UNIVERSITY OF CALGARY Identification of Prolactin-Regulated Factors Contributing to Breast Cancer-Mediated Osteoclastogenesis

UNIVERSITY OF CALGARY
Identification of Prolactin-Regulated Factors Contributing to
Breast Cancer-Mediated Osteoclastogenesis
by
Amanda Nicole Forsyth
A THESIS
SUBMITTED TO THE FACULTY OF GRADUATE STUDIES
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
DEGREE OF MASTER OF SCIENCE
GRADUATE PROGRAM IN BIOLOGICAL SCIENCES
CALGARY, ALBERTA
SEPTEMBER, 2015
© Amanda Nicole Forsyth 2015
Abstract
High serum levels of prolactin increase risk of breast cancer as well as increase
metastasis in breast cancer patients. Bone is a preferential site of breast cancer metastasis
and breast tumors are known to secrete soluble factors that enhance bone breakdown by
stimulating differentiation and maturation of osteoclasts. This project conducted gene and
cytokine arrays to elucidate the mechanism by which prolactin contributes to breast
cancer-mediated osteoclastogenesis. Here we demonstrate that sonic hedgehog is a
prolactin-regulated cytokine in breast cancer cells and is part of the mechanism that
induces osteoclast differentiation. Furthermore, sonic hedgehog is expressed more
frequently in breast invasive carcinoma tissue than normal tissue, supporting its role in
tumor progression and metastasis. This mechanism can lead to the development of novel
therapies to help alleviate osteolysis experienced by breast cancer patients.
ii
Acknowledgements
I extend my deepest appreciation to my supervisor, Dr. Carrie Shemanko, for her
support, extreme patience and infectious enthusiasm towards academic sciences and
cancer research. My experience in her laboratory has provided me the inspiration and
means to pursue a career as a clinician-scientist. To that end, I also thank the Cumming
School of Medicine Leaders in Medicine Program for allowing me to simultaneously
pursue medical school and this graduate degree. I would like to thank my committee
members, Drs. Andre Buret and Frank Jirik, for their encouragement and their ability to
show me aspects of this project that I had not yet thought of. Thank you to the
supervisors and students of the Cobb, Hansen, Habibi and Muench labs for teaching me
different scientific techniques, and for the use of your equipment to conduct them. I thank
Dr. Kyla Flanagan for imparting her knowledge of statistics to me.
Thank you to the members of the Shemanko lab, past and present, who have
provided guidance and made coming into school every day fun. To the graduate students
Anna Urbanska and Ashley Sutherland, thank you for showing me the ropes of the lab
and laying the groundwork for my project. I thank Odul Karayazi-Atici for endlessly
fielding my questions and, more importantly, for being my friend. Thank you to Lin Su
for always helping when needed and teaching me so many techniques. Ying Ying Cong,
thanks for being such a cheerful, friendly presence in the lab. I am indebted to the
undergraduate students who contributed to this project: Heather Gibling, Vanessa Lam,
Jessica Howlett, Sara Mirzai, and Laurel Grant. I thank Heather Gibling in particular for
her humour, friendship, and for putting on a copy-editor’s cap and ridding my thesis of
poor grammar.
A very special thank you is extended to my family and friends. Mom and Dad, thank
you for every step you have taken to ensure that the only thing limiting my success is
myself. Grandma and Grandpa, thank you for checking in on me to make sure I am
keeping up with “thesising”. Thank you to my talented sister Heidi Forsyth, who took
my chicken scratch diagrams and turned them into the beautiful illustrations present in
this thesis. I thank Winston for providing a furry shoulder to bury my head in after a
rough day. Finally, I thank Ryan Greig for loving me at my best and worst. I could not
have chosen a better, more supportive partner.
iii
Table of Contents
Abstract ............................................................................................................................... ii Acknowledgements ............................................................................................................ iii Table of Contents ............................................................................................................... iv List of Figures and Illustrations ....................................................................................... viii List of Symbols, Abbreviations and Nomenclature ............................................................ x Epigraph ............................................................................................................................ xii CHAPTER 1: INTRODUCTION ....................................................................................... 1 1.1 Breast Cancer ............................................................................................................ 1 1.1.1 Breast Cancer Statistics in Canada ................................................................... 1 1.1.2 Breast Cancer Classification............................................................................. 2 1.2 Prolactin .................................................................................................................... 4 1.2.1 Prolactin Signaling and Function ..................................................................... 4 1.2.2 The Role of Prolactin in Mammary Gland Development ................................ 8 1.2.3 The Role of Prolactin in Breast Cancer ............................................................ 8 1.3 Bone ........................................................................................................................ 11 1.3.1 Bone Structure and Function .......................................................................... 11 1.3.2 Bone Remodeling ........................................................................................... 12 1.3.3 Osteoclastogenesis and Bone Resorption ....................................................... 14 1.3.4 Common Factors in Both the Mammary Gland and Bone ............................. 18 1.4 Breast Cancer Metastasis to Bone .......................................................................... 18 1.4.1 The Process of Metastasis .............................................................................. 18 1.4.2 Mechanisms of Breast to Bone Metastasis ..................................................... 19 1.4.3 The ‘Vicious Cycle’ of Breast Cancer Bone Metastases................................ 20 1.5 Hedgehog Signaling ................................................................................................ 24 1.5.1 Hedgehog Signaling ....................................................................................... 24 1.5.2 The Role of Hedgehog Signaling in Vertebrate Development ....................... 25 1.5.3 The Role of Hedgehog Signaling in Cancer ................................................... 27 CHAPTER 2: HYPOTHESIS ........................................................................................... 29 2.1 Project Aims ........................................................................................................... 29 CHAPTER 3: MATERIALS AND METHODS .............................................................. 31 3.1 Cell Lines ................................................................................................................ 31 3.1.1 Culture and Maintenance of Breast Cancer Cell Lines .................................. 31 3.1.2 Culture and Maintenance of Osteoclast Precursors ........................................ 32 3.2 Prolactin-Induced Breast Cancer-Mediated Osteoclastogenesis ............................ 32 3.2.1 Preparation of Breast Cancer Cell Condition Medium................................... 32 3.2.1.1 Preparation of Serum Free or Reduced Serum Conditioned Medium .. 33 3.2.1.2 Preparation of Conditioned Media Using a Prolactin Dose Response . 33 3.2.2 Conditioned Media-Induced Differentiation of RAW264.7 Pre-Osteoclasts. 33 3.2.3 Hedgehog Signaling Inhibition in Osteoclast Differentiation ........................ 33 3.3 Methods of Quantifying Osteoclastogenesis .......................................................... 34 3.3.1 Tartrate Resistant Acid Phosphatase (TRAP) and Nuclear Staining.............. 34 3.3.2 para-Nitrophenyl Phosphate (pNPP) TRAP Activity Assay .......................... 34 iv
3.3.3 Fluorescent Staining ....................................................................................... 35 3.3.3.1 Plasma Membrane Staining .................................................................. 35 3.3.3.2 Actin Ring Staining .............................................................................. 36 3.4 Gene Array .............................................................................................................. 36 3.5 Cytokine Array ....................................................................................................... 36 3.6 Quantitative Polymerase Chain Reaction ............................................................... 37 3.6.1 RNA Preparation and Extraction .................................................................... 37 3.6.2 Assessment of RNA Quantity and Quality ..................................................... 37 3.6.3 Complimentary DNA Synthesis ..................................................................... 38 3.6.4 Quantitative Polymerase Chain Reaction ....................................................... 38 3.6.5 Endpoint PCR and Visualization of PCR Products ........................................ 39 3.7 Enzyme-Linked Immunosorbent Assays (ELISA) ................................................. 40 3.8 mRNA expression analysis of PRLR and SHH in human breast tissue ................. 40 3.9 Statistical Analysis .................................................................................................. 41 CHAPTER 4: RESULTS: OPTIMIZATION OF OSTEOCLAST
DIFFERENTIATION AND QUANTIFICATION.................................................. 42 4.1 Optimization of Osteoclast Differentiation............................................................. 42 4.1.1 PRL Enhances Breast Cancer-Mediated Osteoclastogenesis ......................... 42 4.1.2 Treatment of SKBR3 with hPRL for 48 hr is Optimal for Induction of
Osteoclastogenesis .......................................................................................... 42 4.1.3 Prolactin Does Not Increase Breast Cancer Cell Proliferation During
Conditioned Media Preparation ...................................................................... 45 4.2 Methods of Quantifying Osteoclastogenesis .......................................................... 47 4.2.1 TRAP Staining................................................................................................ 47 4.2.2 para-Nitrophenyl Phosphate (pNPP) TRAP Activity Assay .......................... 47 4.2.3 Morphological Identification using Fluorescent Microscopy ........................ 52 CHAPTER 5: RESULTS: IDENTIFICATION OF PRL-REGULATED, BREAST
CANCER CELL SECRETED FACTORS .............................................................. 55 5.1 Gene Array .............................................................................................................. 55 5.1.1 Gene Array Sample Preparation and Testing ................................................. 55 5.1.2 Regulation of Gene Transcription by PRL ..................................................... 55 5.1.3 Gene Array Validation ................................................................................... 59 5.1.3.1 Establishing TBP as a Reference Gene................................................. 59 5.1.3.2 Gene Array Validation .......................................................................... 59 5.1.4 Gene Array Candidate Selection and Assessment.......................................... 61 5.1.4.1 Candidate Selection .............................................................................. 61 5.1.4.2 FLRT1 and FSTL4 are Not PRL-Regulated Genes .............................. 65 5.2 Cytokine Array ....................................................................................................... 71 5.2.1 Preparation of Serum Free SKBR3 and MCF7 Conditioned Media
Samples ........................................................................................................... 71 5.2.2 PRL-Regulated, Breast Cancer Cell Secreted Candidate Selection ............... 71 5.2.3 Luminex Cytokine Array Validation .............................................................. 80 5.3 Detection of SHH, IHH and LIGHT in Conditioned Media Samples by ELISA ... 80 5.4 SHH Signaling Inhibition in Osteoclast Differentiation ......................................... 87 5.5 Expression of the PRLR and SHH in Human Breast Tumours .............................. 87 v
CHAPTER 6: DISCUSSION ............................................................................................ 92 6.1 Optimization of Osteoclast Differentiation Assays ................................................ 92 6.1.1 Optimization of Differentiation Conditions ................................................... 92 6.1.2 Osteoclastogenesis Quantification.................................................................. 93 6.2 Identification of PRL-Regulated, Breast Cancer Cell Secreted Factors ................. 95 6.2.1 Establishing TBP as a Reference Gene .......................................................... 95 6.2.2 Characterization of PRL-induced factors ....................................................... 95 6.3 The Role of SHH in PRL-Induced, Breast Cancer-Mediated Osteoclastogenesis 100 6.4 Future Directions .................................................................................................. 103 6.5 Overall Significance ............................................................................................. 105 CHAPTER 7: REFERENCES ........................................................................................ 106 vi
List of Tables
Table 1. Validation Genes and Primers. ........................................................................... 62 Table 2. Summary of gene array validation. ..................................................................... 64 Table 3.1. Summary of candidate genes. .......................................................................... 66 Table 3.2. Candidate genes identified based on fold change and p-value. ....................... 67 Table 3.3. Candidate genes identified based on fold change, p-value and biological
function. .................................................................................................................... 68 Table 3.4. Candidate genes identified based on biological function. ............................... 69 Table 4. Luminex detection of osteoclastogenic breast cancer factors. ............................ 83 vii
List of Figures and Illustrations
Figure 1. PRL Signaling. .................................................................................................... 5 Figure 2. Bone remodeling. .............................................................................................. 13 Figure 3. Osteoclastogenesis. ............................................................................................ 15 Figure 4. The vicious cycle of breast cancer bone metastases. ......................................... 22 Figure 5. Hedgehog signaling. .......................................................................................... 26 Figure 6. Hypothesis of novel mechanism responsible for PRL-induction of
osteoclastogenesis. .................................................................................................... 30 Figure 7. PRL enhances breast cancer-mediated osteoclastogenesis................................ 43 Figure 8. Conditioned media from SKBR3 treated with human or ovine PRL
enhances osteoclastogenesis, with 48 hr PRL treatment optimal for induction........ 44 Figure 9. PRL does not increase breast cancer cell proliferation. .................................... 46 Figure 10. TRAP and hematoxylin staining of osteoclasts. .............................................. 48 Figure 11. Osteoclast phenotypes. .................................................................................... 49 Figure 12. Blind counting of TRAP and hematoxylin stained osteoclasts. ...................... 50 Figure 13. A colorimetric TRAP activity assay potentially detects CM induction of
osteoclastogenesis but not PRL induction. ............................................................... 51 Figure 14. RAW264.7 stained for the plasma membrane and nuclei. .............................. 53 Figure 15. RAW264.7 stained for the actin ring and nuclei. ............................................ 54 Figure 16. Conditioned media from SKBR3 used in gene array enhances
osteoclastogenesis. .................................................................................................... 56 Figure 17. PRL regulated genes. ...................................................................................... 57 Figure 18. PRL regulated genes assessed by GO terms.................................................... 58 Figure 19. Gene expression of TBP is not regulated by PRL. .......................................... 60 Figure 20. Gene array validation. ..................................................................................... 63 Figure 21. FLRT1 and FSTL4 are not PRL regulated. ..................................................... 70 Figure 22. Serum free CM maintains breast cancer cell-mediated osteoclastogenesis
with and without PRL induction. .............................................................................. 72 viii
Figure 23. Serum free CM from SKBR3 and MCF7 prepared for the cytokine array
induces osteoclastogenesis. ....................................................................................... 73 Figure 24. PRL regulation of breast cancer cell secreted factors selected by less
stringent criteria and their associated biological functions. ...................................... 75 Figure 25. PRL regulation of breast cancer cell secreted factors selected by less
stringent criteria and their associated biological functions. ...................................... 76 Figure 26. PRL regulation of breast cancer cell secreted factors selected by stringent
criteria and their associated biological functions. ..................................................... 78 Figure 28. Luminex detection of breast cancer secreted factors. ...................................... 82 Figure 29. SHH is upregulated by PRL in SKBR3 but not MCF7. ................................. 84 Figure 30. SHH is responsive to an hPRL dose response in SKBR3. .............................. 85 Figure 31. SHH is PRL regulated in T47D. ...................................................................... 86 Figure 32. An anti-hedgehog antibody inhibits PRL induced osteoclastogenesis. ........... 88 Figure 33. Expression of the PRLR and SHH in human breast tumors. ........................... 90 Figure 34. PRLR and SHH are co-expressed in human breast tumors. ............................ 91 Figure 35. SHH is a novel PRL-enhanced, breast cancer secreted factor contributing
to osteoclastogenesis. .............................................................................................. 101 ix
List of Symbols, Abbreviations and Nomenclature
Symbol
Atv6v0d2
CTR
CTSK
CXCR4
DHH
E2
ER
GLI
G-CSF
GM-CSF
hPRL
HER2
Hh
Hhat
IGF-1
IHH
IL-1
IL-6
IL-8
IL-11
IP-10
Jak2
M-CSF
MAPK
MCP
MITF
MMP
NF- κB
NFATc1
OPG
oPRL
PAK1
PDGF-BB
PI3K
PIP
pNPP
PR
PRL
Definition
d2 isoform of vacuolar ATPase Vo domain
proton pump
calcitonin receptor
cathepsin K
C-X-C chemokine receptor type 4
desert hedgehog
estrogen
estrogen receptor
glioma associated oncogene
granulocyte colony-stimulating factor
granulocyte macrophage colony-stimulating
factor
human recombinant prolactin
human epidermal growth factor receptor-2
hedgehog
hedgehog acetyltransferase
insulin-like growth factor 1
indian hedgehog
interleukin 1
interleukin 6
interleukin 8
interleukin 11
interferon gamma-induced protein 10
janus kinase 2
macrophage colony-stimulating factor
mitogen activated protein kinase
monocyte chemotactic protein
micro-ophthalmia-associated transcription
factor
matrix metalloproteinase
nuclear factor-κB
nuclear factor of activated T-cells,
cytoplasmic 1
osteoprotegerin
ovine prolactin
p21 activated kinase 1
platelet derived growth factor-BB
phosphatidylinositol 3-kinase
prolactin induced protein
para-nitrophenyl phosphate
progesterone receptor
prolactin
x
PRLR
PTCH1
PTHrP
RANK
RANKL
SCF
SDF-1
SHH
SHH-N
SMO
STAT
SUFU
TBP
TGF-β
TNF-α
TRAF6
TRAP
VEGF
prolactin receptor
patched 1
parathyroid hormone-related peptide
receptor-activator of nuclear factor-κ B
receptor-activator of nuclear factor-κ B ligand
stem cell factor
stromal derived factor/CXCL12
sonic hedgehog
physiologically active SHH N-terminal
fragment
smoothened
signal transducer and activator of transcription
suppressor of fused
TATA-binding protein
transforming growth factor-β
tumor necrosis factor-α
tumor necrosis factor receptor-associated
factor
tartrate resistant acid phosphatase
vascular endothelial growth factor
xi
Epigraph
I love deadlines. I like the whooshing sound they make as they fly by.
- Douglas Adams, Author
xii
CHAPTER 1: INTRODUCTION
1.1 Breast Cancer
1.1.1 Breast Cancer Statistics in Canada
Breast cancer is the most commonly diagnosed cancer among Canadian women. It
accounts for 26% of an estimated 96,400 newly diagnosed cancer cases in women, and
this incidence has remained steady since 2002 (Canadian Cancer Society's Advisory
Committee on Cancer Statistics 2015). Encouragingly, the overall 5-year survival rate for
all stages of breast cancer is 88%. However, this prognosis drastically decreases to 20%
in patients experiencing late stage, metastatic breast cancer (Canadian Cancer Society's
Advisory Committee on Cancer Statistics 2015). It is therefore not the primary tumour
that is the main cause of mortality, but the metastatic disease.
It is estimated that 10-15% of breast cancer patients advance to late stage disease,
which has spread to the lymph nodes or other parts of the body. Most patients will have
detectable metastases within three years of initial diagnosis, though through recurrent
disease metastasis may occur 10 years or more after detection of the primary tumour
(Harris et al. 2012). Lung, liver and bone are the most common sites of breast metastasis.
Bone is often the earliest organ affected, and it is reported that 70-85% of patients with
late stage disease experience bone metastatic lesions (Coleman and Rubens 1987;
Galasko 1986; Singletary et al. 2003). There are limited treatment options for bone
metastases, none of which are curative. The current standard treatment is
bisphosphonates, which is also used to treat other conditions of bone loss such as
osteoporosis. They bind with high affinity to mineralized bone surfaces and inhibit
osteoclastic bone resorption (Mundy 2002). While bisphosphonates provide some
therapeutic relief, they are complicated by the need to take a “drug holiday” due to renal
toxicity and some patients become refractory to the treatment (Rose and Siegel 2010).
More recently a monoclonal antibody against receptor-activator of nuclear factor-κ B
ligand (RANKL), a cytokine important in the differentiation, function and survival of
osteoclasts, has provided greater clinical benefit (Goessl et al. 2012). Three randomized
phase III trials demonstrated significantly delayed time to skeletal related events (for
1
example a bone fracture, or need for bone surgery) and better tolerability of denosumab
than the most commonly used bisphosphonate, zoledronic acid. However, there was no
difference in overall survival and disease progression between treatments (Drooger et al.
2013). The median survival for breast cancer patients with bone metastases is 22-55
months (Ahn et al. 2013; Domchek et al. 2000). An infrequent, but serious, side effect
that is also common to both treatments is osteonecrosis of the jaw (Drooger et al. 2013).
While studies are being conducted to determine if denosumab could act as a preventative
therapy for bone metastases in early disease, it is currently providing no survival benefit
to patients. Furthermore, RANKL-independent pathways of osteoclast differentiation and
survival exist creating a need for research into other therapeutic options for breast cancer
bone metastases.
1.1.2 Breast Cancer Classification
Historically breast cancer classification was based upon histopathological type and
grade, largely being subdivided into in situ (ductal or lobular) and invasive disease
(Vuong et al. 2014). A clinically heterogeneous disease, it is most often histologically
categorized into groups based on traditional biomarkers, such as the estrogen receptor
(ER), progesterone receptors (PR) and human epidermal growth factor receptor-2
(HER2), which provide an indication of prognosis and response to therapeutic options.
Up to 80% of breast cancers are ER+ (Anderson et al. 2002), which may be treated
through antagonism of the ER (tamoxifen) or inhibition of estrogen production
(aromatase inhibitors). It is estimated that 40% of ER+ breast tumours do not
concurrently express the PR (Rakha, Reis-Filho, Ellis 2010), which is normally regulated
by estrogen and is indicative of a functioning ER pathway (Horwitz and McGuire 1978).
ER+/PR- tumours are suggestive of aberrant growth factor signaling and generally have
poorer prognosis and are less responsive to treatment with tamoxifen than ER+/PR+
tumours (Arpino et al. 2005). Overexpression of HER2 is present in 13-20% of invasive
breast cancers (Rakha, Reis-Filho, Ellis 2010). While often interpreted as a poor
prognostic factor, it has also proven to be a clinical success due to effective therapeutic
targeting of HER-2 using the specific anti-HER2 monoclonal antibody, trastuzamab
2
(Herceptin) (Slamon et al. 1987). The smallest subset is triple-negative breast cancers
(TNBC), which lack the ER, PR and HER2 overexpression. TNBCs have more limited
chemotherapeutic options than the other clinical subsets and are of increased incidence in
patients with BRCA1 mutations or of African ancestry (Carey et al. 2006; Foulkes et al.
2003).
A seminal study by Perou et al. (2000) used microarray-based gene expression
profiling to molecularly classify tumours into four intrinsic breast cancer subtypes:
luminal, basal-like, HER2-positive enriched and normal like (Perou et al. 2000). Later
work further subdivided luminal cancers into luminal A and luminal B (Sorlie et al.
2001). The luminal categories are comprised of ER+ tumours and are therefore the most
common. Luminal A tumours are ER+, PR+, HER2- and low-grade (slow growing, welldifferentiated and non-aggressive). Luminal B tumours are higher-grade, may be PR+ or
PR- and HER2+ or HER2-, but have a high Ki-67 score (Cheang et al. 2009). Ki-67, a
nuclear histone protein, is a marker of cell proliferation associated with shorter survival
in breast cancer patients (De Azambuja et al. 2007). Prognosis is generally better for
patients with luminal A breast tumours, as they tend to respond better to therapy and have
higher overall survival rates (Cheang et al. 2009). Basal-like breast tumours, which lack
expression of the ER, PR and HER2 and are often referred to as TNBCs, are also
characterized by expression of cytokeratins C5/6 and CK14, EGFR, c-KIT, FOXC1 and
have a high incidence of p53 mutations (Badve et al. 2011). Basal-like tumours have a
high proliferation index and a study comparing them to luminal A cancers found that
basal tumours are overall larger, of a higher grade and more invasive (Ibrahim et al.
2009). Though some HER2+ cancers may be categorized as luminal B due to coexpression of the ER, HER2+ tumours cluster with a very specific subset of genes (Perou
et al. 2000). A heterogeneous category, the HER2-positive enriched subtype features
multiple subgroups (with mixed stage, histologic grade and ER status) which each have
differing prognoses and may be clinically useful in identifying patients in need of
additional treatment in targeting the HER2 pathway (Staaf et al. 2010). The normal-like
breast cancer subtype clusters with genes associated with adipose tissue and stromal cell
types (Perou et al. 2000), however, it has been suggested that this group represents
3
normal cell contamination as opposed to being a true tumour cell subtype (Vuong et al.
2014).
1.2 Prolactin
1.2.1 Prolactin Signaling and Function
Prolactin (PRL) is a 23 kDa polypeptide hormone primarily secreted by the
lactotrophs of the anterior pituitary gland (Freeman et al. 2000). A member of the longchain cytokine family, PRL shares 23% sequence identity and is similar in structure to
growth hormone and placental lactogen (Gertler and Djiane 2002; Teilum et al. 2005).
While both growth hormone and placental lactogen have the ability to bind multiple
receptors, including the PRL receptor (PRLR), PRL is unique in that it binds only the
PRLR (Gertler and Djiane 2002; Goffin et al. 1996). There are seven described isoforms
of the PRLR: the long isoform (Bazan 1990), intermediate isoform (Kline, Roehrs,
Clevenger 1999), ΔS1 isoform (Kline, Rycyzyn, Clevenger 2002), PRL binding protein
(PRLBP) and the transmembrane-intracellular domain (TM-ICD) (Kline and Clevenger
2001), and short 1a (S1a) and short 1b (S1b) isoforms (Hu, Meng, Dufau 2001). The
intermediate form, ΔS1, S1a and S1b isoforms are produced through alternative splicing
of the long form, while the PRLBP and TM-ICD are results of proteolysis (Hu, Meng,
Dufau 2001; Kline, Rycyzyn, Clevenger 2002; Kline, Roehrs, Clevenger 1999; Kline and
Clevenger 2001). The most functional of the receptor isoforms is the long form, though
the ΔS1 isoform (which contains the full intracellular domain but lacks the extracellular
ligand binding S1 domain) is also capable of signaling to a lesser degree (Broutin et al.
2010).
In the mammary epithelium PRL/PRLR promotes cellular proliferation,
differentiation, survival, migration and invasion through numerous signaling pathways
(Figure 1). Upon ligand binding the PRLR undergoes dimerization resulting in the
association and activation of Janus kinase 2 (Jak2), a molecule constitutively expressed
with the PRLR that is responsible for tyrosine phosphorylation of the PRLR and Jak2
itself (Pezet et al. 1997). There is unclear evidence as to whether Jak2 activation is
required for PRL signaling, however it plays an important role in multiple downstream
4
Figure 1. PRL Signaling. The major downstream signaling pathways include
JAK2/STAT5, Ras/Raf/MAPK, Src, Tec/Vav/Rac, PAK1 and PIK3/AKT. Through these
pathways PRL contributes to breast cancer cell survival, motility, migration, proliferation
and differentiation (reviewed in Clevenger et al. 2003; Radhakrishnan et al. 2012).
5
pathways (Clevenger et al. 2003). Phosphorylation of the PRLR allows binding of
proteins containing SH2 domains, notably signal transducers and activators of
transcription (STATs) 1, 3, and 5 (DaSilva et al. 1996). STAT5 is the most active of the
STATs in PRL signaling, and the Jak2/STAT5 pathway is the best characterized
signaling pathway activated by the PRL/PRLR complex. Once phosphorylated, STAT5
dissociates from the receptor, homo- or heterodimerizes with other STAT molecules and
translocates to the nucleus where it binds to the promoter of target genes (Freeman et al.
2000). Also activated through Jak2 is the Ras-Raf-MAPK pathway via association with
SHC, GRB2 and SOS (Das and Vonderhaar 1996). Furthermore, PRL-induced
phosphorylation of HER2 by Jak2 has been shown to synergistically activate the RasMAPK cascade via interaction with GRB2 (Yamauchi et al. 2000). There is evidence of
direct MAPK phosphorylation of STAT1 and STAT3, though such cross talk with
STAT5 in response to PRL is likely more complex and involves multiple pathways (Gao
and Horseman 1999; Yamashita et al. 1998). Jak2 tyrosine phosphorylation of p21activated kinase 1 (PAK1) facilitates signaling to induce MMP-1 and MMP-3 in a
MAPK-dependent manner, and also allows for formation of a paxillin/GIT1/βPIX/pTyrPAK1 complex that phosphorylates filamin A (Hammer and Diakonova 2015). Other
kinases including phosphatidylinositol 3-kinase (PI3K), Src, and Tec are also involved in
PRL signaling. The PRLR activates the PI3K/AKT pathway via direct association with
PI3K at the p85 regulatory subunit, which leads to recruitment and phosphorylation of
AKT at the plasma membrane (Tokunaga et al. 2008). The PRLR also induces
PI3K/AKT through association with Src, which plays a role in precipitating cross talk
between multiple pathways downstream of the PRL/PRLR complex (al-Sakkaf, Dobson,
Brown 1997). It is additionally involved in activation of the STAT5 and Tec/Vav
pathways, and potentially directly interacts with Jak2 via the tyrosine phosphatase Shp2
(Clevenger et al. 2003; Kline, Moore, Clevenger 2001; Martín-Pérez et al. 2015). For a
complete overview of PRL signaling see Radhakrishnan et al. (2012) (Radhakrishnan et
al. 2012).
PRL secretion from the anterior pituitary is under inhibition by dopamine and is
released by thyrotropin-releasing hormone from the hypothalamus in response to stimuli
6
such as suckling, stress and increased estrogen (Freeman et al. 2000). It is most markedly
upregulated during pregnancy and lactation, with serum concentrations increasing up to
10-fold, where it is responsible for stimulation of breast milk production (Holt and
Hanley 2012). During the post-partum period PRL, and thus also lactation, is maintained
through a neural reflex stimulated by infant suckling; its loss results in cessation of milk
production. PRL also inhibits luteinizing hormone and follicle stimulating hormone by
the anterior pituitary gonadotrophs resulting in a post-partum physiological secondary
amenorrhea (Holt and Hanley 2012).
In addition to reproduction, PRL has over 300 described functions, with some
notable roles in immunomodulation, osmoregulation and the cardiovascular system. In
the immune system, PRL is involved in proliferation and survival of cells of both the
innate and adaptive immune responses, and hyperprolactinemia is a risk factor for the
development of autoimmune disease (Blanco-Favela et al. 2012; Neidhart 1998). The
osmoregulatory function of PRL is related to renal Na+ and K+ excretion, as it has been
shown to activate Na+-K+-adenosine triphosphatase (ATPase) activity in the rat outer
medulla and inhibit Na+-K+-ATPases in rat proximal tubules through the renal
dopaminergic system (Ignacak et al. 2012). PRL, along with growth hormone and
placental lactogen, is angiogenic but interestingly the 16kDa and 14kDa proteolytic
fragments of PRL are inhibitory of angiogenesis (Freeman et al. 2000). Furthering PRL’s
role in the cardiovascular system, high serum levels have been linked to essential
hypertension and there is some evidence that PRL is involved in the pathogenesis of
pregnancy-induced hypertension. Urinary PRL excretion is also a reliable marker for
preeclampsia and its severity (Ignacak et al. 2012). Multiple extra-pituitary sources of
PRL, in which it acts as an autocrine/paracrine factor, have also been described: the
mammary epithelium, decidua, the ovaries, male reproductive system, immune system,
brain, hair follicles and skin, adipose tissue, cochlea, endothelial cells and bone marrow
(Bellone et al. 1997; Marano and Ben-Jonathan 2014).
7
1.2.2 The Role of Prolactin in Mammary Gland Development
Mouse models negative for PRL (PRL-/-) or the PRLR (PRLR-/-) have demonstrated
that PRL is a primary driver of development in the mature virgin mammary gland, and
during pregnancy and lactation (Hennighausen and Robinson 2005; Horseman et al.
1997; Ormandy et al. 1997). Normal pre-pubertal mammary growth is observed in both
PRL and PRLR knockout mice, but post-pubertal ductal side branching is markedly
decreased (Oakes et al. 2008). Interestingly, transplantation of PRLR-/- mammary tissue
to a normal host restores ductal side branching, suggesting an indirect role for PRL
(Brisken et al. 1999). Further studies found PRL contributes by regulating the release of
progesterone, which was then directly responsible for stimulating ductal side branching
(Binart et al. 2000; Ormandy et al. 2003). During pregnancy and lactation, PRL is
responsible for alveolar morphogenesis and secretory activation. The mammary
epithelium of PRLR-/- -mice fails to undergo proliferation and differentiation during
alveolar development, resulting in failed milk secretion (Brisken et al. 1999). Studies
using PRL-deficient mice have found autocrine PRL to be critical for inducing mammary
epithelial cell proliferation responsible for alveolar morphogenesis (Naylor et al. 2003;
Vomachka et al. 2000). Furthermore, PRL is also thought to play an indirect role by
contributing to a transcriptional regulatory network controlling spatial and temporal
development cues (Oakes et al. 2008).
1.2.3 The Role of Prolactin in Breast Cancer
In addition to the normal mammary epithelium, PRL is produced by the majority of
breast tumours (Ginsburg and Vonderhaar 1995; Reynolds et al. 1997). This autocrine
expression has been correlated with proliferation, differentiation and survival of tumour
cells, accelerated tumour growth, and poor survival outcomes (Liby et al. 2003;
Muthuswamy 2012; Wu et al. 2011). PRLR mRNA is also upregulated in multiple human
breast cancer cells lines, as well as in tumour samples when compared to contiguous
tissue (Peirce, Chen, Chen 2001; Touraine et al. 1998). A proliferative effect is observed
in breast cancer cells in response to PRL, in part due to activation of HER2 via
Jak2/MAPK (Yamauchi et al. 2000). PRL activation of the Jak2/STAT5 pathway induces
8
cancer cell proliferation and differentiation (Miyoshi et al. 2001). A recently identified
alternative STAT5a splice variant, expressed at higher ratios in invasive ductal
carcinoma, also increases proliferation and is thought to be a contributor to the
pathogenesis of ductal carcinoma (Tan et al. 2014). PRL enhances survival of breast
cancer cells though STAT5 upregulation of carboxypeptidase-D, and by signaling
through the PI3K/AKT pathway (Hutchinson et al. 2001; Koirala, Thomas, Too 2014).
Furthering PRL-mediated tumour cell proliferation and survival is PRL-induced protein
(PIP), which is expressed in the majority of breast cancers and promotes cell cycle
progression. PIP has also been shown to aid invasion of breast cancer cell invasion
through degradation of fibronectin (Naderi 2015). Aiding in cell motility and tumour
invasion is PAK1, which is activated by the PRL/Jak2 complex (Hammer and Diakonova
2015). PRLR interaction with Tec/Vav induces GDP/GTP exchange on Rac which also
results in increased cell motility (Kline, Moore, Clevenger 2001; Maus, Reilly, Clevenger
1999). Promisingly, PRLR antagonists have been shown to disrupt these signaling events
in breast cancer cells, thereby preventing PRL induced proliferation, differentiation and
survival (Llovera et al. 2000). A PRLR antagonist was also able to inhibit tumour growth
in ovarian cancer models by promoting autophagy-induced cell death (Wen et al. 2014).
High serum levels of PRL are associated with increased risk of breast cancer as well
as increased metastasis in breast cancer patients. Epidemiological studies show a positive
association between high plasma PRL and hormone receptor-positive breast cancer risk
in pre- and postmenopausal women (Tworoger et al. 2004; Tworoger, Sluss, Hankinson
2006; Tworoger et al. 2007). A recent 20-year prospective study demonstrated the
strongest association with ER+ disease and postmenopausal women, among whom ER+
and metastatic (lymph node-positive) cancers were of highest risk. The risk for
postmenopausal women is with serum PRL concentrations above 11 ng/mL, which is
well within the normal range (Tworoger et al. 2013). Another recent study found high
circulating PRL levels to have a positive association with breast cancer in situ in all (preand postmenopausal) women (Tikk et al. 2015). High serum PRL has also been
associated with aggressive breast cancers and poor survival (Bhatavdekar et al. 2000;
Holtkamp et al. 1984; Patel et al. 1996; Wang et al. 1995), while PRLR overexpression
9
has been included in gene expression profiles linked to poor prognosis (Bertucci et al.
2002; Waseda et al. 1985). Unsurprisingly, it has been suggested that screening for PRL
improves breast cancer risk prediction and can potentially help identify patients in need
of additional screening or chemotherapy (Tworoger et al. 2014). The long form PRLR
has recently been shown to promote metastatic spread to the liver and lungs (Yonezawa
et al. 2015). While PRL is correlated with breast cancer metastasis, the precise role of
PRL in breast to bone metastasis is uncertain (Mujagic and Mujagic 2004). Factors
regulating bone metastasis such as interleukin-1 (IL-1), interleukin-8 (IL-8), macrophage
colony stimulating factor (M-CSF), parathyroid hormone hormone-related peptide
(PTHrP), and vascular endothelial growth factor (VEGF) are known to be PRL regulated
in other cell types outside the context of the bone, and may provide clues in elucidating a
clearer role for PRL (D’Isanto et al. 2004; DeVito et al. 1995; Goldhar et al. 2005;
Kacinski 1997; Thiede 1989).
PRL cross talk with estrogen (E2) and the ER has tumour-promoting effects in breast
carcinomas. PRL has the capability of activating the unliganded ER, stimulating similar
transcriptional activity as ligand-bound ER (González et al. 2009). This effect is achieved
through serine phosphorylation of the ER, which also results in a slower decrease in
activated ER than when stimulated with E2 (Chen et al. 2010). PRL and E2 have also
been shown to upregulate genes that were un-induced following treatment with the
hormones individually. Furthermore, PRL/E2 also had additive and synergistic effects on
gene expression in breast cancer cells (Rasmussen et al. 2010). Dense collagen-I matrices
have been found to enhance PRL/E2-induced breast tumour growth and drive invasion by
modifying the alignment of collagen fibres (Barcus et al. 2015). This is potentially
important in breast metastases to the bone, where the mineralized matrix is primarily
comprised of type I collagen (Boron and Boulpaep 2012). Interestingly, a constitutively
active alternatively spliced variant of the S1b PRLR isoform has the ability to inhibit the
stimulatory effect of E2 on breast cancer cells (Huang et al. 2015).
10
1.3 Bone
1.3.1 Bone Structure and Function
Bone is a highly specialized form of hard connective tissue that acts as a structural,
mechanical, endocrine, and hematological organ. It provides support for the body,
protects vital organs and cavities, provides a mechanical basis for movement by acting as
an attachment point for skeletal muscle, tendons and ligaments, aids in mineral
homeostasis, and is the primary site of hematopoiesis (Moore, Dalley, Agur 2013).
Organized by shape, there are five major classes of bone in the body: long bones are
tubular in structure and found in the limbs; short bones are cuboidal and found only in the
tarsus and carpus; flat bones, such as those found in the cranium, have a protective
function; irregular bones, such a facial bones, have shapes other than tubular or cuboidal;
and sesamoid bones, such as the patella, are found where tendons cross the ends of long
bones to protect from repetitive wear (Moore, Dalley, Agur 2013).
Bone is comprised of a few specialized cell types in a mineralized extracellular
matrix comprised of 95% type I collagen, and 5% non-collagenous proteins and
strengthening hydroxyapatite crystals. The three primary cell types found in bone are
osteoblasts, which promote bone formation, osteoclasts, which promote bone resorption,
and osteocytes, which are derived from osteoblasts that have become encased in the
mineralized matrix. Each class of bone is comprised of two types of osseous tissue.
Cortical bone, which accounts for 80% of the total bone mass, forms the weight bearing
exterior layer of bone. It is predominantly composed of dense mineral matrix and
osteocytes, which communicate with osteoblasts on the bone surface to control calcium
transfer from the interior of the bone (Boron and Boulpaep 2012). Surrounding the
exterior cortical bone is a protective layer of fibrous connective tissue called the
periosteum. Trabecular tissue is comprised of a network of bone spicules that form the
spongy interior of bone. This inner medullary cavity is lined with a thin layer of
connective tissue forming the endosteum, which is the primary site of action of
osteoclasts (Moore, Dalley, Agur 2013). Between the spicules of trabecular bone is red
and yellow bone marrow. Red marrow is most commonly located at the proximal ends of
long bones and is the primary site of hematopoiesis. Yellow marrow is found within the
11
hollower central portion of bones and is comprised primarily of adipose tissue (Custer
1933).
1.3.2 Bone Remodeling
Bone is a highly dynamic tissue that is constantly undergoing remodeling to preserve
skeletal size, shape and structural integrity, as well as regulate mineral homeostasis
(Raggatt and Partridge 2010). Turnover is highest in trabecular bone, where the network
of spicules is lined with clusters of osteoclasts and osteoblasts in a specific temporalspatial arrangement (Boron and Boulpaep 2012; Hauge et al. 2001). This arrangement is
critical in coordinating the phases of the remodeling sequence (Figure 2):
(i)
Activation
Mechanical forces and endocrine signaling are the main stimuli responsible
for initiation of bone remodeling. Damage to bone or decreased mechanical
loading results in apoptosis of osteocytes, lowering the local concentration
of transforming growth factor-β (TGF-β), which normally acts to inhibit
osteoclastogenesis (Heino, Hentunen, Väänänen 2002; Verborgt et al.
2002). Parathyroid hormone, a calciotropic hormone critical for calcium
homeostasis, is also a potent activator of the bone remodeling process that
acts to recruit osteoclast precursors and induce osteoclastogenesis to
stimulate bone resorption (Swarthout et al. 2002).
(ii)
Resorption
While osteoclasts are responsible for physical bone breakdown (see section
1.3.3 for a detailed explanation of the process), osteoblasts are also involved
by responding to signals generated by osteocytes or the endocrine system to
secrete cytokines that control osteoclast differentiation and activation.
Mature osteoclasts resorb bone by binding to the surface to create a “sealing
zone” where hydrogen ions and enzymes are secreted to dissolve the
mineral matrix (Teitelbaum 2000).
12
Figure 2. Bone remodeling. Bone remodeling is a highly dynamic process that begins
with activation of bone resorption by osteoclasts in response to mechanical and endocrine
signals. The reversal phase occurs when macrophages and cells of osteoblast lineage are
recruited to the resorption lacunae to remove undigested material and signal for the
cessation of resorption and activation of formation. Osteoblasts are the primary cells
involved in bone formation. They synthesize and deposit new osteoid and then undergo
apoptosis or embed in the tissue to form osteocytes. Sharing the osteoblast lineage are
bone lining cells, which thinly coat inactive bone surfaces during quiescence (reviewed in
Raggatt and Partridge 2010).
13
(iii) Reversal
Following resorption reversal cells, proposed to be of osteoblast lineage,
form a layer over the bone surface to remove undigested material and signal
for the cessation of resorption and activation of formation (Everts et al.
2002).
(iv) Formation
The process of bone formation is considerably longer than resorption – three
months to a comparative two weeks of breakdown (Harada and Rodan
2003). Bone forming osteoblasts are derived from mesenchymal stem cells,
which differentiate at the resorption lacunae in response to signals from
reversal cells, osteoclasts, and cytokines released from the degraded bone
surface (Ducy, Schinke, Karsenty 2000). Osteoblasts synthesize and deposit
new osteoid, comprised primarily of type I collagen, and then either
undergo apoptosis or imbed in the tissue to form osteocytes following
mineralization.
(v)
Termination
The mechanism that terminates the remodeling process is yet to be fully
elucidated, though there is an emerging role for osteocytes. Unless
pathologic processes are active, the level of bone replacement is generally
equal to the amount resorbed. The resting bone surface environment is reestablished and maintained until remodeling is initiated again (Raggatt and
Partridge 2010).
1.3.3 Osteoclastogenesis and Bone Resorption
Osteoclastogenesis is the differentiation of hematopoietic stem cell precursors into
highly specialized, multinucleate cells capable of bone resorption (Figure 3). The process
is reliant upon osteoblasts and stromal cells in the local environment to produce two
critical factors, M-CSF and RANKL (Takahashi et al. 1988). Osteoclastogenesis is
negatively regulated by osteoprotegerin (OPG), a soluble decoy receptor for RANKL,
and the degree of differentiation and function is highly dependent on the RANKL/OPG
14
Figure 3. Osteoclastogenesis. Osteoclastogenesis is the differentiation of hematopoietic
stem cells into multinucleate cells capable of bone resorption. Stimulation of osteoclast
precursors by M-CSF commits the stem cells to a monocytic cell lineage by signaling
through transcription factors PU.1 and MITF. Consequent upregulation of the RANK
receptor allows for binding of RANKL, which further promotes differentiation of
osteoclasts via transcription factors c-FOS and NFATc1. NFATc1, considered the master
regulator of osteoclastogenesis, induces transcription of osteoclast specific genes TRAP,
CTSK and CTR. RANKL is negatively regulated by a soluble decoy receptor, OPG,
which prevents RANKL from binding to the cell surface. Factors in the bone
microenvironment, including RANKL, promote pre-osteoclast chemotaxis and fusion
allowing for the formation of a large polykaryon that will eventually mature to become an
active osteoclast capable of bone resorption (reviewed in Raggatt and Partridge 2010).
Image adapted from Biomedical Tissue Research Group, University of York.
15
expression ratio (Simonet et al. 1997). M-CSF is involved early in differentiation by
committing myeloid precursors, formed from hematopoietic stem cells through signaling
from transcription factors PU.1 and micro-ophthalmia-associated transcription factor
(MITF), to a monocytic/macrophage lineage. Consequent upregulation of the RANK
receptor marks the monocytic cells as osteoclast precursors and allows for binding of
RANKL to promote further differentiation (Arai et al. 1999). Interaction of the RANK
cytoplasmic domain with TNF receptor-associated factor (TRAF6) initiates a signaling
cascade culminating in activation and translocation of NF-κB to the cell nucleus, where it
stimulates expression of osteoclastic genes such as c-Fos and nuclear factor of activate Tcells, cytoplasmic 1 (NFATc1) (Iotsova et al. 1997; Song et al. 1997; Takayanagi et al.
2002). RANK/RANKL signaling through c-Fos is seemingly important in committing
precursors to osteoclastogenesis, as mice deficient in c-Fos lack osteoclasts but have an
increased number of macrophages (Grigoriadis et al. 1994). NFATc1 is thought to be the
master transcription factor for osteoclastogenesis, as NFATc1-deficient embryonic stem
cells fail to differentiate in response to RANKL. NFATc1 is also known to regulate
transcription of tartrate resistant acid phosphatase (TRAP), cathepsin K (CTSK), and the
calcitonin receptor, which are all genes specific for osteoclastogenesis (Matsumoto et al.
2004; Takayanagi et al. 2002).
Fusion of mononuclear precursors to form a polykaryon is an important event in the
final stages of osteoclast differentiation. RANK/RANKL induces the chemotactic
cytokines RANTES and monocyte chemotactic protein-1 (MCP-1) (Kim, Day, Morrison
2005). Signaling through NFATc1 increases expression of cell-fusion molecules dendritic
cell-specific transmembrane protein and the d2 isoform of vacuolar ATPase Vo domain
proton pump (Atp6v0d2) (Kim et al. 2008). Still under direction from RANK/RANKL,
the fused polykaryon undergoes cytoskeleton rearrangement and binds to the bone
surface in order for resorption to occur. Formation of a dense, belt-like actin ring creates
a tight attachment to the mineralized bone matrix called the ‘sealing zone’ (Lakkakorpi et
al. 1989). The plasma membrane enclosed within the sealing zone enlarges into the
characteristic ruffled border responsible for bone resorption (Stenbeck 2002). Binding of
an osteoclast to bone involves an integrin αVβ3-filamentous actin complex, called the
16
podosome, which recognizes and binds ArgGlyAsp (RGD) amino-acid motifs in the
organic bone matrix (Aubin 1992). Upon association the β3 domain of the integrin
molecule signals through c-Src, SYK and Vav3 to induce cytoskeletal rearrangement
(Zou et al. 2007). Effective resorption is achieved through acidification of the lacunar
space below the ruffled border. A vacuolar proton pump expels H+ and Cl- ions through
the border membrane to achieve a pH of approximately 4.5, which degrades the strong
hydroxyapatite (Blair et al. 1989). Additionally matrix-degrading proteases including
TRAP, CTSK, and multiple matrix metalloproteinases (MMPs) are released through the
ruffled border to degrade the organic, type I collagen matrix (Delaissé et al. 2003;
Ljusberg et al. 2005; Saftig et al. 1998). While the RANK/RANKL/OPG axis is the most
studied and considered the most important mediator of osteoclastogenesis, RANKindependent mechanisms exist. For example, immune-related induction of
Ca2+/calcineurin signaling activates NFATc1 transcription of osteoclastogenic target
genes (Negishi‐Koga and Takayanagi 2009).
Bone homeostasis, and successful differentiation and maturation of osteoclasts, is
critical for skeletal health. Mutations in genes involved in osteoclast formation and
function, such as CTSK, Atp6v0d2, and tumor necrosis factor 11, lead to osteopetrosis
(Sobacchi et al. 2013). A condition of increased bone mass, inheritance of osteopetrosis
may be autosomal dominant or recessive, and the disease is most often characterized as
being osteoclast-rich, in which many osteoclasts are present but ruffle-border formation,
and thus effective resorption, is inhibited (Sobacchi et al. 2013). The autosomal recessive
type is the more severe form of the disease and patients often feature a short stature, a
large head, frontal bossing, nystagmus, hepatosplenomegaly and genu valgum. Failure to
thrive, delayed dentition, fractures and myelophthisis are common in children with
osteopetrosis. Untreated, the disease is fatal in the first decade of life (Goldman and
Schafer 2012).
17
1.3.4 Common Factors in Both the Mammary Gland and Bone
In addition to its role in the mammary gland, PRL is a known calciotropic hormone.
It indirectly influences bone homeostasis by stimulating intestinal calcium absorption and
decreasing renal calcium excretion (Mainoya 1975; Pahuja and DeLuca 1981; Piyabhan,
Krishnamra, and Limlomwongse 2000). Physiological and pathological
hyperprolactinemia result in direct changes to bone homeostasis. During pregnancy and
lactation high bone turnover acts as a mechanism to supply calcium for fetal growth and
milk production, and suppression of PRL secretion has been shown to reduce maternal
bone turnover in a rat model (Kovacs 2011; Lotinun et al. 2003). Pathological
hyperprolactinemia stimulates high bone turnover, resulting in calcium loss, osteopenia
and osteoporosis (Haddad and Wieck 2004). Osteoblasts, but not osteoclasts, express the
PRLR and PRL can directly increase bone turnover by raising the RANKL/OPG ratio
(Clement-Lacroix et al. 1999; Seriwatanachai et al. 2008).
The RANKL/OPG axis is also active in mammary gland development. In animal
models, RANK-/- and RANKL-/- mice are unable to lactate as they fail to develop lobuloalveolar structures during pregnancy (Fata et al. 2000). Interestingly, they share a similar
breast phenotype with PRLR-/- mice (Brisken et al. 1999). There is evidence for direct
PRL upregulation of RANKL in mammary epithelial cells via Jak2/STAT5 signaling
(Srivastava et al. 2003). RANKL causes increased mammary cell proliferation that
contributes to ductal side-branching and alveolar morphogenesis, a role similar to PRL
(Fernandez-Valdivia et al. 2009; Naylor et al. 2003).
1.4 Breast Cancer Metastasis to Bone
1.4.1 The Process of Metastasis
Metastasis arises when a cancer spreads from a primary site and invades distant
organs to form new tumours. This acquired capability of tissue invasion and metastasis is
described as a “hallmark of cancer”; a functional capability that the majority of cancers
will develop, albeit through differing mechanisms (Hanahan and Weinberg 2000).
Whether metastasis occurs early or late in tumorigenesis is currently an open debate, but
there is evidence for early ductal carcinoma cell dissemination and formation of
18
micrometastases in the bone marrow and lung prior to invasion of the primary tumour
and pretreatment circulating tumour cells have been suggested as early predictors of
metastatic potential (Giuliano et al. 2014; Hüsemann et al. 2008). Metastasis is a multistep process involving (i) the local infiltration of tumour cells into adjacent tissue, (ii)
intravasation, a transendothelial migration of cancer cells into blood vessels, (iii) survival
within the circulatory system, (iv) extravastion and (v) proliferation in competent organs
leading to colonization (reviewed by Friedl and Wolf 2003). The processes of local
infiltration and intravasation into vascular tissue are driven by the epithelial to
mesenchymal transition (EMT) of cancer cells, extracellular matrix (ECM) degradation
and angiogenesis (van Zijl, Krupitza, Mikulits 2011). Angiogenesis, another hallmark of
cancer, is considered crucial for metastasis to occur. Its regulatory pathways have been
extensively targeted for therapeutic intervention, but unfortunately have resulted in only a
moderate survival benefit as monotherapy (Dimova, Popivanov, Djonov 2014; Hanahan
and Weinberg 2000). Survival of cancer cells within the circulatory system is primarily
achieved through priming by platelets and surface shielding from natural killer cells
(Palumbo et al. 2007). Success of metastasis and preference for specific organs is
dependent upon the pre-metastatic niche – microenvironments created by non-cancerous
stromal cells to promote future metastasis. Factors secreted by tumour cells are thought to
prime distant organ sites for colonization by inducing stromal upregulation of
chemokines and receptors that promote metastasis to specific tissues (Kaplan, Rafii,
Lyden 2006). Bone is one of the most common and often the earliest site for breast
cancer metastasis (Singletary et al. 2003). When stratified across breast cancer subtype
bone is the most common site of metastasis in all but basal-like tumours (Kennecke et al.
2010).
1.4.2 Mechanisms of Breast to Bone Metastasis
The most prominent explanation for the preference and ability of breast cancer to
metastasize to bone is based on Stephen Paget’s “Seed and Soil” hypothesis, which
proposes that metastatic “seeds” will only grow if the microenvironment acts as a
hospitable “soil” that promotes migration, growth and survival (Paget 1889). Tropism of
19
circulating breast tumour cells to bone is mediated by CXCR4, a chemokine receptor for
stromal derived factor-1 (SDF-1 or CXCL12) that is highly expressed by breast
neoplasms but only minimally expressed in normal breast tissue. Highest expression of
SDF-1 occurs in cells at the sites of frequent breast cancer metastasis, including
osteoblasts and stromal fibroblasts of the vascular bone marrow (Müller et al. 2001).
Highly metastatic breast cancer cell lines over-expressing CXCR4 display increased
capacity to metastasize to bone, while siRNA knockdown of CXCR4 and a neutralizing
antibody against CXCR4 have been shown to significantly decrease breast cancer bone
metastatic burden in mouse models (Kang et al. 2003; Liang et al. 2005; Richert et al.
2009).
Once at the bone metastatic site the tumour cells must arrest, invade the bone
marrow stroma, induce angiogenesis and migrate to the endosteal bone surface (Mundy
2002). These requirements mean that bone metastases are not equally established in all
bones, and the majority occur in the long bones, ribs, and vertebrae at the trabecular
metaphyses. Trabecular bone is rich in red bone marrow and vascular sinusoids, which
often have slower bloodflow compared to the capillaries and are thought to be an ideal
environment for tumour cells to arrest (Wang, Loberg, Taichman 2006). The chemotactic
gradient created by the CXCR4/SDF-1 axis contributes to the arrest of cancer cells while
integrins and cadherins, namely αvβ3 integrin and cadherin-11, aid in adhesion to bone
marrow stromal cells (Huang et al. 2010; Sloan et al. 2006). At the endosteal bone
surface cancer cells alter the bone microenvironment to create osteolytic or osteoblastic
metastases by activation of osteoclasts or osteoblasts, respectively. Breast cancer
metastases are predominantly osteolytic, though the two disease types are not mutually
exclusive and features of both are often present (Mundy 2002).
1.4.3 The ‘Vicious Cycle’ of Breast Cancer Bone Metastases
In osteolytic metastases, breast cancer cells co-opt cells in the bone
microenvironment to stimulate osteoclastogenesis and increase osteoclast resorptive
activity (Figure 4) (Käkönen and Mundy 2003). Similarly to normal osteoclast
differentiation, the RANK/RANKL/OPG axis is the best-known contributor to breast
20
cancer-mediated osteoclastogenesis. In breast to bone metastases, invading tumour cells
produce cytokines such as PTHrP (Guise et al. 1996), VEGF (Aldridge et al. 2005a;
Aldridge et al. 2005b), and IL-6, IL-8 and IL-11 (Bendre et al. 2003; Manolagas 1995),
which act to increase production of RANKL by osteoblasts and stromal cells while
inhibiting OPG synthesis. This results in an increase in the number and activity of
osteoclasts, shifting normal bone homeostasis to favour bone breakdown (Bussard, Gay,
Mastro 2008; Mundy 2002). Degradation of the rich bone matrix results in release of
factors, namely insulin-like growth factor-1 (IGF-1), TGF-β, and calcium, that act on
metastatic cells to promote growth and survival while further stimulating production of
osteoclastogenic cytokines. In turn this further increases the RANKL:OPG ratio, thereby
maintaining the “vicious cycle” of breast cancer bone metastases (Mundy 2002).
While there are multiple factors affecting the vicious cycle, PTHrP, VEGF, IL-6, IL8 and IL-11 are the best characterized. PTHrP is a major driver of osteolytic metastases,
and its expression is greater in tumours that have metastasized to the bone in comparison
to tumours that have metastasized to soft tissue (Powell et al. 1991). There is opposing
evidence as to whether it is associated with increased bone metastatic potential (Guise et
al. 1996; Powell et al. 1991), or a more favorable outcome with a lesser predisposition for
bone metastasis (Henderson et al. 2001). It interacts with osteoblasts to increase RANKL
and decrease OPG secretion, resulting in a large increase in the RANKL:OPG ratio to
favor osteoclastic lesions (Mundy 2002). VEGF, an angiogenic cytokine often associated
with poor prognosis, is highly expressed in breast cancer metastases to bone (Aldridge et
al. 2005a). In the presence of RANKL, VEGF induces differentiation of multinucleate,
TRAP-positive cells capable of bone resorption via activation of its receptor VEGFR1,
which is highly expressed by osteoclasts in the bone/tumour microenvironment (Aldridge
et al. 2005a; Aldridge et al. 2005b). Other studies have also demonstrated the ability of
VEGF to stimulate bone resorption as well as promote osteoclast survival (Nakagawa et
al. 2000). IL-6, IL-8 and IL-11 contribute to the vicious cycle by directly stimulating
osteoclasts and through interaction with osteoblasts and the stroma. IL-6 is produced by
both the breast tumour and osteoblast cells at the site of metastasis, and promotes preosteoclast differentiation (Fontanini et al. 1999; Kurebayashi 2000; Manolagas 1995).
21
Figure 4. The vicious cycle of breast cancer bone metastases. Metastatic breast cancer
cells secrete factors including PTHrP, VEGF, IL-6, IL-8 and IL-11, which act on
osteoblasts and stromal cells to increase production of RANKL while decreasing
production of OPG. Breast cancer cells also secrete factors such as VEGF, IL-6, IL-8 and
IL-11 that are able to directly induce osteoclast differentiation and activation. As a result,
increased bone resorption occurs leading to characteristic osteolytic breast to bone
metastases. Bone degradation releases growth factors into the microenvironment, such as
IGF-1, TGF-β and Ca2+ that act to further promote growth and survival of metastatic
breast cancer cells, perpetuating the vicious cycle of bone metastasis (reviewed in Mundy
2002).
22
Interestingly, IL-8 promotes breast cancer cell motility, invasion and metastatic potential
while also promoting osteolysis (Bendre et al. 2003; De Larco et al. 2001). Breast cancer
cells both secrete and stimulate osteoblast production of IL-11, which plays a role in
differentiation and the resorptive capabilities of osteoclasts (Girasole et al. 1994; Morgan,
Tumber, Hill 2004). IL-6, IL-8 and IL-11 have all been shown to promote osteolysis
through RANKL-dependent and independent mechanisms (Bendre et al. 2005; Kudo et
al. 2003).
Breast cancer cells also produce a number of other cytokines that interact with the
microenvironment or stimulate osteoclasts directly to perpetuate the vicious cycle:
fractalkine (Koizumi et al. 2009), granulocyte macrophage colony-stimulating factor
(Park et al. 2007), insulin-like growth factors (Sachdev and Yee 2001), IL-1 (Singer et al.
2003; Singh et al. 2006), jagged-1 (Sethi et al. 2011), M-CSF (Mancino et al. 2001),
MCP-1 (Kim, Day, Morrison 2005; Kim et al. 2006), MMPs (Eck et al. 2009; Ohshiba et
al. 2003), platelet-derived growth factor-BB (Bos et al. 2005; Vincent et al. 2009; Zhang,
Chen, Jin 1998), prostaglandin E2 (Kobayashi et al. 2005), RANKL (Mancino et al.
2001; Nicolin et al. 2008), RANTES (Kim, Day, Morrison 2005), relaxin (Ferlin et al.
2010; Tashima, Mazoujian, Bryant-Greenwood 1994), stem cell factor (Demulder et al.
1992), serotonin (Chiechi et al. 2013; Hernandez, Gregerson, Horseman 2012), TGF-β
(Guise and Chirgwin 2003), and tumor necrosis factor-α (Kobayashi et al. 2000). There is
evidence of PRL having a regulatory role over some of these factors. In the mammary
gland PTHrP, VEGF, and M-CSF are upregulated by PRL (Goldhar et al. 2005; Kacinski
1997; Thiede 1989), while IL-6 and some MMPs are downregulated (Deb et al. 1999;
Flint et al. 2006). Outside of the mammary gland IL-8, IL-1 and tumor necrosis factor- α
have also been shown to be PRL regulated (D’Isanto et al. 2004; DeVito et al. 1995).
Together these cytokines act to increase osteolysis and provide potential mechanisms by
which PRL could enhance breast cancer-mediated osteoclastogenesis.
23
1.5 Hedgehog Signaling
The hedgehog signaling (Hh) pathway plays an important role in vertebrate
embryogenesis and development where it controls cell fate, patterning, proliferation,
survival and differentiation (Hooper and Scott 2005). Of particular importance is its
regulation of proliferation and differentiation in a time/position dependent manner,
ensuring that tissue reaches the appropriate size with correct cell type and sufficient
vascularization and innervation (Hooper and Scott 2005). Although significantly reduced
in adults, HH signaling has been identified as a regulator of stem or progenitor cell
formation and self-renewal (Taipale and Beachy 2001; Zhang and Kalderon 2001). It is
therefore not surprising that when aberrantly activated or deregulated Hh signaling also
plays a role in tumourigenesis. This project has identified sonic hedgehog (SHH) as a
PRL-enhanced, breast cancer cell secreted factor that contributes to osteoclastogenesis.
1.5.1 Hedgehog Signaling
The Hh gene was first identified by Christiane Nusslein-Volhard and Eric Wieschaus
as one of multiple involved in the development of body segmentation in Drosophila
melanogaster (Nusslein-Volhard and Wieschaus 1980). In mammals, there are three HH
genes: SHH, Indian hedgehog (IHH) and Desert hedgehog (DHH). The HH homologs are
highly hydrophobic secreted proteins, which diffuse through tissue to establish gradients.
Each is expressed in different tissues at different stages of development, although the
cellular activity of all homologs culminates in transcriptional responses mediated by the
zinc-finger glioma associated oncogene (GLI) transcription factors (reviewed in Rubin
and de Sauvage 2006). There are three forms of GLI transcription factors: GLI1, a
transcriptional activator, GLI2, both an activator and repressor, and GLI3, a
transcriptional repressor. Gene targets of Hh signaling vary by cell type however, GLI1
and PTCH1 are ubiquitously expressed in mammals, providing auto-regulatory feedback
for the pathway (Rubin and de Sauvage 2006). Hh signaling occurs via autocrine or
paracrine mechanisms and the Hh homologs are secreted from the cell through a
transmembrane transporter, Dispatched, after acylation of their N-terminus by the
enzyme Rasp in the endoplasmic reticulum (Micchelli et al. 2002). The Hh signaling
24
cascade is initiated upon Hh ligand binding to the receptor Patched (PTCH), a twelvetransmembrane protein located at the cell surface that acts to inhibit Smoothened (SMO)
when unbound. SMO is a seven-transmembrane G-protein coupled receptor (GPCR)-like
protein located in the membrane of intracellular endosomes (Riobo and Manning 2007).
While SMO is in its inactive state kinases such as PKA and GSK3 phosphorylate GLI2/3,
enabling the repressor forms. Suppressor of Fused (SUFU) and Iguana act to inhibit the
active GLI form in a mechanism that is not well understood. Upon Hh binding, PTCH is
internalized and destabilized resulting in release of SMO inhibition. SMO then
translocates to the plasma membrane, and through not-yet-understood mechanisms causes
accumulation of the GLI1/2 activator forms and subsequent transcription of Hh target
genes (Figure 5) (Riobo and Manning 2007).
1.5.2 The Role of Hedgehog Signaling in Vertebrate Development
SHH, the best characterized of the homologs, primarily acts as a morphogen in anteriorposterior patterning of the limb at the zone of polarizing activity (Riddle et al. 1993). It is
also involved in cusp formation of teeth (Dassule et al. 2000), and dorsal-ventral polarity
of the midline structures of the brain (Herzog et al. 2003), spinal cord (Lewis and Eisen
2001), and thalamus via the zona limitans intrathalamica (Scholpp et al. 2006). In
conjunction with IHH, SHH signaling aids in specifying regional characteristics of the
gut via differentiation and proliferation of epithelial cells (Ramalho-Santos, Melton,
McMahon 2000). IHH plays a major role in chondrocyte differentiation during
endochondral ossification in a mechanism negatively regulated by PTHrP (Vortkamp et
al. 1996). During development of the appendicular skeleton IHH coordinates
endochondral bone growth via mechanisms dependent and independent of PTHrP (Karp
et al. 2000). It is also expressed during fracture healing of long bones (Murakami and
Noda 2000). The least characterized of the three homologs, DHH, is involved in male
germ line regulation through signaling in Sertoli cells (Bitgood, Shen, McMahon 1996).
Schwann-cell secreted DHH aids in development of the peripheral nerve sheath
(Parmantier et al. 1999).
25
Figure 5. Hedgehog signaling. Unbound, the Hh receptor PTCH1 inhibits SMO to
prevent downstream signaling of the Hh pathway. In this state kinases PKA and GSK3β
phosphorylate the repressor GLI2/3 transcription factors. In addition, SUFU and iguana
act to inhibit the activator GLI1/2 transcription factors. Upon binding of SHH, IHH or
DHH, PTCH1 is internalized and destabilized to release inhibition of SMO. SMO
translocates to the nucleus where it causes accumulation of the activator GLI1/2
transcription factors and subsequent transcription of Hh target genes (Rubin and de
Sauvage 2006).
26
Each of the Hh ligands, as well as many components of the Hh signaling cascade, are
expressed in the postnatal mammary gland. Mouse studies have indicated that SHH is
expressed in mammary tissue from puberty to lactation (Lewis et al. 1999). IHH and
DHH are also both detectable during puberty, while IHH mRNA is strongly upregulated
in alveolar cells during pregnancy and lactation (Kouros‐Mehr and Werb 2006; Lewis et
al. 1999). Loss of function studies, collectively using multiple PTCH1 mutants,
demonstrated that PTCH1 is required for normal patterning and elongation of the
mammary ductal tree (Lewis et al. 1999; Li et al. 2008; Moraes et al. 2009). PTCH1 is
expressed in both epithelial and stromal compartments of the virgin mouse mammary
gland, and mRNA expression correlates with IHH, elevating during pregnancy and
lactation. During involution, IHH and PTCH1 are no longer detectable, however, PTCH1
expression again rises following gland remodeling. In addition, the GLI2 transcription
factor is needed for proper ductal development, though it was noted that loss of GLI2
function had no effect on alveolar development during pregnancy (Lewis et al. 2001).
1.5.3 The Role of Hedgehog Signaling in Cancer
The first connection between Hh signaling and cancer was made by the discovery
that Gorlin syndrome (basal cell nevus syndrome), a condition characterized by the
development of numerous basal cell carcinomas, was caused by a mutation in PTCH
(Hahn et al. 1996; Johnson et al. 1996). Further studies determined that the majority of
basal cell carcinomas contained hyper-activation of Hh signaling by observing elevated
expression of GLI1 and PTCH mRNA, which are both components and transcriptional
targets of Hh signaling (Dahmane et al. 1997; Unden et al. 1997). Other cancers have
since been reported to have de-regulation of Hh signaling including breast, prostate,
colon, liver, medulloblastoma and small-cell lung carcinoma (Rubin and de Sauvage
2006). Based on observation of cancers featuring aberrant Hh activity, different
mechanisms of Hh signaling have been determined (Scales and de Sauvage 2009):
(i)
Type I Ligand-Independent – inactivating mutations in PTCH or activating
mutations in SMO result in constitutive activation of Hh signaling
regardless of presence of Hh ligand
27
(ii)
Type II Ligand-Dependent (Autocrine) – tumours secrete and respond to Hh
ligand promoting tumor growth and survival
(iii)
Type IIIa Ligand-Dependent (Paracrine) – tumors secrete Hh ligand which
activates Hh signaling in stromal cells in the tumor environment resulting in
the release of factors that support tumor growth and survival
(iv)
Type IIIb Ligand-Dependent (Reverse-Paracrine) – stromal cells secreted
Hh ligand that activate the Hh pathway in tumor cells promoting tumor
growth and survival
Each of the ligand-dependent methods of signaling (types II, IIIa and IIIb) is of particular
significance to the metastatic bone microenvironment as they provide mechanisms by
which the Hh pathway may contribute to tumour growth and survival. Furthermore, there
is potential for PRL to regulate Hh secretion from the tumour and thus induce breast
cancer cell mediated osteoclastogenesis.
28
CHAPTER 2: HYPOTHESIS
Our lab has shown that PRL and the PRLR are able to induce osteoclastogenesis and
bone resorption via the secretion of osteoclastogenic factors from breast cancer cells,
however the precise mechanism remains uncertain (Sutherland 2010). I hypothesize that
prolactin-induced, breast cancer cell secreted factors contribute to osteoclastogenesis and
bone resorption by stimulating fusion and differentiation of osteoclast precursors and
activation of mature osteoclasts (Figure 6).
2.1 Project Aims
The hypothesis will be tested by meeting the following aims:
1. Optimize osteoclast differentiation and quantification
a. Determine the optimal PRL treatment length for induction of
osteoclastogenesis
b. Test alternative methods of quantifying osteoclastogenesis and determine
which is most suitable
2. Identify PRL-regulated, breast cancer secreted factors and determine if they
contribute to PRL/breast cancer-mediated osteoclastogenesis
a. Conduct a gene array to identify PRL-regulated genes
b. Conduct a cytokine array to identify PRL-regulated, breast cancer cell
secreted factors
c. Conduct osteoclast differentiation assays using inhibitors against
identified factors to determine their contribution to PRL-induced, breast
cancer-mediated osteoclastogenesis
29
Figure 6. Hypothesis of novel mechanism responsible for PRL-induction of
osteoclastogenesis. PRL induces the secretion of osteoclastogenic factors by breast
cancer cells. These factors act directly on pre-osteoclasts to induce differentiation into
mature osteoclasts, and potentially contribute to osteoclast activation. Resulting bone
degradation releases growth factors into the microenvironment that further promote
growth and survival of metastatic breast cancer cells and perpetuate the vicious cycle of
breast to bone metastasis.
30
CHAPTER 3: MATERIALS AND METHODS
3.1 Cell Lines
The human breast cancer cell lines MCF7 (HTB-22), SKBR3 (HTB-30) and T47D
(HTB-133), and mouse macrophage/monocyte cell line RAW264.7 (TIB-71) were
purchased from American Type Culture Collection (ATCC; Manassas, VA, USA).
3.1.1 Culture and Maintenance of Breast Cancer Cell Lines
SKBR3 and MCF7 human breast cancer cell lines were maintained in Dulbecco’s
Modified Eagle Medium (DMEM; Invitrogen, Burlington, ON, Canada) supplemented
with 10% fetal bovine serum (FBS; PAA Laboratories Inc., Etobicoke, ON, Canada), 100
units/mL penicillin and 100 µg/mL streptomycin (Penicillin-streptomycin; Invitrogen),
and 2mM L-glutamine (Invitrogen). MCF7 were additionally supplemented with 1
µg/mL bovine insulin (Sigma-Aldrich Canada Co., Oakville, ON, CAN). T47D were
maintained in Roswell Park Memorial Institute (RPMI) 1640 (Fisher Scientific, Toronto,
ON, Canada) media, also supplemented with 10% FBS, 100 units/mL penicillin and 100
µg/mL streptomycin, and 2 mM L-glutamine. Once plated, MCF7 and T47D were
additionally supplemented with 1 µg/mL bovine insulin (Sigma-Aldrich Canada Co.,
Oakville, ON, CAN). All breast cancer cell lines were cultured on 100mm cell culture
plates (Corning, Tewksbury, MA, USA) at 37°C and 5% CO2 to approximately 85%
confluency for propagation of cultures. To sub-culture, cells were washed twice with
phosphate buffered saline (PBS; pH 7.4, 1.37M NaCl, 26.8mM KCl, 0.1M Na2HPO47H2O and 82mM KH2PO4) and treated with 0.05% Trypsin-EDTA (Invitrogen) for 4 min
at 37°C to disperse the cell layer. Cells were collected with FBS-containing culture
media to deactivate the trypsin, and centrifuged at 400 rcf for 4 min at room temperature.
For culture maintenance cells were re-seeded at a density of 7.5 x 105 cells in 8mL media
per 10cm cell culture dish.
31
3.1.2 Culture and Maintenance of Osteoclast Precursors
The osteoclast precursor RAW264.7 mouse macrophage/monocyte cell line was
cultured in DMEM (ATCC), supplemented with 10% FBS, and 100 units/mL penicillin
and 100 µg/mL streptomycin. For culture maintenance cells were grown in 25cm2 culture
flasks (Sarstedt Inc., Montreal, QC, Canada) at 37°C and 5% CO2 to approximately 85%
confluence. To propagate the culture cells were detached using a cell scraper (Sarstedt
Inc.), collected and centrifuged at 140 rcf for 6 min at room temperature. Cells were replated at a density of 4.5 x 105 cells in 10 mL media per flask.
3.2 Prolactin-Induced Breast Cancer-Mediated Osteoclastogenesis
3.2.1 Preparation of Breast Cancer Cell Condition Medium
To determine the optimal PRL treatment length for induction of osteoclastogenesis,
SKBR3 and MCF7 breast cancer cells were plated at a density of 9.1 x 103 cells/cm2 in
either 100 mm culture dishes, 60 mm culture dishes (Corning) or 6-well culture plates
(Corning) and left for 24 hr at 37°C and 5% CO2. Cells were treated with 5 ug/mL ovine
PRL (oPRL) (Sigma Aldrich Canada Co.) or 25 ng/mL bacterial-derived recombinant
human PRL (hPRL) (Bernichtein et al. 2003) (a gift from Dr. Vincent Goffin, University
Paris Descartes, Paris, France) for 96 hr, 72 hr, 48hr or 24hr. Equal volumes of PBS was
used as a vehicle control. To maintain equal cell confluence between treatment lengths all
plates were cultured for a total of 5 days. PRL or PBS control was added to the 96 hr
treatment group 24 hr after plating, with the remaining treatments spaced 24 hr apart.
Upon PRL treatment, media was aspirated and replaced with fresh media containing
hPRL or the PBS control. Following 5 days of culture the breast cancer cell conditioned
medium (CM) was removed from the plates, centrifuged at 800 rcf for 5 min at 4°C, flash
frozen in liquid nitrogen and stored at -80°C until use or for a maximum of one year.
Once thawed, CM may be stored at 4°C for a maximum of one week. After an optimal
PRL treatment length of 48 hr was established, breast cancer cells were routinely plated
at a density of 1.64 x 104 cells/cm2 on 60 mm or 100 mm culture dishes and left for 24 hr
at 37°C and 5% CO2. Cells were treated with 25 ng/mL hPRL or PBS control for 48 hr
and collected as described above.
32
3.2.1.1 Preparation of Serum Free or Reduced Serum Conditioned Medium
For preparation of serum free or 2% FBS CM, breast cancer cells were
maintained and plated in 10% FBS (as described in section 3.2.1). Media was
aspirated and replaced with media without FBS or with 2% FBS upon PRL
treatment. Cells were treated with 25 ng/mL hPRL for 48 hr. RAW264.7
differentiated with serum free or 2% FBS CM were supplemented with additional
FBS to ensure differentiation conditions contained 10% FBS.
3.2.1.2 Preparation of Conditioned Media Using a Prolactin Dose Response
SKBR3 were plated at a density of 1.64 x 104 cells/cm2 in either 100 mm
culture dishes, 60 mm culture dishes or 6-well culture plates and 2 % FBS CM was
prepared (as described in section 3.2.1.1). Cells were treated with 12.5 ng/mL, 25
ng/mL, 50 ng/mL, or 100 ng/mL hPRL, with the PBS control added in equal
volume to 150 ng/mL hPRL for 48 hr.
3.2.2 Conditioned Media-Induced Differentiation of RAW264.7 Pre-Osteoclasts
RAW264.7 were plated in a 48-well plate (Falcon, Corning) at a density of 7.0 x 103
cells/cm2 and left for 24 hr at 37°C and 5% CO2. Media was then replaced with fresh
DMEM (ATCC) supplemented with 20% breast cancer cell CM, which was refreshed
every second day, for 6 days (adapted from Guo et al. 2008; Nicolin et al. 2008).
3.2.3 Hedgehog Signaling Inhibition in Osteoclast Differentiation
Differentiation assays using 5E1 (Developmental Studies Hybridoma Bank,
University of Iowa, Iowa City, IA, USA), a mouse inhibitory antibody with affinity for
human SHH and IHH as determined by enzyme-linked immunsorbent assay (Wang et al.
2000), were conducted according to section 3.2.2. RAW264.7 were differentiated in
PRL-treated or non-treated CM supplemented with 2.5 µg/mL 5E1 antibody.
33
3.3 Methods of Quantifying Osteoclastogenesis
3.3.1 Tartrate Resistant Acid Phosphatase (TRAP) and Nuclear Staining
Pre-osteoclasts were differentiated as described in section 3.2, and were then fixed
and stained for TRAP using a leukocyte acid phosphatase kit (Sigma-Aldrich Canada
Co.). Cells were fixed in a citrate/acetone solution (pH 5.4, 38mM citrate, 60% acetone in
ddH2O) for 30 sec at room temperature and washed with ddH2O. Cells were then
incubated for 40 min at 37°C in a solution containing naphthol AS-BI phosphate and fast
garnet GBC salt, which couples rapidly under acidic conditions to produce insoluble dye
deposits when the phosphate is hydrolyzed by a phosphatase. Tartaric acid is included in
the solution to identify TRAP. TRAP is known to be expressed by mature osteoclasts and
is therefore used as criteria in identifying osteoclasts (Minkin 1982). Cells were
counterstained with 10% hematoxylin in ddH2O to identify nuclei. Stained cells were
viewed under light microscopy using the Zeiss Axiovert 100 inverted microscope (Carl
Zeiss Canada Ltd., Toronto, ON, Canada) and TRAP-positive, multinucleate cells
(containing three or more nuclei) counted. If required, blind counts were completed by
individuals not involved in the differentiation assay and all identifying information was
removed from the experiment plates prior to quantification.
3.3.2 para-Nitrophenyl Phosphate (pNPP) TRAP Activity Assay
Pre-osteoclasts were differentiated as described in sections 3.2. On the final day of
differentiation media was removed, cells washed twice in PBS and lysed in 0.2% TritonX-100 (OmniPur, EMD Chemicals Inc., Gibbstown, NJ, USA) in PBS for 10 min at room
temperature. TRAP activity of the cell lysates was measured using an Acid Phosphatase
Assay (Cayman Chemical, Ann Arbor, MI, USA) that utilizes pNPP as a colorimetric
substrate for TRAP. To determine TRAP activity, the cell lysates were incubated with the
pNPP substrate in a 96-well plate for 20 min at 37°C. Samples were then treated with 500
mM sodium hydroxide to stop the reaction and absorbance was read at 405 nm on a
SpectraMax M4 plate reader (Molecular Devices, Downington, PA, USA; Buret Lab,
University of Calgary). From the absorbance, TRAP activity was calculated as the
34
amount of acid phosphatase required to release 1 µmol of phosphate from pNPP in one
minute at 37°C.
3.3.3 Fluorescent Staining
RAW264.7 were seeded onto poly-L-lysine coated cover slips or 96-well optical
plates (Costar, Corning) at a density of 7.0 x 103 cells/cm2 and differentiated in SKBR3
CM (described in section 3.2.2). If plated on cover slips, cells were mounted onto glass
slides after staining (see below) using Mowiol (Polysciences Inc., Warrington, PA, USA)
containing 2.5% w/v DABCO (Sigma-Aldrich Canada Co.). Cover slips were sealed with
Insta-Dri Fast Dry clear nail polish (Sally Hansen, Coty Canada Inc., Dorval, QC,
Canada) and allowed to air-dry overnight prior to imaging. Cells were visualized using
the IX81 FV1000 laser scanning confocal microscope (Olympus Canada Inc., Richmond
Hill, ON, Canada) and DeltaVision microscope (Applied Precision Inc., Mississauga,
ON, Canada), both located at the Live Cell Imaging Facility, University of Calgary.
Alternatively, cells were also visualized on a DMR epifluorescence microscope (Leica
Microsystems Inc., Concord, ON, Canada; Muench Lab, University of Calgary) or the
SP5 laser scanning confocal microscope (Leica Microsystems Inc.; Samuel Lab,
University of Calgary, Calgary). If plated in 96-well plates, stained samples were kept in
PBS and shipped to the S.M.A.R.T. Laboratory for High-Throughput Screening
Programs (Mount Sinai Hospital, Toronto, ON, Canada) for imaging on the Opera High
Content Screening System (Perkin Elmer, Waltham, MA, USA).
3.3.3.1 Plasma Membrane Staining
Following differentiation cells were incubated in 5.0 ug/mL CellMask Deep
Red plasma membrane stain (Invitrogen) for 5 min at 37°C and then fixed in 3.7%
paraformaldehyde (PFA) for 10 min at 37°C. Cells were then stained with 300 nM
– 1200 nM DAPI (Invitrogen) for 5 min at room temperature (Tautzenberger et al.
2011). All stains and washes were completed in un-supplemented DMEM
(Invitrogen).
35
3.3.3.2 Actin Ring Staining
Following differentiation cells were fixed in 3.7% PFA for 10 min at room
temperature and then permeabilized in 3% Triton-X-100 for 10 min at room
temperature. Cells were then stained with 300 nM – 1200nM DAPI for 5 min at
room temperature, followed by 1:250 Alexa Fluor 488 Phalloidin (Invitrogen) for
20 min at room temperature (Ghayor C et al. 2011). All stains and washes were
completed in PBS.
3.4 Gene Array
A single genome wide microarray expression analysis, consisting of 110,000 human
gene probes, was performed by Almac (Craigavon, Ireland) according to Affymetrix
GeneChip protocols. Three different experimental samples of SKBR3 were plated as
described in section 3.2.1 and treated with 25 mg/mL hPRL or PBS control for 24 hr.
RNA was extracted by Lin Su (Shemanko Lab) using the Qiagen miRNeasy Mini Kit and
quality was assessed using the BioRad Experion Automated Electrophoresis System
(Mississauga, ON, Canada; S. Huang Lab, University of Calgary). CM collected from
SKBR3 was tested in an osteoclastogenesis assay (described in section 3.2.2) to confirm
the biological activity of the samples.
3.5 Cytokine Array
A single RayBiotech (Norcross, GA, USA) biotin-label based human antibody array,
capable of detecting 507 human cytokines, was performed by RayBiotech on CM from
SKBR3 and MCF7 treated with 25 ng/mL hPRL for 48 hr. As FBS contains innate
cytokines that produce detectable signals on the antibody array chip, it was recommended
that the array be performed in serum free conditions. In order to determine how serum
free conditions affected pre-osteoclast differentiation, five replicate samples of serum
free CM were prepared from SKBR3 and MCF7 and tested in osteoclastogenesis assays
(described in section 3.2.1.1 and 3.2.2, respectively). Additional serum was added to
RAW264.7 treated with serum free CM in order to maintain an even serum concentration
across all pre-osteoclast samples. Proteins present in CM were biotinylated, and then
36
samples were added onto a glass array chip containing cytokine specific antibodies. Each
cytokine was present in duplicate on the array chip. Array chips were incubated with
fluorescent dye-conjugated streptavidin and signals visualized with a laser scanner using
the cy3 channel.
3.6 Quantitative Polymerase Chain Reaction
3.6.1 RNA Preparation and Extraction
Breast cancer cells were plated at a density of 1.64 x 104 cells/cm2 in either 60 mm or
100 mm culture dishes and left for 24 hr at 37°C and 5% CO2. Cells were treated with 25
ng/mL recombinant hPRL or PBS control for 6hr, 24hr or 48hr. Upon PRL treatment, old
media was removed from the plate and replaced with fresh media containing PRL or the
PBS control. RNA was extracted using the RNEasy Mini Kit (Qiagen Inc., Missisauga,
ON, Canada) or the NucleoSpin RNA Kit (Macherey-Nagel Inc., Bethlehem, PA, USA)
according to supplied protocols. RNA extracted using the RNEasy Mini Kit was DNAse
treated on-column with RNase-Free DNase (Qiagen) according to Qiagen protocols.
DNase was provided with the NucleoSpin RNA Kit, which was also applied to the RNA
on-column during purification. RNA was eluted from the columns using provided RNasefree ddH2O or ddH2O treated with 0.1% diethyl pyrocarbonate (DEPC; Sigma-Aldrich
Canada Co.).
3.6.2 Assessment of RNA Quantity and Quality
Quantity and purity of the RNA was determined using the NanoDrop 100
Spectrophotometer (Thermo Scientific, Wilmington, DE, USA; Hansen Lab, University
of Calgary). RNA was considered pure if the 260/280 ratio was ~ 2.0 and the 260/230
ratio was ~2.0-2.2. Quality of RNA was further assessed using a formaldehyde agarose
(FA) gel. The 1.2% FA gel was prepared by combining 1.2% UltraPure agarose
(Invitrogen), 5.4% formaldehyde (Fisher Scientific) and 0.001% ethidium bromide
(Fisher Scientific) in 3-(4-Morpholino)propane sulfonic acid (MOPS) buffer (20 mM
MOPS, 5 mM sodium acetate and 1 mM sodium ethylenediaminetertaacetic acid
(Na2EDTA) in 0.1% DEPC ddH2O. Samples were prepared by mixing 1 µg RNA with
37
14% formamide (Fisher Scientific), 40% formaldehyde and 0.8% MOPS. The samples
were then incubated at 65°C for 10 min and loaded into the gel with 5X RNA loading
buffer (50% glycerol, 1 mM Na2EDTA and 0.4% bromophenol blue). The RNA was
resolved in the gel using MOPS buffer at 80V for 1-1.5 hr. RNA and visualized under
UV light using the BioRad GelDoc 2000 and Quantity One software. Good quality RNA
was characterized by the presence of a small lower band, representing the 18S RNA
fragment, and an upper band approximately twice the intensity representing the 28S RNA
fragment. After extraction, RNA was stored at -80°C.
3.6.3 Complimentary DNA Synthesis
Complimentary DNA (cDNA) was synthesized by reverse transcribing RNA with the
Superscript II Reverse Transcriptase Kit (Invitrogen). To begin the reaction, 2 µg RNA
was incubated with a final concentration of 0.2 mM dNTPs (Fisher Scientific) and 25
µg/mL oligo-dT primer (5’-TTTTTTTTTTTTTTTTTTTV-3’; synthesized by the
University of Calgary Core DNA Services, Calgary, AB) at 65°C for 5 min. The
remainder of the reaction was completed according to the Superscript II Reverse
Transcriptase protocol. Once synthesized, cDNA was stored in -20°C.
3.6.4 Quantitative Polymerase Chain Reaction
Quantitative polymerase chain reaction (qPCR) samples were prepared in triplicate
by combining iTaq Universal SYBR Green Supermix (BioRad) with 12.5 pmol of each
forward and reverse primer and 1 µl cDNA template per 20 µl reaction. Reactions carried
out on the BioRad iQ5 Real-Time PCR System (the lab of Dr H. Habibi or Dr D. Hansen,
University of Calgary) were loaded into 96-well PCR microplates (Axygen, Corning) and
sealed with PlateMax Cyclerseal sealing film (Belfast, PE, Canada). Reactions carried out
on the BioRad MJMini Opticon Real-Time PCR System were loaded into iQ 96-well
segmented PCR plates (BioRad) that were cut in half for the 48-well maximum of the
machine, and sealed with iQ Optical Tape (BioRad).
Primers were designed (with the aid of undergraduate students Heather Gibling and
Vanessa Lam) using the NCBI Primer-Blast program. Possible primer dimers and self38
complementation were identified using the Operon Oligo Analysis tool and IDT Oligo
Analyzer, respectively. All primers were synthesized by the University of Calgary Core
DNA Services. To determine the optimal annealing temperature of each primer set, qPCR
over a temperature gradient ranging from 50°C-60°C or 55°C-65°C was conducted
according to the following protocol: 95°C for 3 min, 40 cycles of 95°C for 10 sec
followed by the gradient temperature for 30 sec, 95°C for 10 sec and a melt curve from
45°C to 95°C with increments of 0.5°C for 5 sec. For a complete list of genes amplified,
the primer sequences and annealing temperatures see Table 1.
When genes were amplified using primers obtained from the University of Calgary
Core DNA Services, the following protocol was used: 95°C for 3 min, 40 cycles of 95°C
for 10 sec followed by the primer-specific annealing temperature for 30 sec, 95°C for 10
seconds and a melt curve from 55°C to 95°C with increments of 0.5°C for 5 sec. All
genes were normalized to either TATA-binding protein (TBP) or tyrosine 3monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide
(YWHAZ) and gene expression fold change in response to PRL treatment determined by
the Pfaffl method (Pfaffl 2001).
3.6.5 Endpoint PCR and Visualization of PCR Products
In the event that real time data was not required for a PCR reaction, samples were
prepared in 0.2 mL thin wall PCR tubes (Axygen, Corning) and genes amplified in the
BioRad MJMini Opticon Real-Time PCR System according to section 3.6.4 with
exclusion of the melt curve. PCR products from both endpoint and qPCR reactions were
visualized on a 1%-2% agarose gel prepared in Tris-borate-EDTA buffer (TBE; 89 mM
Tris, 89 mM boric acid, 2 mM Na2EDTA) with 0.002% ethidium bromide. Samples were
loaded by mixing 9 µL PCR product with 1 µl 10X DNA Buffer (4.5 µM Tris, 2.5
mg/mL bromophenol blue and 0.6% glycerol in ddH2O). The DNA was resolved in the
gel using TBE buffer at 80-120 mA for 45 min. DNA was visualized under UV light
using the BioRad GelDoc 2000 and Quantity One software.
39
3.7 Enzyme-Linked Immunosorbent Assays (ELISA)
ELISAs were used to validate PRL regulation of cytokine array candidates and
determine PRL regulation of the proteins in different cell lines. The LIGHT ELISA was
purchased from R&D Systems Inc. and the SHH and IHH ELISAs were purchased from
Cusabio Biotech Co. Ltd. (Wuhan, China). Three of the five serum free SKBR3 and
MCF7 CM samples prepared for the cytokine array (described in section 3.5) were used
in array validation. CM from SKBR3 treated with an hPRL dose response was prepared
in 2% FBS (described in section 3.2.1.2). Triplicate samples of T47D, treated with 25
ng/mL hPRL or PBS control for 48 hr were also prepared in 2% FBS (described in
section 3.2.1.1). All CM samples were centrifuged prior to performing the ELISAs.
3.8 mRNA expression analysis of PRLR and SHH in human breast tissue
Data for expression of the PRLR and SHH in breast cancer was obtained from The
Cancer Genome Atlas (TCGA) database (generated by the TCGA Research Network:
http://cancergenome.nih.gov). RNASeq Version 2 sequencing data for 822 primary solid
breast invasive carcinoma tumour samples and 104 normal solid tissue controls was
downloaded and sorted in order to acquire specific expression data for the PRLR and
SHH. To determine if the samples had normal distribution a Q-Q plot and Shapiro-Wilk
test was used. An F-test was performed between normal and tumour samples for each
gene to determine if the samples had equal variance. A Students’ t-test was then used to
identify if PRLR or SHH were significantly upregulated in tumour versus normal tissue.
To test if PRLR expression is correlated with whether SHH is expressed or not, a binary
logistic regression was conducted. All statistical analyses were performed using the Real
Statistics for Excel Resource Pack (obtained from www.real-statistics.com). For purposes
of visualization the data was logarithmically transformed by a base of 2 to produce
boxplots depicting expression distribution. Boxplots were produced using BoxPlotR
(accessed at www.boxplot.tyerslab.com).
40
3.9 Statistical Analysis
A paired Student’s t-test or two-way ANOVA was used to determine statistical
significance of treatments in all osteoclastogenesis assays and ELISAs. In addition,
Bonferroni post-tests were applied to all groups to determine significance between
individual treatment samples. A p-value of ≤ 0.05 was required for statistical
significance. Standard deviation from the mean was also calculated and displayed as error
bars.
41
CHAPTER 4: RESULTS: OPTIMIZATION OF OSTEOCLAST
DIFFERENTIATION AND QUANTIFICATION
4.1 Optimization of Osteoclast Differentiation
To determine the potential molecular mechanism by which PRL enhances breast
cancer-mediated osteoclastogenesis a gene array and cytokine array were conducted. To
provide the best opportunity for identifying PRL-regulated, breast cancer secreted factors
through these means, the osteoclastogenesis assay conditions allowing for the greatest
PRL-enhancement in differentiation was determined.
4.1.1 PRL Enhances Breast Cancer-Mediated Osteoclastogenesis
Our lab previously observed that PRL treated breast cancer cells were capable of
inducing osteoclastogenesis to a greater extent than non-treated breast cancer cells
(Sutherland Thesis 2010). RAW264.7 differentiated in untreated SKBR3 CM formed 7.1fold more TRAP-positive, multinucleate osteoclasts than untreated controls. CM from
SKBR3 treated with 25 ng/mL hPRL for 48 hr further enhances osteoclastogenesis above
untreated CM (Figure 7). These results are consistent with what was previously observed.
4.1.2 Treatment of SKBR3 with hPRL for 48 hr is Optimal for Induction of
Osteoclastogenesis
To determine the optimal PRL treatment length for the gene and cytokine arrays,
osteoclastogenesis assays were conducted using CM from SKBR3 treated with 5 µg/mL
oPRL or 25 ng/mL hPRL for a time course of 24 hr, 48hr, 72hr or 96 hr. A treatment
length of 48 hr was found to be optimal for induction of osteoclastogenesis for both ovine
and human PRL (Figure 8). CM from SKBR3 treated with oPRL for 24 hr exhibited a
1.64-fold increase in osteoclastogenesis above non-PRL treated CM. The fold increase
was 2.45 after 48 hr oPRL treatment, 2.00 after 72 hr and 1.68 after 96 hr. PRL
enhancement of osteoclastogenesis was significantly significant at all time points (Figure
8A). CM from SKBR3 treated with hPRL enhanced osteoclastogenesis 1.75-fold after 24
hr, 2.06-fold after 48 hr, 1.83-fold after 72 hr and 1.77-fold after 96 hr. Only the increases
42
Figure 7. PRL enhances breast cancer-mediated osteoclastogenesis. RAW264.7 preosteoclasts were differentiated for 6 days in 20% CM from SKBR3 treated with 25
ng/mL hPRL or PBS control for 48 hr. TRAP positive, multinucleate (MNC) osteoclasts
were quantified for each treatment. Each column represents three internal replicates and
error bars indicate standard deviation from the mean. * indicates statistical significance
between PRL treated and non-treated samples (Student’s t-test: p < 0.05).
43
2
#TRAP+ multinucleate cells/cm
2
B
# TRAP+ multinucleate cells/cm
A
*
140
120
+oPRL
*
*
-oPRL
100
80
60
40
20
0
24 hr
48 hr
72 hr
96 hr
*
140
+hPRL
120
*
100
-hPRL
80
60
40
20
0
24 hr
48 hr
72 hr
96 hr
Figure 8. Conditioned media from SKBR3 treated with human or ovine PRL
enhances osteoclastogenesis, with 48 hr PRL treatment optimal for induction.
RAW264.7 pre-osteoclasts were differentiated for 6 days in 20% CM from SKBR3
treated with 5 µg/mL ovine PRL (A) or 25 ng/mL human PRL (B) for 24 hr, 48 hr, 72 hr
or 96 hr. TRAP positive, multinucleate osteoclasts were quantified for each treatment.
Each graph represents three experimental replicates with three internal replicates. Error
bars indicate standard deviation from the mean. * indicates statistical significance
between PRL treated and non-treated samples (Two-way ANOVA: p < 0.05).
44
following SKBR3 hPRL treatment for 48 and 96 hr were statistically significant, which
can be attributed to variation in overall induction of osteoclastogenesis between replicates
(Figure 8B).
Our lab previously determined, through a Luminex array and ELISA validation, that
commercially available oPRL extracted from sheep pituitary gland contains ACTH and
α-MSH (Sutherland 2010). The recombinant hPRL is free of such contamination and was
able to enhance osteoclastogenesis to levels similar to oPRL. In addition, all breast cancer
cell lines used are human and larger doses of oPRL are required to obtain the results of
normal physiological levels of hPRL (Utama et al. 2009). Based on my results, cells were
treated with hPRL for 48 hr for the cytokine array. Given that less time is required for
increased gene expression, a treatment length 24 hr prior to the observed peak of
biological effect was used for the gene array.
4.1.3 Prolactin Does Not Increase Breast Cancer Cell Proliferation During
Conditioned Media Preparation
PRL has been shown to have a proliferative effect on breast cancer cells through
activation of the Jak2/STAT5 pathway (Miyoshi et al. 2001), or Jak2 activation of the
Ras-Raf-MAPK pathway (Yamauchi et al. 2000). To confirm that PRL-induced cell
proliferation was not contributing to increased osteoclastogenesis, SKBR3 and T47D
breast cancer cells were counted using trypan blue following preparation of CM. While
the T47D cells were at a higher density than SKBR3 upon CM collection, there was no
observed increase in cell density with hPRL treatment in either of the cell lines (Figure
9). Following 48 hr of 25 ng/mL hPRL treatmen,t SKBR3 had an average cell density of
3.4 x 104 cells/cm2 and viability of 94%. Untreated SKBR3 had a density of 3.1 x 104
cells/cm2 and viability of 97%. Untreated T47D had an average cell density of 6.0 x 104
cells/cm2 and viability of 97%, while hPRL-treated cells had a cell density of 6.1 x 104
cells/cm2 and viability of 97%. There was no statistical significance between the densities
of untreated versus PRL-treated SKBR3 or T47D.
45
Figure 9. PRL does not increase breast cancer cell proliferation. SKBR3 and T47D
were plated at a density of 1.64 x 104 cells/cm2 and treated with 25 ng/mL hPRL or PBS
control for 48 hr. Cell viability was then determined using trypan blue staining and viable
cells counted using a hemocytometer. Error bars represent standard error from the mean
(n=3).
46
4.2 Methods of Quantifying Osteoclastogenesis
Multiple methods of osteoclastogenesis quantification were tested in order to find a
technique best suited for our system of differentiation. Methods were screened for their
ability to show PRL enhancement of osteoclastogenesis, ease of quantification, accuracy
of quantification and how amenable to high-throughput studies they are. In addition, the
use of multiple techniques increases the strength of our initial observation of
osteoclastogenesis using TRAP staining.
4.2.1 TRAP Staining
Staining for TRAP, in conjunction with staining for nuclei with hematoxylin, is the
most commonly used method to identify osteoclasts (Figure 10). Within our system of
differentiation, the majority of osteoclasts are compact and have five or fewer nuclei
(Figure 11A). In addition, large osteoclasts often show very faint TRAP staining (Figure
11B). The morphology of these cells contributes to subjectivity of osteoclast
quantification, leaving the process susceptible to counter bias. To aid in correcting
subjectivity strict guidelines for inclusion or exclusion of cells have been established.
Assays may also be double counted by two independent counters, one of which is blind,
to reduce counter bias (Figure 12).
4.2.2 para-Nitrophenyl Phosphate (pNPP) TRAP Activity Assay
An alternate method of quantifying osteoclastogenesis, which removes subjectivity
due to counter bias, utilizes pNPP as a colorimetric substrate for TRAP. Differentiation
of RAW264.7 in untreated SKBR3 CM is able to induce TRAP activity ~2-fold above
controls, however no further PRL induction of TRAP activity is observed (Figure 13).
This may be due to the fact that even single RAW267.4 cells will contain active TRAP,
and may effectively hide the induced signal, which is of a lower proportion. It is also
possible that PRL contributes to osteoclastogenesis in a manner independent of TRAP
activity.
47
Figure 10. TRAP and hematoxylin staining of osteoclasts. RAW264.7 cultures are
TRAP stained and the number of TRAP positive, multinucleate osteoclasts are counted.
Both criteria must be met, as undifferentiated cells can stain positive for TRAP. A,
Undifferentiated RAW264.7 expressing TRAP. B, An example of a mature, TRAP
positive, multinucleate osteoclast (arrow). Scale bar = 20 uM.
48
A
B
Figure 11. Osteoclast phenotypes. RAW264.7 cultures were differentiated for 6 days in
20% breast cancer cell CM. Cells are then TRAP stained and the number of TRAP
positive, multinucleate osteoclasts are counted. Both criteria must be met, as
undifferentiated cells can stain positive for TRAP. (A) An example of a compact
osteoclast with dark TRAP staining. (B) An example of a large spread osteoclast
displaying very faint TRAP staining. Scale bar = 20 uM.
49
# TRAP+ multinucleate cells/cm2
*
140
120
Blind Count
Count
100
80
60
40
20
0
Control
SKBR3
- hPRL CM
SKBR3
+ hPRL CM
Figure 12. Blind counting of TRAP and hematoxylin stained osteoclasts. RAW264.7
pre-osteoclasts were differentiated for 6 days in 20% CM from SKBR3 treated with 25
ng/mL hPRL or PBS control for 48 hr. The number of TRAP positive, multinucleate
osteoclasts was double counted, once with a blind counter. Each column represents three
internal replicates and error bars indicate standard deviation from the mean. * indicates
statistical significance between the initial count and blind count (Student’s t-test: p <
0.05).
50
*
0.010
0.008
0.006
0.004
0.002
0.000
l
tro
n
Co
RL
hP
RL
-hP
3
BR
SK
M
CM
LC
3+
BR
SK
R
hP
Figure 13. A colorimetric TRAP activity assay potentially detects CM induction of
osteoclastogenesis but not PRL induction. RAW264.7 were differentiated for 6 days in
20% CM from SKBR3 treated with 25ng/mL hPRL or PBS control for 48hr. Following
differentiation, cells were lysed in 0.2% triton-x-100 and TRAP activity of lysates
determined using a pNPP as a substrate. Graph represents one experiment containing
three internal replicates. Error bars represent standard error of the mean. * indicates
statistical significance (Two-way ANOVA: p< 0.05).
51
4.2.3 Morphological Identification using Fluorescent Microscopy
Fluorescent staining of cell nuclei, the plasma membrane and actin ring was
developed as an alternate method of identifying mature osteoclasts with the aim of using
this method as a high throughput screening tool. To identify multinucleate cells
RAW264.7 were stained with CellMask Deep Red plasma membrane stain and Hoechst
33342, and imaged using confocal microscopy (Figure 14). Multinucleate cells were
clearly identifiable, however, internalization of the plasma membrane stain (Figure 14B)
posed difficulties for development of an algorithm to identify the cells in a highthroughput manner. In some cases it was also difficult to determine if cells were at an
early fusion stage of differentiation or simply a small number of cells clustered together
(Figure 14C). This also required the adjustment of different focal planes, making this
technique less amenable for quantification (Figure 14C).
Phalloidin, which binds F-actin, and DAPI staining was used as a second screen to
identify mature, multinucleate osteoclasts as actin forms a sealing ring in active
osteoclasts that is essential for osteolytic bone resorption (Ma et al. 2010). While
multinucleate cells with a large actin ring were clearly visible, smaller actin rings were
also present around undifferentiated, mononucleate osteoclast precursors (Figure 15) and
also proved difficult for development of a defining algorithm. Actin ring and plasma
membrane staining in conjunction with a nuclear stain can therefore be useful in
identifying osteoclasts in the process of differentiation.
From these studies oPRL and hPRL were both confirmed to induce breast cancermediated osteoclastogenesis , and a PRL treatment length of 48 hr was found to be
optimal. Three methods of quantifying osteoclastogenesis were explored: TRAP staining,
the pNPP assay, and fluorescent staining. While all had their merits, TRAP staining was
ultimately chosen as the primary method.
52
A
B
C
Figure 14. RAW264.7 stained for the plasma membrane and nuclei. RAW264.7 were
plated on cover slips and differentiated in 20% breast cancer cell CM for 6 days. Cells
were then fixed in 3.7% PFA and stained with 5 ug/mL CellMask deep red plasma
membrane stain and 300nM DAPI. Cells were mounted in mowiol containing 2.5% w/v
DABCO and imaged on the IX81 FV1000 laser scanning confocal microscope (Live Cell
Imaging Facility, University of Calgary). In the top panel the arrow indicates a large
osteoclast with nine nuclei (A), while in the middle row the arrow indicates a smaller
osteoclast with four nuclei (B). In the bottom row the arrow indicates a cell that could
potentially be an osteoclast in the early stages of differentiation, or simply a cluster of
cells (C). Scale bar = 15uM.
53
Figure 15. RAW264.7 stained for the actin ring and nuclei. RAW264.7 were plated on
96-well optical plates and differentiated in 20% breast cancer cell CM for 6 days. Cells
were then fixed in 3.7% PFA, permeabolized in 3% Triton-X-100 and stained with 1:250
phalloidin and 600nM DAPI. Stained samples were maintained in PBS and imaged on the
Opera high content screening system (S.M.A.R.T. Laboratory for High-Throughput
Screening Programs, Mount Sinai Hospital, Toronto). Image from Cyrus Handy,
S.M.A.R.T. Laboratory for High-Throughput Screening Programs.
54
CHAPTER 5: RESULTS: IDENTIFICATION OF PRL-REGULATED, BREAST
CANCER CELL SECRETED FACTORS
5.1 Gene Array
5.1.1 Gene Array Sample Preparation and Testing
To elucidate the potential molecular mechanism by which PRL enhances breast
cancer-mediated osteoclastogenesis, a genome-wide microarray analysis was performed
(by Almac) on triplicate samples of SKBR3 treated with 25 ng/mL hPRL or PBS control
for 24 hr. Prior to the array, biological activity of the CM from SKBR3 samples was
assessed in an osteoclastogenesis assay. CM from the three hPRL-treated SKBR3
samples produced 1.68, 1.60 and 1.58-fold more TRAP positive, multinucleate
osteoclasts than untreated CM in all three samples. PRL induction was statistically
significant within each sample, and there was no statistical variation between the three
sample (Figure 16).
5.1.2 Regulation of Gene Transcription by PRL
Normalized gene array data was filtered by Almac to produce a stringent list
consisting of the most statistically robust PRL regulated gene transcripts, containing 61
of a total of 110,000 gene probes (Figure 17). CHCHD7 was the most highly upregulated
gene and was increased 2.33-fold by hPRL, while the greatest down-regulated probe, an
as yet unidentified gene transcript, had a fold change of -3.46. The smallest fold change
observed on the stringent list was -1.24. Genes were assessed by gene ontology (GO)
terms for biological process, molecular function, and cellular component (Figure 18).
Cell signaling, accounting for 18.3% of genes, was the GO biological process most
associated with PRL-regulation, of which 55% were upregulated and 45% downregulated
(Figure 18A). PRL-regulated genes were also associated with differentiation,
proliferation and transcription. Genes associated with differentiation and proliferation
were upregulated and downregulated evenly. The majority of genes associated with
transcription were upregulated (64%). The GO molecular process term most associated
55
# TRAP+ multinucleate cells/cm2
120
*
*
*
100
+hPRL
-hPRL
80
60
40
20
0
A
B
C
Figure 16. Conditioned media from SKBR3 used in gene array enhances
osteoclastogenesis. RAW264.7 pre-osteoclasts were differentiated for 6 days in 20% CM
from SKBR3 treated with 25 ng/mL human PRL or PBS control for 24 hr. TRAP
positive, multinucleate osteoclasts were quantified for each treatment. Each column
represents three internal replicates and error bars indicate standard deviation from the
mean. * indicates statistical significance between PRL treated and non-treated samples
(Student’s t-test: p < 0.05).
56
A
B
C
MAPK1
TSPAN12
SOCS2
PIK3C2A
FGD2
DOCK2
MDK
MAPRE2
TXLN4
HMBOX1
BCL6
TCEA1
ZNF90
MAMSTR
TBX19
GRK4
MAST3
SUGT1
CEP63
FAM125B
MBNL1
LARP6
KIAA1429
ANG
CDH4
PPFIBP
EXOC4
FIP1L1
TBCD
GRPEL2
PALM2
ANKRD23
HUWE1
--OSBP2
AHCTL2
SERTAD1
OSBP2
--FBXL22
--PGDB4
------CHCHD7
FANK1
EFCAB2
ASTN2
----RNF169
SNOR-ZMYM5
--FAM124A
SNOR-SNOR----
Cell signaling
Cell signaling
Cell signaling
Cell signaling
Cell signaling
Cell signaling
Differentiation
Proliferation
Proliferation
Transcription
Transcription
Transcription
Transcription
Transcription
Organ/multicellular development
Phosphorylation
Phosphorylation
Mitosis
Cell cycle
Protein transport
RNA processing
RNA processing
RNA processing
Angiogenesis
Cell adhesion
Cell adhesion
Exocytosis
mRNA processing
Protein folding
Protein import
Regulation of cell shape
Fatty acid metabolic process
Ubiquitination
Keratinization
Lipid transport
One-carbon metabolic process
Regulation of cyclin-dependent protein kinase activity
Steroid metabolic process
------------------------------3.00
--2.00
--1.00
--0.00
---1.00
---2.00
---3.00
Figure 17. PRL regulated genes. A genome wide gene array was conducted by Almac
on triplicate samples (A,B,C) of SKBR3 treated with 25ng/mL human PRL and PBS
control. The most statistically robust PRL regulated genes are presented by their primary
GO Biological Process term in a heat map. “---” denotes gene probes with no listed
identifiers or GO terms.
57
B
GO Molecular Function
GO Biological Process
A
GO Cellular Component
C
Response to hormone stimulus
RNA processing
Protein transport
DNA damage
Cell division
Cell cycle
Actin cytoskeleton organization
Mitosis
Apoptosis
Phosphorylation
Organ/multicellular development
Transcription
Proliferation
Differentiation
Cell signaling
Downregulated
Upregulated
0
5
10
15
20
25
30
35
0
5
10
15
20
25
30
35
0
5
10
15
20
25
30
35
RNA binding
Binding
Transferase activity
Transcription factor activity
ATP binding
Nucleotide binding
DNA binding
Zinc ion binding
Metal ion binding
Protein binding
Neuronal cell body
Microtubule
Integral to membrane
Extracellular region
Cytoskeleton
Membrane
Intracellular
Golgi apparatus
Nucleolus
Cytosol
Plasma membrane
Cytoplasm
Nucleus
Figure 18. PRL regulated genes assessed by GO terms. A genome wide gene array
was conducted by Almac on triplicate samples of SKBR3 treated with 25ng/mL human
PRL and PBS control. The most statistically robust PRL regulated genes are represented
by histograms indicating the most common functions and locations of PRL regulated
genes by GO biological process (A), GO molecular function (B), and GO cellular
component (C) terms.
58
with PRL regulated genes is protein binding, accounting for 28.3% of stringent regulated
genes with 47% upregulated and 53% downregulated (Figure 18B). Other associated GO
molecular functions were metal ion binding, zinc ion binding and DNA binding, although
individually they represent less than 12% of stringent PRL regulated genes. When
assessed by GO cellular component, the greatest number of PRL-regulated genes encode
for proteins present in the nucleus and cytoplasm (Figure 18C). A total of 31.7% of the
most stringently regulated genes encode for protein in the cell nucleus, of which 53% are
upregulated and 47% are downregulated. The cytoplasm contains proteins encoded by
26.7% of genes, of which 41% are upregulated and 59% are downregulated.
5.1.3 Gene Array Validation
5.1.3.1 Establishing TBP as a Reference Gene
As an alternative to YWHAZ, which has been shown to interact with the PRLR
(Olayioye et al. 2003), TBP was assessed as a potential reference gene for qPCR. TBP is
not known to be regulated by PRL and has been used in other studies examining PRLregulated gene expression (Rasmussen et al. 2010). To ensure that TBP was not PRL
regulated in our cell lines, TBP expression was assessed in triplicate samples of SKBR3
treated with 25 ng/mL hPRL or PBS control for 6 hr, 24hr and 48 hr. Each was tested in a
qPCR reaction and was compared to the previous reference gene, YWHAZ, as well as
genes confirmed to be PRL-regulated in our lab (MAPRE2, SPRED1 or PALM2).
Expression of TBP in SKBR3 was not altered with PRL treatment across the time course
(Figure 19). As such, it was also used as a reference gene to complete gene array
validation and can be used in future experiments examining expression of candidate
genes.
5.1.3.2 Gene Array Validation
From Almac’s stringent list, ten genes with the highest fold change between PRL
treated and non-treated samples were chosen to validate the array. Primers for these genes
were designed (with the aid of undergraduate students Heather Gibling and Vanessa
Lam) using the NCBI Primer-Blast program. Quantitative PCR was conducted
59
Figure 19. Gene expression of TBP is not regulated by PRL. Expression of TBP in
SKBR3 treated with 25 ng/mL human PRL for 6 hr, 24 hr or 48 hr was determined using
qPCR. Fold change between PRL treated and untreated samples was determined by the
Pfaffl method. Three qPCR reactions were conducted in triplicate and error bars represent
standard deviation from the mean.
60
over a temperature gradient ranging from 55°C to 65 °C and annealing temperature of
each primer was determined (Table 1). One of the genes, CHCHD7, was removed from
the validation list as it has a second RNA transcript too similar to be detected
individually. Gene expression of candidate factors LIF, FLRT1 and FLST4 was also
compared to the gene array for further validation.
Triplicate samples of SKBR3 treated with 25 ng/mL hPRL for 24 hrs or SKBR3
samples remaining from the gene array were used in qPCR reactions to validate PRLregulation of each of the targets. Expression of LIF was determined using semiquantitative PCR. Genes were normalized to TBP or YWHAZ and, for purposes of
calculation, each primer was assumed to have 100% efficiency. To accept a gene as
validated, the fold change between PRL treated and untreated samples had to be within
0.5 of the gene array result. Currently 60% of the genes have been successfully validated
(Figure 20; with the aid of undergraduate student Vanessa Lam). Of the genes chosen for
validation from the stringent list, MAPRE2, PALM2 and SPRED1 were tested using
RNA prepared after completion of the gene array. BCL6 and GRK4 were validated using
RNA prepared for the gene array. Attempts at validating LARP6, RNF169, FAM124A
and FAM151B have been unsuccessful as the fold changes obtained do not match the
gene array. The fold change obtained in the gene array and average validation fold
change for each gene can be found in Table 2.
5.1.4 Gene Array Candidate Selection and Assessment
5.1.4.1 Candidate Selection
Candidates identified in the gene array were selected from the stringent list
(described in section 4.3.2) and less stringent list, which consists of 563 gene probes also
statistically regulated by PRL, though less robustly than the stringent list. A list of known
PRL-regulated genes in the mammary gland and breast tumors was generated from
normalized array data to establish the minimum fold change between PRL-treated and
non-treated samples, and maximum p-value that would be accepted in identifying
candidates (provided by undergraduate student Heather Gibling). The cutoff for fold-
61
Tm
Gene
Fold Change
p-Value
Primer Sequence (5’ – 3’)
(°C)
Forward AGGAGGCCGAGGACGTGGA
LARP6
-3.30
0.0065
55
Reverse CTCGCTCGCGCTGCCCCA
Forward GCAGTGTCCTGTTTCCCAGAGG
MAPRE2*
-2.31
0.0063
60
Reverse CATTGACCGCCATTCCCCAACT
Forward TCCAGGCAACAAAGCCAAGGTCA
RNF169*
-2.03
0.0071
55
Reverse GGGAAGCCAAGGATGAAAGGGAGC
Forward GGTTTCTGCCATCAAGCCTCCTCG
BCL6*
-1.58
0.0015
59
Reverse TGTTCTCCACCACCTCACGACC
Forward TACCGTTCTGGGTGAGGCATCC
SPRED1
1.46
0.0185
55
Reverse TGACGCTGCTTAGTCCACTCCT
Forward AGTTTCCTTGGGTGACTACAGCGT
PALM2
1.90
0.0071
63
Reverse TTTCCTTGTGCAATTCCGCCTCTG
Forward AGCCAGATTACAGCAGAAGACGGT
FAM124A*
1.96
0.0095
55
Reverse TCAGTGCCTCATTTGTTTGTGCCT
Forward ACCCCAGCCAGAAGAAAGCGGA
FAM151B*
2.07
0.0117
Forward CGATTGGTGGGGACTTGGCTGT
GRK4*
2.17
0.0094
55
Reverse TCGCCTATGTCCCTCACTCGGA
Reverse GCCGCTTGCTTGGATTCTTGGTGA
61.3
Forward TCGGTAACACAGAGGCTGAGAGAT
CHCHD7
2.33
0.0066
Reverse
TCCA GACATCTGGTGGAAGCATCA
57
Table 1. Validation Genes and Primers. Validation genes were chosen by identifying
the ten genes with greatest fold change in expression between PRL-treated and nontreated samples. Primers were designed using NCBI Primer-Blast. qPCR was conducted
across a temperature gradient of 55°C to 65 °C and optimal annealing temperature was
determined by identifying the temperature at which threshold was reached in the least
number of cycles. * indicates primers designed by undergraduate students Heather
Gibling and Vanessa Lam.
62
3
3
3
2
2
2
1
1
1
0
0
0
-1
-1
-1
-2
-2
-2
-3
-3
GRK4
PALM2
-3
3
3
3
2
2
2
1
1
1
0
0
0
-1
-1
-1
-2
-2
-2
-3
-3
LIF
BCL6
-3
SPRED1
MAPRE2
Figure 20. Gene array validation. Six of the ten genes used to determine the validity of
the gene array were successfully validated. Expression was determined by qPCR using
RNA prepared from SKBR3 treated with 25 ng/mL human PRL for 24 hrs or RNA
samples remaining from the gene array. To determine the fold change between PRL
treated and untreated samples the Pfaffl method was used. Expression of LIF was
determined using semi-quantitative PCR. Each column represents an experimental
replicate. The dotted horizontal lines represent the fold change in gene expression
obtained from the gene array. Data for SPRED1, LIF and MAPRE2 from Vanessa Lam.
63
Fold Change Fold Change
Gene
LARP6
MAPRE2*#
Gene Name
La ribonucleoprotein domain family,
member 6
micotubule-associated protein, RP/EB
family, member 2
Array
Validation
-3.30
1.06
-2.31
-2.20
RNF169
ring finger protein 169
-2.03
-1.24
BCL6
B-cell CCL/lymphoma 6
-1.58
-1.66
LIF*#
Leukemia inhibitory factor
1.22
1.20
1.46
1.37
SPRED1*#
sprouty-related, EVH1 domain
containing 1
PALM2*
paralemmin 2
1.90
1.78
FAM124A
Family with sequence similarity 124A
1.96
1.06
FAM151B
Family with sequence similarity 151B
2.07
1.02
GRK4
G protein-coupled receptor kinase 4
2.17
2.00
2.33
N/A
CHCHD7
Coiled-coil-helix-coiled-coil helix domain
containing 7
Table 2. Summary of gene array validation. Validation genes were chosen by
identifying the ten genes with greatest fold change in expression between PRL-treated
and non-treated SKBR3 samples on a stringent list of PRL regulated genes from the
array. Genes were validated by qPCR using RNA prepared from SKBR3 treated with 25
ng/mL human PRL for 24 hrs (*) or RNA samples remaining from the gene array. To
determine the fold change between PRL treated and untreated samples the Pfaffl method
was used. Expression of LIF was determined using semi-quantitative PCR. # indicates
genes validated by Vanessa Lam.
64
change is |1.32| and the maximum p-value is 0.0496. Using the fold change and p-value
cut-offs, and known biological functions (provided by Vanessa Lam) a list of potential
candidates was generated from the stringent and less stringent lists (Table 3.1). From this
list, only secreted, extracellular or transmembrane factors were selected. Of those, factors
identified by fold change and p-value alone are FLRT1, FSTL4 and CECR1 (Table 3.2).
Factors identified by fold change, p-value and biological function include ANG (Morita
et al. 2008), MDK (Maruyama et al. 2004), LTBP2 (Moren et al. 1994), BMP1 (Aldinger
et al. 1991), and TICAM1 (Yamamoto et al. 2002) (Table 3.3). JAG1 (Sethi et al. 2011),
LTBP3 (Song et al. 2009), and LIF (Bozec et al. 2008) were included in the candidate list
based on biological function, as each is indicated in osteoclastogenesis or bone (Table
3.4). Despite low fold change, it is possible that PRL induces these genes to a greater
extent at a time point other than 24 hr. The two factors with the greatest fold change,
FSTL4 and FLRT1, were chosen to pursue in further studies.
5.1.4.2 FLRT1 and FSTL4 are Not PRL-Regulated Genes
To validate the fold change in expression for FLRT1 and FSTL4 obtained in the gene
array and assess PRL regulation of the candidates at other lengths of PRL treatment,
triplicate samples of SKBR3 treated with 25 ng/mL hPRL for 6 hr, 24 hr or 48 hr were
prepared for qPCR. Neither gene was validated and both were found not to be PRL
regulated. FLRT1, which was downregulated 2.73-fold following 24 hr PRL treatment in
the gene array, was upregulated 1.4-fold following 6 hr hPRL treatment, 1.2-fold
following 24 hr hPRL and 1.3-fold following 48 hr hPRL treatment (Figure 21A; Sara
Mirzaei). FSTL4, which was upregulated 2.57-fold in the gene array, was downregulated
-1.2-fold following 6 hr hPRL treatment and upregulated 1.1-fold and 1.2-fold following
24 hr and 48 hr hPRL treatment, respectively (Figure 21B; Sara Mirzaei). Given the low
fold changes observed, it is likely that neither of the genes is PRL regulated in SKBR3.
65
Gene
Gene Name
FLRT1
fibronectin leucine rich transmembrane
(probe set 1)
protein 1
FLRT1
fibronectin leucine rich transmembrane
(probe set 2)
protein 1
ANG
MDK
LTBP2
angiogenin, ribonuclease, RNase A
family, 5
midkine
latent transforming growth factor beta
binding protein 2
Fold Change
p-value
-2.73
0.0451
-1.31
0.0273
-1.46
0.0069
-1.44
0.0027
-1.43
0.0255
BMP1
bone morphogenic protein 1
-1.4
0.0352
JAG1
jagged 1
1.22
0.0179
LTBP3
latent transforming growth factor beta
(probe set 1)
binding protein 3
1.23
0.0034
LTBP3
latent transforming growth factor beta
(probe set 2)
binding protein 3
1.23
0.0101
LIF
leukemia inhibitory factor
1.25
0.0183
1.54
0.0457
CECR1
cat eye syndrome chromosome region,
candidate 1
TICAM1
toll-like receptor adaptor molecule 1
1.54
0.0303
ACVR2A
activin receptor IIA
1.6
0.0283
FSTL4
follistatin-like 4
2.57
0.0127
Table 3.1. Summary of candidate genes. Candidate genes were identified using a fold
change greater than 1.32 or less than -1.32, a p-value less than 0.0496 or known
biological function (see tables 3.3 and 3.4). All gene products are secreted, extracellular
or transmembrane. The two genes with greatest fold change, FLTR1 and FSTL4, have
been chosen to pursue in further studies (bolded).
66
Gene
Gene Name
FLRT1
fibronectin leucine rich
(probe set 1)
transmembrane protein 1
FLRT1
fibronectin leucine rich
(probe set 2)
transmembrane protein 1
CECR1
FSTL4
cat eye syndrome chromosome
region, candidate 1
follistatin-like 4
Fold Change
p-value
-2.73
0.0451
-1.31
0.0273
1.54
0.0457
2.57
0.0127
Table 3.2. Candidate genes identified based on fold change and p-value. This set of
candidate genes was selected using fold change and p-value. Fold change had to be
greater than 1.32 and have a p-value of less than 0.0496 for inclusion. Each gene product
is secreted, extracellular or transmembrane.
67
Gene
Gene Name
Fold
Change
p-value
-1.46
0.0069
-1.44
0.0027
Osteoclastogenesis [2]
-1.43
0.0255
TGF-beta signaling [3]
angiogenin,
ANG
ribonuclease, RNase A
family, 5
MDK
midkine
Biological Function
Inhibition of osteoclast
differentiation [1]
latent transforming
LTBP2
growth factor beta
binding protein 2
BMP1
TICAM1
bone morphogenic
protein 1
toll-like receptor adaptor
molecule 1
Bone cell differentiation,
-1.4
0.0352 ossification, skeletal system
development [4]
1.54
0.0303
Regulation of NF-κB
signaling [5]
Table 3.3. Candidate genes identified based on fold change, p-value and biological
function. This set of candidate genes was selected by identifying factors with a fold
change greater than 1.32 and a p-value less than 0.0496 that had known biological
functions related to bone metabolism and/or osteoclastogenesis (provided by Vanessa
Lam). Each gene product is secreted, extracellular or transmembrane. [1] (Morita et al.
2008), [2] (Maruyama et al. 2004), [3] (Moren et al. 1994), [4] (Aldinger et al. 1991), [5]
(Yamamoto et al. 2002).
68
Gene
Gene Name
Fold p-value
Change
Biological Function
Osteoblast differentiation
JAG1
jagged 1
1.22
0.0179
via osteoblast signaling,
proliferation [1]
LTBP3
latent transforming growth
(probe 1) factor beta binding protein 3
LTBP3
latent transforming growth
(probe 2) factor beta binding protein 3
LIF
leukemia inhibitory factor
1.23
0.0034
Osteoclastogenesis [2]
1.23
0.0101
Osteoclastogenesis [2]
1.25
0.0183
Osteoclastogenesis [3]
Table 3.4. Candidate genes identified based on biological function. Despite being
below the cutoff for fold-change and p-value, these candidate genes had biological
function relevant to the proposed mechanism (provided by Vanessa Lam). Each gene
product is secreted, extracellular or transmembrane. [1] (Sethi et al. 2011), [2] (Song et
al. 2009), [3] (Bozec et al. 2008).
69
A
B
Figure 21. FLRT1 and FSTL4 are not PRL regulated. Expression of FLRT1 and
FSTL4 in SKBR3 treated with 25 ng/mL human PRL for 6 hr, 24 hr or 48 hr was
determined using qPCR. Fold change between PRL treated and untreated samples was
determined by the Pfaffl method. The dotted lines represent fold change obtained in gene
array following 24 hr hPRL treatment. Error bars represent standard error from the mean
(n=3). Data from Sara Mirzai.
70
5.2 Cytokine Array
5.2.1 Preparation of Serum Free SKBR3 and MCF7 Conditioned Media Samples
A RayBiotech biotin-label based human antibody array, capable of detecting 507
human cytokines, was performed on CM from hPRL treated SKBR3 and MCF7 by Ray
Biotech as a pilot study to identify PRL-regulated, breast cancer secreted factors
contributing to osteoclastogenesis. The RayBiotech antibody array is currently the largest
available, providing the greatest chance of identifying candidate factors. The array was
conducted using serum free CM, as proteins present in FBS produce high background
levels on the antibody array that could interfere with results. Prior to the array, the effect
of serum free conditions on differentiation was tested to ensure that it would not alter
observed trends of breast cancer-mediated, PRL enhanced osteoclastogenesis. Serum free
CM induced osteoclastogenesis 1.7-fold greater than untreated CM from SKBR3 cells,
and serum free CM from PRL-treated SKBR3 cells was 1.6-fold greater than PRL-treated
SKBR3 CM containing 10% FBS. The relative level of PRL enhancement, however, was
similar as PRL enhanced osteoclastogenesis 1.5-fold with normal CM and 1.4-fold with
serum free CM (Figure 22).
Five replicate samples of serum free CM were prepared from PRL treated SKBR3
and MCF7 and tested in an osteoclastogenesis assay. PRL enhanced osteoclastogenesis in
all SKBR3 CM samples (Figure 23A), however, only two of five PRL-treated MCF7 CM
samples significantly enhanced osteoclastogenesis (Figure 23B). Previous data has shown
a trend of PRL induction with MCF7 CM, however, the increase above breast cancermediated osteoclastogenesis has not been consistently significant. As the cytokine array
was used as a pilot study for the identification of PRL-regulated breast cancer cell
secreted factors, the sample of serum free SKBR3 and MCF7 CM exhibiting the highest
PRL-enhancement was used in the study.
5.2.2 PRL-Regulated, Breast Cancer Cell Secreted Candidate Selection
The CM samples were tested in duplicate for each cytokine in the array, with each
sample being tested on an individual array chip: SKBR3 – PRL CM, SKBR3 + PRL CM,
MCF7 – PRL CM and MCF7 + PRL CM. Ray Biotech performed initial data
71
# TRAP+ multinucleate cells/cm2
250
Serum Free CM
*
Serum CM
200
*
150
100
50
0
l
o
ntr
Co
L
M
R
hP
S
R3
KB
-h
LC
PR
L
PR
CM
h
3+
BR
SK
Figure 22. Serum free CM maintains breast cancer cell-mediated osteoclastogenesis
with and without PRL induction. RAW264.7 pre-osteoclasts were differentiated for 6
days in 20% CM from SKBR3 treated with 25 ng/mL human PRL or PBS control for 48
hr. Serum free SKBR3 were placed into serum free media for the duration of PRL
treatment. Additional serum was added to RAW264.7 treated with serum free CM in
order to maintain an even serum concentration across all pre-osteoclast samples. TRAP
positive, multinucleate osteoclasts were quantified for each treatment. Each column
represents three internal replicates and error bars indicate standard deviation from the
mean. * indicates statistical significance between serum and serum free samples
(Student’s t-test: p < 0.05).
72
A
#TRAP+ multinucleate cells/cm2
*
*
*
*
250
+hPRL
-hPRL
*
200
150
100
50
0
Control
A
B
C
D
SKBR3 CM
E
*
#TRAP+ multinucleate cells/cm2
B
*
300
300
+hPRL
*
250
*
-hPRL
200
150
100
50
0
Control
A
B
C
D
MCF7 CM
E
Figure 23. Serum free CM from SKBR3 and MCF7 prepared for the cytokine array
induces osteoclastogenesis. RAW264.7 pre-osteoclasts were differentiated for 6 days in
20% CM from SKBR3 (A) or MCF7 (B) serum starved and treated with 25 ng/mL
human PRL or PBS control for 48 hr. Additional serum was added to RAW264.7 treated
with serum free CM in order to maintain an even serum concentration across all preosteoclast samples. TRAP positive, multinucleate osteoclasts were quantified for each
treatment. Each column represents three internal replicates and error bars indicate
standard deviation from the mean. * indicates statistical significance between PRLtreated and non-treated samples (Student’s t-test: p < 0.05).
73
analysis to produce a normalized list of cytokine expression with background subtracted.
Briefly, signal intensities of each duplicate were averaged and then normalized by
positive control to compare data between array chips. Mean background was determined
by averaging negative control values and was then subtracted from the positive control
normalized values to produce a working list of cytokine expression. To identify potential
candidates, we calculated the fold change between PRL-treated and non-treated samples.
As per Ray Biotech instructions a signal was considered “real” if it was two or three
times greater than mean background. As statistical confidence increases the greater a
signal is above background, we selected cytokines with signal intensities three times
greater than background in the SKBR3 + PRL sample. Significant change in protein
expression was determined by a ≥1.5-fold increase or ≤-1.5-fold decrease in signal
intensity between PRL-treated and non-treated samples. These two criteria were used to
produce a list of 104 potential candidates. Known function and involvement in bone
homeostasis was assessed for each cytokine to further reduce the potential candidate list
to 20 candidates (Figure 24). In addition, cytokines regulated in opposing fashion in
SKBR3 and MCF7 were removed as a similar trend in PRL induction of
osteoclastogenesis is observed in both cell lines. Cytokine array candidates were assessed
by gene ontology (GO) terms for molecular function and biological process. The majority
of PRL-regulated cytokines (65%) from the first, low stringency method of background
subtraction are involved in receptor binding. Half of the cytokines (50%) were also
involved in growth factor activity, while 45% are involved in cytokine activity and 35%
involved in protein binding (Figure 25A). The GO biological processes most commonly
associated with PRL-regulated cytokines from the less stringent list are signal
transduction (60%), immune response (55%), cell differentiation (50%), cell-cell
signaling (50%), cell signaling (45%), apoptosis (35%), cell proliferation (35%) and
chemotaxis (30%) (Figure 24C).
Alternatively, a more stringent method of candidate selection used was to subtract
the background of each candidate individually. While the negative control provides a
reliable measure of background, it is based on the assumption that background is even
across the slide. Candidate expression levels were therefore reassessed by
74
F7
3
C
BR
M
SK
Osteoclast differentiation
Signal transduction
Regulation of cell fate specification
Cell differentiation
Cell differentiation
Chemotaxis
Cell signaling
Cell signaling
Chemotaxis
Multicellular organism growth
Cell differentiation
Cell signaling
Signal transduction
Signal transduction
Cell differentiation
Cell growth
Immune response
Protein kinase activity
Immune response
Cell differentiation
Figure 24. PRL regulation of breast cancer cell secreted factors selected by less
stringent criteria and their associated biological functions. A cytokine array detecting
507 human cytokines was conducted by RayBiotech, Inc. on CM from SKBR3 and
MCF7 treated with 25ng/ml human PRL or PBS control for 48hr. (A) Factors with signal
intensities 3x greater than background calculated from the negative control that were
upregulated at least 1.5-fold by PRL in SKBR3 are represented in a heat map depicting
fold change. Cytokines are presented by their gene’s corresponding GO Biological
Process term.
75
GO Molecular Function
A
GO Biological Process
B
Protein hetero/homodimerization activity
Chemokine activity
Protein binding
Cytokine activity
Growth factor activity
Receptor binding
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
Protein phosphorylation
Metabolic process
Mammary gland development
Cell migration
Cell development
Cell adhesion
Blood coagulation
Angiogenesis
Ossification
Inflammatory response
Chemotaxis
Cell proliferation
Apoptosis
Cell signaling
Cell-cell signaling
Cell differentiation
Immune response
Signal transduction
% cytokines
Figure 25. PRL regulation of breast cancer cell secreted factors selected by less
stringent criteria and their associated biological functions. A cytokine array detecting
507 human cytokines was conducted by RayBiotech, Inc. on CM from SKBR3 and
MCF7 treated with 25ng/ml human PRL or PBS control for 48hr. (A) Factors with signal
intensities 3x greater than background calculated from the negative control that were
upregulated at least 1.5-fold by PRL in SKBR3 are represented by histograms depicting
the most common function PRL regulated secreted factors by molecular function (A) and
biological process GO terms (B).
76
subtracting the local background (intensity value immediately outside the diameter of the
spot) from the positive control normalized values and selecting candidates where the
signal was greater than twice the standard deviation (Figure 26, data from Dr. Carrie
Shemanko). The GO molecular functions PRL-regulated cytokines from the stringent
method of background subtraction are predominantly associated with are protein binding
(50%), receptor binding (37.5%) and receptor activity (33.3%) (Figure 27A). The GO
biological processes most commonly associated with PRL-regulated cytokines from the
stringent list are similar to that of the less stringent list. Cell signaling is the most
associated GO term, representing 58.3% of PRL-regulated proteins. Immune response
(37.5%), signal transduction (29.2%), cell differentiation (29.2%), apoptosis (25%), cell
migration (25%) and cell signaling (25%) are also associated with the stringent list
(Figure 27B). SHH and TNFSF14/LIGHT, both identified using the first method (low
stringency) of background subtraction, were chosen as primary candidates due to their
fold change in expression with PRL and their known function in osteoclastogenesis.
SHH was upregulated 2.33-fold by PRL in SKBR3 and 1.29-fold in MCF7.
Breast cancer cell secreted SHH has been shown to directly induce differentiation of
osteoclast precursors as well as enhance osteoclastic activity by mature osteoclasts
resulting in an increase bone resorption (Das et al. 2010). LIGHT was upregulated 1.69 in
SKBR3 and 1.67 in MCF7. It is an interesting candidate as it is able to promote
osteoclastogenesis through canonical RANKL signaling, as well as independently
(Edwards et al 2006). SHH and LIGHT are potentially novel PRL-regulated factors.
RANKL was upregulated 22.04-fold in SKBR3 in the array, however, it was not present
above background in the untreated CM samples. Thus, the fold change may be more
indicative of PRL-induction of the cytokine as opposed to PRL-enhancement, but may
also reflect a false positive. Luminex analysis of SKBR3 CM has indicated that RANKL
is not elevated by the presence of breast cancer cells in the CM, nor is it PRL-induced
(Sutherland et al. 2015, submitted). RANKL was upregulated 1.56-fold in MCF7 and was
present above background in both PRL-treated and untreated samples.
77
3
C
F7
BR
M
SK
Cell-cell signaling
Apoptosis
Apoptosis
Immune response
Cell signaling
Cell morphogenesis
Angiogenesis
Apoptosis
Cell signaling
Cell-cell signaling
Cell signaling
Cell signaling
Cell signaling
Multicellular organism development
Cell signaling
Cell growth
Osteoblast differentiation
Extracellular matrix disassembly
Protein secretion
Cell differentiation
Proteolysis
Cell differentiation
Cellular response to organic substance
Figure 26. PRL regulation of breast cancer cell secreted factors selected by stringent
criteria and their associated biological functions. A cytokine array detecting 507
human cytokines was conducted by RayBiotech, Inc. on CM from SKBR3 and MCF7
treated with 25ng/ml human PRL or PBS control for 48hr. Factors with signal intensities
2-3x greater than local background that were upregulated at least 1.5-fold by PRL in
SKBR3 are represented in a heat map depicting fold change. Cytokines are presented by
their gene’s corresponding GO Biological Process term.
78
GO Molecular Function
A
GO Biological Process
B
ATP binding
Growth factor activity
Cytokine activity
Receptor activity
Receptor binding
Protein binding
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
Response to cellular stress
Protein phosphorylation
Cellular response to stress
Cel adhesion
Chemotaxis
Cell proliferation
Organ development
Inflammatory response
Angiogenesis
Cell-cell signaling
Cell migration
Apoptosis
Cell differentiation
Signal transduction
Immune response
Cell signaling
Immune response
% cytokines
Figure 27. PRL regulation of breast cancer cell secreted factors selected by stringent
criteria and their associated biological functions. A cytokine array detecting 507
human cytokines was conducted by RayBiotech, Inc. on CM from SKBR3 and MCF7
treated with 25ng/ml human PRL or PBS control for 48hr. Factors with signal intensities
2-3x greater than local background that were upregulated at least 1.5-fold by PRL in
SKBR3 are represented in a heat map depicting fold change. Cytokines are represented
by histograms depicting the most common function PRL regulated secreted factors by
molecular function (A) and biological process GO terms (B).
79
5.2.3 Luminex Cytokine Array Validation
A small scale, 64-plex human Luminex array was performed by Eve Technologies
Corp. on triplicate samples of serum free CM from SKBR3 treated with human PRL or
PBS control (including a sample of the CM used in the Ray Biotech array) in order to
validate the larger cytokine array. Of the 64 cytokines probed 16 were present within
detectable range. No factors were significantly induced by PRL (Figure 28), however,
fourteen known osteoclastogenic factors were present (Figure 28A, Table 4). These
factors are likely contributing to the overall effect of breast cancer-mediated induction of
osteoclast differentiation and resorption. While the arrays did not show significant PRLinduction, it would be beneficial to further characterize these cytokines using ELISAs
and real time, quantitative PCR.
5.3 Detection of SHH, IHH and LIGHT in Conditioned Media Samples by ELISA
ELISAs were conducted to confirm expression and PRL regulation of cytokine array
candidates SHH and LIGHT, which were of greatest functional interest. The antibody
detecting SHH in the cytokine array was against the SHH N-terminus, which shares 91%
identity with another of the hedgehog signaling ligands, Indian hedgehog (IHH; Pathy et
al. 2001). To ensure that IHH was not also detected in the cytokine array it was assessed
via ELISA. It was confirmed with the manufacturer of the ELISAs that the IHH and SHH
ELISA antibodies have no cross-reactivity with other hedgehog ligands.
Triplicate samples of serum free SKBR3 and MCF7 CM prepared for the cytokine
array was used to validate the cytokine array. In addition, protein levels of SHH, IHH and
LIGHT were assessed in CM from SKBR3 treated with an hPRL dose response, as well
as CM from T47D. IHH and LIGHT were not expressed in SKBR3, MCF7 or T47D.
SHH was successfully validated in SKBR3, and was upregulated 3.1-fold in serum free
SKBR3 CM. Expression of SHH in MCF7 was also confirmed, but as in the cytokine
array, no PRL-regulation was observed (Figure 29). Furthermore, SHH expression was
found to increase with hPRL dosage, reaching maximum expression at 100 ng/mL hPRL
(Figure 30), and was upregulated 2.5-fold in T47D (Figure 31).
80
A
B
SKBR3 CM
SKBR3 + hPRL CM
81
Figure 28. Luminex detection of breast cancer secreted factors. A cytokine array
detecting 64 human cytokines was conducted by Eve Technologies Corp. on CM from
SKBR3 treated with 25ng/ml human PRL or PBS control for 48hr. Fourteen
osteoclastogenic factors were identified (A), along with an additional two breast cancer
secreted factors (B). Error bars indicate standard deviation from the mean (n=3).
82
Fractalkine
SKBR3 -PRL
(pg/mL)
58.79
SKBR3 + PRL
(pg/mL)
54.65
G-CSF
16.26
GM-CSF
Cytokine
Fold Change
p-Value
-1.08
0.66
20.68
1.27
0.33
6.80
9.58
1.41
0.06
IL-15
0.81
1.18
1.45
0.23
IL-1RA
8.03
7.31
-1.10
0.90
IL-6
1.43
1.50
1.05
0.76
IP-10*
24.14
29.52
1.22
0.29
MCP-1*
966.86
1136.79
1.18
0.20
MCP-3
5.29
9.13
1.73
0.09
PDGF-BB*
58.27
63.73
1.09
0.20
RANTES
2.65
2.51
-1.06
0.74
SCF*
5.33
7.36
1.38
0.18
TNF-a
3.71
3.49
-1.06
0.94
VEGF*
150.34
157.06
1.04
0.26
Table 4. Luminex detection of osteoclastogenic breast cancer factors. A cytokine
array detecting 64 human cytokines was conducted by Eve Technologies Corp. on CM
from SKBR3 treated with 25ng/ml human PRL or PBS control for 48hr. Factors detected
in the array with known roles in osteoclastogenesis are presented. * indicates factors also
induced by ovine PRL (Sutherland Thesis 2010).
83
Fold change SHH expression
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
SKBR3
MCF7
Figure 29. SHH is upregulated by PRL in SKBR3 but not MCF7. Three independent
samples of serum free CM each from SKBR3 and MCF7 treated with 25 ng/mL hPRL,
prepared for the cytokine array, was used to validate the array. Concentration of SHH
present was calculated using a linear regression standard curve, and fold change in
expression calculated from obtained concentrations. Error bars represent standard error
from the mean (n=3).
84
Figure 30. SHH is responsive to an hPRL dose response in SKBR3. 2% FBS CM
from SKBR3 treated with 12.5 ng/mL, 25 ng/mL, 50 ng/mL, 100 ng/mL or PBS control
was assessed for SHH expression via ELISA. Concentration of SHH present was
calculated using a linear regression standard curve. Error bars represent standard error of
from the mean of duplicate sample concentrations (n=1).
85
Figure 31. SHH is PRL regulated in T47D. Triplicate samples of 2% FBS CM from
T47D treated with 25 ng/mL hPRL were assessed for SHH expression via ELISA.
Concentration of SHH present was calculated by averaging the absorption of two internal
replicates and then using a linear regression standard curve. Fold change in expression
calculated from obtained concentrations. (n=3).
86
5.4 SHH Signaling Inhibition in Osteoclast Differentiation
Since SHH has been previously reported to stimulate breast cancer cell-mediated
osteoclastogenesis, it was a strong candidate. An osteoclastogenesis assay was conducted
using the inhibitory antibody 5E1, which is known to effectively bind IHH and SHH
(Wang et al. 2000). There was no statistical difference between RAW264.7 cultured in
DMEM alone versus DMEM supplemented with 5E1. Treatment with 5E1 reduced
hPRL-induced osteoclastogenesis down to non-PRL treated levels in SKBR3 (Figure 32).
This data is preliminary, as only a single experiment with three internal replicates has
been conducted. Further replicates of this assay, with the inclusion of an IgG1 control
antibody, are required to confirm the effect of 5E1 in our model of breast cancermediated osteoclastogenesis. Additional experiments performed in the lab with the Hh
pathway inhibitor, cyclopamine, have confirmed the role of this pathway in PRL-induced
breast cancer cell-mediated osteoclastogenesis (Sutherland et al. 2015, submitted).
5.5 Expression of the PRLR and SHH in Human Breast Tumours
To determine expression of the PRLR and SHH, mRNA expression data for 822
primary solid breast invasive carcinoma tumour samples and 104 normal solid tissue
controls was obtained from the TCGA data portal (generated by the TCGA Research
Network: http://cancergenome.nih.gov/ ). The PRLR is expressed in all control and
tumour tissue samples, but has significantly higher expression in tumour samples (p=2.28
x 10-37) (Figure 33A). In normal tissue, the range of PRLR expression is 6.72 – 7962.65,
with a mean of 2605.04 ± 1733.14. The range of PRLR expression in tumour samples is
146.39 – 59489.81, with a mean of 6046.06 ± 4835.22. The significant difference
between normal and cancerous tissue is interpreted with a degree of caution as the sample
groups have unequal variances (p=4.96 x 10-27), and deviates from normality (normal:
w=0.97, p=0.01; tumour: w=0.82, p<0.001), thus violating an assumption of the t-test.
Log transformation of the data did not improve distribution, however, given that the pvalue is so small and the unequal variances may be controlled for it is unlikely that this
violation impacts the overall interpretation of the test. I am confident in reporting a
significant increase in PRLR expression in breast carcinoma versus normal tissue.
87
Figure 32. An anti-hedgehog antibody inhibits PRL induced osteoclastogenesis.
RAW264.7 pre-osteoclasts were differentiated for 6 days in 20% CM from SKBR3
treated with 25 ng/mL human PRL or PBS control for 48 hr. Pre-osteoclast cells were
treated with 2.5 µg/mL 5E1 upon addition of CM. TRAP positive, multinucleate
osteoclasts were quantified for each treatment. Graph represents one experimental
replicate with three internal replicates. Error bars indicate standard deviation from the
mean of three internal replicates. * indicates statistical significance between 5E1 treated
and non-treated samples (Two-way ANOVA: p < 0.05). ** indicates statistical
significance between PRL treated and non-treated sample (Two-way ANOVA: p < 0.05).
88
SHH is co-expressed with PRLR in 12.5% (n=13/104) of normal samples and 34.9%
(n=287/822) tumour samples (χ2=330.4) (Figure34). To test the possibility that the level
of PRLR expression is correlated with whether SHH is expressed or not, a logistic
regression was performed. There was no significant association between PRLR and SHH
in normal nor breast carcinoma tissue (normal: p=0.43; tumour: p=0.47). In normal tissue
expressing SHH, the range of expression was 0.32 – 4.61, with a mean of 1.10 ± 1.20.
SHH expression in solid tumours ranged from 0.21 – 297.8, with a mean of 4.43 ± 26.20.
Though significantly more tumour samples express SHH, the level of expression in
tumour samples is not different from normal tissue (Figure 33B). A t-test provided a pvalue of 0.04, however, the sample groups again have unequal variances (p=1.18 x 10-14)
and deviate from normality (normal: w=0.68, p<0.001; tumour: w=0.13, p<0.001). The
larger p-value and degree of test violation for SHH expression makes interpretation of the
t-test difficult, and it is very unlikely that there is a true significant difference between
normal and breast carcinoma tissue. Perhaps of greater interest is difference in sample
variance between the two groups. SHH expression in normal tissue has a sample variance
of 1.43. In contrast, SHH expression in breast carcinoma has a sample variance of
686.51, which could be biologically significant.
The gene and cytokine arrays identified multiple PRL-induced, breast cancer
secreted factors that can be further investigated to determine their contribution to
osteoclastogenesis. Of the factors identified, SHH was confirmed to be upregulated by
PRL in a dose dependent manner and has been shown to contribute to PRL-induced
breast cancer-mediated osteoclastogenesis. Gene expression data from human tumours
showed that the PRLR is more highly expressed in tumours than normal breast tissue, and
that PRLR and SHH are co-expressed in both normal and malignant breast tissue.
89
*
PRLR
A
SHH
B
Figure 33. Expression of the PRLR and SHH in human breast tumors. Data on
mRNA expression of PRLR (A) and SHH (B) in normal tissue and primary breast
invasive carcinomas was obtained from the TCGA data portal. Data was transformed by a
logarithmic base of 2 and displayed in boxplots. * indicates statistical significance
between normal and tumour samples (Student’s t-test: p<0.05).
90
*
Figure 34. PRLR and SHH are co-expressed in human breast tumors. Data on
mRNA expression of PRLR SHH in normal tissue and primary breast invasive
carcinomas was obtained from the TCGA data portal. Percent expression for each gene
was calculated and co-expression was determined. * indicates significance between
percentage of normal and tumour samples expressing SHH (χ2=330.4).
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CHAPTER 6: DISCUSSION
6.1 Optimization of Osteoclast Differentiation Assays
6.1.1 Optimization of Differentiation Conditions
Our lab observed that PRL-treated breast cancer cell secreted factors enhance
osteoclastogenesis (Sutherland 2010). Gene and cytokine arrays were conducted to
identify the factors involved. To optimize osteoclast differentiation and determine the
best PRL treatment length for the arrays, osteoclastogenesis assays were conducted using
CM from SKBR3 treated with hPRL or oPRL over a time course of 24 hr, 48 hr, 72 hr or
96 hr. A treatment length of 48 hr was found to be optimal for induction of
osteoclastogenesis for both ovine and human PRL (Figure 8). It was previously
confirmed, through ELISA assays, that commercially available oPRL extracted from
sheep pituitary gland contains ACTH and α-MSH. While ACTH and α-MSH were
determined not to be responsible for PRL-induced breast cancer-mediated
osteoclastogenesis, the recombinant hPRL is free of such contamination (Sutherland
2010). In addition, all breast cancer cell lines used are human and larger doses of oPRL
are required to obtain the results of normal physiological levels of hPRL. As such, all
experiments were conducted using hPRL. Bacterial-derived recombinant proteins, though
purified, are potentially contaminated by lipopolysaccharides. Lipopolysaccharides are
known to induce osteoclastogenesis and promote survival of osteoclasts (Itoh et al. 2003;
Mörmann et al. 2008). Recombinant hPRL (25ng/mL) was added at 20% v/v in all
RAW264.7 negative controls and was not shown to induce differentiation, confirming
that osteoclasts do not express the PRLR (Clement-Lacroix et al. 1999) and that potential
LPS contamination does not affect differentiation in these experiments. However, an
endotoxin or LPS detection assay could be used to test for hPRL purity if further
confirmation was needed.
Breast cancer cells were treated with hPRL for 48 hr to prepare CM for use in
osteoclastogenesis assays and the cytokine arrays. PRL is known to quickly stimulate
transcription of target genes, with increased mRNA expression observed following only
6hr treatment (Rasmussen et al. 2010). An optimal treatment time of 48hr for
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osteoclastogenesis suggests that indirect PRL-stimulated factors are involved in our
model of differentiation. A treatment time of 24 hr (24 hr prior to the observed peak of
biological effect) was chosen for gene array experiments in order to find a balance
between the time required for transcription of secondary target genes, and translation and
secretion of their associated proteins.
Though PRL has been shown to have a proliferative effect on breast tissue, through
activation of the JAK2/STAT5 pathway (Miyoshi et al. 2001) or JAK2 activation of the
Ras-Raf-MAPK pathway (Yamauchi et al. 2000), PRL did not induce proliferation of
SKBR3 and T47D breast cancer cells (Figure 9). Numerous studies have also shown a
weak or no proliferative response of breast cancer cells to PRL stimulation in vitro (Chen
et al. 2010; Chen et al. 1999; Rasmussen et al. 2010). Other studies demonstrating an in
vitro proliferative response, treated cells with PRL for longer periods of time than 48hr or
co-treated with other factors to achieve a synergistic effect (Llovera et al. 2000;
Rasmussen et al. 2010). Heat inactivation of PRL-treated breast cancer cell CM
ameliorated PRL dependent and independent induction of osteoclastogenesis, suggesting
that a soluble factor was responsible for increased differentiation (Sutherland 2010). Lack
of a PRL-induced proliferative response confirms that that PRL is upregulating a breast
cancer cell-secreted factor, as opposed to an apparent increase in expression due to
increased cell number.
6.1.2 Osteoclastogenesis Quantification
Identifying osteoclasts morphologically as having three or more nuclei and staining
positive for TRAP is the most frequent method of quantification used. It can, however, be
highly susceptible to counter bias and is time consuming, making it unsuitable for highthroughput assays. Within our system of differentiation many osteoclasts are compact and
have five or fewer nuclei, making them difficult to identify (Figure 11A). In addition
large osteoclasts often show very faint TRAP staining, making it difficult to count them
when using strict inclusion criteria (Figure 11B). One potential reason behind the faint
staining of large cells could be that the giant spread osteoclasts are less active phenotypes
and thus have lower TRAP activity than compact, active osteoclasts (Nordstrom et al.
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1995). Enforcing strict inclusion criteria and double counting by two independent
counters (Figure 12) can act as a control for counter bias, but alternate methods of
quantification that completely remove counter bias were also explored.
A colorimetric assay utilizing pNPP as a substrate for TRAP was able to detect
breast cancer-mediated induction of osteoclastogenesis, but no further PRL induction was
observed (Figure 7). As the RAW264.7 cells cannot be pre-stained and must be lysed, it
is impossible to determine if there truly was no PRL-induction present or if the assay is
not suited to our system of differentiation. TRAP is expressed early in osteoclast
differentiation and undifferentiated, mononucleate cells expressing TRAP have been
observed (Figure 10A). This assay could therefore be indicative that PRL-induced, breast
cancer cell secreted proteins act during later stages of osteoclast differentiation, post
TRAP induction, or that the signal from more differentiated multinucleated cells is
masked by a larger number of mononuclear cells. A side-by-side stain for TRAP+multinucleate cells could be beneficial if this method is to be pursued in the future.
Fluorescent staining of cell nuclei, the plasma membrane and actin ring was used
with the aim of developing an algorithm to detect osteoclasts in a high-throughput screen
(Figures 14 & 15). A screening method detecting multiple morphological features of
osteoclasts is beneficial, particularly for high-throughput screens, as therapeutic
inhibitors tested could potentially reduce expression of proteins used for osteoclast
identification, such as an ion pump, without actually inhibiting osteoclast function. A
distinct advantage of using confocal microscopy in identifying osteoclasts is the ability to
image cells at different depths in order to confidently quantify the number of nuclei
present within each cell, as they are often layered upon one another (Figure 14A).
Internalization of the plasma membrane stain (Figure 14B) and the presence of actin rings
in undifferentiated cells (Figure 15) posed difficulties for developing an algorithm to
identify the cells in a high-throughput manner. In some cases it was also difficult to
determine if cells were at an early fusion stage of differentiation or simply a small
number of cells clustered together (Figure 14C). The uneven nature in which RAW264.7
grow also posed difficulties for algorithm development due to dead-space within wells,
ultimately making this method incompatible with high-throughput screening. In spite of
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this, osteoclasts were still easily identified by eye using fluorescent staining for the
plasma membrane, actin ring and nuclei. The advantage of confocal microscopy in
counting nuclei to help reduce subjectivity makes this a useful method for quantifying
osteoclastogenesis. Ultimately, TRAP staining was used to quantify osteoclastogenesis as
it was better established in the lab and in literature.
6.2 Identification of PRL-Regulated, Breast Cancer Cell Secreted Factors
6.2.1 Establishing TBP as a Reference Gene
Previously YWHAZ was used as a reference gene for qPCR reactions involving
PRL-regulated genes. However, YWHAZ has come under scrutiny for its use as a
housekeeping gene as it encodes a member of the 14-3-3 protein family, which has been
shown to interact with the PRLR (Olayioye et al. 2003). TBP encodes a highly conserved
eukaryotic transcription factor that binds to the TATA box. TBP is not known to be
regulated by PRL and has been used in other studies examining PRL-regulated gene
expression (Rasmussen et al. 2010). Expression of TBP in SKBR3 was not altered with
PRL treatment across the time course (Figure 19). As such, it was used as a reference
gene to complete the gene array validation and is a viable control for future experiments.
Data using YWHAZ is still considered valid as YWHAZ was previously tested as a
suitable loading control within our lab and did not display PRL regulation. Both TBP and
YWHAZ were consistent with each other and are both suitable as reference genes in
mammary gland and breast cancer studies within our lab.
6.2.2 Characterization of PRL-induced factors
In the gene array, the most highly upregulated gene was increased 2.33-fold by hPRL
while the greatest down-regulated gene had a fold change of -3.46. It is uncommon for
PRL to induce large fold changes in gene expression, and these results were expected
(Figure 20, Table 2). SPRED1 and BCL6 are known PRL regulated genes and the change
in expression obtained in the gene array is similar to previous reports (Rasmussen et al
2010). A small number of known PRL-regulated genes were not found to be altered in
our array, however, given that PRL generally induces genes within a much shorter
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timeframe we are confident that our array is still reflective of PRL signaling. Two
potentially novel PRL-regulated factors with the greatest fold change, FSTL4 and
FLRT1, were chosen to pursue further. FSTL4 is distantly related to follistatin due to the
presence of a follistatin-like domain module and is virtually uncharacterized (Shibanuma
et al. 1993). FLRT1 was identified as part of a gene family isolated in a screen for
extracellular matrix proteins in muscle (Lacy et al. 1999). While also relatively
uncharacterized, it has been shown to be downregulated in malignant thyroid lesions
(Arora et al. 2009). However, upon further experimentation neither FSTL4 nor FLRT1
were altered in SKBR3 following 6hr, 24hr or 48hr treatment with PRL (Figure 21). It is
highly likely that PRL is acting through an indirect gene target to induce
osteoclastogenesis, which is difficult to identify in the window of observation the gene
array provides; therefore, the cytokine array was more useful in providing more direct
factors.
The cytokine array was used as a pilot study to identify PRL-regulated, breast cancer
secreted factors contributing to osteoclastogenesis. As proteins present in FBS can
produce high background levels on the antibody array that could interfere with results,
the experiment was conducted using serum free CM. The effect of serum free conditions
on differentiation was tested to ensure that it would not alter observed trends of breast
cancer-mediated, PRL enhanced osteoclastogenesis. Interestingly, serum free CM
induced osteoclastogenesis to a greater extent than normal CM in both PRL treated and
non-treated samples. The relative level of PRL induction, however, was similar between
serum free and normal CM (Figure 22). It is possible that an inhibitory factor was present
in the serum and removal of serum enabled further osteoclastogenesis, or serum
starvation of breast cancer cells may have pushed the cells into secreting larger amounts
of cytokines.
PRL-regulated factors identified in both the gene and cytokine arrays were primarily
associated with cell signaling. The cellular location of PRL-regulated genes was within
the nucleus or cytoplasm (Figure 18C), while the cytokine array solely measured secreted
factors. Given the number of signaling molecules and transcription factors downstream of
the PRL/PRLR complex this is an expected finding (Radhakrishnan et al. 2012).
96
Cytokines involved in the immune response made up a large proportion of regulated
factors on both stringent and less-stringent lists (Figures 25B & 27B). PRL plays a wellknown role in both the innate and adaptive immune responses. It aids in
immunomodulation by acting as an adaptive molecule against inflammatory mediators, is
involved with IgA transport through the cellular epithelium during mammary gland
development, and promotes growth and survival of T-lymphocytes by protecting against
apoptosis and promoting proliferation (Blanco-Favela et al. 2012). Of particular interest
was that 50% of secreted factors on the less-stringent list and 35% on the high stringency
list were involved in cell differentiation (Figures 25B & 27B).
SHH and LIGHT, both with known roles in cell differentiation, were identified as
potential novel PRL-regulated factors. LIGHT, a member of the tumor necrosis factor
superfamily, was an interesting candidate as it is able to promote osteoclastogenesis
through canonical RANKL signaling, as well as independently (Edwards et al. 2006).
Expression of RANKL in the array samples was not present over background.
Furthermore, TNFα and IL-8, two cytokines able to stimulate osteoclastogenesis in a
RANKL-independent manner in concert with LIGHT, were highly expressed in SKBR3
and MCF7 samples though were not shown to be PRL regulated in the cytokine array
(Bendre et al. 2005; Kobayashi et al. 2000). Together these factors could potentially be
responsible for breast cancer cell CM induction of osteoclastogenesis, while PRL
induction of LIGHT is responsible for further induction of osteoclastogenesis. When
tested by ELISA, LIGHT was undetectable in breast cancer cell CM and it was not
identified as a PRL-regulated factor by the gene array. As the cytokine array does not
provide concentrations, instead measuring protein by signal intensity on an antibody
slide, it is possible that quantities of LIGHT are present outside the detection range of the
ELISA kit. In addition, LIGHT is a type II transmembrane protein and may be found in
higher concentrations in whole cell protein extracts (Mauri et al. 1998). Given the
potential mechanism provided by LIGHT, further characterization of its synthesis and
PRL-regulation in breast cancer cells with different cell preparations is warranted.
Assessment of mRNA expression should also be conducted in breast cancer cells treated
with PRL across a time course.
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The cytokine array was validated with a smaller-scale Luminex array. When
compared to a similar array previously conducted using SKBR3 treated with ovine PRL it
was found that human and ovine PRL potentially stimulate similar factors: MCP-1,
VEGF, SCF, PDGF-BB and interferon gamma-induced protein 10 (IP-10) (Sutherland
2010). No factors were significantly induced by PRL (Figure 28), however, fourteen
known osteoclastogenic factors were present (Table 4). Some of these factors are known
to be involved in breast cancer-mediated osteolysis. Breast tumor secreted fractalkine
stimulates osteoblast-induced osteoclast formation via migration of bone marrow cells
containing osteoclast precursors and mediation of firm adhesion of osteoclast precursors
(Koizumi et al. 2009). Multiple studies have demonstrated that GM-CSF induces human
osteoclast formation (Hodge et al. 2004; MacDonald et al. 1986; Menaa et al. 1999;
Myint et al. 1999). It has been shown to promote osteolytic bone metastasis by breast
cancer cells by induction from NF-κB, resulting in osteoclast differentiation (Park et al.
2007). Interestingly, a separate in vitro study showed breast cancer-mediated
osteoclastogenesis via IL-11 stimulation and GM-CSF inhibition (Morgan, Tumber, Hill
2004). PDGF-BB has been shown to promote osteoclastic differentiation and bone
resorption (Bos et al. 2005; Vincent et al. 2009; Zhang, Chen, Jin 1998). An in vitro
study using MCF7 breast cancer cells demonstrated that it plays a causative role in
establishing breast to bone metastases (Yi et al. 2002). MCP-1 and RANTES are key
regulators of early osteoclast differentiation by providing the chemotactic signals
required for mononuclear pre-osteoclast fusion. Both proteins are stimulated in the preosteoclast by RANK/RANKL signaling (Kim, Day, Morrison 2005). In addition, MCP-1
stimulates pre-osteoclast expression of TRAP and the calcitonin receptor via NFATc1,
though such multinucleate cells are unable effectively resorb bone in the absence of
RANKL (Kim et al. 2008; Kim et al. 2006). SCF is a growth factor that stimulates
differentiation of hematopoietic cells into osteoclast precursors and stimulates
proliferation of osteoclast precursors (Demulder et al. 1992). It is involved in the
maintenance of normal mammary epithelial growth, and progressive loss of SCF is
associated with malignant transformation of breast tissue (Ulivi et al. 2004). TNF-α is a
potent osteoclastogenic cytokine that mediates RANKL osteoclast differentiation in an
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autocrine manner (Zou et al. 2001). It is involved in osteoclast activation by promoting
actin ring formation that is equipotent, independent and synergistic with RANKL (Fuller
et al. 2002). When in the presence of IL-1α, TNF-α forms osteoclasts capable of
resorbing the bone surface independent of other mechanisms (Kobayashi et al. 2000).
Immunocytochemical studies have shown expression of TNF-α in some breast
carcinomas (Pusztai et al. 1994), however, its role in breast to bone metastasis is yet to be
fully investigated. IL-6 and VEGF are PRL-regulated, breast cancer secreted factors, that
independent of PRL, known to be active in bone metastases. Downregulation of IL-6 was
expected in the Luminex array as literature suggests the ability of PRL to inhibit IL-6,
with such down-regulation independently associated with highly malignant mammary
carcinomas (Deb et al. 1999; Fontanini et al. 1999). However, this regulation was
demonstrated in rat decidual cells and it is possible the same regulatory mechanisms are
not present in human mammary or breast carcinoma tissues and cell lines. VEGF
upregulation has been demonstrated in mouse mammary epithelial cells (Goldhar et al.
2005). Similarly, there may be differing regulatory mechanisms in mouse versus human
mammary tissue. It is also possible that PRL stimulation of breast cancer cells for a
different period of time may induce these specific factors to a greater extent. As VEGF is
present in soluble and membrane-bound forms in breast carcinomas (Liang et al. 2006), it
is also possible that a cell lysis preparation (rather than CM) could reveal PRL-regulation.
Unlike the other factors secreted by SKBR3 breast cancer cells in the cytokine array, IL1RA plays a protective role in bone resorption by decreasing osteoclastogenesis
(Kitazawa et al. 1994).
Other factors are potentially novel breast cancer secreted factors with known roles in
osteoclastogenesis. Granulocyte colony stimulating factor (G-CSF)-mobilized
hematopoietic stem cells from peripheral blood are capable of forming osteoclasts in the
presence of IL-1, IL-3 and GM-CSF (Matayoshi et al. 1996). In the context of
rheumatoid arthritis, IL-15 has been found in synovial fluid and likely contributes to
disease destruction of bone by stimulating differentiation of hematopoietic stem cell
progenitors into pre-osteoclasts (Ogata et al. 1999). IP-10 is also involved in the
pathogenesis of rheumatoid arthritis by inducing RANKL in activated T-cells to
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contribute to osteoclastogenesis and bone resorption (Lee, Lee, Song 2009). MCP-3 has
been shown to play a small role in osteoclastogenesis by acting with macrophage
inflammatory protein 1 to enhance expression of RANK on the surface of osteoclast
progenitors (Binder et al. 2009). These factors are likely contributing to the overall effect
of breast cancer-mediated induction of osteoclast differentiation and resorption.
6.3 The Role of SHH in PRL-Induced, Breast Cancer-Mediated Osteoclastogenesis
SHH is upregulated by PRL, in a dose dependent manner, in SKBR3 and upregulated
in T47D cells, but is only secreted by MCF7 and not increased by PRL (Figures 29-31).
Furthermore, addition of an anti-hedgehog inhibitory antibody reduced PRLenhancement of osteoclastogenesis down to non-PRL treated levels in SKBR3 (Figure
32). Our lab has found that the hedgehog pathway inhibitor cyclopamine reduced PRLdependent and independent osteoclast differentiation and osteoclast maturation,
consistent with the levels of secreted SHH detected by ELISA. Recombinant SHH was
also able to increase osteoclastogenesis (Sutherland et al. 2015, submitted). Taken
together this data indicates that SHH is responsible, in part for PRL induction of breast
cancer-mediated osteoclastogenesis (Figure 35).
The Hh pathway plays an important role in embryonic mammary gland development,
though the Hh ligands and their signaling components have roles in the postnatal
mammary gland despite broad silencing of the pathway postnatally (Hatsell and Frost
2007). PRL, in contrast, is essential for post-pubertal mammary growth and development
during pregnancy and lactation (Oakes et al. 2008). Both signaling molecules, however,
play important roles in breast tumorigenesis. The Hh pathway is constitutively activated
in many breast carcinomas. PTCH1 and GL1 are more highly expressed in breast cancers
compared to normal breast tissue when assessed using real time, quantitative PCR (Hu et
al. 2003). Immunohistochemical studies have also shown high SHH, PTCH1 and GLI1
expression in human breast cancer epithelial cells in comparison the normal breast
epithelium. Furthermore, treatment of human breast cancer cell lines with cyclopamine
reduced viability of 75% of cell lines tested (Kubo et al. 2004). Hh-targeted therapeutics
have been touted as being efficient in targeting tumours arising in the bone or
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Figure 35. SHH is a novel PRL-enhanced, breast cancer secreted factor contributing
to osteoclastogenesis. PRL induces the secretion of SHH by breast cancer cells, which
acts directly on pre-osteoclasts to induce differentiation into mature osteoclasts, and
potentially contributes to osteoclast activation. Resulting bone degradation releases
growth factors into the microenvironment that further promote growth and survival of
metastatic breast cancer cells and perpetuate the vicious cycle of breast to bone
metastasis.
101
metastasizing to bone due to their action on stromal cells within the bone
microenvironment (Hurchla and Weilbaecher 2012). Breast cancer cell secreted SHH has
been shown to influence differentiation of osteoclast precursors through enhancement of
osteoblast differentiation and expression of osteopontin, RANKL and PTHrP; as well as
enhance osteoclastic activity by mature osteoclasts resulting in increased bone resorption
(Das et al. 2012; Das, Samant, Shevde 2011). Knockdown of SHH in a bone metastatic
variant of MDA-MB-231 breast cancer cells decreased bone stromal cell secretion of IL6, and this paracrine activation of osteoclastogenesis is associated with increased
expression of the NFATc1 transcription factor (Heller et al. 2012). Given that we
observed a direct effect of SHH on osteoclastogenesis, it is possible that SHH could also
be inducing osteoclast derived IL-6 which promotes osteoclastogenesis via NFATc1 in an
autocrine manner.
Despite known expression of SHH in normal and malignant breast tissue there is no
previous evidence of PRL regulation, thus we present a potential novel regulator of SHH
in breast cancer. SHH and PRLR are co-expressed in all SHH expressing breast tumours
analyzed from the TCGA database (Figure 34), and though the functionality of this
correlation is uncertain, it is possible that PRL/PRLR regulates SHH at the transcriptional
level. SHH was not upregulated by PRL in the gene array, however, further experiments
assessing PRL-induction of SHH following different PRL-treatment lengths are required.
Alternatively, SHH may be regulated by PRL/PRLR signaling at the protein level, which
in general is common with cytokines (Anderson 2008). The production of secreted SHH
is regulated at several levels. The SHH ligand is first produced as a ~45kDa precursor
protein that undergoes catalytic autoprocessing to produce a 19 kDa N-terminal fragment
(SHH-N) that is responsible for downstream signaling (Lee et al. 1994). The
autoprocessing reaction is catalyzed by a protease within the 26 kDa C-terminal domain
of the preprotein. During the reaction, the C-terminal domain also acts as a transferase to
attach a cholesterol moiety to the C-terminus of SHH-N (Porter, Young, Beachy 1996).
Addition of cholesterol to SHH-N is not required for signaling, but it has been shown to
contribute to the long range signaling capability of SHH (Dawber et al. 2005). Critical for
signaling is the addition of palmitate, a 16-carbon fatty acid that is covalently attached to
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the N-terminal cysteine of SHH-N (Chen et al. 2004; Pepinsky et al. 1998).
Palmitoylation of SHH-N is completed by hedgehog acetyltransferase (Hhat), a
transmembrane enzyme belonging to the membrane bound O-acetyltransferase family
(Buglino and Resh 2008). Post-translational control of SHH by PRL could occur at one
or more of these points. There is currently no report of PRL involvement during SHH
autoprocessing or palmitoylation, however, Hhat has recently been shown to be involved
in hormone receptor-positive breast cancer proliferation (Matevossian and Resh 2015).
Following palmitoylation, SHH-N can remain anchored to the cell membrane or
form a secreted multimeric unit that freely diffuses for long range signaling, though
monomeric forms of secreted SHH-N have also been found (Goetz et al. 2006).
Anchoring of SHH-N to the cell membrane could account for the apparent lack of PRL
induction of SHH observed in MCF7. Additionally, the membrane proteins hedgehog
interacting protein and growth arrest-specific gene, which bind Hh ligands and sequester
them from secretion or activating PTCH1 (Hatsell and Frost 2007), may more highly
expressed in MCF7 contributing to an apparent lack of secreted SHH in response to PRL
stimulation. MCF7 are competent in inducing osteoclastogenesis by culturing preosteoclasts in CM, yet the trend of PRL induction has not been consistently significant.
Direct co-culture of MCF7 and pre-osteoclasts, however, often yielded greater PRLinduction of osteoclastogenesis than SKBR3 (Sutherland 2010). This is suggestive that
SHH is one of potentially multiple PRL-regulated factors capable of inducing breast
cancer-mediated osteoclastogenesis.
6.4 Future Directions
This project identified SHH as a PRL-regulated, breast cancer secreted factor
contributing to osteoclastogenesis. To fully characterize the relationship between
PRL/PRLR and SHH a number of small experiments are needed. Supplementary
experimental replicates using the anti-hedgehog inhibitor 5E1, with the appropriate
antibody control, are required to confirm the findings of the assay. Direct co-culture of
pre-osteoclasts and breast cancer cells using the PRLR antagonist and recombinant SHH
could also further confirm the role of SHH in PRL-induction of osteoclastogenesis.
103
Assessment of SHH regulation at the transcriptional level may be determined by
performing qPCR on breast cancer cells treated with PRL across a time course. As posttranslational regulation is also likely, the use of inhibitors or siRNA knockdown of
molecules involved in SHH processing, particularly Hhat, may be used. Involvement of
the Hh sequestering proteins, hedgehog interacting protein and growth arrest-specific
gene, could also be pursued.
There is evidence that SHH acts by stimulation osteoblast expression of
osteoclastogenic factors (Das et al. 2012). As this project identified SHH as an
osteoblast-independent osteoclastogenic factor, experiments to determine how SHH
contributes to differentiation could be conducted. As previously discussed SHH induction
of IL-6 may be tested, however, experiments identifying which stage of
osteoclastogenesis SHH affects would also be beneficial. The effect of SHH on preosteoclast proliferation may be tested using the colorimetric WST-1 assay; migration may
be tested using trans well assays; cell fusion may be measured by staining with a nuclear
dye and plasma membrane stain, and visualizing cells under fluorescent microscopy; and
actin ring formation could be assessed by staining with phalloidin and visualizing cells
under fluorescent microscopy.
Our model used to study breast cancer-mediated osteoclastogenesis currently uses
human breast cancer cell lines and mouse RAW264.7 leukemia virus-induced tumor cells
as pre-osteoclasts. Though RAW264.7 are commonly used to study osteoclastogenesis,
and there is other evidence of human SHH inducing differentiation of the mouse cell line
(Das, Samant, Shevde 2011), it would be advantageous to see if the same effect may be
observed in differentiation of human osteoclasts. Human osteoclasts may be cultured
from precursors circulating in the mononuclear fraction of peripheral blood (Shalhoub et
al. 2000), or from bone marrow-derived monocytes (Sarma and Flanagan 1996).
Once the mechanism by which PRL regulates SHH is elucidated, SHH must also be
functionally tested to determine whether it stimulates osteoclasts capable of bone
resorption. Pre-osteoclasts can be differentiated on dentine discs or a bone-simulating
hydroxyapatite surface to evaluate if the osteoclasts are capable of osteolysis. Upon
successful characterization of this PRL mediated mechanism in vitro, in vivo models can
104
be explored. One possible option would be to use intracardiac injection of a bone
metastatic variant of MDA-MB-231 breast tumour cells into immune deficient PRLR-/- to
generate a metastasic model. Given that other cells types in the bone metastatic
microenvironment, such as osteoblasts, also play such a large role in the disease process,
in vivo models will be important in determining how PRL contributes overall to the
vicious cycle of breast cancer metastases. Finally, other candidates from the cytokine
array should be selected and characterized, as it is likely that PRL is inducing multiple
factors that contribute to breast cancer-mediated osteoclastogenesis.
6.5 Overall Significance
Determining the mechanism by which PRL mediates breast cancer induced
osteoclastogenesis and bone resorption is important for further characterization of breast
cancer tumor cell interactions in the bone microenvironment. The influence of circulating
hormones upon breast to bone metastasis is a novel and unexplored avenue of research
and drug discovery. This mechanism can lead to the development of novel therapies to
help intercede osteolysis and reduce morbidity and mortality experienced by advanced
breast cancer patients.
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