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Investigating the Role of MicroRNAs in the Pathogenesis of Kidney Cancer Subtypes and Their Clinical Utility as
Cancer Biomarkers
by
Samantha Jane Wala
A thesis submitted in conformity with the requirements for the degree of Degree Master of Science
Laboratory Medicine and Pathobiology University of Toronto
© Copyright by Samantha Jane Wala 2014
ii
Investigating the Role of MicroRNAs in the Pathogenesis of
Kidney Cancer Subtypes and Their Clinical Utility as Cancer
Biomarkers
Samantha Jane Wala
Degree Master of Science
Laboratory Medicine and Pathobiology University of Toronto
2014
Abstract
Renal cell carcinoma (RCC) is the most common adult kidney neoplasm and constitutes a
number of distinct subtypes. Papillary RCC (pRCC), a poorly characterized RCC subtype, can be
subdivided into type 1 or 2. The objective of this study was to understand the pathogenesis of
pRCC type 1. We identified genes and microRNAs (miRNAs) that are dysregulated in pRCC
type 1. Pathway analysis showed enrichment of the focal adhesion pathway. We showed that
miR-199a-3p, which is downregulated in pRCC type 1, regulates members of this pathway.
Moreover, RCC subtypes may be difficult to differentiate by microscopy due to overlapping
features. We demonstrated that miR-221 and -222 can discern chromophobe (chRCC) and renal
oncocytoma, a benign neoplasm, from clear cell RCC (ccRCC) and pRCC. miR-126 can
distinguish ccRCC from pRCC, whereas miR-15b and miR-22 had overlapping expression levels
in chRCC and oncocytoma.
iii
Acknowledgments
I would like to thank my mentor, Dr. George Makram Yousef, for his guidance and support both
inside and outside the lab. I am truly grateful towards Dr. Yousef for giving me the opportunity
to grow into the self-assured and tenacious student that I have become throughout my journey in
graduate school. I would also like to express my gratitude towards my committee members, Dr.
Jason Fish, Dr. Andrew Evans, Dr. Jason Karamchandani and Dr. Alexander Romaschin, for
their expertise and thoughtful questions. These accomplished scientists have helped me further
my research and prepare me as a scientist.
I believe my relationship with Dr. Yousef's lab is reflected by the saying, "it requires a village to
raise a child." The individuals to whom I am indebted for all that I have learned are Dr. Nicole
White, Dr. Zsuszanna Lichner, Dr. Henriett Butz, Dr. Qiang (Colleen) Ding, Dr. Heba Khella
and Fabio Rotondo. I would also like to thank my colleagues: Ashley Di Meo, Andrew Girgis,
Hala Girgis, Rania Ibrahim, Nicole Kim, Dr. David Kolin, Dr. Lorna Mirham, Roy Nofech-
Mozes, Sara Samaan, Joseph Samuel, Dr. Gus Sidiropoulos and Youssef Youssef. I am also
grateful to Dr. Sahar Al-Haddad and Dr. Rola Saleeb from St. Michael’s Hospital for taking the
time to evaluate histological slides. Finally, I would like to especially thank Nicole Daniel for
helping me complete the final touches for my project.
I would not have enrolled myself in a Master of Science degree if it were not for the
undergraduate research opportunities given to me by Dr. Chen Wang from the University of
Toronto and Dr. Bhushan Nagar from McGill University. My time spent in their labs was an
invaluable experience that led to my interest in scientific research. On that note, I'd also like to
thank Edward Parker, Geneviève Virgili and Nisa Mullaithilaga, graduate students who I worked
closely with during my undergraduate studies.
Finally, I want to thank my family and close friends for all their support and patience during my
graduate studies. I would also like to dedicate my thesis to my grandmother ('babcia') who passed
away to pancreatic cancer in April 2010. I aspire that my research will shed new light on novel
strategies to design effective treatments against kidney cancer.
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Table of Contents
!
Acknowledgments iii
Table of Contents iv
List of Tables viii
List of Figures ix
List of Abbreviations x
Chapter 1 Introduction 1
1 miRNAs 1
1.1 Biogenesis of miRNAs 1
1.2 Mechanisms of miRNA Function 2
1.3 miRNAs in Disease 3
2 Renal Neoplasms 4
2.1 Clear Cell RCC 5
2.2 Papillary RCC 5
2.3 Chromophobe RCC 5
2.4 Unclassified RCC 6
2.5 Renal Oncocytoma 6
3 Molecular Classification of RCC: History, Opportunities and Challenges 7
3.1 The Clinical Significance of Accurately Diagnosing RCC Subtypes 8
3.2 Potential Molecular Biomarkers for RCC 8
3.2.1 Copy Number Alterations 9
3.2.2 mRNA 9
3.2.3 Protein 10
3.2.4 Methylation 10
v
3.2.5 miRNA 10
3.3 Current Challenges of RCC Subtyping 12
4 Papillary RCC 13
4.1 Clinical Overview 13
4.2 Papillary RCC Subtypes 14
4.2.1 Papillary RCC Type 1 14
4.2.2 Papillary RCC Type 2 15
4.2.3 Papillary RCC Not Otherwise Specified 16
4.3 The Molecular Pathways Underlining Papillary RCC 16
4.3.1 HGF/MET Signaling Pathway 16
4.3.2 MTOR Signaling Pathway 17
4.3.3 Focal Adhesion and Extracellular Matrix Pathway 18
5 Rationale 19
6 Hypothesis 20
7 Objectives 20
7.1 To investigate a role for dysregulated miRNAs and genes in pRCC type 1 20
7.2 To validate a classification scheme with a limited number of miRNAs that can differentiate
ccRCC, pRCC, chRCC and oncocytoma 20
Chapter 2 21
1 Introduction 21
2 Materials and Methods 22
2.1 Patient Sample Collection 22
2.2 Cell Culture and Transient Transfection 22
2.3 RNA Extraction 23
2.4 Quantitative Reverse Transcription PCR (qRT-PCR) 23
2.5 PCR Array 25
2.6 Protein Extraction and Western Blot Analysis 26
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2.7 Luciferase Reporter Assay 26
2.8 Proliferation Assay 27
2.9 Bioinformatic Analysis 27
3 Results 28
3.1 Genes and miRNAs Are Dysregulated in pRCC Type 1 and Are Enriched in the Focal
Adhesion and ECM Pathways 28
3.2 miR-199a-3p Targets Members of the Focal Adhesion and ECM Pathways 39
3.3 miR-199a-3p Has a Negative Effect on Cellular Proliferation 44
4 Discussion 45
5 Conclusions 47
6 Future Experiments 47
Chapter 3 miRNAs Can Subclassify Renal Cell Carcinoma Subtypes with Accuracy 49
1 Introduction 49
2 Material and Methods 49
2.1 Patient Samples 49
2.2 RNA Extraction 50
2.3 Absolute miRNA Quantification 50
3 Results 50
3.1 A Classification Scheme for RCC Subtypes Using 6 miRNAs 50
3.2 miR-221 and -222 Can Differentiate ccRCC and pRCC from chRCC and Oncocytoma 51
3.3 miR-126 Can Distinguish Between ccRCC and pRCC 55
3.4 There Is Overlap in miR-15b and -22 Expression Between chRCC and Oncocytoma 56
3.5 Expression of miR-221, -222 and -126 in Unclassified RCC 58
4 Discussion 61
5 Conclusions 63
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6 Future Experiments 63
viii
List of Tables
Table 2.1. List of Forward and Reverse Primer Sequences Used for Measuring mRNA
Expression by qRT-PCR....................................................................................................... 25
Table 2.2. A Partial List of the Most Significantly Up and Down-Regulated miRNAs in pRCC
Type 1 ................................................................................................................................... 32
Table 2.3. A Partial List of the Biological Pathways Enriched with Dysregulated Genes in pRCC
Type 1 ................................................................................................................................... 34
Table 2.4. A Partial List of Up- or Down-Regulated Members of the Focal Adhesion and ECM
Pathways in pRCC Type 1 Relative to Normal Kidney Tissue ............................................ 36
Table 2.5. Pathways Significantly Enriched for Predicted Gene Targets of miR-199a-3p .......... 40
ix
List of Figures
Figure 2.1. Hierarchical Clustering of Gene Microarray Data from pRCC Type 1 and Normal
Kidney Tissues...................................................................................................................... 30
Figure 2.2. Dysregulated mirnas in pRCC Type 1 Are Predicted to Target Genes in the Focal
Adhesion Pathway ................................................................................................................ 31
Figure 2.3. Gene Ontology Pie Charts .......................................................................................... 35
Figure 2.4. mRNA Expression of CAV2, ITGB8 and MET Is Higher in pRCC Type 1 Relative to
Normal Kidney Tissue. ......................................................................................................... 37
Figure 2.5. miR-199a-3p Expression Is Down-Regulated in pRCC Type 1 Compared to Normal
Kidney Tissue.. ..................................................................................................................... 38
Figure 2.6. miR-199a-3p Down-Regulates mRNA Expression of CAV2, ITGB8, MET and MTOR
............................................................................................................................................... 41
Figure 2.7. miR-199a-3p May Target Protein Expression Levels of mTOR and p-Akt .............. 42
Figure 2.8. miR-199a-3p Interacts with the 3' UTR of MET and MTOR ..................................... 43
Figure 2.9. A Role for miR-199a-3p in pRCC Type 1 ................................................................. 44
Figure 3.1. Classification Scheme for RCC Subtypes and Renal Oncocytoma............................ 51
Figure 3.2. miR-221 Standard Curve............................................................................................ 52
Figure 3.3. Absolute Quantification of miR-221 in ccRCC, pRCC, chRCC and Oncocytoma ... 53
Figure 3.4. Absolute Expression of miR-222 in ccRCC, pRCC, chRCC and Oncocytoma......... 54
Figure 3.5. Absolute Quantification of miR-126 in ccRCC and pRCC........................................ 55
Figure 3.6. Expression Level of miR-15b in chRCC and Oncocytoma........................................ 56
Figure 3.7. Absolute Expression of miR-22 in chRCC and Renal Oncocytoma.......................... 57
Figure 3.8. Expression Level of miR-221 in ccRCC, pRCC, chRCC, Oncocytoma and
Unclassified RCC.................................................................................................................. 59
Figure 3.9. Level of miR-222 Expression in ccRCC, pRCC, chRCC, Oncocytoma and
Unclassified RCC.................................................................................................................. 60
Figure 3.10. Expression Level of miR-126 in Unclassified RCC Samples Compared to ccRCC
and pRCC.............................................................................................................................. 61
x
List of Abbreviations
CAV2 - caveolin 2
ccRCC - clear cell RCC
chRCC - chromophobe RCC
ECM - extracellular matrix
FFPE - formalin-fixed paraffin-embedded
HGF - hepatocyte growth factor
HPRT1 - hypoxanthine phosphoribosyltransferase 1
miRNA - microRNA
mTOR - mammalian target of rapamycin
p-Akt - phosphorylated Akt
pRCC - papillary RCC
qRT-PCR - quantitative reverse transcription PCR
RCC - renal cell carcinoma
TBP - TATA box binding protein
UTR - untranslated region
VHL - Von-Hippel Lindau
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"Medicine is a science of uncertainty and an art of probability."
- Sir William Osler
1
Chapter 1 Introduction
1 miRNAs microRNAs (miRNAs) constitute a class of short, non-coding RNA molecules that are single-
stranded. In 1993, Lee et al. discovered that lin-14 down-regulates the expression of LIN-14
protein in Caenorhabditis elegans by an RNA-RNA process (1). Ten years later, Drs. Andrew
Fire and Craig Mello propose the use of double stranded RNA as experimental tools to knock-
down the expression of specific proteins (2). Today, miRNAs are known to be evolutionarily
conserved and have important roles in both physiological and pathological processes. Since their
discovery, many findings have been made about miRNAs, including their biogenesis,
mechanisms of gene silencing and involvement in disease. These small molecules are believed to
be associated with almost every biological process that occurs within a living organism (3). In
addition, miRNAs are predicted to target more than a third of human genes (4). Therefore, it is
not surprising that these RNA molecules are related to many diseases, including cancer.
Although miRNAs were first identified more than two decades ago, new findings about these
potent regulators are continuously shedding light on the complex and important role miRNAs
have in both health and disease.
1.1 Biogenesis of miRNAs
miRNAs are encoded within introns or intergenic regions (5, 6). These regulatory molecules are
first transcribed in the nucleus as long primary miRNA transcripts by RNA polymerase II or III
(7, 8). Primary miRNA transcripts that are transcribed by polymerase II have a 5' 7-methyl
guanylate cap and 3' polyadenylated tail (8, 9). Drosha, an RNase III protein with nuclease
activity, then processes these transcripts into ~70 nucleotide long stem loop structures called
precursor miRNAs (10). The DiGeorge syndrome critical region gene 8, also known as Pasha, is
a double stranded RNA-binding protein required for processing primary miRNA transcripts into
precursor miRNAs as well (11). These RNAs are subsequently recognized by Exportin-5 and
transported from the nucleus to the cytosol (12, 13). In the cytoplasm, precursor miRNAs are
processed by another RNase III enzyme, Dicer, to generate an ~20 nucleotide double stranded
RNA (14). One strand of this duplex, called the guide strand, mediates RNA silencing via
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association with the RNA-induced silencing complex (3). The other strand, referred to as the
passenger strand, is degraded (15). In addition to the miRNA guide strand, the RNA-induced
silencing complex includes Argonaute 2, the catalytic subunit, which mediates slicing of the
target mRNA (3). Knowledge of the machinery behind miRNA biogenesis is essential for a
comprehensive understanding of the function of miRNAs and factors affecting their expression.
1.2 Mechanisms of miRNA Function
Briefly, miRNAs downregulate the expression of many gene targets at the post-transcriptional
level by binding to the seed sequence located within the 3' untranslated region (UTR) (5, 6).
miRNAs regulate gene expression in both plants and animals (16). In plants, these molecules
preferentially exhibit complementary base-pairing to their target mRNA via the seed sequence,
which encompasses nucleotides 2-8 from the 5' terminus of the miRNA (17). This leads to
cleavage of its target genes (18). In animals, the seed region of miRNAs predominantly has
imperfect base-pairing to the target sequence, favouring translational repression rather than
mRNA cleavage (19, 20). It has been shown that the miRNA can still execute translational
inhibition if its binding site is cloned into the 5' UTR sequence of a gene (21). Furthemore, these
RNA molecules can repress gene expression throughout the different steps of translation, such as
initiation and elongation (22). Mathonnet et al. (2007) show that let-7 regulates gene expression
by repressing recognition of the 7-methyl guanylate cap by translation initiation factors (23).
Another research group provides evidence that miRNAs may negatively regulate the elongation
process because ribosomes on mRNAs were released more rapidly from the nascent polypeptide
in cells transfected with the targeting small interfering RNA compared to the negative control
(24). The RNA-induced silencing complex can also suppress protein translation by sequestering
target mRNAs in processing bodies (25). Moreover, miRNAs are subject to RNA editing, a
process by which adenosine deaminase acting on RNA enzymes convert an adenosine residue to
inosine (8). This mechanism allows for miRNAs to expand their pool of mRNA targets (9, 10).
Overall, the classical function of gene silencing by miRNAs adds another dimension to the
complex regulation of gene expression in addition to transcription factors, methylation and
chromatin remodeling, among others.
There are emerging research findings that propose additional roles for miRNAs other than
silencing complementary mRNA targets. In fact, several researchers suggest that miRNAs can
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upregulate protein translation. Place et al. (2008) show that miR-373 targets complementary
promoter sites of E-cadherin and cold-shock domain-containing protein C2 to induce gene
expression (26). In addition, Vasudevan et al. (2007) demonstrate that miRNAs have the
potential to induce translation of target mRNAs (27). An independent research group also reveals
that miR-10a can induce the expression of ribosomal proteins via a sequence in the 5' UTR (28).
Therefore, miRNAs may be fine-tuning protein levels by either inducing or repressing gene
expression under given conditions in order to maintain cellular homeostasis. Other mechanisms
of gene regulation by miRNAs have been proposed in recent years. For instance, Hwang et al.
(2007) present the possibility that miRNAs can behave as transcription factors since miRNAs
can be imported back into the nucleus by a motif in their 5' region (29). Moreover, miRNAs may
act as decoys that are able to release mRNA sequences targeted for translational inhibition (30).
Another study provided evidence that miRNA-dependent gene methylation occurs in
Arabidopsis (31). In summary, these non-coding RNA molecules have a diversity of functions
that may further our understanding of the different dimensions of gene and protein expression
regulation.
1.3 miRNAs in Disease
miRNAs are endogenous RNA molecules that are fundamental for maintaining cellular
physiology (32). This is supported by their important function in skeletal development,
inflammation and pregnancy, among others (33-35). However, miRNAs may have aberrant
expression, which can result in the initiation and propagation of disease. miR-135a is
upregulated in diabetic skeletal muscle and downregulates the expression of the insulin receptor
substrate 2 gene, which is linked to the etiology of type 2 diabetes (36, 37). Additionally,
miRNAs may be involved in tumorigenesis by possessing either tumor suppressive or oncogenic
functions. To provide support for miRNA dysregulation in cancer, Calin et al. (2004) perform a
genome-wide study and map a significant number of miRNAs to chromosomal regions that are
frequently altered in cancer (38). The promoters of miRNAs are also methylated in cancer in
contrast to normal tissue (39-41). Extensive investigations have been conducted to establish
putative roles for these regulatory molecules in cancer. Interestingly, the same miRNA can either
drive or repress tumorigenesis depending on the given cancer. This indicates that the tumor
microenvironment may, in part, dictate the miRNA function. For example, in breast and gastric
cancer, miR-10b acts as an oncogene by silencing the expression of homeobox D10, a tumor
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suppressor (42, 43). On the contrary, in neuroblastoma cells, miR-10b indirectly inhibits Myc
expression, suggesting a tumor suppressive role for this miRNA (44). Furthermore, components
of the miRNA biogenesis machinery, such as Dicer, can also be dysregulated in disease,
resulting in aberrant miRNA expression (45, 46).
In addition to having a role in disease, these non-coding RNA molecules can also serve as
biomarkers for various pathological states, including diabetes, cardiovascular disease,
rheumatoid arthritis and cancer (47-50). A comprehensive discussion about miRNAs as potential
biomarkers in cancer, particularly renal cell carcinoma (RCC), is found in Section 3.2.5 of
Chapter 1.
2 Renal Neoplasms RCC is the most commonly diagnosed kidney neoplasm in adults, and it is estimated that there
will be 63,920 new cases of renal cancer in the United States this year (51, 52). RCC is one of
the top 10 most common malignancies diagnosed amongst the Western nations, and more than
half of all RCC cases are diagnosed incidentally (53, 54). This cancer is not a single disease;
instead it encompasses a spectrum of histological subtypes. Clear cell RCC (ccRCC) is the most
prevalent RCC subtype, constituting 80-90% of all RCC diagnoses (51). The five-year cancer-
specific survival rate for patients with ccRCC is approximately 68.9% (55). The second most
common RCC subtype is papillary RCC (pRCC), which comprises 10-15% of RCC cases and
has a cancer-specific survival of 87.4% (51, 55). Chromophobe RCC (chRCC) is the third most
frequent RCC subtype, comprising 4-5% of all RCC diagnoses and has a cancer-specific survival
of 86.7% (51, 55). Finally, there exists an RCC subtype called unclassified RCC, which serves as
a designated subgroup of poorly differentiated RCC tumors that do not conform to the defined
features of any specific subtype. Both ccRCC and pRCC originate from the proximal convoluted
tubule of the kidney, whereas chRCC and oncocytoma, a benign renal neoplasm that shares
morphological features with RCC (56), stem from the distal convoluted tubule. Nevertheless,
there are many more RCC subtypes, such as collecting duct RCC, Xp11 translocation RCC and
clear cell papillary RCC, which may now be recognized as the fourth most common RCC
subtype (57, 58). In addition, there is emerging evidence that there may be more RCC subtypes
yet to be included, such as thyroid-like follicular RCC and succinate dehydrogenase B mutation-
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associated RCC (57). In summary, RCC is an "umbrella" term that encompasses a number of
distinct variants with different survival outcomes.
2.1 Clear Cell RCC
ccRCC, previously referred to as conventional RCC, is the most common RCC subtype and has
an aggressive clinical behavior. Under the microscope, the cytoplasm of ccRCC tumor cells may
appear either clear or eosinophilic (59). The name 'clear cell' is derived from the pale appearance
associated with this RCC subtype due to the accumulation of glycogen and lipid in the cancerous
cells (69). A hallmark of this tumor is loss of chromosome 3p, which harbors the Von-Hippel
Lindau (VHL) gene (60). This gene is an important tumor suppressor in ccRCC as it promotes
proteolytic degradation of angiogenic factors, such as the hypoxia inducible factor-1, in
conditions of normoxia (61). In the majority of patients with ccRCC, the VHL gene is deleted,
hypermethylated or mutated, resulting in a highly hypoxic and vascular tumor (62-64). In
addition to loss of 3p, other common cytogenetic aberrations observed in ccRCC include loss of
14q, 8p, 4q, 9p and 6q, as well as gain of 5q and 8q (70, 71). Certain copy number alterations in
ccRCC patients have been associated with a significantly worse prognosis (72). Moreover,
chromosomal aberrations have been correlated with a distinct gene signature (73). A significant
proportion of ccRCC patients will develop metastasis. Therefore, there is a great interest in
examining prognostic markers for this RCC subtype. From Dr. Yousef's lab, galectin-1 was
shown to retain its prognostic significance independent of other clinicopathologic variables for
disease-free survival in ccRCC patients (p = 0.036, Hazard Ratio: 2.08) (74). Moreover, changes
in chromatin remodeling and metabolic pathways have been reported to be associated with
ccRCC pathogenesis (66-68). Therefore, ccRCC is a highly studied RCC subtype since it is
prevalent and has a dismal prognosis.
2.2 Papillary RCC
An in-depth overview of pRCC is presented in Section 4 of Chapter 1.
2.3 Chromophobe RCC
chRCC was first described as a distinct entity in 1985 (65). This rare cancer has several
characteristic features that allow for discrimination from the other RCC subtypes, as well as
renal oncocytoma, a benign renal neoplasm. Under the light microscope, the tumor cells have
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abundant cytoplasm and defining cell borders (77). In addition, there are three variants of
chRCC: 1) typical, 2) eosinophilic and 3) mixed. The first is characterized by pale cytoplasm,
whereas the second has a granular cytoplasm. Loss of chromosomes 1, 2, 6, 10, 13, 17, and 21
was reported in the majority of chRCC cases using comparative genomic hybridization (66).
Another distinctive feature for chRCC diagnosis is positive and diffuse staining with Hale's
colloidal iron (67). Moreover, chRCC displays characteristic ultrastructural features. For
instance, the presence of microvesicles is indicative of this RCC subtype, although microvesicles
can also be observed in oncocytoma and the eosinophilic variant of ccRCC (68). In addition, the
mitochondria found in the tumor cells of chRCC differ in morphology from the mitochondria
seen in the eoinsophilic variant of ccRCC and oncocytoma (68). The distinct immunostaining
pattern of this RCC subtype has also been examined. In particular, there are many investigators
searching for a marker to differentiate between chRCC and oncocytoma (69-71). One group
proposes that positive immunohistochemical staining for cytokeratin 7 and CD117, and negative
staining for the paired box 2 protein can differentiate chRCC from ccRCC, pRCC and
oncocytoma (72). Finally, although chRCC generally has an indolent clinical course, a study
using 25 chRCC patients demonstrated that individuals with both chRCC and pRCC in the same
kidney had lung metastasis (73). However, it is not clear which RCC subtype metastasized. In
summary, chRCC is a rare RCC subtype and may be difficult to differentiate from oncocytoma.
2.4 Unclassified RCC
Unclassified RCC is a rare and aggressive subtype of RCC with a reportedly higher mortality
rate than ccRCC (74, 75). Treatment options for unclassified RCC are limited. One research
group showed from a study with 31 unclassifed RCC patients that nephrectomy and interleukin-2
based immunotherapy significantly improved patient survival (p < 0.05) (76). However, patients
with ccRCC had a more drastic improvement in survival than those with unclassified RCC (74).
Currently, unclassified RCC is a poorly understood renal neoplasm at the molecular level. The
genetic alterations and molecular mechanisms underlining this rare subtype have yet to be
elucidated.
2.5 Renal Oncocytoma
Oncocytoma is not a malignant renal parenchyma, however it shares overlapping features with
the eosinophilic variant of chRCC (77). This benign tumor is characterized by a highly granular
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cytoplasm (59). There are less chromosomal aberrations observed in oncocytoma compared to
RCC (78). However, loss of chromosomes 1 and Y are frequently observed in oncocytoma cases
(79, 80). Additionally, it was shown by comparing oncocytoma to matched normal kidney tissue
that there is alteration in the mitochondrial DNA as well, which mapped to the mitochondrial
cytochrome c oxidase subunit I (81). Furthermore, a retrospective study using 138 patients from
the Mayo Clinic revealed that 14 cases had renal oncocytoma co-occurring with RCC (82).
Patients with co-existent oncocytoma and RCC had a disease-free survival of 100% with no
reported metastatic disease (82). Moreover, investigators previously have characterized the
molecular profile of oncocytoma, particularly markers that can discern oncocytoma from chRCC
(83). Molecular features of this renal tumor include include weak expression for vimentin and
RCC antigen (84, 85). Nevertheless, there may be significant overlap in morphological features
between chRCC and renal oncocytoma (86). In light of such significant overlap, there is a report
that suggests that chRCC and oncocytoma represent a spectrum of the same disease (87).
Another group suggests that chRCC and oncocytoma may originate from a common progenitor
lesion (88). More investigations are required in order to verify the biological relation between
chRCC and oncocyctoma. In summary, oncocytoma is a benign renal neoplasm, but poses a
challenge for pathologists in discerning this tumor from malignant kidney cancers.
3 Molecular Classification of RCC: History, Opportunities and Challenges
The classification of RCC, a heterogeneous disease, has been subject to many advances in its
history due to our evolving knowledge and understanding of this cancer type. In the late 1980's,
it was proposed that all renal neoplasms, including adenomas and carcinomas, be classified on
the basis of the tumor cell type, growth pattern and cytological grading (89). Subsequently, it
was determined that the different renal tumors have distinct karyotypic features (90). Therefore,
Kovacs et al. (1997) put forward the Heidelberg classification system, which combined both
distinct histological and genetic features of an RCC tumor (80). The designation of the different
RCC subtypes according to the guidelines set forth by the Heidelberg classification is still
commonly referred to and prognostically informative (91). Today, the knowledge that different
chromosomal changes results in specific gene and protein expression profiles has driven many
research efforts to discover robust biomarkers and classification schemes for RCC. However,
microscopy persists as an important tool for differentiating between the distinct subtypes,
8
although there are limitations. Therefore, a molecular marker that can help classify difficult RCC
cases is warranted.
3.1 The Clinical Significance of Accurately Diagnosing RCC Subtypes
Misdiagnosis of ccRCC occurs in approximately 1-20% of all cases (92). It is imperative for the
pathologist to correctly diagnose the RCC subtype, as histology itself is a prognostic marker and
will help the oncologist determine the appropriate course for patient care management (93, 94).
For instance, watchful waiting is commonly restricted to patients with confirmed oncocytoma
(51). Additionally, the different RCC subtypes have varying responses to targeted therapy for
metastatic disease. Currently, patients diagnosed with ccRCC have the most favorable response
to targeted therapy as opposed to patients with non-clear cell RCC (95, 96). More specifically, it
has been shown that a greater percentage of patients with chRCC had a more favorable response
to sunitinib or sorafenib, which are both tyrosine kinase inhibitors, compared to patients with
pRCC (97). Aside from characteristic morphological features, there are molecular markers used
for distinguishing RCC subtypes. For example, carbonic anhydrase 9 has higher expression in
patients with ccRCC compared to patients with other RCC subtypes, such as pRCC, chRCC,
unclassified RCC and Xp11.2 translocation RCC (98). However, the ability to accurately classify
the different RCC subtypes still remains an important challenge.
3.2 Potential Molecular Biomarkers for RCC
A landmark achievement for molecular research was the completion of the Human Genome
Project (99). Now, there are many comprehensive projects being conducted in order to
investigate the genomic, transcriptomic and proteomic changes associated with disease. With
today's advancing technology, high-throughput assays, such as microarray analysis and whole
genome sequencing, allow for the simultaneous screening of an assortment of molecular
markers. Such biomarkers hold the promise of revolutionizing patient care management towards
personalized medicine by: 1) distinguishing healthy individuals from non-healthy, 2)
distinguishing between distinct subtypes or variants of a disease, 3) identifying patients who are
likely to respond to a specific treatment ('predictive marker') and 4) subclassifying patients
according to the natural course of their disease ('prognostic marker') (100). In this report, the
9
ability of molecular markers to distinguish the most common RCC subtypes, namely ccRCC,
pRCC and chRCC, as well as oncotyoma, was examined.
3.2.1 Copy Number Alterations
The different RCC subtypes display unique chromosomal aberrations. In general, loss of
chromosome 3p is restricted to ccRCC cases (101). Other chromosomal aberrations that are
specific to ccRCC include gain of 5q and loss of 8p, 9, 14 and 18 (102). For patients with pRCC,
gain of chromosomes 7, 17, 12, 16 and 20, and loss of chromosome Y are commonly observed
(90). Using 26 ccRCC, 13 pRCC and 11 chRCC cases, loss of heterozygosity at 1p, 6p, 10p, 13q
and 21q were more frequently observed in chRCC compared to ccRCC and pRCC (103). Unlike
RCC, oncocytoma cases can have a normal karyotype. However, investigators have previously
reported the loss of chromosomes 1, 14 and Y in this benign renal neoplasm (79, 102, 104).
Finally, the genes encoded within the chromosomal alterations observed in the different RCC
subtypes have been previously correlated to various biological pathways (105).
3.2.2 mRNA
In addition to chromosomal copy number changes, RCC subtyping can also be based on
differential mRNA expression. Different studies using high-throughput microarray analysis have
shown that distinct RCC subtypes can cluster separately from one another based on cDNA
expression (106). Similarly, Takahashi et al. (2003) found different gene signatures associated
with the different kidney cancers using microarray analysis (107). The differential expression of
several candidate genes was re-evaluated in an independent cohort by immunohistochemistry
(107). Glutathione S-transferase alpha expression was specific to ccRCC, alpha-methylacyl-CoA
was preferentially expressed in pRCC and carbonic anhydrase 2 had the strongest staining in
chRCC and oncocytoma (107). Another study analyzed the expression levels of various matrix
metalloproteinases and tissue inhibitors of matrix metalloproteinases from 30 RCC cases from
different histological subtypes (108). By quantitative reverse transcription PCR (qRT-PCR), they
found that the mRNA expression levels of different matrix metalloproteinases are significantly
different between ccRCC and pRCC (108). Furthermore, a distinguishing characteristic of
ccRCC and pRCC is the level of vascularity and angiogenesis observed in these two neoplasms
(109). Therefore, a research group performed qRT-PCR and determined that the mRNA
expression levels of endothelin 1 and endothelin receptor type A, which are involved in the
10
endothelin axis, are higher in ccRCC than pRCC (110). Thus, differential mRNA expression
levels between the different RCC subtypes may reflect distinct macroscopic features associated
with each variant.
3.2.3 Protein
Proteins are the functional products of genes and therefore can provide a more accurate
understanding of the biological impact arising from differential gene expression in RCC.
Moreover, proteins can undergo post-translational modifications, which can determine whether
the protein is in an active or inactive state. Technologies such as mass spectrometry have
facilitated the ability to analyze for many differentially expressed proteins in cancer as candidate
biomarkers (111). Novel high-throughput technologies, such as reverse phase protein arrays, will
further drive proteomic studies in RCC and other cancers (112). Independent research groups
have reported differential protein expression levels in the RCC subtypes. Dr. Yousef's lab
previously identified dysregulated proteins in ccRCC compared to normal kidney tissue samples
by performing mass spectrometry (113). However, there has not been extensive effort to use this
high-put technology to identify a robust proteomic signature for the different RCC subtypes.
3.2.4 Methylation
Methylation patterns have been shown to serve as potential signatures for different cancers (114).
There is evidence that ccRCC and pRCC can be distinguished from renal oncocytoma based on
the methylation level at the promoter for the Ras association domain family member 1 gene
(115). A more recent study identified distinct methylation patterns that can distinguish beteween
chRCC and renal oncocytoma (101). Therefore, this form of epigenetic regulation may serve as a
potential fingerprint for the unique RCC subtypes.
3.2.5 miRNA
Independent research groups have shown miRNAs to be differentially expressed between the
most common RCC subtypes. One of the first studies to investigate differential miRNA
expression in RCC subtypes was conducted by Petillo et al. (2009). In that study, investigators
used tumor tissues and determined that there are 27 significantly differentially expressed
miRNAs between ccRCC and pRCC, including upregulation of miR-126 and -143, and
downregulation of miR-31 in the former compared to the latter (116). Moreover, 5 miRNAs were
11
identified to be significantly different in their expression levels between chRCC and
oncocytoma, including miR-200b (116). The expression levels of a few of these miRNAs were
validated by qRT-PCR. In addition, Fridman et al. (2010) used 71 formalin-fixed paraffin-
embedded (FFPE) tissues and identified relative ratios between two distinct miRNAs by
microarray analysis to differentiate ccRCC, pRCC, chRCC and oncocytoma using a two-step
classification scheme (117). A ratio > 9.86 of miR-221 relative to miR-210 (miR-221/miR-210)
was indicative of chRCC or oncocytoma, whereas a ratio < 9.86 of miR-221/miR-210 was
suggestive of ccRCC and pRCC (117). Subsequently, a tumor with a ratio > 33.1 of miR-
200c/miR-139-5p would be classified as chRCC instead of oncocytoma (117). Finally, in making
the decision between ccRCC and pRCC, if the ratio for miR-126/miR-31 is > 2.32 then it is
indicative of a ccRCC diagnosis rather than pRCC, with the opposite holding true (117). Using 5
FFPE tissues of each ccRCC, pRCC, chRCC and oncocytoma, Powers et al. (2011) identified
miR-142-3p, -20a and -21 to be upregulated in ccRCC and pRCC compared to chRCC and
oncocytoma, whereas miR-221 and -222 had greater expression in chRCC and oncocytoma
relative to ccRCC and pRCC (118). Moreover, they identified miR-126, -126* and 143 to be
highly expressed in ccRCC compared to pRCC, and the expression levels of miR-124, -200b, -
429 and -629* are elevated in chRCC relative to oncocytoma (118). This study also showed that
the majority of the differentially expressed miRNAs did not correlate with chromosomal
imbalances in the RCC cases (118). However, a weakness of this study was that the miRNA
expression levels were measured only by microarray analysis and not independently confirmed
by qRT-PCR. In 2011, Dr. Yousef's lab published a miRNA-based multistep decision tree for
differentiating between the most common RCC subtypes. The training cohort included
microarray analysis on 50 fresh frozen tissues from ccRCC, pRCC, chRCC and oncocytoma
patients (119). From this analysis, a panel of 65 different miRNAs was identified to subclassify
RCC and oncocytoma with high accuracy (119). It has also been reported that there is differential
miRNA expression between ccRCC, pRCC and clear cell papillary RCC, which shares
overlapping features with ccRCC and pRCC (120).
The ability of these non-coding RNA molecules to differentiate between the different subtypes of
the same cancer is not restricted to RCC. Additionally, miRNAs are strong candidates for
biomarker discovery due to the fact that these molecules can remain stable outside the cell by
being encapsulated by exosomes or complexed with Argonaute 2 (121, 122). Therefore, miRNAs
12
can be isolated from a number of biospecimen, including urine, plasma and sputum (123-125).
Recently, miR-210 was detected in the serum of ccRCC patients and may be a promising non-
invasive test for this renal neoplasm (126, 127). Therefore, miRNAs have the potential to serve
as non-invasive biomarkers for pathological states due to their stability in biological fluids. In
addition, miRNAs can also be extracted from FFPE tissues due to their short length (136).
miRNAs are also more robust biomarkers than mRNA molecules because of their ability to
accurately classify undifferentiated tumors, whereas mRNA could not perform the same
classification (128). This is possibly due to the tissue-specific nature of miRNAs (129).
Moreover, miRNA expression can be evaluated in needle core biopsies. A group has shown that
the expression levels of miR-141 and -200b in fine-needle aspirations can differentiate RCC
from normal kidney with high specificity and sensitivity (130). Finally, the use of miRNAs as
biomarkers is a growing field due to the recent research efforts to establish technologies to
directly quantitate miRNA expression (131, 132).
3.3 Current Challenges of RCC Subtyping
There are impediments to accurately subclassifying the different RCC subtypes, which is of great
importance since these unique entities have varying clinical courses and responses to therapy. A
major challenge to subclassifying RCC with high accuracy is that different subtypes can share
similar cytological elements. For instance, the granular variant of chRCC may be difficult to
discern from oncocytoma, a benign renal neoplasm (86). Therefore, there are many research
efforts towards the discovery of robust biomarkers for RCC stratification.
There are challenges and limitations associated with identifying biomarkers that have high
sensitivity and specificity in subclassifying RCC subtypes. For instance, there is heterogeneity
amongst tumors from the same RCC subtype (‘intratumor heterogeneity’). Several research
groups have proposed that ccRCC may be separated into two distinct subgroups based on
different clinical behavior, gene expression pattern and signaling pathway activation (133-136).
Therefore, there is strong evidence for heterogeneity at the molecular-level within an RCC
subtype. Another challenge for accurately stratifying the RCC subtypes is the occurrence of
neoplasms that display elements observed from two separate subtypes. For example, there are
tumors identified as a hybrid between chRCC and oncocytoma (137, 138). The hybrid oncocytic
chromophobe tumor is currently classified under chRCC (57).
13
In addition, there are technical aspects that challenge the identification of robust biomarkers in
RCC. Collaborations between various research institutions in different regions of the world are
necessary to account for patient heterogeneity when proposing and validating a potential RCC
biomarker (139). Moreover, consistency between laboratories with regards to specimen storage
and handling is necessary to avoid artifacts that may skew results (140). There are proposed
guidelines to help address this issue and thereby, achieve reproducibile results between
independent research groups (141). In summary, there are many challenges facing the ability to
accurately discern between the different RCC subtypes using robust molecular biomarkers.
4 Papillary RCC
4.1 Clinical Overview
The term 'papillary' is not exclusive to pRCC since papillae structures can also be seen in other
RCC subtypes, including ccRCC (59). Foamy macrophages and psammoma bodies are typically
seen in pRCC (142). There is a significantly high incidence of pRCC in patients with end-stage
renal disease in comparison to the general population (143). A recent study provides further
evidence for a relationship between renal injury and the occurrence of pRCC (144). There are
genetic hallmarks associated with this RCC variant that allow for differential diagnosis from the
other RCC subtypes. Trisomy for chromosomes 7 and 17, and loss of chromosome Y are
commonly observed in pRCC patients (145). The MET proto-oncogene has been reported in both
hereditary and sporadic pRCC (146). In addition, this growth factor receptor has also been
reported to be over-expressed in the hereditary and sporadic variants (147, 148). The promoter
region of the serine peptidase inhibitor Kunitz type 2 gene, which negatively regulates Met, has
also been shown to be hypermethylated in 40% of sporadic pRCC cases (149). Furthermore, it
was recently reported that Met expression is significantly greater in pRCC relative to ccRCC
(150). Another marker for pRCC is strong expression for alpha-methylacyl-CoA in comparison
to the other RCC subtypes (151). Although many researchers have studied the molecular
mechanisms underlining pRCC, this RCC subtype still remains a poorly understood malignancy
in comparison to the other RCC subtypes, such as ccRCC.
Patients with non-metasatic pRCC demonstrate therapeutic benefit from cytoreductive therapy
(152). In terms of systemic therapy, there is currently none available in the clinic for patients
with metastatic pRCC. However, one study documented that a subset of patients were treated
14
with rapamycin-like mTOR inhibitors for one year (153). Roos et al. (2011) provide evidence
supporting the up-regulation of hypoxia inducible factor-1α expression in pRCC (154).
However, pRCC is not a highly vascular tumor and in general, has a low response to sorafenib
and sunitinib, which are small molecule multi-tyrosine kinase inhibitors that target receptors,
such as the vascular endothelial growth factor receptor (155). A major challenge for improving
the therapeutic strategy for patients with metastatic pRCC is the rarity of this disease. Therefore,
only through coordinated research studies between different hospital centers can there be major
advancements in instating effective treatment options for this cancer. There is a growing interest
in investigating the pathogenesis of pRCC. This may be in part due to the fact that metastatic
pRCC was shown to have the worst survival compared to other common non-clear cell RCC
subtypes (156). Moreover, one study provides evidence that patients with pRCC or ccRCC had
similar disease recurrence frequency (4.1% and 4.7%, respectively) and cancer-specific survival
(143.8 and 147.8 months, respectively) during a follow-up period that averaged more than 5
years (157). Another study also demonstrates that the 5-year cancer-specific survival of non-
metasatic ccRCC (84%) and pRCC (90%) are not significantly different (158). Nonetheless,
there is currently no standard of care with regards to targeted therapy for patients with pRCC.
pRCC is a heterogeneous disease as it can be further subdivided into type 1 or type 2 (159, 160).
The World Health Organization acknowledges the designation of pRCC into type 1 and type 2
(59). Type 1 and type 2 pRCC demonstrate unique characteristics at both the chromosomal and
molecular level, including differential miRNA expression (161-163). Moreover, pRCC type 1
and type 2 differ in their clinical behaviors. Through multivariate analysis, it has been shown that
the type of pRCC retains prognostic significance independent of other variables, such as tumor
size and grade, for both overall survival and disease-free survival (164). Therefore, future
research studies on pRCC should distinguish between the two distinct types.
4.2 Papillary RCC Subtypes
4.2.1 Papillary RCC Type 1
Under the microscope, small cells with pale cytoplasm surrounding papillae structures are
indictative of pRCC type 1 (159). The presence and extent of tumor necrosis are prognostically
significant in pRCC type 1 and not in pRCC type 2 (165). Another feature that is characteristic
of pRCC type 1 is gain of chromosome 17 (162, 166). There are also molecular differences
15
between the two pRCC variants. For instance, there is greater expression of cytokeratin 7 in
pRCC type 1 than pRCC type 2 (167). Moreover, it has been reported that the mRNA expression
of MET, encoded on chromosomal locus 7q31, is overexpressed in sporadic pRCC type 1 cases
(168). Pathway analysis performed by Yang et al. (2005) have shown that genes dysregulated in
this tumor type are significantly enriched in the G1-S checkpoint regulation (p = 0.018) (169).
Aside from these findings, there is little known about the biological pathways underlining pRCC
type 1. Knowledge about such oncogenic processes may help in the design and development of
effective targeted therapy for this RCC subtype. Although pRCC type 1 has an indolent clinical
course, its metastatic form was previously associated with shorter survival than metastatic pRCC
type 2 (170). Currently, there is no study that has investigated the response of pRCC patients to
systemic therapy according to tumor type.
4.2.2 Papillary RCC Type 2
Large cells with a granular cytoplasm are typical of pRCC type 2 (167). From a study using 22
pRCC type 1 and 35 pRCC type 2 tissue samples, karyotypic analysis revealed that type 2 has a
significantly greater number of chromosomal alterations than type 1 (p = 0.018) (162). In
addition, loss of chromosomal arms 1p and 3p, and gain of 5q were restricted to pRCC type 2
and not observed in pRCC type 1 (162). The immunohistochemical profile of pRCC type 2 has
also been evaluated. Both the vascular endothelial growth factor receptor 2 and 3 have
significantly higher expression in pRCC type 2 compared to type 1 (162). Hereditary pRCC type
2 is characterized by mutations in the housekeeping gene, fumarate hydratase (171). Fumarate
hydratase is a member of the Krebs cycle and converts fumarate to L-malate. Therefore, mutated
fumarate hydratase results in increased levels of fumarate. This aberrant accumulation has
downstream effects, including activation of a stress response pathway (172). Sporadic pRCC
type 2 has been demonstrated to have a gene signature that is associated with loss of functional
fumarate hydratase (173). Additionally, overexpression of Myc and activation of its signaling
pathway were associated with high-grade pRCC type 2 (174). With regards to clinical behavior,
pRCC type 2 is associated with a worse prognosis compared to pRCC type 1 (164). Therefore,
although this is an aggressive variant of RCC, there is currently no effective systemic therapy
available for patients with metastasis.
16
4.2.3 Papillary RCC Not Otherwise Specified
Investigators have reported the occurrence of pRCC tumors displaying mixed features of both
type 1 and 2, which are referred to as not otherwise specified (175). This type of pRCC has
similar age of incidence as pRCC type 1 and 2 (176). Pignot et al. (2007) reported 3 out of 133
(~2%) pRCC cases with mixed features (175). These tumors were classified as type 2 because
they predominantly had type 2 features. Another study that distinguished pRCC type 1 and 2
identified cases of mixed pRCC, which more closely resembled pRCC type 2 and were classified
as such (177). The existence of a mixed pRCC type may support the hypothesis that pRCC type
2 tumors originate from pRCC type 1 (178). A study evaluating whether there are molecular
similarities shared between pRCC not otherwise specified, and pRCC type 1 and type 2 is yet to
be conducted.
4.3 The Molecular Pathways Underlining Papillary RCC
4.3.1 HGF/MET Signaling Pathway
Met is a 190-kDa transmembrane tyrosine kinase receptor encoded by the MET proto-oncogene
(179). It is expressed in various cell types, including hepatocytes and epithelial cells (180). In
addition, this protein interacts with its ligand, the hepatocyte growth factor (Hgf), to stimulate
various cellular processes, such as proliferation, cell migration and invasion (181-183). Over-
expression of Met has been reported in different cancer types, including non-small cell lung
cancer, breast cancer and thyroid cancer (184-186). Additionally, the Met receptor can be
constitutively activated by mutation in cancer (187). Met is a heterodimer and consists of both an
extracellular α-chain and intracellular β-chain, which harbors the kinase activity (188). Upon
binding to Hgf, Met becomes activated through autophosphorylation of specific tyrosine residues
located within its intracellular domain (189, 190). Subsequently, the receptor can promote
activation of downstream targets (191-193). Moreover, it has been demonstrated that Met can be
activated through an Hgf-independent manner by interacting with the fibronectin receptor, α5β1
integrin (194). Met activates a number of downstream pathways, including the focal adhesion
and Akt signaling pathway to regulate various cellular processes (195, 196). Since Met has been
implicated in the etiology of various malignancies, small molecule inhibitors against this
receptor have been designed as therapeutic strategies for cancer treatment (197, 198).
17
4.3.2 MTOR Signaling Pathway
The mammalian target of rapamycin (mTOR) was first identified in 1994 (199). Today, this
protein is highly investigated in different physiological and pathological states due to its
pleiotropic functions (200, 201). mTOR is a serine-threonine kinase and member of the phospho-
inositide 3-kinase-related kinase family (202). This protein can participate in the formation of
two distinct complexes: 1) mTOR complex 1 and 2) mTOR complex 2. Both complexes consist
of mTOR, mammalian lethal with Sec13 protein 8 and DEP-domain-containing mTOR-
interacting protein (202). mTORC complex 1 is known to promote cell growth and proliferation,
whereas the processes regulated by mTOR complex 2 are poorly understood (202). However, the
mTOR complex 2 has been previously shown to phosphorylate Akt at Ser473 (203, 204).
Activation of Akt is dependent upon phosphorylation at Ser473 and Thr308 (205, 206).
Downstream targets and effectors of phosphorylated Akt (p-Akt) include p27, endothelial nitric
oxide synthase and the BCL2-associated agonist of cell death (207-209). Phosphorylation of Akt
(Ser473) may be associated with tumor progression since it has potential prognostic significance
in different cancers (210, 211). Activation of Akt promotes a cascade of molecular changes, from
modulation of mRNA translation to promotion of cell survival (212, 213).
mTOR and its signaling pathway have a significant role in carcinogenesis. Therefore, several
drugs have been developed to inhibit this protein, such as temsirolimus (CCI-779) and
everolimus (RAD001) (214). These small molecules are derivatives of rapamycin and able to
form an mTOR-inhibiting complex with the FK506 binding protein 12 (215, 216). Two
multicentre studies demonstrated the benefit of temsirolimus over interferon-α treatment in
patients with metastatic RCC (217, 218). A phase III clinical trial showed that everolimus has a
therapeutic advantage in patients who have metastatic RCC and are non-responsive to other
targeted treatments used for this cancer type (219). However, blocking mTOR can induce
expression of insulin receptor substrate 1, which can circumvent the inhibitory effects of
rapamycin and activate Akt in breast and prostate cancer cells (220). Tumors treated with
rapamycin-like mTOR inhibitors are at risk of developing resistance (221). Therefore
combination therapy may be necessary in order to target potent downstream effectors of mTOR
signaling.
18
4.3.3 Focal Adhesion and Extracellular Matrix Pathway
The focal adhesion and extracellular matrix (ECM) pathway is a dynamic process that is integral
in the maintenance of cellular physiology. Members of this pathway have been previously shown
to be dysregulated in different cancer types and have a functional consequence. Knockdown of
talin-1 expression in an osteosarcoma cell line led to decreased cell adhesion and migration,
suggesting an oncogenic role for this protein (222). The overexpression of another player in the
focal adhesion and ECM pathway, laminin γ1, may promote cellular invasion and migration in
prostate cancer (223). Moreover, miRNAs have been shown to regulate this central pathway
within various cancers (224-226). miR-199a-3p has been demonstrated to target caveolin 2
(CAV2) in endothelial and breast cancer cells (227). Furthermore, mTOR and Met have been
shown to intersect with members of the focal adhesion and ECM pathway. Met can activate focal
adhesion kinase by phosphorylating tyrosine 194 and causing conformational changes (228).
Moreover, resistance to mTOR inhibitors has been associated with a change in the integrin
cellular localization and expression profile (229). Therefore, this pathway has important
consequences on cancer as it can regulate cellular phenotypes associated with tumorigenesis and
regulate other biological pathways that are frequently targeted by drug inhibitors.
4.3.3.1 Integrins
Integrins facilitate coordination between the ECM and intracellular cytoskeleton and cell-cell
adhesion (230). Integrins accomplish these roles by assembling into heterodimers, which consist
of α and β subunits, and thereby can act as receptors that may bind to components of the ECM or
counter-receptors on adjacent cells (231). These adhesion molecules regulate signal transduction,
which influence various cellular processes, including cell motility and survival (232, 233). From
25 ccRCC with matched normal, Sültmann et al. (2005) reported that ITGB8 has higher
expression in cancer tissue (234). However, the role of aberrant ITGB8 expression in RCC is yet
to be elucidated. Furthermore, ITGB8 can be post-transcriptionally regulated by miRNAs. This
gene has been shown to be a target of miR-93 by luciferase reporter assay in U343 cells (235).
4.3.3.2 Caveolins
Caveolins are the core proteins found in the membranes of caveolae (235, 236). Caveolae are
important in that they are able to mediate endocytosis and transcytosis of macromolecules (237).
19
There are a total of three caveolins currently reported in the literature: 1) caveolin 1, 2) CAV2
and 3) caveolin 3 (237). Caveolin 1 has been investigated in various cancers (238-240). Unlike
caveolin 1, there is little known with regards to the role of CAV2 in carcinogenesis. However, a
research group reported that modulating the expression level of CAV2 with no change in caveolin
1 expression resulted in different effects on cell proliferation depending on the cancerous cell
line (241). In addition, it was also recently shown that knock-down of Cav2 expression by small
interfering RNA in A498 and 786-O cells led to significantly reduced proliferation, wound
healing and invasion compared to negative controls (242).
5 Rationale A single miRNA can regulate the expression of hundreds of genes (243). Lim et al. (2005)
transfected miR-124, which is highly expressed in the brain, into the HeLa cancer cell line and
microarray analysis was performed on RNA isolated from these cells (244). It was determined
that miR-124 transfection led the HeLa cells to take on a gene expression profile similar to that
of the brain (244). This phenomenon was not restricted to miR-124, but was also observed when
miR-1, which is a hallmark for heart and skeletal muscle, was transfected into HeLa cells and led
to cells expressing a muscle-like gene profile (244). In addition, a miRNA and its gene targets
can be enriched in a single biological pathway. Investigators transfected let-7b in two different
cancer cell lines and observed a dysregulation in the expression of genes involved in the cell
cycling pathway (245). Our research group has previously demonstrated that various miRNAs
are dysregulated in RCC and have a role in its initiation and progression (246-248). With this
knowledge, there is strong incentive for identification of a dysregulated biological pathway
enriched with aberrantly expressed miRNAs and their predicted gene targets in a poorly
understood RCC subtype. An understanding about the underlining biological mechanisms
driving a kidney cancer can promote the use or design of effective targeted therapies.
In addition to elucidating cancer-promoting pathways, miRNAs can also serve as strong
candidates for biomarker discovery in RCC subtyping. Gottardo et al. (2007) provided the first
evidence that this class of non-coding RNA molecules can differentiate RCC from normal
kidney tissue (249). From this early study, an interest in distinguishing RCC subtypes from one
another based on differential miRNA expression arose within the research community (116-118,
249). The popularity of miRNAs as diagnostic markers for the different kidney cancer subtypes
20
may be due to their tissue-specificity and stability (129, 250). For instance, miRNAs can be
reliably extracted from FFPE tissues (251). Furthermore, small amount of RNA in the picogram
range are sufficient to precisely quantify miRNA expression by qRT-PCR (252). Therefore,
miRNA isolation can be performed on small tissue samples, such as fine-needle aspirates (253).
In summary, miRNAs hold great promise in serving as biomarkers for distinguishing ccRCC,
pRCC, chRCC and oncocytoma.
6 Hypothesis Aberrantly expressed miRNAs and their predicted gene targets may elucidate pathways involved
in the pathogenesis of pRCC type 1. In addition, a small number of miRNAs may distinguish
between the most common RCC subtypes, namely ccRCC, pRCC and chRCC, and oncocytoma
with high accuracy.
7 Objectives
7.1 To investigate a role for dysregulated miRNAs and genes in pRCC type 1
7.2 To validate a classification scheme with a limited number of miRNAs that can differentiate ccRCC, pRCC, chRCC and oncocytoma
21
Chapter 2 The Focal Adhesion and Extracellular Matrix Pathways Are
Dysregulated in Papillary RCC Type 1
1 Introduction pRCC is the second most common RCC subtype and can be further subdivided into type 1 and 2
(159). pRCC type 1 and 2 share several characteristics, but these two entities also have a number
of distinctions at various levels. With respect to morphology, small basophilic cells are a feature
of pRCC type 1 whereas large eosinophilic cells are commonly observed in type 2 (159). The
two pRCC types also exhibit different molecular aberrations and clinical behaviors. Specifically,
pRCC type 1 is characterized by trisomies 7 and 17, and loss of chromosome Y (254).
Overexpression of MET has been previously reported in sporadic pRCC type 1, therefore it may
have an integral role in the pathogenesis of this kidney cancer subtype (148). Currently, there is a
lack in our understanding of the biological pathways involved in the initiation and progression of
pRCC type 1. Addressing this issue may lead to the design and use of specific and effective drug
treatments for pRCC, which are not currently available in the clinic (150).
Short, non-coding RNA molecules, called miRNAs, downregulate the expression of
target genes at the posttranscriptional level. It has been reported that miRNAs may regulate the
expression of more than 50% of mRNAs in mammals (255). Therefore, it is not surprising that
miRNAs have been implicated in many physiological processes. Moreover, we have previously
reported miRNAs to be differentially expressed in RCC and that their aberrant expression plays a
crucial role in cancer initiation and progression (256). To our knowledge, the biological effect of
aberrantly expressed miRNAs has not yet been studied in pRCC type 1.
Members of the focal adhesion and ECM pathways are integral in maintaining normal
cell function. Therefore, dysregulation of these components may lead to adverse consequences,
such as cancer initiation and propagation. The focal adhesion and ECM pathways have been
investigated in various cancer types, such as prostate cancer (257). Aberrant expression of these
components may be reflected in unregulated cell migration, proliferation and cell survival. The
genes MET and MTOR have been linked to both biological pathways. Met can activate the focal
adhesion kinase and mTOR can regulate the expression of various matrix metalloproteinases
(228, 258, 259). Moreover, several members of focal adhesion and ECM, such as secreted
22
phosphoprotein 1 and vascular cell adhesion molecule 1, have been previously reported to be
differentially expressed in pRCC (260, 261). However, whether this pathway is affected in pRCC
type 1 is yet to be elucidated.
In this study, we examined differential miRNA and gene expression in pRCC type 1
compared to normal kidney. Using this data, we performed pathway analysis and the predicted
targets of significantly differentially expressed miRNAs in pRCC type 1 were found to be
enriched in the focal adhesion and ECM pathways. We provide experimental evidence that a
number of the members involved in this biological pathway have dysregulated expression in
pRCC type 1 compared to normal kidney tissue. Furthermore, we validated the downregulation
of miR-199a-3p in pRCC type 1. We show that miR-199a-3p targets members of the focal
adhesion and ECM pathways, including CAV2, ITGB8, MET and MTOR. We finally provide
evidence that miR-199a-3p regulates proliferation in a kidney cancer cell line. To our
knowledge, this is the first study to propose biological mechanisms contributing to the
pathogenesis of pRCC type 1.
2 Materials and Methods
2.1 Patient Sample Collection
A total of 11 fresh frozen pRCC type 1 tissues with matched normal kidney from the cortex were
collected from St. Michael's Hospital and the Ontario Tumor Bank in Toronto, Ontario with
research ethics board approval. A pathologist confirmed that the cancer tissue was pRCC type 1.
In addition, previously published microarray analysis on miRNA expression in pRCC type 1 and
normal kidney was used in this study (262). We also investigated the gene expression profile of
pRCC type 1 and normal kidney tissue using publically available microarray data sets from Gene
Expression Omnibus, specifically GSE7023 and GSE26574.
2.2 Cell Culture and Transient Transfection
In vitro experiments were performed using the 786-O cell line, which was obtained from the
American Type Culture Collection (ATCC, Manassas, VA). 786-O cells, which are derived from
primary ccRCC and are defective for the VHL gene, were used for in vitro experiments because
there is currently no cell line commercially available for pRCC. We did not use ACHN or CAKI-
1 cells because these cell lines are derived from metastatic sites of ccRCC, and pRCC type 1 has
23
a low rate for metastasis. Cells were grown in RPMI media with 10% fetal bovine serum and
stored at 37°C with 5% CO2.
When performing transfection, cells were first seeded in a cell culture plate and incubated
overnight. On the following day, 786-O cells were given fresh media and then transfected with
30 nM of mirVanaTM miR-199a-3p mimic (Applied Biosystems, Foster City, CA) or mirVanaTM
miRNA negative control #1 (Applied Biosystems) using siPORTTM NeoFXTM Transfection Agent
(Invitrogen, Carlsbad, CA) as per the manufacturer's protocol. Mimics were diluted with Opti-
MEM® Reduced Serum Media (Invitrogen). Each transfection experiment was conducted three
times in order to obtain a biological triplicate.
2.3 RNA Extraction
When extracting RNA from 786-O cells, cells were lysed with QIAzol Lysis Reagent (Qiagen,
Mississauga, ON). RNA was recovered from the cell lysates using the miRNeasy Mini Kit
(Qiagen). The 260/280 and 260/230 ratios of the RNA extracts were measured using the
NanoDrop 2000c spectrophotometer (Nanodrop Technologies Inc., Wilmington, DE). RNA was
stored at -80°C until use.
2.4 Quantitative Reverse Transcription PCR (qRT-PCR)
The expression level of miR-199a-3p and RNU44, which served as a reference gene, were
measured by performing qRT-PCR with 5 ng of RNA and TaqMan® miRNA assays (Applied
Biosystems) (252). For the reverse transcription reaction, the following cycling conditions were
used: 16°C for 30 mins, 42°C for 30 mins and 85°C for 5 mins. Subsequently, we measured for
the cDNA expression level of miR-199a-3p by performing real-time PCR with the
StepOnePlusTM System (Applied Biosystems).
For gene quantification, reverse transcription was performed on 500 ng of extracted RNA using
the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems). The following
cycling conditions were used: 25°C for 25 mins, 37°C for 120 mins and 85°C for 5 mins. The
resulting cDNA was diluted to 100 ng and quantified by real-time (RT) PCR using the Fast
SYBR Green Master Mix (Applied Biosystems) and primers designed with Primer3 (v. 0.4.0)
software (Table 1). The TATA box binding protein (TBP) and hypoxanthine
24
phosphoribosyltransferase 1 (HPRT1) genes were used as endogenous controls for gene
quantification. The real-time PCR reaction was performed on the ViiaTM 7 Real-Time PCR
System (Applied Biosystems) with these cycling conditions: 25°C for 10 min, 37°C for 120 min
and 85°C for 5 mins.
Each experiment represents a summary of 3 technical replicates. The expression levels of both
miRNAs and genes were quantified by the comparative CT method using the following formula:
25
Table 2.1. List of Forward and Reverse Primer Sequences Used for Measuring mRNA
Expression by Quantitative Reverse Transcription PCR (qRT-PCR)
Gene Primer Direction Sequence (5’ 3’)
Forward GGCTCAACTCGCATCTCAAG CAV2
Reverse TGATTTCAAAGAGGGCATGG
Forward TTCTCCCGTGACTTTCGTCT ITGB8
Reverse CTGTCAAAGACAGCACATGGA
Forward ATACCGTCCTATGGCTGGTC MET
Reverse GTTTGGGCTGGGGTATAACA
Forward TTCCGTTCCATCTCCTGGTC MTOR
Reverse AAGGCCTCATTGACATCTGG
Forward TTGCTGACCTGCTGGATTAC HPRT1
Reverse TCTCCACCAATTACTTTTATCTCC
Forward TTCGGAGAGTTCTGGGATTGTA TBP
Reverse TGGACTGTTCTTCACTCTTGG
2.5 PCR Array
Total RNA was extracted from fresh frozen tissues samples of 5 pRCC type 1 and 5 normal
kidney cases, and was separately pooled. Each pool was subject to reverse transcription using
the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Subsequently, a total
of 50 ng of RNA from either the cancer or normal tissue pool was added to each well of the
TaqMan® Array Human Extracellular Matrix & Adhesion Molecules 96-well plate, Fast
(Applied Biosystems). Real-time PCR was performed on the StepOnePlusTM System (Applied
Biosystems) as per the manufacturer’s instructions.
26
2.6 Protein Extraction and Western Blot Analysis
786-O cells were transfected for 72 hrs with 30 nM of mirVanaTM miR-199a-3p mimic (Applied
Biosystems, Foster City, CA) or mirVanaTM miRNA negative control #1 (Applied Biosystems).
Cells were lysed using the Lysis Buffer 11 (R&D Systems, Minneapolis, MN), which was spiked
with protease inhibitor mixture (Roche, Laval, Quebec) and Phosphatase Inhibitor Cocktail 3
(Sigma-Aldrich, Oakville, Ontario). Cell lysates were subsequently centrifuged at 12x103 x g for
10 mins at 4°C, and the resulting supernatants were collected and stored at -80°C until later use.
Protein concentration was quantified using the Micro BCATM Protein Assay Kit (Thermo
Scientific, Rockford, IL) according to the manufacturer's protocol.
Protein extracts were diluted with sodium dodecyl sulfate buffer containing beta-
mercaptoethanol, and denatured at 95°C for 5.5 mins. To each well of a 10% polyacrylamide gel,
25 µg of total protein was added. After running the gel, proteins were transferred onto a blot.
Following transfer, the blot was blocked with 5% skim milk. The blot was then stained with
Phospho-Akt (Ser473) (D9E) XP® Rabbit mAb (Cell Signaling Technology, Danvers, MA) and
mTOR (7C10) Rabbit mAb (Cell Signaling Technology). Blots were incubated with diluted
antibody overnight at 4°C. Following primary staining, anti-Rabit IgG horseradish peroxidase
conjugate (Promega, Madison, WI) was added to each blot. After 1 hr incubation,
chemiluminescence signal was enhanced using the PierceTM ECL Western Blotting Substrate
(Thermo Scientific). The blots were then stripped of protein using Antibody Stripping Buffer
(Gene Bio-Application L.T.D, Israel) for 20-30 mins. Blots were then stained with anti-β actin
antibody (Cell Signaling Technology), the loading control, and secondary staining was
performed with anti-Mouse IgG horseradish peroxidase conjugate (Promega) for 1 hr. Detection
of the signal was carried out as previously described. Experiments were performed as biological
triplicate.
2.7 Luciferase Reporter Assay
786-O cells were plated on a 96-well plate with a white and opaque bottom. After overnight
incubation, 0.1 µl of DharmaFECT Duo Transfection Reagent (Thermo Scientific) was added to
each well to allow for co-transfection of pMirTarget vector encoding for either the 3’ UTR of
MET or MTOR (Origene, Rockville, MD) and 50 nM of mirVanaTM miR-199a-3p mimic or
27
mirVanaTM miRNA negative control #1 (Applied Biosystems) as indicated in the manufacturer’s
protocol. Cells were transfected for 24 hrs before briteliteTM plus (PerkinElmer, Netherlands) was
added to measure resulting luminescence signal. A total of 3 biological and 3 technical replicates
were performed for each experiment.
2.8 Proliferation Assay
786-O cells were seeded on a 96-well plate and after overnight incubation, transfected with 30
nM of mirVanaTM miR-199a-3p mimic or mirVanaTM miRNA negative control #1 (Applied
Biosystems) using siPORTTM NeoFXTM Transfection Agent (Applied Biosystems). Cells were
incubated at 37°C in 5% CO2 for 24 hrs. Subsequently, Cell Proliferation Reagent WST-1
(Roche, Germany) was added to each cell culture well. After 30 minutes, absorbance from the
formazan product was measured at 440 nm. Each experiment represents a summary of 3
biological and 3 technical replicates.
2.9 Bioinformatic Analysis
We analyzed miRNA microarray data in pRCC type 1 and normal kidney by performing
hierarchical clustering using SparseHierarchicalClustering from GenePattern
(http://www.broadinstitute.org/cancer/software/genepattern/) (263). In addition, we conducted
supervised clustering with another algorithm from GenePattern called ClassNeighbors (264). We
obtained a list of statistically significantly (p < 0.05) up- or down-regulated genes in pRCC type
1 using GeneSpring software. Furthermore, we performed miRNA target prediction analysis
using the following softwares: PicTar (http://pictar.mdc-berlin.de/) and TargetScan
(http://www.targetscan.org/vert_50/). We also looked at the enrichment of predicted gene targets
for dysregulated miRNAs in pRCC type 1 in biological pathways using DIANA - mirPath
software (http://diana.cslab.ece.ntua.gr/pathways/). We also used the Database for Annotation,
Visualization and Integrated Discovery (DAVID) to investigate which pathways show
enrichment for the differentially expressed genes in pRCC type 1 (http://david.abcc.ncifcrf.gov/).
We also conducted Gene Ontology analysis to investigate pathways enriched with up- or down-
regulated genes in pRCC type 1 (http://www.pantherdb.org/).
28
3 Results
3.1 Genes and miRNAs Are Dysregulated in pRCC Type 1 and Are Enriched in the Focal Adhesion and ECM Pathways
Using publically available microarray data for pRCC type 1 and normal kidney tissue,
unsupervised clustering was performed and separation between the two groups based on
differential gene expression was observed (Figure 2.1). In addition, a similar distinction was
drawn between pRCC type 1 and normal kidney tissue using previously published miRNA
microarray data (Data not shown). Supervised clustering was conducted to determine
significantly dysregulated genes and miRNAs in pRCC type 1 and normal kidney tissue. Table
2.2 provides a list of the top most significantly dysregulated miRNAs in pRCC type 1 (P < 0.05).
We validated our results to an independent data set (GSE41282). With both lists of significantly
differentially expressed miRNAs and genes in pRCC type 1, pathway analysis was performed. A
common biological pathway observed using significantly dysregulated genes and predicted
targets of differentially expressed miRNAs was the focal adhesion pathway. Figure 2.2 depicts
the predicted interactions between the top 20 up- or downregulated miRNAs in pRCC type 1
listed in Table 2.2 with members of the focal adhesion pathway. Additionally, pathway analysis
was performed for significantly dysregulated genes in pRCC type 1 and different biological
pathways were identified (Table 2.3). Moreover, we show that a member of each pathway has
been previously reported to be dysregulated in pRCC type 1. In addition, these biological
pathways have been investigated in other cancer types. Furthermore, we performed Gene
Ontology analysis using genes that have significantly increased or decreased expression in pRCC
type 1 compared to normal kidney (Figure 2.3).
To validate the in silico findings, the mRNA expression level of 92 different members of the
focal adhesion and ECM pathways was measured in pRCC type 1 and normal kidney tissue
samples by qRT-PCR. Table 2.4 shows a partial list of the most significantly down- or
upregulated genes in pRCC type 1 relative to normal kidney.
Additionally, the mRNA expression levels of other genes involved in the focal adhesion and
ECM pathways were also measured by qRT-PCR. It was determined that CAV2, ITGB8 and
MET have decreased mRNA expression in pRCC type 1 relative to normal kidney tissue (Figure
29
2.4). The mRNA expression of MTOR was not decreased in our patient cohort (Data not
shown).
30
A) B)
Figure 2.1. Hierarchical Clustering of Gene Microarray Data from pRCC Type 1 and
Normal Kidney Tissues. Gene expression data for pRCC type 1 and normal kidney tissues was
collected from publically available datasets. Unsupervised clustering was performed with
SparseHierarchicalClustering from GenePattern, which used the 5000 genes with the highest
variance. A) GSE26574 and B) GSE7023. The pRCC type 1 and normal kidney samples are
shown in the columns, and genes are listed in the rows. Red is representative for overexpression,
whereas blue is indicative of underexpression.
31
Figure 2.2. Dysregulated miRNAs in pRCC Type 1 Are Predicted to Target Genes in the
Focal Adhesion Pathway. Using microarray expression data, a list of the top 20 significantly
up- or down-regulated miRNAs in pRCC type 1 was generated using ClassNeighbors. The
potential interaction between the significantly dysregulated miRNAs and their target genes
within the focal adhesion pathway was determined using the TargetScan miRNA prediction
software. The red boxes indicate predicted gene targets of the dysregulated miRNAs. miR-199a-
3p is highlighted.
32
Table 2.2. A Partial List of the Most Significantly Up and Down-Regulated miRNAs in
pRCC Type 1
Up-regulated Score Down-regulated Score
hsa-miR-197 5.993 hsa-miR-200c 12.129
hsa-miR-23b* 5.368 hsa-miR-199a-5p 9.233
hsa-miR-380 4.576 hsa-miR-199a-3p 9.147
hsa-miR-1229 4.477 hsa-miR-139-5p 8.521
hsa-miR-548l 4.231 hsa-miR-100 8.312
hsa-miR-92b 4.003 hsa-miR-652 7.748
hsa-miR-367* 3.974 hsa-miR-660 7.473
hsa-miR-346 3.941 hsa-miR-502-3p 7.349
hsa-miR-411* 3.726 hsa-miR-532-5p 7.252
hsa-miR-519b-3p 3.683 hsa-miR-145 7.170
hsa-miR-92a 3.670 hsa-miR-126 6.580
hsa-miR-1197 3.655 hsa-miR-532-3p 6.366
hsa-miR-1260 3.507 hsa-miR-500* 6.337
hsa-miR-299-5p 3.466 hsa-miR-214 6.070
hsa-miR-101* 3.401 hsa-miR-215 6.020
hsa-miR-25 3.391 hsa-miR-363 6.003
hsa-miR-520d-5p 3.336 hsa-miR-378 5.993
33
hsa-miR-28-3p 3.321 hsa-miR-187 5.686
hsa-miR-520f 3.320 hsa-miR-127-3p 5.684
hsa-miR-24-2* 3.316 hsa-miR-501-5p 5.629
34
Table 2.3. A Partial List of the Biological Pathways Enriched with Dysregulated Genes in
pRCC Type 1
Biological Pathway p-value Member of Pathway
Dysregulated in PRCC Type
1
Pathway Reported in
Other Cancer Type
Breast cancer (265) Leukocyte transendothelial migration 0.0030
CLDN7 (169)
Melanoma (266)
Breast cancer (267) Complement and coagulation cascades 0.0067
FHL1 (169)
Thyroid cancer (268)
Ovarian cancer (269) Cell adhesion molecules 0.0074
CLDN7 (169)
Endometrial cancer (270)
Ovarian cancer (272) Focal adhesion 0.0348
MET (271)
Prostate cancer (257)
35
Figure 2.3. Gene Ontology Pie Charts. A) The significantly up-regulated genes in pRCC type 1
(p < 0.05) compared to normal kidney tissue were identified using publically available datasets
GSE7023 and GSE26574. Using gene list analysis software, the products of the over-expressed
genes in pRCC type 1 were mapped to different biological processes defined by Gene Ontology.
B) Using GSE7023 and GSE26574, genes that were significantly down-regulated in pRCC type
1 (p < 0.05) relative to normal kidney from both datasets were identified. These genes were then
correlated to biological processes designated by Gene Ontology.
36
Table 2.4. A Partial List of Up- or Down-Regulated Members of the Focal Adhesion and
ECM Pathways in pRCC Type 1 Relative to Normal Kidney Tissue
Gene Symbol Unigene Gene Name Fold Change
CD44 Hs.153304 CD44 molecule 9.5029
CLEC3B Hs.162844 C-type lectin domain family 3, member B -13.4228
CNTN1 Hs.355024 Contactin 1 -13.2626
COL11A1 Hs.266273 Collagen, type XI, alpha 1 38.0566
COL1A1 Hs.164004 Collagen, type 1, alpha 1 19.1859
FN1 Hs.1549976 Fibronectin 1 9.5115
ITGA8 Hs.233321 Integrin alpha 8 -13.5501
ITGAM Hs.355885 Integrin alpha M 9.4734
ITGB2 Hs.164957 Integrin beta 2 9.3786
KAL1 Hs.608006 Kallmann syndrome 1 sequence 9.4786
LAMA3 Hs.165042 Laminin, alpha 3 9.3994
MMP12 Hs.899662 Matrix metalloproteinase 12 19.1859
MMP2 Hs.1548727 Matrix metalloproteinase 2 9.5293
MMP3 Hs.968305 Matrix metalloproteinase 3 174.9229
MMP8 Hs.1029057 Matrix metalloproteinase 8 37.5978
MMP9 Hs.957555 Matrix metalloproteinase 9 76.8927
SPP1 Hs.959010 Secreted phosphoprotein 1 9.5056
37
THBS2 Hs.1568063 Thrombospondin 2 9.3423
TIMP1 Hs.99999139 TIMP metalloproteinase inhibitor 9.436
VCAM1 Hs.103372 Vascular cell adhesion molecule 1 9.0579
VCAN Hs.171642 Versican 37.6786
Figure 2.4. mRNA Expression of CAV2, ITGB8 and MET Is Higher in pRCC Type 1
Relative to Normal Kidney Tissue. The expression levels of CAV2, ITGB8 and MET were
measured from RNA extracted from 11 fresh frozen pRCC type 1 tissues with matched normal
kidney tissue by qRT-PCR. The reference gene used was TBP. The statistical significance of the
results for CAV2 and ITGB8 was determined using the unpaired t-test with Welch's correction,
and the Mann-Whitney test was used for MET. *p < 0.05, ****p < 0.0001.
One of the top miRNAs that had decreased expression in pRCC type 1 compared to normal
kidney tissue was miR-199a-3p. The decreased expression of miR-199a-3p was validated in
fresh frozen samples of pRCC type 1 tissue by qRT-PCR (Figure 2.5).
38
Figure 2.5. miR-199a-3p Expression Is Down-Regulated in PRCC Type 1 Compared to
Normal Kidney Tissue. A) Amplification plot of miR-199a-3p and RNU44 expression in a
pRCC type 1 and normal kidney tissue sample. B) Using 11 fresh frozen pRCC type 1 tissues
with matched normal kidney tissue, miR-199a-3p was relatively quantified by qRT-PCR. The
endogenous control that was used for normalization was RNU44.
A)
B)
39
3.2 miR-199a-3p Targets Members of the Focal Adhesion and ECM Pathways
Pathway analysis revealed that the predicted gene targets of miR-199a-3p are enriched in many
different biological pathways, in particular the focal adhesion and ECM-receptor interactions
pathway (Table 2.5). In vitro experiments were performed to validate the ability of this miRNA
to regulate the expression of genes and proteins associated with the focal adhesion and ECM
pathways. Cells that were transfected with a mimic for miR-199a-3p had a significantly lower
mRNA expression level for CAV2 (p = 0.0321), ITGB8 (p = 0.0168) and MET (p = 0.0282)
compared to cells transfected with a non-targeting miRNA mimic control (Figure 2.6). At the
protein level, mTOR and phosphorylated Akt had higher expression level in the negative control
relative to 786-O cells transfected with the miR-199a-3p mimic (Figure 2.7). However, the
differences in MTOR and phosphorylated Akt expression between the experimental and negative
control groups did not reach statistical significance. Furthermore, luciferase reporter assay was
conducted to evaluate the interaction between miR-199a-3p and the 3' UTR of both MET and
MTOR. 786-O cells co-transfected with the miR-199a-3p mimic and plasmid encoding for the 3'
UTR of MET or MTOR had a significantly lower luminescence signal than the negative
experimental control (Figure 2.8).
40
Table 2.5. Pathways Significantly Enriched for Predicted Gene Targets of miR-199a-3p
Biological Pathway P-value
ECM-receptor interaction <0.001
Regulation of actin cytoskeleton 0.001
Focal adhesion 0.002
ErbB signaling pathway 0.004
Glioma 0.004
mTOR signaling pathway 0.008
MAPK signaling pathway 0.008
Melanoma 0.010
Chronic myeloid leukemia 0.014
Small cell lung cancer 0.029
Prostate cancer 0.036
41
Figure 2.6. miR-199a-3p Down-Regulates mRNA Expression of CAV2, ITGB8, MET and
MTOR. 786-O cells were transfected with a mimic for miR-199a-3p or non-targeting miRNA
mimic for 24 hours. Total RNA, including miRNAs, was isolated from cell lysates. Reverse
transcription using random primers was performed, followed by cDNA amplification with
specific primers against candidate target genes. Relative quantification was performed using the
Comparative CT method with HPRT1 as the reference gene. The experiment was carried out in
triplicate. Statistical significance was measured using the t-test with Welch's correction. *p <
0.05, **p < 0.01. A) CAV2, B) ITGB8, C) MET and D) MTOR.
A) B)
C) D)
42
Figure 2.7. miR-199a-3p May Target Protein Expression Levels of mTOR and p-Akt. 786-
O cells were transfected with a miR-199a-3p and non-targeting miRNA mimic by forward
transfection for 72 hrs. Cells were subsequently lysed on ice and extracted lysates were
centrifuged at 4°C for 10 mins at 12,000 g. The supernatant was isolated and stored at -80°C
until use. Total protein was run on a 10% separating gel, followed by transfer onto a blot.
Proteins of interest were stained with antibodies against mTOR and p-Akt. Actin was used as the
loading control. Experiments were performed in triplicate. Western blot images of A) mTOR and
B) p-Akt. C) Relative expression of mTOR and p-Akt in 786-O transfected with miR-199a-3p
compared to cells treated with non-targeting miRNA mimic. Results did not reach statistical
significance.
A) B)
C)
43
Figure 2.8. miR-199a-3p Interacts with the 3' UTR of MET and MTOR. 786-O cells were co-
transfected with pMirTarget vector encoding the 3' UTR of MET or MTOR and a mimic for miR-
199a-3p or non-targeting miRNA control for 24 hrs. Following transfection, cells were lysed and
treated with luciferase substrate to allow generation of light via the luciferase enzymatic reaction.
After 5 minutes of incubation, luminescence was quantified for both cells treated with the miR-
199a-3p or non-targeting miRNA mimic using a multi-well spectrophotometer. The statistical
significance between the experimental and negative control results was measured by the Mann-
Whitney test. **p <0.01, ***p < 0.001.
44
3.3 miR-199a-3p Has a Negative Effect on Cellular Proliferation
To investigate for a biological impact of miR-199a-3p downregulation in pRCC type 1, 786-O
cells were transfected with a mimic for miR-199a-3p to determine its effect on cellular
proliferation. Cells transfected with a non-targeting miRNA mimic had significantly greater
absorbance for formazan than cells treated with miR-199a-3p mimic (p = 0.013) (Figure 2.9),
indicating a higher number of viable cells from the negative control group after 24 hrs of
transfection.
Figure 2.9. A Role for miR-199a-3p in PRCC Type 1. 786-O cells were seeded in a white,
opaque 96-well plate and transfected with a miR-199a-3p or non-targeting miRNA mimic for 24
hrs. After transfection, cells were treated with a tetrazolium salt for 0.5 hrs before measuring for
absorbance at 440 nm with a multi-well spectrophotometer. The statistical significance between
the differences in formazan absorbance from cells treated with miR-199a-3p or non-targeting
miRNA mimic was quantified using the Mann-Whitney test. *p < 0.05.
45
4 Discussion pRCC type 1 is a poorly understood kidney cancer, for which there is currently no effective
targeted therapy. In this study, we provide evidence that genes are differentially expressed in
pRCC type 1 compared to normal kidney tissue (Figure 2.1). In addition, miRNAs are also
aberrantly expressed in pRCC type 1, including miR-199a-3p (Table 2.2). Down-regulation of
miR-199a-3p expression in this RCC subtype was validated by qRT-PCR (Figure 2.5). Our lab
previously showed that miR-199a-3p is also downregulated in ccRCC (273). Moreover, the focal
adhesion pathway was significantly associated with both the dysregulated miRNAs and genes
identified in pRCC type 1, as shown in Figure 2.2 and Table 2.3, respectively.
The aberrant expression levels of different components of the focal adhesion and ECM pathway
were confirmed by performing a PCR array (Table 2.4). Two of the genes, secreted
phosphoprotein 1 and vascular cell adhesion molecule 1, have been previously reported to be
upregulated in pRCC, which is in agreement with our findings (260, 261). Moreover, trisomy of
chromosome 17 is characteristic of pRCC type 1. We showed that COL1A1, ITGB3 and ITGB4,
which are encoded on chromosome 17, have increased expression in pRCC type 1 compared to
normal kidney tissue. Resistance to mTOR inhibitors has been linked to aberrant integrin
regulation (229). Therefore, dysregulation of integrins in pRCC type 1 may play a role in the
dismal responses of patients to rapamycin-like mTOR inhibitor treatment. Moreover, we provide
evidence that matrix metalloproteinases may be aberrantly expressed in pRCC type 1. We also
measured the mRNA expression of additional members of the focal adhesion and ECM
pathways. CAV2 and MET were significantly upregulated in pRCC type 1 compared to normal
kidney (p < 0.05), whereas upregulation of ITGB8 did not reach statistical significance (Figure
2.4).
Our research group has previously identified miR-199a-3p to be downregulated in RCC (273).
Dysregulation of miR-199a-3p in this cancer has been corroborated by an independent group of
researchers (274). The predicted gene targets of miR-199a-3p are significantly enriched in the
ECM-receptor interaction and focal adhesion pathways (p < 0.05) (Table 2.5). In addition, we
provide evidence that miR-199a-3p regulates the expression of components of the focal adhesion
and ECM pathways. The mRNA expression levels of CAV2, ITGB8, MET and MTOR were
significantly downregulated in 786-O cells transfected with a miR-199a-3p mimic compared to
46
the negative control group (Figure 2.6). miR-199a-3p transfection also led to decreased mTOR
and p-Akt protein expression levels relative to 786-O cells treated with a non-targeting miRNA
mimic (Figure 2.7). Therefore, down-regulation of p-Akt in786-O cells transfected by miR-
199a-3p may be due to suppressed mTOR expression by miR-199a-3p since p-Akt is not a
predicted target of miR-199a-3p. We also show that this miRNA directly regulates the
expression of MET and MTOR using a luciferase reporter assay (Figure 2.8). Furthermore, we
propose that miR-199a-3p may have a tumor suppressive function in pRCC type 1 by inhibiting
cellular proliferation (Figure 2.9). This effect by miR-199a-3p has also been observed in other
kidney cancer cell lines (274). Additionally, miR-199a-3p can inhibit the cellular proliferation of
hepatocellular carcinoma cell lines (275).
The miR-199a stem-loop is encoded on chromosomes 1 and 19 and encodes for both
miR-199a-3p and miR-199a-5p. miR-199a-3p has been previously reported to have decreased
expression in a number of cancer types, including hepatocellular carcinoma and osteosarcoma
(276, 277). The biological impact of the aberrant expression of this miRNA has also been
investigated. For instance, it was demonstrated to have tumor suppressive functions in
hepatocellular carcinoma by inhibiting cell proliferation and cell cycle progression, and
promoting apoptosis (278). In osteosarcoma, miR-199a-3p was shown to also inhibit cell cycle
progression, proliferation, and migration (279). Moreover, miR-199a-3p has been experimentally
validated to target genes, including MET, MTOR, CD44, CAV2 and the Brm subunit of the
SWI/SNF chromatin remodeling complex (280-284). Therefore, the dysregulated expression of
miR-199a-3p may serve as an important event in cancer initiation and progression via the focal
adhesion and ECM pathway.
The clinical benefit of rapamycin-like mTOR inhibitors for metastatic pRCC has been
previously studied. One retrospective study that included 14 patients with metastatic pRCC
reported that 3 of these patients were administered rapamycin-like mTOR inhibitors for more
than one year (285). Therefore, a predictive marker for rapamycin-like mTOR inhibitor treatment
is warranted in advanced pRCC. The interaction between miR-199a-3p and mTOR may
highlight a mechanism by which this therapeutically targetable protein has aberrant expression in
pRCC type 1. We believe it would be warranted to study whether miR-199a-3p has a predictive
value for rapamycin-like mTOR inhibitor treatment in pRCC cases.
47
To our knowledge, we are the first to propose that the focal adhesion and ECM pathways
are dysregulated in pRCC type 1 through analysis of both gene and miRNA expression levels.
More specifically, we provide evidence that miR-199a-3p is downregulated in this RCC subtype
and it regulates members of the focal adhesion and ECM pathways. In addition, we suggest that
miR-199a-3p may contribute to pRCC type 1 by inhibiting cellular proliferation via
downregulation of mTOR. The new information we provide concerning a molecular mechanism
underlining pRCC type 1 may contribute to the design of effective targeted therapy for patients
with this RCC subtype.
5 Conclusions There is differential gene and miRNA expression in pRCC type 1 compared to normal kidney
tissue. Both dysregulated genes and miRNAs were shown to be enriched in the focal adhesion
and ECM pathways by in silico analysis. Many members of the focal adhesion and ECM
pathways, including matrix metalloproteinases, CAV2 and MET, have up- or down-regulated
expression in pRCC type 1 compared to normal kidney tissues. Moreover, miR-199a-3p has
decreased expression in pRCC type 1 relative to normal kidney, and may target members of the
focal adhesion and ECM pathways, including CAV2, ITGB8, MET and MTOR. miR-199a-3p
may serve as a tumor suppressor in pRCC type 1 by inhibiting cellular proliferation and targeting
putative oncogenes, such as MET and MTOR.
6 Future Experiments There are some limitations associated with this study that need to be addressed when conducting
future research on pRCC type 1. For instance, there were a small number of pRCC type 1
samples from patients available for conducting experiments. This RCC subtype is a rare renal
neoplasm; therefore it is warranted that future investigators collaborate with other institutions in
order to collect a high number of pRCC type 1 cases for experimental testing. In addition, we
performed our in vitro experiments in one cell line. It would have been beneficial to use other
cell lines as well to ensure our results are not restricted to the 786-O cell line. Future in vitro
experiments should be conducted on a pRCC cell line generated from a patient sample in order to
gain more accurate insight on the role of miR-199a-3p in pRCC type 1.
48
To confirm the importance of miR-199a-3p in pRCC type 1, there should be an initiative to
investigate mechanisms that downregulate this miRNA in pRCC type 1. Bioinformatic analysis
accompanied by experimental validation should be carried out to answer the question as to
whether chromosomal loss, mutations, and/or methylation decrease miR-199a-3p expression in
this RCC subtype using patient samples. In addition, the transcription factors that regulate the
expression of miR-199a-3p via its promoter should be determined using bioinformatic analysis
and chromatin immunoprecipitation assay. Finally, the expression level of the identified
transcription factors should be quantified in pRCC type 1 samples relative to normal kidney
tissue. Twist-1 and p53 have been previously identified to drive miR-199a-3p expression (286,
287). Moreover, there may be other processes affecting expression of this miRNA, for instance
the unfolded protein response inhibits miR-199a-3p expression in HepG2 cells (288). An
understanding of the mechanisms driving downregulation of miR-199a-3p may elucidate
whether loss of this miRNA is an early or late event in pRCC type 1.
There are future experiments that need to be performed in order to further validate that miR-
199a-3p has a tumor suppressive role in pRCC type 1 via the focal adhesion and ECM pathways.
For instance, it should be further investigated whether miR-199a-3p has an impact on apoptosis
and the cell cycle process in this RCC subtype. Furthermore, commercially available Met and
mTOR inhibitors should be added to cells in culture to compare their negative effect on tumor
cell growth and motility compared to over-expressing miR-199a-3p in the same cell line. In
addition, in vivo experiments need to be conducted in order to gain more insight into the role of
miR-199a-3p on tumor characteristics, such as tumor growth and metastatic potential. This
miRNA should be stably transfected into a cell line, and the cells should then be injected into a
mouse model. Moreover, components of the focal adhesion and ECM pathways, such as the
matrix metalloproteinases, should be knocked-down using small interfering RNA technology to
determine their significance in the pathogenesis of pRCC type 1. Finally, we would like to
examine whether miR-199a-3p is dysregulated in other cancers with papillary architecture in
addition to pRCC type 1, such as papillary thyroid carcinoma, papillary serous ovarian
carcinoma and papillary urothelial carcinoma.
49
Chapter 3 miRNAs Can Subclassify Renal Cell Carcinoma Subtypes with
Accuracy
1 Introduction RCC encompasses a broad spectrum of subtypes, with the most common being ccRCC, pRCC
and chRCC. These RCC subtypes have different survival outcomes and responses to systemic
therapy (55, 156, 289). Therefore, accurate classification of the distinct kidney neoplasm is of
clinical importance. However, studies have shown that there is inter-observer variability amongst
pathologists for subtyping RCC (290). This may be due to the fact that distinct RCC tumors can
share overlapping morphological features. For instance, the eosinophilic variant of chRCC and
renal oncocytoma, a benign neoplasm, exhibit similar cytological elements (77). Moreover, there
is an RCC subtype known as unclassified RCC. This subgroup consists of RCC tumors that do
not conform to one specific subtype. Overall, RCC constitutes an array of distinct tumors that
may be difficult to discriminate from one another
miRNAs are non-coding RNAs, which are dysregulated in a number of different cancer types.
Dr. Yousef’s lab previously identified miRNAs as potential biomarkers for accurate
subclassification of RCC (119). This classification uses the expression of 65 different miRNAs
in order to distinguish normal kidney, ccRCC, pRCC, chRCC and renal oncocytoma (119).
Previous reports have also shown that miRNAs are differentially expressed between the RCC
subtypes (116-118). For this study, we proposed a two-step classification system for
differentiating the most common subtypes, namely ccRCC, pRCC and chRCC, as well as
oncocytoma using a limited panel of miRNAs. These stable molecules may complement
microscopy in order to distinguish RCC cases with inconspicuous features.
2 Material and Methods
2.1 Patient Samples
The discovery set included a total of 40 fresh, frozen primary cancer tissues from 40 patients
with RCC (20 ccRCC, 10 pRCC and 10 chRCC) and 10 patients diagnosed with the benign
kidney neoplasm, oncocytoma. The validation set included FFPE tissues from 30 ccRCC, 28
50
pRCC, 20 chRCC and 12 oncocytoma cases. In addition, 4 unclassified RCC cases were
collected. All specimen were collected from St. Michael's Hospital from Toronto, Ontario with
research ethics board approval.
2.2 RNA Extraction
Sections of the FFPE tissue conforming to the standard morphology of the designated subtype
were used for RNA extraction. Total RNA was extracted from FFPE tissues using the miRNeasy
FFPE Kit (Qiagen, Mississauga, Ontario) as described by the manufacturer's protocol. The
quality of RNA was determined spectrophotometrically by measuring the 260/280 and 260/230
ratios with the NanoDrop 2000c spectrophotometer (Nanodrop Technologies Inc., Wilmington,
DE). RNA samples were stored at -80°C.
2.3 Absolute miRNA Quantification
RNA extracted from FFPE tissues was subject to qRT-PCR using the TaqMan® miRNA assays
(Applied Biosystems, Foster City, CA, USA) (252). A standard curve for each miRNA of
interest was generated using mirVanaTM mimics (Applied Biosystems). Quantification of cDNA
for both the standards and unknowns was conducted on the ViiaTM 7 Real-Time PCR System
(Applied Biosystems) and represent a summary of 3 technical replicates. The unknown absolute
expression level of each miRNA was interpolated from a semi-log curve.
3 Results
3.1 A Classification Scheme for RCC Subtypes Using 6 miRNAs
miRNA microarray data for ccRCC, pRCC, chRCC and oncocytoma was used to generate a
decision tree for tumor classification, as shown in Figure 3.1. From this discovery cohort, we
propose in the first decision to distinguish ccRCC and pRCC from chRCC and oncocytoma
based on miR-221 and -222 expression levels. Subsequently, high miR-126 and low miR-498
expression levels will favour a diagnosis of ccRCC instead of pRCC. Finally, low miR-22 and
high miR-15b will be indicative of chRCC rather than oncocytoma.
51
Figure 3.1. Classification Scheme for RCC Subtypes and Renal Oncocytoma. miRNA
microarray analysis on RNA isolated from fresh frozen tissues of 20 ccRCC, 10 pRCC, 10
chRCC and 10 oncocytoma cases was used to develop a decision-tree model. The classification
scheme is based on the differential expression levels of miR-126, -15b, -22, -221, -222 and -498.
3.2 miR-221 and -222 Can Differentiate ccRCC and pRCC from chRCC and Oncocytoma
The absolute copy number of miR-221 and -222 was quantified for ccRCC, pRCC, chRCC and
oncocytoma using FFPE tissue samples by qRT-PCR. Figure 3.2 shows a representation of a
standard curve generated for miR-221 quantification. In Figures 3.3 and 3.4, there is a
separation observed between ccRCC and pRCC from chRCC and oncocytoma based on the
absolute expression levels of miR-221 and -222, respectively. With respect to miR-221, the
expression level ranged between 16,503 (25th percentile) - 162,011 (75th percentile) copy
numbers in ccRCC and pRCC compared to 374,907 (25th percentile) - 1,617,646 (75th percentile)
in chRCC and oncocytoma. In ccRCC and pRCC, the copy number for miR-222 was between
52
36,209 (25th percentile) - 847,070 (75th percentile), whereas the range was 1,128,772 (25th
percentile) - 7,198,912 (75th percentile) in chRCC and oncocytoma.
Figure 3.2. miR-221 Standard Curve. A commercially available miR-221 mimic was subject
to qRT-PCR using a TaqMan® miRNA assay. Serial dilutions of known concentrations of the
miRNA mimic were amplified using the ViiaTM 7 Real-Time PCR System. The CT values
associated with the expression level of miR-221 in a pRCC and oncocytoma sample are plotted
with the former circled in purple and the latter circled in orange. The experiment was performed
in triplicate.
Papillary RCC
Renal oncocytoma
53
Figure 3.3. Absolute Quantification of miR-221 in ccRCC, pRCC, chRCC and oncocytoma.
miRNAs were extracted from FFPE tissue samples of ccRCC, pRCC, chRCC and oncocytoma.
Absolute quantification of miR-221 was performed by qRT-PCR using TaqMan® miRNA
assays for miR-221. A standard curve was generated using a commercially available mimic for
miR-221. Real-time PCR reactions were performed in triplicate for both the standards and FFPE
tissues.
54
Figure 3.4. Absolute Expression of miR-222 in ccRCC, pRCC, chRCC and oncocytoma.
Total RNA, including microRNAs, were isolated from FFPE tissues of ccRCC, pRCC, chRCC
and oncocytoma. The absolute expression level of miR-222 was determined using TaqMan®
miRNA assays and by performing qRT-PCR. Using a mimic for miR-222, a standard curve was
generated. Experiments were performed in triplicate.
55
3.3 miR-126 Can Distinguish Between ccRCC and pRCC
The expression level of miR-126 was absolutely quantified in ccRCC and pRCC by qRT-PCR.
Majority of the ccRCC cases had much greater expression level for miR-126 compared to the
pRCC samples, as shown in Figure 3.5. The miR-126 copy number ranged from 955,648 (25th
percentile) - 8,163,110 (75th percentile) in the ccRCC cohort. In pRCC, miR-126 absolute
expression level was 81,736 (25th percentile) - 408,136 (75th percentile) copy numbers. The
expression level of miR-498 was low in both ccRCC and pRCC (CT value > 34) (Data not
shown). Therefore, this miRNA could not be reliably quantitated by qRT-PCR, and the decision
was made to exclude this miRNA from the RCC subclassification model.
Figure 3.5. Absolute Quantification of miR-126 in ccRCC and pRCC. The absolute
expression level of miR-126 was measured in FFPE samples of ccRCC and pRCC by qRT-PCR
with TaqMan® miRNA assays. Absolute miR-126 copy number for each sample was
interpolated from a standard curve generated with a commercially available miRNA mimic.
Real-time PCR reactions were performed in triplicate.
56
3.4 There Is Overlap in miR-15b and -22 Expression Between chRCC and Oncocytoma
The expression levels of miR-15b and -22 were quantified in FFPE tissues of chRCC and
oncocytoma. Overall, chRCC had higher miR-15b expression relative to oncocytoma (Figure
3.6). miR-15b expression level in chRCC ranged between 443,078 (25th percentile) - 862,147
(75th percentile) copy numbers, whereas miR-15b expression spanned between 230,965 (25th
percentile) - 514,668 (75th percentile) molecules in oncocytoma. There was significant overlap
in the expression levels of miR-22 between chRCC and oncocytoma, as shown in Figure 3.7.
miR-22 expression ranged between 561,625 (25th percentile) - 865,876 (75th percentile) copy
numbers in chRCC and varied between 367,657 (25th percentile) - 1,733,284 (75th percentile)
copy numbers in oncocytoma.
Figure 3.6. Expression Level of miR-15b in chRCC and Oncocytoma. RNA was
isolated from FFPE tissues of chRCC and oncocytoma, and used for measuring miR-15b
expression by TaqMan® miRNA assays and qRT-PCR. A standard curve for miR-15b
was establised using a mimic for this miRNA. Experiments were performed in triplicate.
57
Figure 3.7. Absolute Expression of miR-22 in chRCC and Renal Oncocytoma. The
expression level of miR-22 was measured in FFPE tissues of both chRCC and oncocytoma by
qRT-PCR using TaqMan® miRNA assays. A standard curve for miR-22 was generated using a
mimic for this miRNA. miR-22 expression was quantified in triplicate for both the standards and
tissue samples.
58
3.5 Expression of miR-221, -222 and -126 in Unclassified RCC
The expression levels of miR-221, -222 and -126, which were included in the classification
scheme for the most comon RCC subtypes and oncocytoma (Figure 3.1), were quantified in 4
cases of unclassified RCC. In Figure 3.8, unclassified RCC sample B had the greatest miR-221
expression compared to the other unclassified RCC cases. In addition, samples A and C had
miR-221 expression levels similar to those observed in chRCC and renal oncocytoma, whereas
miR-221 expression in sample D more closely overlapped with that of ccRCC and pRCC cases.
From Figure 3.9, unclassified RCC sample B had the highest level of miR-222 expression.
Samples A and D had overlapping expression levels of miR-222 observed in ccRCC and pRCC,
and miR-222 expression from sample C was more similar to chRCC and oncocytoma.
Furthermore, sample C had the greatest miR-222 expression from the other unclassified RCC
cases. As shown in Figure 2.10, all unclassified RCC samples had similar miR-126 expression
levels as those seen in the ccRCC cohort.
59
Figure 3.8. Expression Level of miR-221 in ccRCC, pRCC, chRCC, Oncocytoma and
Unclassified RCC. miR-221 was quantified from 4 FFPE tissues of unclassified RCC by qRT-
PCR using TaqMan® miRNA assays. A standard curve was generated for absolute
quantification of miR-221 using a mimic for this miRNA. Experiments were performed in
triplicate.
60
Figure 3.9. Level of miR-222 Expression in ccRCC, pRCC, chRCC, Oncocytoma and
Unclassified RCC. Using 4 FFPE tissues of unclassified RCC, miR-222 expression was
measured by qRT-PCR with TaqMan® miRNA assays. A standard curve was constructed using
known amounts of a miR-222 mimic. Real-time PCR reactions were performed in triplicate.
61
Figure 3.10. Expression Level of miR-126 in Unclassified RCC Samples Compared to
ccRCC and pRCC. Total RNA, including miRNAs, was extracted from 4 unclassified RCC
samples. The expression level of miR-126 was quantified by qRT-PCR using a standard curve
for this miRNA. Real-time PCR reactions were conducted in triplicate.
4 Discussion RCC is a heterogeneous disease encompassing a spectrum of distinct subtypes. The subtypes of
this cancer may be difficult to accurately differentiate from one another due to overlapping
morphologies. Therefore, due to the high tissue specificity and stability of miRNAs, these non-
coding RNA molecules have been proposed to subclassify RCC (142, 291). In this study, we
validated a classification scheme based on the expression levels of 5 different miRNAs (Figure
3.1). From our validation cohort, we observed miR-221 and -222 to distinguish chRCC and
oncocytoma from ccRCC and pRCC (Figure 3.3 and 3.4, respectively). miR-221 and -222 are
62
encoded less than 1 kilobases apart on chromosome X. Both miRNAs have been implicated in
promoting epithelial-mesenchymal transition (291). miR-221 and -222 also have common
targets, such as p27 and B-cell chronic lymphocytic leukemia/lymphoma 2 binding component 3
(292). Moreover, these miRNAs have been previously reported to correspond to RCC tumors
originiating from the convoluted tubule, which is in agreement with our results (118).
We evaluated the expression of miRNAs that can discern between ccRCC and pRCC, and
chRCC and oncocytoma. In Figure 3.5, we demonstrated that the absolute expression level of
miR-126 can differentiate between ccRCC and pRCC. This miRNA is commonly associated
with angiogenesis (293). However, there are emerging findings that suggest an important role for
miR-126 in regulating innate immune function (294-296). Dr. Yousef's lab has previously
reported up-regulation of miR-126 in ccRCC tissue relative to matched normal kidney (248).
Unlike ccRCC, pRCC is a highly hypovascular tumor (109, 248) Thus, it is not surprising that
miR-126 expression is significantly elevated in ccRCC relative to pRCC. In addition, miR-15b
and -22 were candidates for distinguishing chRCC from renal oncocytoma. However, both
miRNAs did not draw a distinct separation between chRCC and this benign renal neoplasm as
shown in Figures 3.6 and 3.7. These two tumors have been shown to share similar molecular
profiles. For instance, chRCC and oncocytoma displayed similar immunostaining for the
receptor tyrosine kinase Ron, which was distinct from the staining pattern observed in the other
RCC subtypes (297). Another research group showed a high level of similarity between chRCC
and renal oncocytoma in the expression of 1,550 different transcripts from microarray analysis
(106). Therefore, measuring the expression levels of multiple miRNAs and other molecular
biomarkers simultaneously may provide accurate classification of chRCC and oncocytoma.
From this analysis, miR-126, -221 and -222 were the most powerful biomarkers for
subclassifying ccRCC, pRCC, chRCC and oncocytoma. Unclassified RCC is a rare RCC subtype
for which we have little understanding in terms of its molecular changes. We quantified miR-
221, -222 and -126 in 4 unclassified RCC subtypes and correlated their expression levels with
those observed in the most common RCC subtypes, as well as oncocytoma. We observed that
unclassified RCC sample B had the greatest miR-221 and -222 expression levels, as shown in
Figures 3.8 and 3.9, respectively, suggesting that this tumor may originate from the distal
tubules. Furthermore, from Figure 3.10, all unclassified RCC cases had similar miR-126
expression levels to those from ccRCC. Therefore, the elevated miR-126 expression observed in
63
unclassified RCC may be a result of high vascularity in the tumors. Therefore, these cases may
receive therapeutic benefit from sorafenib and sunitinib.
In summary, we were able to demonstrate that a small number of miRNAs can subclassify the
most common RCC subtypes and oncocytoma. Interestingly, one of the cases included in our
validation set was initially diagnosed as ccRCC, but upon second evaluation the diagnosis was
changed to chRCC. The miR-222 expression level of this particular case overlapped with that
observed in the ccRCC and pRCC cohort. Therefore, our classification scheme may serve as a
complementary tool to current ones used for subclassifying difficult cases of RCC.
5 Conclusions The expression levels of miR-221 and -222 can distinguish between ccRCC and pRCC from
chRCC and oncocytoma. miR-126 can discern ccRCC from pRCC, which both originate from
the proximal tubules. In addition, there was overlap observed between chRCC and oncocytoma,
which arise from the distal tubules, in their expression levels of miR-15b and -22. However,
miR-15b was a stronger biomarker than miR-22 for discerning chRCC from the benign
neoplasm. To our knowledge, we are the first to measure the expression levels of miR-126, -221
and -222 in unclassified RCC. We provide evidence that this rare RCC subtype may have similar
miRNA expression levels to those observed in the most common RCC subtypes and
oncocytoma. In summary, we demonstrated that a limited panel of miRNAs can sub-classify
ccRCC, pRCC, chRCC and oncocytoma.
6 Future Experiments Our RCC classification scheme needs to be validated using an independent and larger number of
ccRCC, pRCC, chRCC and renal oncocytoma samples. The expression level of our candidate
miRNA biomarkers should also be measured in RCC subtypes that share morphological features
with other subtypes and hybrid tumors. Moreover, future studies should include RCC samples
with sarcomatoid differentiation, which is an independent marker for worse prognosis in RCC, to
verify if classification of the tumor subtype by miRNA expression is affected by sarcomatoid
change (298). Moreover, the clinical application of in situ hybridization against miR-126, -15b, -
22, -221 and -222 should be investigated. In particular, in situ hybridization may shed light on
64
whether pRCC samples with similar miR-126 expression to that of ccRCC cases is due to high
vascularization since miR-126 is a hallmark for angiogenesis (299).
In addition to measuring the level of miRNA expression by in situ hybridization, whether miR-
126, -15b, -22, -221 and -222 can be quantified in blood or urine warrants investigation and if
there is differential expression of these miRNAs between ccRCC, pRCC, chRCC and
oncocytoma. The expression levels of miR-221 and -222 have been previously quantitated in the
plasma of RCC patients (300). Such a biomarker would allow for the RCC subtype to be
diagnosed without a need for surgery, thereby preventing unnecessary complications that may be
a result of biopsy, such as significant blood loss or infection (301).
65
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