Characterization of Low Grade Serous Carcinoma of the Ovary and its
Precursor Lesions
by
Taymaa May
A Thesis Submitted in Conformity with the Requirements
for Degree of Masters of Science
Institute of Medical Sciences
University of Toronto
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1+1 Canada ABSTRACT
Characterization of Low Grade Serous Carcinoma of the Ovary and its Precursor Lesions Taymaa May, Master of Science, Institute of Medical Sciences University of Toronto, 2008
Low-Grade-Serous-Carcinoma (LGSC) is a chemoresistant ovarian neoplasm molecularly linked to the non-invasive Low-Malignant-Potential (LMP) tumors. LMP- with-Micropapillary-features (LMP-MP) have more aggressive behavior. The objectives of this study were to clarify the classification of LMP-MP tumors as borderline or malignant neoplasms and to identify candidate genes involved in low grade serous carcinogenesis. Laser-capture-microdissection was used to isolate epithelial cells from
LMP (n=16), LMP-MP (n=9) and LGSC (n=ll). RNA was extracted, amplified, reverse transcribed to cDNA and hybridized to Affymetrix-U133-Plus-2-genechip-arrays. Data were analyzed by GeneSpring, Significance-Analysis-of-Microarrays (SAM), and protein-interaction-database-I2D. Unsupervised-hierarchical-clustering revealed collective clustering of LMP-MP and LGSC, separate from LMP. SAM-analysis identified differential gene expression between LMP and LMP-MP, LMP and LGSC but not between LMP-MP and LGSC. I2D-analysis highlighted differentially expressed genes in the MAPK and EGFR pathways for validation studies. LGSC appears to have a similar genetic profile to LMP-MP and different from LMP. Selected members of the
MAPK and EGFR pathways may play a role in low-grade-serous carcinogenesis.
Identification of novel genes associated with carcinogenesis and malignant transformation may lead to development of more effective targeted therapy for LGSC.
11 ACKNOWLEDGMENTS
I would like to thank Dr. Ted Brown for his remarkable supervision and generous support. I am truly grateful to have been a part of Dr. Brown's laboratory and to have learned from such an impressive scientist. Thank you, Dr. Brown, for allowing me to independently explore interesting ideas while providing gentle guidance and advice.
I wish to thank Dr. Patricia Shaw for her dedication and support of this project. Thank you, Dr. Shaw, for the countless hours we spent reviewing pathology slides and scoring
TMA slides. The quality of this work would not have been the same without Dr. Shaw's world class expertise and, for that, I am deeply grateful.
In addition, I appreciate Dr. Shaw's initiative in creating and maintaining the University
Health Network Ovarian Tissue Bank, which is an invaluable resource for ovarian cancer research. Above all, I am grateful to all the women who generously provided the tumor specimens used in this work.
Special thanks go to Dr. Joan Murphy, who has been a strong supporter of my clinical journey and her support of my young scientific journey has been equally exceptional. As a member of my research committee, Dr. Murphy has been generous with her time and ideas and provided invaluable clinical and surgical perspectives that have defined and strengthened this project. I am honored to be learning from such an amazing mentor and a true inspiration- Thank you Dr. Murphy.
I am greatly appreciative of the funding support this project received from the Toronto
Ovarian Cancer Research Network through funds raised by the Toronto Fashion Show; an initiative largely headed by Dr. Murphy.
iii Many thanks go to Dr. Susan Done for her input as a member of my research committee.
Her expertise in pathology and gene expression profiling were an asset to the development and progress of this work.
In addition, I wish to thank Mr. Carl Virtanen for sharing his expertise in microarray data analysis and I look forward to working with Carl in the future. Many thanks go to members of the Brown lab for their help and support. Special thanks go to Ms. Alicia
Tone for her procedural teachings and for so much more. I'll miss our morning Starbucks routine.
Wonderful thanks go to the division of Gynecological Oncology at Princess Margaret
Hospital for their continuous help and support. Special thanks go to Dr. Barry Rosen for his insightful comments during our many discussions. I also wish to thank Mrs. Judy
Brusse for coordinating many of the meetings and events related to this work and for doing it with a pleasant smile.
I would like to thank Dr. Alan Bocking for his notable support and for providing my source of funding through the Bernard Ludwig Fellowship award. I am also extremely grateful to Dr. Heather Shapiro for her invaluable advice and for allowing me to go through a smooth transition from bedside to bench and back.
Most of all, I would like to thank my mother, who is a true inspiration and a great role model. Thank you mom for your love, support and all the long talks. Great thanks go to my sister and her family for their unconditional love and support. Thank you sis for being my strongest supporter and best friend.
iv Loving thanks go to my husband, my partner, who has been with me through every major
(and minor) decision of my adult life. I am confident I wouldn't be who I am today if it weren't for his love and support. Thank you Ali.
Lastly, I dedicate this work to the memory of my father, the original Dr. May, who is the inspiration and driving force behind all that I do. To you dad, with love..
v TABLE OF CONTENTS
CONTENT PAGE
Abstract ii
Acknowledgements iii-v
Table of Contents vi-ix
List of Figures x-xi
List of Tables xii
List of Abbreviations xiii-xv
CHAPTER 1: INTRODUCTION PAGE
1.1 Ovarian Carcinoma 1
1.1.1 Early Symptoms of Ovarian Carcinoma 2
1.1.2 Early Detection Strategies 3
1.2 Epithelial Ovarian Carcinoma 4
1.2.1 Embryogenesis of the Ovarian Surface Epithelium 4
1.2.2 Origin of Epithelial Ovarian Carcinoma 5
1.2.3 The Role of the Fallopian Tube Epithelium in
Ovarian Carcinogenesis 5
1.3 Serous Epithelial Ovarian Carcinoma 6
1.3.1 Surgical Stage and Histological Grade in Serous
Ovarian Carcinoma 6
1.3.2 Two-Tier Grading System of Ovarian Serous Carcinoma 7
VI 1.4 High Grade Serous Carcinoma 9
1.5 Low Grade Serous Carcinoma 11
1.5.1 Differences in Molecular Characteristics between
LGSCandHGSC 13
1.5.2 Differences in Invasive Characteristics of
LGSCandHGSC 13
1.6 Serous Low Malignant Potential Tumor 14
1.7 Serous Low Malignant Potential Tumor with
Micropapillary Features 17
1.7.1 Controversies Regarding Histological Classification
ofLMP-MP 19
1.8 The Relationship Between Low Malignant Potential Tumors
and Low Grade Serous Carcinoma 20
1.9 Two Pathway Hypothesis of Ovarian Carcinogenesis 21
1.10 Thesis Hypothesis and Rationale 24
1.11 Objectives 25
CHAPTER 2: MATERIAL AND METHODS PAGE
2.1 Case Selection 26
2.2 Tissue Preparation and Sectioning 27
2.3 Laser Capture Microdissection 28
2.3.1 Slide Staining 28
2.3.2 Microdissection of Epithelial Cells 30
vn 2.4 RNA Extraction
2.5 RNA Quality Testing 31
2.6 RNA Amplification and Reverse Transcription to cDNA 32
2.7 cDNA Hybridization to Genechip Microarray 32
2.8 Data Analysis 33
2.9 Validation Studies 34
2.9.1 Quantitative Real-Time Reverse Transcriptase
Polymerase Chain Reaction 37
2.9.2 Immunohistochemistry 40
CHAPTER 3: RESULTS PAGE
3.1 Study Cases and Clinical Data 42
3.2 Unsupervised Hierarchical Clustering 48
3.3 Significant Analysis of Microarrays 51
3.4 Protein-Protein Interaction Database 63
3.5 Gene Selection 63
3.5.1 Selected Genes 65
3.6 Validation Studies 69
3.6.1 Quantitative Real-Time RT-PCR 69
3.6.2 Immunohistochemistry 69
vm CHAPTER 4: DISCUSSION PAGE
4.1 Study Design 82
4.1.1 Study Limitations 84
4.2 Clinical Association between LMP, LMP-MP
and LGSC 86
4.3 LMP Tumors Have Distinct Gene Expression Profiles
From LMP-MP and LGSC 87
4.4 Gene Expression Profile of LMP-MP Tumors is
Similar to LGSC 88
4.5 Identification of Candidate Genes Involved in Low Grade
Ovarian Carcinogenesis 90
4.5.1 Mitogen Activated Protein Kinase 91
4.5.2 Proposed Protein Cascade Significant in Low Grade
Serous Carcinogenesis 93
4.6 Study Implications 94
4.7 Future Studies 96
4.7.1 Functional Assays 96
4.7.2 Differential Gene Expression between Ovarian
LGSC and HGSC 97
4.8 General Conclusion and Summary 98
REFERENCES 99
IX LIST OF FIGURES
FIGURE PAGE
1. Histological Appearance of High Grade Serous Ovarian Carcinoma 10
2. Histological Appearance of Low Grade Serous Ovarian Carcinoma 12
3. Histological Appearance of Ovarian Serous Low Malignant
Potential Tumor 16
4. Histological Appearance of Ovarian Serous Low Malignant Potential
Tumor with Micropapillary Features 18
5. Two Pathway Hypothesis of Serous Ovarian Carcinogenesis 22
6. Type I Pathway of Serous Ovarian Carcinogenesis 23
7. Laser Capture Microdissection of Ovarian Tumor Epithelium 29
8. Microarray Expression Levels of Validation Genes in all
Study Samples and Selected Sample for real-time RT-PCR 35
9. Unsupervised Hierarchical Clustering of LMP, LMP-MP and LGSC 49
10. Unsupervised Hierarchical Clustering of LGSC and HGSC 50
11. Gene Ontology of Differentially Expressed Genes between
LMP and LMP-MP 54
12. Gene Ontology of Differentially Expressed Genes between
LMP and LGSC 60
13. OPHID/I2D Network: LMP vs LGSC 64
14. Quantitative real-time RT-PCR for eight selected genes
as Compared to Microarray expression Levels 70 15. Immunohistochemistry on Constructed Tissue Microarray
Using Anti-TANK Antibody 74
16. Immunohistochemistry on Constructed Tissue Microarray
Using Anti-PARPl Antibody 77
17. Immunohistochemistry on Constructed Tissue Microarray
Using Anti-CDK2 Antibody 79
18. Diagram Representing the Likely Origin and Progression of
Ovarian LMP, LMP-MP and LGSC 89
19. Candidate Genes and Pathway Potentially Involved in
Low Grade Ovarian Carcinogenesis 92
XI LIST OF TABLES
TABLE
1. 5-year Survival of Patients with FIGO Stage I-TV by
Tumor Grade
2. Quantitative Real Time RT-PCR Primer Sets
3. Clinical Information Associated with Study Cases
4. Disease Free and Overall Survival in Patients with LGSC
5. Differentially Expressed Genes between LMP and LMP-MP
6. Differentially Expressed Genes between LMP and LGSC
7. SAM Analysis Results LMP-MP versus LGSC
xn ABBREVIATIONS
5aRed 5-alpha-Reductase I
ACPM American College of Preventive Medicine
ANOVA Analysis of Variance
ATDA Diamine Acetyl Transferase
BCL2 B-Cell CLL/Lymphoma 2
BER Base Excision Repair
BRCA1 Breast Cancer Associated Gene 1
BRCA2 Breast Cancer Associated Gene 2
BTC Betacellulin
CDK2 Cell Division Protein Kinase 2
CT Cycle Threshold ddH20 double distilled H20
DHT Dihydrotestesterone
EGFR Epidermal Growth Factor Receptor
ERK Extracellular Signal Regulated Kinase
FDA Food and Drug Administration
FDR False Discovery Rate
FIGO International Federation of Obstetrics and Gynecology
Her2/neu Human Epidermal Growth Factor Receptor 2
HGSC High Grade Serous Carcinoma
HPF High Power Fields IHC Immunohi stochemi stry
JNK c-Jun N-terminal kinase
LCM Laser Capture Microdissection
LGSC Low Grade Serous Carcinoma
LMP Low Malignant Potential
LMP-MP Low Malignant Potential with Micropapillary Features
MAPK Mitogen-Activated Protein kinase
MDACC MD Anderson Cancer Center
MEKK1 MEK Kinase 1
MIB1 Mindbomb Homolog 1
MPSC Micropapillary Serous Carcinoma
OPHID Online Protein-Protein Human Interaction Database
PARP1 Poly [ADP-ribose] Polymerase-1
PDGFR Platelet Derived Growth Factor Receptor
PEA15 Astrocytic Phosphoprotein
PPI Phosphatidylinositol Transfer Protein- alpha Isoform
PTEN Phosphatase and Tensin Homolog
RECIST Response Evaluation Criteria In Solid Tumors
RT-PCR Reverse Transcriptase Polymerase Chain Reaction
SAM Significant Analysis of Micro Arrays
SBT Serous Borderline Tumor
SBT-MP Serous Borderline Tumor with Micropapillary Features
SSBR Single Stand Break Repair TANK TRAF Family Member Associated NF-Kappa-B-Activator
TBP TATA Binding Protein
TRAF2 TNF receptor-associated factor 2
UHN University Health Network
VEGFR-2 Vascular Endothelial Growth Factor Receptor 2
WHO World Health Organization
xv CHAPTER 1
INTRODUCTION
1.1 OVARIAN CARCINOMA
Ovarian carcinoma accounts for 4% of all cancers in women and 31% of cancers of the genital tract. A woman's lifetime risk of developing ovarian carcinoma is 1.5% and her risk of succumbing to this disease is 1%. (i). Ovarian carcinoma has the highest fatality-to-case ratio of all gynecological malignancies. Thus, although ovarian cancer is the 2nd most common gynecological malignancy, it accounts for the highest mortality rate among gynecological cancers. In 2006, the American Cancer Society and the Canadian
Cancer Society estimated a total of 20,180 and 2,400 new ovarian cancer cases in the
United States and Canada, respectively (2,3). In that year, a total of 17,010 deaths were attributed to ovarian cancer in North America. Thus ovarian carcinoma has an overall mortality rate of 70%, which far exceeds that of breast carcinoma.
The majority of patients with epithelial serous cancer, the most common ovarian malignancy, present with metastatic disease, which has a 5-year survival rate of less than
25% and a 10-year survival rate approaching 0 (4). This alarming death rate is not only attributed to the advanced stage of disease at diagnosis, but also to the lack of effective therapy for ovarian carcinoma. Current first-line treatment for epithelial ovarian carcinoma includes surgical resection and staging followed by Platinum- and Taxane- based chemotherapy, administered via the intra-venous or intra-peritoneal routes (5).
However, these agents produce variable therapeutic response rates among patients with epithelial ovarian carcinoma (6,7). This is not surprising when one considers that gene
1 profiling studies have demonstrated segregation of epithelial tumors based on specific histotypes (8,9,10). Therefore, as serous, mucinous, endometroid and clear cell epithelial ovarian carcinomas have different genetic identities, they will likely have different behavior patterns and therapeutic responses.
Therefore, what is urgently needed to impact survival in ovarian carcinoma are advances in development of disease-specific, targeted therapy for the various histotypes of ovarian carcinoma. It is thus imperative to understand the biology of ovarian cancer and define the molecular events associated with malignant transformation and carcinogenesis.
1.1.1 Early Symptoms of Ovarian Carcinoma
A major contributing factor to the high mortality rate of ovarian carcinoma is that symptoms of early stage disease may not be readily identifiable. In a prospective, case- controlled study, Goff et al. established that symptoms were present in 43% of women with malignant ovarian neoplasms including increased abdominal girth, bloating, increased urinary urgency and unspecified pelvic pain (ii). While these symptoms may also be present in non-malignant conditions, the authors established that these symptoms were more frequent, more severe, more recent in onset and were present in multiples in patients with malignant ovarian tumors (ii). Nevertheless, these symptoms remain vague and are easily attributed to other potential causes. Hence, they are often ignored by both patients and some front-line physicians, thereby delaying diagnosis and appropriate, potentially curative, treatment.
2 1.1.2 Early Detection Strategies
The lack of clearly noticeable symptoms of early stage ovarian carcinoma is exacerbated by the lack of accurate early detection strategies. The glycoprotein CA-125 is the only presently available serum biomarker for epithelial ovarian carcinoma, yet it lacks sensitivity and specificity for early stage disease. The sensitivity of CA-125 in stage
I ovarian carcinoma is 20-57% and for stage II disease is 90% (12). Although CA-125 is elevated in 80% of epithelial ovarian cancer, it is also elevated in endometrial and pancreatic cancers and several benign conditions such as endometriosis, pelvic inflammatory disease, uterine fibroids and in 1% of healthy women (12). Therefore, CA-
125 is not an effective screening tool for early stage epithelial ovarian carcinoma, but rather it is used as a disease marker to monitor patients' response to treatment and disease recurrence.
Similarly, pelvic ultrasonography has been shown to be an ineffective ovarian cancer screening method for low risk asymptomatic women (13). Moreover, Campbell et al. have predicted that ultrasound screening of 100,000 asymptomatic women would detect 40 cases of ovarian cancer but yield 5,398 false-positive outcomes. This would therefore result in 5,398 unnecessary surgical interventions with 160 predicted complications from laparoscopic surgery (13). Hence, the American College of Preventive Medicine (ACPM) does not recommend routine ovarian cancer screening for asymptomatic women.
Although direct evidence is lacking, the ACPM recommends annual screening of women with germline mutations or familial cancer syndrome using pelvic examination, ultrasonography and serum CA-125 levels, pending prophylactic salpingo-oopherectomy after child bearing is complete (14). A collaborative trial is currently under way evaluating
3 multimodal screening strategies, using both CA-125 and US, for sporadic ovarian carcinoma (15).
1.2 EPITHELIAL OVARIAN CARCINOMA
Ovarian neoplasms may arise from the ovarian epithelium, germ cells or sex cord- stroma. Epithelial tumors comprise 80%-85% of all ovarian neoplasms and serous tumors account for 60% of all epithelial tumors (16). These neoplasms behave in a benign, borderline or malignant fashion.
1.2.1 Embryogenesis of the Ovarian Surface Epithelium
During embryogenesis, the coelomic epithelium covers the nephrogenital ridge which gives rise to the mullerian ducts and the ovaries. Embryological differentiation of the mullerian system gives rise to the fallopian tube epithelium, the endocervical epithelium and the uterine endometrium. While the ovarian surface epithelium is a single pseudostratified layer of mesothelial cells (17), its neoplastic transformation usually involves some elements of differentiation reflecting mullerian duct derivatives. As such, neoplastic transformation of the ovarian coelomic epithelium along the tubal pathway gives rise to serous epithelial tumors; differentiation along the endocervical pathway results in mucinous tumors and differentiation along the endometrial pathway gives rise to endometriod and clear cell ovarian neoplasms. The different types of epithelial ovarian cancers are not only histologically distinct but also exhibit different tumorigenesis and clinical behavior (18,19).
4 1.2.2 Origin of Epithelial Ovarian Carcinoma
While some controversy exists over the cell of origin of ovarian cancer, the surface epithelium has been considered the source for many years (20,21). Some scientists postulate that inflammatory stimulants on the ovarian surface epithelium may lead to irreversible DNA damage and malignant transformation (22). Epidemiological evidence supports this hypothesis as women with inflammatory conditions such as endometriosis or pelvic inflammatory disease are at an increased risk of ovarian carcinoma whereas women who have had operative tubal ligation, where a mechanical barrier prevents transport of toxins to the peritoneal cavity, have a lower incidence of ovarian carcinoma
(23,24). Other protective conditions have been described for ovarian carcinoma including high parity, breast feeding and use of the oral contraceptive pill (25). Since these factors suppress ovulation, some researchers have postulated that incessant ovulation may play a role in the commencement of ovarian carcinoma (26). According to this theory, ovulatory follicular rupture may irreversibly damage the ovarian epithelium and may lead to entrapment of epithelial segments within the ovarian stroma, generating what are known as inclusion cysts, thus exposing the epithelium to a foreign environment rich in cytokines and hormones. According to this theory, these factors may increase the epithelium's susceptibility to malignant transformation (26).
1.2.3 The Role of the Fallopian Tube Epithelium in Ovarian Carcinogenesis
Given the close histological resemblance of ovarian and fallopian tube serous carcinomas, scientists have questioned the role of the fallopian tube in serous ovarian carcinogenesis. Gene profiling studies of serous ovarian carcinoma demonstrated that
5 they resemble the fallopian tube epithelium more than the ovarian surface epithelium.
This suggested that the fallopian tube epithelium, rather than the ovarian epithelium, might be the source of ovarian serous carcinoma (10). Subsequently, Lee et al. have examined surgical specimens of BRCA1 carriers undergoing prophylactic salpingo- oopherectomy and described a potential malignant precursor lesion in the distal end of the fallopian tube, which was not present in the corresponding ovary (27). More recently,
Tone et al. have shown that the genetic profile of high grade ovarian serous carcinoma is similar to that of fallopian tube serous carcinoma in high risk women (28). Due to these and other emerging data, many groups now believe that the fallopian tube epithelium might be an alternative source of serous ovarian carcinoma, particularly high grade.
1.3 SEROUS EPITHELIAL OVARIAN CARCINOMA
The most commonly diagnosed ovarian malignancy is serous ovarian carcinoma.
Based on clinicopathological and molecular studies, it is becoming increasingly apparent that various types of serous tumors have different clinicopathological characteristics and may harbor different genetic and molecular identities (29).
1.3.1 Surgical Stage and Histological Grade of Serous Ovarian Carcinoma
Disease stage at diagnoses is an important predictor of clinical outcome and overall survival in serous ovarian carcinoma (30). Epithelial ovarian tumors are staged using the Federation of Obstetrics and Gynecology (FIGO) staging system, which is based on findings at surgical exploration and describes the extent of disease spread beyond the tissue of origin. Stage I disease describes tumor that is limited to one or both
6 ovaries. Stage II disease refers to ovarian neoplasia with pelvic extension or implants.
Stage III disease describes tumor involving one or both ovaries with peritoneal implants
outside the pelvis and/or positive retroperitoneal or inguinal lymph nodes. In addition,
histologically proven microscopic involvement of the small bowel, omentum or liver
capsule constitute stage III disease. Stage IV disease involves growth in one or both
ovaries with distant metastasis, such as to the lungs, central nervous system or liver
parenchyma (30).
In addition to stage, histological grade of carcinoma is an important indicator of tumor
aggressive behavior and an important prognostic factor (32,33,34) [Table 1]. Nonetheless,
no universal grading system exists for ovarian epithelial serous carcinoma (35,36). Two
commonly used grading systems are the Shimizu-Silverberg system and the International
FIGO grading system, which are mainly based on architectural composition of the tumor
(37). Studies have shown that Silverberg grade 1 serous disease has a distinguished
molecular identity, clinical behavior and overall survival as compared to Silverberg grade
2 and 3 serous cancers (29,34,36).
1.3.2 Two-Tier Grading System of Ovarian Serous Carcinoma
Given the importance of tumor grading, the gynecologic pathology group at MD
Anderson Cancer Center (MDACC) set out to develop a simple, clinically meaningful
and reproducible grading system for serous ovarian carcinoma- the MDACC two tier
grading system. This system is based primarily on the assessment of nuclear atypia, where pathologists examine the uniformity and pleomorphism of the nuclei in the worst
7 TABLE 1
5-year Survival of Patients with FIGO Stage I-IV by Tumor Grade.
Stage I Stage II Stage HI-IV " Grade 1 91% 69% 38%
Grade 2 74% 60% 25%
Grade 3 75% 51% 19%
Adapted from Heinz et al. 2003'
8 area of the tumor. As such, ovarian serous carcinoma was divided into a low grade category, largely corresponding to Silverberg grade 1 tumors and a high grade category, largely corresponding to Silverberg grade 2 and 3 neoplasms (38). This grading system reduces classification of serous cancers from three to two subtypes, which replicates the different categories of serous carcinoma observed at the clinical and molecular levels (29).
The MDACC two-tier grading system was successfully validated in follow-up studies and shown to be easy to learn and highly reproducible amongst general and gynecological pathologists (39).
1.4 HIGH GRADE SEROUS CARCINOMA
This poorly differentiated carcinoma is the most commonly diagnosed ovarian malignancy. Histologically, high grade serous carcinoma (HGSC) is a predominantly solid tumor with sheets of anaplastic cells and pronounced nuclear atypia corresponding to Silverberg grade 2 or 3 [Figure 1]. As such, this is an aggressive, fast growing neoplasm affecting mostly post-menopausal women with no known or readily identifiable precursor lesion.
Fifteen percent of all HGSC occur in patients with germline mutations in the tumor suppressor genes BRCA1 and BRCA2 (40). Carriers of these hereditary mutations have an increased life-time risk of developing ovarian and breast carcinoma. The reported risk of ovarian carcinoma in BRCA1 carriers is approximately 48% and in BRCA2 carriers 30%
(40). Interestingly, the most commonly reported form of ovarian carcinoma in these women is advanced-stage, high-grade serous carcinoma, whereas LGSC and borderline tumors are rarely reported in this patient population (41).
9 FIGURE 1
StuJv mi.r.v Ilk I III\CIMI\ II lili "« I I I ll"« ' i in li n I - •••!
Figure 1: Histological Appearance of High Grade Serous Ovarian Carcinoma
10 Given that the majority of patients with HGSC present with advanced stage disease, they
are aggressively managed with surgical cytoreduction and medical treatment using the chemotherapeutic agents Carboplatinum and Paclitaxel. This strategy often achieves a good initial response in HGSC, particularly in the background of BRCA mutations (5).
Recurrence, however, is common and requires further treatment.
1.5 LOW GRADE SEROUS CARCINOMA
This well differentiated serous carcinoma has unique histopathological and clinical characteristics that distinguish it from HGSC (29,42,43). This tumor has mild nuclear atypia, less than 12 mitotic figures per 10 microscopic High Power Fields (HPF) and fine papillae that may become fused to form slit-like spaces [Figure 2]. Clinically, low grade serous carcinoma (LGSC) is a slow growing tumor, as reflected by its low mitotic figure content, affecting women in their 40s and 50s and is highly resistant to current chemotherapeutic regimens (44). It is therefore considered a surgical disease; however, the diffuse papillary deposits often present on most peritoneal surfaces make complete resection extremely challenging and often not feasible. Hence, many patients are suboptimally debulked, which worsens their prognosis. In a retrospective analysis,
Bristow et al. studied the effect of cytoreduction on overall survival and found that the median survival for patients with stage III/IV low grade serous cancers who were optimally debulked was 115.4 months compared to 43.1 months for patients who had suboptimal debulking (45). It is therefore evident that novel therapeutic strategies to supplement surgical approaches are needed to improve patient survival. To achieve this, one needs to understand the molecular and genetic identity of this disease.
11 FIGURE 2
Study image the University Health Network (UHN) Ovanan Tissue Bank
Figure 2: Histological Appearance of Low Grade Serous Ovanan Carcinoma
12 1.5.1 Differences in Molecular Characteristics between LGSC and HGSC
Molecular studies have shown that LGSC frequently exhibit KRAS, BRAF, PTEN and {J-
catenin mutations and rarely harbor p53 mutations (29). During tumor progression, these
neoplasms appear to acquire increasing genetic abnormalities such as loss of
chromosome 5q, which is associated with malignant transformation, and loss of chromosome lp, which is associated with the acquisition of invasive phenotypes (46).
Conversely, HGSC demonstrate wild-type KRAS and BRAF, frequent mutations in p53,
MIB1, BCL2, HER-2/neu and C-KIT (29;47) and a high level of allelic imbalance even in
early tumor development (42).
1.5.2 Differences in Invasive Characteristics of LGSC and HGSC
A leading cause of morbidity and mortality from epithelial ovarian carcinoma is bowel obstruction. While up to 23% of patients may have a non-malignant cause of
obstruction, such as due to intraperitoneal chemotherapy or abdominal-pelvic radiation, the majority will suffer from an obstruction due to malignant disturbance of the gastrointestinal wall by malignant ovarian cells (48). The median life expectancy after
developing malignant bowel obstruction is 3 months (49). Often, if the surgery is feasible
and if life expectancy exceeds two months, surgical palliation is attempted to alleviate
symptoms and improve quality of life. When surgery is not possible, other palliative measures are used such as narcotics and anti-emetics (50).
Various models of malignant gastrointestinal wall involvement have been described. In one model, carcinomatous cells invade the muscularis propria and the submucosal layers of the intestinal wall causing luminal invasion and significant risk of obstruction. When
13 the bowel lumen has been invaded, the likelihood of obstruction reaches 71% (51). An alternative pattern of gastrointestinal wall disturbance involves formation of carcinomatous nests within the bowel serosa with invasion into the muscularis propria but without interruption of the submucosa or the bowel lumen. This invasion pattern is often associated with lymphatic invasion and leads to obstruction in 30% of cases (50). It is observed that LGSC follows the former gastrointestinal invasion pattern and HGSC follows the latter, although no concrete data are available. Therefore, examining gene expression profiles of LGSC and HGSC may highlight differentially expressed genes significant in gastrointestinal luminal invasion.
1.6 SEROUS LOW MALIGNANT POTENTIAL TUMOR
Serous Low Malignant Potential Tumor (LMP) or Serous Borderline Tumor
(SBT) is a unique entity initially recognized in 1971 by the International Federation of
Obstetrics and Gynecology and in 1973 by the World Health Organization (WHO).
Comprising 15% of all serous tumors, LMP tumors are defined as serous neoplasms that exhibit epithelial proliferation and atypia greater than that seen in benign serous tumors but do not show destructive stromal invasion or high grade cytological atypia characteristic of serous carcinoma (52). They are slow growing neoplasms as evident by their low number of mitotic figures- less than 4 per 10 HPF. The tumor cysts and papillae are lined by stratified cuboidal epithelium of varying thickness with characteristic budding, tufting and branching papillae [Figure 3]. Grossly, these tumors harbor papillary projections that may be focal or abundant and may involve the ovarian surface with little or no solid areas.
14 LMP tumors often present in pre-menopausal women and the majority present as Stage I disease. The risk of malignant transformation of FIGO Stage I LMP tumor is approximately 0.5%, which is similar to gynecological conditions classified as benign, such as uterine fibroids (53). Interestingly, the presence of non-invasive peritoneal implants or microscopic lymph node metastases do not alter the patient's prognosis
(54;55). In fact, much of the morbidity and mortality associated with most LMP tumors is directly associated with the surgical treatment rather than the disease itself (56). However,
some LMP tumors have increased aggressiveness and worse prognosis. These tumors often have one or more of the following factors: advanced FIGO stage, tumor growth on the ovarian surface, post-operative residual tumor, tumor aneuploidy and the presence of micropapillary features (57). These types of LMP tumors have an increased risk of malignant transformation; reaching approximately 10% (34).
Current management for LMP tumors includes surgical resection of all visible disease including an omental biopsy if the omentum is clinically uninvolved. Resection of uninvolved tissue such as the contralateral ovary and uterus are not recommended, nor are lymphadenectomies and random peritoneal biopsies (58). Chemo- and radiation therapies are not routinely offered to patients with LMP tumors, irrespective of their
FIGO stage. If a patient experiences a recurrence of an LMP tumor, secondary cytoreductive surgery appears to be the most effective treatment (58).
15 FIGURE 3
Study image: the University Health Network (UHN) Ovarian Tissue Bank
Figure 3: Histological Appearance of Ovarian Serous Low Malignant Potential Tumor
16 1.7 SEROUS LOW MALIGNANT POTENTIAL TUMOR WITH
MICROPAPILLARY FEATURES
In the past decade, a specific subtype of serous neoplasm has been recognized that appears to have unique clinicopathological features. Representing 6-18% of all serous borderline tumors, this relatively rare neoplasm has small papillae, minimal nuclear atypia and low mitotic activity and has therefore been called ovarian Low Malignant
Potential Tumor with Micropapillary Features (LMP-MP) or non-invasive Micropapillary
Serous Carcinoma (MPSC) (59). This tumor was originally described by Burks et al. in
1996 and is regarded as a low grade invasive tumor even if no invasive implants are detected at initial staging (60). LMP-MP represents a subset of well-differentiated ovarian cancers that do not display destructive, infiltrative growth but are associated with malignant behavior.
On gross examination, LMP-MP tumors exhibit prominent papillary excrescences and are intracystic or exophytic with or without a cystic component (61). Morphologically, they display highly complex micropapillae arising directly from large bulbous papillary structures [Figure 4]. The micropapillae are covered by round to cuboidal cells with a high nuclear-to-cytoplasmic ratio. In fact, the cardinal feature of LMP-MP is the formation of micropapillae lined by small cells with little or no fibrovascular stromal support (60). By convention, the diagnosis of LMP-MP is made when the papillae are at least five times as long as they are wide and the micropapillary architecture described is present in at least a continuous 5 mm section on a single slide (60,61).
Clinically, LMP-MPs are slow growing neoplasms affecting women in their 40s and 50s and are managed surgically. In rare cases, adjuvant chemotherapy is administered.
17 FIGURE 4
} j - • ' >* if- <* w-iJ- «. - r A' % •*•<*
r • ~.- •? ;" • J tr* "*V --«. <*.;-- * *" . ^ , " .>' ,' '-"' *v " 1 if * ^ ^r ; ^ , «,**"^ - * " ="* •" f «•' :i- '.'. , - .-» :->" *V " -> > n "V" , - - ** "**» r -|# % % - ^ .1U ~+K ,«• ' i '-*%.' -""_ "t. .* t\ £-*•:% ^** - •_ V>'* 1 • mix t-fe-* . t "* f *" •. '* ~* I ," .»_•* d,* '• . '. \V' * * - 'i* " ^
Study image: the University Health Network (UHN) Ovarian Tissue Bank
Figure 4: Histological Appearance of Ovarian Serous Low Malignant Potential Tumor with Micropapillary Features
18 1.7.1 Controversy Regarding Histological Classification of LMP-MP
Since their initial description in 1996, these tumors have been subject to controversy. Histologically, these tumors display exuberant cellular proliferation with no stromal invasion and as such fall under the "borderline" category. However, the clinical behavior of these neoplasms is more aggressive than what is expected of a typical borderline neoplasm. Studies have shown that LMP-MP tumors are associated with increased incidence of invasive peritoneal implants, increased bilaterality, advanced stage at presentation, increased incidence of recurrence and higher mortality rates than typical serous LMP tumors (60,62). Therefore, many consider these neoplasms to be low-grade ovarian carcinomas and use the term non-invasive micropapiUary carcinoma (MPSC) to describe them (43).
Appropriate classification of these neoplasms is critical in driving appropriate medical management of women with these tumors. Tumors labeled as "borderline" are often conservatively managed with primary resection of the tumor and other macroscopic disease, but sparing the remainder of the gynecological apparatus (30). Post-operatively, these patients are seldom followed in a cancer center. In contrast, patients diagnosed with a carcinomatous tumor are managed aggressively with total-abdominal hysterectomy and bilateral salpingo-oophorectomy, debulking, complete surgical staging and adjuvant therapy where appropriate (30). Post-operatively, they are followed closely in an oncology center. It is therefore crucial to appropriately classify these tumors as true borderline or malignant neoplasms.
19 1.8 THE RELATIONSHIP BETWEEN LOW MALIGNANT POTENTIAL
TUMORS AND LOW GRADE SEROUS CARCINOMA
Given their inherent "borderline" malignant behavior and potential for malignant transformation, LMP tumors have been the subject of multiple studies. Several publications investigating the molecular biology of these tumors have demonstrated that they frequently harbor mutations of the BRAF-KRAS-MAP Kinase cascade (45,46). In a molecular study, Singer et al. found 61% of LMP tumors to have a gain of function mutation in either KRAS or BRAF- commonly in codons 12 and 13 in BRAF or 599 in
KRAS (42). The researchers found the same mutations in 68% of LGSC cases but in none
of the study's HGSC cases (42). This molecular association led to several studies examining the clinicopathological association of LMP tumors and LGSC of the ovary.
Malpica et al. studied 50 LGSC cases diagnosed between 1972 and 2000 and identified a co-existing LMP lesion in 60% of patients (30/50). Of those LMP tumors, 93% had micropapillary features (28/30) and were thus classified as LMP-MP. This suggested a close histological association of LMP tumors, particularly the subtype featuring micropapillary patterns, and LGSC (38). In another clinicopathological study, researchers examined 41 cases of LGSC that were diagnosed years after an initial diagnosis of borderline/LMP tumors. The clinical behaviors of these neoplasms were compared to those of 112 primary LGSC. The two groups exhibited similar disease-free and progression-free survival; thus, the researchers concluded that LGSC arising in the background of LMP tumors are clinically similar to primary LGSC (63).
To investigate the genetic correlation of these tumors, Bonome et al. conducted a gene expression profiling study examining microdissected LMP tumors, low grade and high
20 grade serous cancers. Their results demonstrated a collective clustering of the majority of
LMP tumors with LGSC, and separate from the HGSC (64). Unfortunately, this study did not specify whether the LMP tumors harbored micropapillary features.
1.9 TWO PATHWAY HYPOTHESIS OF OVARIAN CARCINOGENESIS
The aforementioned studies support the hypothesis that serous ovarian carcinoma may develop through two distinct pathways. A slow-growing pathway whereby the ovarian surface epithelium or inclusion cysts can transform into LMP tumors which in turn can give rise to LGSC, and an alternative, aggressive pathway whereby HGSC arises de novo without a recognizable intermediate lesion (46) [Figure 5].
In an attempt to clarify the role of LMP-MP in the malignancy continuum from LMP tumors to LGSC, Staebler et al. used comparative genomic hybridization to determine the cytogenetic differences between the three neoplasms (65). Chromosomal imbalances were identified in 3 of 9 LMP tumors, 6 of 10 LMP-MP tumors and 11 of 11 LGSC (65). The authors have therefore suggested that LMP-MP tumors may represent an intermediate lesion in the progression from borderline to low grade serous neoplasms [Figure 6].
However, no carefully designed gene profiling study is available that directly compares ovarian LMP, LMP-MP and LGSC. This is essential to further elucidate the proposed
Type I malignancy continuum and would clarify the role of LMP-MP within it.
21 Figure 5
Proposed Type I Pathway
Proposed Type II Pathway
Adapted from Singer et al. Am J Path. 2002. 160;4:1223-1228 (46)
Figure 5: Two Pathway Hypothesis of Serous Ovarian Carcinogenesis
22 FIGURE 6
Proposed Type I Pathway
Adapted from Singer et al. Am J Path. 2002. 160;4:1223-1228 (22)
Figure 6: Type I Pathway of Serous Ovarian Carcinogenesis
23 1.10 THESIS HYPOTHESIS AND RATIONALE
Based upon the studies reviewed above, we hypothesize that LMP-MP tumors represent an intermediate tumor in the type I pathway that resembles LGSC more than
LMP tumors. This would support a more aggressive medical intervention and follow-up in the management of these tumors. Identifying genes differentially expressed between these tumor types will reveal important information regarding the progression of the
disease and could lead to important discoveries leading to identification of new therapeutic targets. Our approach was to generate gene expression profiles of ovarian
LMP, LMP-MP and LGSC tumors to clarify the similarity of these tumors, thereby
supporting whether LMP tumors- with and without micropapillary features- are potential precursor lesions for LGSC. Gene expression profiles also highlight potential differences between LMP-MP and LMP as well as between LMP-MP and LGSC. This may clarify the genetic identity of LMP-MP and allow for appropriate classification of this neoplasm either as a borderline tumor or as a carcinomatous lesion, which is an essential distinction when selecting the appropriate management modalities for patients with this disease.
Furthermore, comparing gene expression profiles of the non-invasive LMP tumors and the invasive LGSC may highlight key differentially expressed genes involved in malignant transformation and progression. This may allow identification of potential targets for novel, more effective therapy for LGSC. Currently, surgical resection is considered first-line management for LGSC but its effectiveness is often limited by the extent of peritoneal disease, which impacts negatively on survival. Incomplete surgical resection leads many physicians to offer Platinum/Taxane based chemotherapy to women with LGSC, despite the fact that it's a chemoresistant neoplasm. Therefore, developing
24 effective medical therapy for LGSC would not only improve overall prognosis but would also minimize morbidity associated with unnecessary chemotherapy.
1.11 OBJECTVES
1. To profile gene expression of serous LMP, LMP-MP and LGSC of the ovary using genetic material from microdissected tumor epithelium. This would allow comparison of the genetic profile of the three tumor types and may indicate whether the genetic profile of LMP-MP is unique or whether it is similar to LMP or LSGC.
2. To study differentially expressed genes between the non-invasive LMP tumors and the invasive LGSC in an attempt to identify genes involved in carcinogenesis and malignant transformation. Key differentially expressed genes would be selected for validation studies using real-time PCR and immunohistochemistry.
25 CHAPTER 2
MATERIALS AND METHODS
2.1 CASE SELECTION
This study received human research ethics approval from the University Health
Network (UHN) Human Research Ethics Board and informed consent was obtained from all patients contributing surgical material included in the study. All patients in the UHN database diagnosed with one of the following from 1996-2006 were identified:
• Low Malignant Potential Tumor (LMP), Serous Borderline Tumor (SBT)
• LMP with Micropapillary Features (LMP-MP), SBT with Micropapillary Features
(SBT-MP)
• Invasive or Non-Invasive Micropapillary Serous Carcinoma (MPSC)
• Grade 1 or 2 Serous Carcinoma
The corresponding histopathology slides were reviewed with Dr. Patricia Shaw, a gynecologic pathologist, and appropriate cases were reclassified into one of the 3 study categories (LMP, LMP-MP or LGSC) using the International Federation of Obstetrics and Gynecology (FIGO) standards and the MDACC two tier classification system as follows:
• LMP: No stromal invasion, budding epithelium, branching papillae, less than 4
mitotic figures per 10 High Power Fields.
• LMP-MP: No stromal invasion, medusa-like micropapillary pattern, projections 5
times longer than they are wide.
26 • LGSC: Invasive carcinoma, low grade nuclear atypia, Silverberg grade 1, less thanlO
mitotic figures per 10 High Power Fields.
The study's inclusion and exclusion criteria were as follows:
• Inclusion Criteria
o Primary diagnosis of: LMP, LMP-MP, or LGSC
o Tissue present in UHN ovarian tissue bank
o Follow up information available from hospital and/or clinic charts
• Exclusion Criteria:
o High grade serous carcinoma or non-serous ovarian carcinoma
o Previous chemotherapy treatment (either neoadjuvant or for other disease)
o Recent personal history of breast cancer (< 10 years)
o Strong family history of breast or ovarian cancer (that would qualify patients
for genetic testing for hereditary mutations)
o Patient is a known BRCA 1/2 mutation carrier
All specimens and the corresponding clinical information were studied according to the protocols approved by the UHN ethics and research review board.
2.2 TISSUE PREPARATION AND SECTIONING
Ovarian tumor tissues were harvested surgically by the gynecological oncologists at the UHN. Sections from the tissue specimens were obtained for histopathological diagnosis and the remaining tumor was processed by the UHN ovarian tissue bank, snap frozen in liquid nitrogen and stored at -80°C.
27 Tumor specimens selected for this study were transferred from the -80°C storage freezer to the cryostat chamber and allowed to equilibrate to -20°C. The procedure was performed in an RNase free manner using RNase away (Sigma-Aldrich, Oakville, ON)to clean all equipment and instruments. An initial 5[xm tissue section was obtained from each tumor specimen and placed on a Snowcoat microscopic slide (Surgipath Medical
Industries, Inc). The slide was then stained with hematoxylin & eosin and reviewed with
Dr. Shaw to ensure the histopathological diagnosis in the microdissected specimen. Once the tumor diagnosis was confirmed, approximately 15-20 7^m sections were cut from each specimen and adhered to Superfrost Plus microscope slides (Fisher Scientific,
Ottawa, ON). The unstained slides were placed on dry ice and transferred to a -80°C freezer for storage pending laser capture microdissection.
2.3 LASER CAPTURE MICRODISSECTION
Laser Capture Microdissection (LCM) is a microscopic technique used to isolate cells of interest from tissue containing a heterogeneous cell population. In this study, this technique was used to isolate ovarian epithelial tumor cells from the surrounding stroma
[FIGURE 7].
2.3.1 Slide Staining
The frozen unstained tissue sections were transferred on dry ice to the laboratory housing the Arcturus PixCell He Microscope (Arcturus, Mountain View, CA) at Princess
Margaret Hospital, Toronto, ON. The slides were dehydrated and stained by transferring through the following solutions: 75% ethanol for 30 seconds, distilled water for
28 FIGURE 7
Figure 7: Laser Capture Microdissection of Ovarian Tumor Epithelium a. Undissected tumor on LCM Superfrost slide b. Stromal tissue remaining on the Superfrost slide post microdissection of epithelium c. Microdissected tumor epithelium isolated on CapSure cap after LCM
29 30 seconds, Hematoxylin for 30 seconds, distilled water for 30 seconds, 75% ethanol for
30 seconds, 95% ethanol for 30 seconds, 100% ethanol for 30 seconds, xylene for 10 seconds and lastly a second xylene wash for 5 minutes. After the xylene treatment, the slides were allowed to air dry for 5 minutes before being placed in a box containing dryite crystals in preparation for laser capture microscopy.
2.3.2 Microdissection of Epithelial Cells
The stained slides were placed on the Arctus PixCell II microscope's stage and the area of interest within the tumor was selected. A CapSure LCM Cap (Molecular
Devices, Sunnyvale, CA) containing a transparent thermoplastic film of ethylene vinyl acetate polymer was applied to the tissue on the glass slide to allow collection of the final cell product. A carbon dioxide laser was then activated and directed towards the cells of interest. After isolating an average 5000-10,000 cells per case, the ethylene vinyl acetate film- with the adherent cells- was removed from the glass slide and placed into a
GeneAmp tube containing lOO^L RNA lysis buffer containing the strong protein denaturant Salt Guanidine Thiocyanate and 0.7u.L (3-Mercaptoethanol. The tube was vortexed cap side down then spun for 1 minute. The liquid buffer containing the lysed cells was then transferred to a 1ml Eppendorf tube and stored at -80°C in preparation for
RNA extraction.
2.4 RNA EXTRACTION
Total RNA was extracted from cell lysates using the Stratagene Absolutely RNA
Microprep purification kit according to the manufacturer's instructions (Cat# 400805).
30 Following adherence of the RNA to the silica-based fiber matrix, the spin cup was then
washed with salt wash buffer in preparation for the DNase treatment step. RNase-free
DNase I mixed with DNase digestion buffer was used to degrade genomic DNA. The
solution was then incubated for 15 minutes followed by multiple washings using high and
low salt wash buffers to remove all contaminants including DNase I. After micro
centrifugation, 15ul of elution buffer were added directly to the spin cup's fiber matrix
and this mixture, containing the RNA, was stored in -80°C pending future steps.
2.5 RNA QUALITY TESTING
The extracted RNA samples were analyzed at the UHN microarray center. To ensure that the obtained RNA samples were free of contaminating proteins, cellular material, organic salts or solvents, each RNA sample's purity was tested using a
NanoDrop ND-1000 Spectrophotometer (NanoDrop Technologies Inc, Wilmington DE).
The Ratio of Absorbance (A) readings at 260nm and 280nm were used as a measure of
acceptable purity. Only RNA samples with A26o:A28o ratios in excess of 1.8 were selected
as ratios lower than 1.8 may result in low amplification yield. In addition to purity, the
RNA samples were tested for integrity using the Agilent 2100 Bioanalyzer RNA 6000
Pico LabChip Kit (Agilent Technologies Inc, Santa Clara, CA). Good integrity RNA had high molecular weight and showed little or no evidence of degradation. Degraded RNA
samples were excluded from the study since amplification of degraded RNA may
significantly alter microarray results. Of note is that the tumor samples' ages ranged from
1 to 10 years, however, the RNA concentration and quality were independent of the age
31 of the sample suggesting that the processing and storing methods used by the UHN tumor bank were effective in keeping the tissues' integrity.
2.6 RNA AMPLIFICATION AND REVERSE TRANSCRIPTION TO cDNA
An aliquot (lOng) of each pure RNA sample underwent one round of linear amplification and reverse transcription to cDNA with the Ovation Biotin RNA
Amplification and Labeling System (NuGEN Technologies, San Carlos, CA) using the
Rbo-SPIA amplification process which was preformed in three steps according to the manufacture's protocol (NuGEN Technologies, Cat# 2300-12). The first step involved synthesis of the first cDNA strand. Step 2 involved synthesis of the second cDNA strand and the third step involved the SPIA isothermal linear amplification. The reaction products were Biotin labeled according to the manufacturer's protocol (NuGEN
Technologies, Cat# D01002). The quality and quantity of the amplified, labeled cDNA product was tested according to the manufacturer's instructions (NuGEN Technologies,
Cat# D01007).
2.7 cDNA HYBRIDIZATION TO GENECHIP MICROARRAY
Amplification and hybridization of the study samples to gene chip arrays was performed in seven separate balanced runs, with each group equally represented in each ran to minimize procedural bias. Each purified, labeled cDNA sample was prepared for hybridization to Affymetrix U133 Plus2 Human GeneChip microarrays according to the
Affymetrix GeneChip expression analysis technical manual, revision 4
(www.affymetrix.com) with the single following exception; the hybridization cocktail
32 was heat denatured at 99°C for 2 minutes, which is an adequate time for cDNA hybridization, instead of 5 minutes, which would be required for cRNA hybridization.
After denaturation, the hybridization cocktail was placed in 45°C heat blocks for 5 minutes then centrifuged at 10,956 x g for 5 minutes prior to loading. An aliquot (200pil)
of the hybridization cocktail containing a single cDNA sample was loaded onto each
GeneChip and was incubated for 17 hours according to the GeneChip fluidics station 400 protocol (EukGE-WS2v4).
2.8 DATA ANALYSIS
Expression data generated in CEL file format were imported into ArrayAssist and a master data table of all hybridization results was produced using the Robust Multi-
Array Average (RMA) algorithm. RMA normalized data were then analyzed using multiple tools. Unsupervised Hierarchical Cluster analysis of LMP, LMP-MP and LGSC was performed using GeneSpring GX software (www.chem.agilent.com). To study differential gene expression between the study groups, RMA normalized data were imported into Microsoft Excel Significance Analysis of Microarrays program (SAM version 3.0, www-stat.stanford.edu/~tibs/SAM/). Comparisons were performed using two-class unpaired analysis of unlogged data and the delta values for each comparison were selected based on an acceptable corresponding median false discovery rate (FDR) associated with each delta value. Gene ontology of differentially expressed genes was obtained from the NetAffx Analysis Center (www.affymetrix.com/analysis/index.affx).
To obtain more detailed information on genes associated with proteins of interest, gene names or probe set IDs were imported into the GeneCard website (www.genecards.org).
33 Differentially expressed genes identified through SAM analysis were integrated into the
Online Predicted Human Interaction Database (OPHID/I2D) network
(http://ophid.utoronto.ca/i2d, version 1.6) and the generated known and predicted protein interaction list was imported into NAViGaTOR software, version 1.1.0 (Network
Analysis, Visualization and Graphing, Toronto). This highlighted differentially expressed proteins with significant interconnectivity and potential networks and pathways of interest. While the 0PHID/12D network identifies both hypothetical and real interactions, we have researched interactions of interest and ensured they were real; as such all interactions described in this study have been previously confirmed as real interactions.
To select genes for validation studies we studied the function of genes differentially expressed between LMP and LGSC and were particularly interested in genes involved in malignant behavior and carcinogenesis. Another important factor in gene selection was the potential for genes to act as therapeutic targets for LGSC. Therefore, genes for which therapy is available were particularly interesting. Hence, after intensive examination of differentially expressed genes coupled with through literature review, eight genes were selected for validation studies.
2.9 VALIDATION STUDIES
The microarray screen was validated using quantitative real time reverse transcriptase polymerase chain reaction (RT-PCR) performed on eight selected genes and selected gene products were validated by immunohistochemistry performed on a larger set of samples contained in an ovarian tissue microarray.
34 FIGURE 8
TANK Microarray Expression Levels TANK Microarray Expression Levels 210458 ALL Samples 210458 Selected Samples Only
50
40
10
0 - LMP LMP LMP MP LGSC
PARP1 Microarray Expression Levels PARP1 Expression Levels 208644 ALL Samples 208644 Selected Samples Only 180 160 *1 140 120 100 •jit" =*- 80 _l3 60 40 20 0 LMP LMP MP LGSC
CDK2 Expression Levels CDK2 Microarray Expression Levels 204252 ALL Samples 204252 Selected Samples Only
PEA15 Expression Levels PEA15 Microarray Expression Levels 200787 ALL Samples 200787 Selected Samples Only
. 250- . 250, i 200 - ? 1 i i§ 150 - o i Expressio n • JL ' T a 50 " 0 2 n. lii LMP LMP MP LGSC LMP LMP MP LGSC
35 ATDA Microarray Expression Levels ATDA Microarray Expression Levels 213988 ALL Samples 213988 Selected Samples Only
Betacellulin Microarray Expression Levels Betacellulin Microarray Expression Levels 241412 ALL Samples 241412 Selected Samples Only
100
80
60
40
20
0
PPI Microarray Expression Levels PPI Microarray Expression Levels 201192 ALL Samples 201192 Amplification Samples Only
LGSC
5 alpha Reductase Microarray Expression Levels 5 alpha Reductase Microarray Expression Levels 204675 ALL Samples 204675 Selected Samples Only
500 -, 600 -, « 450 - | 400 T c 350 - 0 i •i I250- , 300 t 200-
s S 100 — 2 50 if LMP MP LG LMP MP LG
Figure 8: Microarray Expression Levels of Validation Genes in all Study Samples and Selected Samples for real-time RT-PCR for TANK, PARPl, CDK2, PEA 15, ATDA, BTC, PPI and 5aRed
36 2.9.1 Quantitative Real-Time Reverse Transcriptase Polymerase Chain Reaction
Given the minute amount of genetic material obtained in this study, 15 representative study samples, 5 from each tumor group, were subjected to quantitative real-time RT-PCR. This ensured that sufficient genetic material would be available for further validation studies on additional genes and for future experiments. Care was taken to ensure that the microarray expression patterns of the selected cases were true representations of the tumor groups [Figure 8].
The selected cases were subject to real-time RT-PCR on the following 8 selected amplified gene products: TRAF Family Member Associated NF-Kappa-B-Activator
(TANK), Poly [ADP-ribose] polymerase-1 (PARP1), Cell Division Protein Kinase 2
(CDK2), Astrocytic Phosphoprotein (PEA15), Diamine Acetyl Transferase (ATDA),
Phosphatidylinositol Transfer Protein- alpha isoform (PPI), Betacellulin (BTC) and 5-a-
Reductase I (5aRed). Primer sets were designed for each selected gene product and the reference gene TATA Binding Protein (TBP) using the Primer Express Software, version
2.0 (Applied Biosystems, Foster City, Ca). Care was taken to ensure that all amplicons were within 1500bp from the poly A tail of each gene sequence given that 01igo(dT)2o primers were used for reverse transcription of the study and caliber samples [Table 2].
RNA was extracted from SKOV3 ovarian cancer cells using TRIzol reagent (Invitrogen
Corporation, Carlsbad, CA) to use in primer optimization and as caliber sample during
RT-PCR reaction. Potential DNA contamination of the extracted RNA samples was eliminated using the DNA-free kit (Ambion Inc, Austin, TX) and SKOV3 RNA was reverse transcribed to cDNA using Superscript III reverse Transcriptase and 01igo(dT)2o
37 TABLE 2:
Quantitative Real Time RT-PCR primer sets
Oligo Name Oligo Sequence (5'->3') TANK Forward GTGATGCTACAGGACGAAGAGGA TANK Reverse TGCAGAATCTCTATCCATGCATG PARP1 Forward AAGTCCCTTGTTTTGTGTTGTGTC PARP1 Reverse CCAGCCTTTTCTCTATGTCAGTTTT CDK2 Forward CCTTATGAGGCAGGTGAGAGATGT CDK2 Reverse GACGTCAGAGGAAAATGGGATC PEA 15 Forward CCCTCTTCCACTCAGTTGTTCCTA PEA 15 Reverse GGTGGTGTCAGGATTCGTGTTAA ATDA Forward AGTGACATACTGCGGCTGATCA ATDA Reverse TCTCCAAAACCATCTTCTAGCAGA PPI Forward AGCTGTTCTGTTGGCTCGATAAGT PPI Reverse CTTTTGTCTCATTTCATCCAGCTGT BTC Forward CTGAGGAAAACTGTGCAGCTACC BTC Reverse GCATCTCCCTTTGATGCAGTAAT 5aRed Forward GTAACAGGTCAGAATTTCAAGCTCTG 5aRed Reverse GTCTCATACACACTTGGCAAGACATA TBP Forward TGC ACA GGA GCC AAG AGT GAA TBP Reverse CAC ATC ACA GCT CCC CAC CA
38 primers (Invitrogen, Carlsbad, CA). Prior to RT-PCR all cDNA samples were diluted to
1.6ng/ul using sterile double distilled H2O (ddH20). To determine the ideal primer
annealing temperature, optimization experiments were performed using end point PCR
and the reaction products were subject to agarose gel electrophoresis and sequence
verification to ensure the reactions yielded the desired products. RT-PCR was performed
using the QuantiTech SYBER Green PCR Kit (Qiagen Inc, Velencia, CA) according to
the manufacturer's instructions. Each reaction mixture included the corresponding
forward and reverse primers, at concentrations determined during optimization
experiments, lOu.1 of cDNA [16ng], 25^1 of Sybergreen solution and ddH20 to bring the
total reaction volume to 50ul. All reactions included triplicates of each sample for both
target gene and reference gene. The mixtures were placed in a 96-well plate (BrandTech
Scientific) and the reaction was carried out in the ABI-PRISM 7900HT sequence
detection system (AME Bioscience, Toroed, Norway). The initial denaturation stage
temperature was set at 95°C for 10 minutes. This was followed by 40 cycles of
amplification at 95°C for 15 seconds and 60°C for 1 minute. A dissociation stage was
added where the temperature changed from 95°C for 15 seconds to 65°C for 15 seconds to 95°C 15 seconds. A cycle threshold (CT) value was recorded for each sample using the
ABI PRISM Sequence Detection Software, version 2.2.2 (Applied Biosystems, Foster
City, CA).
As the RT-PCR reaction was set up in triplicates for each sample, the mean of the 3 CT
values was calculated and normalized against the housekeeping gene TBP. An arithmetic formula from the comparative CT method was applied to the raw mean CT values to
obtain relative gene expression data for each study sample.
39 2.9.2 Immunohistochemistry
A representative tissue microarray was constructed from 61 paraffin-embedded specimens in the UHN ovarian tissue bank with the following histopathology: 25 LMP tumors, 14 LMP-MP tumors, 18 LGSC, 3 normal fallopian tubes and 1 endosalpingiosis.
Of these cases, 32 were included in the gene expression microarray analysis and 29 were new cases not previously included in the study. The new cases were selected based on the histology and clinical data obtained from the UHN tissue bank database. All malignant cases were nai've to chemotherapy.
H&E sections were examined for each case and 1mm2 area of representative histology was highlighted. The corresponding formalin-fixed paraffin-embedded tissue block was retrieved and the corresponding area of interest was located. Core biopsies (1mm2) were taken at the selected area in duplicate and were used to construct the tissue microarray.
Three gene products with differential gene expression levels in LMP compared to LGSC as suggested by microarray analysis and real-time RT-PCR trends were selected for validation by immunohistochemistry using the following antibodies:
• TANK: Unconjugated goat polyclonal Anti-TANK (N-19) Antibody, Sc# 1998
(Santa Cruz Biotechnology, Santa Cruz, CA)
• PARP1: Unconjugated rabbit polyclonal Anti-PARPl Antibody, GTX# 10955
(GeneTex Inc, San Antonio, TX)
• CDK2: Unconjugated mouse monoclonal CDK2 Pre-diluted Antibody, #Ab 17947
(Abeam Inc, Cambridge, MA)
The selected antibodies were screened by staff at the Pathology Research Center, UHN and optimized on formalin-fixed paraffin-embedded tissue sections as per each
40 manufacturer's instructions prior to their application to the constructed tissue microarrays. Stained slides were scanned using the ScanScope CS slide scanner (Aperio
Technologies Inc, Vista, CA) to create magnified (20X) digital slide images. The images were then imported into ImageScope software, version 6.25 (Aperio Technologies Inc,
Vista, CA). Manual, consensus-style HistoScoring was performed with Dr. Shaw in a blinded fashion. The HistoScore was an additive score of the percent of staining score that ranged from 0 to 3 and the intensity of staining score that similarly ranged from 0 to
3. Therefore, the HistoScore value ranged from 0 to 6.
41 CHAPTER 3
RESULTS
3.1 STUDY CASES AND CLINICAL DATA
Clinical data for all study patients were obtained from the patients' hospital charts, clinic charts and the UHN tissue bank database [Table 3]. Eleven patients were included in the study with clinical and histopathological diagnoses of LGSC.
Seven patients with clinical and histopathological diagnoses of LMP-MP were included in this study. Two additional patients had an overall clinical diagnosis of LGSC and an associated histopathological LMP-MP tumor. Since only the LMP-MP tumor epithelium was microdissected, these cases were classified as LMP-MP, which brings the total of
LMP-MP study cases to nine.
Similarly, three patients with a clinical diagnosis of LGSC had associated LMP tumors on histopathology. Since only the LMP tumors were microdissected in these cases, the cases were labeled as LMP for the purpose of this study. In addition, one patient with primary diagnoses of LMP-MP had an associated LMP tumor that was microdissected, thus the case was classified as LMP. It is of note that in this case the LMP-MP tumor was also microdissected and included in the LMP-MP study group (cases #14 and #24 in
Table 3). Lastly, twelve patients with a clinical and histopathological diagnosis of isolated LMP were included in this group, thus the final number LMP cases included in the study was sixteen.
42 TABLE 3
Clinical information associated with study cases
Disease Micro- Principal Overall Clinical Surgical Chemo Free Case dissected Clinical Age Survival Status Stage Debulking therapy Survival # Tumor Diagnosis [Months] [Months] Alive without 1 LGCA LGSC 68 III Optimal No 38 38 Disease Dead from 2 LGCA LGSC 67 III Suboptimal No 8 10 Disease Dead from 3 LGCA LGSC 72 III Suboptimal No 1 35 Disease Alive with 4 LGCA LGSC 40 III Optimal Yes 12 120 Disease Alive without 5 LGCA LGSC 37 III Optimal No 65 65 Disease Alive with 6 LGCA LGSC 68 III Suboptimal Yes 36 38 Disease Alive with 7 LGCA LGSC 79 III Suboptimal Yes 2 50 Disease Alive with 8 LGCA LGSC 63 III Optimal Yes 17 48 Disease Alive with 9 LGCA LGSC 48 III Suboptimal No 12 31 Disease Alive without 10 LGCA LGSC 59 III Optimal Yes 1 1 Disease Alive without 11 LGCA LGSC 78 III Optimal Yes 2 2 Disease Alive without 12 LMP-MP LGSC 77 III Optimal No 55 55 Disease Dead from 13 LMP-MP LGSC 40 III Suboptimal Yes 1 19 Disease (LGSC) Alive with 14 LMP-MP LMP-MP 50 III Optimal No 2 20 Disease (LGSC) Alive without 15 LMP-MP LMP-MP 73 II Optimal No 12 12 Disease Alive without 16 LMP-MP LMP-MP 73 I Optimal No 70 70 Disease Alive with 17 LMP-MP LMP-MP 45 III Optimal No 39 68 Disease (LGSC) Alive with 18 LMP-MP LMP-MP 36 III Optimal No 34 35 Disease (LGSC) Alive without 19 LMP-MP LMP-MP 61 III Optimal No 34 34 Disease Alive without 20 LMP-MP LMP-MP 47 III Optimal Yes 24 24 Disease
43 Disease Micro- Principal Overall Clinical Surgical Chemo Free Case dissected Clinical Age Survival Status Stage Debulking therapy Survival # Tumor Diagnosis [Months] [Months] Alive without 21 LMP LGSC 42 I Optimal No 29 29 Disease Dead from 22 LMP LGSC 35 IV Optimal Yes 7 9 Disease (LGSC) Alive with 23 LMP LGSC 76 III Suboptimal Yes 1 1 Disease (LGSC) Alive with 24 LMP LMP-MP 50 III Optimal No 2 20 Disease (LGSC) Alive without 25 LMP LMP 29 I Optimal No 72 72 Disease Alive without 26 LMP LMP 52 III Optimal No 46 46 Disease Alive without 27 LMP LMP 59 III Optimal No 37 37 Disease Alive with 28 LMP LMP 22 III Optimal No 11 40 Disease (LMP) Alive without 29 LMP LMP 44 III Suboptimal No 13 13 Disease Alive without 30 LMP LMP 46 I Optimal No 43 43 Disease Alive without 31 LMP LMP 21 I Optimal No 47 47 Disease Alive without 32 LMP LMP 32 I Optimal No 40 40 Disease Alive without 33 LMP LMP 48 I Optimal No 39 39 Disease Alive without 34 LMP LMP 70 I Optimal No 1 1 Disease Alive with 35 LMP LMP 27 I Optimal No 72 104 Disease (LMP) Alive without 36 LMP LMP 44 I Optimal No 28 28 Disease
44 The average age for patients in the LMP, LMP-MP and LGSC categories were 43.51,
55.7 and 61.7 years, respectively. The average age for the twelve cases with an isolated
LMP diagnosis was 41.16. All patients were managed with primary surgical resection and staging. Clinical information and follow-up data listed is as of April 2008.
In the LGSC group, surgical cytoreduction was deemed optimal in six of eleven patients and suboptimal in five of eleven patients. Suboptimal surgical resection described the presence of unresectable residual malignant disease in the peritoneal cavity measuring more than 1cm at the end of cytoreductive surgery (66). The average disease free survival and overall survival for patients who were optimally debulked were 22.5 months and 45.6 months, respectively. The average disease free survival and overall survival were significantly less in patients who underwent suboptimal surgical debulkmg- 11.8 months and 32.8 months, respectively [Table 4a].
Six patients with LGSC received post-operative adjuvant Carboplatinum and Paclitaxel chemotherapy- four patients with optimal debulking and two with suboptimal debulking.
Of these six patients, four experienced disease recurrence and two did not. The average disease free survival for patients treated with chemotherapy was 10.8 months and overall survival was 40.3 months. Five patients with LGSC did not receive adjuvant chemotherapy- two with optimal debulking and three with suboptimal debulking. Of these five patients, three experienced disease recurrence and two did not. For patients who were not treated with adjuvant chemotherapy, the average disease free survival was
25.8 months and the overall survival was 39.2 months [Table 4b]. Thus, disease free survival was longer in patients not treated with adjuvant chemotherapy compared to those treated with chemotherapy, whereas the overall survival was similar between the groups.
45 TABLE 4
Disease free and overall survival in patients with LGSC a. after optimal versus suboptimal surgical resection b. after adjuvant chemotherapy versus no therapy
a. Optimal Resection (n=6) Suboptimal Resection (n=5)
Disease Free Survival [Months] 22.5 11.8
Overall Survival [Months] 45.6 32.8
b.
Adjuvant Chemotherapy No Adjuvant Chemotherapy (n=6) (n=5) Disease Free Survival [Months] 10.8 25.8
Overall Survival [Months] 40.3 39.2
46 Included in the LMP-MP study group were two patients with an overall diagnosis of
LGSC. One was optimally debulked, did not receive adjuvant chemotherapy and did not experience disease recurrence, whereas the second patient was suboptimally debulked, was treated with post-operative chemotherapy and has succumbed to her disease. The seven patients with a clinical diagnosis of LMP-MP were all optimally debulked. One patient received adjuvant chemotherapy and did not experience disease recurrence. Of the remaining six patients who did not receive chemotherapy, three are alive without disease and three suffered disease recurrence/progression to LGSC. The average disease free survival and overall survival for patients with a clinical diagnosis of LMP-MP were 30.7 months and 37.4 months, respectively.
In the LMP study group, three patients had an overall clinical diagnosis of LGSC. Two patients were optimally debulked and one suboptimally debulked. Two received chemotherapy, and both experienced disease recurrence. The third patient did not receive post-operative adjuvant therapy and is alive without disease. Moreover, one patient in the
LMP group had an overall diagnosis of LMP-MP. This patient was optimally debulked, was not treated with chemotherapy and experienced disease recurrence/progression to
LGSC. Of the twelve remaining patients with clinical diagnoses of isolated LMP tumors, eleven were optimally debulked and one was suboptimally debulked. None of the patients in this sub-group received adjuvant chemotherapy and none experienced recurrence/progression to LGSC. However, two patients experienced recurrent LMP lesions in the contra-lateral ovaries, which were successfully resected.
47 3.2 UNSUPERVISED HIERACHICAL CLUSTERING
GeneSpring 1-way ANOVA unsupervised hierarchical cluster analysis of all study samples revealed two major branches of the hierarchical clustering. The first branch contained only LMP tumors whereas the second branch contained all LMP-MP tumors, all LGSC tumors and two LMP tumors [Figure 9]. The two LMP tumors that clustered with the LMP-MP and LGSC cases were cases 23 and 24 in Table 3, which have an overall diagnosis of LGSC and LMP-MP, respectively.
A second GeneSpring 1-way ANOVA unsupervised hierarchical cluster analysis was performed comparing gene expression profiles of the eleven LGSC cases to thirteen
HGSC- seven ovarian and six fallopian tube- obtained in our laboratory by A. Tone using an identical experimental platform (28). The cluster analysis demonstrated complete separation of the LGSC and the HGSC into two distinct clusters without any outlier samples [Figure 10].
48 FIGURE 9
anovaomndication
LMP-MP
I indication [Sample Name Sample Number •g J JTI* jj File Name ^ortgindication
Selected Gene Tree anovaomndication Colored by allsamplesmp (Default Interpretation) Selected Condition Tree anovaonlndication Gene List anovaonindicaoonfromrawgt200 (688) Branch color parameter indication
Figure 9: Unsupervised Hierarchical Cluster Analysis of LMP, LMP-MP and LGSC using GeneSpring.
49 FIGURE 10
1-WayANOVA..
HGSC-OV
Selected Gene Tree: 1-Way ANOVAonlndlcation Colored by: IflVShgse (Default Interpretation) Selected Condition Tree: 1-Way ANOVAonindication Gene List: 1-Way ANOVAonlndlcation (281) Branch color parameter: indication
Figure 10: Unsupervised Hierarchical Cluster Analysis of LGSC and HGSC using GeneSpring.
50 3.3 SIGNIFICANT ANALYSIS OF MICRO ARRAYS
Two-way SAM analysis has highlighted 45 probe sets, representing 43 genes, as differentially expressed between LMP and LMP-MP at a False Discovery Rate (FDR) of
2%. Specifically, 41 probe sets representing 40 genes were upregulated in LMP-MP compared to LMP and 4 probe sets representing 3 genes were down-regulated in LMP-
MP compared to LMP [Table 5].
Further analysis of these 45 differentially expressed probe sets using Gene Ontology
Database (www.geneontology.org) indicated twelve gene products to be localized to the nucleus (27%), eight to the cell membrane (18%) and six to the cytoplasm (13%).
Products of seven genes appeared to be involved in transcription (16%), three in protein transport (7%), two in cellular proliferation (4%) and two in cell growth (4%) [Figure
11].
51 TABLE 5
Differentially Expressed Genes between LMP and LMP-MP, FDR 2%
LMP vs LMP-MP, FDR 2%, 40 Upregulated genes (41 probe sets) Gene ID Gene Name Fold Change 210774 s at nuclear receptor coactivator 4 1.418806863 1555905 a at hypothetical protein DKFZp313N0621 2.447010749 227801 at tripartite motif-containing 59 2.081543274 201814 at TBC1 domain family member 5 1.625905765 225278 at protein kinase AMP-activated beta 2 non-catalytic subunit 2.227893435 1555889 a at cartilage associated protein 1.626428745 225474 at BA11-associated protein 1 1.70507087 201380 at cartilage associated protein 1.913521496 201200 at cellular repressor of E1 A-stimulated genes 1 1.721019474 226656 at Hypothetical protein LOC253263 2.071789092 203319 s at zinc finger protein 148 (pHZ-52) 1.647515856 221897 at tripartite motif-containing 52 1.80112379 225768 at nuclear receptor subfamily 1 group D member 2 1.544679212 229139 at junctophilin 1 3.207242593 200705 s at eukaryotic translation elongation factor 1 beta 2 1.575411589 202800 at solute carrier family 1 (glial high affinity glutamate transporter) member 3 2.483192159 226402 at cytochrome P450 family 2 subfamily U polypeptide 1 2.654852989 1553858 at zinc finger and BTB domain containing 3 1.82643191 http://genome-www4.stanford.edu/cgi- 238191 at bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria= 1.906698424 208935 s at lectin galactoside-binding soluble 8 (galectin 8) 1.436451842 206653 at polymerase (RNA) III (DNA directed) polypeptide G (32kD) 2.366467919 222129 at chromosome 2 open reading frame 17 1.450117829 226282 at CDNA FLJ32401 fis clone SKMUS2000339 2.052829708 207547 s at TU3A protein 4.748297193 226508 at polyhomeotic like 3 (Drosophila) 1.509875534 http://genome-www4.stanford.edu/cgi- 235733 at bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria= 3.288281802 steroid-5-alpha-reductase alpha polypeptide 1 (3-oxo-5 alpha-steroid delta 204675 at 4-dehydrogenase alpha 1) 1.986901767 219054 at hypothetical protein FLJ14054 2.597582449 222651 s at trichorhinophalangeal syndrome I 1.962850639 adaptor protein containing pH domain PTB domain and leucine zipper motif 218158 s at 1 1.658743116 212239 at phosphoinositide-3-kinase regulatory subunit polypeptide 1 (p85 alpha) 1.683836483 225087 at hypothetical protein FLJ31153 1.495632742 http://genome-www4.stanford.edu/cgi- 226360 at bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria= 1.864338324 1558123 at hypothetical protein FLJ35390 1.836330905 1555906 s at hypothetical protein DKFZp313N0621 1.965244495 http://genome-www4.stanford.edu/cgi- 244533 at bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria= 2.305064359 http://genome-www4.stanford.edu/cgi- 230818_at bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria= 2.161725195
52 MASK-4E-BP3 alternate reading frame gene /// ankyrin repeat and KH domain 208773 s at containing 1 1.496177724 235104 at leukocyte-derived arginine aminopeptidase 2.158636316 205677 s at deleted in lymphocytic leukemia 1 2.117759288 233878_s_at 5'-3' exoribonuclease 2 1.410443296
LMP vs LMP-MP, FDR 2%, 3 Downregulated genes (4 probe sets) Gene ID Gene Name Fold Change 207358 x at microtubule-actin crosslinking factor 1 0.64746791 203216 s at myosin VI 0.611700848 208634 s at microtubule-actin crosslinking factor 1 0.738092936 203395_s_at hairy and enhancer of split 1 (Drosophila) 0.728636635
53 FIGURE 11 a.
Differentially Expressed Genes LMP vs LMP-MP Cellular Location
D Nucleus D Membrane B Cytoplasm D Unkown p Other
Differentially Expressed Genes LMP vs LMP-MP Biological Processes
D Unknown 1 Transcription B Protein Transport D Cell Proliferation • Cell Growth D Other
Figure 11: Gene Ontology of Differentially Expressed Genes between LMP and LMP- MP a. Venn Diagram Representing Cellular Location of differentially Expressed Genes between LMP and LMP-MP b. Venn Diagram Representing Most Frequent Biological Process of Differentially expressed genes between LMP and LMP-MP
54 Furthermore, SAM analysis highlighted 135 probe sets as differentially expressed between LMP and LGSC at an FDR of 2%. Specifically, 91 genes were upregulated in
LSGC compared to LMP and 44 genes were down-regulated in LGSC compared to LMP
[Table 6].
Further analysis of the differentially expressed genes using Gene Ontology Database
(www.geneontology.org) indicated forty-two gene products to be localized to the nucleus
(31%), thirty-six to the cytoplasm (27%) and twenty-nine to the cell membrane (21%).
Twelve gene products appear to be involved in protein transport (9%), ten in transcription
(7%), seven in the ubiquitination cycle (5%), four in cellular proliferation (3%), three in
DNA repair (2%), three in cell survival (2%) and two in the cell cycle (1%) [Figure 12].
Lastly, no differential gene expression was detected using SAM analysis when comparing the gene expression profiles of LMP-MP and LGSC at an FDR of 89% [Table 7].
55 TABLE 6
Differentially Expressed Genes Between LMP and LGSC, FDR 2%
LMP vs LG, FDR 2%, 91 Upregulated genes Gene ID Gene Name Fold Change 220631_at O-sialoglycoprotein endopeptidase-like 1 2.511423679 218576 s at dual specificity phosphatase 12 1.886780775 226154 at dynamin Mike /// CDNA FLJ33469 fis clone BRAMY2002005 1.374729201 200788_s_at phosphoprotein enriched in astrocytes 15 2.41829425 223068 at echinoderm microtubule associated protein like 4 1.753683497 1558692 at kidney predominant protein NCU-G1 2.033451976 221229_s_at hypothetical protein FLJ20628 /// hypothetical protein FLJ20628 1.722491092 218389 s at likely ortholog of C. elegans anterior pharynx defective 1A 1.715623914 225469 at hypothetical protein LOC144363 1.914995328 241017_at TBC1 domain family member 8 (with GRAM domain) 2.734761571 209363 s at SRB7 suppressor of RNA polymerase B homolog (yeast) 2.049060981 200842 s at glutamyl-prolyl-tRNA synthetase 1.614426365 212765 at KIAA1078 protein 1.486160438 218699 at RAB7 member RAS oncogene family-like 1 2.044064871 203340 s at solute carrier family 25 (mitochondrial carrier Aralar) member 12 1.610620747 202793 at putative protein similar to nessy (Drosophila) 1.859296841 202964 s at regulatory factor X 5 (influences HLA class II expression) 2.167912333 217802 s at nuclear ubiquitous casein kinase and cyclin-dependent kinase substrate 1.274096112 http://genome-www4.stanford.edu/cgi- 1568853_at bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria= 2.971894559 208935 s at lectin galactoside-binding soluble 8 (galectin 8) 1.546469916 218082 s at upstream binding protein 1 (LBP-1a) 1.458766965 210458_s_at TRAF family member-associated NFKB activator 2.400198599 235253 at RAD1 homolog (S. pombe) 1.960758474 225904 at LOC126731 2.16798377 200028 s at START domain containing 7 /// START domain containing 7 1.349547527 218283 at synovial sarcoma translocation gene on chromosome 18-like 2 1.431889906 217835 x at chromosome 20 open reading frame 24 1.645000045 224428 s at cell division cycle associated 7 /// cell division cycle associated 7 2.162974139 228351 at protein BAP28 1.751512594 214431 at guanine monphosphate synthetase 1.715673879 218768 at nucleoporin 107kDa 1.561025491 http://genome-www4.stanford.edu/cgi- 204010 s at bin/SMD/source/sourceResult?choice=Gene&option==Name&criteria= 1.723811398 steroid-5-alpha-reductase alpha polypeptide 1 (3-oxo-5 alpha-steroid delta 4- 204675 at dehydrogenase alpha 1) 1.984289148 http://genome-www4.stanford.edu/cgi- 230523 at bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria= 2.314628665 225547 at U87HG mRNA complete sequence 1.733496314 235413_at gamma-glutamyl carboxylase 1.983532326 206102 at KIAA0186 gene product 2.190740335 http://genome-www4.stanford.edu/cgi- 226177_at bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria= 1.505376892
56 236026 at G patch domain containing 2 2.119033354 218894 s at hypothetical protein FLJ10292 1.649745909 http://genome-www4.stanford.edu/cgi- 238191 at bin/SMD/source/sourceResult'?choice=Gene&option=Narne&criteria= 2.285470382 226711 at human T-cell leukemia virus enhancer factor 1.517296442 209825 s at undine-cytidine kinase 2 2.383530755 226880_at Nuclear ubiquitous casein kinase and cyclin-dependent kinase substrate 1.293749849 221486 at endosulfine alpha 2.08143462 http.//genome-www4.stanford.edu/cgi- 243880_at bin/SMD/source/sourceResult'?choice=Gene&option=Name&criteria= 4.145095121 214801 at CDNA FLJ11392 fis clone HEMBA1000575 1.648895202 http://genome-www4.stanford.edu/cgi- 242294_at bin/SMD/source/sourceResult?choice=Gene&option=Name&cntena= 2.122435223 219569 s at transmembrane protein 22 3.911755925 225370 at pygopus 2 1.771288695 218344 s at REST corepressor 3 1.893271804 200690 at heat shock 70kDa protein 9B (mortahn-2) 1.610249569 224571 at interferon regulatory factor 2 binding protein 2 1.540894076 207391 s at phosphatidyhnositol-4-phosphate 5-kinase type I alpha 1.552551075 chromosome 20 open reading frame 24 /// chromosome 20 open reading 224376_s_at frame 24 1.724023047 204839 at processing of precursor 5 nbonuclease P/MRP subunit (S. cerevisiae) 1.730752366 218229 s at pogo transposable element with KRAB domain 1.857343579 226845 s at helicase/pnmase complex protein 1 466283319 201517 at nuclear cap binding protein subunit 2 20kDa 1.383945792 234950_s_at constitutive photomorphogenic protein 1.834582973 213480 at vesicle-associated membrane protein 4 1.643366622 211761 s at Siah-interacting protein /// Siah-mteractmg protein 1.90583826 202382 s at glucosamme-6-phosphate deaminase 1 1.719860969 222552 at CGI-141 protein 1.564059435 218111 s at cytidine monophosphate N-acetylneuraminic acid synthetase 1.710860295 212126 at Chromobox homolog 5 (HP1 alpha homolog Drosophila) 1.489489943 209340 at UDP-N-acteylglucosamme pyrophosphorylase 1 1.788139926 203774 at 5-methyltetrahydrofolate-homocysteine methyltransferase 1.958792291 226443 at chromosome 9 open reading frame 42 1.410783515 204252 at cyclin-dependent kinase 2 1.881306462 218053 at formin binding protein 3 1.327983402 225463 x at G protein-coupled receptor 89 1.606299165 212160 at exportin tRNA (nuclear export receptor for tRNAs) 1.753302108 v-abl Abelson murine leukemia viral oncogene homolog 2 (arg Abelson- 231907 at related gene) 2.102636992 http://genome-www4.stanford.edu/cgi- 235873 at bin/SMD/source/sourceResult?choice=Gene&option=Name&critena= 2.3192648 1554178 a at hypothetical protein MGC39518 1.86628662 222480 at ubiquitin-conjugating enzyme E2Q (putative) 1.535214956 227801 at tripartite motif-containing 59 2.114055981 http://genome-www4.stanford.edu/cgi- 227815 at bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria= 1.718926982 242905 at putatative 28 kDa protein 1.919109587 203560 at gamma-glutamyl hydrolase (conjugase folylpolygammaglutamyl hydrolase) 5.230533776
57 208644 at poly (ADP-ribose) polymerase family member 1 1.570614155 212885 at M-phase phosphoprotein 10 (U3 small nucleolar ribonucleoprotein) 1.454798373 212408 at lamina-associated polypeptide 1B 1.616575698 1554148 a at solute carrier family 33 (acetyl-CoA transporter) member 1 1.925924318 200787 s at phosphoprotein enriched in astrocytes 15 2.484167443 219267 at glycolipid transfer protein 1.995248068 206550 s at nucleoporin 155kDa 1.753276572 203580 s at solute carrier family 7 (cationic amino acid transporter y+ system) member 6 2.355285676 226371 at Jumonji AT rich interactive domain 1A (RBBP2-like) 1.784270737 202373_s_at rab3 GTPase-activating protein non-catalytic subunit (150kD) 1.48856281
LMP vs. I_G» FDR 2%, 44 Downregulated genes Gene ID Gene Name Fold Change 207624_s_at retinitis pigmentosa GTPase regulator 0.414637436 207358_x_at microtubule-actin crosslinking factor 1 0.612216508 1553971 a at opposite strand transcription unit to STAG3 0.437637822 204075_s_at glycine- glutamate- thienylcyclohexylpiperidine-binding protein 0.594450693 227081 at dynein axonemal light intermediate polypeptide 1 0.378909869 226075_at SPRY domain-containing SOCS box protein SSB-1 0.478750105 217122_s_at solute carrier family 35 member E2 0.62334533 209627 s at oxysterol binding protein-like 3 0.441866632 205186_at dynein axonemal light intermediate polypeptide 1 0.292790177 203450 at PKD2 interactor golgi and endoplasmic reticulum associated 1 0.524842806 209489_at CUG triplet repeat RNA binding protein 1 0.752347888 208634 s at microtubule-actin crosslinking factor 1 0.704290264 241412 at betacellulin 0.30313946 229909_at beta 1 4-N-acetylgalactosaminyltransferase-transferase-lll 0.343073851 210561 s at WD repeat and SOCS box-containing 1 0.537227725 226297 at homeodomain interacting protein kinase 3 0.800730449 solute carrier family 25 (mitochondrial carrier; peroxisomal membrane protein 34kDa) member 17 /// solute carrier family 25 (mitochondrial carrier; 211754 s at peroxisomal membrane protein 34kDa) member 17 0.690248269 243386 at CDNA FLJ27486 fis clone RCT02754 0.368096936 203395 s at hairy and enhancer of split 1 (Drosophila) 0.715478703 219396 s at nei endonuclease Vlll-like 1 (E. coli) 0.568941489 203910 at PTPL1-associated RhoGAP 1 0.456592027 213039 at rho/rac guanine nucleotide exchange factor (GEF) 18 0.627829939 222108 at amphoterin induced gene 2 0.193418957 225589 at SH3 multiple domains 2 0.526505783 209447 at spectrin repeat containing nuclear envelope 1 0.310714304 213988 s at spermidine/spermine N1 -acetyltransferase 0.673985282 208988 at F-box and leucine-rich repeat protein 11 0.833282491 202239 at poly (ADP-ribose) polymerase family member 4 0.590709633 211950 at retinoblastoma-associated factor 600 0.608317636 213355 at sialyltransferase 10 (alpha-2 3-sialyltransferase VI) 0.408047392 230936 at testis spermatogenesis apoptosis-related protein 6 0.260279656 213726_x_at tubulin beta 2 0.500824976 214779 s at RUN and TBC1 domain containing 3 0.491776608 204193_at carnitine palmitoyltransferase 1B (muscle) 0.318270491
58 227680 at hypothetical protein FLJ20403 0.645223477 223838 at testis specific 10 0.378477771 204703 at tetratricopeptide repeat domain 10 0.537332373 204084 s at ceroid-lipofuscinosis neuronal 5 0.533806801 208718 at DEAD (Asp-Glu-Ala-Asp) box polypeptide 17 0.80916512 201192 s at phosphatidylinositol transfer protein alpha 0.435936861 204813 at mitogen-activated protein kinase 10 0.261039401 213940 s at form in binding protein 1 0.578157908 http://genome-www4.stanford.edu/cgi- 228400 at bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria= 0.447694898 209502 s at BAH-associated protein 2 0.461252078
59 FIGURE 12
Differentially Expressed Genes LMP vs LGSC Cellular Location
D Nucleus B Cytoplasm B Membrane D Unkown a Other
Differentially Expressed Genes LMP vs LGSC Biological Processes
D Unknown D Protein Transport ^\ m Transcription 20% \ • Ubiquitine Cycle 51% i£f -9% • Cell Proliferation B Anti Apoptosis
Kj^k ^EpHwi/ • DNA Repaire Wk«;^Qy/ p Cell Cycle D Other
1
Figure 12: Gene Ontology of Differentially Expressed Genes between LMP and LGSC a Venn Diagram Representing Cellular Location of differentially Expressed Genes between LMP and LGSC b Venn Diagram Representing Most Frequent Biological Process of Differentially expressed genes between LMP and LGSC
60 TABLE 7
SAM Analysis Results LMP-MP versus LGSC delta # med false pos 90th perc false pos # called median FDR 90th perc FDR 0 25417.56927 27606.48048 28334 0.897069573 0.974323444 0 25417.56927 27606.48048 28334 0.897069573 0.974323444 0 25417.56927 27606.48048 28334 0.897069573 0.974323444 0 25417.56927 27606.48048 28334 0.897069573 0.974323444 0 25417.56927 27606.48048 28334 0.897069573 0.974323444 0 25416.51285 27604.36763 28332 0.897095611 0.974317649 0 25414.92821 27600.14193 28330 0.897103008 0.974237272 0 25414.92821 27600.14193 28330 0.897103008 0.974237272 0.01 25414.4 27599.08551 28329 0.89711603 0.974234371 0.01 25407.53324 27592.32439 28321 0.897126981 0.974270838 0.01 25385.87654 27562.95579 28306 0.896837298 0.973749586 0.01 25385.87654 27562.95579 28306 0.896837298 0.973749586 0.01 25382.70727 27559.57524 28303 0.896820382 0.973733358 0.01 25380.06621 27556.40596 28302 0.896758752 0.973655783 0.02 25353.12739 27535.06619 28285 0.89634532 0.973486519 0.02 25353.12739 27535.06619 28285 0.89634532 0.973486519 0.02 25327.24499 27517.31826 28272 0.895841999 0.97330639 0.02 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.03 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.03 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.03 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.04 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.04 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.04 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.05 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.05 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.05 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.06 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.06 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.07 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.07 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.08 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.08 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.09 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.09 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.1 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.1 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.11 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.12 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.12 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.13 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.13 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.14 25082.15455 27239.16174 28018 0.895215738 0.972202218
61 0.15 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.15 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.16 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.17 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.18 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.18 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.19 25082.15455 27239.16174 28018 0.895215738 0.972202218 0.2 0 0 0 #DIV/0! #DIV/0!
62 3.4 PROTEIN-PROTEIN INTERACTION DATABASE
Genes identified by SAM analysis as differentially expressed between LMP and
LGSC were entered into the online predicted human protein interaction database,
OPHED/ I2D. Forty of the 135 gene products mapped into the protein interaction database and 25 of the mapped proteins displayed significant interconnectivity amounting to a single network which included the Mitogen-Activated Protein kinase 1/3 (MAPK1/3) cascade [Figure 13]. A second minor network was also identified that centered on the epidermal growth factor receptor (EGFR).
3.5 GENE SELECTION
To select genes for validation studies for potential future functional studies, genes differentially expressed between the non-invasive LMP tumor and the invasive LGSC were examined in respect to ontology and proposed gene product function. Literature review was conducted to examine the role some of these genes play in ovarian cancer carcinogenesis in general and in low grade carcinogenesis in particular. Genes with significant interconnectivity with members of the MAPK and EGFR pathways were of particular interest as the interconnectivity could signify a key role in the MAPK or EGFR pathways. Also, genes for which targeted therapy is available were of particular significance as identifying potential therapeutic targets for LGSC was an ultimate goal of this research. As such, two members of the EGFR pathway and 5 members of the MAPK pathway were selected for validation studies. In addition, the gene encoding for the steroid hormone 5-a-Reductase was selected for validation studies as an independent, non-member of the MAPK or EGFR pathways.
63 FIGURE 13
LMP vs LGSC
Figure 13: 0PHED/I2D Network: LMP versus LGSC Differentially Expressed Genes
64 3.5.1 Selected Genes
MAPK 1&3 Pathway
1- TRAF Family Member Associated NF-Kappa-B-Activator. (TRAF interacting
protein) (I-TRAF) (TANK)
• Upregulated in LGSC as compared to LMP 2.4-fold
• Gene localized to chromosome 2p61.3
• Protein localized to cytoplasm where it plays a role in cellular fate and
organization
• Involved in signal transduction by binding TRAF 1, 2, or 3 to regulate their
function
• Overexpression of TANK inhibits TRAF2 modulated NF-Kappa B activation by
CD40/TNFR1/TNFR2 (66)
2- Poly [ADP-ribose] polymerase-1. (ADPRT) (NAD(+)ADP-ribosyltransferase-l)
(Poly[ADP-ribose] synthetase-1). (PPOL) (PARP1)
• Upregulated in LGSC as compared to LMP 1.57-fold
• Gene localized to chromosome Iq41-q42
• Protein localized to nucleus where it plays a role in genomic stability
• Also involved in DNA break-sensing and signaling when single stand break
repair (SSBR) or base excision repair (BER) pathways are engaged thus
enhancing survival of proliferating cells (67)
65 • Interacts with and modulates poly(ADP-ribosyl)ation of p53 and topoisomerase I
(both participate in DNA recombination) thereby altering the surveillance
function of p5 3 (68)
3- Cell Division Protein Kinase 2. (p33 Protein Kinase) (CDK2)
• Upregulated in LGSC as compared to LMP 1.88-fold
• Gene localized to chromosome 7q36.1
• Protein localized to nucleus and cytoplasm and plays a role in genome
maintenance and cell cycle control
• Interacts with Cyclin A/E and allows nuclear to cytoplasmic shuttling (69,70)
• Phosphorylation of residues with the conserved glycine-loop motif (residues 11 to
16) inactivates its kinase activity whereas phosphorylation at Thr-160 activates its
kinase activity (71)
4- Astrocytic Phosphoprotein (PEA15)
• Upregulated in LGSC as compared to LMP 2.48-fold
• Gene localized to chromosome 1 q21.1
• Protein localized to cytoplasm where it plays a role in cellular Fate and
organization
• Predominantly expressed in astrocytes in central nervous system
• PEA 15 binds ERK1/2 (MAPK1/3) inhibiting their nuclear localization thus
blocking cell proliferation (72)
66 • Phosphorylation of PEA 15 at SER104 or SER 116 blocks its interaction with
ERK1/2 thus allowing ERK1/2 to translocate into the nucleus and drive cell
proliferation. Therefore, phosphorylated PEA 15 has anti-apoptotic function (72)
5- Diamine Acetyl Transferase. (Spermidine/Sperimine N(l)-Acetyl Transferase)
(Putresamine Acetyl Transferase) (SSAT) (ATDA))
• Downregulated in LGSC as compared to LMP 0.67-fold
• Gene localized to chromosome Xp22.1
• Protein localized to cytoplasm where it regulates transport of polyamines and
catalyzes their acetylation, thereby controlling intracellular polyamine
concentration
• ATDA overexpression leads to alterations in cellular polyamine content, depletion
of polyamine synthesis and reduced cell proliferation while its suppression has the
opposite effect and increases cell proliferation (73)
EGFR Pathway
1- Phosphatidylinositol Transfer Protein- alpha isoform (PPI)
• Downregulated in LGSC as compared to LMP 0.43-fold
• Gene localized to chromosome 17pl3.3
• Cytosolic protein with ubiquitous expression
• Involved in phospholipid transport from the endoplasmic reticulum and golgi
apparatus to the cell membrane and in exocytosis (74)
• Involved in EGF and f-Met-Leu-Phe signaling
67 2- Betacellulin (BTC).
• Downregulated in LGSC as compared to LMP 0.3-fold
• Gene localized to chromosome 4q 13.3
• A transmembrane protein involved in genome maintenance
• Member of the epidermal growth factor family with EGF Motif that undergoes
proteolytic cleavage of the extracellular domain
• Common in intestine and pancreas where it promotes islet P-cell re-growth in
diabetes modules and in epithelium of endometroid endometrial cancer (75)
5-alpha reductase type I
1- Steroid-5-alpha-reductase alpha polypeptide 1 (3-oxo-5 alpha-steroid delta 4-
dehydrogenase alpha 1) (5aRed)
• Upregulated in LGSC as compared to LMP 1.98-fold
• Gene localized to chromosome 5
• Nuclear enzyme that converts testosterone to dihydrotestesterone (DHT), which is
a more potent androgen that binds and activates the androgen receptor
• Inhibition results in decreased production of DHT, increased levels of testosterone
and potentially increased levels of estradiol
• Inhibitory drugs can reduce the risk of prostate cancer and can be used in the
treatment of prostate cancer and benign prostatic hyperplasia (76)
68 3.6 VALIDATION STUDIES
To validate the microarray screen and verify that the differential gene expression patterns seen between the three tumor groups were present in the extracted genetic material, real-time RT-PCR was performed on fifteen representative study samples. To validate selected genes, which were demonstrated to be differentially expressed between the three tumor groups in the microarray screen, immunohistochemistry was performed on tissue microarray that includes most cases also included in the microarray screen and an equivalent number of new cases with the same histopathological diagnoses. This would verify whether any differential gene expression is translated to the protein level and whether this difference is present in all tumors, outside of the initial study sample.
3.6.1 Quantitative Real-Time RT-PCR
The eight selected genes underwent quantitative real-time RT-PCR. The results for all genes indicated a similar differential expression pattern to that observed in the microarray screen although these did not reach statistical significance [Figure 14].
3.6.2 Immunohistochemistry
Immunohistochemistry on the constructed tissue microarray using Anti-TANK antibody demonstrated statistically significant differential cytoplasmic staining between
LMP and LMP-MP compared to LGSC (p<0.05) [Figure 15]. The staining was limited to the cytoplasm with no nuclear staining observed. Both intensity of stain and percent of cells stained were significantly elevated in LGSC as compared to LMP and LMP-MP tumors.
69 FIGURE 14
TANK Microarray Expression Levels TANK Microarray Expression Levels 210458 ALL Samples 210458 Amplification Samples Only
TANK RT-PCR Expression Levels
LMP
b.
PARP1 Microarray Expression Levels PARP1 Expression Levels 208644 ALL Samples 208644 Amplification! Samples Only 0 (J 1 o o Level s 8 s Microarra y Expressio n LMP LMP MP LGSC
PARP1 RT-PCR Expression Levels
70 c.
CDK2 Expression Levels CDK2 Microarray Expression Levels 204252 ALL Samples 204252 Amplification Samples Only
I120- " 100 -
1 60- ifl 40 - 1 2°J LMP LMP-MP LGSC
CDK2 RT-PCR Expression Levels
2 5
2
1 5 1 h 0 5
d.
PEA15 Expression Levels PEA15 Microarray Expression Levels 200787 ALL Samples 200787 Amplification Samples Only
250 - 200 - 150 - Pfe 100 - 50 - -** LMP LMP MP LGSC PEA15 RT-PCR Expression Levels 71 e. ATDA Microarray Expression Levels ATDA Microarray Expression Levels 213988 ALL Samples 213988 Amplification Samples Only ATDA RT-PCR Expression Levels mm F* f. PPl Microarray Expression Levels PPl Microarray Expression Levels 201192 ALL Samples 201192 Amplification Samples Only 180 100 80 60 40 20 0 PPl RT-PCR Expression Levels *C**e#' 72 g- Betacellulin Microarray Expression Levels Betacellulin Microarray Expression Levels 241412 ALL Samples 241412 Amplification Samples Only ^ 100 BTC RT-PCR Expression Levels 6 , 2 0 - '*• ^ta 5 alpha Reductase Microarray Expression Levels 5a tpha Reductase Microarray Expression Levels 204675 ALL Samples 204675 Selected Samples Only 600 soo T . 1 450 o 400 T » 500 350 2%'- 1 "00- t- 300 250 S. 300 • -•- ,-" ""• ^ T 200 —.— 8 • i - 150 S- 200- 100 • 8 100 '• .-• 50 "1 V • u Sri g 0 LMP MP LG 5-alpha-Reductase RT-PCR Expression Levels 40 35 30 25 20 15 10 -- 5 0 LMP-MP LGSC Figure 14: Quantitative real-time RT-PCR mRNA expression levels compared to microarray screen gene expression levels in all study samples and in the fifteen selected samples for RT-PCR for the following genes: a. TANK, b. PARPl, c. CDK2, d. PEA15, e. ATDA, f. PPI, g. BTC, h. 5aRed 73 Figure 15 a. TANK Microarray Expression Levels TANK Cytoplasmic Stain 210458 ALL Samples HistoScore 45 40 35 30 - 25 20 15 10 5 0 TANK Cytoplasmic Stain TANK Cytoplasmic Stain Intensity of Staining Score Percent of Staining Score 3 -, - - 1 " * 1-5 i IBSill 0.5 - iisi BSltll LMP LMP-MP LGSC b. LMP LMP-MP '^V»'.T5-.' LGSC J '-••"*' ;,, *' ;".*. -I ' ; j- 'A * C« * *-i i ~ - 1- '-: ^ - .• *^ , *"',,. •" / -v , • i * ' "*"" ''• -•* i' i' *•>' t '• '• -•"• -• • *•• ..,-«. I V.1 i : i V^V».:}- •) f» 74 c. =i 7 i 6 • — LU DC 5 o 4 O oCO 3 I- co 2 1 TANI K 0 LMP LMP-MP LGSC Figure 15: Summary of quantitation of immunohistostaining of TANK performed on microarrayed tissue samples a. Mean intensity score, percent score, and combined histoscore of TANK staining compared to microarray levels b. LMP, LMP-MP and LGSC images of tissue microarray staining with Anti-TANK antibody c. Data were analyzed by Kruskal-Wallis one way ANOVA followed by Mann-Whitney U-test. Bars represent the mean +/- S.E. Box plots of histoscores. *p<0.001, LMP vs. LGSC; •p=0.006, LMP-MP vs LGSC 75 Immunohistochemistry on the constructed tissue microarray using Anti-PARPl antibody demonstrated statistically significant differential nuclear staining between LMP and LMP-MP compared to LGSC (p<0.05) [Figure 16]. Staining was limited to the nucleus with no observed cytoplasmic staining. The intensity of staining was significantly elevated in LGSC as compared to LMP and LMP-MP tumors. The percent of stained cells was significantly elevated in LGSC as compared to LMP tumors, but not as compared to LMP-MP tumors. LMP-MP tumors generally displayed weak, diffuse staining involving the majority of epithelial cells. The overall HistoScore was significantly elevated in LGSC as compared to both LMP and LMP-MP tumors. Immunohistochemistry on the constructed tissue microarray using Anti-CDK2 antibody demonstrated statistically significant differential cytoplasmic staining between LMP compared to LMP-MP and LGSC (p<0.05) [Figure 17]. The intensity of staining was significantly elevated in LGSC as compared to LMP tumors, but not LMP-MP tumors. Similarly the percent of cells stained were significantly higher in LGSC as compared to LMP tumors but not LMP-MP tumors. As such, the overall HistoScore is significantly elevated in LGSC and LMP-MP as compared to LMP tumors. 76 Figure 16 a. PARP1 Microarray Expression Levels PARP1 Nuclear Stain HistoScore 208644 ALL Samples PARP1 Nuclear Stain PARP1 Nuclear Stain Intensity of Staining Score Percent of Staining Score ljjjljl§ s | 2.75 - ~ 2.7 - 2.65 - ffifEsI . fJSJSsliB HHHi LMP LMP-MP LGSC b. I LMP I W LMP-MP I. "Hf.' mmiimsi 77 c. 7 6 1X1 DC 5 O o 4 CO O 3 i- co 2 X 1 PAR P1 0 LMP LM P-M P LG SC Figure 16: Summary of quantitation of immunohistostaining of PARPl performed on microarrayed tissue samples a. Mean intensity score, percent score, and combined histoscore of PARPl staining compared to microarray levels b. LMP, LMP-MP and LGSC images of tissue microarray staining with Anti-PARPl antibody c. Data were analyzed by Kruskal-Wallis one way ANOVA followed by Mann-Whitney U-test. Bars represent the mean +/- S.E. Box plots of histoscores. *p<0.001, LMP vs. LMP-MP; •p=0.007, LMP vs LGSC 78 Figure 17 CDK2 Expression Levels 204252 ALL Samples 120 100 h m CDK2 Cytoplasmic Stain CDK2 Cytoplasmic Stain intensity of Staining Score Percent of Staining Score 3 -| r-J- . . 1* 1 5 "• :*2 - * ." •' . • r:; OS - 0 5 - .. _. L* . . LMP LMP MP LGSC LMP LMP MP LGSC A * # % 79 c. -i CDK2 < 7 =i 6 T LU 5 occ ± o 4 CO o 3 H 1 y- co x 2 1 0 LMP LMP-MP LGSC Figure 17: Summary of quantitation of immunohistostaining of CDK2 performed on microarrayed tissue samples a. Mean intensity score, percent score, and combined histoscore of CDK2 staining compared to microarray levels b. LMP, LMP-MP and LGSC images of tissue microarray staining with Anti-CDK2 antibody c. Data were analyzed by Kruskal-Wallis one way ANOVA followed by Mann-Whitney U-test. Bars represent the mean +/- S.E. Box plots of histoscores. *p<0.001, LMP vs. LMP-MP; •p<0.001, LMP vs LGSC 80 DISCUSSION Since their initial description in 1996, LMP-MP tumors have been subject to controversy. Histologically, these lesions display exuberant cellular proliferation with no stromal invasion; thus they fall under the "borderline" category. However, the clinical behavior of these tumors is more aggressive than what is expected of a typical borderline neoplasm. Studies have shown that LMP-MP tumors are associated with increased incidence of invasive peritoneal implants, increased bilaterality, advanced stage at presentation, increased incidence of recurrence and higher mortality rates than typical serous LMP tumors (60, 62). Therefore, some consider these neoplasms to be frank low- grade ovarian carcinomas and use the term non-invasive micropapillary serous carcinoma (MPSC) to describe them (43). Appropriate classification of these neoplasms is critical in driving appropriate medical management of women with LMP-MP tumors. Tumors labeled as borderline are often conservatively managed with primary resection of the tumor and any macroscopic disease, but sparing the remainder of the gynecological apparatus (30,77). Post-operatively, these patients are seldom followed in a cancer center. In contrast, patients diagnosed with a carcinomatous tumor are managed aggressively with total-abdominal hysterectomy, bilateral salpingo-oopherectomy, debulking, complete surgical staging and adjuvant therapy where appropriate (30,77). Post-operatively, these patients are followed closely in an oncology center. It is therefore crucial to appropriately classify these tumors as borderline or malignant neoplasms. 81 The slow-growing ovarian neoplasm LGSC is traditionally resistant to chemotherapeutic regimens with an average response rate not exceeding 10-15%. Moreover, not all women with this neoplasm benefit from the advantages of complete surgical debulking since this tumor is traditionally metastatic at diagnosis (45). Typically, the peritoneal cavity is coated with diffuse peritoneal implants that, when invading critical structures, are not resectable. Consequently, many women are suboptimally managed both surgically and medically. Therefore, development of new therapy for LGSC is critical and would improve management and significantly impact patient survival. 4.1 STUDY DESIGN Our goals for this study were to correctly classify LMP-MP tumors and to begin to identify candidate genes that may potentially lead to development of effective therapeutic regimens for LGSC. To achieve these objectives, we set out to isolate genetic material from the tumor epithelium of three tumor types believed to be at different stages of the same malignancy continuum- LMP, LMP-MP and LGSC. Evidently, different approaches to gene expression profiling are available. We believed that epithelial microdissection would ensure collecting a pure genetic material sample of the presumptive cell of origin without variable levels of contaminating material, which would have confounded the analysis. While the stroma is a likely player in the development and behavior of carcinomatous lesions, a principle goal of this work was to recognize the genetic identity of the selected neoplasms and therefore a pure epithelial collection was gathered. 82 In contrast to this approach, the investigators of the Australian Ovarian Cancer Study performed bulk extractions on LMP, LMP-MP and LGSC ovarian tumors. Their preliminary findings were recently presented at an international meeting and indicated no segregation or clustering in accordance with histopathological diagnoses (78). When comparing their data to the data obtained in this work, the principle investigator of the Australian Ovarian Cancer Study hypothesized that contaminating genetic signals from the stroma may have overpowered the specific epithelial gene signatures of the tumors. Similarly, the superiority of LCM over bulk extraction in identifying unique gene expression profiles was recently described by Harrell et al. (79). The researchers compared whole tumor xenografts and their matched whole lymph node metastases to LCM captured cancer cells from the same tumors and matched lymph node metastases. Whole tumor extraction approach yielded 1930 genes whereas LCM extraction yielded 1281 genes as differentially expressed between the tumors and the lymph node metastases. Importantly, only 1% of genes were common to both approaches. After comparing the gene expression lists to previously published significant genes in metastatic breast carcinoma, the authors concluded that LCM-derived data are likely more accurate than whole tumor-derived data. Another important factor that may have contributed to the lack of collective histological clustering in the Australia Ovarian Cancer Study is the lack of unified central pathological review of all study cases. Given the controversial and difficult classification of the tumor subtypes, particularly LMP-MP, inaccurate classification may greatly influence the results. We have therefore reviewed all cases with an expert gynecological pathologist, Dr. Patricia Shaw, and ensured all cases were classified according to a single 83 unified classification regimen. In addition, after LCM extraction all microdissected slides were reviewed with Dr. Shaw to ensure that a pure tumor epithelial sample was harvested with minimal stromal contamination. Lastly, to minimize procedural bias associated with RNA amplification and hybridization, all hybridization runs were performed at the UHN microarray center by one technician, Ms. Monika Sharma, and all study groups were equally represented in each amplification run. 4.1.1 Study Limitations After careful review of the SAM analysis, OPHID/I2D results and thorough literature review, eight genes were selected for validation with RT-PCR and three genes for validation with immunohistochemistry. When designing the RT-PCR experiments, representative study samples from each tumor group were selected due to the scarcity of the genetic material obtained after microdissection and amplification. Care was taken to use samples with a similar gene expression profile as the general tumor group and not samples with outlying values. Five samples with high cDNA concentration levels from each tumor group were selected for real-time RT-PCR validation. Notably, this represents more than half of the LMP-MP cases and approximately half of the LGSC samples. While it is clear that using the entire sample pool would have been ideal and may have yielded enhanced RT-PCR data, this would have limited the utility of this genetic material to the currently selected genes. Since LMP-MP and LGSC are relatively rare neoplasms, more genetic material may not be readily available and since additional genes of interest may be highlighted when different questions are asked, the decision was made to retain some of study samples for future experiments as indicated by further data 84 mining. This approach has been successfully used by others in our group yielding meaningful scientific results (28). The RT-PCR mRNA expression levels demonstrated similar trends to the microarray screen for all genes; however, these trends did not reach statistical significance. This may reflect the modest microarray fold changes of the selected genes, which ranged between 1.5 and 2.5 fold-change. Since the goal of this work was to identify genes with potential functional significance, the validated genes were selected by pathway analysis according to proposed function and potential to act as therapeutic targets for LGSC rather than simply based on their fold change values in the microarray screen. Despite the modest gene expression changes seen in the microarray and RT-PCR reactions, some of the selected gene products are believed to be members of a protein kinase cascade. Therefore, the changes in them are likely additive and minute changes in each gene product may potentially lead to significant changes in the cascade output and thus to important functional effects. Importantly, gene validation was further addressed using immunohistochemistry on a constructed tissue microarray. This array contained all initial study samples for which paraffin embedded tissue was available (n=32) and a corresponding number of new cases not previously included in this study (n=29). To date, three genes have been successfully validated using immunohistochemistry. The proteins tested exhibited statistically significant differential expression between LMP and LGSC in the same direction as the microarray screen. This demonstrated that the differential gene expression levels initially identified using the microarray screen were translated to the protein level for the three validated genes and, importantly, that the observed expression differences were not 85 limited to the microdissected cases but are also present in other tumors with the same histopathological diagnosis. 4.2 CLINICAL ASSOCIATION BETWEEN LMP, LMP-MP AND LGSC Our clinical data has demonstrated that the average age for patients with LGSC is higher than patients with LMP and LMP-MP tumors suggesting that these tumors may occur earlier in life and, in the case of LMP-MP, may slowly progress to LGSC. Also, some cases with LGSC had a co-existing LMP or LMP-MP tumor on histopathological examination. This demonstrates the close correlation of these tumors and is in keeping with the results of Malpica et al., who in a clinicopathological review demonstrated that 60% of LGSC had associated serous borderline tumors, 93% of which were LMP-MP neoplasms (38). Furthermore, our findings are also consistent with the known advantages of optimal surgical debulking in LGSC, as patients who were suboptimally debulked appeared to have worse disease free and overall survival. The importance of surgical debulking was originally described by Bristow et al. demonstrating a six year overall survival advantage in patients with stage Ill/TV LGSC who were optimally debulked compared to those undergoing suboptimal debulking (45). This would support aggressive surgical intervention in patients with LGSC to achieve optimal debulking status. Significantly, our findings are in keeping with clinical experience suggesting that only 10-15% of patients with LGSC respond to chemotherapeutic drugs (44), as adjuvant chemotherapy did not provide survival advantage to study patients with LGSC. Arguably, the minimal response rate provided by chemotherapy in LGSC may not justify exposing 86 patients to the morbidity associated with this therapeutic regimen and further emphasizes the urgency associated with the limited management options available for women diagnosed with LGSC. Moreover, recent evidence by Gershenson et al. suggests recurrent LGSC is also resistant to various chemotherapeutic regimens. Thus management of recurrent disease proves as challenging as management of the original diagnosis (80). 4.3 LMP TUMORS HAVE A DISTINCT GENE EXPRESSION PROFILE FROM LMP-MP AND LGSC Our patient study population included two patients with LMP tumors who were subsequently diagnosed with recurrent LMP neoplasms, which were definitively managed with conservative surgical resection. Importantly, none of the LMP cases progressed to cancer and none of the patients succumbed to their disease (as of April 2008). This suggests that LMP tumors have a non-malignant clinical course and generally recur in the form of a second LMP tumor rather than a frank malignancy. This is in sharp contrast to the clinical course of LMP-MP tumors and LGSC that behave in a generally more aggressive fashion. The distinction between LMP tumor and LMP-MP/LGSC neoplasms was clearly displayed using unsupervised hierarchical cluster analysis that demonstrated near complete separation of the LMP tumors from the LMP-MP and LGSC cases. Two LMP cases were an exception and clustered in the LMP-MP/LGSC branch of the hierarchical tree. These two cases were LMP tumors that co-existed with LMP-MP or LGSC tumors, suggesting that the more aggressive lesions may have influenced the gene expression profiles of the LMP tumors. Thus, unsupervised hierarchical clustering indicated that the genetic profile of LMP tumors was distinct from LMP-MP tumors and 87 LGSC. This was further confirmed with SAM analysis. Forty-three genes were identified to be differentially expressed between LMP and LMP-MP most of which are involved in protein transport, transcription, cell proliferation and cell growth. Furthermore, SAM analysis identified 135 genes as differentially expressed between LMP tumors and LGSC. The majority of these genes are involved in protein transport, transcription, cell proliferation, apoptosis and DNA repair. This demonstrates that LMP tumors are unique neoplasms with distinct gene expression profiles that differ from LMP-MP tumors and LGSC. 4.4 GENE EXPRESSION PROFILE OF LMP-MP TUMORS IS SIMILAR TO LGSC In contrast to LMP tumors, LMP-MP neoplasms are more aggressive as evidenced by the fact that three of seven clinical LMP-MP neoplasms progressed to LGSC. Unsupervised hierarchical clustering demonstrated that LMP-MP tumors clustered with LGSC, separate from LMP tumors. This indicated that the gene expression profile of LMP-MP tumors closely resembled that of LGSC. This was further confirmed with SAM analysis showing no differential gene expression between LMP-MP and LGSC at a FDR below 89%. This finding may have been due to tumor heterogeneity within the LMP-MP neoplasm thus contributing to the lack of detection of any differentially expressed genes between LMP-MP and LGSC. Nevertheless, Collectively, the unsupervised hierarchical cluster and SAM analysis data demonstrate that LMP tumors are different from LMP-MP and LGSC whereas LMP-MP tumors are similar to LGSC [Figure 18]. 88 FIGURE 18 ^aurface^ Epithelium/ Inclusion Figure 18: Diagram representing the likely origin and progression of ovarian LMP, LMP-MP and LGSC 89 The similarities in gene expression between LMP-MP tumors and LGSC may underlie the more aggressive clinical course of LMP-MP neoplasms compared to other borderline malignancies, including the associated increased risk of transformation/progression to LGSC. It is therefore important to distinguish between LMP and LMP-MP tumors at diagnosis and once the diagnosis of LMP-MP tumor is confirmed by a gynecological pathologist, patients with LMP-MP tumors would likely require aggressive surgical staging and debulking and would benefit from regular follow-up in an oncology center. On the other hand, women with a simple LMP tumor without micropapillary features would not require aggressive surgical management and may not necessarily require follow-up in an oncology center. Our findings thus support classifying LMP-MP tumors as malignant, rather than borderline, lesions. As such, using the term non-invasive micropapillary serous carcinoma (MPSC) instead of low malignant potential tumors with micropapillary features (LMP-MP) may be appropriate and would underscore the true genetic identity of these lesions. 4.5 IDENTIFICATION OF CANDIDATE GENES INVOLVED IN LOW GRADE OVARIAN CARCINOGENESIS A key objective of this study was to identify candidate genes involved in low grade serous carcinogenesis. To achieve this, differentially expressed genes between the non-invasive LMP tumors and the invasive LGSC were studied. While several genes appeared significant, four genes were particularly interesting as potential candidates for targeted therapy based on overt pathway analysis. These genes are likely members of a 90 cascade involving the extracellular signal regulated kinases 1 and 2 (ERK1/2) of the Mitogen-activated protein Kinase (MAPK) pathway. 4.5.1 Mitogen Activated Protein Kinase The Mitogen-activated protein kinases (MAPK) are serine/threonine specific protein kinases that regulate various cellular processes in response to external stimuli. To date, three MAPK cascades have been characterized in mammalian cells: the extracellular signal regulated kinase (ERK), also known as the classic MAPK, c-Jun N-terminal kinase (JNK) and p38 (81). All three serine/threonine kinases appear to act sequentially to transmit extracellular signals to the nucleus through phosphorylation of transcription factors. The ERK pathway, which has been implicated in low grade ovarian carcinogenesis (42), is preferentially activated in response to growth factor receptors such as the epidermal growth factor receptor (EGFR) and the platelet derived growth factor receptor (PDGFR). K-Ras and B-Raf have been shown to be mutated in approximately 68% of ovarian LGSC, leading to constitutive activation of these proteins (42,45). K-Ras is a small G-protein that activates the downstream MAP Kinase Kinase Kinase B-Raf, which in turn activated the MAP Kinase Kinase MEK, which most commonly activates the MAP Kinases ERK1 (p24) or ERK2 (p44). Subsequently, the ERK1/2 kinases translocate to the nucleus where they regulate cell proliferation, differentiation and apoptosis (81). 91 FIGURE 19 t PEA15 —X- ERK1/2 Figure 19: Candidate genes and pathway potentially involved in low grade ovarian carcinogenesis • Upwards arrow represents gene products that are upregulated in LGSC compared to LMP tumors • Downwards arrow represents down-regulation of a reaction • 'X' represents blocking a reaction 92 4.5.2 Proposed Protein Cascade Significant in Low Grade Serous Carcinogenesis Four genes differentially expressed between LMP and LGSC may act in a cascade fashion to effect nuclear translocation of ERK1/2. TANK, PARP1, CDK2 and PEA15 are overexpressed in LGSC compared to LMP tumors. These changes separately and jointly act to translocate ERK1/2 to the nucleus and thus drive cell proliferation [Figure 19]. TANK is thought to activate TNF receptor-associated factor 2 (TRAF2), which activates MEK Kinase 1 (BRAF), which in turn activates MEK1/2, which activates ERK1/2 (82). Through the action of this cascade, overexpression of TANK, as seen in LGSC, leads to increased ERK1/2 activation and thus increased cell proliferation. TANK staining was found to be significantly increased in LGSC compared to LMP tumors. Another gene overexpressed in LGSC was PARP1. Notably, overexpression of PARP1 also activates TRAF2, which augments the effects of TANK on this factor (83). Tissue microarray nuclear staining demonstrated elevated PARP1 protein expression levels in LGSC compared to LMP tumors. PARP1 has a likely inhibitory effect on p53, which has an activating effect on cyclin dependent kinase inhibitor 1A (p21), which is a CDK2 inhibitor (68,84). Thus, the overall effect of PARP1 overexpression would be to increase activity of CDK2. CDK2 is a cell cycle protein involved in Gl to S phase transition and is independently overexpressed in LGSC. This overexpression has been validated with immunohistochemistry on the constructed tissue microarray. Lastly, unphosphorylated PEA 15 has been previously shown to inhibit ERK1/2 activity (72). However, phosphorylation of PEA 15 at SER104 or SER 116 blocks its interaction with ERK1/2, thus removing the inhibitory effect and increasing cell proliferation (72). Immunohistochemical staining of the constructed tissue microarray using phosphorylated 93 and unphosphorylated PEA 15 antibodies is currently underway to determine the post- translational modified protein form potentially overexpressed in LGSC. 4.6 STUDY IMPLICATIONS Members of the MAPK pathway have been targets of therapeutic anti-cancer modalities for years. Our findings suggest that targeting the MAPK pathway could provide therapeutic advantage to patients with LGSC. One such class of drug targeting the MAPK cascade is the MEK1/2 inhibitors. The first generation drug CI-1040 was tested in phase II clinical trials in patients with non-small cell lung cancer, breast cancer, colon cancer and pancreatic cancer (85). Although the drug was generally well tolerated, it had insufficient anti-tumor activity. Consequently, second generation MEK1/2 inhibitors were developed. PD 0325901 appears to strongly suppress MAPK activity at low doses and is currently being tested in phase I clinical trial in patients with colon cancer, breast cancer and melanoma and the study is expected to be completed in June 2008 (86). An alternative promising drug with dual inhibition is the RAF Kinase inhibitor BAY43-9006, also called Sorafenib. This compound targets ERK, thus inhibiting proliferation, and targets the vascular endothelial growth factor receptor 2 (VEGFR-2) and the platelet derived growth factor receptor p (PDGFR- (3), thus inhibiting angiogenesis. Sorafenib has been shown to increase progression free survival in patients with advanced clear cell renal cell carcinoma and received FDA approval in 2005 (87). In vivo studies have demonstrated effective Sorafenib anti-tumor activity in SKOV3 cells and xenograft models (88). Phase II clinical trials of Sorafenib and Gemcitabine in patients with recurrent or refractory ovarian or peritoneal carcinoma have concluded with preliminary 94 results suggesting beneficial effects (89). Furthermore, a phase II clinical trial of Sorafenib and Bevacizumab is currently underway in patients with recurrent/refractory epithelial ovarian carcinoma, primary peritoneal carcinoma and preliminary results are pending (90). Additionally, recent in vivo and in vitro studies in ovarian carcinoma have tested different PARP1 inhibitors as potential therapeutic agents for this disease. One such inhibitor is AZD 2281; a potent oral PARP1/2 inhibitor. This drug was tested in a phase I trial in a cohort of 46 patients with sporadic and hereditary ovarian carcinoma (91). As the treatment was generally well tolerated, with substantial PARP1 inhibition noted in tumors, blood and hair follicles, the drugs was tested on a second cohort of 46 patients with recurrent hereditary ovarian carcinoma. The preliminary analysis indicates the drug had a substantial anti-tumor activity by Response Evaluation Criteria In Solid Tumors (RECIST) and CA-125 measures (91). Future trials are planned in patients with sporadic ovarian carcinoma. Presently, a promising phase II clinical trial, which includes patients with LMP/Micropapillary tumors, is underway using the drug PXD101 or Belinostat. This is a histone deacetylase inhibitor with anti-tumor activity demonstrated in multi-drug resistant ovarian cancer cell lines and ovarian cancer xenograft models. Belinostat is currently being tested in a phase II clinical trial on patients with micropapillary/LMP tumors and metastatic/recurrent platinum-resistant ovarian carcinoma (92). While the study is still on going, preliminary data indicate that within the group of patients with micropapillary/LMP diagnoses (n=12), 1 experienced a partial response, 9 have stable disease and 2 were non-evaluable. In contrast, in the group with metastatic recurrent platinum-resistant ovarian carcinoma (n=18), 9 patients had stable disease, 5 had 95 progressive disease and 4 were non-evaluable. If the final results indicate a therapeutic advantage of Belinostat on LMP-MP disease, patients with LGSC may be candidates for future trials with Belinostat given the similar gene expression profiles of LMP-MP tumors and LGSC. Lastly, given the significant interconnectivity observed between some of the differentially expressed genes in LGSC, it is likely that combination therapy targeting multiple genes and different pathways is required to achieve a clinical response in LGSC. 4.7 FUTURE STUDIES Additional immunohistochemical studies are currently underway using phosphorylated and unphosphorylated PEA 15 antibodies to validate the protein expression level of PEA 15 in LMP, LMP-MP and LGSC and determine whether post- translational phosphorylation of PEA15 takes place in LMP, LMP-MP or LGSC. In addition, further data mining is currently underway using different statistical tools to highlight new candidate genes for future studies. 4.7.1 Functional Assays A likely next step is to perform functional study assays on the key selected genes with proposed role in malignant transformation and carcinogenesis. While studies using mouse models would be ideal to study the impact of key genes, these are not readily available as models for LGSC. Therefore, in vitro models may be used to initially determine the impact these genes have on proliferation, cell motility, and invasion. For those genes with large impact on these functions, and thus suspected to play a key role in 96 malignant transformation, one can alter their expression in cultured ovarian epithelial cells and determine the impact this has on colony forming ability in soft agar and anchorage independent growth. 4.7.2 Differential Gene Expression between Ovarian LGSC and HGSC Unsupervised hierarchical cluster analysis indicated complete segregation of LGSC and HGSC suggesting that these are two distinct diseases. A logical next step is to perform 2-way SAM analysis comparing the gene expression profiles of LGSC generated in this study to profiles of HGSC generated by Tone et al. in our laboratory using an identical experimental platform (28). This comparison will likely reveal differential gene expression given the separate clustering of these tumors and their distinct molecular, histological and clinical behavior (29,47). An important difference between LGSC and HGSC is that LGSC tends to invade all layers of the gastrointestinal wall, which leads to bowel obstruction in 71% of cases, whereas HGSC tends to form carcinomatous nests within the bowel serosa invading only the underlying submucosa, which leads to obstruction in 30% of cases (48,49,50). Comparing gene expression profiles of LGSC and HGSC may highlight differentially expressed genes significant in gastrointestinal luminal invasion. This may allow discovery of potential targets to prevent or delay bowel obstruction and may prove essential in prolonging disease-free and overall survival in women with LGSC. 97 4.8 GENRAL CONCLUSION AND SUMMARY Borderline tumors are a unique group of neoplasms that harbor both benign and malignant characteristics. However, LMP-MP, although labeled a borderline neoplasm, has the clinical behavior and the gene expression profile of a carcinomatous lesion and should be managed accordingly. Reclassifying this tumor as a non-invasive micropapillary carcinoma may serve to emphasize the distinction between it and simple LMP tumors and may drive appropriate referral and management of women with this diagnosis. LMP-MP tumors often progress to LGSC; a disease that poses a true medical predicament. These slow growing LGSC neoplasms are resistant to current chemotherapeutic regimens and therefore the standard therapy is surgical resection. 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