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UNDIFFERENTIATED SARCOMAS: UNDERSTANDING
EARLY SARCOMA DEVELOPMENT
A DETAILED GENOMIC AND TRANSCRIPTOMIC ANALYSIS OF
PAEDIATRIC UNDIFFERENTIATED SARCOMAS
BY
Cassandra Graham
A thesis submitted in conformity with the requirements for the degree of Master of Science
Department of Laboratory Medicine and Pathobiology University of Toronto
Date of Defense: November 3rd, 2010
© Copyright by Cassandra Graham, 2011
ii
ABSTRACT
A Detailed Genomic and Transcriptomic Analysis of Paediatric Undifferentiated Sarcomas
Cassandra Graham
Master of Science, 2010
Department of Laboratory Medicine and Pathobiology
University of Toronto
November 3rd, 2010
Paediatric undifferentiated soft tissue sarcomas (USTSs) are a diagnostically challenging
group of neoplasms. We hypothesized that USTSs contain distinct subgroups that can be
identified based on their morphology, genomic aberrations and expression profiles. We sought
to characterize genomic aberrations within primitive round cell (PRC) sarcomas which may
underlie aberrant expression patterns. Using molecular and cytogenetic analyses, we identified 5
of 18 CIC-DUX4-positive PRC sarcomas. The consistent involvement of the CIC-DUX4 fusion
in a subset of PRC sarcomas suggests a central role for the fusion transcript in such tumours.
These analyses also identified a cohort of CIC-DUX4-negative USTSs with no established
genetic markers. We performed integrative copy number and expression profiling, and identified
significant genomic and transcriptomic changes. We propose that these genes are involved in
biological pathways that are important to the initiation and progression of undifferentiated
sarcoma, and may provide novel insights into the biological events responsible for
sarcomagenesis.
iii
ACKNOWLEDGEMENTS
I would like to express my heartfelt appreciation to those people that have made the completion
of this thesis a reality.
This Master’s thesis is dedicated to my parents, whose never-ending patience and unconditional
support have allowed me the latitude to explore all of my personal and academic pursuits. I
thank my brother and sister for their endless teasing and loving support, which always allow me
to keep things in perspective. I would also like to thank my supportive extended family, and my
Toronto family at 470 Markham Street, who have been there for me every step of the way.
I would like to express my sincerest gratitude to my supervisors, Dr. Gino Somers and Dr. Maria
Zielenska. I am so grateful for the confidence with which you have always supported my
research. The encouragement and guidance that I have received from you were invaluable to the
successful completion of this thesis, and have helped me grow significantly both academically
and personally.
I would also like to acknowledge Dr. Jeremy Squire, who has provided me with so many
opportunities, and without whom I may not have chosen this path. I would like to thank Dr.
Maisa Yoshimoto, Dr. Georges Maire, Jane Bayani and Paula Marrano who have mentored me
for the past 6 years and have served as knowledgeable resources throughout the course of this
project. Without our lunch dates, I might have gone crazy. To Devina Ramsaroop, I couldn’t
have asked for a better labmate or partner in crime at our lab meetings and conferences.
I wish to acknowledge Dr. Cynthia Hawkins and Dr. Mary Shago, for providing me with
direction, advice and genuine enthusiasm over the course of this project. I would also like to
iv
thank the following friends at Sick Kids for all of their help with this thesis: Susan Chilton-
MacNeill, Dr. Bekim Sadikovic, Michael Ho, Pawel Buczkowicz and Dr. Paul Thorner.
Lastly I would like to thank Graeme Mask, who has been so encouraging and supportive over the
past 2 years – I couldn’t have done it without you.
My deepest appreciation to each and every one of you.
v
TABLE OF CONTENTS
ABSTRACT ii
ACKNOWLEDGEMENTS iii
TABLE OF CONTENTS v
LIST OF ABBREVIATIONS vii
LIST OF TABLES AND FIGURES ix
CHAPTER ONE: INTRODUCTION 1
1.1 Overview of Paediatric Soft Tissue Sarcomas 2
1.2 Classification of Paediatric Soft Tissue Sarcomas 2
1.3 Diagnostic Importance of Chromosomal Translocations in Sarcomas 3
1.4 Overview of Paediatric Undifferentiated Soft Tissue Sarcomas 5
1.4.1 Morphological Subtypes of USTS 6
1.4.2 Models of Sarcomagenesis: Cell of Origin & Acquisition of Malignant
Properties 7
1.4.3 Molecular Studies of USTS 15
1.5 Hypothesis, Objectives & Expected Outcomes 18
CHAPTER TWO: MATERIALS & METHODS 19
2.1 Tumour Specimens 20
2.2 DNA and RNA Extraction 20
2.3 Cytogenetic and Spectral Karyotyping (SKY) analyses 21
2.4 Array Comparative Genomic Hybridization (CGH) 22
2.5 Interphase Fluorescence in situ Hybridization (FISH) 23
2.5.1 Isolation of DNA from Bacterial Artificial Chromosomes (BACs) 23
2.5.2 Pilot Project Four-Colour Interphase FISH 24
2.5.3 FISH Validation of Copy Number Gain of EPHA3 25
2.6 Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) for
Detection of CIC-DUX4 26
2.7 Copy Number Arrays 27
2.8 Expression Arrays 28
2.9 Analysis and Integration of Microarray Data 28
2.9.1 Partek Genomics Suite: Data Analysis and Integration 28
2.9.2 Ingenuity Pathway Analysis: Network Identification and
Pathway Analysis 30
2.10 Quantitative Polymerase Chain Reaction (qPCR) 30
2.10.1 Comparative CT Analysis of qPCR Data 31
2.11 Immunohistochemistry (IHC) 31
vi
2.11.1 IHC for CD99 32
2.11.2 IHC for EPHA3 32
CHAPTER THREE: RESULTS & DISCUSSION 33
3.1 Clinicopathological Features of Tumour Samples 34
3.2 t(4;19) Pilot Project 36
3.2.1 Genomic Imbalances in Undifferentiated Sarcomas 36
3.2.2 Karyotypes of Undifferentiated Sarcomas 39
3.2.3 Detailed Comparison of Cytogenetic Karyotyping and Array CGH 40
3.2.4 Four-Colour FISH Validation of 19q Rearrangement and Detection
of Translocation Binding Partners 42
3.2.5 Detection of CIC-DUX4 Fusion Transcripts by RT-PCR 46
3.2.6 Discussion of t(4;19) Pilot Project 48
3.3 Screening of USTS Cohort for the CIC-DUX4 Fusion Transcript 49
3.3.1 Detection of the CIC-DUX4 Fusion by RT-PCR 50
3.3.2 DNA Sequence Analysis of the CIC-DUX4 Positive PCR Products 52
3.3.3 Discussion of Overall Screening of USTS cohort of CIC-DUX4
Fusion Transcript 55
3.4 Discovery of Novel Markers for CIC-DUX4-Negative Tumours 58
3.4.1 DNA Copy Number Analysis 59
3.4.2 Gene Expression Analysis 60
3.4.3 Integration of Copy Number and Gene Expression Analyses 62
3.4.4 Gene Network Analysis 64
3.4.5 Identification of Candidate Genes 65
3.4.6 Gene Expression Validation of Candidate Genes 66
3.4.7 Copy Number Validation of EPHA3 68
3.4.8 Protein Validation of EPHA3 68
3.4.9 Discussion of Discovery of Novel Markers for
CIC-DUX4-Negative Tumours 71
CHAPTER FOUR: SUMMARY, GENERAL CONCLUSIONS & FUTURE DIRECTIONS 77
4.1 Summary & General Conclusions 78
4.2 Future Directions 79
REFERENCES 81
vii
LIST OF ABBREVIATIONS
ARMS Alveolar Rhabdomyosarcoma
BAC Bacterial Artificial Chromosome
CGH Comparative Genomic Hybridization
CSC Cancer Stem Cell
DAPI 4’,6’-diamidino-2-phenylindole
DEPC Diethylpyrocarbonate
DNA Deoxyribonucleic Acid
EDTA Ethylenediaminetraacetic acid
ERMS Embryonal Rhabdomyosarcoma
ES/PNET Ewing Sarcoma/Primitive Neuroectodermal Tumour
FFPE Formalin-Fixed Paraffin-Embedded
FISH Fluorescence in situ Hybridization
IHC Immunohistochemistry
IPA Ingenuity Pathway Analysis
MPNST Malignant Peripheral Nerve Sheath Tumour
MSC Mesenchymal Stem Cell
NSC Neural Stem Cell
PGS Partek Genomic Suite
PRC Primitive Round Cell
qPCR Quantitative Polymerase Chain Reaction
RMS Rhabdomyosarcoma
RNA Ribonucleic Acid
RT-PCR Reverse Transcriptase-Polymerase Chain Reaction
viii
SNP Single Nucleotide Polymorphism
SSC Saline Sodium Citrate
SDS Sodium Dodecyl Sulfate
SKY Spectral Karyotyping
SS Synovial Sarcoma
t() Translocation
TCAG The Centre for Applied Genomics
USTS Undifferentiated Soft Tissue Sarcoma
ix
LIST OF TABLE AND FIGURES
Chapter 1
Figure 1.1 Subclassification of paediatric soft tissue sarcomas 4
Table 1.1 Sarcomas characterized by recurrent chromosomal translocations 5
Figure 1.2 Euclidean clustering of sarcoma tumours based on eight stem cell markers 11
Figure 1.3 Immunohistochemical staining of CD56 12
Figure 1.4 Decoding the early events of sarcoma development 14
Chapter 2
Table 2.1 Primer sequences used for RT-PCR screening of USTS cohort 26
Chapter 3
Figure 3.1 Histological features and CD99 staining of USTSs used in
t(4;19) pilot study 34
Table 3.1 Clinicopathological features, CD99 staining patterns, and CIC-DUX4
fusion transcript status of the cohort of tumours used in study. 35
Figure 3.2 Composite analysis of genomic imbalances in USTSs used in
t(4;19) pilot study 38
Table 3.2 Summary of USTS karyotypes by G-banding and SKY analyses 39
Figure 3.3 SKY of USTSs with 19q13 rearrangement 40
Figure 3.4 Comparative analysis of array CGH and SKY results 42
Figure 3.5 Four-colour FISH validation of 19q rearrangement and detection
of translocation binding partners in USTS1 44
Figure 3.6 Four-colour FISH validation of 19q rearrangement and detection
of translocation binding partners in USTS2 46
Figure 3.7 RT-PCR confirmation of the CIC-DUX4 fusion for t(4;19) pilot study 47
Figure 3.8 Identification of the CIC-DUX4 fusion by RT-PCR screening 51
x
Figure 3.9 Nucleotide sequences of CIC and DUX4 sequences denoting
primer sequences and translocation breakpoints 53
Figure 3.10 Nucleotide and predicted amino acid sequences of the different
CIC-DUX4 fusions 54
Figure 3.11 Predicted protein structure of CIC-DUX4 fusion protein variants 55
Figure 3.12 Overall genomic profiling views from PGS of all USTS samples
interrogated for copy-number changes 60
Figure 3.13 Cluster analysis from PGS of all USTS samples interrogated for
changes in gene expression 61
Figure 3.14 Tumour-specific integration of copy number and gene expression
analyses 62
Figure 3.15 Integration of cumulative copy number and gene expression analyses 63
Figure 3.16 Gene network analysis of copy number and gene expression changes 64
Table 3.3 Candidate genes with USTS-specific genomic and transcriptomic changes 65
identified by integrative and gene network analyses.
Figure 3.17 Changes in Gene expression of candidate genes 67
Figure 3.18 FISH Validation of Copy Number Gain of EPHA3 69
Figure 3.19 IHC for EPHA3 70
1
CHAPTER ONE
INTRODUCTION
2
1. INTRODUCTION
1.1 Overview of Paediatric Soft Tissue Sarcomas
Paediatric soft tissues sarcomas (STSs) are a diverse group of mesenchymal tumours arising in
the connective tissues of the body. These tumours are generally found to be quite aggressive,
particularly if the tumour cells are poorly differentiated, and often present with early hematogenous
metastasis [1-6]. Collectively, STSs account for up to 10% of all paediatric cancers, making them
proportionately more common among children than adults (1% of all adult cancers) [5, 7].
Sarcomas have long been a challenging group of tumours to diagnose and classify due to the
morphological similarities between established subgroups, and the relative rarity of these
neoplasms [8]. However, advances in immunohistochemical analysis and the development of
molecular and cytogenetic techniques have allowed for the discovery of mechanisms by which we
can differentiate between different sarcoma subtypes [9]. Treatment of paediatric STSs consists of
surgery and preoperative chemotherapy. Chemotherapy has been shown to effectively manage
localized, chemosensitive tumours, and when the tumour is caught early enough, surgery can be
curative [6]. Though advances in therapeutics have improved long-term survival, the prognosis of
chemoresistant and metastasized sarcomas remains very poor, with a 5-year survival rate of less
than 60% [6, 10].
1.2 Classification of Paediatric Soft Tissue Sarcomas
Morphologically, sarcomas are divided into rhabdomyosarcomas (RMS), the most common
subtype of STS accounting for up to 50% of paediatric sarcomas, and non-rhabdomyomatous
sarcomas (NRSTSs) (Figure 1.1) [11]. NRSTSs include a very large number of pathologically
diverse tumours including Ewing sarcoma/primitive neuroectodermal tumour (ES/PNET),
3
malignant fibrous histiocytoma (MFH), synovial sarcoma (SS), and many others [11]. At the
genomic level, sarcomas are divided into two major categories (Figure 1.1). The first category is
composed of sarcomas with simple, near-diploid karyotypes which consistently present with
recurrent chromosomal rearrangements [12]. Members of this group include ES/PNET which are
associated with rearrangements of the EWS gene including t(11;22) and t(21;22); alveolar
rhabdomyosarcoma (ARMS), which is associated with t(1;13) and t(2;13); and SS which is
associated with t(X;18) resulting in an SYT-SSX gene fusion product [13-15]. To date,
approximately 41 gene fusions have been associated with 17 different subtypes of sarcomas [16].
The second category is composed of tumours that have very complex karyotypes, but in which no
reproducible chromosomal aberrations have been identified. This group includes malignant
peripheral nerve sheath tumour (MPNST), embryonal rhabdomyosarcoma (ERMS) and
osteosarcoma (OS) [12].
1.3 Diagnostic Importance of Chromosomal Translocations in Sarcomas
The accurate subclassification of paediatric sarcomas has important therapeutic and prognostic
implications [17, 18]. Subclassification is achieved using a variety of diagnostic techniques
including morphological, immunohistochemistal, molecular and cytogenetic analyses [19-21]. In
some cases, the combination of specific morphological features (e.g. biphasic histology for SS, or
spindled and myxoid histology for RMS), together with positive staining for specific antibodies
(e.g. CD99 for ES [19], or myogenin for RMS [22]) allows for the identification of specific
sarcoma subtypes [23]. However certain tumours are particularly difficult for the pathologist to
diagnose due to significant overlap in the histologic and immunohistochemical features of the
tumours [24]. Because the treatment protocols and prognoses of these different sarcoma subtypes
vary immensely, a correct diagnosis is crucial.
4
The discovery of specific chromosomal translocations associated with sarcoma subtypes has
markedly improved the diagnostic accuracy of paediatric sarcomas (Table 1.1) [23-27]. Tumours
with these translocations generally show low genomic complexity, with well-defined recurrent
chromosomal translocations that result in fusion genes with oncogenic fusion protein products. Not
only has the identification of these fusion genes improved the precision of sarcoma
subclassification, it has provided an increased understanding of sarcoma biology by providing a
logical starting point for more thorough functional studies of tumour development [27, 28]. The
identification of specific translocations harboured by different tumours has played a pivotal role in
differentiating between different neoplasms. As such, it has become imperative to use molecular
Figure 1.1. Subclassification of Pediatric Soft Tissue Sarcomas. Morphologically,
pediatric sarcomas are divided into rhabdomyosarcomas (RMS) and non-rhabdomyomatous
sarcomas (NRSTS) (Top). At the genomic level sarcomas are divided into two categories
(Bottom); those with simple, near-diploid karyotypes which consistently present with
recurrent chromosomal rearrangements and those with very complex karyotypes, but in
which no recurrent chromosomal aberrations have been identified.
5
and cytogenetic techniques, in conjunction with traditional histopathological techniques, to identify
these chromosomal translocations so that a correct diagnosis can be made.
TUMOUR TRANSLOCATION FUSION PRODUCT
Alveolar Rhabdomyosarcoma t(2;13)(q35;q14) PAX3-FOXO1A
t(1;13)(p36;q14) PAX7-FOXO1A
Alveolar Soft- Part Sarcoma t(X;17)(p11.2;q25) TFE3-ASPL
Clear-cell Sarcoma t(12;22)(q13;q12) EWS-ATF1
Congenital Fibrosarcoma t(12;15)(p13;q25) ETV6-NTRK3
Desmoplastic Small Round
Cell Tumour t(11;22)(p13;q12) EWS-WT1
Ewing Sarcoma/PNET
t(11;22)(q24;q12) EWS-FLI1
t(21;22)(q22;q12) EWS-ERG
t(7;22)(p22;q12) EWS-ETV1
t(17;22)(q21;q12) EWS-ETV4
t(2;22)(q33;q12) EWS-FEV
Myxoid Chondrosarcoma t(9;22)(q22;q12) EWS-NR4A3
Myxoid Liposarcoma t(12;16)(q13;p11) FUS-CHOP
t(12;22)(q13;q12) EWS-CHOP
Synovial Sarcoma t(X;18)(p11;q11) SYT-SSX1
Table 1.1. Several sarcoma subtypes are characterized by specific chromosomal translocations that
produce fusion oncogenes with protein products. (Adapted from [11]).
1.4 Overview of Paediatric Undifferentiated Soft Tissue Sarcomas
Despite the advancement of diagnostic applications which allow for the identification of
sarcoma subtypes, approximately 5% of sarcomas remain unclassifiable [29-32]. These tumours,
termed undifferentiated soft tissue sarcomas (USTSs), show no specific lineage differentiation,
exhibit no well established or consistent histologic or immunohistochemical profile, and harbour no
compelling recurrent molecular aberrations associated with the traditional sarcoma subtypes [32,
33]. Furthermore, the karyotypes of these sarcomas are largely variable, with some tumours having
a very complex genomic make-up, and other tumours harbouring a near-diploid genome [34]. As
6
such, a diagnosis of undifferentiated sarcoma is largely considered a diagnosis of exclusion,
evoking much debate as to whether this group is composed of tumours with common histological
and biological features, or if these tumours represent a heterogeneous group of primitive
mesenchymal tumours that can differentiate along various soft tissue lineages [9]. USTSs have
been found to be most commonly located in the extremities (46%), the trunk (38%), and the head
and neck (16%) [8, 29]. Treatment protocols included surgery, VAC (vincristine, adriamycin,
cyclophosphamide) chemotherapy and radiation therapy [30]. At the present time, patients
diagnosed with USTS have a 40-50% risk of developing distant metastases within 5 years of
diagnosis [4]. USTSs generally have a poorer prognosis compared to RMSs [8, 30, 35, 36], with a
44% overall 5-year survival rate [8, 30], though these results may be skewed due to small sample
size and inaccurate diagnoses.
1.4.1 Morphological Subtypes of USTS
Due to the rarity of these tumours, only a few studies have been published which seek to
more fully elucidate the pathology behind a USTS diagnosis. Somers et al initiated a study which
sought to examine the clinical and pathological features of the largest cohort of USTSs studied at
the time, in an attempt to identify specific characteristics which could be useful for the diagnosis,
prognosis and treatment of USTS tumours [32]. This study was unable to identify any specific
features shared by the majority of these tumours, but noted that many of the tumours showed a
sheet-like proliferation of densely packed round to plump spindled tumour cells [32]. In a similar
study, Alaggio et al. [9] examined the histology of a smaller cohort of USTSs, and furthered this
notion, determining that USTSs can be broken down into two morphological groups. The first
group contains tumours with a spindled/myxoid morphology, that have elongated cells arranged in
poorly formed bundles or fascicles [9, 32] (see Figure 3.1c taken from Somers et al., 2006 [32]).
7
The second group, which our lab has entitled paediatric primitive round cell (PRC) sarcomas,
contains tumours composed of primitive mesenchymal cells showing no evidence of differentiation,
that are arranged in sheets or nests [9, 32] (see Figure 3.1b taken from Somers et al., 2006 [32]).
These PRC sarcomas represent the most primitive paediatric sarcomas described to date, and thus
form an attractive model in which to study the early events involved in sarcomagenesis.
1.4.2 Models of Sarcomagenesis: Cell of Origin and Acquisition of Malignant Properties
Much research has gone into studying the molecular mechanisms of sarcomagenesis, or the
initiation and development of sarcoma. While significant progress has been made, this complex
process remains highly debated. Two generally accepted models of tumourigenesis are the ‘cancer
stem cell’ (CSC) hypothesis and the ‘dedifferentiation’ hypothesis. The dedifferentiation
hypothesis suggests that cells with more primitive features, such as ability to self-renew and
differentiate down different lineages, may arise in an adult through the process of dedifferentiation
[37]. Overlapping with this model is the stochastic model of tumour formation which proposes that
any cell from a tumour is capable of limitless growth and the ability to spread throughout the body
[38]. These theories are less accepted by modern cancer biologists due to the heterogeneous nature
of most tumours, as well as evidence to the contrary that all tumour cells can re-initiate tumour
formation. The CSC hypothesis suggests that tumours are initiated and sustained by a fraction of
cancer cells derived from tissue stem cells which maintain pluripotency and the capacity to self-
renew [39-41]. In general, these cells are thought to be resistant to chemotherapy, and thus the cells
that are responsible for relapse and metastasis. This theory appears to hold true for numerous
cancer types including leukemia [42, 43], pancreatic cancer [44], and brain tumours [45], in which
specific tumour- initiating cells have been identified. The CSC hypothesis is not without criticisms
8
however, as certain cancers, such as melanoma, have been identified which seem to harbour
numerous cells that are able to initiate and maintain a tumour [40, 46].
Though controversial, a number of publications have suggested that some sarcoma subtypes
may also follow this CSC hypothesis [41, 47-49]. Other studies however, have found that the
ability of some sarcoma-initiating cells to actually initiate tumour formation in vitro and in
xenograft models is inconsistent, and varies from tumour to tumour [50]. Thus, the initiation and
progression of sarcomas remains a complex process that is poorly understood. Crucial to our
understanding of sarcomagenesis are two central and related themes: the cell of origin and the
mechanisms by which cancer-initiating cells acquire malignant properties. Due to the complete
lack of lineage differentiation in the majority of the tumour population, PRC sarcomas are
hypothesized to be the most primitive paediatric STSs that have been identified, and understanding
the molecular processes responsible for the initiation and progression of these tumours will provide
novel insights into the critical biological events responsible for the development of sarcomas in
general.
Cell of Origin
The discovery of the cell of origin is extremely important in understanding the molecular
mechanisms involved in the formation of different sarcoma subtypes. Due to the fact that sarcomas
are mesenchymal-derived tumours, recent studies have focused on mesenchymal stem cells (MSCs)
as the cells of origin for sarcomas [37]. MSCs are capable of differentiating along numerous
differentiation pathways including adipocytic, chondrocytic, osteogeneic and myogenic lineages.
Recent studies have made progress in identifying the supposed cells of origin of specific paediatric
sarcomas, particularly mxyoid liposarcoma, ES/PNET and RMS. Data from several sources has
supported the notion that these tumours arise from mesenchymal stem cells (MSCs), with
9
expression of the tumour-specific fusion proteins in mesenchymal stem cells (TLS/CHOP in
myxoid liposarcoma [51]; PAX3/FKHR in ARMS [52]; EWS/FLI1 in ES [53-55]) inducing
phenotypic changes similar to those seen in the parent tumour. Furthermore, inhibition of the
EWS/FLI1 fusion gene in ES cell lines has been shown to shift the expression profiles of these cells
lines towards the MSC expression profile [56]. These cell lines were then shown to be able to
differentiate along both adipogenic and osteogenic lineages [56]. While increasing amounts of
evidence are being published to support the notion of MSCs as the cell of origin for many sarcoma
subtypes, this concept has been challenged by theories suggesting ES may emanate from the
neuroectodermal lineage. ES has been shown by immunohistochemistry (IHC) to express neural
and neuroectodermal markers, and one study has shown that the expression of the EWS/FLI1 fusion
transcript in neural crest-derived stem cells (NSCs) induces phenotypic and migratory changes [56,
57].
Traditionally, paediatric USTSs have been classified with ERMSs for the purposes of
treatment and therapeutic trials because these tumours often share a similar response to therapy [8,
58, 59]. More recent reports have suggested that at least some paediatric USTSs have more Ewing-
like characteristics, including sheet-like, round cell morphology, focal CD99 positivity (albeit
cytoplasmic), and occasional ultrastructural features of primitive neural differentiation [9, 33, 34,
60]. As previously mentioned, there is much debate as to whether MSCs or NSCs serve as the cell
of origin of many sarcomas. A recent study from our laboratory applied antibodies against several
MSC and NSC-associated proteins to a variety of sarcoma subtypes (ES, USTS, MPNST, ARMS,
ERMS, SS) in an attempt to determine whether differential expression of stem cell-associated
proteins could be used to aid in the subclassification of paediatric sarcomas [61]. It was found that
based on the expression of eight stem cell-associated proteins, paediatric sarcomas cluster into two
major subgroups (Figure 1.3). The first cluster included ES and USTS, and the second cluster
10
contained the majority of the remaining tumours. Lack of expression of CD56 was significantly
associated with the ES/USTS cluster (Figure 1.4). Interestingly, CD56 is a neural stem cell marker,
and the lack of expression in ESs and USTSs is suggestive of a mesenchymal stem cell origin for
these tumours [61]. However, the results of this study were not conclusive enough to definitively
establish a cell of origin for either ES or USTS. On the other hand, the results from this study do
indicate that USTS is much more closely related to ES than to RMS. Thus, the cell of origin of
most primitive sarcomas remains elusive. However based on the primitive nature of these tumours,
their pathological similarity to ES, and the increasing amounts of evidence supporting MSCs as the
cell of origin for ES, it is likely that MSCs serve as the cell of origin for USTSs.
11
Fig
ure 1
.2.
Eu
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Clu
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arcom
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um
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rs
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igh
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.
12
Acquisition of Malignant Properties
The striking specificity of individual translocations for different sarcoma subtypes leads one
to question the functional basis of a specific gene rearrangement in a given tumour. More
specifically, does the acquired gene fusion directly influence the histology of the tumour, or does
the histology of the tumour influence which cell types are able to support the oncogenic activity of
the gene fusion product [28]? These concepts speak to the timing of translocation acquisition;
whether fusion transcripts are acquired at later stages of cellular differentiation and tumour
Figure 1.3. Immunohistochemical Staining of CD56. Immunohistochemical
staining of CD56 in USTS (A), ES (B), MPNST (C) and SS (D). Lack of
expression of CD56, a neural stem cell marker, was significantly associated with
the ES/USTS cluster, where positive staining was found in all other tumour types.
A-D, CD56 immunostain with hematoxylin counterstain, original magnification x
400. (Adapted from [61]).
13
formation, and simply facilitate tumour development, or if the acquisition of a translocation occurs
in more primitive cells, and is the critical event in the formation of a specific histologic subtype of
sarcoma. Conflicting evidence has been shown in ARMS. Some studies have suggested that the
critical event for RMS sarcomagenesis occurs after the tumour cells have acquired myoblastic
properties [37, 62]; whereas others suggest that the malignant phenotype occurs in the more
primitive MSCs [28, 37]. The latter theory is being more widely supported, as numerous studies
have now shown that PAX3 is an important myogenic regulator [63, 64], and that dysregulated
PAX3 (in the form of the ARMS fusion transcript PAX3-FOXO1) represses myogenic
differentiation [64-66]. The ability of PAX3 to commit cells to the myogenic lineage is however
only seen in specific cell types such as those of mesenchymal origin, and cannot induce muscle
commitment in endothelial cells [67]. Thus it is conceivable that dysregulation of PAX3 through a
genomic rearrangement in a very primitive cell may generate a skeletal muscle-derived tumour
[37]. Overall much evidence is supporting the notion that the critical events responsible for tumour
formation occur in more primitive cells as opposed to fully differentiated cells. Furthermore, the
degree of tumour differentiation is being linked to the point of cellular differentiation at which a
specific translocation is acquired [37]. For example, if the translocation is acquired early in lineage
commitment, the resulting tumour is much more primitive, however if the translocation is acquired
later in lineage commitment, the tumour will be well-differentiated. This has led our lab to develop
a hypothetical model of USTS molecular aberration acquisition (Figure 1.5), in which PRC
sarcomas acquire molecular aberrations at a much earlier stage of differentiation relative to other
sarcoma subtypes. The critical events leading to the development of most sarcoma subtypes are
believed to occur in mesenchymal progenitor cells. USTSs, on the other hand, are believed to be
the proliferation of uncommitted mesenchymal cells. It is this difference in the timing of
acquisition of molecular aberrations that results in the more primitive morphology of USTSs.
14
Gaining a better understanding of this process in more primitive tumour types may offer insight
into the specific mechanisms of sarcomagenesis.
Figure 1.4. Decoding the early events of sarcoma development. Many sarcomas, such as
rhabdomyosarcoma, show evidence of differentiation down a specific connective tissue
lineage. Undifferentiated sarcoma shows no specific lineage differentiation, leading to the
hypothesis that the critical molecular events leading to undifferentiated sarcoma development
are likely happening at an earlier stage of differentiation compared to other sarcoma
subtypes.
15
1.4.3 Molecular Studies of USTS
Identifying diagnostic and prognostic markers which help to more accurately identify
sarcoma subtypes is invaluable. Unfortunately, as with the morphological studies of USTS, the
molecular studies of these tumours have been rather uncommon and sporadic. However, in the past
5 years progress has been made in terms of characterizing some of the molecular abnormalities
arising in these tumours, although small sample sizes and minimal clinical correlation have been
limiting factors in these studies.
Immunohistochemical Screens
In general, immunohistochemical screening of USTS have been unsuccessful because of the
uniform negativity seen for most immunohistochemical markers, and inconsistent positivity for
others. However, Somers et al. [32] recently performed a thorough molecular analysis of 13
USTSs and identified certain molecular features of this specific USTS cohort.
Immunohistochemistry identified positivity for vimentin (92%), CD117 (92%), VEGF (69%),
HER-2 (54%), WT1 (46%), and Cox-2 (31%), among others. Alaggio et al. [9] performed a similar
immunohistologial study on 7 USTS tumours and found variable positivity for smooth muscle
actin, muscle-specific actin, CD34, cytokeratin, S-100, CD117, nestin, FLI1, CD105, survivin and
INI1. Discrepancies between these studies are likely a result of the small sample sizes used in each
study. While some of these immunohistochemical results are promising, more confirmatory studies
are necessary in order to determine the importance of these findings.
INI1
A specific type of sarcoma called malignant rhabdoid tumour has been found to harbour a
characteristic loss or mutation of the INI1 gene on chromosome 22q11.2. The INI1 protein is part
16
of the SWI/SNF chromatin-remodelling complex. This complex can act as either a transcriptional
activator or repressor, and has been found to be constitutively expressed in all cells [68, 69]. In a
recent study Kreiger et al [33] sought to investigate whether USTSs show a loss of INI1 protein
expression due to underlying genomic abnormalities in the INI1 gene. By immunohistochemisty,
the study found 5/17 (29%) cases of USTS showing loss of nuclear expression of INI1. They
further identified by fluorescence in situ hybridization (FISH), reverse transcription-polymerase
chain reaction (RT-PCR) and/or mutational analysis that 4 of these 5 tumours harboured genetic
abnormalities of the INI1 gene. The report goes on to suggest that this category represents a unique
subgroup of tumours. Unfortunately others have found data inconsistent with these results,
suggesting that a category of USTSs showing INI1 loss may not be significant [9, 32].
IGF2
A recent publication by Somers et al. applied gene expression array analysis and post-array
validation to a series of USTSs and identified consistent upregulation of the excitatory components
of the insulin-like growth factor pathway. Particularly high expression of the insulin-like growth
factor 2 (IGF2) ligand was identified and confirmed using RT-PCR. IHC identified overexpression
of the IGF2 protein in 19 of 21 tumours (90%) with 2 distinct staining patterns (diffuse cytoplasmic
(16/19) and punctuate perinuclear (3/19)). Convincing and consistent upregulation of this pathway
in USTS suggests that the IGF signalling pathway may be a critical early event in sarcomagenesis,
however functional studies are necessary to confirm these studies.
CIC-DUX4 translocation
Over the past decade there have been sporadic case reports of tumours with PRC
morphology that harbour a rearrangement involving chromosome 4 and chromosome 19. In 1996,
17
Richkind et al. [70] reported a balanced t(4;19)(q35;q13.1) translocation in a tumour diagnosed as a
malignant extraskeletal sarcoma. This translocation did not resurface in the literature again until
2006, when Kawamura-Saito et al. [60] reported two primitive Ewing-like sarcomas harbouring
t(4;19)(q35;q13) translocations. This group further deduced that in both cases, this translocation
resulted in the fusion of the CIC gene (19q13) to the C-terminal portion of the DUX4 gene (4q35).
Finally they found that overexpression of the CIC-DUX4 transcript in vitro increased anchorage-
dependent colony formation in murine NIH3T3 fibroblasts and directly induced over-expression of
downstream ETS-family transcription factors [60]. A more detailed description of the CIC and
DUX4 genes, as well as the CIC-DUX4 translocation, can be found in section 3.3.3. Most recently,
Rakheja et al. [71] published the 4th
case of a PRC sarcoma harbouring a t(4;19) translocation.
Unfortunately no group has attempted to characterize the prevalence of this translocation in a larger
cohort of USTS.
18
1.5 Hypothesis, Objectives & Expected Outcomes
The accurate sub-classification of sarcomas is of utmost importance in order to determine an
accurate prognosis and to plan optimal therapy for the patient. Based on the limited literature that
has been published on USTSs, I hypothesize that paediatric undifferentiated sarcomas contain
distinct subgroups that can be identified based on their morphology, genomic aberrations and
expression profiles. To test this hypothesis, three objectives are proposed. Firstly, we seek to
develop an assay with which to screen our cohort of USTSs for the t(4;19)(q35;q13.1), and
ultimately determine the prevalence of this translocation in our cohort of USTSs. Secondly we seek
to characterize genomic aberrations within the t(4;19)(q35;q13.1)-negative USTSs, looking
specifically for any novel recurrent regions of gain, loss or rearrangement. Lastly, we seek to
characterize the expression profiles of the t(4;19)(q35;q13.1)-negative USTSs with the aim of
identifying a unique expression signature. With these objectives, we hope to identify specific
genomic changes which will aid in the characterization and diagnosis of USTSs, establish unique
genomic and protein expression profiles for USTSs for diagnostic and prognostic use, and to
identify potential genes or proteins for targeted treatment.
19
CHAPTER TWO
MATERIALS & METHODS
20
2. MATERIALS & METHODS
2.1 Tumour Specimens
An electronic search of the pathology database at the Hospital for Sick Children was
performed to identify children diagnosed with USTS between 1987 and 2007. Primary tumours
involving viscera and bone, and intradural tumours, were excluded. Pretreatment biopsy specimens
were studied if available, and all tumours underwent extensive immunohistochemical and
molecular genetic screening before being included in the series of USTS (see [32] for methodology
used). Thirteen of the 22 USTSs (USTS1-4, USTS6-11, USTS16-18) were part of previous studies
looking at the clinical, pathologic and cytogenetic features of paediatric USTSs [31, 32, 34, 72].
Three of the USTSs were originally diagnosed as atypical Ewing sarcomas (USTS20-22), due to
the fact that none of these tumours harboured a demonstrable rearrangement of the EWS gene by
RT-PCR, but had PRC morphology. Primary tumour samples were used when available; for one
patient, the primary tumour specimen was not available and therefore one post-therapy specimen
was studied. Frozen tissue from five additional paediatric USTSs (USTS12-15, USTS19) were
obtained from the files of the Co-Operative Human Tissue Network (Columbus, OH). Clinical
details were not available for one such tumour. All studies were performed in accordance with the
guidelines of the Hospital for Sick Children’s Research Ethics Board.
2.2 DNA and RNA Extraction
High molecular weight genomic DNA was extracted from snap-frozen tissue digested in a
proteinase K buffer and purified by standard phenol/chloroform methods [73]. All samples were
treated with RNase A (Roche Diagnostics, Laval, QC, Canada) for 30 minutes at 37oC. Total RNA
was extracted from snap-frozen tumour tissue samples using the TRIzol reagent according to the
21
manufacturer’s instructions (Invitrogen, Carlsbad, CA), and treated with RNase-free DNase I
(Message Clean; GeneHunter Corporation, Nashville, TN) for 30 minutes at 37oC to remove DNA
contaminants. RNA extraction from formalin-fixed paraffin-embedded (FFPE) tissue samples was
performed as previously described [31]. In brief, tissue sections were deparaffinized in xylene and
subsequently washed in absolute ethanol. Following centrifugation, the tissue pellet was air-dried
and digested overnight at 55oC in 350 μl of RNA lysis buffer (20mM Tris pH 7.5, 10mM EDTA,
1% SDS, 500mg of Proteinase K). RNA extraction was then carried out using the TRIzol reagent
according to the manufacturer’s instructions (Invitrogen, Carlsbad, CA), and treated with RNase-
free DNase I (Message Clean; GeneHunter Corporation, Nashville, TN) for 30 minutes at 37oC to
remove DNA contaminants. Quality and concentration of extracted RNA and DNA samples were
quantified by the NanoDrop ND 3300 fluorospectrometer (NanoDrop products, Wilmington, DE).
2.3 Cytogenetic and Spectral Karyotyping (SKY) Analyses
Tumour specimens were processed for short-term culture in high-glucose Dulbecco’s
modified Eagle’s medium (4.5g/L) supplement with 15% fetal calf serum, 1% penicillin
streptomycin, and 1% L-glutamine [34]. The cultures were harvested within 5 days using 0.1
mg/mL colcemid (Gibco/BRL) for 2-4 hours, hypotonically treated with 0.075M KCl, and fixed in
3:1 methanol:acetic acid [34, 74]. The slides were dropped and aged for 2 to 3 days, after which
they were stained with Giemsa [34]. By convention, a minimum of 10 metaphases were analyzed
using conventional G-banding methods according to standard procedures, and were immediately
destained and dehydrated for subsequent fluorescence SKY analysis. The SKY analysis was
performed on the tumour metaphase cells according to the manufacturer’s instructions (Applied
Spectral Imaging, Carlsbad, CA) and as previously published [34, 74, 75]. Briefly, slides were
destained, formalin fixed, and denatured in 70% formamide/2XSSC at 75oC for 40 seconds. The
22
SKYPaints were denatured, preannealed and hybridized to the denatured slides for 24 hours at
37oC. Posthybridization washes and detections were conducted according to the manufacturer’s
instructions. Spectral images were acquired with an SD 200 spectral bioimaging system (ASI,
Migdal Ha’Emek, Israel), and were analyzed using ASI SkyView software (version 1.2). When
sufficient metaphase cells of adequate quality were present, 10 metaphase cells were analyzed.
2.4 Array Comparative Genomic Hybridization (CGH)
All tumour specimens were selected to include only those tissue samples containing >80%
of identifiable neoplastic tissue by routine histological characterization. The Agilent human
genome CGH microarray 44 k (USTS1, USTS16) and 244 k (USTS2) (Agilent Technologies, Santa
Clara, CA) were used for the array CGH experiments. Three micrograms of human genomic DNA
from multiple anonymous male donors (Promega, Madison, WI) and 3 μg of test genomic DNA
sample were digested with AluI (5 units) and RsaI (5 units) (Promega, Madison, WI) for a
minimum of 2 hours at 37oC. Digestion quality was assessed by the DNA 1000 LabChip Kit
(Agilent 2100 Bioanalyzer). Labeling reactions were performed using the Agilent genomic DNA
labeling kit PLUS according to the manufacturer’s instructions. Briefly, the reference and sample
DNA were labeled with 1.5-3 mmol/L Cy5-dUTP or Cy3-dUTP (Agilent), and purified using a
Centricon YM-30 filter (Millipore, Billerica, MA). Probe mixture of Cy3-labeled sample DNA,
Cy5-labeled reference DNA, 50 μL of 1.0 mg/mL of human Cot-1 DNA (Invitrogen Canada,
Burlington, ON, Canada), 52 μL of Agilent 10x blocking agent and 260 μL of Agilent 2X
hybridization buffer was denatured at 100oC for 1 minute 30 seconds and incubated at 37
oC for 30
minutes. The probe was applied to the array using an Agilent microarray hybridization chamber,
and hybridized for 40 hours at 65oC in a rotating oven (Robbins Scientific, Sunnyvale, CA) at 20
rpm. Dye swaps were performed for both USTS1 and USTS2 samples. Arrays were washed
23
according to the manufacturer’s recommendations, and then air dried and scanned using an Agilent
2565AA DNA microarray scanner. For each tumour, the data analysis of array CGH was
performed by applying rank segmentation, with a significance threshold of 1.0x10-6
, a maximum
contiguous probe spacing of 1,000 kb, and a minimum of 5 probes per segment (Nexus Copy
Number v.4; BioDiscovery, El Segundo, CA). Genomic imbalances were assigned as either gain
[log(3/2) or threshold of 0.2] or loss [log(1/2) or threshold of -0.3], considering only genomic
intervals >100 kb. The aberrations on X and Y chromosomes were excluded from the analysis to
eliminate the sex mismatching bias.
2.5 Interphase Fluorescence in situ Hybridization (FISH)
Interphase FISH was used in the pilot project portion of this thesis for the purposes of
identifying the translocation partners for chromosome 19 in samples USTS1 and USTS2.
Interphase FISH was also used in the exploratory array portion of this thesis in an attempt to
validate copy number changes of the EPHA3 gene in USTS12-15 and USTS19.
2.5.1 Isolation of DNA from Bacterial Artificial Chromosomes (BACs)
DNA was isolated from individual BAC clones grown overnight in a 20ml culture of Luria-
Bertani (LB) medium containing 12.5 μg/ml chloramphenicol (Sigma-Aldrich Canada Limited,
Oakville, ON, Canada). BAC DNA was isolated using the Qiagen Plasmid Midi Kit as per the
manufacturer’s instructions. Briefly, cell pellets were resuspended in Tris-EDTA (50mM Tris-
Chloride, pH 8.0, 10mM EDTA) and lysed in sodium hydroxide (200mM NaOH, 1% SDS).
Following neutralization with potassium acetate (3M KOAc, pH 5.5), BAC DNA was precipitated
in isopropanol for 1 hour at -80oC, and resuspended in DEPC water. Samples were then treated
with RNase A (Roche Diagnostics, Laval, QC, Canada) for 30 minutes at 37oC to remove RNA
24
contaminants and DNA was isolated by standard phenol-chloroform methods, precipitated with
isopropanol and resuspended in sterile water. Quality and concentration of extracted DNA was
quantified by the NanoDrop ND 3300 fluorospectrometer (NanoDrop products, Wilmington, DE).
2.5.2 Pilot Project Four-Colour Interphase FISH
For the pilot project portion of this study, a four-colour interphase FISH method was
applied to USTS1 and USTS2 paraffin sections to validate the genomic rearrangement involving
chromosomes 4 and 19. The following BAC clones were used for BACs located at chromosome 19
(a-e), chromosome 4 (f,g), and chromosome 20 (h): (a) 19p13.12~p13.11 (16.00-16.42 Mb, control
probe): RP11-121I1 and RP11- 451E20; (b) 19q13.2 (47.02-47.33 Mb, probe A): RP11-688M4 and
RP11-108I20; (c) 19q13.2 (47.26-47.44 Mb, probe B): RP11-317E13; (d) 19q13.2~q13.31 (47.52-
47.91 Mb, probe C): RP11-374A11 and RP11- 1029C16; (e) 19q13.2 (47.44-47.61 Mb, probe E):
CTC- 790D18; (f) 4q33 (17.05-17.07 Mb): RP11-157C21 and RP11-242A14; (g) 4q35.2 (19.10-
19.11 Mb, 111 kb upstream of DUX4, probe D): RP11-521G19; and (h) 20p12.3 (7.39-7.55 Mb,
probe F): RP11-19D2. The linear order and approximate distances are based on the March 2006
assembly (Hg18) of the University of California, Santa Cruz, genome browser
(http://www.genome.ucsc.edu). DNA from all BAC clones was extracted by standard methods and
labeled with Vysis Spectrum Green-dUTP, Spectrum Red-dUTP, Spectrum Orange-dUTP (Abbott
Molecular, Des Plaines, IL), or Cy5-dUTP (PerkinElmer Life and Analytical Sciences, Waltham,
MA), using a Vysis nick-translation kit according to manufacturer’s instructions. The presence of
CIC, DEDD2, and ERF sequences and correct chromosome location of all BAC clones were
verified by PCR and by hybridization to metaphase spreads from normal peripheral lymphocytes,
respectively. Samples were scanned using the Zeiss Axio Imager.M1 microscope (Carl Zeiss
25
Canada, Toronto, ON) equipped with appropriate filter sets and analyzed with ISIS imaging
software (MetaSystems, Altlussheim, Germany).
2.5.3 FISH Validation of Copy Number Gain of EPHA3
A BAC clone was selected that covers the EPHA3 gene region on 3p11.2 (89.13-89.30 Mb,
RP11-23D18). A commercially available CEP3 DNA probe labeled with Spectrum Orange-dUTP
and specific for the centromeric region 3p11.1-q11.1 (Vysis
Inc., Downers Grove, IL, USA) was
used as a control for both locus-specificity and ploidy analyses. DNA from all BAC clones was
extracted by standard methods and labeled with Spectrum Green–dUTP using the Vysis nick-
translation kit according to manufacturer’s instructions. Locus-specificity of the selected BAC
clones was verified by hybridization to metaphase spreads
from normal peripheral lymphocytes.
Five micrometer histological formalin-fixed and paraffin-embedded tissue sections were pre-treated
as follows: deparaffinization with a series of xylene followed by immersion in 100% ethanol,
incubation in 10mM NaCitrate (pH 6.4) at 80oC for 90 minutes followed by 2XSSC rinse, pepsin
digestion (4mg/ml pepsin in 100ml 0.01N HCl) at 45oC for 20 minutes followed by 2XSSC rinse
and a final ethanol dehydration series (70%, 90% and 100%, 2 minutes each) [76]. Slides and
probes were co-denatured on a hot plate at 75oC for 10 minutes and incubated overnight at 37
oC.
Following a post-hybridization rapid-wash technique consisting of one wash in 0.4xSSC and 0.3%
NP-40 at 72 °C for 3 min, followed by a 5-min wash at room temperature in 2XSSC and 0.1% NP-
40, slides were mounted in DAPI/Antifade (Vector Laboratories, Burlington, ON, Canada) and
visualized with a Zeiss Axioscope fluorescence microscope (Carl Zeiss Canada). The establishment
of EPHA3 gene copy number status was defined relative to the CEP 3 DNA probe gene copy
number.
26
2.6 Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) for Detection of CIC-DUX4
Fusion Transcripts
To test for the presence of the CIC-DUX4 translocation, RT-PCR amplification was
performed using the GeneAmp RNA PCR core kit (Applied Biosystems, Foster City, CA).
Complementary DNA was synthesized by reverse transcription (RT) using 1-2μg of total RNA and
primed using random hexamers. The RT reaction consisted of 15 minute incubation at 42oC
followed by inactivation at 99oC for 5 minutes. Specific amplification of the CIC-DUX4 fusion
transcript was performed using combinations of CIC forward primers and DUX4 reverse primers at
a concentration of 0.2μM for each primer (Table 2.1).
Primer Name Primer Sequence (5'-->3')
DUX4RTr2 [60] TGAGGGGTGCTTCCAGCG
DUX4-R2 ATGCCTTGCATCTGCCC
CIC4120 [60] TGAGTTGCCTGAGTTTCG
CIC2F ATCATGCAGGCTGCCACT
CIC3F CCCTGGAGCTGAGGCT
CIC4F ACTGGCACCGCTGCT
Table 2.1. Primer sequences for the RT-PCR reactions described in the
materials and methods. (Adapted from [30]).
27
Primer sets were designed such that the PCR products were small enough so that the CIC-
DUX4 fusion transcript could be detected from RNA extracted from paraffin, and that primer sets
would detect all known breakpoints. The cycling parameters for each PCR reaction consisted of 40
cycles of 94oC for 30 seconds, 62
oC for 45 seconds, 72
oC for 45 seconds, followed by a final
elongation step at 72oC for 7 minutes. RT-PCR products were size-fractionated by gel
electrophoresis on a 1.5% agarose gel. To determine RNA integrity, cDNA from the same RT
reaction was also amplified with primers from the endogenous housekeeping gene
phosphoglycerate kinase 1 (PGK1). A single product of 247bp was visualized on a 1.5% agarose
gel with ethidium bromide staining (data not shown). A full-length CIC-DUX4 cDNA subcloned
into pGEM (Promega, Madison, WI; a kind gift from Dr. Takuro Nakamura, Department of
Carcinogenesis, Japanese Foundation for Cancer Research, Japan) was used as a positive control for
the CIC-DUX4 fusion transcript.
2.7 Copy Number Arrays
Copy number analysis was performed using the Affymetrix Genome-Wide Human SNP 6.0
array platform (Affymetrix, Santa Clara CA, USA). High molecular weight genomic DNA was
extracted from five snap-frozen t(4;19)(q35;q13.2)-negative USTS tumours (USTS12-15, USTS19)
as described in section 2.2. 500ng of genomic DNA was labeled and hybridized to the array as per
the manufacturer’s instructions at the Centre for Applied Genomics (The Hospital for Sick
Children, Toronto ON, Canada). Quality control was performed by Contrast QC calculation. Data
collected by the International HapMap project [77] was used as the normal control reference
(Affymetrix SNP 6.0 data on 270 normal samples).
28
2.8 Expression Arrays
Genomic RNA expression analysis was performed using the Affymetrix Gene 1.0 ST array
platform (Affymetrix, Santa Clara CA, USA). Total RNA was extracted from five snap-frozen
t(4;19)(q35;q13.2)-negative USTS tumours as described in section 2.2. To test the purity of the
RNA samples, total RNA was run on the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa
Clara, CA). The software for this program provided an RNA Integrity Number (RIN) for each
sample that allowed for estimation of the integrity of the total RNA extracted from each sample
[78]. Normal Adult Human Dermal Fibroblasts were obtained from Cedarlane Canada and
ScienCell Research Laboratories (Cedarlane Canada, Burlington On, Canada; ScienCell Research
Laboratories, Carlsbad CA, USA) and used as normal reference controls. Total RNA (200ng) from
normal human fibroblasts and tumour samples was analyzed as per manufacturer’s instructions at
the Centre for Applied Genomics (The Hospital for Sick Children, Toronto ON, Canada).
2.9 Analysis and Integration of Microarray Data
For the purposes of array analysis and integration two programs were used: Partek
Genomics Suite (PGS) and Ingenuity Pathway Analysis (IPA).
2.9.1 Partek Genomics Suite: Data Analysis and Integration
Gene copy number and RNA expression analyses were performed using the PGS Software.
Data from copy number array experiments and RNA expression array experiments were received in
the form of .cel files (GCOS 1.3 software), and were imported into, analyzed, and integrated using
the PGS software.
The analysis of copy number changes followed the PGS Copy Number Workflow, which
compares signal log2 ratios to a reference of 270 normal HapMap samples [79]. The PGS Genomic
29
Segmentation tool was used to detect amplified and deleted segments with the following
parameters: minimum continuous probes set at 10, p-value threshold of 0.01, and signal to noise
ratio of 0.1. Regions of significant gain or loss were annotated to the corresponding genes on the
Affymetrix Gene 1.0 Array using the publically available HuGene-1_0-st-v1.na24.hg18.-
transcript.csv file. A gene list was subsequently created which contained all genes that were
significantly gained or lost in at least 3/5 (60%) of tumours.
The analysis of the expression array data used the PGS one-way analysis of variance
(ANOVA) tool at P < 0.05 and a cut-off of ±2-fold enrichment. Changes in gene expression were
cumulatively analyzed across all tumours versus normal fibroblast controls. Significantly over- and
under-expressed genes were annotated using the Affymetrix Gene 1.0 Array, which in turn uses the
publically available HuGene-1_0-st-v1.na24.hg18.-transcript.csv file. A gene list was subsequently
created which contained all genes that were significantly over- and under-expressed cumulatively
across all tumours analyzed.
The integration of genes lists of significantly gained and lost, and significantly over- and
under-expressed genes was performed using the PGS Venn analysis tool. The genes in the cross-
section area of the Venn diagram represent genes found to have significant changes in both copy
number and gene expression. From the list of genes found in the Venn diagram cross-section,
genes that showed copy number gain and under-expression or copy number loss and over-
expression were excluded from further analysis (5 of 51 genes). Copy number and gene expression
analysis excluded sex chromosomes to avoid bias in the identification of significant regions owing
to sex differences between some tumour samples and controls.
30
2.9.2 Ingenuity Pathway Analysis: Network Identification and Pathway Analysis
Functional identification of gene networks was performed using the IPA program as
previously described [80, 81]. Excel (.xls) tables representing genomic changes (as per analysis of
the Affymetrix SNP 6.0 microarray data) and differentially expressed genes (as per analysis of the
Affymetrix Gene 1.0 ST microarray data relative to normal fibroblasts) in the tumour samples were
imported as individual experiments using the IPA Core Analysis tool. The analysis was performed
using the Ingenuity Knowledge Database for Humans only, and was limited to known direct
molecular interactions.
2.10 Quantitative Polymerase Chain Reaction (qPCR)
A quality control RT-PCR using primers for the PGK1 housekeeping gene demonstrated
that RNA from all five samples used for the microarray studies were of suitable quality for RT-PCR
experiments. Thus, RNA from the five tumour samples and two normal adult human fibroblasts
were reverse transcribed into complementary DNA using 1μg of total RNA and primed using
random hexamers. The RT reaction consisted of a 15 minute incubation at 42oC followed by
inactivation at 99oC for 5 minutes. Real-time PCR was performed as previously published [82, 83].
0.5μl of cDNA was used for real-time PCR using the SYBR green master mix as per
manufacturer’s instructions and primers at a concentration of 50μM each on the Roche
Lightcycler480 (Hoffmann-La Roche Limited, Mississauga, ON, Canada). Each combination of
tumour sample and gene primer sets was run in triplicate. Reactions were incubated for 5 minutes
at 95oC, followed by 50 cycles of 95
oC for 15 seconds, 60
oC for 40 seconds, and 72
oC for 15
seconds. Product specificity was confirmed by melt-point analysis. Primer sets for the four genes
of interest (EPHA3, SNX10, ADAM9, CDC73) as well as the housekeeping control were obtained
from SABiosciences (SABiosciences: A Qiagen Company, Frederick MD, USA). Data was
31
analyzed by the comparative CT method (see below) using the TATA box-binding protein (TBP) as
the housekeeping control.
2.10.1 Comparative CT Analysis of qPCR Data
Comparative CT analysis, or 2-ΔΔC
T, is a method of presenting quantitative gene expression
[83]. This method calculates the relative gene expression of a gene of interest compared to an
internal control gene using the CT, or threshold cycle, of each test sample [83]. The CT is defined
as the PCR cycle at which the fluorescent signal of the reporter dye crosses an arbitrarily placed
threshold [83]. Plots of cDNA dilutions versus delta CT were performed, and the resultant plots
were consistently close to zero, indicating that the amplification efficiencies of the target and
housekeeping genes were similar for all gene primers. The following formula [83] was used to
determine relative fold-change for genes of interest in each tumour sample:
2-ΔΔC
T = [(CT gene of interest in tumour – CT internal control gene in tumour) – (CT gene of interest
in normal – CT internal control gene in normal)]
Where TBP was used as the internal control reference gene, and pooled adult human dermal
fibroblasts were used as the normal reference sample.
2.11 Immunohistochemistry (IHC)
IHC for CD99 was performed in all tumours in order to determine CD99 expression status.
IHC for EPHA3 was performed on the t(4;19)(q35;q13.1)-negative tumours used in the array
portion of the study in order to attempt to confirm the gain of EPHA3 in some of these tumours.
32
2.11.1 IHC for CD99
Immunohistochemistry was performed on dewaxed slides cut at 4-µm using the Ventana
Benchmark XTTM autostainer (Ventana Medical Systems, Tucson, Arizona, USA) and the
Ventana i-VIEW LSAB DAB kit. The CD99 antibody (Leica-Novocastra, Richmond IL; clone
HO36-1.1) was used at 1:50 following heat-induced epitope retrieval. CD99 staining was
performed on all tumours.
2.11.2 IHC EPHA3
Immunohistochemistry with an EPHA3 antibody (1:300; Abnova, Walnut CA, USA) was
performed on 4-µm thick sections of each tumour. Tumours used for IHC were the same as those
used for array analysis (USTS12-15, USTS19). Optimal conditions of heat-induced epitope
retrieval using the Ventana 3,3’diaminobenzidine tetrachloride kit according to the manufacturer’s
instructions (Ventana Medical Systems Inc., Tucson, AZ). Sections were scored using a previously
established scoring system [32]. Intensity of staining was scored as 1 for low, 2 for moderate, and
3 for high, where the positive control was used as the standard for high intensity. Distribution of
staining was as 1 if less than 10% of cells were positive, 2 if 11-50% of cells were positive, and 3 if
greater than 50% of cells were positive. A combined score of 4 or more was considered positive,
and a combined score of less than 4 was considered negative. Optimization was performed on
tissues identified as highly expressing EPHA3 by the Human Protein Atlas version 6.0
(www.proteinatlas.org) [84-87]. Tissues identified as most favorable for optimization were testis,
kidney, placenta, and breast carcinoma. Ultimately breast carcinoma was identified as the optimal
positive control reference tissue.
33
CHAPTER THREE
RESULTS & DISCUSSION
34
3. RESULTS & DISCUSSION
3.1 Clinicopathological Features of Tumour Samples
In total 22 USTSs were used over the course of this thesis work. Table 3.1 summarizes the
clinicopathological features of the tumours; for one patient, the age and sex was not available. The
clinicopathological features of 13 tumours have been previously described [31, 32, 34, 72]. Briefly,
17 cases were composed of sheets of primitive round to plump spindle cells; one case was
composed of nests of PRCs; and four cases were composed of pure spindle cells within a myxoid,
collagenous or cellular fascicular background (Figure 3.1). There was a slight male predominance
(12:9) and the mean and median age was 9.3 and 11 years, respectively. The most common sites of
involvement were the trunk (50%), the extremities (27%) and the head & neck (23%). CD99
showed cytoplasmic positivity in 3 cases (USTS13-15) and membranous positivity in 5 cases
(USTS1-3, USTS20-21) (Figure 3.1).
Figure 3.1. Histological features and CD99 staining pattern of USTSs used in t(4;19) pilot study.
(A,B) USTS1 and USTS2 exhibited a primitive round cell phenotype. (C) USTS16 exhibited a more
spindled and myxoid phenotype. (D) CD99 staining of USTS2 exhibited weak and inconsistent membranous
staining. (A-C) Hematoxylin-eosin stain, x 200, hematoxylin and eosin. (D) CD99 antibody (Dako,
Carpinteria, CA, 1:50) with hematoxylin countertain. Magnification x 400. (Adapted from [32]).
35
Sample Sex Age Site of Primary Morphology CD99 CIC-DUX4
status
USTS1 F 16 yr Trunk (abdominal wall) PRC, sheets Pos (m) POS
USTS2 M 14 yr H&N PRC, sheets Pos (m) POS
USTS3 F 11 yr Trunk (flank) PRC, sheets Pos (m) POS
USTS4 F 9 yr Trunk (paraspinal) PRC, nested Neg POS
USTS5 F 11 yr Trunk (inguinal) PRC, sheets Neg POS
USTS6 F 12 yr Trunk (chest wall) PRC, sheets Neg NEG
USTS7 F 14 yr Trunk (back) PRC, sheets Neg NEG
USTS8 M 13 yr H&N (neck) PRC, sheets Neg NEG
USTS9 M 12 yr Extremity (knee) PRC, sheets Neg NEG
USTS10 M 3 yr Extremity (toe) PRC, sheets Neg NEG
USTS11 M 4 yr Trunk (shoulder) PRC, sheets Neg NEG
USTS12 M 9 m Trunk (retroperitoneal) PRC, sheets Neg NEG
USTS13 M 9 m Extremity (arm) PRC, sheets Pos (c) NEG
USTS14 F 11 yr H&N (neck) PRC, sheets Pos (c) NEG
USTS15 n/a n/a Extremity (thigh) PRC, sheets Pos (c) NEG
USTS16 M 12 yr Trunk (paraspinal) SC, myxoid Neg NEG
USTS17 M 11 yr H&N (orbit) SC, myxoid Neg NEG
USTS18 F 7 yr Trunk (chest wall) SC, fascicular Neg NEG
USTS19 M 10 yr Trunk (flank) SC, collagenous Neg NEG
USTS20 M 13 yr Extremity (leg) PRC, sheets Pos (m) NEG
USTS21 M 11 yr Extremity (toe) PRC, sheets Pos (m) NEG
USTS22 F 9 m H&N (orbit) PRC, sheets Neg NEG
Table 3.1. Clinicopathological features, CD99 staining pattern, and CIC-DUX4 fusion
transcript status of the cohort of tumours used in the study. USTS, undifferentiated soft tissue
sarcoma; N/A, not available; H&N, head and neck; PRC, primitive round cell; SC, spindled cell; pos,
positive; neg, negative; m, membranous; c, cytoplasmic. (Table modified from [31]).
36
3.2 t(4;19) Pilot Project
The following data has been published as follows: Yoshimoto M., Graham C., Chilton-MacNeill
S. et al., Cancer Genet Cytogenet. 2009;195:1-11 [31].
Prior to the commencement of this thesis project, our laboratory initiated a pilot project to
investigate the presence of a 19q13 rearrangement in our cohort of USTSs. This study applied
high-throughput array comparative genomic hybridization (CGH) together with spectral
karyotyping (SKY), four-colour fluorescence in situ hybridization (FISH), and reverse
transcriptase-polymerase chain reaction (RT-PCR) to a series of three paediatric USTS samples
(USTS1, USTS2, USTS16). Two of these samples have PRC morphology with CD99 positivity,
and the third sample has a spindled and myxoid appearance and is negative for CD99. G-banding
and SKY analyses were initiated and completed prior to my addition to the study group. Array
CGH studies had been initiated prior to my addition to the study, however analysis of the array
CGH results were completed by both myself and Dr. Maisa Yoshimoto. I subsequently played a
major role in the development and application of the four-colour FISH and RT-PCR assays.
3.2.1 Genomic Imbalances in Undifferentiated Sarcomas
High-resolution array CGH analysis was performed to identify: (a) copy number changes of
a whole chromosome; (b) copy number changes affecting a chromosomal arm; (c) small genomic
imbalances (>750kb or ≤ 10Mb); and (d) cryptic microaberrations (>100kb or ≤ 750kb) in the
paediatric USTS samples [34]. From comparison analysis of genomic gains and losses among the
three tumour samples, USTS16 exhibited the least genomic copy number imbalance relative to the
other samples (Figure 3.2). USTS1 and USTS2 exhibited the highest frequency of genomic
complexity (15 and 26 respectively), whereas USTS16 exhibited only 1 imbalance by array CGH.
37
USTS2 harboured the most genomic losses (17/26), compared with USTS1 (3/15) and USTS16
(0/1). Although small genomic imbalances were widely detected in USTS1 (7/15), the high-
resolution genome-wide analysis also identified chromosome-arm (2/15) and whole chromosomal
copy number changes (1/15). USTS2 showed chromosome-arm copy number changes, small
genomic imbalances and cryptic microaberrations (1/26, 10/26, and 15/26, respectively). These
cryptic microaberrations were randomly distributed across the genome. Each genomic imbalance
interval in USTS1 and USTS2 was converted to the corresponding cytoband at 850-band resolution.
Notably, the 19q13 region was consistently rearranged in both USTS1 and USTS2, but not
USTS16. This common 19q13 region rearranged in USTS1 and USTS2 was further interrogated
using SKY and four-colour FISH.
38
Fig
ure 3
.2.
Com
posi
te a
naly
sis
of
gen
om
ic i
mb
ala
nces
in t
hree U
ST
S s
am
ple
s, w
ith
wh
ole
gen
om
ic p
rofi
les
of
(A)
US
TS
16,
(B)
US
TS
1,
an
d (
C)
US
TS
2.
Reg
ions
of
gai
n a
re s
how
n o
n t
he
right
side
of
each
ver
tica
l pro
file
and a
re h
ighli
ghte
d a
s gre
en
sect
ions
on t
he
chro
moso
mal
rep
rese
nta
tions.
Reg
ions
of
loss
are
show
n o
n t
he
right
side,
and a
re h
ighli
ghte
d a
s re
d.
Aber
rati
ons
wer
e as
sign
ed a
s ei
ther
gai
n [
log(3
/2)
or
thre
shold
of
0.2
] or
loss
[lo
g(1
/2)
or
thre
shold
of
0.3
]. G
enom
ic i
mbal
ance
s w
ere
ass
igned
as e
ither
gai
n [
log(3
/2)
or
thre
shold
of
0.2
) o
r lo
ss [
log
(1/2
) o
r th
resh
old
of
0.3
], t
akin
g i
nto
acc
ou
nt
on
ly g
eno
mic
inte
rval
s >
10
0 k
b.
(Adap
ted f
rom
[30])
.
39
3.2.2 Karyotypes of Undifferentiated Sarcomas
Metaphase cells from the 3 tumours were characterized by G-banding (data not shown) and
SKY analysis. All of the tumours demonstrated both balanced and unbalanced structural
rearrangements. The karyotypes are given in Table 3.2.
Sample Karyotype
USTS16
a 46,XY,t(1;15)(q?31;q?21),t(2;6)(q?33;q12~q14),der(8)t(8;20)(p23;q11.2),t(11;20)(q12
~q13;p11.2),del(20)(q11.2)
USTS1b 47,XX,i(1)(q10),der(4)t(4;19)(q33~q35;q13.1),+8,t(15,17)(q24;p11.2~p12),der(19)t(19
;20)(q13.1;p11.2),der(22)t(20;22)(q13;q13)
USTS2 46,XY,del(4)(q33),der(6)t(6;19)(p21.1;p13.3),der(7)t(7;19)(q36;q13.2),der(13)t(6;13)(
p21.1;p11.2),del(19)(p13.3),der(19)t(6;19)(p21.3;q13.2)
SKY analysis confirmed that the 19q13 region was consistently rearranged in the PRC sarcoma
samples. In sample USTS1, chromosome 19 was involved in two unbalanced translocations, one
with chromosome 4 and one with chromosome 20 (Figure 3.3A). In sample USTS2, chromosome
19 was involved in unbalanced translocations with chromosome 6 and chromosome 7 (Figure
3.3B). In both cases it was subsequently necessary to determine which chromosome was the
binding partner for the region of interest on chromosome 19 (19q13.1).
Table 3.2. Summary of the undifferentiated sarcoma karyotypes according to G-banding and
spectral karyotyping analyses. a Cytogenetic analysis previously described by Selvarajah et al
[33]. b Cytogenetic analysis previously described by Somers et al [36].
40
3.2.3 Detailed Comparison of Cytogenetic Karyotyping and Array CGH
Comparison of the array CGH profiles to the SKY results showed that apparent balanced
rearrangements affecting chromosomes 1, 2, 6, 8, 11, 15, and 20 were associated with no
acquisition of genomic imbalances in USTS16 (data not shown). In contrast, both USTS1 and
USTS2 showed unbalanced translocations associated with genomic imbalances. Array CGH
profiling of USTS1 revealed loss and gain of the short and long arm of chromosome 1, respectively.
These genomic imbalances were consistent with the presence of i(1q), as previously described from
Figure 3.3. Representative spectral karyotyping (SKY) image of two USTS samples
showing multiple structural alterations in a diploid karyotype. (A) SKY and G-band
(data not shown) analysis of USTS1 showing a complex karyotype including
isochromosome of 1q, trisomy of chromosome 8 and rearrangements of chromosomes 4,
15, 17, 19, 20 and 22. (Adapted from [33]). (B) SKY analysis of USTS2 showing
chromosomal rearrangements on chromosomes 4, 6, 7, and 19. (Adapted from [30, 36]).
41
SKY analysis [34]. In addition, the extra copy of chromosome 8 was detected by both array CGH
and SKY analyses [88]. The derivative chromosomes, such as der(4)t(4;19)(q33~q35;q13.1),
der(19)t(19;20)(q13.1;p11.2), and der(22)t(20;22)(q13;q13) were also associated with gain and loss
of genomic regions (Figure 3.4A). For example, genomic gains of chromosome 20 and genomic
loss of chromosome 22q were likely generated through unbalanced translocation mechanisms
involving chromosomes 20 and 22, respectively. The der(19)t(19;20)(q13.1;p11.2) unbalanced
translocation was accompanied by a genomic loss on chromosome 19. USTS2 also showed
genomic imbalances consistent with unbalanced chromosomal rearrangements. Small genomic
losses on chromosome 4 were associated with a deletion at the 4q33 region (Figure 3.4B).
Genomic gain of chromosomal arm 6p was likely generated through mechanisms involving
unbalanced translocations. USTS2 exhibited the recurrent 19q13 region rearrangement, and the
der(19)t(6;19)(p21.3;q13.2) and der(7)t(7;19)(q36;q13.2) unbalanced translocation were
accompanied by a genomic loss and gain on chromosome 19q13 (Figure 3.4B). These recurrent
19q abnormalities may lead to haploinsufficiency or gene fusion events affecting one or more genes
mapping to either the deleted or gained interval, the translocation breakpoints, or flanking genomic
regions. Notably, array CGH analysis indicated at least two candidate genes, CIC and ERF,
potentially affected by aberrations on chromosome 19.
42
3.2.4 Four-colour FISH Validation of 19q Rearrangement and Detection of Translocation Binding
Partners
In order to further investigate the binding partners of the region of interest on chromosome
19, four-colour interphase FISH analysis was performed on corresponding formalin-fixed, paraffin-
embedded sections. This approach was also used to validate the genomic imbalances and to test the
sensitivity of the platform in detecting a small gain of interest in proximity to the microdeletion.
Copy number changes for the chromosomal 19q13 region frequently showing alterations were
recorded in 50 tumour cells for USTS1 and USTS2.
43
USTS1
As predicted by cytogenetic analysis, the USTS1 tumour cells did not exhibit co-
localization of the control probe (pink) with probe C (yellow). Instead, an extreme nuclear
separation of these two probes was apparent and concurrent with the detection of losses of probe A
and B (green and red signals, respectively), confirming the presence of the 19q abnormalities
described by array CGH and SKY (Figure 3.5A). The BAC probes hybridizing to the unaffected
chromosome 19 showed the normal pink, green, red and yellow co-localization pattern.
Subsequently, we sought to investigate the binding partners of the chromosomal 19q
rearrangement in USTS1. Based on hybridization in the control sample (data not shown) and
tumour samples, the detection of chromosomal 19q rearrangement was defined when the distance
between two signals was more than three times the estimated signal diameter [89]. Fusion FISH
strategy was used in the analysis of 19q rearrangement, with BAC DNA probes, producing two-
colour fusions on the derivative chromosome. To determine the translocation partner for USTS1,
the four-colour FISH approach consisted of BAC DNA probe sets proximal to the breakpoint
region involving the CIC gene on chromosome 19 (probe E, red signal), the region upstream of the
DUX4 gene on chromosome 4 (probe D, green signal), a control region on chromosome 19 (probe
A, pink signal), and region of interest on chromosome 20 (probe F, yellow signal). Co-localization
of probes for the CIC gene and the DUX4 gene confirmed the presence of a t(4;19) translocation
(Figure 3.5B). Lack of co-localization of the red and yellow signals indicates that CIC is not
involved in a rearrangement with chromosome 20p.
44
Figure 3.5. Four-colour FISH validation of 19q abnormalities and detection of
translocation binding partners in USTS1. (A) FISH probes used to validate genomic
imbalances in USTS1 (arbitrarily designated as probes A-C; see Materials and Methods for
details). Gene and BAC locations are taken from the UCSC Genome Browser (May 2004
assembly, Hg17; http://www.genome.ucsc.edu). (B) Representative FISH images
confirming deletions on chromosome 19 (green and red signals). Extreme nuclear
separation of the yellow and pink signals is indicative of a rearrangement involving 19q,
confirming array CGH and SKY results. BAC probes hybridizing to the unaffected
chromosome 19 showed the normal pink, green, red, and yellow colocalization pattern. (C)
FISH probes used to determine binding partner of 19q13 in USTS1 (arbitrarily designated
as probes D-F; see Materials and Methods for details). Gene and BAC locations are taken
from the UCSC Genome Browser (May 2004 assembly, Hg17;
http://www.genome.ucsc.edu). (D) FISH analysis identifies a t(4;19)(q35;q13.1)
translocation in USTS1. Probes for the CIC gene (probe E, red signal) showed
colocalization with a probe just upstream of DUX4 (probe D, green signal), confirming the
presence of the CIC-DUX4 fusion rearrangements. Lack of colocalization of the red and
yellow (probe F) signal indicates that CIC is not involved in a rearrangement with 20p. All
captured signals were converted to default false colours: Spectrum Orange to red, Spectrum
Red to pink, and Cy5 to yellow. (Adapted from [30]).
45
USTS2
As predicted by array CGH, the USTS2 tumour showed cell populations with extra copies
of probes A and B (green and red signals, respectively), ranging from three to four copies for the
chromosomal loci analyzed, and hemizygous loss of probe C (yellow signal) (Figure 3.6A). The
BAC probes hybridizing to the unaffected chromosome 19 showed the normal pink, green, red and
yellow co-localization pattern.
To determine the translocation partner for USTS2, four-colour FISH was performed using
probes for the CIC gene, a control region on chromosome 19, the region of interest on chromosome
6 and the region of interest on chromosome 7. Surprisingly, a lack of co-localization was seen for
chromosome 19 with either chromosome 6 or 7 (data not shown). To be thorough, the FISH assay
used to detect the t(4;19) translocation in USTS1 was applied to both USTS2 and USTS16. No co-
localization of the CIC and flanking DUX4 probes was seen in USTS16 (data not shown).
However, in USTS2 the CIC probe signal showed co-localization with the probe immediately
upstream of DUX4 (probe D, green signal), indicating that this sample harboured a similar t(4;19)
translocation (Figure 3.6B). The probe for DUX4 falls within a region of hemizygous deletion on
chromosome 4 (as detected by array CGH), thus co-localization is indicative of a fusion of the CIC
gene to the unaffected chromosome 4. Therefore, both USTS1 and USTS2 showed evidence of a
t(4;19) CIC-DUX4 fusion event by FISH analysis.
46
3.2.5 Detection of CIC-DUX4 Fusion Transcripts by RT-PCR
A t(4;19)(q35;q13.1) translocation had been previously identified in 4 samples with a
similar PRC morphology over the past 15 years [60, 70, 71]. One group determined that in 2 cases,
Figure 3.6. Four-colour FISH validation of 19q abnormalities and detection of
translocation binding partners in USTS2. (A) FISH probes used to validate genomic
imbalances in USTS2 (arbitrarily designated as probes A-C; see Materials and Methods for
details). Gene and BAC locations are taken from the UCSC Genome Browser (May 2004
assembly, Hg17; http://www.genome.ucsc.edu). (B) Representative FISH images showing
hemizygous loss of the yellow signal and gain of the red and green signals, confirming array
CGH results. BAC probes hybridizing to the unaffected chromosome 19 showed the normal
pink, green, red, and yellow colocalization pattern. (C) FISH probes used to validate binding
partner for 19q13 in USTS1, applied to USTS2 (arbitrarily designated as probes D-F; see
Materials and Methods for details). Gene and BAC locations are taken from the UCSC
Genome Browser (May 2004 assembly, Hg17; http://www.genome.ucsc.edu). (D) FISH
analysis identifies the presence of a t(4;19)(q35;q13.1) rearrangement in USTS2. The red
signal (probe E, CIC) showed extreme nuclear separation from the chromosome 19 control
probe signal (purple); however, this signal showed colocalization with a probe immediately
upstream of DUX4 (probe D, green signal). The probe for DUX4 falls within a region of
hemizygous deletion on chromosome 4; thus, colocalization is indicative of a fusion of the
CIC gene to the unaffected chromosome 4. All captured signals were converted to default
false colours: Spectrum Orange to red, Spectrum Red to pink, and Cy5 to yellow. (Adapted
from [30]).
47
this translocation resulted in the fusion of the CIC gene (19q13) to the C-terminal portion of the
DUX4 gene (4q35). The primer sequences and positive control were obtained from [60], and RT-
PCR was run with the CIC4120 and DUX4RTr2 primers. Of the three tumours analyzed by RT-
PCR, the two tumours with PRC morphology (USTS1 and USTS2) were confirmed to harbour a
CIC-DUX4 fusion transcript (Figure 3.7). One of the two positive CIC-DUX4 fusion products was
consistent in size with published findings; however the other tumour sample (USTS2) contained a
novel variant CIC-DUX4 fusion transcript. Automated DNA sequencing of gel-purified transcripts
from the positive control and USTS2 confirmed fusions of the CIC gene with the DUX4 gene.
Unfortunately USTS1 had insufficient material for sequence analysis. The positive control was
890bp, with exon 20 of the CIC gene fused to the end of exon 1 of the DUX4 gene. The novel
variant CIC-DUX4 transcript was 710bp, with a 50bp deletion in CIC and a 130bp deletion in
DUX4 relative to the positive control. Interestingly, the fusion gene event involving CIC and
DUX4 was detected by RT-PCR and FISH analysis in both PRC sarcoma cases, but by SKY
analysis in only one case.
Figure 3.7. Confirmation of the CIC-DUX4 fusion transcript in two of
the three USTS samples. RT-PCR products from the three cases (USTS16,
USTS1, and USTS2) were sized against a 100-bp ladder marker using
electrophoresis on a 1.5% agarose gel. Both USTS1 (lane 4) and USTS2
(lane 5) showed the presence of the CIC-DUX4 fusion transcript. The full-
length CIC-DUX4 cDNA was subcloned into a pGEM vector (Promega,
Madison, WI) and used as a positive control (lane 2). (Adapted from [30]).
48
3.2.6 Discussion of t(4;19) Pilot Project
This pilot project describes the results of a detailed analysis of the genomic aberrations in
three paediatric USTSs. Two of the tumours were of PRC morphology, and one tumour had
spindled, less cellular architecture with a focal hemangiopericytomatous-like pattern. All three
tumours had in common a lack of lineage-specific differentiation and no common translocations
associated with paediatric sarcomas. Both tumours with PRC morphology also exhibited CD99
positivity to varying degrees. Both tumours exhibiting the PRC phenotype with CD99
immunoreactivity harboured a recurrent rearrangement between 19q13 and 4q35, as detected by a
combination of SKY, array CGH, FISH and RT-PCR. As previously discussed, four cases of PRC
sarcomas have been previously reported that harbour similar rearrangements involving a
translocation between 19q13 and 4q35 [60, 70, 71]. One of these studies identified two tumours
with this rearrangement, and found that the fusion event between the CIC gene and the DUX4 gene
resulted in a protein with transforming properties. The human CIC gene codes for a protein that is
a member of the HMG-box superfamily of transcription factors [90]. The DUX4 gene is a double-
homeobox gene belonging to the family of double homeodomain transcriptional activators [91].
The biological roles of the CIC and DUX4 genes will be discussed in more detail in section 3.3.3.
As confirmed by RT-PCR, both t(4;19)-positive cases in this pilot project harboured the CIC-DUX4
fusion transcript. Notably, one tumour had a PCR product of a different size than those previously
published, suggesting a different site of rearrangement within one or both genes. Thus, both
tumours are members of the novel category of paediatric primitive sarcomas with a t(4;19)
rearrangement resulting in a CIC-DUX4 fusion transcript.
One particularly intriguing aspect of these analyses was that the original SKY analysis of
USTS2 did not reveal the presence of a t(4;19) rearrangement. By RT-PCR, and subsequent
49
sequence confirmation, a CIC-DUX4 fusion transcript was shown to be present in this sample.
Finer mapping of the genome of USTS2 did indeed confirm the presence of a t(4;19)(q35;q13)
rearrangement, together with a gain, followed by a loss of chromosome 19q at the CIC region. As
chromosomal breakage generally occurs as part of chromosomal rearrangements [92], it is probable
that a small region of CIC was fused to DUX4, and that the size of the chromosome 19 locus that
was translocated was below the threshold of detection for SKY analysis. Thus, the absence of
specific translocations by SKY analysis does not exclude the presence of cryptic or complex
rearrangements below the threshold of detection, and RT-PCR remains the method of choice for
confirming the presence of this fusion gene.
Ultimately, molecular and cytogenetic analyses identified two tumours with PRC
morphology that harbour a t(4;19)(q35;q13) translocation involving the CIC gene on chromosome
19 and the DUX4 gene on chromosome 4. One tumour of spindled and myxoid morphology was
not found to harbour this translocation. These initial findings suggest that the CIC-DUX4 fusion
events are limited to tumours with an undifferentiated round cell phenotype. More importantly, this
pilot study allowed us to optimize RT-PCR and FISH conditions that can be used to screen the
remaining USTSs in our cohort.
3.3 Screening of USTSs Cohort for CIC-DUX4 Translocation
The following data has been submitted for publication as follows: Graham et al., Pediatr Dev
Pathol, Submitted October 2010 [93].
In this portion of the study, an additional 19 tumours were screened for the CIC-DUX4
fusion transcript with an RT-PCR assay developed for both frozen and paraffin-based tissues. A
combination of primer sets were used which cover all known breakpoints of the CIC-DUX4 fusion
50
transcript. Some of the clinicopathological features of these tumours have been previously
described [32, 34, 72, 88]. Fifteen cases were composed of sheets of primitive round to plump
spindle cells, one case was composed of nests of PRCs; and three cases were composed of pure
spindle cells within a myxoid, collagenous or cellular fascicular background. CD99 showed
cytoplasmic positivity in three cases (USTS9-11) and membranous positivity in 3 cases (USTS3,
USTS20, USTS21). The ultrastructural features of USTS3, USTS4, USTS6-11 and USTS17 have
been previously described [34, 72]. For seven USTSs, tissue was not available for ultrastructural
analysis. The remaining 2 USTSs with PRC morphology showed sparse intracytoplasmic
neurosecretory granules, variable amounts of intracytoplasmic glycogen and cytoplasmic processes.
3.3.1 Detection of the CIC-DUX4 fusion by RT-PCR
Total RNA from ten fresh-frozen USTS samples and nine FFPE USTS samples were
subjected to RT-PCR analysis for the CIC-DUX4 fusion transcript. All samples were subjected to a
series of PCR reactions using a combination of CIC forward primers and DUX4 reverse primers
(Table 2.1). CIC4120 and DUX4RTr2 were selected as the optimal primer set for fresh-frozen
samples as they result in the largest PCR product, however all primer sets successfully amplified
specific PCR products (data not shown). CIC4F and DUX4-R2 were selected as the optimal primer
set for RNA extracted from FFPE tissue samples. Of the nineteen tumour samples analyzed by RT-
PCR, three were positive for the CIC-DUX4 fusion transcript (USTS3, USTS4 and USTS5) (Figure
3.8). All three CIC-DUX4 positive tumours had PRC morphology, and one tumour showed
membranous positivity for CD99. Two of the three positive CIC-DUX4 fusion products (USTS3,
USTS4) were consistent in size and sequence with previously published findings [60], whereas one
product (USTS5) contained a novel variant of the CIC-DUX4 fusion transcript.
51
Figure 3.8. Identification of the CIC-DUX4 fusion by reverse transcriptase-polymerase chain
reaction (RT-PCR) screening. (A) Total RNA samples from 10 fresh-frozen USTS tumours were
subjected to RT-PCR analysis for the CIC-DUX4 fusion transcript using the CIC4120 forward and
DUX4RTr2 reverse primers. These products were sized against a 100-bp ladder marker using
electrophoresis on a 1.5% agarose gel. USTS5 (lane 3) showed the presence of the CIC-DUX4 fusion
transcript. The full-length CIC-DUX4 cDNA was subcloned into a pGEM vector (Promega, Madison,
WI) and used as a positive control (lane 2). (B) Total RNA from 9 formalin fixed paraffin-embedded
USTS tumours was subjected to RT-PCR analysis for the CIC-DUX4 fusion transcript using the CIC4
forward and DUX4-R2 reverse primers. These products were sized against a 100-bp ladder marker
using electrophoresis on a 1.5% agarose gel. USTS3 (lane 3) and USTS4 (lane 4) showed the presence
of the CIC-DUX4 fusion transcript. The full-length CIC-DUX4 cDNA was subcloned into a pGEM
vector (Promega, Madison, WI) and used as a positive control (lane 2). (Adapted from [93]).
52
3.3.2 DNA Sequence Analysis of the CIC-DUX4 Positive PCR Products
Gel-purified transcripts from the positive control, USTS3, USTS4 and USTS5 were directly
sequenced using automated DNA sequencing. Sequencing confirmed fusions of the CIC gene with
the DUX4 gene in all three cases, as well as the positive control. Using the CIC4120 forward
primer and DUX4rtR2 reverse primer, the positive control was 890bp. Using the same primer set
USTS5 was 821bp, with an additional 10bp in CIC and a 75bp deletion in DUX4 relative to the
positive control.
Using the CIC4F forward primer and the DUX4-R2 reverse primer, the positive control was
170bp. USTS3 and USTS4 shared the same breakpoint as the positive control. In all cases, exon
20 of the CIC gene was fused to exon 1 of the DUX4 gene. Combining the sequencing analysis of
these three additional cases with the sequencing analysis from the pilot project resulted in the
identification of three breakpoints associated with the CIC-DUX4 fusion transcript in our cohort
(Figure 3.9). Based on the sequence analysis results, the amino acid structure of the fusion
proteins was predicted (Figure 3.10). This results in an in-frame fusion between CIC and DUX4
with the CIC open reading frame. This fusion leaves intact virtually all functional regions of the
CIC gene, including the DNA-binding high-mobility group (HMG)-box and fifteen of sixteen
putative MAPK phosphorylation sites [94-98]. On the other hand, this translocation results in the
loss of the majority of the DUX4 functional regions, including both DNA-binding homeodomains
(Figure 3.11).
53
CIC mRNA Exons 17-20
...GGTCCTGTCAGAAGTGGACTTCGAAGAGCGCTTTGCTGAGTTGCCTGAGTTTCGGCCTGAGGAGGTGCTGCCCTCCCCCACCCTGCAG
CIC4120
TCTCTGGCCACCTCACCCCGGGCCATCCTGGGCTCTTACCGCAAGAAGAGGAAGAACTCCACGGACCTGGATTCAGCACCCGAGGACCCCA
CCTCGCCCAAGCGCAAGATGAGAAGACGCTCCAGCTGCAGCTCGGAGCCCAACACCCCCAAGAGTGCCAAGTGCGAGGGGGACATCTTCAC
CTTTGACCGTACAGGTACAGAAGCCGAGGACGTGCTTGGGGAGCTAGAGTATGACAAGGTGCCATACTCCTCCCTGCGGCGCACCCTGGAC
CAGCGCCGGGCCCTGGTCATGCAGCTCTTTCAGGACCATGGCTTCTTCCCGTCAGCCCAGGCCACAGCCGCCTTCCAGGCCCGCTATGCAG
ACATCTTTCCCTCCAAGGTTTGTCTGCAGTTGAAGATCCGTGAGGTGCGCCAGAAGATCATGCAGGCTGCCACTCCCACGGAGCAGCCCCC
CIC2F
TGGAGCTGAGGCTCCTCTCCCTGTACCGCCCCCCACTGGCACCGCTGCTGCCCCTGCCCCCACTCCCAGCCCCGCAGGGGGCCCTGACCCC
CIC3F CIC4F
ACCTCACCCAGCTCGGACTCTGGCACGGCCCAGGCTGCCCCGCCACTGCCTCCACCCCCAGAGTCGGGGCCTGGACAGCCTGGCTGGGAGG
GGGCTCCCCAGCCCTCCCCCCCACCCCCAGGTCCCTCCACAGCTGCCACAGGCAGGTGA
- Positive Control, USTS3 & USTS4 breakpoint
- USTS1 breakpoint
- USTS5 breakpoint
DUX4 mRNA
...CCGCCCCCGCGCTGCAGCCCAGCCAGGCCGCGCCGGCAGAGGGGGTCTCCCAACCTGCCCCGGCGCGCGGGGATTTCGCCTACGCCGC
CCCGGCTCCTCCGGACGGGGCGCTCTCCCACCCTCAGGCTCCTCGGTGGCCTCCGCACCCGGGCAAAAGCCGGGAGGACCGGGACCCGCAG
CGCGACGGCCTGCCGGGCCCCTGCGCGGTGGCACAGCCTGGGCCCGCTCAAGCGGGGCCGCAGGGCCAAGGGGTGCTTGCGCCACCCACGT
CCCAGGGGAGTCCGTGGTGGGGCTGGGGCCGGGGTCCCCAGGTCGCCGGGGCGGCGTGGGAACCCCAAGCCGGGGCAGCTCCACCTCCCCA
GCCCGCGCCCCCGGACGCCTCCGCCTCCGCGCGGCAGGGGCAGATGCAAGGCATCCCGGCGCCCTCCCAGGCGCTCCAGGAGCCGGCGCCC
DUX4R2
TGGTCTGCACTCCCCTGCGGCCTGCTGCTGGATGAGCTCCTGGCGAGCCCGGAGTTTCTGCAGCAGGCGCAACCTCTCCTAGAAACGGAGG
CCCCGGGGGAGCTGGAGGCCTCGGAAGAGGCCGCCTCGCTGGAAGCACCCCTCAGCGAGGAAGAATACCGGGCTCTGCTGGAGGAGCTTTA
DUX4RTr2
G
- Positive Control, USTS3 & USTS4 breakpoint
- USTS1 breakpoint
- USTS5 breakpoint
Figure 3.9 Nucleotide sequences of CIC and DUX4 sequences denoting primer sequences and translocation
breakpoints. (Top) Partial nucleotide sequence of the CIC gene, with CIC forward primer sequences underlined,
and different translocation breakpoints denoted by coloured arrows. (Bottom) Partial nucleotide sequence of the
DUX4 gene, with DUX4 reverse primer sequences underline, and different translocation breakpoints denoted by
coloured triangles. Arrows represent direction in which the genes are fused to one another. (Adapted from [93]).
54
USTS1 Fusion
... GCC ACT CCC ACG GAG CAG CCC CCT GGA GCT GAG GCT CCT CTC CCT GTA CCG CCC CCC
A A T P T E Q P P G A E A P L P V P P P
ACT GGC ACC GCT GCT GCC CCT GCC CCC CTC CCC TGC GGC CTG CTG CTG GAT GAG CTC CTG
T G T A A A P A P L P C G L L L D E L L
GCG AGC CCG GAG TTT CTG CAG CAG GCG CAA CCT CTC CTA GAA ACG GAG GCC CCG GGG GAG
A S P E F L Q Q A Q P L L E T E A P G E
CTG GAG GCC TCG GAA GAG GCC GCC TCG CTG GAA GCA CCC CTC AGC GAG GAA GAA TAC CGG
L E A S E E A A S L E A P L S E E E Y R
GCT CTG CTG GAG GAG CTT TAG
A L L E E L *
Positive Control, USTS3 and USTS4 Fusion
... GCC ACT CCC ACG GAG CAG CCC CCT GGA GCT GAG GCT CCT CTC CCT GTA CCG CCC CCC
A A T P T E Q P P G A E A P L P V P P P
ACT GGC ACC GCT GCT GCC CCT GCC CCC ACT CCC AGC CCC GCA GGG GGC CCT GAC CCC ACC
T G T A A A P A P T P S P A G G P D P T
TCA CCC AGC TCG GAC TCT GGG GGT GGA CCC CAA GCC GGG GCA GCT CCA CCT CCC CAG CCC
S P S S D S G G G P Q A G A A P P P Q P
GCG CCC CCG GAC GCC TCC GCC TCC GCG CGG CAG GGG CAG ATG CAA GGC ATC CCG GCG CCC
A P P D A S A S A R Q G Q M Q G I P A P
TCC CAG GCG CTC CAG GAG CCG GCG CCC TGG TCT GCA CTC CCC TGC GGC CTG CTG CTG GAT
S Q A L Q E P A P W S A L P C G L L L D
GAG CTC CTG GCG AGC CCG GAG TTT CTG CAG CAG GCG CAA CCT CTC CTA GAA ACG GAG GCC
E L L A S P E F L Q Q A Q P L L E T E A
CCG GGG GAG CTG GAG GCC TCG GAA GAG GCC GCC TCG CTG GAA GCA CCC CTC AGC GAG GAA
P G E L E A S E E A A S L E A P L S E E
GAA TAC CGG GCT CTG CTG GAG GAG CTT TAG
E Y R A L L E E L *
USTS5 Fusion
... GCC ACT CCC ACG GAG CAG CCC CCT GGA GCT GAG GCT CCT CTC CCT GTA CCG CCC CCC
A A T P T E Q P P G A E A P L P V P P P
ACT GGC ACC GCT GCT GCC CCT GCC CCC ACT CCC AGC CCC GCA GGG GGC CCT GAC CCC ACC
T G T A A A P A P T P S P A G G P D P T
TCA CCC AGC TCG GAC TCT GGC ACG GCC CAG CAA GGC ATC CCG GCG CCC TCC CAG GCG CTC
S P S S D S G T A Q Q G I P A P S Q A L
CAG GAG CCG GCG CCC TGG TCT GCA CTC CCC TGC GGC CTG CTG CTG GAT GAG CTC CTG GCG
Q E P A P W S A L P C G L L L D E L L A
AGC CCG GAG TTT CTG CAG CAG GCG CAA CCT CTC CTA GAA ACG GAG GCC CCG GGG GAG CTG
S P E F L Q Q A Q P L L E T E A P G E L
GAG GCC TCG GAA GAG GCC GCC TCG CTG GAA GCA CCC CTC AGC GAG GAA GAA TAC CGG GCT
E A S E E A A S L E A P L S E E E Y R A
CTG CTG GAG GAG CTT TAG
L L E E L *
Figure 3.10. Nucleotide and predicted amino acid sequences of the different CIC-DUX4 fusions. (Top) In-frame
fusion between CIC and DUX4 in USTS1. (Middle) In-frame fusion between CIC and DUX4 in the positive control,
USTS3 and USTS4. 2 additional glycine residues are present at the fusion point which do not natively belong to
either CIC or DUX4. (Bottom) In-frame fusion between CIC and DUX4 in USTS5. Legend: CIC nucleotide sequence,
DUX4 nucleotide sequence, nucleotide sequence not belonging to CIC or DUX4, CIC amino acid sequence, DUX4
amino acid sequence, amino acid sequence not belonging to CIC or DUX4.
55
3.3.3 Discussion of Overall Screening of USTS cohort of CIC-DUX4 Fusion Transcript
Paediatric USTSs are a very poorly understood group of tumours on which very little data
had been published. Recently however, several groups have identified a particular variant of USTS
having a PRC phenotype, and harbouring recurrent translocations involving chromosomes 4q35 and
19q13 [31, 60, 70, 71]. One such study determined that in two cases, this rearrangement resulted in
the fusion of the CIC gene on chromosome 19q13.1 and the DUX4 gene on chromosome 4q35 [60].
Characterization of the genes involved in the t(4;19)(q35;q13.1) translocation has provided the
necessary information to develop an RT-PCR approach for the detection of the fusion transcript
resulting from this translocation. As such, the aims of this study were two-fold: (i) to identify
Figure 3.11. Predicted protein structure of CIC-DUX4 fusion protein variants.
Predicted structure of the CIC–DUX4 protein is based on the results of sequence analysis and
protein prediction. This fusion leaves intact most of the functional regions of the CIC gene,
including the DNA-binding high-mobility group-box and the majority of the putative MAPK
phosphorylation sites. However, this translocation results in the loss of the majority of the
DUX4 functional regions, including both DNA-binding homeodomains. (Adapted from [93]).
56
primer sets and RT-PCR conditions that could be used to effectively detect the CIC-DUX4 fusion
transcript on RNA extracted from both fresh-frozen and FFPE tissue and (ii) to screen a large
cohort of paediatric USTSs for the presence of this rearrangement.
Overall our institution has screened 22 USTS tumours and identified 5 of 18 (28%) tumours
with PRC morphology that harbour the CIC-DUX4 fusion transcript. Three of these 5 tumours
show CD99 positivity. Given that CIC-DUX4-positive tumours have been previously described as
‘atypical Ewing sarcomas’ [31, 88] or ‘Ewing-like sarcomas’ [60], the distinction of ESs and CIC-
DUX4-positive tumours on morphological and immunohistochemical grounds appears to be
challenging. Both exhibit some degree of CD99 positivity and ultrastructural evidence of neural-
type differentiation. However, previous reports have highlighted that the PRC sarcomas appear less
differentiated, with some lacking evidence of neural differentiation [31, 71] and an overall
inconsistent CD99 staining pattern [31, 60, 71]. Nevertheless, definitive diagnosis rests upon
cytogenetic and molecular diagnostic assays for identification of this specific rearrangement.
Interestingly, 4 distinct breakpoints were identified in the 5 positive cases (Figure 3.9),
though we were unable to determine the specific breakpoint in USTS1 due to insufficient material
for sequencing. The breakpoint is variable in both CIC and DUX4, suggesting that the structure of
both of these genes may facilitate multiple rearrangements with different genomic sequences [99].
In all cases, however, exon 20 of the CIC gene was fused to exon 1 of the DUX4 gene, leaving
intact the functional regions of CIC, but resulting in a loss of all DNA-binding portions of DUX4
(Figure 3.10). This suggests that while a specific nucleotide breakpoint may not be necessary, the
fusion between specific higher-level functional domains may be of particular importance.
57
The human CIC gene is an ortholog of the Drosophila capicua gene, and is a member of the
HMG-box superfamily of transcription factors [90]. This gene has 20 exons encoding a protein of
1608 amino acids [100], and contains an N-terminal DNA-binding HMG-box and sixteen possible
MAPK phosphorylation sites [94-98, 101]. Expression of CIC is predominantly limited to
immature granule cells in the central nervous system, and is important for their development [90,
101]. CIC has been shown to be involved in mediating two oncogenic signaling pathways, EGFR
and Wnt, by transcriptional repression. In cancer, CIC has been shown to be differentially
expressed in medulloblastomas [101], and has been shown to play a role in the pathogenesis of
PRC sarcomas [31, 60]. Furthermore, CIC has been shown to play a role in spinocerebellar ataxia
type 1 (SCA1) through association of the CIC protein with ATAXIN-1 (ATXN-1) [102, 103].
Binding of ATXN-1 to the N-terminal region of CIC alters the repressional activity of the CIC
protein [103].
The DUX4 gene is a double-homeobox gene belonging to the family of double
homeodomain transcriptional activators [91]. DUX4 is located within the tandem repeat locus
D4Z4 on chromosome 4 [104], and contains two DNA-binding homeoboxes at its N-terminus [91].
DUX4 has been implicated in the pathogenesis of facioscapulohumeral muscular dystrophy, in
which the D4Z4 copy number is significantly reduced [104, 105], and in ERMS where DUX4 is
involved in a chromosomal rearrangement with the EWSR1 gene [106]. Under normal conditions
DUX4 is believed to be involved in myogenic differentiation and cell-cycle control [106], and over-
expression of this gene in cultured primary myoblasts has been shown to induce caspase activity
and promote cell death [107].
58
The CIC-DUX4 fusion preserves the majority of the CIC gene, including the DNA-binding
HMG-box and the majority of the MAPK phosphorylation sites, but both DUX4 homeobox
domains were lost. Preliminary evidence suggests that the chimeric protein has transforming
properties, and acts as a strong transcriptional activator of downstream CIC targets, with CIC-
DUX4 showing 130-fold enhancement of transcriptional activity against wild-type CIC [60, 108].
Nonetheless, full characterization of the CIC-DUX4 fusion product will be required to fully
understand the functional role that this rearrangement plays in oncogenesis.
The CIC-DUX4-positive cohort forms a distinct subcategory of tumours with PRC
morphology and variable membranous CD99 positivity. Of note are another 17 cases of USTS
lacking the CIC-DUX4 transcript by RT-PCR. Such tumours may harbour variants of the t(4;19)
rearrangement not detected in the current analysis, or may represent a different category of
primitive sarcomas altogether.
3.4 Discovery of Novel Markers for CIC-DUX4-Negative Tumours
The t(4;19) pilot project and screening portions of this project allowed for the identification
of 5 novel cases of paediatric PRC sarcomas that harbour the CIC-DUX4 fusion transcript.
Furthermore, this screening allowed for the identification of a cohort of CIC-DUX4-negative
USTSs with no established genetic markers. As such we sought to characterize novel genomic
aberrations that may play a role in the deregulation of gene expression in these CIC-DUX4-negative
tumours. Recent developments in microarray technologies have revolutionized the way in which
we study the relationships between genomic changes and gene expression in cancer cells, allowing
us to study this relationship on a whole genome scale [81]. Identifying changes in gene expression,
as well as the mechanisms responsible for these changes, serves to significantly enhance our
59
understanding of oncogenesis [109]. Moreover, the integration of platforms that identify genomic
and transcriptomic aberrations will aid in determining genetic variations that control the
development and progression of cancer. As such, in order to study the effects of genomic
mechanisms on sarcoma-related gene networks, we used an integrative approach for genome-wide
profiling of genomic and gene expression changes. To do so we have tested DNA and RNA from
five CIC-DUX4-negative tumours on the Affymetrix Human SNP 6.0 microarray and the
Affymetrix Gene 1.0 ST expression microarray, respectively.
3.4.1 DNA Copy Number Analysis
In order to identify genome-wide copy number changes in each tumour sample, high
molecular weight genomic DNA was sent to The Centre for Applied Genomics (TCAG) microarray
facility to be run on the Affymetrix Genome-Wide Human SNP Array 6.0 microarray platform
(Affymetrix Inc., Santa Clara, CA, USA). Copy number analysis was conducted in 5 primary
tumour samples – USTS12, USTS13, USTS14, USTS15 and USTS19. Copy number analysis was
performed using the PGS software, using the publicly available normal control SNP data from the
HapMap consortium [79] as the reference for determining copy number alterations. While copy
number analysis revealed gross heterogeneity between the individual tumour samples (Figure 3.12),
numerous smaller regions could be identified which showed similar patterns of aberration across
the majority of the tumour samples. Tumour specific copy number changes ranged between 250
(USTS13) and 10019 (USTS12). In all tumours there was more copy number gain relative to loss.
A cumulative gene list was created that contained the genes showing statistically significant copy
number changes (P ≤ 0.05) in at least 3 of the 5 tumour samples.
60
3.4.2 Gene Expression Analysis
To characterize the expression profiles of these tumours, total RNA from each tumour as
well as two adult human dermal fibroblast samples was sent to the TCAG microarray facility to be
run on the Affymetrix Gene 1.0 ST microarray platform (Affymetrix Inc, Santa Clara, CA, USA).
The RNA extracted from USTS15 was not of high enough quality to be used for the expression
microarray analysis. As such the tumour-specific integration analysis, cumulative expression
microarray analysis and cumulative integration analysis did not include USTS15. Expression
Figure 3.12. Overall genomic profiling view from Partek Genomic Suite (PGS). High
molecular weight DNA from five t(4;19)(q35;q13.2)-negative USTS tumour samples were
tested on the genome-wide human SNP 6.0 microarray platform , and results were analyzed
using PGS to identify tumour-specific and cumulative copy number alterations. Copy number
analysis was normalized against the publicly available data collected by the International
HapMap consortium. A cumulative gene list was created that contained genes showing
statistically significant copy number changes (P ≤ 0.05) in at least 3 of the 5 tumour samples.
The x-axis contains chromosomes 1-22, X and Y in order.
61
analysis was performed using the PGS software, using human fibroblasts as reference RNA
samples for determining differential expression. Clustering analysis showed distinct clustering of
tumours relative to the normal human fibroblast samples (Figure 3.13). Tumour specific changes
ranged between 726 genes in USTS14 and 1370 genes in USTS13. An overall cumulative analysis
was performed, which compared the cumulative expression profiles of all tumours to the combined
profiles of the normal fibroblasts. This analysis produced a gene list with genes showing at least
two-fold, statistically significant (P ≤ 0.05) differential gene expression, cumulatively across the
tumours.
Figure 3.13. Clustering analysis shows distinct clustering of tumours relative to normal
human fibroblast samples. (A) Hierarchical clustering using average Euclidean cluster analysis
on 4 tumours tested on the Gene 1.0 ST expression microarray platform. Tumour samples (blue)
clustering separately from the normal fibroblast control expression profiles. (B) Principal
component analysis (PCA) of individual tumour samples and normal human fibroblast control
samples from the expression analysis. The PCA tool in PGS subjects the raw expression data to
3-dimensional clustering analysis. The PCA-generated ellipsoids denote separate clustering
between normal and tumour samples in the gene expression analysis.
62
3.4.3 Integration of Copy Number and Gene Expression Analyses
Tumour-Specific Integration
Microarray and statistical analyses using PGS software allowed for the identification of
genomic and transcriptional alterations in individual tumour samples. Genes with significant
changes (P ≤ 0.05) in copy number and gene expression in individual tumour samples were
analyzed using the PGS-Venn analysis tool. Integration of these data revealed gene-specific
overlap in each tumour (Figure 3.14A). The number of overlapping genes in each tumour sample
varied from 9 genes in USTS13 to 225 genes in USTS12. In order to characterize the correlation
between gene-specific changes in the Venn analysis intersects of each tumour, the genes were
plotted based on copy number and gene expression status (Figure 3.14B). This analysis clearly
showed the strongest correlation between copy number gain and over-expression.
Figure 3.14. Tumour-specific integration of copy number and gene expression analyses. (A)
Integration of expression and copy number data in individual USTS tumours. Genes with significant
(P≤0.05) changes in copy number relative to the HapMap and gene expression relative to normal
human fibroblasts in individual tumours were analyzed using the Partek Genomics Suite Venn
Analysis tool. (B) Distribution of significant tumour-specific changes. The line graph represents the
percentage of genes (y-axis) identified with specific genomic and transcriptomic changes (x-axis).
63
Cumulative Integration
Microarray and statistical analyses using the PGS software also allowed for the
identification of genomic and transcriptional alterations across all tumours. This analysis allowed
us to identify the most significant recurrent changes in USTSs as a group. Parameters for this
analysis were as follows: (1) Copy number changes present in at least 3 samples with P ≤ 0.05; and
(2) Transcriptional changes at a magnitude of at least 2-fold over- or under-expression with P ≤
0.05 cumulatively across all tumour samples relative to normal fibroblasts. Based on these
parameters, 438 genes were identified in the cumulative copy number gene analysis, and 2296
genes were identified in the gene expression analysis. Integration of these analyses was performed
using the PGS-Venn analysis tool, and resulted in the identification of 51 genes with overlapping
copy number and expression level changes (Figure 3.15A). These genes were plotted based on
copy number and gene expression status to characterize the correlation between gene-specific
changes in the Venn analysis intersect. This analysis displayed a strong correlation between copy
number gain and over-expression (66.7%), and copy number loss and under-expression (23.8%)
(Figure 3.15B).
Figure 3.15. Cumulative analysis of gene-specific changes in copy number and gene expression in
undifferentiated sarcoma. (A) Integration of cumulative copy number and expression analyses in USTS
tumours. Genes with significant (P≤0.05) changes in copy number relative to the HapMap, and gene expression
relative to normal human fibroblasts across all tumours were analyzed using the PGS Venn analysis tool. (B)
Distribution of significant overall genomic changes in undifferentiated sarcoma. The pie chart represents the
percentage of genes identified with specific genomic and transcriptomic changes.
64
3.4.4 Gene Network Analysis
In order to identify USTS-related gene networks, Ingenuity Pathway Analysis (IPA) of the
genes identified in the cumulative copy number and cumulative expression analyses was performed.
This analysis allowed us to identify significant biological pathways that have been disrupted at the
genomic and transcriptomic levels, and to identify genes of interest within those pathways. We
compared the most significantly affected biological functions from copy number analysis to the
most significantly affected biological functions from gene expression analysis (Figure 3.16). All
tumours showed disruption in copy number and gene expression in similar biological functions.
Overall the biological functions that were most significantly affected by the changes in copy
number and gene expression were cell death, cell movement, cellular growth and proliferation, cell
development, cell cycle, and cellular assembly and organization.
Figure 3.16. Biological functions significantly affected by changes in copy
number and gene expression. The top six most significantly affected biological
functions in individual USTS tumours detected using the Ingenuity Pathway
Analysis (IPA) Comparative Analysis tool. IPA analysis of copy number (dark
blue bars) and gene expression (light blue bars) . Yellow line is indicative of P-
value threshold of 0.05.
65
3.4.5 Identification of Candidate Genes
By combining the results from the PGS-based integrative analysis with the IPA network
analysis, a list of candidate genes were selected. This selection process focused on genes which
were either gained and over-expressed or deleted and under-expressed, and are directly involved in
the biological pathways identified in the network analysis. The candidate genes identified are
presented in Table 3.3.
Gene Genomic
Region
Copy
Number
Cumulative
Expression
Brief Description
EPHA3 3p11.2 Gain 12.11
· Transmembrane protein belonging to the Eph
receptor tyrosine kinase family.
· Eph receptors and ephrin ligands known to play
an important role in many biological functions
including axon guidance, cell migration,
angiogenesis and cytoskeletal regulation.
SNX10 7p15.2 Gain 2.3
· Member of sorting nexin family which play a role
in cellular functions including intracellular protein
trafficking, endocytosis and cell-to-cell signaling.
· May control endosome homeostasis.
ADAM9 8p11.22 Deletion -6.83
· Member of the ADAM (a disintegrin and
metalloprotease domain) family which have a role
in ectodomain shedding of membrane-bound
molecules.
· Known to cleave and release many molecules
associated with tumourigenesis and angiogenesis.
CDC73 1q25 Deletion -2.08
· Tumour suppressor involved in transcriptional
control pathways.
· Part of the PAF1 transcriptional regulatory
complex.
· Loss of function results in increased cell
proliferation and over-expression of proto-
oncogenes.
Table 3.3. Candidate genes with USTS-specific changes identified by integrative copy number
array, expression array, and gene network analysis.
66
3.4.6 Gene Expression Validation of Candidate Genes
Integrative microarray analysis identified significant genomic and transcriptomic changes in
the following genes: (1) a gain in chromosome 3p11.2 and over-expression of EPHA3, (2) a gain in
chromosome 7p15.2 and over-expression of SNX10, (3) a loss in chromosome 8p11.22 and under-
expression of ADAM9, and (4) a loss in chromosome 1q25 and under-expression of CDC73. To
validate the results of the expression analysis (Figure 3.17A), quantitative PCR was performed in
all 5 tumours (Figure 3.17B). Over-expression of the EPHA3 gene was found in 4/5 tumours, with
an average of 11.3-fold over-expression relative to fibroblasts. Cumulative microarray expression
analysis found a 12.1-fold over-expression of the EPHA3 gene relative to fibroblasts. Over-
expression of the SNX10 gene was found in 3/5 tumours, with an average of 3.7-fold over-
expression relative to fibroblasts. Cumulative microarray analysis found 2.3-fold over-expression
of the SNX10 gene relative to fibroblasts. Under-expression of the ADAM9 gene was found in 5/5
tumours, with an average of 27.7-fold under-expression relative to fibroblasts. Cumulative
microarray analysis found 6.8-fold under-expression of the ADAM9 gene relative to fibroblasts.
Under-expression of the CDC73 gene was found in 5/5 tumours, with an average of 2.1-fold under-
expression relative to fibroblasts. Cumulative microarray analysis found 2.1-fold under-expression
of the CDC73 gene relative to fibroblasts.
67
Figure 3.17. Expression analysis of candidate genes. (A) PGS dot plot visualization of
candidate genes from expression microarray analysis. Significant over-expression of EPHA3 and
SNX10 (top), and under-expression of ADAM9 and CDC73 (bottom) were seen cumulatively
across the four samples tested. The y-axis represents the raw lo2 signal intensity for each probe
set. (B) Quantitative RT-PCR validation of candidate gene expression. The y-axis represents fold
enrichment values generated using the comparative CT method. TATA-box binding protein
(TBP) was used as the endogenous housekeeping reference gene and human adult fibroblast
samples were used as the control samples. Each qPCR reaction was performed in triplicate and
average values were used for enrichment calculations.
68
3.4.7 Copy Number Validation of EPHA3
Quantitative PCR confirmed over-expression of the EPHA3 gene in 4/5 USTS cases.
Ephrins and ephrin receptors have been found to play a role in the development of numerous types
of cancer [110-112]. Due to the high correlation between copy-number gain (3 copies; Figure
3.18A) and over-expression, subsequent qPCR validation, and the prominent role that EPHA3 has
been found to play in cellular development, we decided to further validate this potential biomarker
in our samples. We performed validation of the copy number status of EPHA3 by performing dual-
colour FISH on all five tumour samples using a commercial Spectrum-Orange labeled centromere 3
probe as a control. In order to confirm locus specificity and optimize the EPHA3-specific probe,
FISH was first performed on normal metaphase spreads and on FFPE normal tonsil tissue (Figure
3.18B and C). The expected low-level gain of EPHA3 (3 copies) was not identified in any of the 5
tumours samples. Instead, 2 copies of both the control probe (orange probe) and test probe (green
probe) were seen in the majority of the tumour cells (Figure 3.18D and E).
3.4.8 Protein Validation of EPHA3
Immunohistochemistry was performed on FFPE sections from all 5 tumours in order to
determine the protein expression patterns of the EPHA3 gene in these tumours. The EPHA3
antibody (Abnova) was applied to USTS12-15 and USTS19 using methodology described
previously. USTS12 and USTS13 showed weak, consistent cytoplasmic staining in <10% of cells;
the remaining 3 tumours showed no staining (Figure 3.19). All 5 tumours were thus scored as
negative.
69
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70
Figure 3.19. Immunohistochemical staining for EPHA3. Positive control tissue
included breast, both normal (A) and cancer (B). The photomicrographs show strong
cytoplasmic positivity for EPHA3 in the normal glandular epithelium (A), as well as
moderate to weak cytoplasmic positivity in ductal carcinoma and infiltrating histiocytes
(B). The negative controls show negativity in the same tissues, as well as focally positive
inflammatory cells that stain for endogenous peroxidase (C, D). Representative sections
of two tumours (E, F; USTS12 & USTS13) show very focal, weak and inconsistent
cytoplasmic staining, scored as negative. Scale bar = 100 µm.
71
3.4.9 Discussion of Discovery of Novel Markers for CIC-DUX4-Negative Tumours
As previously described, paediatric USTS remains a diagnosis of exclusion, despite the
advancement of highly specific and sensitive diagnostic applications. The similarities that these
tumours share with other sarcoma subtypes (e.g. Ewing Sarcoma) has evoked much debate as to
whether this group comprises tumours with common histological and biological features
representing a distinct subgroup of sarcomas or if these tumours have simply not been accurately
sub-classified into known sarcoma subgroups [9]. Recent studies on the CIC-DUX4 fusion
transcript have suggested the former – that there are distinct morphological and molecular
subgroups which can be identified from within the USTS family [31, 60]. However, identification
of the subgroup of USTSs harbouring the CIC-DUX4 fusion transcript left a group of USTSs
devoid of any known molecular identifiers. As such, the final studies in this thesis project sought to
identify novel genomic markers in a series of CIC-DUX4-negative USTS tumours using an
integrative genomic and transcriptomic analysis. Using bioinformatic whole-genome approaches,
the genomic and transcriptomic profiles of 5 USTS tumours were analyzed, and a series of
biological networks were identified that may play a role in sarcomagenesis. While we did find that
there was gross heterogeneity between the tumours at both the DNA and RNA levels, significant
changes were identified that were present in the majority of the tumours studied. Cumulative
analysis of the tumours found that there was a correlation between copy number gain and
overexpression, as well as copy number loss and underexpression, with more than 90% of the genes
identified following these patterns. However, these molecular correlations are present in a
relatively small proportion of the genes identified in any given tumour sample. Many of the genes
found to have changes at the copy number level, did not exhibit changes at the expression level and
vice-versa. This can be partially explained as a result of other biological phenomena present in the
cellular environment such as the actions of regulatory elements and epigenetic mechanisms such as
72
DNA methylation [113, 114]. However, by identifying genes which do in fact show a
genomic/transcriptomic correlation, and investigating the biological pathways in which these genes
are involved, it may be possible to identify specific genes that are responsible for driving USTS
tumourigenesis.
Integrative analysis of changes in copy-number, gene expression, and USTS-related gene
networks, identified a set of candidate genes which may play a role in the early event involved in
USTS development. The genes identified from this analysis were EPHA3, SNX10, ADAM9 and
CDC73. EPHA3 is a transmembrane protein that belongs to the Eph receptor tyrosine kinase
(RTK) family, which can be subdivided into 2 groups (EphA and EphB receptors) based on
sequence homology of the extracellular domain (ECD) of the receptor [115]. The Eph-receptor
ECD is composed of a ligand-binding domain, a cysteine-rich region, and two fibronectin type III
repeats. The cytoplasmic domain is composed of a juxtamembrane domain, a classical tyrosine
kinase domain, a sterile α-motif and a PDZ-domain. The Eph-ephrin receptor-ligand interaction
serves as a guidance system which regulates cell positioning and the modulation of cell morphology
[116]. Eph receptors and ephrin ligands are known to play an important role in many biological
functions including axon guidance, cell migration, angiogenesis and cytoskeletal regulation [117-
119]. Over-expression of the Eph receptors may promote cellular growth by increasing
vascularization, disrupting cell-cell adhesion, and providing potential adherence in a new tumour
microenvironment [120]. As such, it is not surprising that over-expression of EPHA3 has been
implicated in the pathogenesis of numerous cancers including lung cancer, prostate cancer,
melanoma, rhabdomyosarcoma and lymphocytic leukemia [110, 112, 120, 121].
73
SNX10 is a member of the sorting nexin family which are characterized by the presence of
an extracellular PX domain [122]. There are 29 known or predicted members of the sorting nexin
family, and the function of the majority of these proteins remains unknown [123]. Sorting nexins
have been shown to play a role in numerous cellular functions including intracellular protein
trafficking, endocytosis and cell-to-cell signaling [124, 125]. SNX10 specifically has been shown
to be capable of generating giant vacuoles in mammalian cells, suggesting that this protein may
serve to control endosome homeostasis [126]. EPHA3 and SNX10 both showed significant copy
number gains in 4/5 USTS samples. Interestingly, preliminary analysis also found EPHA3 to be
gained in the fifth USTS sample, however this gain was not found to be statistically significant.
These genes were also found to be significantly over-expressed in the cumulative expression
analysis.
ADAM9 is a member of the ADAM (a disintegrin and metalloprotease domain) family,
which have a predominant role in ectodomain shedding of membrane-bound molecules [127].
ADAMs are critical regulators of cell-cell signaling during development, and have been shown to
specifically play a role in muscle development, neurogenesis, cell adhesion and cell migration
[128]. ADAM9 specifically is known to cleave and release many molecules associated with
tumourigenesis and angiogenesis [129], and as such dysregulation of this protein has been
implicated in the pathogenesis of breast, prostate and lung cancer, melanoma, adenocarcinoma, and
metastasis to the brain [127, 130-133].
CDC73, or parafibromin, is a tumour suppressor that has been found to be involved in
transcriptional and post-transcriptional control pathways. Human parafibromin binds to RNA
polymerase II as part of the PAF1 transcriptional regulatory complex, and facilitates the association
74
of 3’ mRNA processing factors with chromatin that is being actively transcribed [134]. It has been
documented that loss of function of CDC73 can lead to oncogenesis, though the mechanisms
underlying this phenomenon are poorly understood. Furthermore, it has been shown that down-
regulation of CDC73 expression leads to increased cell proliferation and increased expression of
known proto-oncogenes [135]. Not surprisingly, deletions and loss-of-function mutations in the
CDC73 gene have been associated with various cancers including pancreatic cancer and
parathyroid carcinoma [136, 137]. ADAM9 and CDC73 both showed copy number loss in 3/5
USTS samples, and significant under-expression in the cumulative expression analysis.
Quantitative PCR analysis confirmed that USTS12, USTS13, USTS15 and USTS18 showed over-
expression of the EPHA3 gene, USTS12, USTS14, USTS15 and USTS18 showed over-expression
of SNX10, and that all 5 tumours showed under-expression of ADAM9 and CDC73. Each of these
genes plays an important role in the USTS-related biological pathways identified by gene network
analysis.
However, EPHA3 was identified as the most biologically relevant marker in this study as
this gene showed the most significant correlation between genomic gain and over-expression. As
such, we sought to validate the copy-number gain identified in EPHA3 by dual-colour FISH.
Microarray analysis identified a low-level gain in a 29-kb region in EPHA3 in all samples, but this
gain was not found by FISH in any of the tumour samples. This does not however, negate the
results of the microarray analysis. This 29-kb region of EPHA3 encompasses the entire exon III
coding region, as well as portions of introns II and III. It is possible that this copy number gain is a
result of a segmental duplication of exon III of the EPHA3 gene. If this were the case, it is possible
that separate EPHA3 probe signals could not be seen due to the size of the duplication, and the
proximity of the duplicate signals to one another. It would have been possible to visualize this
75
signal if the low-level copy-number gain were a result of rearrangement of this region, though this
does not seem to be the case. Interestingly, the ephrin ligand-binding domain has been found to be
specifically restricted to the N-terminal extracellular region of EPHA3 encoded by exon III.
Duplication of exons coding for the ligand-binding domain have been similarly documented in
another RTK, epidermal growth factor receptor (EGFR), in human gliomas [138-140]. Duplication
of the ligand-binding domain of EGFR has been shown to induce constitutive receptor
phosphorylation, while retaining the ability to bind the EGF ligand. Furthermore, it has been
shown that this segmental duplication may prevent efficient ligand-mediated receptor
internalization, resulting in prolonged RTK signaling [138-140]. Hypothetically, the same may be
true for the EPHA3 receptor. Duplication of the EPHA3 ligand-binding domain may promote
oncogenesis by increasing receptor activation, and impairing receptor attenuation. In order to
elucidate the role of this duplication in EPHA3, detailed functional studies are essential.
Furthermore, the IHC for EPHA3 was negative in all 5 samples tested, suggesting that
EPHA3 is not overexpressed at the protein level in these samples. However, negative staining for
EPHA3 may be a result of a poor quality antibody, as optimization on the positive control tissues
proved challenging. These results may also be a result of poor quality tissue received from the
Cooperative Human Tissue Network, as both FISH and IHC were difficult to perform on these
FFPE slides. Alternatively, it is possible that the EPHA3 protein is simply not expressed, or is
expressed at very low levels in these tissues. More studies using a larger cohort of samples, as well
as a different EPHA3-specific antibody are necessary to the EPHA3 expression status in USTSs.
This microarray study was limited in terms of the number of samples that were studied, as
well as the number of genes that were validated. Further studies which include more samples and
more comprehensive validation will be imperative to determine the role of these genes in
76
sarcomagenesis. Importantly, the general pathways identified in this study may provide novel
insights into the critical biological events responsible for the development of USTS as well as
sarcomas in general.
77
CHAPTER FOUR
SUMMARY, GENERAL CONCLUSIONS
&
FUTURE DIRECTIONS
78
CHAPTER 4. SUMMARY, GENERAL CONCLUSIONS & FUTURE DIRECTIONS
4.1 Summary and General Conclusions
Paediatric sarcomas have long been a challenging group of tumours to accurately diagnose
due to the similarity in histology and clinical presentation between different subgroups [141].
Though different sarcoma subtypes may appear very similar at the histological level, the prognosis
and treatment of these subtypes varies immensely, making correct sub-categorization paramount for
optimal therapy. The development of ancillary techniques such as immunohistochemistry,
cytogenetics and RT-PCR has significantly enhanced the pathologists’ ability to make an accurate
diagnosis [142]. Until very recently no markers had been identified in USTS, and consequently
undifferentiated sarcoma has remained a diagnosis of exclusion.
In this study, extensive and detailed genomic analysis of paediatric undifferentiated soft
tissue sarcomas has identified a specific variant of USTS with PRC morphology harbouring the
CIC-DUX4 rearrangement. Using RT-PCR and FISH, we have established a reliable and specific
screening platform with which any tumour can be screened for this rearrangement. In our cohort of
22 USTS samples, we identified 18 tumours with PRC morphology, 5 of which harbour the CIC-
DUX4 rearrangement (Objective 1).
Following this screening we were left with 17 tumours in which no specific molecular
markers had been identified. In order to identify potentially novel molecular markers for these
tumours, we performed an integrative copy-number and expression microarray analysis to identify
genes with changes at both the DNA and RNA levels. This study identified specific genes
(EPHA3, SNX10, ADAM9 and CDC73) which may play an important role in sarcomagenesis.
More importantly this study identified key USTS-related genomic pathways that may play a pivotal
79
role in the acquisition of the USTS phenotype (Objectives 2 and 3). While it remains unclear if it
will be possible to sub-classify the 17 tumours without the CIC-DUX4 translocation, we have
certainly made progress into identifying pathways responsible for the development of USTS.
Although USTS currently remains a diagnosis of exclusion, the cumulative data surrounding
the CIC-DUX4 translocation suggests that a portion of these tumours may in fact be identifiable by
a distinct chromosomal rearrangement. In CIC-DUX4-negative tumours, we have made progress in
identifying specific genes and biological pathways that may play a role in the development and
progression of these tumours. These findings help to further define this novel genetic subset of
paediatric sarcomas and provide an additional diagnostic tool for their classification and diagnosis.
4.2 Future Directions
The results of this project have given rise to several issues that are of utmost importance to
address in future studies. This study identified a subset of USTSs with PRC morphology that
harbour the CIC-DUX4 fusion gene. These tumours show minimal evidence of differentiation,
suggesting that they are able to maintain a stem cell-like phenotype. Thus, it is believed that the
presence of the CIC-DUX4 fusion transcript is associated with transforming properties [60], as well
as the maintenance of the undifferentiated state. However, significantly more evidence is needed in
order to prove both of these concepts. Future work in our laboratory will seek to determine the
specific biological and pathological consequences of the CIC-DUX4 fusion transcript. To do so, it
will be necessary to identify specific target molecules of the CIC-DUX4 fusion protein, as well as
to identify overall biological pathways that affected by this fusion. Furthermore, the diagnosis of
CIC-DUX4-positive tumours would be greatly aided by the identification of novel diagnostic
markers for these tumours. For example, the development of an immunohistochemcial-based
80
diagnostic assay for these tumours would allow for a timely and cost-efficient diagnosis to be made
where RT-PCR is unavailable. Additionally, the testing of other sarcoma subtypes for the presence
of this fusion transcript with help further elucidate the diagnostic utility of the CIC-DUX4 fusion
transcript. Future studies on CIC-DUX4-positive tumours will help to further elucidate the
biological features of these tumours, the prognostic implications of carrying this translocation, and
potential therapeutic options for this challenging subgroup of sarcomas.
A more in-depth study of the CIC-DUX4-negative tumours will provide a greater
understanding of the specific mechanisms of sarcomageneis in these tumours. Analysis of a greater
number of CIC-DUX4- negative tumours will allow for the accurate determination of both genomic
and transcriptomic features of these tumours, and will certainly enhance our understanding of
paediatric primitive sarcomas. Furthermore, it will be necessary to perform functional studies on
the candidate genes in order to gain a better understanding of the roles that these genes play in
sarcomagenesis. Additional work will hopefully give rise to diagnostic markers that can be used to
recognize this challenging group of tumours.
Future work in this field will provide a greater understanding of the early events involved in
the acquisition of the sarcoma phenotype. Furthermore, the analysis of both the CIC-DUX4 fusion
transcript and dysregulated pathways identified in CIC-DUX4-negative tumours will help to
improve diagnosis and treatment of such aggressive and poorly-understood sarcomas. These
findings will have relevance not only for paediatric sarcomas, but for sarcomas in general.
81
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