat cancer and AML
In the immune system of cancer patients CD47 applies major effects. First the high increase in
expression of CD47 on the cellular membrane has been noted in many cancers, to protect the cancer
cells against phagocytosis by macrophages through CD47-SIRPα activation. Second, CD47 activates
and inactivates immune responses through reverse signalling pathways to protect the cancer cells. In
the treatment of cancer, CD47 therapies are being utilised in three main ways; SiRNA and miRNA-
based approaches, antibody-based approaches and peptide-based approaches. These anti-CD47
antibody therapies will usually be used in conjunction with cancer targeting chemotherapies, the aim
of this combination of therapies is to achieve higher anti-cancer efficacies (Chao et al., 2010).
Many studies in both cell lines, and mouse models have utilised anti-CD47 miRNAs and siRNAs to
reduce the expression of CD47, leading to the increased phagocytosis of cancer cells. Wang et al and
Lee et al studies used mouse models to show a suppression of the tumour growth in hepatocellular
carcinoma (Lee et al., 2014), and melanoma (Wang et al., 2013). CD47-targeting siRNAs studies have
also focused on oesophageal squamous cell carcinoma where a reduction of CD47 expression and a
suppressed tumour progression was shown and in colon cancer where tumour growth was inhibited
45
(Broom et al., 2009; Suzuki et al., 2012). Therapies using siRNAs and miRNAs to silence the CD47 gene
have exhibited low bioavailability and poor cellular uptake, resulting in poor delivery to intended site
leading to untargeted effect of the siRNAs and miRNAs causing safety concerns and limiting their
therapeutic application (Gao & Huang, 2013).
Antibody-based approaches use anti-CD47 antibodies to target specific cancer cells for macrophage-
mediated phagocytosis (Weiskopf et al., 2016). Unlike with siRNAs and miRNAs targeted therapies
anti-CD47 antibodies do not target non-specific cells, instead the antibodies tend to target cancer
cells more than normal cells due to the higher levels of CD47 on the cell surface (Gheibi Hayat et al.,
2019). Anti-CD47 antibodies are effective due to the specific “eat me” and “do not eat me” signals
that are expressed from dying cells and cancer cells, which encourage binding to their receptors on
the surface of macrophages resulting in phagocytosis (Tajbakhsh et al., 2019). Other pro-phagocytic
markers have been found to aid phagocytosis, via supporting CALR translocation through the cell from
the endoplasmic reticulum to the cytoplasmic membrane, where it binds the cancer cell to the
macrophages low-density lipoprotein receptor-related protein (LRP) initiating immune attack (Gardai
et al., 2005). Several different antibody-based compounds have shown promising signs. Preliminary
studies in non-human primates show the anti-CD47 humanized antibody Hu5F9-G4 prevents the
binding of CD47-SIRPα in haematological malignancies by binding to CD47, thus promoting
phagocytosis of cancer cells and giving the ability to control the growth of the malignancies in vivo
(Piccione et al., 2015). In vivo studies of the anti-CD47 antibody ALX148 is being used to treat solid
and hematologic malignancies. Studies have shown that ALX148 treatment increases phagocytosis by
macrophages, a shift in tumour-associated macrophages towards an inflammatory phenotype,
increased T cell effector function and increased proinflammatory cytokine production whilst reducing
the expression of suppressive cells in the tumour microenvironment (Kauder et al., 2018).
Peptide-based approaches use a mimicry peptide compound to bind to the target or use mimicry
peptides to bind to its receptor. Anti-CD47 peptide-based approaches will target either CD47 or its
46
receptor SIRPα rendering it inactive. 4N1K is a well-known anti-CD47 mimicry peptide, which binds to
CD47, mimicking part of the TSP-1 C-terminal domain (peptide sequence - KRFYVVMWKK) (Barazi et
al., 2002). In breast cancer cell lines 4NK1 activation of CD47 initiates caspase-independent apoptosis
(Manna & Frazier, 2004b). TTI-621 is a mimicry peptide that promotes phagocytosis of cancer cells
and is effective in controlling the growth of different tumour cells. It is a fully human recombinant
fusion protein that is used as a SIRPα Fc decoy, binding to the SIRPα N-terminal domain rendering
CD47 activation (Petrova et al., 2017).
Specially, AML leukemic stem cells and AML cells express CD47 highly, the high expression of CD47
can be considered as an independent prognostic factor for poor prognosis shown in two adult cohorts
of AML patients. Phagocytosis of AML leukemic stem cells in vitro has been initiated with the
treatment of monoclonal anti-CD47 antibodies (B6H12.2 and BRIC126), B6H12.2 and BRIC126 have
also shown to inhibit their growth in mice models (Jaiswal et al., 2009; Majeti et al., 2009). The anti-
CD47 antibody magrolimab has shown early promising results when combined with azacytidine in a
combination treatment of AML and MDS (Chao et al., 2020).
Earlier treatment with chemotherapeutic agents prior to the addition of anti-CD47 treatments have
been noted to stimulate the adaptive immune response against cancer-specific antigens resulting in a
better outcome (Liu et al., 2015). A different strategy of combining anti-CD47 antibodies with the
administration of anti-programmed death-ligand 1 (PD-L1) antibodies an immune checkpoint
inhibitor, has shown promising results in preclinical mice studies (Sockolosky et al., 2016). These early
preclinical results highlight the power and potential of chemotherapeutic drugs in combination with
anti-CD47 therapeutics in the future of myeloid malignancies.
47
1.10: Purposed aims of research
Whilst a role of CALR and CD47 has been established in cancer and to some extent investigated in
AML, less is known about MDS and MPN and as such this thesis will be looking to establishing the
implication of CALR and CD47 in treatment response and prognosis in MDS and MPN.
Chapter 3, will focus on establishing the differences in CALR and CD47 expression levels in untreated
vs treated MDS and MPN cell line models, which will provide insight on the relationship between the
expression rates of CALR and CD47 and how current therapeutic interventions alter CALR and CD47
localisation in untreated and treated cell line models in response to treatments.
Chapter 4 will analyse CALR/CD47 expression in MDS and MPN patient’s samples to confirm data in
cell lines.
Chapter 5 will assess the interactions, or lack thereof, between CALR and CD47 by determining what
effect knocking down one of them has on the other, and the impact on chemotherapy response in
MDS and MPN cell models.
49
2.1: Ethics
This study gained ethical permission for this work to be undertaken via the University of Lincoln, UK
ethics board for the use of human derived cell line models and clinical samples. Clinical sample details
have been given later and further information on them and ethical approval is detailed in section -
Patient Samples Processing and Analysis.
2.2: Cell Line Models of MDS & MPN
Due to there not being perfect cell lines that completely follow the phenotype of these diseases, the
following 5 cell line models were used with 2 cell lines similar to MPN, 1 x Intermediate (which has
specific phenotypes of each disease) and 2 cells line similar to MDS (all have been extensively used
within the literature to explore these diseases). Cell line models used were purchased from DSMZ
(Deutsche Sammlung von Mikroorganismen, Website; www.dsmz.de) and ATCC (Website;
www.lgcstandards-atcc.org/Products/Cells_and_Microorganisms/Cell_Lines.aspx?geo_country=gb).
Details on the suspension cell lines, where they were derived from, recommended seeding volumes
and exact location acquired from as seen in Table 6.
Cell line Recommended
seeding volume
Type Acquired from
K-562 100,000 cell per
mL
established from the pleural effusion of a 53-
year-old woman with chronic myeloid leukemia
(CML) in blast crisis in 1970; cells carry the BCR-
ABL1 e14-a2 (b3-a2) fusion gene
ATCC
MOLM-
13
400,000 cells
per mL
established from the peripheral blood of a 20-
year-old man with acute myeloid leukemia AML
FAB M5a at relapse in 1995 after initial
DMSZ
50
myelodysplastic syndromes (MDS, refractory
anemia with excess of blasts, RAEB)
SKM-1 400,000 cells
per mL
established from the peripheral blood of a 76-
year-old Japanese man with acute monoblastic
leukemia (AML M5) in 1989 following MDS
DMSZ
HEL-92 200,000 cells
per mL
Established from the bone marrow of a 30-year-
old Caucasian male with AML following MPN
ATCC
GDM-1 500,000 cells
per mL
established in 1979 from the peripheral blood of
a 66-year-old woman with a Philadelphia
chromosome negative myeloproliferative
disorder (resembling chronic myeloid leukaemia,
CML, in blast crisis) after transformation to
refractory acute myelomonocytic leukaemia
(AML)
ATCC
Table 9: Table of MDS cell line models (MOLM-13/SKM-1), intermediate cell line model (K562) and MPN cell line models
(HEL-92/GDM-1). Each cell line was acquired as stated from ATCC or DMSZ, were the descriptions were abbreviated from
DSMZ (Deutsche Sammlung von Mikroorganismen, Website; www.dsmz.de) and ATCC (Website; www.lgcstandards-
atcc.org/Products/Cells_and_Microorganisms/Cell_Lines.aspx?geo_country=gb).
2.3: Cell Culture
All cells were initially grown from frozen cell stocks kept in the liquid phase of liquid nitrogen, with
each vile containing 1x106 cells stored in a solution of 90% RMPI medium (1% penicillin/streptomycin
and 10% FBS) and 10% DMSO (a cell cryogenic preservative). Cells were cultured in class 2 cabinets
in sterile tissue culture rooms throughout using aseptic conditions as standard. Each cell line was
cultured in similar conditions. Any specific changes to conditions or supplementation will be
51
addressed in the methods section of each results chapter. All cell lines were grown in growth media
of RPMI-1640 (Invitrogen, Paisley, UK) supplemented with 1% penicillin/streptomycin (Invitrogen,
Paisley, UK) and 10% foetal bovine serum (Invitrogen, Paisley, UK) to prevent any infections and
support cell growth. Cells were incubated at 37oC with 5% CO2 throughout to support cell growth.
Upon removing cells from liquid nitrogen, they were thawed in a water bath at 37oC for 2-3 minutes,
then added to a falcon tube with 4 mL RMPI media (heated to 37oC to avoid shocking the cells) and
then centrifuged at 13,500 x RPM for 3.5 minutes at room temperature to pellet the cells (Micro Star
17R, VWR, Lutterworth, UK). The supernatant was then poured off to remove any DMSO contained
in the media, which may damage the cells and prevent future cell growth. The pellet was then
resuspended in 2 mL of fresh RPMI media initially to maintain density to support cell growth, added
to a T25 Flask (Sarstedt, Leicester, UK) and left to incubate at 37oC for 3 days to initially access cell
growth.
2.3.1: Testing cell lines for mycoplasma via qPCR
A premix solution was prepared for each cell line from the MycoSEQ™ Mycoplasma Real-Time PCR
Detection Kit (Thermo Scientific, Loughborough, UK). Each premix included 15 µL of Power SYBR™
Green PCR Master Mix, 2x and 3.0 µL of Mycoplasma Real‑Time PCR Primer Mix, 10x. Four microamp
tubes were set up for each cell line; Negative control - 18 µL of the premix solution with 12 µL of
Negative Control (water), Unknown sample reaction - 18 µL of the premix solution with 10 µL of
unknown sample and 2 µL of Negative Control (water), Inhibition-control reaction - 18 µL of the
premix solution with 10 µL of unknown sample and 2 µL of the Discriminatory Positive Control,
Positive control reaction - 18 µL of the premix solution with 10 µL of Negative Control (water) and 2
µL of the Discriminatory Positive Control. The samples were ran according to the 2.11 qPCR protocol.
The results would show different readings for the unknown samples compared to the controls in the
presence of mycoplasma.
52
2.4: Cell counting and seeding After 3 days incubation the cells were assessed to see if they had grown to confluency. To assess cell
growth, cell solutions were pipetted into a sterile 15 mL falcon and centrifuged at 13,500 x RPM for
3.5 minutes at room temperature to pellet the cells. Once pelleted, the supernatant was removed,
and cells were resuspended by gently pipetting up and down in 3 mL of pre-warmed RMPI media
and then counted. To perform the cell counting 20 µL of cell suspension was added to 20 µL 0.4%
trypan blue solution (Sigma-Aldrich, Poole, UK) and left to incubate at room temperature for 5
minutes to allow for the distinction between living and dead cells. Once incubated, 20 µL was
pipetted onto a haemocytometer chamber (Counting Chamber Improved Neubauer; Hawksley,
Sussex, UK), with a cover slip placed over the top to secure single layer of cell suspension. Cells were
counted in each of four corner quadrants (1 mm x 1 mm x 0.1 mm) of the 16-square grid. The sum of
the counted quadrants was divided by 4 (to get an average of the quadrants counted) and then
multiplied by the dilution factor of the trypan blue (2), this gave cell count per 1 x 10-4 ml. Final cell
count per mL was determined by multiplying by 10,000, to get number of cells per ml. Cell counts
were used to seed cells at required seeding density appropriate for specific cells (Table 6), and then
re-seeded in fresh T25 flasks, to continue growth/enable experiments to be undertaken.
2.5: Growth curves
Growth curves of untreated and treated cell lines were conducted using 12-well microtitre plates
(Sarstedt, Leicester, UK). Cells were seeded (1 x 105 cells per well) in 1 mL of RPMI 1640 growth
complete media. Cells to be drugged were seeded at the same density with the intended drug at
different dilutions ranging from control to high drug concentration. Drug treated samples were
gently mixed using a pipette. In 24 hour increments the cells were retreated with the specific drug
without changing the media, repeated until the time-period has ended. Running at the most for 96
hours. Both drugs half-life was around 8 hours. Therefore, retreatment was completed every 24
hours.
53
At each 24 hour time point cells were agitated to make sure all are in suspension and all of the cells
were removed from the wells. To assess cell growth at each time point, 20 µL of cell suspension was
added to an equal quantity of 20 µL 0.4% trypan blue solution (Sigma-Aldrich, Poole, UK) in a 500ul
Eppendorf and left to incubate at room temperature for 5 minutes. From this solution, 10 µL was
pipetted onto a haemocytometer chamber with cover slip attached. Dead cells with permeable
membranes took up the dye and stained blue; live cells did not stain and reflected light. Cells were
counted in each of four corner quadrants (1 mm x 1 mm x 0.1 mm) of the 16-square grid as
described earlier in the cell counting section with a modification that the number of both live and
dead cells were counted.
2.6: Drug titrations and drugging of cell lines
Cells lines were treated with azacytidine and ruxolitinib, drugs that are specific to MDS and MPN,
respectively, (as described in the Introduction) to investigate how therapy could impact upon
pathways within these cell line models. Azacytidine (Sigma-Aldrich, Poole, UK) and ruxolitinib
(Selleckchem, Newmarket, UK) (supplied at 5mg) were dissolved in 1 mL DMSO (Sigma-Aldrich,
Poole, UK) for drug stocks and applied to cell cultures diluted in media at a 1/100 dilution of drug to
final volume of culture media. Drug titrations were first performed to work out final concentration of
drug required. Drug titrations took place using 12-well microtitre plates (Sarstedt, Leicester, UK).
Determination of possible drug concentrations gathered from published data were used as a base
line then with the low/high concentrations adapted from them. Cells to be drugged were seeded at
5 x105 with the intended drug at different dilutions ranging from control to high drug concentration.
Cell solutions were then gently mixed to ensure equal distribution of drug through the cell media
using a pipette. In 24 hour increments, the cells were retreated with the specific drug every 24 hours
without changing the media to ensure a constant level of drugging, repeated up to a maximum of 96
hours.
54
At the desired time points, cells were agitated to make sure all are in suspension and all the cells
were removed from the wells using a 1 mL graduated pipette and placed into a sterile 1.5 mL
Eppendorf. To determine the impact of the drugging on the cell viability, as previously described 20
µL of cell suspension was added to 20 µL 0.4% trypan blue in separate 0.5ml Eppendorf and left to
incubate at room temperature for 5 minutes. After incubation, 10 µL was pipetted onto a
haemocytometer chamber with cover slip attached. Dead cells with permeable membranes took up
the dye and stained blue; live cells did not stain and reflected light. Live/dead cells were then
counted (as described below).
The titration optimisation experiments were measured for 96 hours with measurements taken every
24 hours. This revealed that an appropriate time point of 48 hours and 5µg/ml of drug every 24
hours, were chosen for cells to be drugged. This is within the ranges that were supported by the
literature.
2.7: Cell viability assay
After optimising drug dosing these were done to check toxicity. Trypan blue assays were conducted
to show cell viability and drug toxicity. Cells were grown over a period of 96 hours with live and dead
cells counted using a microscope after each 24-hour stage, using same method previously
mentioned in growth curves of cell line models. A percentage of live cells could then be calculated
from the total cells. Cell viability percentage was calculated by dividing live cells by total cells (dead
and live) and multiplying by 100. The non-treated cells were considered 100% viable with the
calculations of the viability of the drugged cells as a percentage of the control. Calculating the
percentage of the cell viability indicates the drug toxicity. If toxicity is to high all cells would die
including healthy cells. Therefore, the aim is to affect the cells enough that some cells survive and
are affected by the drug without destroying all of them.
55
2.8: Harvesting of cells to undertake RNA and Protein Analysis
At the desired time point (48 hours), cells were counted (in the presence or absence of drugging)
and 1x106 cells were pelleted to undertake the following RNA extractions and protein analysis.
2.9: Extraction of RNA from cell lines
RNA extraction on cell line models (1 x 106 cells) was performed using the Quick RNA Mini-Prep
(Zymo Research, USA) as per manufacturer’s instructions. The kit supplied all reagents unless
specified. Cell pellets were resuspended in 300 µL RNA lysis buffer and an equal amount of 100%
ethanol (Fisher Scientific, Loughborough, UK) was added to lyse the cells. The lysate ethanol mixture
was pipetted into a Zymo-Spin™ IIICG Column placed on top of a clean 2 mL collection tube and then
centrifuged (Micro Star 17R, VWR, Lutterworth, UK) at 15,000 x g for 30 seconds to filter out debris,
and the flow through was discarded. The spin column which retained the mRNA was prewashed with
400 µL of RNA wash buffer and centrifuge at 15,000 x g for 30 seconds. A DNase reaction mixture (75
µL DNase digestion buffer and 5 µL DNase I) was added to the spin column membrane then
incubated at room temperature for 15 minutes, to remove any DNA products. After incubation, 400
µL of RNA Prep Buffer was added to the spin column and centrifuged at 15,000 x g for 30 seconds to
wash the filter: with the flow through discarded. A further washing step was undertaken with 700
µL RNA wash buffer added to the spin column before being centrifuged at 15,000 x g for 30 seconds
and discarding the flow-through. As a final wash step another 400 µL RNA wash buffer was added to
the spin column and centrifuged at 15,000 x g for 2 minutes. The spin column was then transferred
to a clean RNase free 1.5mL Eppendorf and 50 µL of nuclease-free water pipetted directly on to the
membrane of the spin tube to help elute the mRNA from the column. The column was then
centrifuged at 15,000 x g for 30 seconds, the spin column was discarded and flow-through
containing the purified mRNA was collected.
RNA yield and purity were determined by absorbance readings at 230/260/280 nm on a Nanodrop
2000 spectrophotometer (Thermo Scientific, Loughborough, UK). Purity is assessed on the 230/260
56
absorbance for nucleic acids with protein contamination being registered at 280 nm (Olson &
Morrow, 2012). The nanodrop is used to determine the RNA quantity and purity through
absorbance. To use the nanodrop the nucleic acid option was chosen on the software and the
pedestal was cleaned with ultra-pure water. The nanodrop was blanked using 2µL of the same
nuclease-free water used to dilute the RNA, then 2µL of the sample obtained from the spin column
was added to the pedestal and read by the nanodrop in duplicate, this was repeated for each
individual sample.
2.10: cDNA synthesis from RNA
To obtain cDNA from the mRNA, required to enable quantitative qPCR to be undertaken, iScript
cDNA synthesis kit (Bio-Rad, Hercules, USA), was used to synthesise complementary DNA from total
RNA as per manufacturer’s instruction, with the kit suppling all reagents unless specified. Initially a
reaction mixture was prepared as follows (20 µL total).
4 µL iScript reaction mix
1 µL iScript Reverse Transcriptase
1000 ng Total RNA (as worked out from Nanodrop)
Nuclease-free H2O to make up to a 20 µL reaction
Controls were performed in each synthesis in the form of a no-reverse transcriptase control (NRT) in
which nuclease-free water was substituted for the reverse transcriptase control, to ensure reagents
had no contamination. No-template controls (NTC) were also used where nuclease-free water was
used in place of RNA sample, to ensure the nuclease free water was not contaminated. The 20 µL
reaction mixture was then run in a thermocycler (Model: T100, Manufacturer: Bio-Rad, Watford, UK)
per the following settings to support synthesis:
57
Step1: 25ºC 5 minutes
Step 2: 42ºC 30 minutes
Step 3: 85ºC 5 minutes
Step 4: 4ºC Hold
After the RNA extraction, the RNA product was immediately used in cDNA analysis or stored at -20oC
until needed.
2.11: Quantitative PCR (qPCR)
Using the cDNA produced above, qPCR reaction was completed on a StepOne Plus thermocycler
(Applied Biosystems). SYBR Green chemistry probe methods were used. The SYBR Green method is
commonly used in qPCR. Fluorescent dye binds to the cDNA samples that have been specifically
labelled with the forward and reverse primers. Once bound energy is released as fluorescence which
increases/decreases along with the concentration of cDNA in the sample (Bookout, Mangelsdorf, &
Hughes, 2003).
Samples were prepared in MicroAmp Fast 96-well Reaction Plate (Applied Biosystems, UK). SYBR
Green reaction mixture comprised of: 0.5 µL of 10 mM primer (Sigma-Aldrich, Dorset, UK) of each
forward and reverse primer. In addition, 5 µL iTaq SYBR Green (Bio-Rad, Hercules, USA) and 14 µL
1/50 diluted cDNA in nuclease-free water was also added (Sigma-Aldrich, Dorset, UK), this was
achieved by adding 4µL of cDNA generated in the previous step to 196µL of nuclease-free water.
Primers were selected from predesigned primers previously designed within labs or from published
data, then a series of quality checks were performed on the efficiency and melt curves to select the
used primers as follows. Internal validation of the primers was conducted by running several (3-5) n
numbers making sure that the cT values and melt curves were similar. Primers for CALR and CD47
along with GAPDH are shown in Table 7. GAPDH is a housekeeping gene that is readily found
58
throughout the cell, allowing for levels of CALR and CD47 to be standardised (Nygard, Jørgensen,
Cirera, & Fredholm, 2007).
Forward Reverse
GAPDH TGCACCACCAACTGCTTAGC GGCATGGACTGTGGTCATGAG
CALR TGTCAAAGATGGTGCCAGAC ACAACCCCGAGTATTCTCCC
CD47 TATCCTCGCTTGGTTGGAC TCTTTGAATGCATTAAGGGGTTCCT
Table 10: Table of primer sequence use for GAPDH, CALR and CD47
Duplicates of each sample were ran for n3 (each n was of the same cell line from different time
points in the cell lines growth), with NRT and NCT controls, as described earlier for the cDNA
amplification step. qPCR reactions were run on a predesigned SYBR Green fast protocol. Conditions
of the cycle times and temperatures for the qPCR are shown below as per recommended.
Step 1: 95ºC 30 seconds
Step 2: 95ºC 3 seconds
Step 3: 60ºC 30 seconds
Repeat Step 2 – 3 x 39
Step 4: 60ºC + 0.3ºC incremental to 95ºC 3 seconds
Analysis of the qPCR was then completed using the method detailed in the statistics and data
analysis section.
2.12.1: Western blotting
Western blotting is a technique used for protein analysis. Using a primary antibody, the target gene
is located and then visualised using a secondary antibody as shown in Figure 2.1. Individual proteins
are separated from a sample on an SDS gel. This is then transferred to an appropriate membrane
59
using electrical currents. Once on the membrane the primary antibodies from one host and
secondary antibodies from a different host can be used to visualise the proteins.
Figure 2.1: Depiction of western blots antibodies on a membrane and the fluorescent image of proteins (Fluorescent
Western Blotting | Sino Biological, 2020).
2.12.2: Buffer formulations for SDS-PAGE and Western blot experiments
A series of common buffers and solutions are used throughout. Buffers used for SDS-PAGE and
Western blot are given below:
All generic buffer were made up as detailed in table 8;
Modified RIPA buffer
Used for protein lysis 50 mM Tris pH 7.5
150 mM NaCl
1% NP-40
10% Glycerol
Fisher Scientific, Geel, Belgium
Fisher Scientific, Geel, Belgium Thermo Scientific, Rockford, USA
Fisher Scientific, Loughborough, UK
Fisher Scientific, Geel, Belgium Model: Purelab Flex,
60
5 mM EDTA
Ultrapure (18.2 MΩ) H2O (to make up to specific volume)
Manfacturer: Elga Lab Water, High Wycombe, UK
SDS Loading buffer
Used to load protein into the SDS gel
10% SDS
50% Glycerol
200 mM Tris pH 6.8
Ultrapure H2O
Bromophenol Blue
Fisher Scientific, Geel, Belgium
BDH Chemicals, Poole, UK
SDS Running Buffer
Used to run the SDS gel
25 mM Tris
192 mM Glycine
0.1% w/v SDS
Melford Biolaboratories, Chelsworth, UK
Transfer buffer
Used to transfer proteins from SDS gel to membrane
25 mM Tris
192 mM Glycine
20% Methanol
Fisher Scientific, Loughborough, UK
Phosphate-buffered saline (PBS)
Used to dilute block solution and to mix with tween
PBS tablets Sigma-Aldrich, Poole, UK
PBS-Tween (PBS-T)
Used to wash the membrane
0.1% Tween-20 Fisher Scientific, Loughborough, UK
Blocking buffer
Used to block membrane from unspecific binding and to dilute antibodies
PBS
Blocking solution
Sigma-Aldrich, Poole, UK
LI-COR Biosciences, Cambridge, UK
Table 11: Buffer formulations for SDS-PAGE and Western blot experiments
2.12.3: Protein isolation and quantification in preparation for SDS-PAGE and Western blotting
Cells were harvested at 1x106 as stated earlier. Once cells were harvested to undertake protein
analysis, they were then lysed within an 1.5ml Eppendorf using 100µL of modified RIPA buffer
61
(Sigma-Aldrich, Poole, UK) with protease inhibitors, to prevent degradation of the protein during
storage. Bradford assay was performed to determine the total protein concentration, using Bradford
reagent (Bio-Rad, Watford, UK) and reading the absorbance at 595nm. A serial dilution was
performed using BSA (bovine serum albumin), 5µL of each serial dilution and 5µL sample were
added to 250µL Bradford reagent and absorbance read at 595nm (infinite M20 PRO, Tecan). A
standard curve was composed from the serial dilution and protein concentrations were calculated
from this as depicted in Figure 2.2.
Figure 2.2: Depiction of a Bradford assay standard curve.
2.12.4: SDS-PAGE
To create the acrylamide gel within which to run the protein samples, a 10% acrylamide gel was
made; composed of 1.5 M Tris- pH 8.8 (Fisher Scientific, Geel, Belgium), 0.5 M Tris- pH 6.8, 30%
acrylamide/bisacrylamide (Bio-Rad, Watford, UK), N,N,N',N'-tetramethylethylene diamine (TEMED)
(Bio-Rad, Watford, UK), 10% SDS (Fisher Scientific, Geel, Belgium) and 10% ammonium persulfate
0
0.5
1
1.5
2
2.5
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Abso
rban
ce 5
95 n
m
BSA standard mg/ml
Bradford assay - Absorbance 595
62
(Fisher Scientific, Geel, Belgium) which were mixed together in a universal tube. The 10% acrylamide
solution was then pipetted between 1mm SDS glass plates (Bio-Rad, Watford, UK), previously
secured in a gel casting station (Bio-Rad, Watford, UK) with a 15 well comb inserted to create the
wells. The gel was then left to set for 20-30 minutes.
Using the concentrations generated from the Bradford assay, 10mg of lysates were mixed with
loading buffer and loaded onto a 10% SDS-PAGE gel (together with a 0-120 bp ladder) within a mini
protean SDS-PAGE tank (Bio-Rad, Watford, UK) which was then filled with running buffer and ran at
120-150V (PowerPac™ Basic Power Supply, Bio-Rad, Watford, UK) until the proteins have separated
and the dye front had moved an adequate distance down the plate or reached the end of the plate.
2.12.5: Transfer to PVDF membrane
Proteins within the polyacrylamide gel were then transferred to a polyvinylidene fluoride (PVDF)
membrane using a wet-blot apparatus by passing a current across the gel to the membrane.
Tris/Glycine + 20% methanol transfer buffer (Fisher Scientific, Geel, Belgium) is then used during the
transfer step to aid in the disassociating of proteins from the SDS and to aid in the absorption of the
protein to the membrane. Proteins were transferred for 1 hour at +2 – 8ºC (to prevent overheating)
at 100V (Mini-PROTEAN tetra, Bio-Rad, Watford, UK).
2.12.6: PVDF membrane blocking and antibody incubation
The PVDF membrane was then incubated in blocking solution at room temperature for 1 hour, to
prevent any nonspecific binding. An antibody buffer was created using an appropriate amount of
primary antibody (see table 9), together with the required amount of fresh blocking buffer (antibody
dilutions are listed in table 8), and applied to the membrane and incubated overnight at 2 – 8ºC.
After incubation the primary antibody solution was removed, and the membrane washed 3 times for
5 minutes with PBS-T to remove any excess primary antibody left on the membrane.
63
Secondary fluorescent antibody dilutions were then prepared in 10ml of blocking buffer (see table 8)
and added to the membrane and left to be incubated in a light tight falcon tube for an hour at room
temperature, whilst gently rolling on a DigiRoll6 roller (SLS, Nottingham, UK) to ensure even
coverage. Use of a light tight falcon tube ensured that the florescent signal of the secondary
antibody would not be affected. After incubation the secondary antibodies were removed, and the
membrane washed 3 times for 5 minutes with fresh PBS-T each time to remove any excess
secondary antibody.
Visualisations of the fluorescent bands were completed using a digital developer (LI-COR
Biosciences, Cambridge, UK). Visualisation is achieved when the digital developer fires a targeted
light source onto the membrane. This excited the fluorescent dye (the secondary antibody) and
releases photons at its emission wavelength to return the molecule to its ground electronic state.
The light emitted from the protein is then captured at different wavelengths using specific filters by
the digital developer. The bands can then be further analysed, and densitometry undertaken as
detailed later in the statistics and data analysis section.
Table 12: Antibodies and dilutions used in Western blot experiments
Antibodies Primary or secondary antibody
Host Dilution in blocking buffer Manufacturer
CALR Primary Rabbit 1:2000 Abcam, Cambridge, UK
CD47 Primary Mouse 1:500 Abcam, Cambridge, UK
GAPDH Primary Rabbit 1:20000 Amsbio, Abingdon, UK
800 goat anti rabbit
Secondary Goat 1:50000 LI-COR Biosciences,
Cambridge, UK
700 donkey anti mouse
Secondary Donkey 1:50000 LI-COR Biosciences, Cambridge, UK
64
2.13: Fractionations of protein in cells
To investigate cellular localisation fractionation was undertaken, two kits were used to split samples
into the membrane, cytoplasm, cytosol and nucleus as no one kit for all for components is available.
Both kits work by using a series of buffers and centrifugations to break down and separate the
different components of the cell. The Nucper (Thermofisher scientific, Altringham, UK) kit was used
to, separate the sample into cytoplasm and nucleus samples and the Membranplus (Thermofisher
scientific, Altringham, UK), separating the sample into membrane and the cytosol.
In brief for the Nucper kits: Cell pellets (1x106) were resuspended in 100µL of nuc 1 solution then
incubated on ice for 10 minutes as per manufacturers guidelines. Then 5.5µL of nuc 2 solution was
added to the resuspended pellet solution and incubated for 1 min on ice. The sample was then spun
in a micro centrifuge at 16000g for 5 minutes. Once completed the supernatant of the sample
containing the cytoplasm is collected into a clean RNA free Eppendorf tube and placed on ice. The
remaining pellet was then resuspended in 50µl of nuc 3 solution. Samples were vortexed (VX100,
Labnet) for 15 seconds to fully suspend the content, then incubated for 40 mins at room
temperature, with a re-vortex every 10 minutes to ensure content doesn’t settle at the bottom of
the Eppendorf. The sample was spun in a micro centrifuge at 16000g for 10 minutes. Into a clean
RNA free Eppendorf tube the supernatant of the sample containing the nucleus, is collected and
placed on ice ready for protein extraction as detailed earlier.
For the Membrane plus kits: Cell pellets were resuspended in 100µL of mem 1 solution, then
incubated for 10 minutes at 4ºC whilst being gently agitated. The sample was spun in a micro
centrifuge at 16000g for 15 minutes. The supernatant containing the cytosol was collected and put
into a clean RNA free Eppendorf tube and placed on ice. The pellet was resuspended in 100µl of
mem 2 solution, then incubated for 30 minutes at 4ºC whilst being gently agitated. The supernatant
containing the membrane was collected and put into a clean RNA free Eppendorf tube and placed on
65
ice. All samples are either then used straight away for protein or frozen down at -80ºC. Protein was
extracted/analysed as described in earlier sections.
2.14: CALR and CD47 Knockdowns
CALR and CD47 knock downs using siRNA and lipofectamine LTX (Life technologies, California, US)
were performed on cell line models. siRNA transfections are used to supress a specific gene in cell
cultures. The technology degrades the mRNA after transcription preventing translation, therefore
the gene and the protein will not be expressed within the cell. As the exact relationship between
how CALR & CD47 work together is currently unknown in MDS and MPN, transfections of both genes
was undertaken to see how gene expression is altered without the other genes being present.
Using a 6 well plate (Sarstedt, Leicester, UK), for each cell line 2x105 cells per mL were seeded per
well. For each well seeded with cells, the following mixture was created in a clean 1.5ml Eppendorf -
1µg of specific siRNA and 1µL of plus reagent were added to 200µL of Opti-MEM media (Invitrogen,
Paisley, UK) and mixed gently and left to incubate for 5 minutes at room temperature. After
incubation, 2µL of lipofectamine LTX were added to each tube and incubated for 20 minutes at room
temperature to form DNA-lipofectamine complexes. Once incubation was completed, 200µL of the
DNA-lipofectamine complexes were added to each specific well and mixed by gently rocking back
and forth. The transfected cell was transferred into an incubator at 37ºC with 5% CO2 and left for 48
hours to enable knockdown to occur. After incubation, the cells were collected into a clean
Eppendorf tube and spun at 13500g for 5 mins to pellet them. The supernatant was discarded, and
the pellet frozen at -80ºC ready for future protein and RNA assays.
CALR siRNA sequence –
Sense -GGAGCAGUUUCUGGACGGAtt Anti-sense-UCCGUCCAGAAACUGCUCCtt
CD47 siRNA sequence –
66
Sense -GGUGAAACGAUCAUCGAGCtt Anti-sense-GCUCGAUGAUCAUUUCACCtt
2.15: Patient Samples Processing and Analysis
2.15.1: Isolation of Peripheral Blood Mononuclear cells (PBMCs) from human donors
All patients gave written informed consent before peripheral blood and bone marrow samples were
taken. All patients were greater than 18 years of age and had been diagnosed with an MPN or MDS
at the time of sampling via the doctors in charge of their treatment/care. Trained phlebotomists
were used to obtain the peripheral whole blood sample via venepuncture (vacutainer, vacutainer
tube EDTA and needles, Benton Dickinson, Winnersh, UK), and a surgeon took the bone marrow
samples. Each patient gave up to 12ml peripheral blood/6ml of bone marrow divided into 4mL
vacutainers containing EDTA (Ethylenediaminetetraacetic acid), to prevent the blood from clotting.
Three cohort groups were recruited for this research project;
Cohort 1: CALR and CD47 Expression levels in peripheral blood of patients with MPN
Patients recruited for this study were diagnosed according to the 2008 WHO criteria with essential
thrombocythaemia, polycytemia vera or myelofibrosis.
Exclusion criteria for this study were:
• Patients with any other myeloid neoplasms.
Cohort 2: CALR and CD47 Expression levels in peripheral blood of patients with MDS
Patients recruited for this study were diagnosed according to the 2008 WHO criteria with MDS.
Exclusion criteria for this study were:
• Patients with any other myeloid neoplasms.
67
Cohort 3: CALR and CD47 expression levels in peripheral blood of healthy aged matched controls.
Ethical approvals for the study are documented in the appendices.
Peripheral blood was obtained from MDS patients (n = 16) and MPN patients (n = 16), and healthy
control donors (n = 4). The healthy control samples were age matched and either taken from donors
at the Hospital or the University.
2.15.2: Processing and collection of mononuclear cells from patient blood samples
To separate out the blood samples the patient samples (whole blood) were diluted in a 15ml falcon
tube (Thermofisher, Oxoid, Basingstoke, UK) to a 1 in 3 dilution with the PBS/EDTA solution
(Thermofisher, Oxoid, Basingstoke, UK) and gently mixed by inversion. Diluted blood mixture was
then gently layered over 4ml (room temperature) of Ficoll-Paque Premium 1.077 g/ml (GE
Healthcare Life Sciences, Buckinghamshire, UK) in a 15 mL Falcon tube using an automated pipetboy
(Integra, Berkshire, UK) to provide a density gradient media to support fractionation of whole blood.
Layered blood samples were centrifuged at 400 x g for 40 minutes with the acceleration set to a
minimum in a swing bucket rotor centrifuge (Allegra X-15R, Manufacturer: Beckman-Coulter, High
Wycombe, UK) set to 18 ºC, which is the temperature for optimum yield for peripheral blood
mononuclear cell (PBMCs). Additionally, reduced acceleration and putting the brake to a minimum
reduced agitation to the contents of the falcon tube, keeping the separated layers intact. After
centrifugation, the blood sample and the Ficoll-Paque solution split into distinct layers. The interface
between the Ficoll-Paque and plasma layers is the PBMC layer, which was carefully extracted using a
Pasteur pipette (MERCK, Poole, UK), using a gentle circular motion around the falcon tube. The
collected PBMCs were then transferred to a clean 15 mL Falcon tube.
The PBMC layer was then washed in 10 mL of PBS/EDTA and centrifuged for 10 minutes at 300 x g
with brake switched on, then repeated at 200 x g for 10 minutes to support cell pelleting.
68
Supernatant was then discarded, and cells were gently resuspended in 5mL PBS. A cell count was
performed using 0.4% trypan blue (Sigma-Aldrich, Poole, UK) as described earlier. Using information
generated from the cell count, cells were split into 1x106/ml and aliquoted into 1.5mL Eppendorfs.
The sample was then centrifuged at full speed in a microcentrifuge (16000 rpm) to pellet remaining
PBMCs. The supernatant was aspirated completely, and the pellet stored at -80ºC freezer until use
ready for future RNA, protein or fractionation studies.
2.15.3: Extraction of RNA using TRIzol® method from PBMCs
Cell pellets containing 1 x 106 PBMC cells in a 1.5ml Eppendorfs were removed from -80oC and
solubilised from frozen in 500 µL TRIzol® reagent (Thermo-Fisher, Paisley, UK), to breakdown the
cells components whilst keeping the integrity of the RNA intact for 5 minutes. Then 100 µL of
chloroform (Sigma-Aldrich, Dorset, UK) was added and the tubes, shaken vigorously by hand for 15
seconds to mix the solution before being left to settle for 3 minutes. The TRIzol – chloroform mixture
was then centrifuged at 12,000 x g for 15 minutes at 4ºC to prevent RNA degradation (Micro Star
17R, VWR, Lutterworth, UK). After centrifugation, the mixture split into two layers, one containing
the RNA and the other containing DNA. The upper aqueous layer containing the RNA was removed
and placed in a clean RNA/DNA free Eppendorf where 0.25 µL of glycogen (20 µg/µL) (Life
Technologies, Paisley, UK) was also added to support co-precipitate of the RNA from the aqueous
phase, before being mixed gently by inversion. To the aqueous phase/glycogen mixture, 500 µL of
analytical grade isopropanol kept at room temperature (Fisher Scientific, Loughborough, UK) was
added and then left to incubate at room temperature for 10 minutes. After incubation, the sample
was centrifuged for 10 minutes at 12,000 x g at 4ºC in the micro centrifuge. Supernatant was
discarded and the pellet was washed with 75% ethanol (Fisher Scientific, Loughborough, UK) and
vortexed for 15 seconds. The pellet was then centrifuged for a further 5 minutes at 7500 x g at 4ºC.
Supernatant was discarded and the pellet was air-dried for 10 minutes at room temperature. After
air-drying, the pellet was dissolved in 50 µL of nuclease-free water, then incubated at 55-60ºC on a
69
block heater (Model: SBH130D/3, Manufacturer: Stuart, Stone, UK) for 10 minutes to support
resuspension of RNA before being taken forward ready for the clean-up phase.
2.15.4: Clean-up of PBMC RNA obtained using the TRIzol® extraction method
After phenol-chloroform extraction, a Qiagen RNeasy Mini kit (Qiagen, Hilden, Germany) was used
to clean-up the RNA as per manufactures details. Briefly, a 50 µL sample of previously prepared RNA
solution was placed in an Eppendorf and made up to 100ul with RNAse free water (Qiagen, Hilden,
Germany). To this solution, 350 µL of buffer RLT from the RNeasy kit was added to the sample and
mixed well, to which 250 µL of analytical grade ethanol (Fisher Scientific, Loughborough, UK) was
added and further mixed well by pipetting. The 700 µL sample was then transferred to the spin
column and centrifuged for 15 seconds at 10,000 x g (Micro Star 17R, Manufacturer: VWR,
Lutterworth, UK) at 4ºC using a RNeasy Mini kit spin column with a 2 mL collection tube; this allows
the spin column with a separate collecting tube below it to filter, clean and capture the RNA before
eluting. The flow-through was discarded.
DNase treatment was performed on the sample in the RNeasy Mini kit spin column using the RNase-
free DNase set (Qiagen, Hilden, Germany) to remove any potential remaining DNA contamination.
Then 350 µL of buffer RW1 was added directly to the column and centrifuged for 15 seconds at
10,000 x g, flow-through was discarded. An 80ul DNase solution (10 µL DNase I stock solution with
70 µL buffer RDD), was then added to the spin column membrane and incubated at room
temperature for 15 minutes. After incubation, 350 µL of buffer RW1 was added to the spin column
and centrifuged for 15 seconds at 10,000 x g, flow-through was discarded. Following centrifugation,
500 µL of buffer RPE was added to the spin column and centrifuged for 15 seconds at 10,000 x g,
flow-through was discarded and then this step repeated. After the wash steps, the spin column was
placed in a clean fresh collection tube and centrifuged at 17,000 x g for 1 minute to dry the
membrane. After spin drying 50 µL of nuclease-free water was added directly to the spin column
70
placed in a 1.5 mL sterile Eppendorf tube for collection, then centrifuged for 1 minute at 10,000 x g
and flow-through collected. RNA yield and purity were determined by absorbance readings at
230/260/280 nm on a Nanodrop 2000 spectrophotometer (Thermo Scientific, Loughborough, UK), as
previously described.
Protein was extracted from PBMCs using same method used for protein extraction in cell line models
(as previously described).
2.16: Data Analysis and Statistics
2.16.1: Calculating relative gene expression
Relative gene expression was calculated using the ΔΔCT method where the gene of interest was
normalised against the control gene GAPDH. Cell line and patient samples were processed into cDNA
and analysed on a qPCR machine which generated results in CT values. CT values were acquired from
the StepOne instrument software (v2.3).
ΔCT = CT (Gene of Interest) - CT(GAPDH)
ΔΔCT = ΔCT(Sample) – (Average ΔCT(Control Sample))
Relative quantification given by following transformation;
RQ = 2-(ΔΔCT)
Gene expression of patient data was collected then split into desired groups and the median gene
expression of the samples were compared against the healthy controls. Mann-Whitney U-tests were
utilised to determine the significance. All tests were carried out using Excel (Microsoft, New Mexico,
USA).
71
Gene expression of cell line data were calculated as described for patient sample data, with the
sample data being compared against the median gene expression. A t-test (α-= 0.05) (Excel) was
used to determine significance.
2.16.2: Calculating relative protein expression
Total relative protein expression was calculated by normalising the band intensity of the protein of
interest against the control protein GAPDH. Protein band intensity were acquired from the Licor
image studio software (digital developer FC, LI-COR Biosciences, Cambridge, UK), with relative
protein expression calculated by dividing the protein of Interest gene expression by the house
keeping gene GAPDH gene expression. Standard deviation error bars were calculated and a t-test (α-
= 0.05) (Excel, Microsoft, California, USA) was used to determine significance.
Samples that were fractionised were split into the four compartments then, using the total protein
expression of the sample’s, the percentage expression of each of the protein of interest were
calculated for each fraction. Standard deviation error bars were calculated as a percentage and a t-
test (α-= 0.05) (Excel) was used to determine significance. Significance stars are given as follows:
(*) p < 0.05; (**) p < 0.01; (***) p < 0.001; (****) p < 0.0001
A p value of less >0.05 was considered as significant. A T-test would be used on each sample Vs a
control to determine the significance, with all tests undertaken within Excel.
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Chapter 3: Establishing basal expression levels and location percentage of CALR and CD47 in MDS and MPN cell line models
73
3.1: Introduction
Currently there are a lack of specific gene biomarkers that can differentiate between disease
subtypes or stage of the disease progression within myeloid malignancy. Evidence has started to
emerge within solid tumours that alternations in CALR and CD47 levels, two genes linked to a
possible apoptosis vs survival mechanism in malignant cells, could dictate successful response to
chemotherapy (Boasman et al., 2019). Whilst the role of CALR and CD47 in solid tumours has been
less well established; it has been proposed that their role within MDS and MPN could be similar to
their roles in solid tumours, where a concomitant upregulated expression is seen between CALR and
CD47 on the surface of the cell (Boasman et al., 2019), (Figure 3.1).
CALR and CD47 are both present and functional in cell homeostasis and are located throughout the
different components of the cell, from the nucleus through to the membrane (Eladl et al., 2020;
Fucikova et al., 2020). CALR acts as a pro-phagocytic signal and presence of CALR on the cell surface
acts as a marker for the clearance of aged or damaged cells by the immune system (Gardai et al.,
2005). CD47 is involved in many processes; cellular proliferation, apoptosis, adhesion, migration,
immune system homeostasis and signalling to inhibit phagocytosis; giving CD47 the title of the
“don’t eat me signal” (Oldenborg et al., 2016) (Figure 3.1).
74
Figure 3.1: (A) Macrophages ignore normal cells as a result of negative interactions in which the CD47–SIRP-α pair promote
the “don’t eat me” signal. (B) Cancer cells showing high levels of CD47 can avoid phagocytosis despite having a higher level
of calreticulin but (C) blocking CD47 with antibody favours their uptake (Unanue, 2013).
In different human cancers (i.e. AML, acute lymphocytic leukaemia, chronic myeloid leukaemia,
ovarian cancer, glioblastoma, and bladder cancer) (Chao et al., 2010) CALR plays a role in pathways
including activation of the unfolded protein response (UPR), which controls the upregulation of
molecular chaperones and degrading misfolded proteins, calcium signalling pathways and
macrophage mediated immune evasion. CALR upregulation is often counterbalanced by the
concomitant expression of the antiphagocytic CD47 in solid tumours (Chao & Gotlib, 2014). During
oncogenesis upregulation of CALR on the surface of the cell, targeted cancers cells can be detected
and bound by the LDL receptor related protein 1 (LRP1), which is present on the surface of a
macrophage and induce phagocytosis (Chao et al., 2010). When a total upregulation of CALR is
expressed in cancer cells, a concomitant upregulation of the anti-phagocytic CD47 protein on the cell
75
surface also occurs. This response is a key survival mechanism by cancer cells to evade
immunosurveillance and keep the cancer cells alive (Feng et al., 2015). Cells being able to evade
immunosurveillance is a key hallmark for cancer and is achieved by various different mechanisms
(Hanahan and Weinberg, 2011). Within cancer cells high CALR expression promotes signalling for
phagocytosis, while high expression of CD47 protects the cancer cell from an immune response. It is
this ratio of CALR to CD47 that directs the immune response, depending on which signal is
predominate in the cell’s fate (Feng et al., 2015).
Interestingly, it is overexpression of CALR with a concomitant CD47 expression (both mRNA and
protein cell surface levels) that has been correlated with poor prognosis in many different cancers
including mantle cell lymphoma, superficial and invasive bladder cancer, and neuroblastoma (Chao
et al., 2010). Chao et al. (Chao et al., 2010) showed a correlation between CALR overexpression,
together with counteracting CD47 overexpression, exhibiting lower survival rates. Patients across
different solid tumour cancers (lung, colon and ovarian) and in haematological cancers including
AML have exhibited higher expression rates of CD47 which correlate clinical outcomes (Majeti et al.,
2009; Willingham et al., 2012). When CD47 expression increases on the surface of cancer cells it
signals for survival by evading immune surveillance, acting as a marker of anti-phagocytosis (Tsai et
al, 2010; Galli et al., 2015).
Within the haematological disorders, including MDS and MPN, the role of CALR and CD47 has not
been as well characterized. However CD47 over expression on the membrane of cancer cells has
been measured in AML (Jaiswal et al., 2009; Majeti et al., 2009), suggesting CD47 as a potential
therapy. Patients with MDS, can progress on to AML throughout the course of their treatment and
these patients show a gradual increase of CD47 expression throughout the stages of their disease
progresses (Jiang et al., 2013). Jiang et al showed that MDS expressed the CALR/CD47 ratio
differently to solid tumours (Jiang et al., 2013). Initially over expression of CALR and lower levels of
CD47 were witnessed in low risk patients, with this ratio altering as disease progress with increasing
76
CD47 expression on the cell surface being seen with concurrent lower levels of CALR seen in higher
risk patients, suggesting a potential role of CALR/CD47 ratio in myeloid malignancies (Chao et al.,
2010). This suggests that the role of CALR/CD47 ratio in MDS and MPN could act differently to that
seen in solid tumours.
3.2: Aims
The overarching aim of the project is to establish the differences of CALR and CD47 expression levels
in untreated and treated MDS and MPN cell lines, which will provide insight on how current
therapeutic strategies could affect levels of CALR and CD47 in MDS and MPN patients. Specifically,
this chapter will:
• Examine the differences between CALR and CD47 basal levels mRNA and protein comparing
MDS and MPN cell line models
• Expose each cell lines to azacytidine and ruxolitinib to determine the CALR and CD47
expression modifications when cells are treated
• Identify changes in CALR and CD47 within cell compartments during and after treatment
3.3: Methods
Methods are described in brief here only where they differ from the main Methods in Chapter 2 (p
43).
3.3.1: Cell culture and drug treatment
Cell lines were cultured and treated as previously described, with cell survival rates calculated using
trypan blue assay. All cell lines were drugged and grown/harvested to produce n3 (The experiment
was completed three independent times). All treatment concentrations were derived through an
optimisation process. Initially, an appropriate concentration range would be selected informed
through data available across multiple literature sources. Concentrations ranges below and above
77
this given starting concentration were then tested on each cell line for each treatment and then a
suitable concentration for each treatment was picked for use across each cell line model. The final
drug dose used across each cell line was 5µg/ml.
3.3.2: Protein analysis Protein expression analysis was conducted using protein lysate extracted from untreated and
treated samples of all cell line models. Western Blotting was completed. Densitometry readings
were taken from each band for each cell line model on western blots and qualitative analysis was
generated from the densitometry readings, resulting in normalised signals of the target bands.
3.3.3: Gene analysis
Gene expression analysis of CALR and CD47 was conducted using qPCR generated from RNA
extracted from untreated and treated samples of all cell line models.
3.3.4: Fractionisations
All the cell line models were fractionated into the cellular components of membrane, cytoplasm,
cytosol, and nucleus using two fractionation kits.
3.4: Results
3.4.1: CALR and CD47 changes and impact on survival in cell line models
To determine the effect of standard-of-care drugs on cell survival, cell counts from 24h and 48h after
incubating with 5µg/mL of specific drug with re-drugging occurring at 24 hours were evaluated. All
cell lines were subjected to treatment with both azacytidine (MDS treatment) and ruxolitinib (MPN
treatment) as shown in Figure 3.2. Survival rate was calculated as fraction of alive cells from the
total cell pool treated and expressed as %.
78
When treated with azacytidine MOLM-13 and SKM-1 (MDS models) exhibited an overall survival rate
of 71% and 74% respectively. HEL-92 and GDM-1 (MPN models) exhibited an overall survival rate of
86% and 92% respectively, with K562 (MDS/MPN overlap model) exhibiting a survival rate of 77%.
When treated with ruxolitinib, the HEL-92 and GDM exhibited an overall survival rate of 71% and
76%, respectively. MOLM -13 and SKM-1 exhibited an overall survival rate of 97% and 93%,
respectively, K562 exhibiting a survival rate of 81%. The cell line models acted as expected showing
higher death rate when exposed to the appropriate drugs as per specificity.
Figure 3.2: Overall percentage survival rate of each cell line model treated with both azacytidine and ruxolitinib. All cell
lines and drugging were grown/harvested at different time points to the n3 (mean of three independent experiments).
Total cell counts were determined after 48 hours from Live and dead cells after treatment with 5µg/mL of specific drug
with re-drugging occurring at 24 hours. The error bars represent the standard deviation between the n3.
3.4.2: Alterations in CALR and CD47 mRNA expression in untreated & treated cell lines In both MDS (MOLM-13 and SKM-1) cell line models, low expression of CALR and CD47 was seen at
baseline (Figure 3.3). When treated with azacytidine, increased median fold expression of CALR was
shown in both MOLM – 13 and in SKM – 1 (p=0.017); (19-fold and 42-fold, respectively). Increases in
0
20
40
60
80
100
MOLM-13 SKM-1 k562 HEL - 92 GDM - 1Perc
enta
ge o
f liv
ing
cells
pos
t tre
atm
ent
Overall % survival rate of each cell line model treated with azacytidine and ruxolitinib
Azacytidine Ruxolitinib
79
CD47 median fold expression were also shown in both MOLM – 13 (p=0.023) and SKM – 1 (68-fold
and 5-fold respectively). When treated with ruxolitinib, a similar increase of CALR expression to 35-
fold was shown in MOLM-13, however, CD47 expression was unchanged compared to the untreated.
SKM-1 exhibited a significant increase in both CALR and CD47 (10-fold; p=0.045 and 15-fold;
p=0.044) when treated with ruxolitinib.
80
Figure 3.3: Mean expression of CALR and CD47 mRNA in the MDS cell line models - MOLM-13 (A) and SKM-1 (B). Each cell
line model was treated with azacytidine and ruxolitinib. Each mean was comprised from n3 for each cell line model.
Significant increases between the drug treated compared to the untreated were seen in: MOLM-13 showed no significance
in CALR azacytidine treated or in CALR and CD47 ruxolitinib treated. CD47 azacytidine showed significance (T Test; p =
0.023). SKM-1 showed no significance in CD47 levels when treated with azacytidine. Significance increases was shown in
0
10
20
30
40
50
60
70
80
90
Untreated Azacytidine Ruxolitinib
2^(-Δ
ΔCt)
fold
cha
nges
*
Expression of mRNA in untreated and treated MOLM-13 cell line models
CALR CD47
A
0
10
20
30
40
50
60
70
80
90
Untreated Azacytidine Ruxolitinib
2^(-Δ
ΔCt)
fol
d ch
ange
s
* * *
Expression of mRNA inuntreated and treated SKM-1 cell line models
CALR CD47
B
81
CALR levels upon azacytidine treatment (p = 0.017) and in CALR upon ruxolitinib treatment (p = 0.045) and CD47 (p = 0.044).
The error bars represent the standard deviation of the n3 between the drug treated samples compared to the control.
When treated with azacytidine and ruxolitinib the intermediate K562 cell line model showed a
similar trend to the ones witnessed in the MDS cell line models with CALR having the greatest
increase when treated with azacytidine and ruxolitinib (70-fold and 35-fold respectively) with
azacytidine treatment showing significance (p=0.034) (Figure 3.4). Increases in CD47 were also seen,
with 11-fold (p=0.035) and 13-fold (p=0.012) increases seen upon azacytidine and ruxolitinib
treatment, respectively.
Figure 3.4: Mean expression of CALR and CD47 mRNA in K562 cell line models. Each cell line model was treated with
azacytidine and ruxolitinib. Each mean was comprised from n3 of the cell line model. Significant increases between the
drug treated compared to the untreated were seen: K562 showed no significance in CALR ruxolitinib treated. Significance
was shown in azacytidine treated CALR (p = 0.034) CD47 (p = 0.035) and in ruxolitinib treated CD47 (p = 0.012). The error
bars represent the standard deviation of the n3 between the drug treated compared to the control.
0
10
20
30
40
50
60
70
80
90
100
Untreated Azacytidine Ruxolitinib
2^(-Δ
ΔCt)
fol
d ch
ange
s
* * *
Expression of mRNA in untreated and treated K562 cell line models
CALR CD47
82
When treated with azacytidine the MPN cell lines (HEL-92 and GDM-1) (Figure 3.5) exhibited a
similar trend, showing an increase in both CALR and CD47 expression. CALR in both MPN cell line
models showed greater increases than the CD47 genes, however, in HEL-92 the CALR expression
levels showed a larger fold increase than in GDM-1 (110-fold; p=0.044 and 25-fold, respectively).
CD47 expression levels showed a smaller fold increases than CALR in both HEL-92 and GDM-1 cell
lines (5-fold and 8-fold, respectively). This pattern of gene expression matches the K562 cell line
model but differs to the MDS cell line models.
When treated with ruxolitinib the MPN cell line models showed a different pattern of expression to
the MDS cell line models. Both HEL-92 and GDM-1 exhibited increasing expression of CALR and
CD47, with HEL-92 cells having a higher expression of CALR 49.9-fold (p=0.031) and CD47 68.2-fold
(p=0.041). GDM-1 has lower expression of CALR and CD47 (25-fold and 5-fold, respectively) while
still showing increases, GDM-1 followed a similar pattern to the K562 cell line model. Every cell line
showed an increase in CALR and CD47 expression, indicating that there is a gene response to
treatment in MPN and MDS cell lines.
83
Figure 3.5: Mean expression of CALR and CD47 mRNA in HEL-92 (A) and GDM-1 (B) cell line models. Each cell line model
was treated with azacytidine and ruxolitinib. Each mean was comprised from n3 of each cell line model. Significant
increases between the drug treated compared to the untreated were seen: HEL-92 showed no significance in azacytidine
treated. Ruxolitinib treated showed significance in CALR (p = 0.031) and CD47 (p = 0.041). GDM-1 showed no significance in
0
20
40
60
80
100
120
140
160
Untreated Azacytidine Ruxolitinib
2^(-Δ
ΔCt)
fol
d ch
ange
s
* *
Expression of mRNA in untreated and treated HEL-92 cell line models
CALR CD47
A
0
5
10
15
20
25
30
35
40
Untreated Azacytidine Ruxolitinib
2^(-Δ
ΔCt)
fol
d ch
ange
s
*
Expression of mRNA in untreated and treated GDM-1 cell line models
CALR CD47
B
84
CD47 azacytidine treated and ruxolitinib treated. Significance was shown in CALR azacytidine treated (p = 0.044). The error
bars represent the standard deviation of the n3 between the drug treated compared to the control.
3.4.3: CALR and CD47 protein expression of untreated and treated cell line models
To determine whether alternations seen in CALR and CD47 mRNA in cell lines translated into protein
levels changes, western blotting was performed. Sample Western Blot plots for CALR, CD47 and
GAPDH in untreated (A), azacytidine-treated (B) and ruxolitinib-treated (C) in all cell line can be seen
in Figure 3.6. A full Western blot example with molecular markers can also be found depicted within
Appendix 3.1.
CALR CD47 GAPDH
A B C A B C A B C MOLM - 13
SKM – 1 K562
HEL – 92 GDM - 1
Figure 3.6: Representative example of western blot samples of CALR, CD47 and GAPDH proteins. Each protein had an
untreated sample = A, azacytidine treated sample = B and ruxolitinib treated = C, for each cell line model. Each protein was
conducted to the n3 of each cell line model
CALR and CD47 protein expression data was generated from the densitometry readings that were
taken from each band for each cell line model on western blots shown in Figure 3.6 Qualitative
analysis was generated from the densitometry readings, resulting in normalised signals of the target
bands. Showing the proteins response to treatment, shown in Figure 3.7.
85
Figure 3.7: Mean protein expression of CALR (A) and CD47 (B) rate expression (normalised against GAPDH expression) in
untreated sample, azacytidine treated sample and ruxolitinib treated for each cell line model. Normalised ratio (NR)
expression rate was determined from normalised western blot analysis. Significant increases between the drug treated
compared to the untreated were seen: Significant changes were witnessed in azacytidine treated MOLM - 13 CALR (p =
0.033) and K562 CALR (p = 0.044), ruxolitinib treated MOLM -13 CALR (p = 0.035), K562 CALR (p = 0.029), HEL - 92 CALR (p =
0.035) and SKM -1 CD47 (p = 0.016). The error bars represent the standard deviation of the n3.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
MOLM - 13 SKM - 1 K562 HEL - 92 GDM - 1
Nor
mal
ised
rate
exp
ress
ion
* * * * *
Expression of CALR protein in untreated Vs treated cell line models
Untreated Azacytidine Ruxolitinib
A
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
MOLM - 13 SKM - 1 K562 HEL - 92 GDM - 1
Nor
mal
ised
rate
exp
ress
ion
*
Expression of CD47 protein in untreated Vs treated cell line models
Untreated Azacytidine Ruxolitinib
B
86
Overall protein normalised ratio (NR) expression upon treatment with azacytidine and ruxolitinib,
showed increased expression of CALR in MOLM-13 cells (0.70-NR; p=0.033 and 0.72- NR; p=0.035 vs
control 0.59- NR), respectively (Figure 3.7). Increased CALR protein expression was also observed in
HEL-92 when treated with ruxolitinib (0.51- NR: p=0.035 vs control 0.50- NR). Interestingly CALR
protein showed a decrease expression in K562 cells upon treatment with azacytidine and ruxolitinib
(0.61- NR; p=0.044 and 0.62- NR; p=0.029 vs control 0.64- NR). CD47 protein exhibited a range in
expression over the cell lines, with only a significant reduction seen when treated with ruxolitinib in
SKM-1 cells (0.27- NR: p=0.016 vs control 0.30- NR)
3.4.4: CALR and CD47 localization in MDS in response to treatment
Whilst basal RNA and protein expression has shown alterations within CALR and CD47 expression
upon treatment with azacytidine and ruxolitinib this does not provide insights into how this could
impact the immune response and in particular in terms of cell surface expression. Cell fractionation
was employed to analysed the movement of CALR and CD47 within cell compartments (membrane,
cytoplasm, cytosol and nucleus) when cells were exposed to drugs, enabling the determination of
how treatment impacts upon cellular localisation/membrane expression to start to better link to
how the cells are recognised by macrophages.
In both MDS cell line models CALR protein expression rates on the membrane are seen to
significantly decrease when treated with azacytidine and ruxolitinib (MOLM-13 – 18% membrane
expression in untreated Vs 13% and 15% (p =0.0001), respectively, see Figure 3.8) & SKM-1 - 17%
membrane expression in untreated Vs 7% (p=0.004) and 3%(p=0.001), respectively, see Figure 3.9.
At the same point higher CD47 membrane expression was detected (MOLM-13 –13% upon
azacytidine treatment and 9% upon ruxolitinib treatment (p=0.03) vs 8% in untreated: SKM-1 –8%
upon azacytidine and 7% upon ruxolitinib vs 5% untreated), although only MOLM - 13 treated with
ruxolitinib showed significance. However, higher CD47 expression is witnessed in the azacytidine
87
treated cells which is the drug specific to their treatment. Localisation of the CD47 indicates that it is
being externalised as the cytoplasm and the cytosol as a combined average decrease, this is the
opposite pattern of expression compared to CALR localisation where upon treatment it is becoming
internalised.
88
B MOLM-13
UNTREATED AZA RUXO M CP CS N M CP CS N M CP CS N
CALR
CD47
Figure 3.8: Percentage expression of CALR and CD47 within fractionated cells are shown taken from densitometry of
western blots (A). Representative example of western blot samples of CALR and CD47 proteins in different fractions of MDS cell line model MOLM-13 (B). Each cell was split into cellular fractions (membrane (M), cytoplasm (CP), cytosol (CS) and
nucleus (N)), proteins for each fraction have been shown in the untreated sample, azacytidine treated sample and ruxolitinib treated sample for each cell line model. Mean protein expression of CALR and CD47 in fractionated MDS cell line
model MOLM-13 with untreated sample, azacytidine treated sample and ruxolitinib treated sample for each cell line model. These bands were achieved using the same concentration of cells using the same protocol and volumes in the fractionation kits. Each set of protein fractionations then had their average band expressions calculated across the 4 fractions giving a total % for each fraction. Azacytidine treated cells showed no significance. Significance was seen in ruxolitinib treated cells in the membrane in both CALR (p= 0.0001) and CD47 (p=0.03). The error bars represent the
standard deviation of the n3.
0
10
20
30
40
50
60
70
80
90
100
% w
ithin
the
cells
*** *
Percentage of cellular protein location in untreated Vs treated MOLM-13 cell line models
CALR CD47
A
89
B SKM1
UNTREATED AZA RUXO M CP CS N M CP CS N M CP CS N
CALR
CD47
Figure 3.9: Percentage expression of CALR and CD47 within fractionated cells are shown taken from densitometry of
western blots (A). Representative example of western blot samples of CALR and CD47 proteins in MDS cell line model SKM-1 (B). Each cell was split into cellular fractions (membrane (M), cytoplasm (CP), cytosol (CS) and nucleus (N)), proteins for
each fraction have been shown in the untreated sample, azacytidine treated sample and ruxolitinib treated sample for each cell line model. Mean protein expression of CALR and CD47 in fractionated MDS cell line model SKM-1 with untreated
sample, azacytidine treated sample and ruxolitinib treated sample for each cell line model. These bands were achieved using the same concentration of cells using the same protocol and volumes in the fractionation kits. Each set of protein
fractionations then had their average band expressions calculated across the 4 fractions giving a total % for each fraction. Significant increases between the drug treated compared to the untreated were seen: SKM1 showed significance in
azacytidine treated cells and ruxolitinib treated cells in the membrane in CALR (p= 0.004 and 0.001, respectively). The error bars represent the standard deviation of the n3.
0
10
20
30
40
50
60
70
80
90
100
% w
ithin
the
cells
** **
Percentage of cellular protein location in untreated Vs treated SKM -1 cell line models
CALR CD47
A
90
3.4.5: Examining changes in cellular locations of CALR and CD47 in intermediate MDS/MPN
As with MDS models the fractionated K562 intermediate protein samples were also analysed (Figure
3.10). CALR expression shows a relatively similar percentage expression on the membrane across the
untreated, azacytidine treated and ruxolitinib treated cells of around 2%. Interestingly, the CD47 is
seen to significantly decrease in expression when the untreated is compared to the azacytidine and
ruxolitinib treated cells (26% Vs 21% p=0.012 and 22% p=0.048, respectively). The intermediate
K562 cell line showed much higher level of CD47 both in untreated and after treatment compared to
the MDS lines (as seen in Figure 3.8 and 3.9).
91
B K562
UNTREATED AZA RUXO M CP CS N M CP CS N M CP CS N
CALR
CD47
Figure 3.10: Percentage expression of CALR and CD47 within fractionated cells are shown taken from densitometry of
western blots (A). Representative example of western blot samples of CALR and CD47 proteins in intermediate cell line model K562 (B). Each cell was split into cellular fractions (M = membrane, CP=cytoplasm, CS=cytosol and N= nucleus),
proteins for each fraction have been shown in the untreated sample, azacytidine treated sample and ruxolitinib treated sample for each cell line model. Mean protein expression of CALR and CD47 in fractionated intermediate cell line model K562 with untreated sample, azacytidine treated sample and ruxolitinib treated sample for each cell line model. These
bands were achieved using the same concentration of cells using the same protocol and volumes in the fractionation kits. Each set of protein fractionations then had their average band expressions calculated across the 4 fractions giving a total %
for each fraction. Significant increases between the drug treated compared to the untreated were seen: SKM1 showed significance in azacytidine treated cells and ruxolitinib treated cells in the membrane in CD47 (p= 0.012 and 0.048,
respectively). The error bars represent the standard deviation of the n3.
0
20
40
60
80
100
120
% e
xpre
ssio
n w
ithin
the
cells
* *
Percentage of cellular protein location in untreated Vs treated K562 cell line models
CALR CD47
B
92
3.4.6: Examining changes in cellular locations of CALR and CD47 in MPN
Fractionations of the cell line models were continued in the MPN cell line models HEL-92 (Figure 3.11) and GDM-1 (Figure 3.12).
93
B HEL-92
UNTREATED AZA RUXO M CP CS N M CP CS N M CP CS N
CALR HEL -92
CD47
Figure 3.11: Percentage expression of CALR and CD47 within fractionated cells are shown taken from densitometry of
western blots (A). Representative example of western blot samples of CALR and CD47 proteins in the MPN cell line model HEL-92 (B). Each cell was split into cellular fractions (membrane (M), cytoplasm (CP), cytosol (CS) and nucleus (N)), proteins for each fraction have been shown in the untreated sample, azacytidine treated sample and ruxolitinib treated sample for
each cell line model. Mean protein expression of CALR and CD47 in fractionated MPN cell line model HEL-92 with untreated sample, azacytidine treated sample and ruxolitinib treated sample for each cell line model. These bands were achieved using the same concentration of cells using the same protocol and volumes in the fractionation kits. Each set of protein fractionations then had their average band expressions calculated across the 4 fractions giving a total % for each
fraction. Azacytidine treated cells showed no significance. Significance was seen in ruxolitinib treated cells in the membrane in CD47 (p=0.01). The error bars represent the standard deviation of the n3.
0
10
20
30
40
50
60
70
80
90
100
% e
xpre
ssio
n w
ithin
cel
ls
*
Percentage of cellular protein location in untreated Vs treated HEL-92 cell line models
CALR CD47
A
94
B GDM -1
UNTREATED AZA RUXO M CP CS N M CP CS N M CP CS N
CALR
CD47
Figure 3.12: Percentage expression of CALR and CD47 within fractionated cells are shown taken from densitometry of
western blots (A). Representative example of western blot samples of CALR and CD47 proteins in the MPN cell line model GDM-1(B). Each cell was split into cellular fractions (membrane (M), cytoplasm (CP), cytosol (CS) and nucleus (N)), proteins for each fraction have been shown in the untreated sample, azacytidine treated sample and ruxolitinib treated sample for
each cell line model. Mean protein expression of CALR and CD47 in fractionated MPN cell line model GDM-1 with untreated sample, azacytidine treated sample and ruxolitinib treated sample for each cell line model. These bands were achieved using the same concentration of cells using the same protocol and volumes in the fractionation kits. Each set of protein fractionations then had their average band expressions calculated across the 4 fractions giving a total % for each
fraction. Significant increases between the drug treated compared to the untreated were seen: SKM1 showed significance in azacytidine treated cells and ruxolitinib treated cells in the membrane in CD47 (p= 0.04 and 0.008, respectively).
Ruxolitinib showed CALR significance in the membrane (P=0.03). The error bars represent the standard deviation of the n3.
0
10
20
30
40
50
60
70
80
90
100
% e
xpre
ssio
n w
ithin
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ls
* * **
Percentage of cellular protein location in untreated Vs treated GDM- 1 cell line models
CALR CD47
A
95
The MPN cell line models show that HEL-92 cells exhibit larger protein alterations in CALR/CD47
upon drugging. CALR is seen to decrease from 29% in the untreated cells to 24% in the azacytidine
treated and 13% in ruxolitinib treated. CD47 is seen to increase from 2% in the untreated cells to
13% in the azacytidine treated and a significant increase of 33% in ruxolitinib treated (p=0.01).
Greater differences in CALR and CD47 membrane expression were seen in the ruxolitinib treated
cells in the MPN cell line models, which is the intended treatment for MPN. Mimicking the HEL-92
cell line model trend the GDM-1 cell line model exhibited a small decrease in CALR and an increase
of CD47 membrane expression. CALR is seen to decrease from 16% in the untreated cells to 14% in
the azacytidine and a significant decrease of 11% in ruxolitinib treated (p=0.03). CD47 is seen to
significantly increase from 11% in the untreated cells to 12% in the azacytidine (p=0.04) and 17% in
ruxolitinib treated (p=0.008).
The protein expression percentage exhibits a trend that shows the internalisation of CALR from the
membrane Vs an externalisation of the CD47 gene after treatment with both azacytidine treated
sample and ruxolitinib compared to untreated cells. The membrane protein in the MPN cell lines
supports what is also seen in the MDS model SKM -1 and K562 cell lines with regards to changes in
CALR and CD47 upon treatment.
3.5: Discussion
Overexpression of CALR has been documented to produce a pro-phagocytic signal in solid tumours
and it is often counteracted by a concomitant over expression of the antiphagocytic CD47 (Daitoku
et al., 2016), however this function is unknown in MDS and MPN. Once treated with either
azacytidine or ruxolitinib the MDS and MPN cell line models elicited changes in CALR and CD47 in
respond to both treatments. Unsurprisingly, each disease had a greater response to the drug
intended for that disease (MDS – azacytidine and MPN – ruxolitinib). The MDS cell line models upon
treatment with azacytidine (used clinically to treat MDS) showed a reduced overall survival rate of
71% for MOLM-13 and 74% for SKM-1, compared to the overall survival rate when treated with
96
ruxolitinib of 97% and 93% respectively. Alternately, the MPN cell lines models showed an opposite
pattern when treated. Where upon treatment with azacytidine, HEL-92 and GDM-1 showed an
overall survival rate of 86% for and 92%, respectively, compared to MOLM-13 and SKM-1 overall
survival rate of 71% and 76, respectively. Azacytidine has been shown to dramatically reduce survival
rate in both MDS cell line models (Dubois et al., 2019); with the cell survival rates corresponding
with published data that MDS has a sensitivity to azacytidine whereas MPN has less sensitivity to
azacytidine. This sensitivity to drug treatment was also seen in MPN cell lines models where they
showed greater cell sensitivity to ruxolitinib (used to treat MPN) without inducing complete cellular
death (Ishida et al., 2018). Each cell line model behaved as expected to the treatment before looking
specifically at CALR and CD47 expression upon treatment.
Survival rates of the treated cell line models showed that a specific cell line models does not have a
sensitivity in response to the unintended drug, indicating that intended drug for the specific disease
induces cells into cell death. However, lower expression rates in the anti-phagocytotic signal of
CD47 (Brightwell et al., 2016) and the pro-phagocytosis signals of CALR (Araki & Komatsu, 2017) in
response to the untreated cells and cells treated with the intended drug treatment, indicating that
the unintended drug could have the potential for better treatment response compared to intended
drug treatment in certain settings.
CALR and CD47 exhibit a concomitant expression, reflecting a mechanism of apoptosis vs survival in
response to chemotherapy (Boasman et al, 2019). Increasing expressions of CALR and CD47 are also
witnessed in K562 and the MPN GDM-1 cell line models. However, they exhibit higher CALR
expressions compared to CD47 in the treated samples, following the same expression patterns as
seen in solid tumours (Chao et al., 2010). MDS cell line models (MOLM-13 and SKM-1) and the MPN
cell line model HEL-92, when treated with azacytidine, show an increase of both CALR and CD47,
however CD47 is shown to be higher than the CALR gene expression. Interestingly in response to
treatment with unintended drug for that disease the CALR and CD47 gene still increases in
97
expression, however, the mean expression is a lot lower than when treated with intended drug.
Indicating that a similar process was occurring but less specifically.
The protein translations do not fully support the mRNA data in response to treatment as different
cell lines exhibits increases in CALR and CD47 expression to treatment while others exhibit
decreases. Upon treatment with azacytidine decreases in CALR expression were seen in SKM-1,
K562, HEL-92 and GDM-1. While upon ruxolitinib treatment increases in CALR expression was seen in
MOLM-13, SKM-1, HEL-92 and GDM-1 cell lines, CD47 only showed increases in azacytidine treated,
K562, HEL-92 and GDM-1 cells and ruxolitinib treated K562 and GDM-1 cells. Total expressions of
CALR were larger than CD47 in all models: untreated, azacytidine and ruxolitinib treated cells in
both MDS and MPN cell line models. This differs to the published data where we would expect to
see an increase of protein in response to treatment where CALR is expressed greater than CD47
(Chao et al., 2010), instead it is shown that CD47 is expressed greater making it the dominate signal.
CALR and CD47 membrane expression is vital to determining cell fate. Interestingly, when
fractionated, the location of the proteins followed a similar pattern across the samples and differed
to what was previously witnessed in other cancers (Chao et al., 2010). CALR has different roles
within the cell, one of which is to signal for cell death in regular and cancer cells (Unanue, 2013).
CALR interacts with the protein LDL receptor-like protein on the macrophage, telling it that the cell is
damaged and needs to be destroyed (Fucikova et al., 2018); a mechanism that is widely witnessed in
cancer cell phagocytosis (Chao et al., 2010). Total CALR expression percentage was seen to be
internalised away from the membrane into the cytoplasm and cytosol, when comparing untreated to
treated samples. CD47 showed an opposite reaction to treatment, exhibiting greater expression on
the cell membrane. CD47 on the cell membrane interacts with SIRPα on the cell membrane of the
macrophage (Unanue, 2013). Interaction between the CD47 on the diseased cell and the SIRPα on
the macrophage cell signals a recognition of “self”, preventing the macrophage from phagocytosing
the diseased cell (Oldenborg & Per-Arne, 2013). Many types of cancers exhibit large amounts of
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CD47 leading them to avoid phagocytosis by evading recognition of macrophages (Chao et al, 2012).
The percentage expression of CD47 increases upon treatment with azacytidine and ruxolitinib
indicating how the cell is attempting to protect itself, hiding from the immune system.
Lots of data was generated in this chapter. The significant results can be found in the mRNA data
azacytidine treated showed CALR expression in SKM-1, K562, and GDM-1 cells were shown to
increase significantly with ruxolitinib treated SKM-1 and HEL-92 cells shown to also significantly
increase CALR expression. CD47 was also seen to be significantly increase in MOLM-13 and K562
were shown to increase significantly with ruxolitinib treated SKM-1 and HEL-92 cells shown to also
significantly increase CD47 expression. Protein expression in MOLM-13 azacytidine and ruxolitinib
cells showed significant increases in CALR as did ruxolitinib treated HEL-92 cells. Interestingly, K562
cells exhibited a significant decrease in expression. CD47 only showed a decrease in expression in
ruxolitinib treated SKM-1 cells. The expression of proteins on the membrane showed significant
decreases in CALR expression in azacytidine treated SKM-1 cells and ruxolitinib treated MOLM-13,
SKM-1, GDM-1 cells. Significant increases in CD47 protein expression were seen in azacytidine
treated GDM-1 cells and ruxolitinib treated MOLM-13, HEL-92 and GDM-1 cell. Interestingly, CD47
membrane expression was seen to significantly decrease in both azacytidine and ruxolitinib treated
K562 cells.
This work has shown that in the untreated vs treated samples there is a concomitant increase of
total gene expression of CALR and CD47 and some of the cell lines protein expressions. Solid
tumours have been witnessed across different cancers to show increases of CALR and CD47 in mRNA
and protein expression (Chao et al., 2010). Some MDS and MPN cell lines showed no increases with
CALR, whilst still being expressed greater compared to CD47 upon treatment. This indicates that in
MDS and MPN cell lines models CALR and CD47 do not correlate to the published data showing no
concomitant expression as an increase in CALR is not always shown by an increase in CD47. In some
cell lines CD47 is shown to increase independently of CALR. Indicating that CALR and CD47 do not
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work together and CD47 does not react to increases in CALR in response to treatment. The location
of CALR and CD47 were then looked at from the total expression. It was also demonstrated that the
percentage of the protein that had migrated to the cell membrane was a lot higher for CD47 which
externalised its expression compared to the CALR, which internalised its expression upon treatment.
This differs to the published data on solid tumours, concluding that myeloid malignancies MDS and
MPN could potentially behave differently to solid cancers.
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4.1: Introduction
Previously in chapter 3, work suggested that CALR and CD47 act differently in MDS and MPN cell
lines compared to solid cancers. In 2007 Obeid et al observed increased levels of CALR on the cell
surface when patients developed cancer, as an attempt to signal abnormal cells to the immune
system (Klampfl et al., 2013). (Obeid et al., 2007). Many different solid tumours (Chao et al., 2010),
including: Gastric cancers (Lee et al., 2019), breast cancer (Wu., et al 2019), pancreatic (Sheng et al.,
2014) and neuroblastoma (Hsu et al., 2005) have shown a degree of CALR overexpression.
Whilst not currently extensively studied, some investigation of CALR in MPN patients has been
undertaken. Interestingly, high levels of CALR have been observed in MPN, in both CALR wild-type
and mutated forms of the disease (Klampfl et al., 2013), despite CALR mutations only being found in
only 4.4% of MPN patients (Mikic et al., 2019). Interestingly, patients that have mutated CALR has
been seen to have a greater survival rate upon treatment compared to patients that over express
wild-type CALR (Heuser et al., 2014).
CD47 expression strongly correlates with worsening clinical outcomes in many solid tumours (Jaiswal
et al., 2009; Willingham et al., 2012) and it is found elevated together with CALR in lung cancers
(Arrieta et al., 2020), colorectal cancers (Fujiwara-Tani et al., 2019) breast cancer and glioblastomas
(M. Liu et al., 2019). As previously described, inhibition of phagocytosis occurs when CD47 binds to
the signal regulatory protein alpha (SIRPα) on the surface of the macrophage, tumours then use this
mechanism to evade immune surveillance (Zhao et al., 2018). Taken together with the cell line work
in MDS and MPN undertaken in Chapter 3, the data suggests that further investigating of CALR and
CD47 within patient samples is now warranted
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4.2: Aims
In this chapter, the level of expression of CALR and CD47 was measured in patient peripheral blood
samples collected from MDS and MPN patients to determine any correlation with disease subtype
and stage of progression. The following steps were performed:
• Initial screening of MDS and MPN patient samples baseline CALR and CD47 expression was
undertaken to better understand potential differences between MDS and MPN in
comparison to healthy age matched controls.
• Determine if any changes in CALR and CD47 total RNA and protein expression within MDS
and MPN also correlated with alterations in the subcellular localisation.
• Correlate the data generated on CALR and CD47 with different stages of disease progression
and/or subgroupings within the MDS and MPN.
4.3: Methods
Methods are described in brief here; for full methodology refer to Chapter 2 (p 48).
4.3.1: Processing of patient samples
Patient samples peripheral blood were collected from Boston and Lincoln hospitals, UK. In this study
mutation status was not looked at, but samples were collected from 16 MDS patients – 4 low risk, 4
intermediate-1 risk, 4 intermediate-2 risk and 4 high risk patients. All MDS patients were receiving
treatment. MPN patients consisted of 8 ET patient samples (4 untreated, 4 treated), 4 PV patients (2
untreated, 2 treated) and 4 MF patients (2 untreated, 2 treated). Sample collection was performed
as previously described in Chapter 2 using ficol-plaque. Optimisations on technique and ficol-plaque
used were conducted on healthy volunteer patient samples. Once processed optimisations on the
number of cells used per technique was conducted, it was determined that 1x106 /ml of cells gave
the most consistent results across the different techniques and was therefore employed across all
experiments.
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4.3.2: Protein analysis
Protein expression analysis was conducted using protein lysate extracted from untreated and
treated samples of patient samples. Western Blotting was completed. Densitometry readings were
taken from each band for each cell line model on western blots and qualitative analysis was
generated from the densitometry readings, resulting in normalised signals of the target bands.
4.3.3: Gene analysis
Gene expression analysis of CALR and CD47 was conducted using qPCR generated from RNA
extracted from untreated and treated samples of all cell line models.
4.3.4: Fractionisations
All the cell line models were fractionated into the cellular components of membrane, cytoplasm,
cytosol, and nucleus using two fractionation kits.
4.4: Results
4.4.1: Initial analysis of Total RNA & Protein Expression of CALR and CD47 in MDS and MPN patient samples
Total RNA and protein expression of both CALR and CD47 was measured in a cohort of patients with
MDS (16 samples) and MPN (16 samples) and compared against a control group (4 controls) (Figure
4.1), with increases in both CALR and CD47 observed in both MDS and MPN patients compared to
controls.
MDS patients show a greater increase in CALR mRNA levels (11.07-fold) (P=0.041) and a smaller
increase in CD47 mRNA (2.02-fold) when compared to the healthy controls. These changes were
mirrored in the protein expression showing a large increase in CALR normalised ratio (NR) expression
(14.2-NR) rather than a smaller increase in the CD47 expression (2.37- NR).
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MPN showed a different expression pattern with no increase in CALR when compared to the control
(1.50-fold) but a significant increase in CD47 (4.83-fold). Similarly, the mRNA results, the protein
expression of CALR and CD47 in MPN patients shows a small decrease in the CALR (4.36- NR), with a
larger increase in the CD47 (11.3- NR). The presence of large error bars was seen in MDS, indicating a
potential further analysis required into the different MDS sub-groups.
C Whole protein CALR CD47 GAPDH
MDS MPN
Figure 4.1: RNA (A) and protein (B) expressions were gathered from patient samples. Mean expression of cDNA from RNA and protein of CALR and CD47 in control, MDS and MPN are depicted. Representative example of western blot samples of
CALR and CD47 proteins (C). Each group was comprised of patient across the disease and treatment progression. Each
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mean was comprised from a minimum of 4 patient per group. Significant increases between the patient samples compared to the healthy controls were seen: MDS patients showed significance in CALR RNA expression (p = 0.041) against controls.
Representative example of western blot samples of CALR, CD47 and GAPDH proteins in the MDS and MPN patient samples. Each cell was lysed with RIPA buffer then loaded to a gel at 10mg/well. Each sample was run in duplicate on the same blot
then replicated. The error bars represent the standard deviation across the group of patient samples, resulting in high error bars.
4.4.2: In-depth look at MDS disease severity sub-groups
When MDS patients were stratified according to the IPSS, low risk, intermediate-1, intermediate-2
and high risk (Figure 4.2) the levels of CALR and CD47 mRNA increased with disease progression
overall, however the pattern is not consistent, with intermediate-1 patients exhibiting an increase
that is still greater than the control group, albeit at a lower level than the other stages (low risk =
6.28-fold, intermediate-1 = 2.02-fold, intermediate-2 = 18.76-fold and high risk = 17.93-fold). CD47
initially exhibits a decrease in expression in low- risk patients compared to the control (0.04-fold vs
1.01-fold, respectively), however as disease progresses a progressive increase in CD47 expression is
observed, from low to high risk (intermediate-1 0.36-fold, intermediate-2 1.47-fold and high risk
7.34-fold). Interestingly, CD47 throughout the disease progression shows a positive association with
an increase at each stage, with low risk patient’s expressing lower levels of CD47 and high-risk
patients showing higher expression levels. Intermediate-2 exhibited some variability between
samples in mRNA levels as demonstrated by the high error bars.
When looking at corresponding protein levels, CALR showed an initial slight decrease in expression in
low risk patients compared to controls (from 5.1 NR in controls to 3.7- NR in low risk), but then
increased expression in the following MDS subgroups (intermediate-1 = 6.1-fold, intermediate-2 =
31.83- NR p = 0.037 and high risk = 10.39- NR), similar to the mRNA. CD47 initially exhibited a
significant decrease in expression in low- risk patients compared to the control (0.41- NR (p=0.0033)
vs 2.2- NR, respectively). A progressive increase in expression is then observed as disease
progressed in CD47, from low to high risk (intermediate-1 = 0.47- NR, intermediate-2 = 3.9- NR
(p=0.0012) and high risk = 4.87- NR). Interestingly, CD47 throughout the disease progression
increases at each stage, with low risk patient’s expressing lower levels of CD47 and high-risk patients
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showing higher expression levels. This suggests the protein backs up the mRNA expression as they
show similar patterns.
Figure 4.2: Mean expression of cDNA from RNA (A) and protein (B) of CALR and CD47 in different MDS risk groups (low, intermediate – 1, intermediate – 2, High) Vs a control group. Each group was comprised of patient across disease
progression. Significant increases between the MDS disease progression groups compared to the healthy controls were seen: MDS low risk patients showed significance in RNA expression in CD47 expression (p = 0.027). MDS patients showed
significance in protein expression in Int-2 CALR expression (p = 0.037), Low risk CD47 expression (p=0.0033) and Int-1 CD47
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(p=0.0012). The error bars represent the standard deviation across the group of patient samples, resulting in high error bars.
4.4.4: Examining changes in cellular locations of CALR and CD47 in MDS subgroups
The work in this chapter was informed by the results from chapter 3. Therefore, although the other
compartments were examined, membrane expression was therefore focused on. Upon examining
cellular localisation of CALR and CD47 protein within the MDS subgroups (Figure 4.3), when
comparing the protein localisation to the controls, the CALR membrane expression showed large
increases in membrane protein expression (Low risk MDS – 26.73% CALR membrane expression Vs
control – 12.98%), however this was more variable amongst the proceeding subgroups with Int-1
group showing a large decrease compared to the low risk group (5.05%) and both int-2 and high risk
showing similar levels of protein to that seen in the low group (28.07% and 24.22%, respectively)
compared to the control, suggesting the CALR is being internalised.
CD47 membrane protein shows an initial decrease in the low risk subgroups vs the controls and then
a progressive increase in expression was seen at every corresponding subgroup. Significant
decreases were seen in the low risk and int-groups when compared to the controls with a further
decrease seen in Int-2, followed by a large increase witnessed in the high-risk group (Low – 1.41%
(p=0.00014), Int-1 – 3.33% (p=0.034),Int-2 - 14.66%, high-risk - 47.30% Vs Control 21.91%). The
expression rates indicate a trend where CALR is internalised away from the membrane as the
disease progresses, while CD47 is externalised.
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B Fractionated protein CALR CD47 M CY CS N M CY CS N
MDS
MPN
Figure 4.3: Protein (A) expressions were gathered from patient samples. Mean expression of CALR and CD47 protein on the membrane of the cells in different stages of (low, intermediate – 1, intermediate – 2, High) Vs a control group. Each group was comprised of patient across treatment progression. Representative example of western blot samples of CALR and CD47 proteins (C). Each cell was split into cellular fractions (membrane (M), cytoplasm (CY), cytosol (CS) and nucleus
(N)). Significant increases between the MDS disease progression groups compared to the healthy controls were seen: MDS patient’s protein showed significance in CD47 low risk and int-1 expressions (p=0.00014 and p=0.034, respectively) vs
controls. Representative example of western blot samples of CALR and CD47 proteins in MDS and MPN patients. The error bars represent the standard deviation across the group of patient samples, resulting in high error bars.
4.4.5: In-depth look at MPN untreated Vs treated patient samples
To understand the impact of treatment in CALR and CD47 expression, the level of expression in
treated MPN patient’s vs untreated was measured (Figure 4.4) and then stratified according to the
MPN subtypes (Figure 4.5). Different lines of therapy (hydroxyurea, interferon alpha or ruxolitinib)
were used, but due to the small size of each treatment sub-group, we could not further stratify
according to different cytoreductive drug. As described in the introduction use of different
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treatment options could account for the variability seen in the overarching subgroups above and
variability seen by the error bars.
CALR protein shows a down regulation in MPN cells upon treatment (untreated - 16.45- NR vs
treated – 3.44- NR). CD47 protein expression shows a large increase (untreated – 8.38- NR vs
control – 10.75- NR). These changes indicate that as the disease progress, there is a recognition of an
unhealthy cell and upregulation of CALR occurs to signal for its removal. An upregulation of CD47 in
treated patients comparing with untreated suggesting a strong “do not eat me” signal expressed by
cells when attacked by a cytotoxic agent. Interestingly, despite a reduction in CALR being observed
after treatment, the level of protein reduced suggesting how the “do not eat” message prevails over
the pro-phagocytic signal.
Figure 4.4: Mean expression of CALR and CD47 protein in untreated (8 samples) and treated MPN (8 samples) patients. Each group was comprised of patient across subgroups treatment progression. The error bars represent the standard
deviation across the group of patient samples, resulting in high error bars.
When MPN samples are split into MF, PV and ET subgroups, they still maintain similar changes in
CALR and CD47 protein expression in response to treatment (Figure 4.5). MF patients showed CALR
and CD47 protein responded to treatment with a decrease in CALR expression (untreated - 9.37- NR
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vs treated 4.20- NR) andCD47 protein exhibited a down regulation upon treatment (untreated –
33.5- NR vs treated 5.30- NR).
PV patients showed CALR and CD47 protein responded to treatment with a decrease in CALR
expression (untreated – 1.30- NR vs treated 0.20- NR), while CD47 protein exhibited a large
upregulation upon treatment (untreated – 6.34- NR vs treated 15.96- NR). ET patients exhibited a
decrease in CALR expression (untreated – 36.7- NR vs treated 1.19- NR), while CD47 protein
exhibited an upregulation upon treatment (untreated – 0.84- NR vs treated 1.99- NR).
Figure 4.5: Mean expression of protein of CALR and CD47 in untreated and treated MPN subgroups patients (ET, PV, MF). Each group was comprised of patient across subgroups treatment progression. MPN patients showed no significance in expression. The error bars represent the standard deviation across the group of patient samples, resulting in high error bars.
4.4.6: Examining the cellular locations of CALR and CD47 between different MPN subgroups
To understand whether the changes seen in mRNA and protein levels between untreated and
treated MPN subgroups reflect in modifications in cell membrane levels of CALR and/or CD47
proteins cell fractions membrane were analysed (Figure 4.6).
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The membrane expression is shown as a percentage of the overall expression. Interestingly MF and
PV membrane proteins responded differently upon treatment compared to ET. MF patients had
significant reduction in CALR levels in membrane levels after treatment (untreated – 43.3% vs
treated 2%) while CD47 protein exhibited a large upregulation on membrane expression (untreated
– 11.8% vs treated 34.3%). PV patients also showed a decrease in CALR membrane expression
(untreated – 36% vs treated 2.2%) and increase in CD47 membrane protein expression upon
treatment (untreated – 11.4% vs treated 35.9%). Interestingly, ET patients showed a decrease in
both CALR and CD47 membrane expression upon treatment: CALR (untreated – 21% vs treated
1.19%), CD47 (untreated – 22% vs treated 16.6%), suggesting perhaps a separate mechanism of
immune-response within this MPN subgroup.
Figure 4.6: Mean expression of CALR and CD47 protein of membrane expression in untreated and treated MPN subgroups patients (ET, PV, MF). Each group was comprised of patient across subgroups treatment progression. The error bars
represent the standard deviation across the group of patient samples, resulting in high error bars.
4.5: Discussion
When compared to healthy aged-matched controls, MDS and MPN patients exhibited an increase in
CALR and CD47 expression. Overall, level of CALR and CD47 increased as seen in solid cancers
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although the ratios of these seen in MDS and MPN vary (Chao et al., 2010). MDS showed larger
expression of CALR whereas MPN exhibited larger expression of CD47.
Upon closer analysis of MDS, when split according to the IPSS, inconsistent patterns of CALR were
seen with an initial decrease in the low risk group before increases were then seen in intermediate-
1, intermediate-2 and high-risk groups while CD47 showed a more consistent increase as the disease
progressed. Fractionation data confirmed a large upregulation of CALR on the membrane of the cells
initially and then a decrease in expression during evolution into high-risk MDS. CD47 externalised
throughout disease progression with lower percentages of CD47 in low risk and progressive increase
moving into high risk. The Initial upregulation of CALR indicates that the cell wants to signal for cell
phagocytosis (Schcolnik-Cabrera et al., 2019) but is quickly counteracted by CD47 upregulation with
the anti-phagocytic message to prevail (Liu et al., 2017).
Total protein expression in MPNs upon treatment showed decreases of CALR in all subgroups (ET,
PV, MF), interestingly, CD47 expression significantly increases during therapy in MF and PV but not
in ET, suggesting a potential different mechanism of interaction with the immune system in ET
patients comparing with MF and PV. Fractionations of the MPN subgroups showed patients of all
subgroups responded similarly with decreases in membrane CALR protein percentage expression
upon treatment but increases in CD47 externalization in MF and PV (Jaiswal et al., 2009). Overall
prognosis in ET patients is favourable, and the reduced expression of CD47 in response to treatment
may correlate with the positive outcome because the ET cells can be more easily recognised and
destroyed by the immune system.
Further data are required to understand if ET progression into MF or AML causes changes to the
CD47/CALR ratio during transformation. Protein expression of CALR have shown little consistencies
in expressions patterns in MDS and MPN. CD47 however, follows a more consistent pattern in MDS
progression as well as after therapy in PV and MF and could represent a new therapeutic target. For
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example, the inhibition of CD47 interaction with macrophages could suppress the do not eat
message and help favour the immune response against MDS and MPN cells.
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Chapter 5: Investigating whether CALR and CD47 work together directly or independently in determining response to therapy within MDS and MPN cell models
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5.1: Introduction
The phagocytic message expressed via CALR in cancer cells is thought to counteract the strong “do
not eat me” immune escape mechanism deployed via CD47 overexpression (Chao et al., 2010).
Interestingly, previous work has looked at therapeutically targeting CALR and CD47 in solid cancers
separately but do not expand on the relationship between the two.
Targeting CALR as an anti-cancer strategy, has been mainly focused on targeting molecules able to
affect pathways indirectly implicated in enhancing CALR expression, and as a result should have
stimulated phagocytosis of the cancer cell (Michalak et al., 2009). An increased rate of apoptosis has
been show in prostate and lung cancer cells when treated with CALR targeting adenovirus (Ad-
CALR/MAGE-A3) which upregulates total surface CALR, inhibiting cell proliferation (Liu et al., 2015).
Treatment with anthracycline, a chemotherapeutic drug used across a variety of different cancers
(Fucikova et al., 2016), stimulates the activation of a tumour-targeting immune response through
increasing CALR expression on the cell surface, with levels of CALR membrane expression correlating
with the overall removal of cancer cells and survival of patients with AML, neuroblastoma, non-small
cell lung carcinoma, ovarian cancer, and colorectal carcinoma (Fucikova et al., 2016).
Targeting CD47 interaction with the key SIRPα dependent phagocytosis pathway (as described in
detailed in section in the introduction) using specific anti-CD47 monoclonal antibodies has been
tested in several clinical trials in solid tumours with a certain degree of success, suggesting a
potential new anti-cancer strategy (Majeti et al., 2009). Application of CD47 monoclonal antibodies
blocking CD47 activity, lead to increases in the SIRPα dependent phagocytic signals resulting
macrophage activation, promoting them to phagocytose cancer cells, inducing cancer cell death
(Feng et al., 2015) . In leukemic, lymphoma, pancreatic, and ovarian carcinomas cell lines the
combination of anti-CD47 and anti-CD49 antibodies (CD49 mediates general cell surface signalling),
showed an increase in cell death, through the same mechanism of SIRPα activation (Dheilly et al.,
2017). In addition, use of anti-CD47 antibody Hu5F9-G4 in brain tumours demonstrated inhibition of
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CD47, leading to increased presence of activated macrophages (Gholamin et al., 2017). Blocking
CD47 using CD47 siRNA could also represent a therapeutic strategy, with CD47 siRNA administered
intravenously in melanoma and lung metastases resulting in a reduction of tumour size (Wang et al.,
2013).
Whilst chemotherapy drugs are considered the first line treatment for cancer, cytotoxic drugs are
not always effective so work has also been done looking at whether therapeutically targeting CALR
and CD47 molecules could be used in combination with chemotherapy to enhance treatment
efficacy (Xiang & Liu, 2017). In AML, humanised anti-CD47 antibody (Hu5F9-G4) when administered
in combination with ARA-C or Azacytidine, resulted in an initial reduction of all blasts in vivo in a pre-
clinical assessment (Liu et al., 2015). With initial trials showing how altering expression in CALR and
CD47 in AML could lead to improvement in disease, this suggest there is potential to target CALR or
CD47 with a therapeutic effect in MDS and MPN. As earlier work within this thesis has shown a role
for alteration in CALR and CD47 cell surface expression in MDS and MPN disease
progression/response to therapy different to that seen in other cancer settings, it will be important
to understand the relationship between the separate CALR and CD47 pathways and explore
potential cross interaction between the two specifically in the MDS and MPN setting.
5.2: Aims
The overarching aim of the chapter is to assess the interactions, or lack thereof, between CALR or
CD47 within an MDS/MPN specific setting by determining what effect knocking down one of them
has on the other, and the potential effect on chemo-response in vitro. This will be achieved by the
following steps
• Transfect cell line models with either CALR and CD47 siRNAs to generate knockdowns, and
evaluate the impact upon mRNA and protein expression on the other molecule
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• Treat the transfected cell line models with azacytidine and ruxolitinib, to enlighten how
knocking down either CALR or CD47, impacts upon both molecules response to treatment
5.3: Methods
Methods are described in brief here only where they differ from the main Methods in Chapter 2 (p
48).
5.3.1: Cell culture
Cell culture and drug treatment was performed as previously described in Chapter 2 (p 52) using
azacytidine (MDS specific) and ruxolitinib (MPN specific), each drug was used to treat both MDS and
MPN cell lines as previously used in Chapter 3 (p72).
5.3.2: Transfections
Transfections using siRNAs were performed using the optimised protocol as described in Chapter 2,
section 2.14: CALR and CD47 Knockdowns (p 65). Cell lines were incubated for 48 hours with siRNAs
(Sigma, Dorset, UK) and lipofectamine LTX Reagent (Thermo Fisher, Paisley, UK) to achieve this. To
optimise the transfections different time points and concentrations of lipofectamine were assessed
across all cell lines. Due to different cell line models reacting to knockdowns differently, total
knockdowns were not achieved. A reasonable concentration was chosen that suited all the cell line
models whilst showing no effect on the lipofectamine control or the scatter siRNA (1µg/2x105 cells).
Having no effect on the control and the scatter siRNA indicates that the knockdown is specific to the
siRNAs used. Graph to show efficiency is included in the Appendix 4.1.
5.3.4: Gene Expression qPCR Analysis
Gene expression analysis of CALR and CD47 was conducted using qPCR (as described in Chapter 2:
Quantitative PCR (qPCR) (p 57)).
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5.3.5: Western Blotting Analysis
Protein of the transfected samples were collected to assess via western blotting (as described in
Chapter 2: 2.12.1 Western blotting (p 59-64)). Densitometry readings were taken from western blots
for each cell line model and qualitative analysis was gathered from the densitometry resulting in
normalised signals of the target bands against the GADPH housekeeping gene. Figure 5.1 is an
example of n1 for CALR, CD47 and GAPDH in all cell line models. With each sample there was
untreated, azacytidine treated and ruxolitinib treated.
Anti-CALR CALR CD47 GAPDH A B C A B C A B C
MOLM - 13 SKM – 1
K562 HEL – 92
GDM - 1 Anti-CD47
A B C A B C A B C MOLM - 13
SKM – 1
K562
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Figure 5.1: Representative example of western blot samples of CALR, CD47 and GAPDH proteins. Each protein had an
untreated sample = A, azacytidine treated sample = B and ruxolitinib treated = C, for each transfected cell line model. Full
representative blot in Appendix 4.1.
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5.4: Results
5.4.1: CALR and CD47 changes and impact on survival in transfected MDS & MPN cell line models
To determine the effect of transfections on cell survival, cell counts were evaluated 48h after
incubating with either CALR or CD47 silencing RNA. All cell lines were subjected to treatment with
both CALR silencing and CD47 silencing siRNAs as shown (Figure 5.2). Survival rates were calculated
as fraction of alive cells from the total cell pool treated and expressed as a percentage. When
treated with CALR silencing siRNAs, MOLM-13 and SKM-1 (MDS models) exhibited an overall survival
rate of 68% - p=0.029 and 67% - p=0.013, respectively. HEL-92 and GDM-1 (MPN models) exhibited
an overall survival rate of 64% p= 0.0016 and 69% - p=0.017 respectively, with K562 (MDS/MPN
overlap) exhibiting a survival rate of 66% - p=0.0016. When treated with CD47 silencing siRNAs
however, the MOLM -13 and SKM-1 exhibited an overall survival rate of 58% - p=0.01 and 58% -
p=0.01, respectively. HEL-92 and GDM exhibited an overall survival rate of 64% and 59% - p=0.0049,
respectively, and K562 exhibiting a survival rate of 60% - p=0.0056, suggesting that when the anti-
phagocytic signal CD47 is reduced, cells died more independently of the type of cell line. As
expected, cells transfected with either CALR or CD47 silencing siRNAs exhibited an overall decrease
in survival across all cell lines.
120
Figure 5.2: Overall mean percentage survival rate of each cell line model treated with CALR and CD47 silencing siRNAs. All cell lines and transfected at different time points to the n3 (representing 3 independent experiments). Total cell counts
were determined after 48 hours from live and dead cells after transfections. Statistics were performed against non-transfected cells. The error bars represent the standard deviation of the n3 between the treated compared to the control.
5.4.2: CALR silenced transfection efficiency in cell line models
CALR knockdowns were confirmed in all cell lines. Each cell line was transfected with CALR silencing
siRNAs which knocked down the expression of CALR (Figure 5.3) and CD47 (Figure 5.4) within the
cells. After transfection, cell lines showed suppression of CALR rather than a complete knockdown,
in fact complete knockdown was shown to result in all cells dying (data not shown). Along with CALR
silencing siRNAs a scatter siRNA was also used to treat cells. Within the scatter treated cells, no
change in expression was observed (data not shown).
MDS cell line models (MOLM-13 and SKM-1), showed low expression of CALR mRNA in transfected
cells compared to non-transfected (0.42-fold and 0.22-fold Vs 1.32-fold and 1.09-fold, respectively).
The MPN cell line models (HEL-92 and GDM-1) showed decreased CALR mRNA expression in
transfected cells compared to non-transfected (0.38-fold and 0.82-fold Vs 1.05-fold and 1.59-fold,
respectively). The K562 cell line showed a similar change in CALR mRNA expression patterns to those
0
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Overall percentage survival rate of each transfected cell line models after 48 hours of transfection
Anti-CALR Anti-CD47
121
seen in the MDS cell lines. These same patterns of alterations in CALR expression were also seen
when investigating protein expression.
The impact of CALR suppression on CD47 expression was also determined. Interestingly, in MDS cell
lines (MOLM-13 and SKM-1) transfected with CALR silencing, CD47 mRNA in responded similarly to
the CALR mRNA showing large decreases in expression (0.35-fold and 0.31-fold Vs 1.32-fold and
1.02-fold in non-transfected cells, respectively). Within the MPN cell line GDM-1 CD47 mRNA
expression showed a small decrease in (0.98-fold Vs 1.05-fold in non-transfected cells). In contrast to
other cell lines, the HEL-92 MPN cell line showed an increase in expression of CD47 upon CALR
silencing (1.39-fold Vs 1.13-fold in non-transfected cells) in and the MDS/MPN intermediate K562
CD47 mRNA was also seen to decrease upon CALR silencing transfection (0.40-fold Vs 1.21-fold).
CD47 protein expression was also affected similarly to the mRNA patterns as reported in Figure 5.4.
122
Figure 5.3: Overall mean mRNA (A) and protein (B) levels of CALR in each cell line model when treated with CALR silencing siRNAs. All cell lines and drugging were grown/harvested at different time points to the n3. Showing how the siRNAs are
affecting the cell line models upon transfection. The error bars represent the standard deviation of the n3.
0
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Protein expression of CALR in anti-CALR siRNA transfected cells line models
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123
Figure 5.4: Overall mean mRNA (A) and protein (B) transfection rate of CD47 in each cell line model when treated with CALR silencing siRNAs. All cell lines and drugging were grown/harvested at different time points to the n3. Total cell counts
were determined after 48 hours. The error bars represent the standard deviation of the n3.
5.4.4: CD47 silenced transfection efficiency in cell lines
The impact of CD47 suppression was also analysed using CD47 silencing siRNAs. As previously
described, when total knockdown was obtained, cells did not survive (data not shown), so the assay
was optimised to produce a significant, but not complete, gene suppression. Upon transfection with
0
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Protein expression of CD47 in anti-CALR siRNA transfected cells line models
Non transfected Transfected
B
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CD47 silencing siRNA all cell line models showed decreases in CD47 mRNA and protein (Figure 5.5),
when compared to non-transfected cells. Along with CD47 silencing siRNAs a scatter siRNA was also
used to treat cells. Within the scatter treated cells no change in expression was observed.
Interestingly, also in this case, the suppression of CD47 resulted in reduction of CALR expression in
most of the cell lines (Figure 5.6).
MDS cell line models (MOLM-13 and SKM-1), showed low expression of CALR mRNA in CD47 silenced
transfected cells compared to non-transfected (0.63-fold and 0.53-fold Vs 1.32-fold and 1.09-fold,
respectively). MPN cell line HEL-92 showed a decrease in CALR mRNA expression (0.54-fold Vs 1.05-
fold in non-transfected cells), whereas the GDM-1 MPN cell line showed only a small decrease in
CALR expression upon CD47 silencing (1.55-fold Vs 1.59-fold in non-transfected cells). Upon
analysing protein expression in all cell line models upon which CD47 silencing transfections had been
undertaken, MDS cell line models (MOLM-13 and SKM-1) showed low expression of CALR protein in
CD47 silenced transfected cells compared to non-transfected (0.02- NR and 0.26- NR Vs 0.59- NR and
0.34- NR, respectively). The MPN cell line models (HEL-92 and GDM-1), also showed decreased CALR
protein expression in transfected cells compared to non-transfected (0.29- NR and 0.026- NR Vs
0.50- NR and 0.59- NR, respectively).
Upon CD47 silencing transfections, in MDS cell lines (MOLM-13 and SKM-1) CD47 mRNA responded
similarly to the CALR mRNA with large decreases in expression seen Vs non-transfected cells (0.75-
fold and 0.50-fold Vs 1.32-fold and 1.02-fold, respectively). The MPN cell line models (HEL-92 and
GDM-1), also showed decreases in CD47 mRNA expression in transfected cells compared to non-
transfected (0.44-fold and 0.64-fold Vs 1.13-fold and 1.05-fold, respectively). Protein expression
supported the mRNA in both MDs and MPN cell line. CD47 protein responded with decreases in the
MDS (MOLM-13 and SKM-1) (0.02- NR and 0.24- NR Vs 0.48- NR and 0.30- NR in non-transfected
cells, respectively), upon CD47 silencing transfections. While HEL-92 and GDM-1 cell lines also
125
showed CD47 expression to decreased upon CD47 silencing (0.15- NR and 0.02- NR Vs 0.26- NR and
0.48- NR in non-transfected cells, respectively).
The K562 cell line showed a similar protein expression pattern to the MDS cell lines with CALR mRNA
was shown to decrease upon transfection (0.55-fold Vs 1.29-fold), while CD47 mRNA was also seen
to decrease upon CD47 silencing transfection (0.49-fold Vs 1.21-fold). The K562 cell line showed a
similar expression pattern to the MDS and MPN cell lines. CALR protein was shown to decrease upon
transfection (0.08- NR Vs 0.64- NR), while CD47 protein was also seen to decrease upon CD47
silencing transfection (0.04- NR Vs 0.28- NR).
126
Figure 5.5: Overall mean mRNA (A) and protein (B) transfection rate of CD47 in each cell line model treated with CD47 silencing siRNAs. All cell lines and drugging were grown/harvested at different time points to the n3. Total cell counts were
determined after 48 hours. The error bars represent the standard deviation of the n3.
0
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MOLM-13 SKM-1 K562 HEL-92 GDM-1
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RNA expression of CD47 in anti-CD47 siRNA transfected cells line models
Non-transfected transfected
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Protein expression of CD47 in anti-CD47 siRNA transfected cells line models
Non transfected Transfected
B
127
Figure 5.6: Overall mean mRNA (A) and protein (B) transfection rate of CALR in each cell line model treated with CD47
silencing siRNAs. All cell lines and drugging were grown/harvested at different time points to the n3. Total cell counts were
determined after 48 hours. The error bars represent the standard deviation of the n3.
0
0.5
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MOLM-13 SKM-1 K562 HEL-92 GDM-1
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RNA expression of CALR in anti-CD47 siRNA transfected cells line models
Non-transfected transfected
A
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Protein expression of CALR in anti-CD47 siRNA transfected cells line models
Non transfected Transfected
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128
5.4.5: CALR and CD47 silenced cells survival rate upon treatment
with chemotherapeutics
To model the effects of chemotherapeutic agents upon cell survival within MDS or MPN cells where
either CALR or CD47 has been knocked down, the MOLM-13 cell line was used to represent MDS
and the HEL-92 cell line was used to represent MPN. Each MDS and MPN cell line model showed
decreases in the percentage of cell survival in response to treatment with chemotheraputic agents
compared to the untreated transfected cells (Figure 5.7). Both cells exhibited a decreased cell
survivall in response to both intended treatment options (azacytidine - MDS and ruxolitinib – MPN)
and non-intended treatment option in both CALR silencing and CD47 silencing. When the pro-
phagocytic CALR is silenced MOLM-13 showed reductions in cell survival upon treatment with
azacytidine and ruxolitinib (43% and 54%, respectively) compared to normal cell survival when
previously treated in Chapter 3 (p 78). However, as expected when the anti-phagocytic CD47 is
silenced, cells showed a deeper drop in survival upon treatment with azacytidine and ruxolitinib
(38% and 42%, respectively). MPN cell line model HEL-92 responded with a similar cell survival
pattern, showing a greater percentage of cell death when CD47 is suppressed, after exposure to
azacytidine and ruxolitinib (survival rate 42% and 39%, respectively) when compared to CALR
suppressed cells (51% and 47%, respectively).
129
Figure 5.7: Cell survival upon treatment with azacytidine and ruxolitinib in representative MDS (MOLM-13) (A) and MPN
(HEL-92) (B) cell lines. The error bars represent the standard deviation of the n3 between the drug treated compared to the control.
5.5: Discussion
In this chapter the effect of CALR and CD47 on cell survival and the impact of each gene on the other
using knockdown models in MDS and MPN cell lines was studied. Transfection efficiency has been
challenging throughout this work, as only partial knockdowns could be undertaken due to the
triggering of complete cell death upon higher doses of the silencing siRNAs, however a significant
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Azazytidine Ruxolitinib
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Overall percentage survival rate of transfected HEL-92 cells after treatment with chemotherapeutic agents
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130
reduction in both genes and protein expression was successfully obtained. Upon knockdown with
CALR or CD47 silencing siRNAs, a reduction of CALR and CD47 expression of mRNA and protein was
seen in all cell lines. In all cells it is interesting to note that when CALR was knocked down using CALR
silencing siRNAs, CD47 expression was also seen to decrease. Similarly, when CD47 was knocked
down using CD47 silencing siRNAs, CALR expression was also seen to decrease suggesting a link
between CALR and CD47 expression in MDS and MPN cell lines. It was also shown that when either
of them is silenced, cells survival rate decreased. This is expected as both CALR and CD47 are
involved in many homeostatic functions in the cell and therefore, when reduced, the cell may not
function as needed (Adachi et al., 2018).
When cells were treated, survival rates of MDS and MPN cell lines are seen to further decrease, as
expected but the cell lines where the pro-phagocytic CALR was silenced had a lower reduction in cell
survival, in comparison with the anti-phagocytic CD47 slienced cells which showned a larger
reduction in cell survival. This indicates that when CALR is silenced, CD47 is still being expressed to
keep the cell alive and when CD47 is silenced the expression of CALR allows for more cells to induce
phagocytosis (Feng et al., 2018).
Whilst this work suggests that modulation of CALR or CD47 expression could play an important role
in MDS and MPN cell survival, it also suggests that a simple reduction in levels and altered ratio
between the two might not be enough to direct the cell toward either survival or death. In solid
tumours, strategies used to reduce CD47 expression levels demonstrated some efficacy in reducing
tumour size and increasing macrophage activity (Wang et al., 2013; Gholamin et al., 2017). Also,
increased CALR expression signals for cell death by interacting with the LDL receptor-like protein on
the macrophage, telling the cell is damaged and needs to be destroyed (Fucikova et al., 2018). Lui et
al. 2019, previously showed that manipulating linked signalling systems via siRNA knock downs was
not successful in inducing in vitro phagocytosis of tumour cells or in vivo anti-tumour responses (Liu
et al., 2019), which findings from Chapter 5 further support. Due to the cross interaction between
131
the two genes expression, knocking out one of the genes will potentially affect the other and the
overall signal might not be therapeutically effective. Leaving the gene and protein intact but directly
interfering with the CD47 anti phagocytic signal with a compound/antibody able to disrupt the
interaction with macrophages, could potentially allow the pro phagocytic message transduced by
CALR to prevail (Lian., 2019) and provide a more appropriately way of therapeutically targeting CALR
and CD47 expression in MDS and MPN in the future.
133
6.1: Conclusions The knowledge and understanding of the molecular mechanisms underpinning the role of CALR and
CD47 in cancers have progressed in recent years but are still not fully understood. Established
research in to CALR and CD47 have given insights into solid cancer and some of the myeloid
malignancies, with CALR and CD47 shown to increase in their expression. Little research had been
done in CALR and CD47 expression in MDS and MPN, therefore, this project aimed to investigate and
establish CALR and CD47 role in MDS and MPN and their implication in therapy response and disease
prognosis.
Within MDS and MPN cell line models CALR and CD47 expression was shown to change when cell
lines were exposed to chemotherapy drugs such as azacytidine and ruxolitinib, and often the
changes in total expression or membrane exposure reflected the treatment specificity as described
in Chapter 3 (p 72-97). Not surprisingly, localisation studies in both MDS and MPN models, found
that when cells are exposed to cytotoxic drugs, CD47 is externalised more than CALR indicating a
strong ‘don’t eat me’ signal released by the cell in danger in the attempt to stay alive. However, this
reflects a substantial difference with previous studies in solid cancer, where CD47 and CALR are
concomitantly expressed on the membrane during chemotherapy exposure and might represent a
very important feature in myeloid neoplasms to target for future therapeutical strategies.
Chapter 4 (p 98-111) focused on the expression levels of CALR and CD47 in patients with MDS and
MPN, to determine whether the changes observed in cell lines could be replicated in patient
samples. When compared to healthy aged-matched controls, overall levels of CALR and CD47 have
been shown to increase in solid cancers although the ratios of these in MDS and MPN vary (Chao et
al., 2010). MDS showed larger expression of CALR whereas MPN exhibited larger expression of CD47.
When the MDS cohort was split into subgroups in accordance to IPSS, changes in total CALR and
CD47 seemed to follow a pattern related to disease progression, with CALR expression seen to be
higher in intermediate-2 and high-risk patients as previously described by Schcolnik‑Cabrera et al.
134
2019 (Schcolnik-Cabrera et al., 2019), suggesting the attempt by the leukemic cell to still try to
identify itself as abnormal particularly during the late evolution into AML. CD47 expression showed a
consistent pattern during progression, demonstrating a progressive increase from low risk into
intermediate-2 and high-risk, indicating the cells are counteracting CALR signalling and trying to
prevent phagocytosis (Jaiswal et al., 2009). Not surprisingly, most of the relevant changes in CALR
and CD47 happened on the membrane of the cells as confirmed by fractionation study. The MDS cell
exposes more CALR and CD47 on the membrane during AML progression, but the CALR/CD47 ratio
indicate a prevalent “do not eat” message, indicating that this may be an immune-escape
mechanism needed by the leukaemia cell throughout MDS evolution.
Fractionation data confirmed a large upregulation of CALR on the membrane of the cells initially and
then a decrease in expression during evolution into high-risk MDS. CD47 externalised throughout
disease progression with lower percentages of CD47 in low risk and progressive increase moving into
high risk. The Initial upregulation of CALR indicates that the cell wants to signal for cell phagocytosis
but is quickly counteracted by CD47 upregulation with the anti-phagocytic message to prevail.
When separately analysing MPN patients split by PV, ET and MF classification, they showed different
patterns of expression. Total protein expression in MPN groups upon treatment showed decreases
in CALR in all subgroups, while CD47 protein expression was shown to increase in PV and MF but not
in ET. Total protein expression in MPNs upon treatment also showed decreases of CALR in all
subgroups (ET, PV, MF), interestingly, CD47 expression significantly increases during therapy in PV
and MF but not in ET, supporting the mRNA data, and suggests a potential different mechanism of
interaction with the immune system in ET patients comparing with MF and P, and with it potentially
differences in prognosis.
Fractionations of the MPN subgroups showed patients of all subgroups responded similarly with
decreases in membrane CALR protein expression upon treatment but increases in CD47
externalization in MF and PV. Overall prognosis in ET patients is favourable, and the reduced
135
expression of CD47 in response to treatment may correlate with the positive outcome because the
ET cells can be more easily recognised and destroyed by the immune system. Further data are
however required to understand if ET progression into MF or AML changes the CD47/CALR ratio
during transformation.
To start investigating the therapeutic potential of manipulating CALR and CD47 in treating MDs and
MPN, transfection assays were undertaken to alternatively supress CALR or CD47 to evaluate the
impact of on to the other. In all cell line models, when CALR or CD47 were knocked down, both
decreased, suggesting a potential direct interaction of CALR and CD47 intra-cellular pathways. This
hypothesis however needs further corroborating by additional studies.
When transfected cells were treated azacytidine and ruxolitinib, survival rates of MDS and MPN cell
lines were seen to further decrease, as expected, but the pro-phagocytic CALR silenced cells reduced
their survival less, in comparison with the anti-phagocytic CD47 slienced cells. This ndicates that
when CALR is silenced, CD47 is still being expressed to keep the cell alive and when CD47 is silenced
the expression of CALR allows for more cells to induce phagocytosis. Chapter 5 (p 112- 128) supports
the results found Lui et al. 2019 studies, where manipulating linked signalling systems siRNA knock
downs were not successful on their own at inducing in vitro phagocytosis of tumour cells or in vivo
anti-tumour responses (Liu et al., 2019). Due to the cross interaction between the two genes
expression, knocking out the genes will potentially affect the other and the overall signal might not
be therapeutically effective. Leaving the gene and protein intact but directly interfering with the
CD47 anti phagocytic signal with a compound/antibody able to disrupt the interaction with
macrophages, could potentially allow the pro-phagocytic message transduced by CALR to prevail.
In summary, this work describes the role of CALR and CD47 in MDS and MPN. Changes in CALR and
CD47 expression not only follow different diseases but also disease progression. The expression
relative ratio between the pro-phagocytic signal CALR and the do not eat me message CD47 seem to
play a key role in the immune response of both MDS and MPN cells in both cell lines and patient’s
136
samples. CALR and CD47 expression pathways, even if transducing different messages via very
different intra-cellular pathways, might be interdependent but further studies are required to
confirm this. Studies within MDS have specifically shown that blocking the CD47 signal leads to
selective phagocytosis of the MDS cells by reducing the “don’t eat me” signal (Pang et al., 2013). The
availability a of monoclonal antibody targeting CD47 could represent a great opportunity for
therapeutic intervention round these newly elucidated CALR and CD47 pathways in myeloid
malignancy. Phase 1 trials using Magrolimab in AML have demonstrated efficacy in monotherapy
and in combination (Chao et al., 2020). This study suggests the possible impact of such therapy in
MDS and MPN as well. Targeting the “do not eat signal” CD47 in MDS and MPN patients could slow
down progression and increase efficacy of the standard chemotherapy.
6.2: Future studies Future research following on from this project could have a few different directions. The first is to
continue with the CD47 story. This would progress by looking at using a drug such as Magrolimab in
both MDS and MPN (Chao et al., 2020). By looking in both cell lines and eventually patient samples
the effects of targeting the “do not eat signal” CD47, either in combination with current therapies or
by itself, to determine any potential benefits for the treatment of MDS and MPN patients.
Alternately looking at other genes such as BCL-2, calnexin, GATA-1 and PU-1 that have been
proposed to play a role in MDS/MPN onset and seeing their reaction in relation to CALR and CD47.
Techniques that could be utilised to aid in this work would include flow cytometry, immune
precipitations and florescent microscopy to determine interactions between these molecules and
the impact on the immune response or response to treatment. This would elaborate on any possible
connection within MDS and MPN patients.
137
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Appendix
EA1 [doc version 09.02]
Ethical Approval Form: Library/Desk/Lab/Studio-based Research Projects
This form must be completed for each piece of research activity whether conducted by academic staff, research staff, graduate students or undergraduates. Applications by students must be endorsed by an academic member of staff acting as Principal Investigator/supervisor. The completed form must be sent to the designated Ethics Committee within the College.
Please complete all sections. If a section is not applicable, write N/A.
1 Name of Applicant
Kristian Boasman
2 School
LIFE SCIENCE
3 Position in the University
PhD Student
4 Role in relation to this research
Postgraduate student
5 Name(s) of collaborators/co-workers and their relationship to the project (e.g. supervisor, assistant etc.)
Name, and role in project: 1. Rachael Simpson (postgraduate student) 2. 3. 4. 5.
6 Brief statement of main Research Question or Project Title
Elucidating the molecular mechanisms of calreticulin and CD47 in myelodysplastic syndrome (MDS) and myeloproliferative neoplasms (MPN) – using model cell systems.
7 Ethical checklist Does the research involve living human participants, or human tissue? Yes No If you answered “yes”, submit form EA2 for Ethical Approval. Does the research involve living animals, or animal tissue? Yes No If you answered “yes”, submit form EA3 for Ethical Approval. Does the research involve confidential data, or data not in the public domain? Yes No Does the project potentially put you or your collaborators at physical or psychological risk? Yes No Could the topic or results of this research be seen as illegal, or attract legal action against the University from an outside agency? Yes No Could the topic or results of this research attract unwelcome media attention, or affect the reputation or standing of the University? Yes No Could the topic, results or conduct of this research be regarded as offensive, immoral or destructive by some reasonable people? Yes No Does this research need to be undertaken under a relevant professional code of conduct? Yes No Are there any potential conflicts of interest in conducting this research, including financial gain for the researchers, or for individuals or external organizations affiliated with the researchers? Yes No Are there any factors inhibiting the application of the University’s ethical guidelines, including those on proper treatment of data, research design and publication of results? Yes No Does the research require the approval of any external body? Yes No
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Does the research involve living human participants, or human tissue? Yes No If you answered “yes”, submit form EA2 for Ethical Approval. Does the research involve living animals, or animal tissue? Yes No If you answered “yes”, submit form EA3 for Ethical Approval. Does the research involve confidential data, or data not in the public domain? Yes No Does the project potentially put you or your collaborators at physical or psychological risk? Yes No Could the topic or results of this research be seen as illegal, or attract legal action against the University from an outside agency? Yes No Could the topic or results of this research attract unwelcome media attention, or affect the reputation or standing of the University? Yes No Could the topic, results or conduct of this research be regarded as offensive, immoral or destructive by some reasonable people? Yes No Does this research need to be undertaken under a relevant professional code of conduct? Yes No Are there any potential conflicts of interest in conducting this research, including financial gain for the researchers, or for individuals or external organizations affiliated with the researchers? Yes No Are there any factors inhibiting the application of the University’s ethical guidelines, including those on proper treatment of data, research design and publication of results? Yes No Does the research require the approval of any external body? Yes No If the answer to all questions above is “No”, you may complete section 8 to certify that there are no ethical issues, submit this form to the relevant Ethics Committee, and proceed with the research immediately. You accept professional responsibility for this decision, and if unsure should instead submit to the Committee.
If the answer to any of the above questions is “Yes”, complete the rest of the form, submit to the relevant Ethics Committee, and await approval before proceeding with the research. Answering “Yes” does not necessarily imply that the research is problematic, only the Ethics Committee needs to consider the research to ensure that it can proceed, and that the research design conforms to best practice. Having reviewed the ethical implications of this research, I certify that there are no issues requiring Ethical Approval. I certify that the research will be carried out in compliance with the University’s ethical guidelines for library/desk/laboratory/studio-based research, with Health and Safety regulations, and with all other relevant University policies and procedures. If there are any changes to the research requiring ethical clearance, I shall apply for such clearance before continuing with the research. Signed: Principal Investigator Note. This section must be endorsed by the member of academic staff responsible for the project. In the case of research by students, the supervising member of academic staff must sign. The signed form should then be submitted to the relevant Ethics Committee within the College, and the research may proceed. Yes No If “No”, provide an ethical justification for your project and explain why you wish to continue with the research in breach of normal ethica
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13 If there are any other ethical issues, to which the attention of the approving committee should be drawn, please state them in this section, and explain how you have taken the issues into account, so that the research should be approved. Please consult the University’s ethical guidelines for advice.
Please also include here, or attach separately, a brief description of the research, to allow the approving committee to reach judgement.
Introduction - The myelodysplastic syndromes (MDS), and myeloprolife disorders that have the potential to progress to acute leukaemia (cancer MDS/MPN to acute myeloid Leukaemia (AML) occurs not just in blood b Overall survival rates can range from low risk (4-18 months) to high risk CALR is a multi-process calcium buffering chaperone of the endoplasmic used as a biomarker of cancer. In recent studies it has been show that cancer patient’s show increased (CALR) (2), and anti-phagocytic signal CD47 (4). The high levels presen needed to protect the cells from CALR mediated phagocytosis. CD47 an marrow cells. CALR has been shown to be upregulated in certain AML s contrast with previous studies in solid tumours, there is a down-regulatio with MDS (1).Whilst the full mechanisms of CALR and CD47 in relation t understanding of these molecules could potentially be exploited for use a and bone marrow samples. Aim- The overarching aim of the project is to dissect the molecular mech haematopoietic transcription factors such as: CALR and CD47, and their will access the levels of CALR and CD47 mRNA in a larger cohort of MD will explore the role of CALR in MPN/MDS to elucidate the signalling me Elucidating these signalling mechanisms will indicate future therapeutic t MDS and MPN. Methods -The purpose of this study is to dissect the molecular pathwa and partner transcription factors in a series of commercially available ce and further investigate any changes in these pathways. Total RNA and pro RT-qPCR for changes in gene transcription. Proteomic investigations of employed to look for changes in protein expression in commercially avai Ethical Issues - For the methodological work-up of these investigations and procedures, we will use commercially available cell lines, with no im of disease’. However, due to the very nature of MDS disease there is available. We will also use commercially available mouse ES cells to ge cell model with hematopoietic potential as an additional model of disease techniques as already stated. For further information on these cells lines please see associated EA3 fo References
(1) Panga, 2016. PNAS. 3011–3016 (2) Park, 2015. Plos ONE, 10. 1-8. (3) YI, 2016. Oncology Letters,1861-1867 (4) Chao, 2010. Sci Transl Med 2: 63-94. (5) Kourie, 2016 Myeloma Leuk , 264-268 (6) Park, 2015. Cancer Genet 630-635
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EA2 Ethical Approval Form: Human Research Projects
Please word-process this form, handwritten applications will not be accepted
This form must be completed for each piece of research activity whether conducted by academic staff, research staff, graduate students or undergraduates. The completed form must be approved by the designated authority within the College.
Please complete all sections. If a section is not applicable, write N/A.
1 Name of Applicant
Kristian Boasman School: Life Sciences
College: Science
2 Position in the University
PhD Student
3 Role in relation to this research
Postgraduate researcher
4 Brief statement of main Research Question
Elucidating the role of Calreticulin and CD47 on myelodysplastic syndrome (MDS) and myeloproliferative neoplasms (MNP). (Human samples)
5 Brief Description of Project
Introduction - The myelodysplastic syndromes (MDS), and myeloproliferative neoplasms (MPN), are a group of blood disorders that have the potential to progress to acute leukaemia (cancer of the white blood cells). The progression of MDS/MPN to acute myeloid Leukaemia (AML) occurs not just in blood but initiates in the bone marrow once it is affected. Overall survival rates can range from low risk (4-18 months) to high risk (4-6 months). CALR is a multi-process calcium buffering chaperone of the endoplasmic reticulum. The over expression of CALR is used as a biomarker of cancer. In recent studies it has been show that cancer patient’s show increased levels of the pro-phagocytic signals calreticulin (CALR), and anti-phagocytic signal CD47. The high levels present in cancer cells potentially show that CD47 is needed to protect the cells from CALR mediated phagocytosis. CD47 and CALR are present in regular blood and bone marrow cells. CALR has been shown to be upregulated in certain AML studies. However recent data suggest, in contrast with previous studies in solid tumours, there is a down-regulation of CALR mRNA in the bone marrow of patients with MDS. Whilst the full mechanisms of CALR and CD47 in relation to MPN and MDS are not fully understood, greater understanding of these molecules could potentially be exploited for use as a biomarker, especially if seen in both blood and bone marrow samples. Aim: The purpose of this study is to dissect the molecular pathways involved in the expression of Calreticulin, CD47, and partner transcription factors. We will use bone marrow and peripheral blood samples from 80 patients affected by MDS/MPN (20 low risk; 20 Int-1; 20 Int-2 and 20 high risk patients). Samples will be collected by a trained phlebotomist. Method: Mononuclear cells will be separated using Ficoll method and total RNA and proteins will be extracted, rendering samples acellular. RNA and protein will be stored only and not functional cells. All processes are exempt from the Human Tissue Act. We will measure, evaluate, and further investigate any changes in these pathways. Total RNA and protein will be extracted and assessed by quantitative RT-qPCR for changes in gene transcription. Proteomic investigations of western blot and mass spectrometry will also be employed to look for changes in protein expression. FACS analysis will also be carried out to show cell surface abundance.
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9 Statement of the ethical issues involved and how they are to be addressed –including a risk assessment of the project based on the vulnerability of participants, the extent to which it is likely to be harmful and whether there will be
Obtaining Informed Consent and Exclusion Criteria to Prevent any Vulnerable Participants Taking Part: The following measures will be employed
- An easy to read yet comprehensive leaflet detailing the full study will be provided and all and any questions will be answered in full before any consent is given (Please see the participation information sheet).
- No volunteer below the age of 18yrs will be permitted to take part.
- A signed informed consent form will be required before any samples are taken and a cool off period will be allowed.
5 Brief Description of Project
Introduction - The myelodysplastic syndromes (MDS), and myeloproliferative neoplasms (MPN), are a group of blood disorders that have the potential to progress to acute leukaemia (cancer of the white blood cells). The progression of MDS/MPN to acute myeloid Leukaemia (AML) occurs not just in blood but initiates in the bone marrow once it is affected. Overall survival rates can range from low risk (4-18 months) to high risk (4-6 months). CALR is a multi-process calcium buffering chaperone of the endoplasmic reticulum. The over expression of CALR is used as a biomarker of cancer. In recent studies it has been show that cancer patient’s show increased levels of the pro-phagocytic signals calreticulin (CALR), and anti-phagocytic signal CD47. The high levels present in cancer cells potentially show that CD47 is needed to protect the cells from CALR mediated phagocytosis. CD47 and CALR are present in regular blood and bone marrow cells. CALR has been shown to be upregulated in certain AML studies. However recent data suggest, in contrast with previous studies in solid tumours, there is a down-regulation of CALR mRNA in the bone marrow of patients with MDS. Whilst the full mechanisms of CALR and CD47 in relation to MPN and MDS are not fully understood, greater understanding of these molecules could potentially be exploited for use as a biomarker, especially if seen in both blood and bone marrow samples. Aim: The purpose of this study is to dissect the molecular pathways involved in the expression of Calreticulin, CD47, and partner transcription factors. We will use bone marrow and peripheral blood samples from 80 patients affected by MDS/MPN (20 low risk; 20 Int-1; 20 Int-2 and 20 high risk patients). Samples will be collected by a trained phlebotomist. Method: Mononuclear cells will be separated using Ficoll method and total RNA and proteins will be extracted, rendering samples acellular. RNA and protein will be stored only and not functional cells. All processes are exempt from the Human Tissue Act. We will measure, evaluate, and further investigate any changes in these pathways. Total RNA and protein will be extracted and assessed by quantitative RT-qPCR for changes in gene transcription. Proteomic investigations of western blot and mass spectrometry will also be employed to look for changes in protein expression. FACS analysis will also be carried out to show cell surface abundance. Approximate Start Date: Oct 2016
Approximate End Date: Oct 2019
6 Name of Principal Investigator or Supervisor
Dr Ciro Rinaldi Dr Matt Simmonds Email address: [email protected] [email protected]
Telephone: 01205 446311 01522 886896
7 Names of other researchers or student investigators involved
Rachael Simpson
8 Location(s) at which project is to be carried out
Molecular Research Lab, Joseph Banks Laboratories
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9 Statement of the ethical issues involved and how they are to be addressed –including a risk assessment of the project based on the vulnerability of participants, the extent to which it is likely to be harmful and whether there will be significant discomfort. (This will normally cover such issues as whether the risks/adverse effects associated with the project have been dealt with and whether the benefits of research outweigh the risks)
Obtaining Informed Consent and Exclusion Criteria to Prevent any Vulnerable Participants Taking Part: The following measures will be employed
- An easy to read yet comprehensive leaflet detailing the full study will be provided and all and any questions will be answered in full before any consent is given (Please see the participation information sheet).
- No volunteer below the age of 18yrs will be permitted to take part.
- A signed informed consent form will be required before any samples are taken and a cool off period will be allowed.
Obtaining Samples and Sample Anonymization:
- All consent forms and patient information will be kept at Boston hospital in a secured area by Professor Ciro Rinaldi and the samples will be labelled with a specific code to ensure anonymity to the patient from their given sample.
- Peripheral blood samples from healthy volunteers will be drawn by a registered haematologist ensuring no risk to the volunteer.
- No information regarding the identity of the donor volunteer will be held at the University of Lincoln, UK and only anonymised sample ID and matched anonymised data will ever be given.
Sample Processing at the University of Lincoln:
- All samples will be rendered acellular immediately, by extracting the Mononuclear cells using Ficoll method. The total RNA and proteins will be extracted and kept for a maximum of 5 years.
Data Protection:
- All data protection and sample preparation will be processed and held in conjunction with the NHS and university guidelines.
- Data on these samples will be kept for six years after it is published in line with journal guidelines on how long raw data should be kept.
8 Location(s) at which project is to be carried out
Molecular Research Lab, Joseph Banks Laboratories
158
s research require
of an external body
Yes No If “Yes”, please state which body:- NHS IRAS form
cal approval
n from that body ?
N/A Yes -Please append documentary evidence to this form. No If “No”, please state why not:- An IRAS form has been sent and is currently awaiting a decision. Previously approval has been given for a project using MNP samples and there is currently approval for MDS samples to be used. IRAS has been sent to confirm the approval of both for this specific project. Please note that any such approvals must be obtained and documented before the project begins.
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CONSENT FORM – Healthy Volunteer Consent
Elucidating the role of Calreticulin and CD47 on MDS/MPN Name of Researcher: Dr Ciro Rinaldi – Consultant Haematologist, Dr Matt Simmonds (Senior Lecturer) & Dr Ciaren Graham (Senior Lecturer)
Please initial box each box
1. I confirm that I have read and understand the information sheet dated 07/01/2015 (Final Version 1.0) for the above study. I have had the opportunity to consider the information, ask questions and have had these answered satisfactorily. 2. I understand that my participation is voluntary and that I am free to withdraw at any time 3. I understand that data collected during the study, may be looked at by authorised individuals from the University of Lincoln, the NHS Trust and from regulatory authorities, where it is relevant to my taking part in this research. I give permission for these individuals to have access to my records. 4. I agree to take part in the above study.
Name Signature Date ----------------------------------------- ----------------------------------- ------------- Name of Person taking consent Signature Date ----------------------------------------- ----------------------------------- -------------
160
Appendix 3.1: Example of full western blots ran to the n3 depicting CALR & GAPDH, CD47 & GAPDH
40kDa CALR 46kDa 36kDa GAPDH 36kDa
40kDa CD47 47-50kDa 36kDa GAPDH 46kDa
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Figure 4.1. Example of knockdown optimisation blot and graph
1 2 3 4 5 6 7 8 9 40 kDa CALR 47kDa 30kDa GAPDH 36kDa
Figure 4.1 representative western blot of transfected k562 cells (A). This cell line was transfected with anti CALR siRNA (1µg/2x105cells). 1- ladder, 2- 3- anti CALR treated siRNA, 4-5 -untreated cells, 6-7 - scatter siRNA treated cells, 8-9 -
lipofectamine only treated cells. A graph representing the normalised bands of the western blot (B).
00.10.20.30.40.50.60.70.80.9
CALR siRNA Untreated Scatter siRNA Lipofectamine
Nor
mal
ised
ratio
exp
ress
iom
Expression of CALR and CD47 in anti CALR siRNA treated K562 cells
CALR CD47
B
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