THE REGULATIONS OF MIR-30C-3-3P AND ITS ANTITUMOR MECHANISM IN OVARIAN
CANCER
by
HA THU NGUYEN
(Under the Direction of Dr. Mandi Murph)
ABSTRACT
Lysophasphatidic acid (LPA) is a mitogenic phospholipid present within the ovarian tumor microenvironment that induces ovarian cancer progression through multiple intracellular signaling cascades, leading to cell growth, motility and proliferation.
MicroRNAs (miRNAs) are small, non-protein-coding entities with important roles in post- transcriptional regulation of most of the human genome. Previously, we found that the expression of miR-30c-2-3p is induced by LPA and has an important role in the regulation of cell proliferation in ovarian cancer cells. The goals of this study were to examine the correlation between LPA and miR-30c-2-3p expression as well as mechanisms of miR-30c-
2-3p antitumor effects. We observed that Dicer and epigenetic modifications, particularly
DNA methylation and histone methylation, were not the major regulators of miR-30c-2-3p overexpression. Applying a combination of bioinformatics, qRT-PCR, immunoblotting and luciferase assays, we uncovered a regulatory pathway between miR-30c-2-3p and the expression of the transcription factor, ATF3. LPA triggers the expression of both miR-30c-
2-3p and ATF3 in SKOV-3 and OVCAR-3 serous ovarian cancer cells. The 3´-untranslated region (3´-UTR) of ATF3 was a predicted, putative target for miR-30c-2-3p, which we confirmed as a bona-fide interaction using a luciferase reporter assay. Furthermore, the presence of anti-miR-30c-2-3p enhanced ATF3 mRNA and protein after LPA stimulation.
Thus, the data suggest that after the expression of ATF3 and miR-30c-2-3p are elicited by
LPA, subsequently miR-30c-2-3p negatively regulates the expression of ATF3 through post- transcriptional silencing, which prevents further ATF3-related outcomes as a consequence of LPA signaling. Our in vivo pilot study shows evidence that miR-30c-2-3p can be a potential therapy for ovarian cancer. To date, there is limited information on miRNA mechanisms associated with LPA. Thus our findings bring in more understanding about the signaling circuits initiated by LPA, especially at the level of post-transcriptional silencing regulated by miRNAs. Furthermore, we provide experimental data to support the regulation of ATF3, another gene transcript targeted via miR-30c-2-3p, extending the current list, which includes BCL-9, HIF2A, X-box binding protein 1, Cyclin E1 and an adaptor protein of the NF-κB signaling pathway.
INDEX WORDS: Ovarian cancer, MicroRNAs, miR-30c-2-3p, Lysophosphatidic acid
(LPA), Activating Transcription Factor 3 (ATF3).
© 2015
Ha Thu Nguyen
All Rights Reserved
THE REGULATIONS OF MIR-30C-3-3P AND ITS ANTITUMOR MECHANISM IN OVARIAN
CANCER
by
HA THU NGUYEN
Pharm.D., Hanoi University of Pharmacy, Vietnam, 2005
M.P.H., Emory University, 2009
A Dissertation Submitted to the Graduate Faculty of The University of Georgia in Partial
Fulfillment of the Requirements for the Degree
DOCTOR OF PHILOSOPHY
ATHENS, GEORGIA
2015
THE REGULATIONS OF MIR-30C-3-3P AND ITS ANTITUMOR MECHANISM IN OVARIAN
CANCER
by
HA THU NGUYEN
Major Professor: Mandi Murph
Committee: Michael Bartlett Eileen Kennedy Nancy Manley Jianfu (Jeff) Chen
Electronic Version Approved:
Suzanne Barbour Dean of the Graduate School The University of Georgia December 2015
DEDICATION
To my son, Tiger, thank you for your unconditional love. This work is for, and because of you.
To my loving husband, Jeff, for always being supportive.
To my parents, who encouraged me to see the world for myself and to explore all aspects of my life.
To Dr. Yunzhi Li, who taught me how to balance between both professional and motherly roles. Your endless support has given me precious time to finish this research.
To my big brother, big sister and younger brother: you always stand by my side.
iv
ACKNOWLEDMENTS
I would like to express my gratitude to my advisor, Dr. Mandi Murph, for offering me an opportunity to conduct this research in her lab. I am truly grateful for her expertise, her patience and the awesome research atmosphere she provided in the lab. Working under her direction was one of the most important and informative experiences in my life.
I deeply appreciate the other members of my committee, Dr. Michael Bartlett, Dr.
Eileen Kennedy, Dr. Nancy Manley and Dr. Jeff Chen, for their insightful comments and encouragement, which motivated me to widen my research from various perspectives.
My special thanks goes to Dr. Bartlett for continuously supporting me ever since I was admitted to UGA and for helping me improve my writing, as well as for advising me on my career. I appreciate Dr. Kennedy for her technical and others warm-hearted advices.
Thanks to Dr. Manley for joining my committee at a very late time and providing scientific insight into my research. Thanks to Dr. Chen for his always-optimistic stance and his confidence in my research.
My sincere thanks goes to Dr. Aaron Beedle, who helped me with troubleshooting experiments and who generously gave me access to the equipment in her lab. I also thank
Dr. Robert Arnold and Dr. Brian Cummings for their help during the early years of my research.
v
I would like to say to Dr. Shelley Hooks, during the hard time, your kindness meant so much. Furthermore, I am pleased to tell my lab mates and friends, Molly Altman, Wei Jia,
Ali Alshamrani and Sudeepti Kuppa, I enjoyed working with you guys.
Finally, I want to express my gratitude to the Department of Pharmaceutical and
Biomedical Sciences, the College of Pharmacy and the University of Georgia for their generous financial support.
vi
TABLE OF CONTENTS
Page
DEDICATION ...... iv
ACKNOWLEDGEMENTS ...... v
TABLE OF CONTENTS ...... vii
LIST OF TABLES ...... ix
LIST OF FIGURES ...... x
CHAPTER
1. INTRODUCTION AND LITERATURE REVIEW ...... 1
2. DICER AND MICRORNAS: ONCOMIRS ARE THE NEXT FRONTIER OF ONCOGENES
AFFECTING CANCER ETIOLOGY AND TUMOR PROGRESSION ...... 32
3. MIR-30C-2-3P AND DICER IN OVARIAN CANCER ...... 58
4. MOLECULAR EPIGENETICS IN THE MANAGEMENT OF OVARIAN CANCER:
ARE WE INVESTIGATING A RATIONAL CLINICAL PROMISE? ...... 70
5. EPIGENETIC REGULATION OF MIR-30C-2-5P ...... 95
6. LYSOPHOSPHATIDIC ACID MEDIATES ACTIVATING TRANSCRIPTION
FACTOR 3 EXPRESSION WHICH IS A TARGET FOR POST-TRANSCRIPTIONAL
SILENCING BY MIR-30C-2-3P ...... 107
7. CONCLUSION ...... 130
REFERENCES ...... 139
vii
Page
APPENDICES ...... 196
A. MIRNA-30C-2-3P AND OVARIAN CANCER ...... 196
B. ADDITIONAL DATA OF ATF3 ...... 216
C. SUPPRESSION OF THE GTPASE-ACTIVATING PROTEIN RGS10 INCREASES
RHEB-GTP AND MTOR SIGNALING IN OVARIAN CANCER CELLS ...... 222
D. VINYL SULPHONE ANALOGS OF LYSOPHOSPHATIDYLCHOLINE IRREVERSIBLY
INHIBIT AUTOTAXIN AND PREVENT ANGIOGENESIS IN MELANOMA...... 228
viii
LIST OF TABLES
Page
Table 1.1 LPA receptors and their roles in cancer ...... 4
Table 1.2 Differences in molecular alterations between low-grade and
high-grade ovarian cancer ...... 14
Table 1.3 Dichotomous roles of ATF3 in cancer ...... 21
Table 2.1 miRNAs associated with cancer and their known targets ...... 45
Table 2.2 DICER alterations in cancers ...... 54
Table 3.1 Absolute Ct values of LPA receptor 1-5 expression in ovarian
cancer cells SKOV-3 ...... 63
Table 4.1 Alterations in multiple miRNAs among ovarian cancer ...... 78
Table 4.2 Epigenetic drugs in gynecological cancer trials ...... 87
Table 4.3 miRNAs involved in chemoresistance ...... 90
Table 5.1 Absolute Ct value comparisons of miR-30c-2-3p after
different treatments ...... 103
Table A.1 Potential targets of miR-30c-2-3p ...... 199
Table A.2 Absolute Ct values of miR-30c-2-3p and 18S from exosomes
and intact adipose stem cells ...... 212
ix
LIST OF FIGURES
Page
Figure 1.1 Chemical structure of LPA ...... 2
Figure 1.2 Biogenesis of LPA and its signal cascades through
LPA receptors ...... 8
Figure 1.3 miRNA biogenesis pathway ………………………………………………………… ...... 12
Figure 1.4 Regulation of miR-30c-2-3p and its downstream target ...... 18
Figure 1.5 Induction of ATF3 and its dichotomous roles in cancers ...... 20
Figure 2.1 The schematic structure of the human DICER protein ...... 50
Figure 2.2 Haploinsufficiency of DICER in cancer ...... 52
Figure 3.1 Effects of LPA treatment on Dicer mRNA expression in
ovarian cancer cells SKOV-3 ...... 62
Figure 3.2 Effects of LPA receptor knockdown on Dicer expression
after LPA treatment ...... 64
Figure 3.3 Effects of Dicer knock-down on SKOV-3 cell viability ...... 64
Figure 3.4 Effect of Dicer knockdown on expression levels of
pri-miR-30c, miR-30c-2-5p and miR-30c-2-3p ...... 65
Figure 3.5 Effects of metformin on ovarian cancer cell viability ...... 66
Figure 3.6 The expression of Dicer, pri-miR-30c and miR-30c-2-3p in
ovarian cancer cell lines, SKOV-3 and HeyA8, after metformin
treatment ……………………………..…………………………………………………… ...... 67
x
Page
Figure 4.1 Outline of the functional effect resulting from specific
epigenetic modifications in malignancy ...... 72
Figure 4.2 Location of molecular epigenetic mechanisms
dynamically affecting gene expression ...... 74
Figure 5.1 The effects of 5-aza-CdR treatment on expression
levels of miR-30c-5p ...... 101
Figure 5.2 Treatment effects of DZNeP on expression level
of miR-30c-2-5p ...... 102
Figure 5.3 Treatment effects of 5-aza-CdR and DZNeP on miR-30c-2-5p ...... 102
Figure 5.4 Effects of 5-aza-CdR and DZNep treatment on miR-30c-2-5p ...... 104
Figure 6.1 Lysophosphatidic acid induces the expression of ATF3 ...... 117
Figure 6.2 MiR-30c-2-3p inhibits ATF3 expression ...... 118
Figure 6.3 Anti-miR-30c-2-3p augments ATF3 expression ...... 119
Figure 6.4 ATF3 modulates miR-30c-2-3p expression ...... 120
Figure 6.5 ATF3 expression induces cellular stress on ovarian cancer cells ...... 121
Figure 6.6 Model of auto-regulatory feedback loop between ATF3
and miR-30c-2-3p ...... 123
Figure 6.S1 ATF3 gene transcription is increased among primary ovarian
cancer tumors among patients with a high level of depression ...... 126
Figure 6.S2 Schematics of the interaction ...... 127
Figure 6.S3 MiR-30c-2-3p is predominantly expressed in ovarian
and renal cancer cell lines ...... 128
xi
Page
Figure 7.1 A proposed model of miR-30c-2-3p regulation by LPA ...... 136
Figure A.1 In vivo delivery of miR-30c-2-3p ...... 198
Figure A.2 Potential miR-30c-2-3p targets in JNK pathway ...... 200
Figure A.3 Potential targets of miR-30c-2-3p in ovarian cancer cells ...... 201
Figure A.4 mRNA expressions of potential gene targets of
miR-30c-2-3p after treatment ...... 202
Figure A.5 Developing scheme of cell line with inducible expression
of gene of interest ...... 204
Figure A.6 Expression level of miR-30c-2-3p in Tet-Off inducible HeyA8 cells ...... 205
Figure A.7 Fluorescence images of HeyA8 cells with inducible
miR-30c-2-3p expression ...... 206
Figure A.8 ATF3 mRNA expression in Tet-Off HeyA8 cells with
and without doxycycline in media ...... 207
Figure A.9 Expression ratios of miR-30c-2-3p and 18S in exosomes
compare to their expression in intact SKOV-3 cells ...... 210
Figure A.10 The comparisons of miR-30c-2-3p expressions from exosomes
in the media of co-cultured SKOV-3 ASC cells and SKOV-3 cells ...... 211
Figure B.1 Location of potential ATF3 binding sequence ...... 217
Figure B.2 Results obtained from gelshift assay ...... 218
Figure B.3 Expressions of multiple genes 48 hours after ovarian cancer
cells were transfected with ATF3 overexpression vector ...... 219
Figure C.1 Evaluation of the GAP activity of RGS10 ...... 225
xii
Page
Figure D.1 The autotaxin inhibitors HA-130 and PF-8380 did not impact
tumor progression in a xenograft model of melanoma ...... 229
xiii
CHAPTER 1
INTRODUCTION AND LITERATURE REVIEW
Ovarian cancer
Ovarian cancer has the highest mortality rate among gynecological cancers. It is ranked the fifth leading cause of cancer death among American women with about
5% of total cancer deaths. In 2015, it is estimated that ~21,000 cases of ovarian cancer will be diagnosed in the United States, and ovarian cancer deaths will reach
~14,000. Despite advances in surgery and chemotherapy, comparing the ~36% overall five-year survival rate of ovarian cancer during 1975-1977, there has only been limited improvement to reach ~45% in the period during 2010-2014 [1].
The limited survivability is multifactorial; one major issue is that early stage disease does not show any obvious symptoms. For example, the reported early symptoms are non-specific bloating, feeling full quickly, urinary urgency, and pelvic and/or abdominal pain (which may be induced by the accumulation of fluid), which are symptoms usually attributed to irritable bowel syndrome [1-3]. If ovarian cancer is diagnosed at an early stage when the disease is still locally confined to one or both ovaries, the 5-year survival rate can reach ~92%. However, this only accounts for 15% of cases. The majority of cases are diagnosed at a later more distant stage, which is responsible, in part, for the diminished survival rate, which drops down to 27% [1]. Moreover, epithelial ovarian carcinomas make up to approximately 90% of ovarian cancers in the U.S [4, 5]. As it is the sixth most
1 common cancer in women, epithelial ovarian cancer’s high malignancy and aggressive nature lead to more than 125,000 deaths worldwide each year [6] even the developed countries like the United States accounts for more than 11% of ovarian cancer death [7].
Lysophosphatidic acid
This research focuses on lysophosphatidic acid, a well-known mitogenic phospholipid, present within the ovarian tumor microenvironment and induces ovarian cancer progress through multiple intracellular cascades [8].
Lysophosphatidic acid (1-acyl-2-hydroxy-sn-glycero-3-phosphate, LPA) is a growth factor-like phospholipid (Figure 1.1). It is a simple and water soluble glycerophospholipid owing to its free hydroxyl group and phosphate moiety [9].
LPA was originally known as a precursor of phospholipid biosynthesis. It was revealed later that it could be released by platelets to stimulate wound healing and tissue generation, suggesting its role as an intercellular signaling molecule [10, 11].
Figure 1.1: Chemical structure of LPA (1-acyl-2-hydroxy-sn-glycero-3-phosphate)
LPA produces various cellular effects through its binding to G-protein coupled receptors (GPCRs). The wide range of LPA’s biological functions is due to GPCR coupling to distinct α-subunits of G protein coupled receptor subunits, such as Gq, Gi and G12/13 [12-16]. All of LPAs receptors are G protein-coupled receptors, and can be divided into two major groups based on their origins, the Edg family members and
2 the purinergic family members (Table 1.1). Among the eight members of this family
(Edg1-8), five receive an activating signal from sphingosine-1-phosphate (S1P), they are Edg1 (S1P1), Edg5 (S1P2), Edg3 (S1P3), Edg6 (S1P4) and Edg8 (S1P5)[17]. The remaining three are LPA specific receptors, Edg2 (LPA1), Edg4 (LPA2) and Edg7
(LPA3) [17].
The purinergic receptor family traditionally receives signals from purine nucleotides. They are divided into P1 receptors and P2 receptors. P1 receptors are most sensitive to adenosine stimulation. P2 receptors are activated mainly by ATP, and can be further divided into two major groups: P2X, the ligand-gated ion channels, and P2Y, the GPCRs [18, 19]. Several P2Y receptors were later discovered to play roles as LPA receptors, such as P2Y9 (LPA4), P2Y5(LPA6) and P2Y10 [20-
22]. A recently discovered LPA receptor GPR87 is also suggested to have closer origin to purinergic receptors than Edg receptors [20, 21, 23, 24].
LPA receptor 1, LPA1 (or EDG2), was the first LPA receptor identified [25]. It is expressed in brain, uterus, testis, heart, stomach, kidney, spleen and skeletal muscle
[26, 27]. LPA1 couples with Gq, Gi and G12/13 to convert the LPA signal to activate phospholipase C, MAPK, Akt and Rho. Specifically, LPA1 is internalized upon specific binding to LPA in a dose dependent manner, which leads to activation of the MAPK signaling pathway [28]. The signals from LPA1 activation lead to multiple cellular responses, including cell proliferation, survival and migration [27, 29, 30].
Interestingly, LPA1 may act as a negative regulator of ovarian tumor progression since its overexpression induced apoptosis and anoikis in ovarian cancer cells [31,
32].
3 P2Y10 GPR87 LP (GPR92) LPA5 (P2Y9) LPA4 (EDG7)LPA3 (EDG4)LPA2 (EDG2)LPA1 receptors receptors A6 (P2Y5)A6 LPA
Purinergic Purinergic Purinergic Purinergic Purinergic receptor Edg receptor Edg receptor Edg Origin Origin G G G G G G G G G G G G 16 16 12/13 12/13 s i q q 12/13 12/13 12/13
proteins , G , G , G α and G and
coupled coupled Table 1.1LPATable receptors their and rolescancer in q i i
, G , G and and
i q/11 q
and
Unknown PI3K-Akt EGFRcAMP, cAMP Rho/ROCK phospholipase C, Adenylyl cyclase, phospholipase C, MAPK, Akt, MAPK, Ras, PI3K, Rho Rho phospholipase C, MAPK Rho Downstream pathway pathway
Unknown Oncogenic Unknown Anti Anti Oncogenic Oncogenic Oncogenic tumorigenic tumorigenic tumorigenic Effects in Effects in cancers - - Unknown Lung, liver development Normal follicle Hair Mouse cell melanoma Colon Melanoma Ovarian, colonOvarian, Ovarian
Type of cancers Type ofcancers
[20] 48] [24, 47, [22] [44- [21, 41- [37- [33- 32] [28, 31, Reference 46] 40] 36] s s
43]
4 The second LPA receptor discovered was LPA receptor 2 (LPA2 or EDG4), a G protein coupled receptor with 55% amino acid similarity to LPA1 [34, 49]. LPA2 is expressed at a high level in the testes and leukocytes [26, 27]. Similar to LPA1, LPA2 couples to Gq, Gi and G12/13 to initiate downstream signals through Ras, PI3K, MAPK, phospholipase C, diacylglycerol and Rho [50]. The cellular effects of LPA2 activation ultimately lead to cell survival and cell migration [33, 51, 52]. LPA2 is expressed at a higher level in ovarian and colon cancers in correlation with invasiveness and angiogenesis [35, 36, 53-58]. The major pathways for mitogenic signaling by LPA2 activation in cancer are activation of Akt and Erk1/2 [36].
LPA receptor 3 (LPA3, EDG7) was discovered through homology searches for orphan GPCRs with about 54% amino acid identical to LPA1 and about 49% amino acid identical to LPA2 [37, 38]. LPA3 couples with Gi and Gq and transfers signals through adenylyl cyclase, phospholipase C and MAPK [39]. In a recent publication from our lab, we discovered that LPA3 had an important role in melanoma cell viability, and this function is critically supported by the Src homology ligand binding domain within the third intracellular loop of this receptor [40].
LPA receptor 4, LPA4, is a former orphan purinergic receptor P2Y9 [21]. It shares only about 20% amino acid identity with the Edg LPA receptors, LPA1-3 [59,
60]. The receptor’s expression is high in brain, platelet, adipose tissue, ovary, uterus and placenta [61]. LPA4 couples with Gs, Gq, Gi and G12/13 for subsequent signal transfer through the Rho/ROCK pathway [41, 42]. Interestingly, activation of LPA4 inhibits migration and invasion induced by LPA via LPA1, suggesting a potential role
5 of LPA4 in inhibiting cell mobility and preventing EDG-receptor induced metastasis in cancer [43].
LPA receptor 5 (LPA5/GPCR92) was first described in 2006 with about 35% amino acid identical to LPA4 and about 22% amino acid similarity to LPA1-3 [44,
45]. LPA5 is found expressed in spleen, heart, small intestine, placenta, colon, liver and brain [44, 45, 61]. LPA5 couples with Gq and G12/13 [44, 45]. In mouse melanoma cell line B16F10, the activation of LPA5 by LPA shows inhibition effects on migration [46]. However, information about the roles of LPA5 in human melanoma is still limited, and more studies are needed to provide broad understanding about this receptor’s functions.
LPA receptor 6, LPA6 (P2Y5), is a recent discovered LPA receptor. LPA6 couples to G12/13, and is reported to be involved in human hair follicle growth [22].
However, the understanding about this receptor’s role in ovarian cancer still needs further investigation [61].
GPR87, originally an orphan G-protein coupled receptor, is recently proved to be a LPA receptor [23, 24]. The gpr87 gene is located in a cluster containing p2y12,13,14 .GPR87 has more than 40% amino acid identical with P2Y12,13,14 , about
25% identical with LPA4 and LPA5 receptors, but has low sequence homology with
LPA1-3, suggesting GPR87 has a origin closer to 2YP receptors than LPA receptors
[24] . Analysis from mouse tissues shows that GPR87 is expressed in the ovaries, placenta, testis, prostate, brain and skeletal muscle [24]. In human, GPR87 is shown to be expressed in placenta [62]. Because placenta is derived from the ovarian surface epithelial cells, GPR87 is suggested to have a potential role in normal
6 ovarian development, and probably ovarian cancer progression [24]. Furthermore,
GPR87 is overexpressed in lung squamous cell carcinoma and the receptor promotes the growth and metastasis of cancer stem-like cells with CD133 [47, 48].
Activation of GPR87 triggers the PI3K/Akt survival pathway, resulting in phosphorylation and activation of Mdm2, the major inhibitor of p53, which ultimately destabilizes p53 and inhibits its activity [63, 64].
In addition to the receptors described above, P2Y10 is another orphan GPCR uncovered to function as a lysophosphatidic acid receptor, which interacts with Gα16 to increase intracellular Ca2+ [20]. However, information about the roles of P2Y10 in biological systems is still limited. Therefore, further studies are needed to better understand if this receptor is involved in ovarian cancer.
LPA is synthesized from precursor phospholipids (phosphatidyl choline (PC)) mainly through two pathways. In the first and most common pathway observed in cancers, phospholipids (PC) are converted into lysophospholipids
(lysophosphatidylcholine LPC) by phospholipase 1 and 2 (PLA1, PLA2) then eventually transformed to LPA through autotaxin (Lysophospholypase D, ATX) activity. In the second pathway, phospholipids are converted to phosphatidic acids by phospholipase D1 or D2 and then to LPA by phospholipase A1 or A2 [8]. Figure
1.2 summarizes the biogenesis of LPA and its signaling cascades in cancer through different LPA receptors.
Ovarian cancer patients usually have a significant increase in the level of LPA present in peritoneal ascites [65]. The presence of ascites fluid is considered a sign of an advanced disease stage; however, such fluid may be found in the peritoneal
7 washings of patients presenting with stage 1C ovarian cancer [6]. Ascites formation is explained as the failure to absorb physiologically produced peritoneal fluid (1 liter/day) by subperitoneal lymphatic channels due to obstruction caused by cancer cells [65]. In addition, ovarian carcinoma cells secrete vascular endothelial growth factor (VEGF) thus increasing vascular permeability to promote ascites formation
[66]. On the other hand, there is evidence showing that ascites fluid contains growth factors necessary for ovarian cancer cell growth in vitro and in vivo [67-69].
Figure 1.2: Biogenesis of LPA and its signal cascades through LPA receptors. (PLD1-2: phospholipase D1-2, PLA1-2: phospholipase A1-2, ATX: autotaxin)
LPA is believed to play a significant role in the overall mitogenic activities of ascites fluid with concentrations ranging from 1 to 80 μM, which is a significant increase above normal levels of <2 μM [70, 71]. The source of increased LPA concentration in ovarian cancer ascites fluid is probably the high level of LPA
8 production by ovarian cancer cells [72, 73]. Furthermore, previous research suggested that LPA could act in autocrine networks to stimulate the production of itself in ovarian cancer cells [72-74]. In addition, ATX mRNA and protein levels as well as ATX activity are increased in ovarian cancer ascites fluid [75-77]. The influence of LPA on ovarian cancer is further strengthened by LPA2 and LPA3 overexpression observed in ovarian cancer cells while the LPA4 receptor has a particularly higher expression level in normal ovary [53, 72, 78, 79].
Ovarian cancer patients usually have a significant increase in the level of LPA
LPA treatment in ovarian cancer cells induces a transcriptional profile increase among 39 genes that are correlated with serous ovarian carcinoma and worsened prognosis. Among these targets, claudin-1 is identified as an independent biomarker of serous epithelial ovarian carcinoma. Particularly, the overexpression of claudin-1 by LPA stimulation leads to increased adhesion in OVCAR-3 cells [80]. LPA concentration is also elevated in patients’ plasma and this phenomenon has been suggested for exploitation for the use of LPA as a biomarker to diagnosis malignancy; unfortunately, LPA alone cannot achieve the specificity and sensitivity required for an FDA-approved biomarker test of a rare disease [75, 81-83].
Furthermore, LPA is shown to induce p53 degradation in other cancers, such as in lung carcinoma cells A549 [84].
Others factors in normal ovarian tissue and ovarian cancer development
Normal ovarian epithelial cells regulate their cell cycles by secreting and receiving growth and differentiation signal factors. The roles of these factors in ovarian carcinomas have been extensively studied. The first important factor is
9 gonadotropin-releasing hormone, GnRH, a key hormone regulating the pituitary gonadal axis. GnRH is reported to have an autocrine role in ovarian tumorigenesis, and GnRH receptors are detected in several ovarian cancer cell lines [85, 86].
Researchers applied GnRH analogs through their negative feedback loop on GnRH receptors to treat hormone responsive ovarian tumors [87], which are putatively stimulated by endogenous ligands to signal cell proliferation [87, 88].
Gonadotropins (FSH, LH and hCG) are also reported as contributing factors of ovarian tumorigenesis. Multiple pregnancies, breast-feeding and oral contraceptive use are indicated as preventive factors of ovarian cancer due to reducing level of gonadotropins [89-92]. Furthermore, increased level of gonadotropins facilitates the growth of ovarian carcinoma through induction of tumor angiogenesis [93].
Sex steroids are other known factors in the pathogenesis and growth of ovarian cancer [94, 95]. Usage of hormone replacement therapy in long duration increases the risk of developing ovarian cancer [96]. On the other hand, breast- feeding’s tumor prevention tendency may have originated from reduction of serum estradiol [97-99]. Clinical data showed that high plasma levels of estradiol, progesterone and testosterone are correlated with tumor volume [100-104].
Growth factors, another type of signaling ligand, have mixed influence on ovarian cancer. TGFβ, a peptide involved in cell growth regulation and many other crucial cellular functions, plays an inhibitor role to prevent abnormal proliferation of ovarian surface epithelial cells which can potentially lead to cancer [105-108]. In contrast to TGFβ protective effects, TGFα is described as an ovarian cancer growth stimulator [109] because the treatment of exogenous TGFα promotes ovarian
10 cancer cell growth [110, 111]. Patients with malignant ovarian tumors have higher levels of TGFα in serum than those with benign tumors [112]. EGF receptor, a membrane tyrosine kinase activated by EGF and TGFα, is reported to have increased levels in ovarian tumors compared to the normal ovary [113-118].
MicroRNAs and ovarian cancer
MicroRNAs are small, non-coding RNAs about 18-25 nucleotides in length with the ability to suppress gene expression through binding to complementary 3’ untranslated regions of target mRNAs [119, 120]. The mechanisms of miRNA gene silencing are either direct cleavage of homologous mRNAs or indirect inhibition of specific protein synthesis. Figure 1.3 represents miRNA biogenesis and its mechanisms of function, more details are discussed in Chapter 2. Because of their critical function, miRNAs are important regulators of cellular processes, involving in proliferation, differentiation, development and cell death [121]. As a result, microRNAs also play pivotal roles in cancers, both in tumor suppression and tumorigenesis because there is significant distinction of miRNA profile signatures between cancer cells and normal cells in a variety of cancers tissues and corresponding normal tissues [122, 123].
In the field of ovarian cancer, many research groups have screened miRNA profiles in ovarian cancer to identify changes in miRNA expression compared to normal ovarian samples. In a comprehensive study reported by Iorio and colleagues, a panel of highly overexpressed miRNAs in ovarian cancer was identified, such as, miR-200a, miR-141, miR-200c and miR-200b. In the same study, multiple miRNAs
11
Figure 1.3: miRNA biogenesis pathway
12 had shown significantly down regulation in the disease such as miR-199a, miR-140, miR-145 and miR-125b [124].
Further research has been carried out to identify miRNA profile changes with the ability to categorize the subtype of ovarian cancer. Due to the heterogeneous nature of the disease, ovarian cancers are divided into type I low-grade and type II high-grade cancers based on histology and molecular profile [125] (Table 1.2). Low- grade cancers only account for about 10% of ovarian cancer cases. These cancers grow slowly with progression lasting as much as 20 years, however, the patients do not response well to platinum and taxane-based therapy compared to high-grade cancers [126]. Molecular alterations associated with low-grade cancers includes mutations of BRAF, KRAS, β catenin and PTEN [125].
High-grade cancer patients are initially chemosensitive but the acquisition of resistance increases after each recurrence [7]. High-grade cancers are characterized by somatic mutations in TP53, BRCA1, and BRCA2, multiple copy number abnormalities and epigenetic changes [125]. Furthermore, about 50% of high-grade patients have defects in the homologous recombination repair of DNA damage [7].
The high-grade serous tumor histology is described as becoming solid with increased architectural complexity, glands are elongated by clefts occupied with foci of necrosis [7]. Screening results from NCI-60 panel of 60 human cancer cell lines suggest that mutations of BRAF and PTEN affect miRNA expressions [127]. In low- grade serous ovarian cancer, four miRNAs related to BRAF mutation are up- regulated compared to normal fallopian tube, which are miR-509-3p, miR-30d, miR-
30b-3p and miR-30b-5p [128]. In high-grade ovarian cancers, eight miRNAs are
13 identified as being up-regulated (miR-183-3p, miR-15b-3p, miR-15b-5p, miR-590-
5p, miR-18a, miR-16, miR-96 and miR-18b) and nine miRNAs are down-regulated
(miR-140-3p, miR-145-3p, miR-143-5p, miR-34b-5p, miR-145-5p, miR-139-5p, miR-
34c-3p, miR-133a and miR-34c-5p) [129]. MiR-34 family is known to be up- regulated by wild-type p53. Interestingly, in ovarian tumors and cell lines, TP53 mutation, a common characteristic in high-grade serous ovarian cancers, is correlated with the decrease of miR-34a, miR-34b and miR-34c [128, 130].
Table 1.2: Differences in molecular alterations between low-grade and high-grade ovarian cancer
Characters Low-grade ovarian cancers High-grade ovarian cancers
Mutations of p53, BRCA1, Molecular Mutations of BRAF, KRAS, β- BRCA2, multiple copy alterations catenin and PTEN abnormalities and epigenetic changes
Chemotherapy Grow slowly, do not Sensitive initially but acquire responses response well to platinum resistance once recurrent
Upregulate miR-183-3p, miR- 15b-3p, miR-15b-5p, miR-590- 5p, miR-18a, miR-16, miR-96 and miR-18b Upregulate miR-509-3p, Changes in Down regulate miR-140-3p, miR-30d, miR-30b-3p and miRNA profile miR-145-3p, miR-143-5p, miR- miR-30b-5p 34b-5p, miR-145-5p, miR-139- 5p, miR-34c-3p, miR-133a and miR-34c-5p, miR-34a, miR- 34b and miR-34c
Epigenetic alterations are other factors that affect miRNA profiles in ovarian cancers. For example, treatment of DNA methyltransferase inhibitor (5-aza-2- deoxycytidine) and histone deacetylase inhibitor (4-phenylbutyric acid) restored 16
14 out of 44 down-regulated miRNAs in advanced-stage ovarian cancer [131]. On the other hand, incubation with 5-aza-2-deoxycytidine further increased miR-21, miR-
203 and miR-205 expressions, which are already elevated in ovarian cancer cell line
OVCAR-3 [124].
Because of their putatively important roles in ovarian cancer, miRNAs may serve as disease biomarkers. In addition, miRNAs generally have stable, yet altered, levels in blood, serum and tumor-derived exosomes of cancer patients [132]. Two down-regulated miRNAs, let-7 family and miR-155, together with five up-regulated miRNAs, miR15/16 cluster, miR-20a, miR-92, miR-203 and miR-205 are indicated as promising biomarkers for early diagnosis of cancer since they were found in peripheral circulation of ovarian cancer patients [132]. Several miRNAs may also be potential ovarian cancer prognostic biomarkers. For instance, poor prognosis ovarian cancer cases showed overexpression of the miR-200 family miRNAs and miR-519a, as well as underexpression of the let-7 family and miR-153 [133-135].
Previous results from our lab showed that treatment of LPA on the ovarian cancer cell lines, SKOV-3 and OVCAR-3, dramatically induced the expression of miR-
30c-2-3p, a passenger miRNA, whereas the expression level of the major strand, miR-30c-5p did not change significantly [136]. Our data indicated that miR-30c-2-3p reduces cell viability and inhibits proliferation in ovarian cancer. Though miR-30c-
2-3p may have multiple targets, we identified BCL-9 as one direct target that miR-
30c-2-3p down-regulates to exert its antitumor effects [137]. Shortly after the publication of our miR-30c-2-3p study, in 2012, Byrd and colleagues provided evidence that miR-30c-2-3p is also induced by stress in the endoplasmic reticulum
15 [138]. ER stress activates the unfolded protein response (UPR) in order to rescue cells from the accumulation of unfolded or misfolded proteins in the lumen of ER.
The UPR has three aims, halting protein translation, degrading misfolded proteins and increasing the production of chaperons to assist protein folding [139]. To carry out these three aims, UPR has three mediators: inositol-requiring protein-1 (IRE1), activating transcription factor 6 (ATF6) and protein kinase RNA-like ER kinase
(PERK) [139]. PERK is an ER-localized transmembrane protein with a protein kinase domain located in the cell cytoplasm. Under ER stress stimuli, the kinase domain phosphorylates the α subunit of eukaryotic translation initiation factor-2 (eIF2α) and inhibits the recycling of this protein to its active form, resulting in a lower level of translational initiation and reduction in global protein synthesis [139]. ER stress induced by thapsigargin, an inhibitor of the ER Ca+ATPase, initiates the PERK pathway and activates NF-κB through depleting the production of its inhibitor IκB.
Free NF-κB travels to the nucleus, binds to the upstream region of miR-30c-2-3p and induces gene expression [138]. miR-30c-2-3p overexpression targets X-box binding protein 1 (XBP1) and is considered a contribution to overall cellular stress adaptation [138].
MiR-30c-2-3p is later indicated in several publications as an antitumor miRNA.
A report on human clear cell renal cell carcinoma demonstrated that miR-30c-2-3p inhibits expression of hypoxia-inducible factor HIF2 alpha, an angiogenesis factor, through direct binding to its transcript [140]. In contrast, inhibition of miR-30c-2-3p expression enhances HIF2 alpha level, resulting in cell proliferation, angiogenesis and xenograft tumor growth [140]. Interestingly, a recent study confirms miR-30c-
16 2-3p’s antitumor role in breast cancer by negatively regulating NF-κB, which was previously described as its direct transcription factor [138, 141]. However, the down-regulation of NF-κB by miR-30c-2-3p is indirect through its targeting tumor necrosis factor 1-associated death domain protein (TRADD), a known activator of
NF-κB [141, 142]. The impact of inhibiting miR-30c-2-3p on breast cancer cell proliferation and invasion is explained through its direct targeting of CCNE1, the gene encoding cyclin E1, which is involved in G1 to S transition [141, 143]. Figure
1.4 summarizes information about miR-30c-2-3p regulations as well as its known targets from both published literature and ongoing studies. My research will focus on identifying the specific mechanisms that LPA modulates miR-30c-2-3p expression in ovarian cancer and the potentials of miR-30c-2-3p in ovarian cancer treatment.
Transcription Regulation of miR-30c-2-3p through Activating Transcription
Factor 3 (ATF3)
MiRNA gene translation is suggested to be similar to other genes, but with some specific characteristics. If miRNA genes are located within intronic regions, their transcription is initiated together with the host genes. If miRNA genes are located in intergenic regions, mainly they are believed to have independent transcription units [144-148]. Bioinformatic tools have been developed to predict miRNA promoters with several indicators. For example, the core transcriptional regulatory circuitry of embryonic stem cells (Oct4/Sox2/Nanog/Tcf3) was utilized to connect the microRNA transcription initiation and promoter locations [149].
17 Figure 1.4: Regulation of miR-30c-2-3p and its downstream target
Most miRNA genes are either bona-fide or predicted to be RNA polymerase II- transcribed and the miRNA cluster is reported to be transcribed by RNA polymerase
III [150-153]. This evidence suggests RNA polymerase II binding sequences, such as the TATA box (5’-TATAAA-3’), can be transcriptional start sites. Alternatively, trimethylation of histone 3 at lysine 4 (H3K4me3) is proposed in several publications as a marker for promoter regions of common genes and miRNA genes
[154, 155]. The majority of miRNA transcription start sites are within 2kb from the gene start site; the further the distance from this point reduces the possibility of locating the miRNA transcription site [155]. However, most of the miRNA promoters and transcriptional mechanisms described above are computationally predicted.
Experimentally, many transcription factors have been shown to be involved in miRNAs transcription, such as MITF, a melanoma oncogene and master
18 transcriptional regulator of melanocyte development, revealing more transcriptional mechanism possibilities for specific miRNAs [155].
The focus of my study is on the transcription factor named Activating
Transcription Factor 3 (ATF3). We hypothesize that LPA mediates ATF3 expression, which is a target for post-transcriptional silencing by miR-30c-2-3p. ATF3 belongs to a transcription factor family called ATF/CREB (ATF stands for Activating
Transcription Factor, CREB stands for cAMP responsive element binding protein)
[156, 157]. Members of this family are ATF1, CREB, CREMm ATF2, ATF3, ATF4,
ATF5, ATF6, ATF7, and B-ATF [158]. Members of the ATF/CREB family bind to a concensus DNA sequence (TGACGTCA). They all have the basic region leucine zipper domain (bZip), which allows them to form homodimers or heterodimers with each other [157]. Early research on ATF3 revealed ATF3’s transactivating ability through inducing proenkephalin gene expression through homodimerization or the formation of heterodimers with Jun-D [159, 160]. On the other hand, ATF3 is also reported to bind to DNA and act as a transcriptional repressor, probably through stabilizing inhibitory cofactors at the DNA promoter [161, 162].
With expression generally maintained at low/undetectable levels in quiescent cells, ATF3 is known as a stress response element due to its rapid increase in expression after a wide range of stresses, such as hepatoxicity, genotoxic agents and physiological stresses (Figure 1.5) [163-165]. ATF3 is induced quickly after DNA damage by UV radiation or methyl methanesulphonate (MMS) through promoter induction by MEKK1 [166]. ATF3 expression also increases after receiving stress signals from the MKK6 (MAPK (mitogen-activated protein kinase) kinase 6) and the
19 p38 signaling pathways [167]. Upon DNA damage by camptothecin, ATF3 forms a complex with p53 and induces expression of death receptor 5 (DR5), a death domain-containing transmembrane receptor with capability to trigger cell death through binding to its ligand TRAIL [168]. It is also documented that ATF3 is regulated post-translationally by SUMOylation, and this modification leads to complete repression of TP53 gene [169].
Figure 1.5: Induction of ATF3 and its dichotomous roles in cancers
ATF3 has long been known to have a dichotomous role in cancer (Table 1.3)
[170]. As a tumor suppressor, ATF3 suppresses Ras-stimulated tumorigenesis in mouse fibroblasts [162]. ATF3 binds to cyclin D1 promoter and represses it transcription, leading to cell cycle arrest [162]. Inhibition of ATF3 on cell growth are
20 supp Tumor Types effe ressive ressive
cts cts of colon cancer colon cancer carcinoma, Lung cancer cancer Prostate carcinomas squamous cell Esophageal lung Non Bladder Colorectal Types of cancers -small cell
Inhibit invasionInhibit cell Inhibit proliferation Supress signaling Akt signaling androgenRepress meta Suppress and invasion supp proapoptotic andtumor p53Stabilize Suppress metastasis celldeath apoptotic sensitivityto Enhance Impacts on cancer cellsImpacts oncancer ressor functions ressor functions stasis Table Table ichotomous roles of ATF3 in cancer ichotomous cancer rolesofATF3 in 1. 3: D
(su Adenylate 4 (AK4) (repre Cholesterol inhibitor) (topoisomerase II acidRetigeric B Dexrazoxane, n/a n/a n/a reagents damage Camptothecin, DNA (inducer) Zerumbone Upstream effectors Upstreameffectors ppressor) ppressor) ssor)
Repress GADD153Repress rece Su Induce P53 MMP2Degrade Blockp53 activate MDM2, Induce Gelsolin (D Induce Death receptor 5 R5) ppress Androgen Downstream targets ptor
[179] [175- [174] [173] [172] 171] [168, References References 178]
21 Oncogenic supp Tumor Types of effects ressive ressive
carcin Breast cancerBreast can Prostate fibroblasts associated Cancer- Colon cancer squ Cutaneous carcinoma Epidermoid Cervical cancer can Colorectal Types of cer cer amous cell amous effects oma
Promotes Promotes metastasis development Induce cancer inhibition Antagonize growth apoptosisInhibit growthCell arrest development cancerPromote metastasis Promote tr cancerrelated Facilitate Ta anscription anscription ble 1. Types of effects Types ofeffects 3: Dichotomous cancer rolesofATF3(continued) in
NRF2) NRF2) n/a NDRG1 (suppressor) n/a n/a Ult Cyclosporin Aand n/a myco (fungal Patulin Types of effects Types ofeffects ra violet ra A (induce toxin) toxin)
cave FN-1, TWIST, PAI1/ PLAU, KAI1 CLDN1Repress CXCL13Promote RGS4 and vascularization Promote GADD153 p21Repress GADD153Repress ROS generation olin-1, olin-1, Slug Types of effects Types ofeffects in vivo
[187] [186] [185] [184] [183] [182] [181] [180] Types of effects
22 also shown when overexpression of ATF3 slows down cell progression from G1 to S phase
[166]. ATF3 activates p53 by blocking p53’s MDM2-mediated degradation [173].
Interestingly, ATF3 has opposing effects on mutated p53, which often becomes oncogenic, promoting cell proliferation, inducing angiogenesis and promoting cancer cell invasion and metastasis [188, 189]. ATF3 interacts with mutated p53 at its C terminus and counteracts its oncogenic functions in TP53-mutated epidermoid carcinoma and breast cancer cell lines
[188]. In esophageal squamous cell carcinomas, ATF3 expression was suggested to be an independent prognosis factor. Overexpression of ATF3 arrests cell growth and decreases invasion [190].
ATF3 also facilitates p53 nuclear translocation and forms an ATF3/MDM2/MMP-2 complex, leading to MMP-2 degradation. Since MMP-2 is capable of degrading type IV collagen, the most abundant collagen in the basement membranes, which provide structural support for cells, MMP-2 is an important factor in the metastatic progression of cancer. Antitumor ATF3 enables MMP-2 degradation and helps minimize the invasive properties of esophageal squamous cell carcinomas [174].
The nuclear DNA damage triggered by dexrazoxane, a topoisomerase II inhibitor, induces ATF3 protein expression, resulting in p53 accumulation and cell death [176]. ATF3 is also involved in another mechanism whereby retigeric acid B, another topoisomerase II inhibitor, inhibits cell proliferation and induces apoptosis in prostate cancer [177]. Another study in prostate cancer reveals that ATF3 binds to the androgen receptor and represses androgen signaling, helping prostate epithelial cells restore homeostasis and maintain integrity [191]. In a mouse model of prostate cancer induced by knockout of the tumor suppressor PTEN, ATF3 deficiency promotes the activation of oncogenic Akt signaling,
23 leading to cell proliferation, survival and the progression of prostate lesions to invasive adenocarcinoma [175] Also in prostate cancer, ATF3 acts as a node in a cholesterol-sensing network, where depletion of cholesterol elevates ATF3 expression and inhibits proliferation [178]. In lung cancer, adenylate kinase 4 (AK4), a metastasis protein, promotes invasion through down regulation of ATF3, leading to unfavorable outcomes
[179]. Increased ATF3 expression, which results in G1/S accumulation and cell growth arrest, is suggested as one anti-cancer mechanism of patulin, a fungal mycotoxin, in colorectal cancer [180].
On the opposite side of ATF3’s anticancer functions, an early study in HeLa cells indicated that ATF3 could bind the GADD153 promoter and repress its transcription [181].
GADD153 is known as an inducer of cell cycle arrest and apoptosis in response to cellular stresses. The protein is also a promoter of apoptosis in cancer cells under anti-tumorigenic treatment [192-194]. Repressing GADD153 expression is the evidence indicating ATF3’s anti- oncogenic effects.
ATF3 can act as an oncogene in skin cancer. Overexpression of this protein in epidermoid carcinoma cells antagonizes the growth inhibition effects of trichostatin A, a histone deacetylase (HDAC) inhibitor. The mechanism is ATF3 deregulates p21 expression, whereas p21 is a cyclin-dependent kinase inhibitor, which plays an essential role in growth inhibition [182]. In non-small cell lung cancer, the aberrant expression of ATF3 is linked to tumorigenesis [195]. Cancer-associated fibroblasts from prostate, breast and lung cancers have ectopic ATF3 expression, which promotes cell proliferation and induces the growth of adjacent tumor cells [185]. Data from the same study also shows evidence that ATF3 facilitates a cancer related transcriptional program, which induces CXCL13 and RGS4
24 transcription and represses CLDN1 transcription [185]. Interestingly, in vitro and in vivo data in prostate cancer describes ATF3 as a mediator between two metastasis suppressors,
NDRG1 and KAI1 [186]. NDRG1 ectopic expression activates KAI1 through inhibition of
ATF3, whereas ATF3 represses KAI1 transcription through cooperation with NFκB [186].
In the aggressive breast cancer cell line MCF10CA1a, ATF3 up-regulates several genes involved in tumor metastasis, such as TWIST1, fibronectin-1, plasminogen activator inhibitor-1, urokinase-type plasminogen activator, caveolin-1 and Slug [170]. Constitutive overexpression of ATF3 in female transgenic mice leads to frequent development of mammary carcinoma with elevated levels of Snail and Slug, two proteins with known roles in epithelial-mesenchymal transition [187].
Current therapies for cancer and specific therapies for ovarian cancer
Current chemotherapies have efficacy, but are limited and do not always provide cures, mainly due to the development of chemoresistance. Conventional chemotherapy induces cytoxicity in proliferating cancer cells through damaging DNA or disrupting the functions of cell organelles, such as microtubules and the cytoskeleton. However, these reagents do not only target cancer cells but all rapidly dividing normal cells; thus, they cause toxicity among the bone marrow, hair follicles and the gastrointestinal tract, leading to undesired side effects, such as anemia, myelosuppression, infection, bleeding and bruising (thrombocytopenia), diarrhea, fatigue, hair loss (alopecia), pain, nausea and vomiting. To overcome this major limitation, the idea of targeted therapy was introduced with the specific intention of blocking molecular targets that are only involved in tumor formation and progression [196]. However, this ideal in some cases is unachievable and in
25 other cases the adverse effects of targeted therapy are just as severe as chemotherapy, without any ability to provide a cure (e.g. EGFR inhibitors).
One example is tyrosine kinases, the enzymes that transfer the phosphate group from
ATP to tyrosine residues in proteins. Many important tyrosine kinases are transmembrane receptors. They consist of a ligand binding extracellular domain and a catalytic intracellular kinase domain. When ligand is absent, tyrosine kinase receptors are in an unphosphorylated and monomeric form with their kinase domain inactived. Once receptor tyrosine kinases are bound and activated by ligand, the receptors will oligomerize; the autoinhibitory effect is disrupted, leading to autophosphorylation of a regulatory tyrosine and generation of intracellular binding sites for signaling proteins. This process recruits the signaling proteins to the membrane and triggers multiple downstream signaling pathways.
In conditions like cancer, the dysregulation of tyrosine kinases and tyrosine kinase receptors is very common. For example, mutation in the kinase domain of epidermal growth factor receptor (EGFR) increases the receptor’s sensitivity to ligand and distorts receptor signaling in small-cell lung cancer [197, 198]. EGFRs are mutated in many types of cancer, leading to continuous activation and amplification of downstream signals, such as
Ras/Raf MAPK and PI-3K/Akt pathways, resulting in oncogenic transformation and cancer cell survival/proliferation [199]. Multiple compounds have been developed to target EGFR family and many are approved for cancer treatment, such as gefitinib, afatinib, cetuximab and erlotinib. Some of these drugs target ErbB1 for treatment of advanced non-small-cell lung cancer or after the failure of chemotherapy [196, 200, 201]. Nevertheless, despite efficacy, the treatment with these inhibitors still results in significant toxicity, which sometimes requires the discontinuation of therapy.
26 Another approach in cancer therapy is monoclonal antibody-based therapies, which have the advantage of a track-record of faster FDA-approval than traditional chemotherapies [202]. Many monoclonal antibody drugs have been developed for cancer treatment, especially for ovarian cancer, mainly targeting VEGF and VEGF receptors, such as bevacizumab, pazopanib, ceriranib and nintedanib [203-206]. In colorectal cancer treatment, cetuximab, a monoclonal antibody against epidermal growth factor receptor
(EGFR), showed significant effects and benefits for patients with K-ras mutations [207,
208]. Monoclonal antibody therapy has been used in combination with chemotherapy and radiotherapy for multiple types of cancers, such as breast, colorectal, lung, head and neck cancers, with increased clinical benefits and improved disease-free survival [209-212].
However, there are still limitations of antibody-based cancer therapy, such as difficulty in penetrating tumor tissues efficiently due to antibodies’ physical properties and pharmacokinetics [213, 214]. Furthermore, tumors can develop resistant mechanisms to antibody treatment, for instance, down-regulation of target proteins or developing compensation mechanisms by activating other survival signaling pathway [215-217].
Finally, since cancer is a heterogenic disease, the possibility to successfully treat it through suppressing a single target, either in target therapy or monoclonal antibody therapy, is not high.
Current treatments for ovarian cancer include debulking surgery usually followed with combination chemotherapy. The standard first-line chemotherapy for advanced disease is carboplatin, alone or in a combination with paclitaxel [7]. However, about 70-
80% of patients will relapse after first-line chemotherapy. For this reason, new treatment strategies for the disease have been considered to overcome this problem [218].
27 Up to 50% of ovarian cancer cases utilize homologous recombination repair for double-stranded DNA damage due to mutations in BRCA genes. In order to recover DNA previously damaged by chemotherapy, cancer cells will rely more heavily on the single- strand break repair pathway, of which, the enzyme poly ADP ribose polymerase (PARP) plays a critical role. With PARP being inhibited, together in patients lacking functional
BRCA1/2 proteins, cells are deprived of both mechanisms to repair DNA damage, resulting to death after chemotherapy. Following this direction, a PARP inhibitor, Olaparib
(Lynparza), has been approved for recurrent ovarian cancer in 2014 [219].
Another approach for specific targeting in combination chemotherapy is inhibiting the process of angiogenesis. In order to grow, tumors need a network of blood vessels to provide them with additional oxygen and nutrients. In some cases, tumor metastasis needs vascular transportation to carry cancer cells to distant sites [220]. The vascular endothelial growth factor (VEGF) is one of the most common pro-angiogenic factors that facilitates the development of vessels by stimulating endothelial cells to form such structures.
On a molecular level, VEGF binds to VEGF receptors on the cell membrane leading to dimerization and auto-phosphorylation of the receptors. The binding triggers several signal transduction pathways, including Akt/PKB, PI3K and Ras, and results in cell survival, migration and proliferation. In adults, VEGF has the highest expression in ovary, heart, placenta, small intestine and the thyroid gland. Epithelia ovarian cancer cells frequently express VEGF, and this expression is directly associated with ascites formation [221].
Disease progression is supported largely by the formation of blood vessels. A significant decrease in the level of VEGF is correlated with a reduction in tumor vascularization and angiogenesis, coupled with prolonged patient survival [222]. For these
28 reasons, targeting factors that regulate angiogenesis, specifically VEGF, is a promising direction in ovarian cancer treatment [223]. Bevacizumab (Avastin), developed by Roche, is a monoclonal antibody targeting VEGF. Bevacizumab binds to VEGF and inhibits its coupling to VEGF receptors, thus preventing the growth of tumor blood vessels. The drug has been approved by the FDA to treat epithelial ovarian cancer patients whose disease does not response to platinum chemotherapy [206]. Bevacizumab is shown to be modestly beneficial when added to carboplatin and paclitaxel chemotherapy in maintenance treatment for patients with residual tumor after primary surgery [224, 225]. Beside bevacizumab, more VEGF and VEGF receptor antibodies have been developed and show positive results in trials with ovarian cancer, such as pazopanib, ceriranib and nintedanib
[203-205].
MicroRNAs for cancer therapy
MiRNAs are known for their ability to silence multiple target genes simultaneously
[119]. This is a benefit of miRNA-based therapy in treatment of cancer as a heterogenic disease, where many oncogenic genes, especially mediators in common cancerous pathways, share similar nucleotide sequences [226]. Also, as small natural nucleotides, miRNAs may induce less immune response and have lower toxicity to the body compared to protein-based drug molecules. Moreover, miRNAs’ characteristics, such as small size, low molecular weight and ionic charge gives the advantage of formulation in a delivery system
[227]. In fact, mimics of antitumor miRNAs and antagonists of oncomiRs have been developed to treat cancers as well as other conditions in vitro and in vivo. Some designs have proceeded into clinical trials, such as MRX34 (miR-34 mimic) for liver cancer by
29 Mirna Therapeutics or Miravirsen (anti-miR-122 agent) for treatment of HCV infection by
Santaris Pharma A/S [123, 228].
Studies on miRNAs’ pharmacokinetics and pharmacodynamics showed that highly water soluble synthetic miRNAs (either mimics or antagonists) are suitable for both intravenous and subcutaneous injections [229]. After intravenous administration, miRNAs distribute widely but are not retained in blood circulation for a long time [229]. Indeed, they accumulate mainly in the liver and kidney, while the concentrations in the brain, heart and lung quickly decline [229]. On the other hand, once inside cells, the synthetic miRNA concentrations remain stable; allowing prolonged therapeutic effects [229]. Multiple strategies for miRNA delivery have been developed; each comes with specific advantages and limitations. Viral delivery of miRNAs is an efficient method to deliver miRNAs in vivo using adenovirus or lentivirus with significant effects [230, 231]. Despite promising results in animal cancer models, viral vectors face challenges for clinical applications due to their immunogenic tendency, potential of viral pathogenetic issues and difficulties in scaled-up industrial manufacturing [227, 232]. In order to avoid these limitations, non-viral delivery systems for miRNA have been proposed in diverse platforms. Inorganic nanoparticles such as gold and silica are demonstrated to have positive anticancer results with low toxicity
[233, 234]. The obstacles for inorganic miRNA delivery systems are low loading efficiency and inefficient endosomal escape [227, 233, 234]. Lipid-based nanocarriers are another appealing option for miRNA delivery due to its advantages in easy chemical modification and availability for tracing by fluorescence [235, 236]. Cationic lipoplex based systems are favorable design since its positive charges help enhance endocytosis through interaction with the negatively charged cell membrane [237, 238]. However, charged lipids are more
30 likely to bind with serum proteins, making it challenging for particles to reach target cells to release loaded miRNAs; thus having longer retention in circulation as well as a higher possibility of triggering an immune response [239-241].
Other approaches include vectors made from polymeric materials, such as polyethylenimine (PEIs), poly(lactide-co-glycolide) (PLGA) and atelocollagen [242-245].
Among them, atelocollagen is the material chosen for miRNA delivery in our study.
Atelocollagen is a cationic protein produced from type-I collagen by pepsin treatment to remove the immunogenic amino and carboxyl terminal peptides [246]. The protein retains nucleic acid through electrostatic interactions to form a complex for sustained delivery.
Furthermore, atelocollagen’s characteristics, staying in liquid form at 4oC and readily turning to gel form at 37oC, facilitate intratumoral injection and prolonged release of miRNAs [246]. Atelocollagen has been studied for delivery of miRNAs in several types of cancer models. MiR-34a in nanoparticle complex with atelocollagen showed a significant inhibition of colon tumor growth in vivo [247]. MiR-516-3p delivered intratumoral with atelocollagen is reported to have potential in suppressing gastric cancer metastasis [248].
In this study, atelocollagen is used as a mediator to deliver miRNA-30c-2-3p intratumorally in a xenograft mouse ovarian cancer model.
31
CHAPTER 2
DICER AND MICRORNAS: ONCOMIRS ARE THE NEXT FRONTIER OF ONCOGENES
AFFECTING CANCER ETIOLOGY AND TUMOR PROGRESSION1
1 H. Nguyen and M. Murph (2013), Oncogenes: Classification, Mechanisms of Activation and Roles in Cancer Development, 85-125. Reprinted here with permission of the publisher
32 Abstract
Recent understanding of oncogene regulation has uncovered an emerging new field of molecular cancer biology in which tumor suppressors and mediators are controlled through RNA regulation. Among the most critical players are microRNA
(miRNA), also referred to as ‘oncomirs’, and Dicer, the major enzyme responsible for cleaving double-stranded RNA and forming the RNA induced silencing complex. These components are aberrantly expressed in cancer and among some tumor types are hypothesized to be causative to the etiology of malignancy. For example, prostate and colorectal cancers express abundantly high levels of Dicer mRNA while lung, ovarian and endometrial cancers express low levels of Dicer, which is believed to correlate to poor cancer prognosis. In addition, mutations of the Dicer encoding gene (DICER1) occur in non-epithelial ovarian cancers and pediatric tumor pleuropulmonary blastoma.
Paradoxically, Dicer is a haploinsufficient tumor suppressor; the loss of a single allele of
DICER1 enhances tumor growth where the loss of the second allele results in halting tumor proliferation. MicroRNA regulates oncogenic signaling pathways as well as the expression of tumor suppressors and oncogenes, making it a major contributor to the overall status of the cell and its malignant potential. The let-7 miRNA family members are well-known as tumor suppressor genes, which target and silence the Ras oncogene.
On the other hand, miRNAs may induce tumor growth; one example is miR-17-19 cluster negatively regulating two tumor suppressor genes, PTEN and Bim (Bcl-2 interacting mediator of cell death). In this chapter, the regulation of oncomirs will be discussed with focus on the post-transcriptional control by miRNA biogenesis machinery, which consists of Dicer as a major player.
33 Introduction
MicroRNAs (miRNAs) are noncoding RNAs 18-25 nucleotides in length, which regulate gene expression by targeting messenger RNAs (mRNAs). Through miRNA binding to mRNA’s complementary base pair sequences, mRNA is then degraded, which ultimately silences gene expression in the cell. Alternatively, gene silencing may also be achieved by translation inhibition without a requirement for exact complementarity. Profiling miRNAs in human cancer showed that miRNA expression differs between normal and cancer tissues, and also between tumor types [249]. MicroRNAs are suggested to have roles in cancer development both as oncogenes as well as tumor suppressors [120, 250]; therefore they are referred as ’oncomirs’. To further complicate the issue, other miRNAs like miR-146 and miR-29 are context-dependent [251], which means they can function as either an oncogene or tumor suppressor, depending on the cell type and the specific regulatory pathways that are either functional or deficient in that system.
MicroRNA and cancer
MicroRNAs regulate mRNA levels by directing the RISC to the 3’-unstranslated region (UTR) of target mRNAs by complementary sequences on miRNAs to achieve gene silencing [119, 252]. The RISC complex contains Ago, an RNase III endonuclease. Although human cells have four Ago proteins (Ago1-4], only Ago2 in the miRNA-RISC complex processes mRNA cleavage [253]. The extent of complementarity between miRNAs and target mRNAs also determines whether the mRNA will be cleaved or just inhibited from translation [119]. A single miRNA can regulate different target genes and one target mRNA can also be regulated by multiple miRNAs [120].
34 Since miRNAs regulate multiple fundamental biological processes, alterations in their normal functioning could result in contributing to the etiology of cancer. As such, research continues to elucidate the roles of miRNAs in tumorigenesis as both tumor suppressors and oncogenes. Analysis of tumor and normal tissues from different types of cancers showed altered expression levels of many miRNAs, either via overexpression or downregulation [249, 254-260]. To date many miRNAs have been identified for their crucial roles in cancer, among them the let-7 family and the miR-17-92 cluster are well studied as tumor suppressors and oncogenes.
MicroRNA-based therapies in cancer
As increasing evidence shows significant regulatory roles of miRNAs, their aberrant expression and function in cancer, miRNAs and their antagomirs have become appealing therapeutic potentials against cancer. There are three principal directions or applications for miRNA therapies. The first application for miRNAs as therapeutics consists of replacing the lost miRNA or using antagomirs as direct targets against malignancy, which means overexpressing antitumor miRNAs and/or repressing oncogenes. Combinations of appropriate miRNAs or antagomirs might increase specificity to the target and reduce the risk for acquired resistance. In order for this approach to be successful at the level of molecular mechanisms, an in-depth knowledge of all the biological targets and regulatory pathways, including the ensuing off-target effects would have to be elucidated for a given miRNA first.
The goal of the second application is to increase drug sensitization of cancer cells through modulation of related miRNAs. This direction is based on the results from multiple labs, which show that despite some miRNAs not displaying significant direct effects on
35 cancer cell survival, their alterations increase cancer cell sensitivity to drug treatment or radio therapy [261-263]. With this approach, a new era in pharmacogenomics would emerge since it is highly likely there would be significant patient variability to drug resistance and miRNA expression. The third application of miRNA therapies is inhibiting specific aberrant functions of cancer cells in a context-dependent fashion (e.g. cancer invasion and migration) through the modulation of specific miRNAs, such as miR-34a, miR-
34b, and miR-21 [264-266]. To sum up, miRNA therapies are attempts to block “bad miRNAs” and increase “good miRNAs” to achieve the desired therapeutic effects.
Since foreign oligonucleotides like synthetic small-interfering RNA (siRNA) molecules in general are subject to degradation by nucleases, many modifications have been developed to increase their stability. At the same time, increases in potency and reductions in toxicity have been improved from early modifications such as phosphorothioate antisense oligonucleotides, 2’-O-methyl or 2’-O-methoxy-ethyl anti-sense oligonucleotides [267-271]. Synthetic anti-sense oligonucleotides, which have complementary sequences to oncogenic miRNAs, are used to inactivate miRNAs at tumor sites. An improvement to these is the cholesterol conjugation of anti-sense oligonucleotides, which are very stable in vivo and improves therapeutic efficacy [272-
274].
Among all the chemical modifications of antisense oligonucleotides, LNA is likely the most common. For example, it is used to create antagomirs for miR-34a and the miR-17-92 cluster in vivo and in vitro [275-278]. Locked nucleic acid (LNA) is defined as an oligonucleotide that contains one or more 2’-O,4’-methylene--D-ribofuranosyl monomer(s) [275]. Furthermore, LNA mediated silencing of miRNA-122 has reached
36 clinical trials in non-human primates [279-281]. It will be intriguing to follow this progress and learn whether or not these modifications are sufficient for the application of miRNA therapy.
Since miRNAs are highly tissue specific, another challenge is to deploy these therapeutics at the appropriate site. In this regards, multiple efforts have been spent on developing carriers to deliver antitumor miRNAs to tumors and even a greater understanding of how miRNAs are transported in biological systems. Interestingly, studies have demonstrated that miRNAs are highly stable and abundant extracellular entities found in circulating plasma and protected from degradation by endogenous RNases, although subject to degradation by proteases [282, 283]. Thus, circulating miRNAs are protected through several mechanisms, including binding and forming a complex with
Argonaute2 [283], secretion into microvesicles from the cell and high-density lipoproteins
[284].
The scenario of the normal regulation of miRNAs in circulation may differ from lessons learned through the usage of foreign siRNAs, which have several challenges during the process to reach targets. First, foreign siRNAs are degraded in plasma by nucleases
[285] (whereas endogenous miRNAs have natural protection). Second, siRNAs are cleared from circulation rapidly through kidney filtration due to their small size [286]. Third, oligonucleotides’ negative charge prevents them from passing through the cellular lipid bilayer, which is impermeable to ions and polar molecules. Fourth, even after cellular internalization, foreign oligonucleotides still need to escape endosomal entrapment and degradation in order to get loaded by the RISC complex and target complimentary mRNAs.
37 To overcome barriers and facilitate therapeutic usage of miRNAs, several delivery systems have been introduced. Viral vector-based systems were successfully tested in murine models of lung and liver cancer [231, 287]. However, because safety and host immune responses are a considerable concern for viral vectors, lipid based delivery systems are a more promising option for clinical use. Using a liposome, which is a lipid bilayer vesicle, has been developed to carry nucleic acids for an unusually long time. The lipid cage allows the liposome to remain in circulation for a much longer time [288]. The particle itself is biodegradable to optimize safety administration to the host body [286].
Moreover, the liposome lipid membrane can be cationized to improve cellular fusion and uptake [289]. Antibody incorporation into the liposomal membrane increases target site specificity, thus reduces undesirable off-target effects [290, 291]. Frther techniques help facilitate liposomes to escape from endosomal engulfment, such as ion-pair formation, the
“proton sponge” effect or de-assembly [286].
Other methods and rising trends in cancer therapy include using nanoparticles, either lipid-based or nonlipid-based, to deliver miRNAs or antagomir oligonucleotides.
Interfering nanoparticles (iNOPs), a lipid nanoparticle with its surface modified with cationic lysines, has succeeded in delivering anti-miR-122 to mouse liver [292, 293].
Nanoparticles coated with cell-penetrating peptides were used to increase the efficiency of anti-miRNA-155 delivery to cancer cells [294]. Gold nanoparticles are another promising delivery system due to reduced toxicity, a favorable ability to penetrate cells, a longer retention in circulation and easiness in biosensing [295-299]. Gold nanoparticles conjugated with miRs or antagomirs have good transfection and delivery results in vitro
[233, 300, 301].
38 MicroRNA biogenesis and degradation
MicroRNAs are transcribed by RNA polymerase II into large RNA precursor primary-miRNA (pri-miRNA) [153, 302]. Similar to mRNAs, pri-miRNAs have caps at the 5’ end and are poly-adenylated at the 3’ end [152]. Pri-miRNAs form stem-loop secondary structures and are processed in the nucleus by Drosha and DGCR8, reducing the structure from hundreds of nucleotides to approximately 70-nucleotide pre-miRNAs [303]. Pre- miRNAs are then exported to the cytoplasm by Exportin-5 (Exp-5) [304]. In the cytoplasm, pre-miRNAs are cleaved by Dicer, with assistance from TARBP2, to generate a 22- nucleotide, double-stranded miRNA-miRNA* duplex, which is also referred to as the miR-
5p/miR-3p duplex [305, 306]. Mature miRNAs and miRNAs* are then separated and miRNAs are loaded into the RNA-induced silencing complex (RISC) while miRNAs* are more often degraded [307, 308].
However, recent publications, including our own, have altered our understanding of the fate of miRNAs* and questioned whether they are always degraded. More work is needed to elucidate the roles and contributions of these so-called “passenger” strands because studies have shown significant effects of miRNAs* in cancer and also suggested they have important regulatory abilities on their own [137, 309, 310]. It is no longer the accepted dogma that miRNAs* are simply non-functional byproducts of the miRNA-miRNA* duplex and have no purpose other than degradation. This is an exciting time in the field of miRNA research because there is still much to uncover in our understanding of these strands and which biological effects they regulate. In particular, are they involved when things go awry and lead to cancer?
39 Let-7 family
Let-7 in Cell Differentiation
Let-7, together with lin-4, was the first known miRNA [311-313]. Let-7 was first identified in the nematode Caenorhabditis elegans as a component involved in early development [311]. In C. elegans, let-7 is important for and highly expressed during the larval-to-adult transition. In this period, a type of C. elegans stem cell called seam cells switches from proliferating in the early larval stage to terminal differentiating [311]. In the absence of functioning let-7, seam cells fail to exit the cell cycle at this transition point, thus continuing to divide instead of differentiating. As a result, the let-7 mutant animals die by bursting through vulvas, and that is the origin of the name let-7, which means lethal-7
[311]. The sequence of let-7 and its temporal expression are conserved in a wide range of animal species, from as simple as C. elegans to as complicated as humans [314]. In zebra fish let-7 is also detected at 48 hours after fertilization, in annelids and mollusks, the time points are adult stages [314]. Later sequencing in mouse and humans extends let-7 to a family of identical mature miRNAs encoded by 13 genomic loci from let-7a to let-7i [315].
Among them, let-7a has its sequence identical across species; while the other members keep the seed sequence but vary in other nucleotides. Current hybridization-based techniques, such as microarray and northern blot, can hardly distinguish closely related miRNAs. Thus, it is a challenge to quantify each single let-7 family member in one sample.
For this reason, in this book chapter we will refer to let-7 as a term to generally describe this whole let-7 family. Let-7 is reportedly absent in embryonic stem cells or pluripotent cells but up-regulated in differentiating cells, such as brain cells, or breast stem-cell progenitors at the differentiating phase [316-320]. Lower expression of let-7 is observed as
40 one feature of certain types of stem cells while overexpression of let-7 seems to reduce the dividing [320]. Let-7 is considered one factor involved in switching cells from the proliferation phase to differentiation phase [321].
Let-7 and Cancer: Ras, HMGA2 and the Cell Cycle
The early evidence of let-7’s role in cancer was the observation that let-7 expression levels are reduced in lung cancer tissues and this event is associated with shorter postoperative survival among patients [322]. Furthermore, let-7 miRNAs are mapped to the frequently deleted chromosomal sites in lung cancer [323]. More importantly, the introduction of let-7 to a low-let-7 lung cancer cell line inhibited cell growth [322].
Based on this information, a later study showed that down regulating Ras is one important mechanism whereby let-7 ceases lung cancer cell growth [324]. Ras is an enzymatic protein at the cell membrane, which is activated through binding to GTP and then triggering a cascade of downstream signaling events, including ERK/MAPK activation
[325]. Ras signaling cascades favor cell proliferation and survival [326, 327]. Mutations in
RAS genes (e.g. HRAS, NRAS and KRAS), which cause increased Ras expression and activity, are reported in a number of different cancers, including pancreatic and colorectal [328-
330]. Similarly, overexpression and increased activity of Ras due to RAS gene mutations occurs in lung cancer [331, 332]. There are multiple let-7 complementary sites in the 3’
UTRs of human RAS genes, allowing let-7 to bind and block the expression of Ras through
Ras mRNA silencing [324]. The antitumor effect of let-7 was proved in vivo when the delivery of let-7 reduced tumor burden in K-Ras G12D, a Ras mutated, human lung cancer xenograft model [333]. Beside Ras, HMGA2 is another oncoprotein regulated by let-7 [334].
HMGA2 is a member of the high mobility group AT-hook (HMGA) family, a family of
41 nonhistone chromatin proteins that act as architectural transcription factors [335]. HMGA2 is involved in the transcription of various genes and is essential for growth during embryonic development [335-337]. HMGA2 is reported to be up-regulated in tumors, such as lung cancers and liposarcomas [337-339]. Taken together, HMGA2 is suggested to be an oncogenic factor in differentiated tissues, which express negligible HMGA2 under normal conditions. Multiple studies have provided evidence of let-7 as a negative regulator of
HMGA2 in cancers [334, 340, 341] Sequencing and reporter assays confirm that the 3’UTR of HMGA2 contains let-7 complementary sites [334, 340]. In fact, removing the let-7 complementary sites in HMGA2 3’UTR causes HMGA2 overexpression and tumorigenesis
[340]. Interestingly, a study on self-renewing-tumor-initiating breast cancer cells showed that increased let-7 levels had negative effects on both Ras and HMGA2, while Ras seemed to be involved in self renewal and HMGA2 was involved in differentiation [342]. However, the antitumor effects of let-7 though Ras and HMGA2 are not always equal. In non-small cell lung cancer, overexpression of let-7 caused a reduction in both Ras and HMGA2, but ectopic expression of Ras showed a reversal of effects to let-7 mediated tumor suppression more so than ectopic expression of HMGA2 [287].
It is predicted that one miRNA strand can target hundreds of mRNAs.
Correspondingly, Let-7 exerts antitumor effects through not only Ras and HMGA2 but also many other targets that are important to the regulation of the cell. For example, a large number of cell-cycle genes have been identified to have let-7 complementary sites [343].
Among them, CDC25A, which is an oncogene, CDK6 and cyclin D1 are confirmed to be directly regulated by let-7 [343-346]. Upsetting the delicate balance of the regulation of the
42 cell cycle could wreak havoc upon normal cells and homeostasis. In some systems these alterations are involved in the etiology of cancer.
Regulation of let-7
Lin28 inhibits the processing of let-7 family members through binding to pri-let-7, then blocks the let-7 precursor from Drosha-DGCR8 cleavage in the nucleus [347, 348].
This binding and blocking process is mediated through the recognition of Lin-28 to several conserved nucleotides shared by let-7 family members, thus it seems to be selective for the let-7 family with little or no effect observed on other miRNAs [347-350]. Another step that
Lin-28 uses to negatively regulate let-7 is inducing the uridylation and recruiting the uridylating enzyme TUT4 to pre-let-7 in the cytoplasm [351-354]. All these modifications block pre-let-7 from being processed by DICER to form mature let-7 and ultimately cause it to be degraded in the cytosol [353]. LIN28 facilitates cellular transformation and is overexpressed in multiple human primary tumor types in concert with let-7 reduction
[355]. LIN28 is believed to be involved in let-7 regulation from the observation that LIN28 expression is high in early developmental stages and low during differentiation, which is reciprocal to let-7 levels [356]. The same reciprocal pattern is also observed in cancer cells
[357, 358]. Furthermore, LIN28 is identified as one of the four genes that together can convert human adult fibroblasts to pluripotent stem cells (the others three names are Oct4,
Nanog and Sox-2) [359]. Even though it is regulated by LIN28, let-7 creates a regulatory loop through direct targeting of LIN28 mRNA 3’UTR [360, 361]. These pieces of evidence suggest that let-7 can escape Lin-28 mediated down-regulation and amplify itself [361,
362].
43 In the process of transforming from somatic cells to pluripotent stem cells, LIN28 can be replaced by Myc [363, 364]. Myc is an oncogenic transcription factor, which induces tumorigenesis through transactivating multiple genes involved in proliferation and survival [365-368]. Interestingly, very similar to LIN28, Myc also inhibits let-7 expression through binding to promoters or sequences upstream of genes encoding the let-7 family and directly repressing transcription [369]. On the other hand, Myc induces LIN28B expression, which in turn further represses let-7 maturation as described above [370].
Again, similar to the case of LIN28, let-7 comes back to directly target and down-regulate
Myc expression [371-375]
MiR-17-92 Family and Organization of miRNA Clusters
The miR-17-92 family of miRNA includes six miRNAs (miR-17, miR-18a, miR-19a, miR-20a, miR-19b-1, and miR92a), which share the same seed sequence, and are encoded tightly in an intron on human chromosome 13 [376, 377]. This cluster has two paralogs: the miR-106b-25 located on human chromosome 7, and the miR-106a-363 located on chromosome X. The miR-106a-363 cluster consists of six miRNAs: miR-106, miR-18b, miR-
20b, miR-19b-2, miR92a-2, and miR-363. The miR-106b-25 cluster consists of three miRNAs: miR-106b, miR-93, and miR-25 [377]. The miR-106b-25 cluster consists of three miRNAs: miR-106b, miR-93, and miR-25 [377]. Fifteen members of the microRNA-17-92 family clusters are grouped into 4 sub-families, in which members share the same seed: miR-17 (miR-17, miR-20a, miR-20b, miR-106a, miR106b, and miR-93), miR-18 (miR-18a and miR-18b), miR-19(miR-19a and miR-19b), and miR-92 (miR-25, miR-92a, and miR363)
[377, 378].
44 Table 2.1. miRNAs associated with cancer and their known targets OC- Oncogene, TS- Tumor Suppressor.
miRNA Cancer association Function Targets References miR-15a B-cell lymphocytic TS BCL2, MCL1, CCND1, [379-381] miR-16-1 leukemia WNT3A miR-122 Breast, liver cancer TS ADAM17, IGF1R [382-384] miR-143 Pancreatic, colorectal, TS GEF1, GEF2, Ras [385-387] prostate cancer miR-144 Colorectal cancer TS mTOR [388] miR-155 Leukemia OC HDAC4 [389]
Let-7 Lung cancer TS HMGA2, Ras [333, 334] family miR-21 Colorectal and lung OC PTEN [267, 390] cancer miR-27a Acute leukemia TS 4-3-3 theta [391] miR-17- Lung cancer OC PTEN, Bim, CDKN1A [392-394] 19 family (p21Waf1/Cip1), E2F miR- Breast cancer, OC Smad7 [395, 396] 106b-25 Prostate cancer p21 miR-182 Ovarian cancer OC BRCA1, HMGA2, FOXO3 [397] miR-200c Ovarian cancer, breast TS TUBB3 (class III beta- [398, 399] cancer tubulin gene), TrkB, NFkB miR-23b Prostate cancer TS Src kinase, Akt [400] miR-218 Cancer metastasis to OC/TS Wnt inhibitors: SOST [401, 402] bone (Sclerostin), Dikkopf2 Gastric cancer (DKK2), SFRP2 (Secreted frizzled-related protein2) Robo1 receptor miR-34c Resistance to OC Bmf (Bcl-2-modifying [403]
45 Table 2.1. miRNAs associated with cancer and their known targets OC- Oncogene, TS- Tumor Suppressor.
apoptosis induced by factor), Myc paclitaxel in lung cancer miR-302- Cervical cancer TS CyclinD1, Akt1, indirectly [404] 367 up-regulate p271, p21 cluster
Regulation of the miR-17-92 family and its roles in cancer
The miR-17-92 family is potential oncogenes: studies showed that this cluster facilitates tumor development in a mouse B-cell lymphoma model [405]. The oncogenic effects of this family are confirmed in many other types of cancers, such as lung, colorectal, liver and thyroid cancer [406-410]. Studies showed that the miR-17-92 family induces cancer through multiple mechanisms. For example, they induce proliferation through repression of Bim, a proapoptotic protein, and PTEN, a tumor suppressor [394]. High expression of miR-17-92 inhibits hypoxia-induced apoptosis because the key transcription factor involved in this process, hypoxia-inducible factor 1α (HIF-1α), is a direct target [409,
411]. MiR-17-92, and miR106b down-regulate CDKN1A (p21Waf1/Cip1), a well-known tumor suppressor, which stops cell proliferation by suspending the cell-cycle progression
[392, 412]. Myc is the factor that transactivates the cluster through binding to the promoter regions [413]. The E2F family of transcription factors also up-regulate miR-17-92 expression through direct binding to its promoter [393, 414]. In an interesting twist, miR-
17 and miR-20 come back to inhibit translation of E2F1, E2F2, and E2F3 [393, 413, 414].
46 Causes of alterations in miRNA expression
Chromosomal Instability
Large portions of miRNAs are located at cancer-associated genomic regions. These regions contain oncogenes, tumor-suppressor genes and fragile sites, which are sensitive to sister-chromatid exchange, translocation, deletion and amplification. MicroRNA loci are also prone to have alterations in human cancers [415]. Furthermore, multiple miRNAs with roles in cancer are located in cancer fragile regions. For example, the cluster miR-17-92 is located at chromosome 13q31, a region amplified in B-cell lymphomas, malignant lymphomas and lung cancers [405, 406, 416].
Epigenetic Regulations
MicroRNAs are also subject to epigenetic regulations, processes which can be involved in tumorigenesis. Epigenetic alterations are changes in gene expression caused by factors other than DNA coding, including DNA methylation, histone modifications and miRNA regulation. Multiple genes encoding miRNAs are methylated in cancer progression, such as multiple myeloma and chronic lymphocytic leukemia [417, 418]. Histone modification is also reported to have an essential role in miRNA regulation in hepatocellular carcinoma [419]. Furthermore, simultaneous inhibition of DNA methylation and histone deacetylation with a combination of 5-aza-2’-deoxycytidine and 4- phenylbutyric acid (PBA) significantly changes miRNA expression in bladder cancer cells
[420].
Abnormalities in miRNA Biogenesis Machineries
Since miRNAs regulate many crucial pathways to maintain normal biological functions, the regulation of miRNAs themselves is strictly controlled. After transcription,
47 pri-miRNAs need to go through multiple steps in order to transform into mature and fully functional miRNAs, which can target and silence complementary mRNAs. MicroRNA microbiogenesis is a complex process involving multiple proteins, any alteration in such factors may lead to changes in miRNAs expression and furthermore, cancer.
Drosha
Drosha expression in cancer is controversial. In triple-negative breast cancer tissue, the expression of Drosha is reported to be significantly higher than in normal breast tissue
[421]. The same pattern is also observed in ovarian serous carcinoma [422]. However, in another clinical study, Drosha mRNA and protein expression were shown to be reduced in epithelial ovarian cancer specimens [423] and nasopharyngeal carcinoma [424]. BRCA1 regulates miRNA biogenesis through interaction with Drosha [425]. The alterations of
Drosha in cancer are usually coupled with changes in overall miRNA expression. However, there is not yet any report showing a correlation between pathological Drosha expression and any specific miRNA(s).
DGCR8
In order for Drosha to process pri-miRNA, it needs to form a complex with DGCR8, a double-stranded RNA binding protein [424, 426]. DGCR8 is encoded by a gene located in a region frequently deleted in DiGeorge syndrome, the most common genetic deletion syndrome in humans [427]. DGCR8 binds pri-miRNAs while Drosha cleaves them and the two proteins interact with each other and form a so-called microprocessor complex [428].
Interestingly, while DGCR8 is known to stabilize Drosha through protein-protein interactions, knockdown of Drosha increases both DGCR8 mRNA and protein levels in cells
48 [428, 429]. This could be interpreted as a mechanism cells use to maintain certain levels of these important proteins in the miRNA biogenesis complex.
Exportin-5
After being processed by the microprocessor complex, pre-miRNAs are transported from the nucleus to the cytoplasm by Exportin-5, a nucleocytoplasmic transport factor. In order to transport pre-miRNAs, Exportin-5 creates a complex with RanGTP and migrates from the nucleus to the cytoplasm. In the cytoplasm, pre-miRNA is released when RanGTP is hydrolyzed to RanGDP, which will be transported back to the nucleus with Exportin-5
[430-433]. Exportin-5 is reported to have tumor suppressor features since cancer cells have a genetic defect in Exportin-5, leading to the accumulation of pre-miRNAs in the nucleus [434].
Dicer
a) The Structure of DICER
DICER is an endonuclease III that cleaves pre-miRNA into its final products, miRNA and miRNA*. The protein structure of DICER from N-terminal to C-terminal includes an
ATPase/helicase domain, DUF283 domain, PAZ domain, RNAseIIIa and RNAaseIIIb domain, and dsRBD domain (Figure 1a). The ATPase/helicase domain helps DICER to differentiate
RNA substrates through binding to their terminal loops. Deletion of the ATPase/helicase domain leads to equal enzyme activity on both pre-miRNA and dsRNA substrates, while wild-type DICER has favorable activity toward pre-miRNA [435].
DUF283 is a function unknown domain [436]. The PAZ domain anchors the ds-RNA end for RNAse cleavage; therefore, the distance between the PAZ domain and the active sites of the RNAse III domains determines the size of DICER products [435].
49 Besides that, the dsRBD domain in DICER is required for dsRNA binding only when the PAZ domain is absent [435]. The RNAse IIIb domain is required for miRNA cleavage while the RNAse IIIa domain is preferred for miRNA* cleavage [437, 438] (Figure 1b).
Figure 2.1. The schematic structure of the human DICER protein.
b) DICER in Early Development
DICER is essential for development in the early stages [439, 440]. The deletion of
DICER can lead to deficient and abnormal sperm cell formation. [441]. Similarly, DICER has critical roles in meiosis of the female germline [442]. DICER null oocytes appear to have non-efficient chromosome-microtubule engagement, triggering a spindle checkpoint and delayed arrest, which results in a failure in mitotic chromosome segregation [442]. DICER heterozygous mutant mice embryos are not viable [439]. Elimination of DICER during embryogenesis causes perinatal death with loss of skeletal muscle mass and abnormal myofiber morphology, thus arresting embryonic development before the body plan is configured [439, 443]. Heterozygous mutation of DICER leads to small and abnormal
50 embryonic morphology [439]. More interestingly, DICER mutant embryos have reduced
Oct4 expression, which is one of key components to maintain embryonic stem cell proliferation [359, 439]. DICER is also important for the later stages of development. It plays crucial roles in the development of lung epithelial morphogenesis and the normal maintenance of the mature pancreas [444, 445]. Deletion of DICER in somatic cells such as ovarian granulosa cells, mesenchyme-derived cells of the oviducts and uterus resulted in female sterility and multiple reproductive defects [446].
c) The Role of DICER in Cancer
Alterations of DICER expression are observed in multiple genetic diseases. For example, “DICER1 syndrome” is a term used to describe familial pleuropulmonary blastoma, a rare malignant lung tumor that occurs in children under age of six [447].
Results from sequencing of DNA from a large amount tumors show that germ-line mutations in the DICER gene are the main causes of pleuropulmonary blastoma. Besides that, mutations of DICER may lead to different types of tumors, preferentially cystic nephroma and ovarian Sertoli-Leydig tumors [447]. Germline mutation of DICER1, the gene encoding DICER, plays an important role in Embryonal rhabdomyosarcoma, the most common childhood soft tissue sarcoma and its somatic mutation may have certain influence on the cause of the disease [448].
DICER is a haploinsufficient tumor suppressor, which means the deletion of one
DICER allele leads to tumorigenesis while complete loss of DICER suppresses tumors [449].
Mouse models of human cancers show that deletion of one DICER copy reduces survival compared to controls and DICER deleted animals. In a DICER conditional knockout mouse model of lung cancer, tumor progression showed selection against complete DICER
51 recombination, which means growing tumors consisted mainly of DICER haplodeficient cells instead of DICER wholly deficient cells. On the other hand, forced complete deletion of
DICER led to a significant decrease in tumor burden compared to tumors where one DICER allele remained (Figure 2.2). Surveys of human tumors also showed frequent loss of one allele of the gene encoding DICER in several tumor types, including breast, kidney, liver, lung, ovarian, pancreas and stomach [449]. The expression of DICER is altered in many types of cancer. In epithelial ovarian cancer, low DICER mRNA expression is recorded in the majority of specimens [423].
Figure 2.2. Haploinsufficiency of DICER in cancer.
Reduction of DICER is also correlated to advanced tumor stage, poor prognosis and lower survival [423]. Low DICER reduces the cells’ ability to process siRNAs [423]. DNA sequencing of DICER showed several mutations from tumor samples but there is not enough evidence to prove that DICER mutations are associated with the reduction of DICER mRNA expression [423]. In endometrioid endometrial cancer, lower DICER transcription is
52 associated with disease recurrence and worse disease free survival [450]. The cause of low
DICER expression in this type of cancer was neither gene deletion nor DNA methylation and still remains unknown [450]. A study on chronic lymphocytic leukemia, the most common leukemia, showed that lower DICER expression is associated with more aggressive cancer stages based on Binet clarification and higher prognostic factors, such as
CD38 and ZAP-70 [451]. Furthermore, a reduction of DICER is also believed to be one indicator of shorter overall survival and lower disease free survival [451].
DICER is over-expressed in many human breast cancer cells but the pattern is not consistent across all breast cancer subtypes [452]. High expression of DICER increases breast cancer resistant protein, which effluxes tamoxifen from cells and increases resistance to tamoxifen treatment [452]. DICER expression is reported to be higher in triple-negative breast cancer, another type of breast cancer with very poor prognosis due in part to its absence of drug targets (e.g. ER, PR and HER2 receptors) [421]. Interestingly, high concentrations of DICER protein in triple-negative breast cancer samples are detected in the nuclear compartment while in normal breast tissue it is located mainly in the cytosol
[421]. Thus, it is hypothesized that in triple-negative breast cancer, there are changes in the subcellular localization mechanisms of DICER [421]. On the other hand, DICER tends to be down-regulated in the non-luminal (ER negative) subgroup of breast cancer through correlations with high histological grade, lack of Bcl-2, high proliferation and expression of basal-like markers [453]. Basal-like breast cancer is associated with high grade, poor prognosis and younger patient age, and is identified by specific markers, such as EGFR, CKs,
CAV1, CAV2 and nestin [454, 455]. The loss of DICER is linked with breast cancer malignancy and a possible cause of miRNAs down-regulation in breast cancer [456, 457].
53 However, low DICER expression does not affect the outcomes of breast cancer adjuvant anthracyclin-based therapy [453]. In lung cancer, DICER is reduced in invasive adenocarcinoma but over-expressed in non-invasive precursor lesions (atypical adenomatous hyperplasia and bronchioloalveolar carcinoma) areas [458]. It is suggested that the up-regulation of DICER can be an early sign of lung peripheral adenocarcinomas
[458]. In another lung cancer study, reduced expression of DICER was reported to be associated with poor prognosis and a shorter postoperative survival [459]. The cause of
DICER reduction was not known but it appears to be another mechanism than DNA methylation in the promoter region of DICER [459].
Table 2.2. DICER alterations in cancers
Types of cancer Regulation of References DICER
Ovarian Down [423]
Endometrial Down [450]
Breast Down/Up [421, 452, 453]
Leukemia Down [451]
Lung Down [458, 459]
Colorectal Up [460]
Prostate Up [461]
Melanoma Up [462]
In contrast, in certain types of cancers, such as colorectal cancer, high expression of
DICER is associated with poor overall survival and low disease free survival [460]. Since a decrease in overall miRNAs is believed to happen frequently in more aggressive tumors,
54 dysfunction of alternative miRNA processing pathways might be an explanation for the occurrence of up-regulated DICER in certain types of cancer [460, 463-465]. In prostate adenocarcinoma, overall miRNAs expression is increased and DICER levels increase as a correlation with disease stage, lymph node status and Gleason score [461]. Since it is believed that luminal cells tend to be the origin of prostate cancer, the observance that
DICER is overexpressed in neoplastic luminal prostate cells suggested that DICER might be an early sign of cancer development, probably facilitating the overall up-regulation of miRNAs [461, 466]. Also in cutaneous melanoma there is an up-regulation of DICER mRNA in tumor cells, especially metastatic melanoma specimens, which suggests that DICER overexpression may be a biomarker of cutaneous metastatic melanoma [462].
d) Regulation of DICER
Currently it is still unknown exactly what or how DICER is regulated. However, evidence suggests that the change in DICER expression levels could be a consequence of feedback loops in miRNA processing machinery. MicroRNAs are important factors that regulate DICER. For example, let-7 directly down-regulates DICER in multiple normal and cancer cell lines, both at the mRNA and protein level [467]. Sequencing results showed that members of the let-7 family are complementary to the 3’ UTR region of DICER [467]. In fact, DICER regulation is considered an intermediate step for let-7 to regulate other miRNA expression [467]. This result requires further investigation on whether let-7 acts as tumor suppressor gene through DICER down-regulation.
Another miRNA family that directly targets DICER is the miR-103/107 family [468].
Similar to the let-7 family, miR-103/107 family directly targets DICER and by doing that, it attenuates miRNA biosynthesis [468]. In contrast to the let-7 family, the miR-103/107
55 family is a biomarker for worse prognosis in breast cancer [468]. Since they are oncogenic miRs, DICER down-regulation is an intermediate step before these miRNAs are capable of exerting oncogenic effects. For example, miR-103/107 can trigger the events of metastasis or epithelial to mesenchymal transition (EMT) [468]. In others words, in spite of the complicated expression pattern of DICER in breast cancer, it is the miR-103/107 family, not
DICER, that should be targeted for cancer therapy.
Exportin-5, the factor responsible for miRNA transport across the nuclear membrane, also regulates DICER post-transcriptionally [469]. Inhibition of Exportin-5 leads to the accumulation of DICER mRNA in the nucleus and ultimately the reduction of cytosolic DICER mRNA and functional DICER protein [469]. Furthermore, an increase in pre-miRNA saturates Exportin-5, thus preventing DICER mRNA transport to the cytoplasm for translation [469]. This event is suggested to be a cross-regulation mechanism in miRNA biosynthesis with the purpose of maintaining miRNA homeostasis inside the cells [469].
Beyond miRNA processing machineries, several different factors are shown to regulate DICER. One study of multiple cell lines with different DICER expression levels showed that DICER protein is repressed by reactive oxygen species, phorbol esters, the Ras oncogene, Type 1 interferon and double-stranded RNAs [470]. These data suggest that
DICER has a role in the cellular stress response and proposed that interferons are regulators of DICER proteins [470]. Metformin, a medication for diabetes, increases DICER mRNA and thus protein expression levels [471]. Indeed, metformin enhances the binding of transcription factor E2F3 and inhibits the transcriptional repressor E2F5 at the promoter of the DICER gene [471]. Metformin exerts its anticancer effects by up-regulating DICER and multiple miRNAs, many of which are involved in metabolism [471].
56 Interestingly, a recent study reported that DICER has other functions beyond miRNA microbiogenesis. In fact, DICER processes transcripts from RD2 Alu repeats into small
RNAs (28-65nt), which will target critical stem-cell RNAs, including Nanog mRNA [472].
These data suggest that our understanding of this multifaceted protein is incomplete; further research is needed to identify the complex nature of DICER.
Argonautes
After being processed by DICER, the miRNA:miRNA* duplex is loaded into the RISC
(RNA-induced silencing complex) [473-475]. MicroRNA* is primarily released (although in some systems it may exert significant biological effects) while miRNA is used to target mRNA for cleavage [476, 477]. Argonaute proteins (AGO1-4) are essential components of the RISC complex. Even though all four AGOs can repress mRNA expression, only AGO2 plays an essential role in mRNA cleavage [478, 479]. AGO1 and AGO2 are shown to up- regulated in serous ovarian cancer [422]. Furthermore, promoting expression and activation of AGO2 has positive effects on cancer by enhancing multiple tumor-suppressive miRNAs [480].
57
CHAPTER 3
MIR-30C-2-3P AND DICER IN OVARIAN CANCER
Abstract
Background: Dicer, an RNAase that processes pre-miRNAs into mature miRNAs, is known to have roles in multiple types of cancers, especially ovarian cancer. Metformin, a guanidine drug used in diabetes treatment, is documented to have potential effects on gynecological cancers and induces Dicer expression. miR-30c-2-3p is a microRNA modulated by lysophosphatidic acid (LPA) specifically in ovarian cancer. The purpose of this project is to examine the link between Dicer and miR-30c-2-3p in an ovarian cancer context.
Results: Quantitative PCR shows that LPA treatment does not alter Dicer mRNA expression in ovarian cancer cells but knocking down Dicer significantly reduces the expression of pri- miR-30c, miR-30c-2-5p and miR-30c-2-3p. Metformin, on the other hand, slightly reduces
Dicer mRNA expression but does not significantly change miR-30c-2-3p level.
Conclusion and future directions: There is no direct evidence showing that dicer mediates the signaling from LPA to miR-30c-2-3p expression in ovarian cancer cells. Likewise, metformin does not show significant inducing effects on Dicer in the ovarian cancer context.
Introduction
Dicer is an RNAase enzyme that plays a crucial role in microRNA biogenesis by processing miRNA precursor hairpins (pre-miRNAs) into mature miRNAs. The deletion of
Dicer and Pten genes induces high-grade serous ovarian cancer with 100% mortality in
58 mice [481]. Dicer repression is linked to an increase in proliferation, migration and cell cycle progression in ovarian cancer [482]. The depletion of Dicer decreases ovarian cancer cell sensitivity to cisplatin treatment [482]. Dicer and Drosha are down regulated in hypoxic conditions, leading to dysregulation of miRNA biogenesis and progression of ovarian tumor [483]. A similar phenomenon is observed in breast cancer. In this condition, hypoxia inhibits demethylation and silences Dicer, leading to miRNA biogenesis impairment, stem cell phenotypes and poor prognosis [484]. Overexpressed and nonfunctional p53 are frequent mutations often observed in serous ovarian cancer [485].
Interestingly, in a p53-mutated human keratinocyte cell line, Dicer silencing results in cell cycle arrest at G1 phase, facilitates pro-apoptotic signals and sensitizes cells to 5-fluoro- uracil chemotherapy [486]. A study on human tissues showed that the expression level of
Dicer is significantly lower in cervical cancer specimens compared to corresponding normal tissues, and this lower expression is reported to be associated with tumor stage and metastasis [487]. Dicer mRNA is documented to be up-regulated in tumor progression of serous ovarian cancer [488].
Ovarian cancer is associated with height and, among never-users of hormone therapy, with high body mass index (≥30) [489]. The disease is also believed to have some correlation with diabetes since diabetes worsens the survival of epithelial ovarian cancer patients, though this association is independent of obesity [490]. Metformin is a biguanide drug commonly used for diabetes treatment through inducing AMP-actived protein kinase
(AMPK) activation [491]. Additionally, this effect is reported to inhibit breast cancer cell growth and inactivate mTOR [492, 493]. Furthermore, multiple studies have documented metformin’s potential in the treatment and prevention of gynecologic cancers, including
59 endometrial and ovarian cancer [494, 495]. There is evidence indicating that metformin treatment induces Dicer expression in cancer cells, suggesting a link between metformin and microRNAs [496]. A recent study on ovarian cancer cell lines A2780 and TaraR127 indicated an indirect link between metformin and a miRNA, let-7, via H19. H19, a long non- coding RNA with multiple let-7 binding sites, acts like a molecular sponge to absorb and deactivate let-7. As a result, as H19 increases, circulating let-7 decreases, leading to an increased in let-7’s metastasis-promoting target genes, such as HMGA2, c-Myc and
IGF2BP3. Metformin facilitates the methylation of the H19 promoter and silences this gene’s expression, inhibiting cell growth and migration [497].
Previous results in our lab showed that treatment of LPA on the ovarian cancer cell lines SKOV-3 and OVCAR-3 dramatically induced the expression of miR-30c-2-3p, a passenger miRNA but kept the major strand, miR-30c-2-5p, levels unchanged [137]. Since
Dicer has a critical role in producing mature miRNAs, both major and passenger strands, there is a possibility that the enzyme is involved in the modulating process of miR-30c-2-3p initiated by LPA. In this project, we examine the correlation between Dicer and miR-30c-2-
3p regulation in ovarian cancer. The data can help explore the correlation between miRNA biogenesis and ovarian cancer pathology.
Methods
Cell culture and reagents: Ovarian cancer cell line, SKOV-3 was obtained from the
American Type Culture Collection (ATCC, Manassas, VA). All cells were maintained at 37oC,
5%CO2. SKOV-3 cells were cultured in Dulbecco’s Modification of Eagles’s medium
(DMEM); HeyA8 cells were cultured in RPMI medium (Corning cellgro, Mediatech,
60 Manassas, VA). Media were supplemented with 10% fetal bovine serum (Atlanta biological,
Flowery Branch, GA).
LPA (18:1, 1 oleoyl-2-hydroxy-sn-glycero-3-phosphate) was purchased from Avanti
Polar Lipids Inc. (Alabaster, AL). For LPA treatment, cells were starved from FBS overnight and then treated with serum free media containing LPA reconstituted in 1% charcoal striped FBS. Metformin (1,1-Dimethylbiguanide hydrochloride) was purchased from
Sigma-Aldrich (St. Louis, MO).
SiRNA knockdown of Dicer: SKOV-3 cells were transfected with Dharmacon SMART pools of siRNA (DharmaconRNAi, Lafayette, CO, USA) targeting the Dicer mRNA using
DharmaFECT reagent (DharmaconRNAi, Lafayette, CO, USA) and following the protocol provided by the manufacturer. Control samples were treated with non-targeted siRNA
(DharmaconRNAi, Lafayette, CO, USA). A final concentration of 25nM siRNA was used for siDicer and siLPA1-5, and the expression level of Dicer mRNA was measured 48 hours after transfection.
mRNA isolation and real-time PCR assay : Total mRNAs from cells were isolated using Trizol reagent (Invitrogen, Carlsbad, CA, USA). Total cDNAs and specific cDNAs from miRNAs were generated using iScriptTM cDNA Synthesis kit (BioRad, Hercules, CA). The RT-
PCR Taqman primer set for miR-30c-2-3p was purchased from Life Technologies (Carlsbad,
CA). Realtime PCR was assessed by 7900HT Fast Real-Time PCR system (Applied
Biosystems, Foster City, CA) using Power SYBR Green Real-time PCR Master Mix or Taqman
Gene Expression Master Mix (Applied Biosystems, Foster City, CA). The primers used were based on algorithm-generated sequences from either Primer Bank
(http://pga.mgh.harvard.edu/primerbank) or NCBI Primer-BLAST. The primers used were
61 as followed: Dicer (5’-GAGCTGTCCTATCAGATCAGGG-3’ and 5’-
ACTTGTTGAGCAACCTGGTTT-3’), and 18S (5’- AGAAACGGCTACCACATCCA-3’, 5’-
CCCTCCAATGGATCCTCGTT-3’). The reactions were normalized using 18S as a housekeeping gene.
Results
Effects of LPA treatment on Dicer expression
SKOV-3 cells were treated with LPA at 5μM and total RNAs were collected at 0.5, 1,
6, 12 and 24 hours after treatment for analysis. Experiment results showed that up to one day after LPA treatment, Dicer mRNA expression was not significantly altered compared to control (Figure 3.1). The data suggested that LPA may not be the factor that regulates Dicer transcription in this ovarian cancer cell line within 24-hour time period.
SKOV-3, n=10
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Figure 3.1: Effects of LPA treatment on Dicer mRNA expression in ovarian cancer cells SKOV-3. Cells were treated with LPA at concentration of 5 μM for one hour then total RNAs were collected and quantified by q-PCR.
62 Effects of LPA receptors on Dicer expression
The next experiments were designed to examine LPA receptors’ regulating roles on
Dicer expression. Basal expressions of different LPA receptors were tested on SKOV-3 cells.
Absolute Ct values showed that LPA receptors 1-4 are expressed at relatively high levels in this cell line while LPA receptor 5 expression is relatively low (Table 3.1).
Table 3.1. Absolute Ct values of LPA receptor 1-5 expression in ovarian cancer cells SKOV-3. (Data was kindly provided by Wei Jia).
LPA receptor Average Ct values Standard Deviations
LPR1 22.7 0.9
LPR2 23.1 0.0
LPR3 23.9 0.2
LPR4 26.0 1.0
LPR5 33.1 4.5
To examine the influence of LPA receptors on Dicer expression, we knocked down each receptor individually using siRNAs and then treated SKOV-3 cells with LPA. After one hour of treatment, total RNAs were collected and quantified for Dicer expression. The results show that knocking down LPA receptors 4 and 5 led to a slight increase in Dicer impact after LPA treatment while the absence of other receptors did not show the same
(Figure 3.2). However, as mentioned in Table 3.1, the expression levels of LPA receptor 4 and 5 were relatively low in SKOV-3 ovarian cancer. For this reason, an option to do further investigation on the role of these LPA receptors on Dicer regulation would only be considered if we can obtain solid evidence of the link between Dicer and miR-30c-2-3p in ovarian cancer.
63 SKOV-3, n=6
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Figure 3.2. Effects of LPA receptor knockdown on Dicer expression after LPA treatment. SKOV-3 cells were treated with siRNA specifically targeted LPA receptors 1, 2, 3, 4 or 5. After 48 hours of siRNA treatment, cells were then treated with 5μM of LPA for one hour and total RNAs were collected for Dicer quantification.
Effects of Dicer on miR-30c-2-3p regulation
To test the impact of Dicer on miR-30c-2-3p regulation, we knocked down Dicer
with siDicer and evaluated SKOV3, n=8
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( 0 2 4 6 Time after transfection (days) any significant impact on Figure 3.3: Effects of Dicer knock-down on SKOV-3 cell viability. Ovarian cancer cells SKOV-3 were treated with ovarian cancer cells SKOV- siDicer at concentration of 25 μM for 2, 4 and 6 days. Cell viability after treatment was evaluated by CellTiter-Blue®. 3 viability. At day 6, cells
treated with siDicer
showed some reduction in viability compared to control cells (Figure 3.3).
64 After determining the impact of Dicer absence in ovarian cancer cell survival, we
evaluated the role of Dicer in miR-30c-2-3p biogenesis and regulation. SKOV-3 cells were
treated with 25 μM siDicer for 6 days. Total RNAs was collected every 2 days to evaluate
Dicer (Figure 3.4A), pri-miR-30c 3.(4B), miR-30c-2-5p (3.4C) and miR-30c-2-3p (3.4D)
SKOV3, n=6 SKOV3, n=6
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m 0.0 m 0.0 0 2 4 6 0 2 4 6 Time after siDicer treatment (days) Time after siDicer treatment (days) Figure 3.4: Effect of Dicer knockdown on expression levels of pri-miR-30c, miR-30c-2-5p and miR-30c-2-3p. Ovarian cancer cells SKOV-3 were treated with siDicer at concentration 25 μM. After 2, 4 and 6 days RNAs were collected. The expression of Dicer (A), pri-miR-30c (B), miR- 30c-2-5p (C) and miR-30c-2-3p (D) were quantified to compared with their basal expression levels.
expression levels. The data showed that Dicer was successfully knocked down after day 2
(50%) and maintained about 20% of basal level at day 6. In a similar trend, pri-miR-30c
expression was reduced significantly after day 4 and maintained until day 6. The absence of
Dicer also led to significant reduction of miR-30c-2-5p and miR-30c-2-3p expression levels
at day 2 after siDicer treatment. The impact lasted up to six days post transfection.
65 Metformin and the regulation of Dicer and miRNAs in ovarian cancer
Metformin was reported previously to have anticancer effects on breast cancer
[496]. Blandino and colleagues showed evidence that metformin induces Dicer mRNA and protein expression. This created changes in microRNA expression profiles and ultimately led to a reduction in breast cancer cell numbers. However, in our ovarian cancer model using SKOV-3 cells, treatment of metformin does not reduce the cell number. A range of metformin concentration from 0.5 μM to 8 μM did not decrease SKOV-3 viability after up to
48 hours of treatment. Similarly, a high concentration of metformin at 8 μM, instead of inhibiting, slightly increases cell viability after 4 days of treatment (Figure 3.5).
A SKOV3, n=8 B SKOV-3, n=10 150 Metformin 200 l **
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Figure 3.5: Effects of metformin on ovarian cancer cell viability. Ovarian cancer cells SKOV- 3 were treated with metformin at a range of concentrations from 0.5 to 8 μM. Cell viability was measured after 48 hours by CellTiter-Blue® (A). SKOV-3 cells were treated with metformin at concentration 8 μM for 2, 4 and 6 days and cell viability was measured by CellTiter-Blue® (B).
In contrast to a previous report, in our experiment, metformin treatment reduces
Dicer mRNA expression after 2 to 4 days. Furthermore, metformin treatment in ovarian cancer cells also significantly reduces pri-miR-30c expression levels after 4 days. When tested in another ovarian cancer cell line, HeyA8, treatment of metformin causes significant
66 reduction of pri-miR-30c after 2, 8 and 10 days. However, the overall impact of metformin treatment on miR-30c-2-3p is not significant (Figure 3.6).
A SKOV-3, n=6 B SKOV-3, n=6
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Figure 3.6: The expression of Dicer, pri-miR-30c and miR-30c-2-3p in ovarian cancer cell lines, SKOV-3 and HeyA8, after metformin treatment. SKOV-3 cells were treated with 8μM of metformin, after 2, 4 and 6 days, total RNAs were collected and quantified for Dicer (A) and pri-miR-30c (B) expressions. Similarly, HeyA8 cells were treated with 8μM of metformin, after 2, 4 and 6 days, miRNAs were collected and quantified for pri-miR-30c (C) and miR-30c-2-3p (D) expressions.
Discussion
A previous publication from our lab indicated that treatment of LPA in ovarian cancer cells significantly induces the expression of miR-30c-2-3p, a passenger strand,
67 whereas it does not alter miR-30c-2-5p, the major strand expression [137]. We originally hypothesized that Dicer, an RNAse known for its pivotal role in processing pre-miRNAs into mature miRNAs by cleaving and separating the major strand and passenger strand, might be a mediator of this LPA modulating miR-30c-2-3p process. It is also mentioned in multiple publications that Dicer is a factor that impedes ovarian cancer progression. Our data shows that knocking down Dicer has a insignificant impact on ovarian cancer cells
SKOV-3 viability after 6 days. Overall, treatment of LPA on SKOV-3 cells does not alter Dicer mRNA expression. Knocking down LPA receptors 4 and 5 caused a slight increase in Dicer mRNA expression after LPA treatment. However, due to the relatively low expression of
LPA receptors 4 and 5 in ovarian cancer cells SKOV-3, the potential that Dicer is regulated by LPA receptors 4 and 5 are of limited interest. On the other hand, knocking down Dicer reduces expression of pri-miR-30c, miR-30c-2-5p and miR-30c-2-3p. In contrast, increasing
Dicer expression, may have potential impact on up-regulating miR-30c-2-3p [136].
Metformin is a familial drug used in Type II diabetes treatment through inducing
AMP-actived protein kinase (AMPK) activation, which was recently shown to have antitumor potentials in gynecological cancers [491, 492, 494, 495]. Interestingly, metformin induces Dicer expression and has an indirect role in let-7 regulation in other ovarian cancer cell lines [471, 497]. In this study, our results show that metformin treatment does not affect SKOV-3 ovarian cancer cell viability. The treatment reduces Dicer mRNA expression in this cell line but does not significantly change miR-30c-2-3p expression in HeyA8, another ovarian cancer cell line. This data suggests that metformin may not be an effective treatment in this model of ovarian cancer and substantiates our previous results with Dicer.
68 In conclusion, Dicer is not a regulator of miR-30c-2-3p under LPA stimulation. Since miR-30c-2-3p induction by LPA is a peripheral event, only lasting for about one hour, perhaps miR-30c-2-3p is rapidly secreted in to the extracellular matrix, probably via encapsulation in exosomes. In addition, it is possible that the modulation of miR-30c-2-3p by LPA is mediated by other factors, such as epigenetic modifications or transcription factors.
69
CHAPTER 4
MOLECULAR EPIGENETICS IN THE MANAGEMENT OF OVARIAN CANCER: ARE WE
INVESTIGATING A RATIONAL CLINICAL PROMISE?2
2 Ha T. Nguyen, Geng Tian and Mandi M. Murph (2014), Frontiers in Oncology, 4:71. Reprinted here with permission of publisher.
70 Abstract
Epigenetics is essentially a phenotypical change in gene expression without any alteration of the DNA sequence; the emergence of epigenetics in cancer research and mainstream oncology is fueling new hope. However, it is not yet known whether this knowledge will translate to improved clinical management of ovarian cancer. In this malignancy, women are still undergoing chemotherapy similar to what was approved in
1978, which to this day represents one of the biggest breakthroughs for treating ovarian cancer. Although liquid tumors are benefiting from epigenetically related therapies, solid tumors like ovarian cancer are not (yet?). Herein, we will review the science of molecular epigenetics, especially DNA methylation, histone modifications and microRNA, but also include transcription factors since they, too, are important in ovarian cancer. Pre-clinical and clinical research on the role of epigenetic modifications is also summarized.
Unfortunately, ovarian cancer remains an idiopathic disease, for the most part, and there are many areas of patient management, which could benefit from improved technology.
This review will also highlight the evidence suggesting that epigenetics may have pre- clinical utility in pharmacology and clinical applications for prognosis and diagnosis.
Finally, drugs currently in clinical trials (i.e., histone deacetylase inhibitors) are discussed along with the promise for epigenetics in the exploitation of chemoresistance. Whether epigenetics will ultimately be the answer to better management in ovarian cancer is currently unknown; but we hope so in the future.
Introduction to Epigenetic Modifications
Although genetic alterations, such as gene copy-number variations, contribute to the development of cancer, classical genetics alone does not account for all acquired
71 characteristics of cancer cells. For this reason, it is generally appreciated that epigenetic abnormalities are involved in tumorigenesis. The definition of epigenetics is the potentially permanent and heritable change in gene expression, which is not attributed to any alteration in the underlying DNA sequence, but results from structural adaptations and responsive outcomes on chromosome regions [498, 499]. Epigenetic modifications among cancer cells result in aberrant gene expression via DNA methylation, histone modifications, and non-coding microRNAs (miRNAs) and can also include alterations among transcription factors[500], although the latter is less often emphasized in epigenetics. These modifications are associated with initiation and progression of ovarian cancers (Figure
4.1).
Figure 4.1. Outline of the functional effect resulting from specific epigenetic modifications in malignancy.
DNA methylation is the most frequently studied epigenetic phenomenon. DNA methylation occurs among cytosine residues in cytosine–guanine (CpG) dinucleotides, which are mostly distributed in the CpG-rich regions referred to as “CpG islands” [4, 270,
501]. This type of methylation is achieved by DNA methyltransferases (DNMTs), which are
72 a family of enzymes that serve to transfer methyl groups onto DNA [502]. In humans,
DNMTs are divided into two groups: DNMT1 and DNMT3.
Changes in DNA methylation regulating gene expression are widespread, appearing in both normal and cancerous cells. For example, roughly 80% of CpG dinucleotides in the human genome are subject to methylation changes throughout life. In addition, nearly 70% of all CpG islands are methylated at any given time [503]. Furthermore, in normal cells,
DNA methylation regulates the silenced allele of imprinted genes and also represses expression of potentially harmful DNA transposon sequences [7, 273, 504]. Interestingly, alterations and deregulation of epigenetic events precede the transformation that generates cancer cells [505].
Epigenetic Modifications in Cancer
Among cancer cells, DNA hyper-methylation is associated with gene silencing and
DNA hypo-methylation with gene expression, both of which are widespread characteristics of malignancy (Figure 4.2). Most often, hypermethylated CpG islands within the DNA silence critical tumor suppressor genes, wreak havoc on the cell’s ability to repair DNA damage, control cell growth, and inhibit proliferation. On the other hand, DNA hypo- methylation contributes to oncogenesis when previously silenced oncogenes become transcriptionally activated. In addition, DNA hypo-methylation can activate latent transposons and cause chromosomal instability in specific pericentromeric satellite regions
[506-511].
Histone modifications also play important roles in epigenetic regulation. Histones are dynamic proteins that can become methylated or acetylated on specific amino acid residues, which correlates with active or repressive transcription [512, 513]. An octamer of
73
Figure 4.2. Location of molecular epigenetic mechanisms dynamically affecting gene expression. histones make up the nucleosome, which is the fundamental building-block unit of chromatin. The nucleosome contains lysine-rich histone tails extending outward from the four constituent core histone proteins (H2A, H2B, H3, and H4). These histone tails provide sites for reversible modifications to alter chromatin structure and thus, gene expression. By tightly winding and condensing chromatin or loosening up the structure of chromatin, transcription factors and other proteins are prevented or permitted access to the DNA for transcription, respectively. The target residues of histone modifications are lysine
(acetylation, methylation, and ubiquitination), arginine (methylation), and serine and
74 threonine (phosphorylation). The crosstalk between histone modifications is complicated and varied based on chromosomal domains.
Overall, the combination of histone modifications contributes largely to chromatin pattern and gene expression [514]. In general, histone acetylation adds more negative charges to positive lysine, thus loosening the electrostatic interaction between histones and the DNA backbone. For this reason, the condensation of chromatin is partially regulated by histone deacetylases (HDAC), a class of deacetylating enzymes that remove acetyl groups from lysine residues of histones, ultimately causing the repression of gene expression [515, 516]. If methylation also targets the same lysine residue, which means excluding acetylation, the histone methylation will have the opposite effect, compared to acetylation, and repress gene expression. However, it is not an all-encompassing rule for every single case. In fact, the situation is much more intriguing. Indeed, methylation can block repressive factors and act as a transcription-facilitating element [517].
The extent of methylation status (mono-, di-, and tri-methylation) and other types of histone modifications (phosphorylation or ubiquitination) are involved in a network of sophisticated crosstalk, determining chromatin condensation status [514]. Furthermore, histone H3 phosphorylation is also suggested to interfere with the electrostatic interaction between histones and the DNA backbone, thus favoring transcription factor-induced gene expression [518]. (Other types of histone modifications, such as ubiquitination and
SUMOylation, are not discussed in this review.) To further convolute this process, evidence suggests that regulating gene expression may occur through crosstalk between histone modifications and DNA methylations [519-521].
75 MicroRNAs are small, non-coding RNAs, which are about 18–25 nucleotides in length. They negatively regulate gene expression through complementary binding to the 3′
UTR region in the promoter of targeted mRNAs, leading to mRNA degradation or translational repression, which is dependent on the level of complementarities [119, 522].
Because of their unique functions, miRNAs regulate many biological changes and contribute to cancer progression. For instance, studies comparing miRNA profiles between normal and cancerous specimens identified alterations of multiple miRNA during cancer development and progression [249, 523]. MicroRNAs can have dual roles in cancer progression, as tumor suppressors that repress oncogenes or as tumorigenesis factors that deregulate tumor suppressor genes [524].
Epigenetic Changes in Ovarian Cancer
Role of DNA methylation in the development of malignancy
As previously stated, DNA methylation can prevent the transcription of tumor suppressor genes. Examples of this occurrence in ovarian cancer include the human MutL homolog 1 (hMLH1) and breast cancer susceptibility gene 1 (BRCA1), which are two critical genes that transcribe proteins involved in the DNA damage response and DNA mismatch repair. These processes are critical in maintaining a stable chromosome and fixing damage. In ovarian cancer, the promoter regions of genes encoding these two proteins are hypermethylated, leading to the low expression levels of hMLH1 and BRCA1
[525, 526]. Indeed, women with genetic mutations in BRCA1/2 are susceptible to breast, ovarian, and (sometimes) pancreatic cancer [527] due to this aberration. Among older women with ovarian cancer, tumors are hypermethylated, leading to suppression in transforming growth factor (TGF)-beta pathway activity [528]. Other silenced genes in
76 ovarian cancer include Ras-association domain gene family 1A (RASSF1A), lost on transformation 1 (LOT1), death-associated protein kinase (DAPK), target of methylation- induced silencing (TMS1)/apoptosis speck-like protein containing a CARD (ASC) [529-
532], and insulin-like growth factor binding protein (IGFBP-3) [33, 303, 533]. These genes encode proteins involved in regulation of the cell cycle and the promotion of apoptosis, which are important to maintain homeostasis.
Role of histone modifications in malignant tumorigenesis
Carcinogenesis and tumorigenesis are multifaceted; how normal tissue precisely undergoes stepwise changes to yield ovarian cancer and then how that progresses unregulated by mechanistic controls is largely debated. However, many aspects involved in the progression of ovarian malignancy are reported, including the role of histones in this process. For example, normal epithelial ovarian cells repress the expression of claudin-3 and claudin-4, yet these proteins are highly overexpressed in ovarian cancer. For claudin-3, this change is exclusively attributed to repressive histone marks, whereas claudin-4 repression occurs through histone modifications and DNA methylation [534]. These findings explain the molecular mechanisms of repressive histone marks likely occurring during tumorigenesis; the rationale is that claudins are critical components of tight junctions and other claudin family members comprise gene signatures leading to worsened outcomes in ovarian cancer [80].
Another example of histone modifications affecting cell–cell interactions occurs in the TGF-beta1 receptor. This receptor is an important regulator of cell growth, cell cycle, and it also activates SMAD transcription factors. Interestingly, aberrant signaling of TGF- beta1 receptor results in histone modifications and repressive chromatin in ovarian cancer,
77 which prevents the expression of ADAM19, the protein containing A Disintegrin and A
Metalloprotease [535]. ADAM19 is a metalloproteinase involved in cell–cell interactions and cell adhesion. Taken together, these studies suggest that histone modifications may be important epigenetic events allowing cells to alter contact with their environment.
MicroRNA alterations in the malignant progression of ovarian cancer
In ovarian cancer, miRNAs play a role in malignant progression. Evidence of this comes from reports that 37.1% of the miRNA genomic loci exhibit alterations in DNA copy- number [415]. Other molecular mechanisms of miRNA deregulation include DNA methylation and histone modification of miRNA genes [536]. Many profiling studies performed in ovarian cancer models illuminate abundant alterations. The results show that the miR-200 family and let-7 family are aberrantly regulated (Table 4.1) along with deregulated tumor suppressor miRNAs: miR-15a, miR-34a, and miR-34b [536, 537].
Table 4.1. Alterations in multiple miRNAs among ovarian cancer.
Type of cancer Up-regulated Down-regulated References
Ovarian cancer miR-200 family (miR- miR-199a, [538-541] 200a, miR-140, miR-200b, miR-145 miR-200c, miR-125b1, miR-141, mir-429), miR-100, let-7b miR-214, miR-125b, let-7f miR-21, miR-106b miR-141, miR-134 miR-221, miR-155 miR-146b, miR-346 miR-508 miR-424
In addition to genetically related explanations for miRNA deregulation, there are also changes in regulatory proteins that affect miRNA processing machinery. For example,
78 there is a reduction in the protein expression levels of Dicer and Drosha. These two proteins are essential factors involved in the biogenesis of miRNAs [423]. A loss of one dicer allele facilitates tumorigenesis while a loss of both alleles in lethal to the cell [542].
Furthermore, low expression levels of Dicer and Drosha correlates with poor clinical outcomes [423].
Significant interplay is likely between miRNAs and other molecular epigenetic mechanisms of DNA methylation and histone modifications. For instance, let-7a-3 is hypermethylated in ovarian tumor samples, and the suppression of this miRNA correlates with good prognosis [543]. As another example, the down-regulation of miR-101 will de- repress its target EZH2, which is a catalytic subunit of the polycomb repression complex 2
(PRC2). Because the complex tri-methylates histone H3 lysine 27, its restoration aberrantly silences multiple tumor suppression genes in cancer [538, 544, 545], MiR-140, which targets histone deacetylase 4 (HDAC4), is also reported to be down-regulated in ovarian cancer [538, 546].
Emerging Potential of Epigenetics in the Diagnosis or Prognosis of Ovarian Cancer
DNA Methylation Techniques in Ovarian Cancer Diagnostics and Prognostics
Analysis of DNA methylation status among tumor specimens is the most favored approach for developing a biomarker diagnostic/prognostic due to methylation stability, amplification ability, high-sensitivity, and relatively low cost. In fact, DNA methylation has demonstrated diagnostic and prognostic use in other types of cancers, in particular glioma
[547]. To detect DNA methylation, the simplest approach is to treat cells with DNA methyltransferase inhibitors (DNMTIs). The treatment will reverse the DNA methylation and result in re-expression of genes that were silenced by this mechanism. Comparison of
79 mRNA expression levels before and after treatment will suggest candidates of methylation in cancer, which can be confirmed using additional methods. An alternative approach uses
HpaII, a methylation-sensitive restriction enzyme to digest genomic DNA samples prior to the amplification of digested DNA (using PCR) to compare differences in methylation levels between samples [548]. In a more straightforward way, another method uses an antibody against 5-methylcytosine to precipitate methylated DNA fragments (DNA immunoprecipitation or MetDIP) [549]. Collected genomic DNA fragments are then identified with array-based comparative genome hybridization to reveal human methylome maps [550].
Despite their readiness, array-based DNA methylation analysis approaches provide limited information about the extent and pattern of methylation in specific CpG regions. To overcome this drawback, bisulfite sequencing methods have been developed. Bisulfite treatment converts unmethylated cytosine residues to uracil, while methylated cytosine residues stay intact. After treatment, specific primers are used in methylation-specific PCR to amplify and help differentiate unmethylated and methylated DNA regions [551]. This method is used to identify the difference in DNA methylation profiles in three major types of gynecological cancers: ovarian, endometrial, and cervical cancers [552].
To date, multiple other techniques applying bisulfite treatment have been introduced for whole-genome methylation sequencing and profiling. These include bisulfite padlock probes (BSPP), solution hybrid selection bisulfite sequencing (SHBS-seq), array capture bisulfite sequencing (ACBS-seq), and bisulfite-patch PCR [553-558]; comprehensive information on methylation profiling is reviewed in Ref. [555, 559]. In ovarian cancer, MethylCap-Seq for whole-genome DNA methylation profiling is a method
80 using specific protein to capture methyl-CpG followed by high-throughput sequencing.
MethylCap-Seq has been applied to analyze methylomic patterns of ovarian tumors and results suggest that hedgehog signaling pathway members (ZIC1 and ZIC4) are DNA methylation prognostic biomarkers for ovarian cancer [560, 561].
A Prospective of Histone Modifications in Pharmacology
Although histone modifications lag behind DNA methylation in this potential application, experimental data elude to future emergence for this field. In support of this concept, research indicates that the loss of H3 histone methylation correlates with significantly reduced overall survival in ovarian cancer patients [562]. In cell-based assays, proteomic techniques have been applied to profile expression-level changes, like histone modification enzymes, after treatment with a heat shock protein 90 inhibitor (HSP90).
Maloney et al. suggested that similar analyses might aid pharmacology by illuminating genes and proteins involved in drug responses [563]. Indeed, in ovarian cancer cells, histone de-acetylation at the RGS10-1 promoter correlates with suppression of RGS10 and chemoresistance [564]. This data suggest the possibility of using histone biomarkers to determine the appropriate selection of therapeutics, particularly in cases of ovarian cancer chemoresistance, moving toward “precision” medicine in the clinic [564, 565].
The growing list of experimental techniques to examine histone modifications further alludes to emerging potential. The traditional experimental techniques available include chromatin immune precipitation (ChIP), which uses antibodies specific to acetylated histone H3 and H4 to detect histone acetylation and mass spectrometry-based proteomics to quantify histone modifications [566-571] and screen post-translational modifications among enzymes involved in epigenetic processes, like DNMT and HDAC
81 [571]. Other approaches are required to identify specific DNA sequences paired with modified histones. In this regards, the ChIP assay is coupled with a genomic tiling array
(ChIP-chip) or direct sequencing (ChIP-seq). In these techniques, DNA extracted from ChIP is further processed to reveal the whole sequence, allowing a detailed mapping of histone modifications affecting the whole genome[572].
MiRNA – A Biomarker for Ovarian Cancer?
Regarding the diagnosis of ovarian cancer among unsuspecting patients, there is no early detection biomarker that is used during routine gynecological examinations of otherwise “healthy” individuals. Although there are many candidates and extensive ongoing research for biomarkers of early stage ovarian cancer, biomarkers like CA-125 and
CEA are limited to management of confirmed cases. Since it is always desirable to detect malignancy in the early stages with a minimally invasive method to collect samples, the bar is set very high for this endeavor. In addition, the accuracy requirement of a biomarker in a rare malignancy like ovarian cancer is exceedingly challenging.
In ovarian cancer, it is hypothesized that the detection of miRNAs present in circulation may be able to meet this challenge. The rationale for miRNAs as favored candidate biomarkers is due to the simplicity of obtaining blood samples and high- sensitivity detection methods. In addition, miRNAs are found in circulation within protected exosomes, which are small vesicles released into the extracellular environment from many types of cells, including tumor cells [573, 574]. Interestingly, the transfer of circulating miRNAs among cells is hypothesized to be a method for internal communication within the body, similar to hormones; thus, supporting the idea of a screening approaches involving miRNAs [574, 575].
82 There are many examples from the literature supporting the concept of miRNA biomarkers for ovarian cancer. Studies show that the expression levels of eight miRNAs have prognostic value in ovarian cancer: miR-21, miR-141, miR-200a, miR-200b, miR-200c, miR-203, miR-205, and miR-214 [538]. Another study identified the expression of 21 miRNAs significantly different between ovarian cancer and normal serum specimens, including three known oncogenic miRNAs (or “oncomirs”) with biomarker potential: miR-
21, miR-92, and miR-93 [576]. Additionally, a large study using 300 plasma samples from ovarian cancer patients and 200 healthy controls evaluated circulating miRNAs and concluded that these are stable and specific. In this study, miR-205 and let-7f were significantly reduced in cancer specimens compared to normal controls. Moreover, let-7f has a lower expression level, which correlates with poor prognosis [577]. Finally, another study suggests that among tumors, miR-9 and miR-223 deregulation is a biomarker of recurrent ovarian cancer [578].
Since miRNAs are released by cells into exosomes, studies have examined the viability of using exosomal miRNA as a potential biomarker. In this regard, research successfully used anti-epithelial cell adhesion molecule (EpCAM) to isolate exosomes secreted from ovarian tumors in plasma and compared exosome-containing miRNA expression profiles between samples from cancer patients versus healthy controls [579].
Taylor and Gercel-Taylor intriguingly demonstrated that the level of tumor-derived circulating exosomes is higher in cancer patients than in normal controls. Furthermore, the level of increase correlates with higher disease grade when the exosomes are presented as concentrated protein. Finally, this study also showed that miRNAs profiles between cancer and benign specimens are significantly different [579]. Because of the laboratory success in
83 using these approaches, several groups have suggested the use of miRNAs and/or exosomes as surrogate or complementary biomarkers for biopsy profiling [577, 579, 580].
Epigenetic Therapy in Ovarian Cancer
Exploiting DNA Methylation for Therapeutic Management
Approaches to exploit DNA methylation changes for ovarian cancer therapy
To reiterate, regions of the DNA experience changes in both hyper- and hypo- methylation during cancer initiation and/or progression. In ovarian cancer, data suggest a correlation between global and satellite DNA hypo-methylation with malignancy since an overall increase in hypo-methylation is observed among ovarian cancer tissues, in comparison with normal controls [581, 582]. Furthermore, the phenomenon of satellite
DNA hypo-methylation is an independent marker of poor prognosis [583].
Methylated genes are known in ovarian cancer and exhibit diagnostic potential. A study using methylation-specific PCR to screen ovarian tumor samples for six tumor suppressor genes (BRCA1, RASSF1A, APC, p14ARF, p16INK4a, and DAP-kinase) indicated that this “hyper-methylation panel” provides diagnostic information in ovarian cancer. In addition, this study further suggests that the panel is 82% sensitive and 100% specific for the detection of ovarian cancer using patient serum DNA in stage 1 [584].
The technology also holds potential use for ovarian cancer-specific prognostic information. For example, methylation-specific PCR analysis of tumor tissues from 270 patients identified that IGFBP-3 gene promoter hyper-methylation is associated with a higher risk of disease progression and mortality. Thus, hyper-methylation of IGFBP-3 is hypothesized as a biomarker for ovarian cancer outcomes, especially for patients in early stages of the disease [585].
84 Due to the extensive aberrant DNA methylation in cancer and the reversible nature of these events, inhibition of DNMTs is a worthy therapeutic approach to re-express tumor suppressors. DNMTIs are categorized into nucleoside and non-nucleoside analogs based on their chemical structures and mechanisms of action. DNMT nucleoside inhibitors incorporate into DNA, trap and inactivate DNMTs in the form of a covalent-DNA adduct. On the other hand, non-nucleoside DNMTIs directly block DNMT activity without covalently trapping the enzyme, thus appearing to have less toxicity [586]. 5,6-Dihydro-5-azacytidine
(DHAC) is a nucleoside analog of DNMTI and is in clinical trials for the treatment of ovarian cancer [587]. Hydralazine, a vasodilator that treats hypertension, is also a non-nucleoside
DNMTI in clinical trials for cervical cancer as both monotherapy and combination therapy
[588, 589].
Pre-clinical studies and clinical trials exploiting DNA methylation for re-sensitization
Ovarian cancer is a treatment-intensive disease and clinics are most often re- populated by their own patients. First-line chemotherapy is initially very effective in the treatment of ovarian cancer patients, but the period of remission they achieve is often short-lived. Thus, many approaches seek to re-sensitize tumors to the previously effective drugs. In contrast, others hypothesize that because previous attempts to re-sensitize recurrent ovarian tumors to first-line therapeutics has failed, they suggest that initial combinations of compounds aimed at preventing chemoresistance is the best approach
[590]. However, since neither approach has achieved bona-fide, proof-of-principle, research into both approaches is ongoing. Researchers are evaluating the application of
DNA methylation for chemotherapy re-sensitization. For example, the hyper-methylation of hMLH1 (human MutL homolog 1) inhibits the apoptotic response to platinum-DNA abduct
85 formation from platinum chemotherapy. Thus, this hyper-methylation is considered a major molecular cause of acquired resistance to platinum chemotherapy in ovarian cancer
[591]. In addition, the presence of methylated hMLH1 DNA in plasma after chemotherapy predicts poor survival for ovarian cancer patients [592]. Interestingly, the hMLH1 activity is restored after treatment with 5-aza-2′-deoxycytidine (decitabine) and so is the re- sensitization of ovarian cancer to cisplatin [593].
Another example of DNA methylation and chemotherapy re-sensitization surrounds
RAS-associated domain family protein 1a (RASSF1A). The promoter methylation of
RASSF1A is highly associated with ovarian cancer [529]. RASSF1A binds to tubulin and promotes microtubule polymerization and stabilization [594, 595]. The presence of
RASSF1A blocks genome instability induced by RAS [577, 596]. RASSF1A also causes cell cycle arrest through blocking Cyclin D1 accumulation [597]. For all these reasons, RASSF1A is an interesting target for restoration.
Many pre-clinical studies present evidence that DNMT inhibitors are efficient in de- repressing tumor suppressor genes. This intimates that DNMT inhibitors may have therapeutic potential in combination regimens to overcome resistance and/or provide synergistic effects [598, 599]. For example, decitabine re-sensitizes chemoresistant ovarian tumor xenografts to cisplatin, carboplatin, temozolomide, and epirubicin [593]. Restoration of RASFF1A by inhibiting DNMT also increases ovarian cancer cell sensitivity to paclitaxel
[600].
Indeed, DNMT inhibitors are also showing some success in the clinic (Table 4.2).
Decitabine is undergoing clinical trials with carboplatin for patients with recurrent, platinum-resistant ovarian cancer [601]. A report of a phase II clinical trial of low-dose
86 decitabine combined with carboplatin for heavily treated and platinum-resistant ovarian cancer patients showed positive results. Low-dose decitabine altered the methylation of genes in tumorigenesis pathways, including the demethylation of hMLH1, RASSF1A,
HOXA10, and HOXA11, leading to re-sensitization to carboplatin, increased response rate, and prolonged progression-free survival [602].
Table 4.2. Epigenetic drugs in gynecological cancer trials.
Drugs Other Names Group Types of Disease
Valproic acid HDAC inhibitors Cervial, ovarian cancers
Belinostat HDAC inhibitors Gynecological cancers
Decitabine DNMT inhibitors Ovarian cancer
Hydralazine DNMT inhibitors Ovarian cancer
Dihydro-5-azacytidine DHAC DNMT inhibitors Ovarian cancer Hypo-methylation treatment, on the other hand, due to its non-specific effects, can be detrimental. One known example is the Fanconi anemia (FANC)–BRCA pathway in ovarian cancer. The malfunction of genes in FANC pathway leads to devastating mutagen hypersensitivity [603]. In cancer treatment, the FNAC–BRCA pathway plays a critical role in the response of cells to DNA-crosslinking agents. However, it was observed that, in ovarian cancer, FANC is inactivated due to hyper-methylation, and the demethylation of FANC is associated with ovarian tumor progression and acquired cisplatin resistance [604]. In addition, there are oncogenic genes overexpressed by hypo-methylation in ovarian cancer, such as synuclein-γ and mammalian heparanase (endo-beta-glucuronidase) [605-607].
Histone Modifications: Histone Deacetylase Inhibition in Clinical Trials
Histone deacetylases are enzymes that remove acetyl groups and have long been studied for treatment of cancer, in general, as well as of gynecological cancers, specifically.
87 Although HDAC overexpression occurs in many types of cancers [608, 609], siRNA silencing
HDAC1 and HDAC2 inhibits growth and promotes apoptosis in ovarian and cervical cancer cells [610, 611]. Similarly, HDAC6 facilitates oncogenic transformation in ovarian cancer
[612]. Thus, there is sufficient support for the rational targeting and inhibiting HDAC within the treatment of this malignancy.
Based on their chemical structures, HDAC inhibitors are divided into four majors groups: short-chain fatty acid, hydroxamic acid, cyclic tetrapeptide, and benzamide [613].
For example, valproic acid, a reagent belonging to the short-chain fatty acid group (also known as an anti-epileptic and mood stabilizer) is in clinical trials for the treatment of cervical and ovarian cancers [614-616]. Scriptaid, another HDAC inhibitor in the hydroxamic group, showed growth inhibition and apoptosis-inducing potential in ovarian and endometrial cancers [617]. Apicidin, an HDAC inhibitor in the cyclic tetrapeptide group, is also studied for its anti-growth effects in ovarian and endometrial cancer cells
B126 [618].
The aberrant expression of HDACs in gynecological cancers is likely associated with de novo resistance and/or poor chemotherapeutic efficacy and thus, chemoresistance development. As with nearly all new drugs, HDAC inhibitors are proposed for combination therapy to strengthen therapeutic efficacy as well as to minimize chemoresistance. Valproic acid has been studied in combination with several cytotoxic drugs, such as methotrexate or epirubicin, for synergistic or antagonistic effects in other types of cancer [619, 620].
Belinostat (PDX101), a novel HDAC inhibitor in the hydroxamic acid group, displayed anticancer effects as a single agent as well as in combination by increasing the acetylation of tubulin induced by docetaxel and the phosphorylation of H2AX induced by carboplatin
88 [621]. Belinostat is under phase II clinical trials for gynecological cancer treatment in combination with platinum or paclitaxel to enhance effectiveness and help overcome resistance [622-626]. OSU-HDAC42 (or AR-42), another new short-chain fatty acid HDAC inhibitor, has anti-growth effects on ovarian cancer cells but not normal epithelial cells. The compound re-sensitizes platinum-resisted ovarian tumors in vivo to cisplatin and may have great potential for combinations with platinum agents [627].
Exploiting miRNAs for Re-Sensitization of Chemoresistant Disease
The goal of targeting miRNAs in cancer treatment is to down-regulate oncomirs, to inhibit mRNAs that will become oncogenic proteins, or to restore tumor suppressor miRNAs. Multiple techniques have been developed to target oncomirs, such as locked nucleic acid (LNA), miRNA sponges, miRNA masking, or small-molecule inhibitors [279,
628-630]. On the other hand, the most straightforward way to restore tumor suppressor miRNAs is to deliver pre-miRNA precursors or miRNA mimics. However, straightforward it may appear, it is the targeted delivery of these molecules that represents a major obstacle.
A critical clinical problem in ovarian cancer is chemoresistance. Multiple studies in the field have focused on the roles of miRNAs in overcoming resistance to chemotherapeutic agents. Many miRNAs are reported as expressed differently between chemosensitive and chemoresistant ovarian cell lines, such as miR-30c, miR-130a, miR-
335, among those, miR-130a is confirmed to target resistant factor M-CSF (Table 4.3) [631].
In addition, the enforced expression of miR-30c-2-3p into chemo-resistant and chemoinsensitive ovarian cancer cells significantly reduces their viability, independently of cisplatin or paclitaxel, without affecting immortalized ovarian surface epithelial cells [137].
Although the miR-200 family is a potential prognostic factor for ovarian and endometrial
89 Table 4.3 miRNAs involved in chemoresistance
Trend in Resisted miRNAs Target genes References resistance drugs miR-200 family B-tubulin III TGF- Paclitaxel [632, 633] beta2, ZEB1
Let-7i Reduced Cisplatin [634] miR-30c, miR-130a, Reduced M-CSF (target of Paclitaxel, [631] mir-335 miR-130a) cisplatin miR-214 Increased PTEN Cisplatin [539] miR-27a, miR-21, Increased MDR1 (indirectly Paclitaxel miR-451 through HIPK2, in case of miR-27a)
Let-7g Reduced MDR1 (indirectly Taxane [635] through IMP-1) agents miR-27a, miR-23a, Increased Platinum [636] miR-30c, let-7g, miR- agents 199a-3p, miR-378, miR-625
Let-7a Caspase-3 Paclitaxel [637, 638] miR-130b Decreased CSF-1 Cisplatin, [639] paclitaxel miR-141 Increased KEAP1 Cisplatin [640] miR-106a, miR-591 Increased (miR- BCL-10, caspase-3, Paclitaxel [641] 106a) Decreased ZEB1 (miR-591) miR-29 Decreased COL1A1 Cisplatin [642] miR-182 Increased PDCD4 TCEAL7 Paclitaxel [643, 644]
90 cancer [579, 645], it may have a role in re-sensitization. The low expression of miR-200c in cancer leads to an increase in the expression of its target, class III β-tubulin (TUBB3) [632].
Since the expression of TUBB3 is required for chemoresistance to microtubule-binding agents (e.g., taxanes and vinca alkaloids), restoration of miR-200c down-regulates TUBB3, and effectively re-sensitizes ovarian cancer cells to paclitaxel [632, 633, 646].
In addition to the miR-200 family, several members of the let-7 family are well documented as down-regulated in ovarian cancer, including let-7a, let-7b, let-7c, let-7d, and let-7i [537-541, 634]. Among these, let-7a is a potential biomarker for the selection of chemotherapy in ovarian cancer. Patients with low let-7 showed good response using platinum-paclitaxel combination therapy, while patients with higher let-7a had better survival using platinum without paclitaxel; adding paclitaxel to this group produced worse progression-free and overall survival [637]. The down-regulation of another member of the let-7 family, let-7i, is associated with resistance of ovarian cancer cells to cisplatin, which suggests that let-7i could be used as a therapeutic target to overcome platinum resistance and as a biomarker to predict chemotherapy response in ovarian cancer patients [634].
Another study observed that the let-7 family member, let-7g, down-regulates the multiple drug resistance 1 (MDR1) gene, one of the major factors causing paclitaxel resistance in ovarian cancer [635].
There are numerous other miRNAs that have roles in ovarian cancer chemoresistance with known mechanisms. These include, but are not limited to, miRNAs like miR-214, miR-27a, and miR-451. MiR-214 targets PTEN, a known tumor suppressor, therefore, inducing cell survival and cisplatin resistance [539]. MiR-27a increases MDR1/P- glycoprotein expression in ovarian cancer cells by targeting HIPK2 as an intermediate
91 [647]. Similarly, miR-451 and miR-21 also facilitate MDR1/P-glycoprotein overexpression, leading to paclitaxel resistance in ovarian cancer cells [648, 649].
Targeting Transcription Factors in Ovarian Cancer
Cancer is often a condition with aberrant gene expression, specifically involving the overexpression of oncogenes. Altered transcription factors are recognized as an epigenetic entity comprising the “ovarian cancer cell epigenome” [500]. This is not surprising given the relationship between transcription factors and structural (not sequence) alterations of the DNA (via DNA methylation and histone modifications).
There are numerous examples of aberrant transcription factors in cancer. Perhaps the most prominent of all is the tumor suppressor protein p53. Mutations of TP53, the gene encoding p53, are very common in ovarian cancer [650]. In fact, nearly 100% of patients with high-grade serous epithelial ovarian cancer have mutations in p53. Overall, at least
50% of all ovarian tumors have mutations in p53, most of which are point mutations leading to amino acid substitutions. These are detrimental to the p53 protein because they affect the DNA-binding domain of the transcription factor [651]. Unfortunately, therapeutic intervention using p53 as the target molecule has not yet achieved measurable success
[590].
The transcription factor and tumor suppressor protein p53 are critical to the signaling pathways of cell cycle arrest and apoptosis. Once activated by DNA damage detection or UV radiation, p53 induces the expression of many well-known apoptosis inducers and other tumor suppressors, such as p21Cip1, BAX, PTEN, and TSP-1. Because of this important role, the inactivation of p53 facilitates many phases of tumor progression as
92 DNA damage cannot be repaired and apoptotic pathways cannot be activated when necessary [651].
Besides p53, other transcription factors have important roles in ovarian cancer pathology. For example, Gil1 (glioma-associated oncogene homolog 1) expression is elevated in advanced serous ovarian cancer and this event is correlated with unfavorable survival [652]. Since transcription factor alterations can have a tremendous impact on the balance of the entire biological system, targeting transcription factors is an emerging trend in cancer therapy research. The possibility of exerting broad control over the system could be a powerful method of regaining regulatory control. This is especially true in light of lessons learned in other cancers whereby targeting one particular kinase or protein in a larger signaling pathway leads to the rapid acquisition of therapeutic resistance.
On the other hand, inhibiting particular transcription factors could provide specificity toward malignant overexpression events in cancer (e.g., oncogenes, oncomirs, etc.). Furthermore, this approach is appealing because it might produce more tolerance among healthy cells due to redundancies in normal signaling pathways. Two major approaches in targeting transcription factors are post-transcriptional silencing (using siRNAs or miRNAs) or blocking the binding of transcription factors to DNA during activation. Another indirect approach is regulating histone-modified enzymes and DNA methyltransferase if the target transcription factor is mis-regulated through histone modification and/or DNA methylation.
Many well-known transcription factors are studied as potential targets in general cancer treatment, such as STATs, NF-κB, and Notch1 [653-655]. In gynecological cancers, multiple studies have reported the involvement of transcription factors in cancer
93 progression and described them as potential targets for cancer treatment. In ovarian cancer, the blockage of STAT3 using a decoy oligodeoxynucleotide inhibits cancer cell growth [656]. Another study in ovarian cancer also showed that suppression of NF-κB activity using minocycline, a tetracycline, had beneficial effects both in vitro and in vivo
[657]. More research is needed in this area to refine this approach and evaluate its worthiness.
Conclusion: Epigenetic Therapy
By undertaking research projects focused on epigenetic-related translational applications, are basic scientists investigating a rational clinical promise? To address this question, it is necessary to review the successful progression of ideas from the laboratory into clinic therapeutics. Although no epigenetic drugs have advanced into the clinic for use against ovarian cancer, there are several FDA-approved therapeutics (e.g., vorinostat, decitabine, and romidepsin) for other types of cancer, especially liquid tumors. Clinical trials are ongoing for ovarian cancer with epigenetic therapeutics (Table 2). Since first-line therapy often results in disease remission, predictions support using new drugs in combination therapy. Although hope lingers for PARP inhibitors, this class of drugs may only treat a specific population of women [658]. Whether using epigenetic modifiers will achieve significant improvements in overall survival is incalculable. Nevertheless, to advance patient outcomes in ovarian cancer, new approaches are required – the previous breakthroughs occurred in 1978 (cisplatin) and 1992 (paclitaxel). An improved therapeutic regimen for ovarian cancer is long overdue. Epigenetics provide hope in a new direction.
94
CHAPTER 5
EPIGENETIC REGULATION OF MIR-30C-2-5P
Abstract
Background information: Among the genetic alterations that occur in malignant cancer cells, tumor suppressor genes may be silenced by DNA promoter hypermethylation or histone deacetylation. Genes containing miRNAs are also under the influence of epigenetics affecting their expression. In this study, we tested the hypothesis that epigenetics was a major contributing factor that influenced the expression of miR-30c-2-3p, an anti- proliferative miRNA and putative tumor suppressor, in ovarian cancer cells.
Methods: Ovarian cancer cells, SKOV-3 and HeyA8, were treated with 10nM of 5-aza- deoxycytidine (5-aza-CdR), a DNA methyltransferase inhibitor, and 5μM of 3-
Deazaneplanocin A (DZNeP), a histone methyltransferase inhibitor for long durations up to
10 days. MiRNAs were then collected and quantified by qRT-PCR. The same procedure was also repeated on immortalized ovarian surface epithelial cells, IOSE.
Results: Treatment of anti-methylation reagents, 5-aza-CdR and DZNeP, modestly reduced miR-30c-2-3p expression in SKOV-3 cells. In contrast, these reagents, either alone or in combination, appeared to increase miR-30c-2-3p expression levels in HeyA8 cells; however, the absolute Ct values after treatment were similar to the undetectable threshold.
The trend is similar in normal ovarian epithelial cells, IOSE.
95 Conclusion: The evidence collected nullifies our hypothesis that the major regulator of miR-
30c-2-3p expression is epigenetic modifications. Therefore, our new hypothesis and future direction will seek alternative mechanisms of gene regulation.
Introduction
In normal human cells, most gene-associated CpG islands are maintained in an unmethylated condition. However, this pattern is altered during tumorigenesis due to tumor-specific methylation, which leads to changes in gene expression [659]. Screening from patients’ ovarian cancer tissues indicated the down-regulation of Kruppel-like transcription factor 11 (KLF11) by promoter DNA methylation in tumorigenesis [660]. In addition, DNA methylation, together with histone modifications and miRNAs, is known to play a role in the development of inherent and acquired resistance in ovarian cancer [661].
For example, miR-9 is an independent prognostic factor of epithelial ovarian cancer and the down-regulation of miR-9 by DNA methylation is an important mechanism for paclitaxel resistance via targeting CCNG1 [662]. In some other cases, DNA methylation may have an anti-tumorigenic impact. Using an ovarian cancer cell model A2780, let-7, a known anti- miR targeting metastatic promoting genes, is down-regulated by H19, a long noncoding
RNA, leading to cell migration and invasion. Interestingly, this phenomenon is reversed by treatment with metformin, a diabetes drug, which induces DNA methylation of H19 gene and represses its expression [497].
Evidence from multiple studies indicates that tumor-specific changes in DNA methylation and modifications of histones affect miRNA gene expression. For example, when bladder cancer cells were treated with DNA methyltransferase inhibitor 5-aza-2’- deoxycytidine and histone deacetylase inhibitor 4-phenylbutyric acid, 17 of 313 human
96 miRNAs were up-regulated by more than 3 fold [420]. Another study on colon cancer cells compared miRNA expression profiles between wild-type cells and cells that lack DNA methyltransferases DNMT1 and DNMT3B. The results revealed that 18 of 320 miRNA genes were up-regulated more than 3 fold among mutated cells [663].
Computational screening for predicting promoter sites suggests that 55%-64% of miRNA transcription start sites are associated with a CpG island [664]. In the majority of cases, the mechanism is direct methylation of the CpG islands within the miRNA gene promoter region or modifications of histones adjacent to miRNA coding genes or both
[665-672]. Interestingly, previous research also reported that bivalent chromatin domains on miRNA promoters could be an epigenetic mark for later DNA hypermethylation in human tumors [673]. In addition, indirect epigenetic regulation through factors involved in the regulation of miRNAs is another possibility affecting miRNA expression. For example,
DNA methylation of the regulator H19 leads to an increase in let-7 expression in ovarian cancer cells [497, 674, 675].
In general, if miRNA genes are located within intronic regions their transcription is believed to be initiated together with the host genes. However, the majority of miRNA genes are located in intergenic regions, suggesting that they have independent transcription units [144-148]. Bioinformatic tools have been developed to predict miRNA promoters with several indicators. For example, the core transcriptional regulatory circuitry of embryonic stem cells (Oct4/Sox2/Nanog/Tcf3) was utilized to connect the microRNA transcription initiation and promoter locations [149].
Most miRNA genes are either bona-fide or predicted to be RNA polymerase II- transcribed and the miRNA cluster is reported to be transcribed by RNA polymerase III
97 [150-153]. This suggests traditional RNA polymerase II binding sequences, such as TATA box (5’-TATAAA-3’), can be transcriptional start sites for miRNA genes. Alternatively, trimethylation of histone 3 at lysine 4 (H3K4me3) is proposed in several publications as a marker for promoter regions of common genes and miRNA genes [154, 155]. The majority of miRNA transcription start sites are within 2kb from the gene start site; the further the distance from this point the lower the possibility of locating the miRNA transcription site
[155]. However, most of the miRNA promoters and transcriptional mechanisms described above are computationally predicted. Specific experiments are used to identify the factors involved in miRNA transcription, such as MITF, a melanoma oncogene and master transcriptional regulator of melanocyte development, revealing more transcriptional mechanism possibilities for specific miRNA [155].
MiR-30c-2-3p is a miRNA up-regulated dramatically and significantly by LPA in ovarian cancer cells. The induction of miR-30c-2-3p by LPA is very specific since no other miRNA among the human genome produced such a high fold change during miRNA screening (Taqman Array MicroRNA, Applied Biosystems) [136]. Although the growth factors EGF and PDGF did stimulate an increase in miR-30c-2-3p, it was not significant compared to unstimulated controls; however, this suggests that miR-30c-2-3p is responsive to proliferation-initiating stimuli and growth factor agonists.
After investigating the upstream sequence of miR-30c-2-3p, we found that there is no primary gene encoded up to 5kb in front of this miRNA. In addition, miR-30c-2-3p is not an intronic sequence of another gene. Furthermore, there is a lack of CpG islands appearing in the upstream DNA, which is comprised of >60% A and T sequences, and there is only one
TATA box located about 4.5kb above the miR-30c-2 gene start site. Therefore, we examined
98 the possibility that miR-30c-2-3p is regulated through DNA methylation and histone modifications.
To test the involvement of DNA methylation in miR-30c-2-3p regulation, we used a compound named 5-aza-2-deoxycytidine (5-aza-CdR). 5-aza-CdR induces degradation of
DNA methyltransferases (DNMTs), especially DNMT1, through the proteasomal pathway
[676]. Treatment with 5-aza-CdR will impede DNA methylation and transactivate genes previously inhibited by this epigenetic mechanism.
In order to understand the impact of histone modifications on miR-30c-2-3p, we treated cells with 3-Deazaneplanocin A (DZNeP), a histone methyltransferase inhibitor.
DZNep selectively inhibits trimethylation of lysine 27 of histone H3 (H3K27me3) and lysine
20 of histone H4 (H4K20me3) [677]. DZNeP is also known to degrade EZH2, a core component of the polycomb repressive complex 2 (PRC3), which plays an important role in transcriptional repression [678]. If a gene is repressed by H3K24me3 and/or H4K20me3, treatment of DZNep will reverse this inhibition and transactivate this gene again.
Methods
Cell culture and reagents: The ovarian cancer cell line, SKOV-3 was obtained from
American Type Culture Collection (ATCC, Manassas, VA). All cells were maintained at 370C,
5%CO2. SKOV-3 cells were cultured in Dulbecco’s Modification of Eagles’s medium (DMEM),
HeyA8 cells were cultured in RPMI medium (Corning cellgro, Mediatech, Manassas, VA).
Media were supplemented with 10% fetal bovine serum (Atlanta biological, Flowery
Branch, GA). Immortalized ovarian surface epithelial cells, IOSE, were maintained in 1:1 mixture of medium 199 and MCDB 105 (Sigma-Aldrich, St. Louis, MO).
99 5-aza-CdR was purchased from Sigma-Aldrich (St. Louis, MO). DZNeP was purchased from Cayman Chemical (Ann Arbor, MI). Cells were plated in 6-well plates at a density of 1x105 cells/well. After overnight incubation, cells were then treated with 10nM
5-aza-CdR, or 5μM DZNeP, or a combination of 10nM 5-aza-CdR and 5μM DZNeP.
mRNA isolation and real-time PCR assay : Total mRNAs from cells was isolated using
Trizol reagent (Invitrogen, Carlsbad, CA, USA). Total cDNAs and specific cDNAs from miRNAs were generated using iScriptTM cDNA Synthesis kit (BioRad, Hercules, CA). The RT-
PCR Taqman primer set for miR-30c-2-3p was purchased from Life Technologies (Carlsbad,
CA). Quantitative, real-time PCR was assessed by 7900HT Fast Real-Time PCR system
(Applied Biosystems, Foster City, CA) using Power SYBR Green Real-time PCR Master Mix or Taqman Gene Expression Master Mix (Applied Biosystems, Foster City, CA). The primers used were based on algorithm-generated sequences from either Primer Bank
(http://pga.mgh.harvard.edu/primerbank) or NCBI Primer-BLAST. The primers used were as followed: 18S (5’- AGAAACGGCTACCACATCCA-3’, 5’- CCCTCCAATGGATCCTCGTT-3’). The reactions were normalized using 18S as a housekeeping gene.
Results
The effects of 5-aza-deoxycytidine treatment on miRNA expression were examined using SKOV-3 ovarian cancer cells. After 2, 4 or 6 days of treatment at a concentration of
10 nM of 5-aza-CdR, total RNA was collected and miR-30c-2-5p and miR-30c-2-3p expression levels were evaluated using quantitative RT-PCR (Figure 5.1).
Treatment of 5-aza-CdR slightly increased the major strand, miR-30c-2-5p, expression after 2 days. However, in an unexpected way, 5-aza-CdR reduced miR-30c-2-5p expression after 4 and 6 days of treatment (Figure 5.1A). The pattern is similar in the passenger
100 strand, miR-30c-2-3p (Figure 5.1B). 5-aza-CdR significantly reduced the miR expression level after 4 to 6 days of treatment. These observed results are opposite to the original hypothesis, which had predicted that the ability of 5-aza-CdR to unmethylate DNA would increase gene expression.
miR-30c-2-5p miR-30c-2-3p A SKOV-3 , n=3 B SKOV-3 , n=3
1.5
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m m 0.0 0.0 2 4 6 2 4 6 Time after treatment (days) Time after treatment (days) Figure 5.1: The effects of 5-aza-CdR treatment on expression levels of miR-30c-5p (A) and miR-30c-2-3p (B) in ovarian cancer cells SKOV-3. MiRNAs were isolated after 2, 4 and 6 of 10nM 5-aza-CdR treatment and quantified by q-PCR.
Since there are other epigenetic modifications outside of DNA methylation, we wanted to identify the impact of histone methylation on miR-30c-2-5p and miR-30c-2-3p expression. To measure this, SKOV-3 cells were treated with DZNeP, a histone methyltransferase inhibitor (Figure 5.2). If these genes are repressed by histone methylation, treatment of DZNep will most likely re-express them.
Surprisingly, DZNeP did not create any significant change on miR-30c-2-5p expression (Figure 5.2A). MiR-30c-2-3p expression was slightly reduced after 9 hours of
DZNeP treatment but overall, the inhibition of histone methyltransferase did not have a significant impact on the miR expression (Figure 5.2B).
To confirm the effect of epigenetic modifiers on miR-30c-2-3p expression in ovarian cancer cells, we tested 5-aza-CdR and DZNep on another ovarian cancer cell line, HeyA8, both individually and in combination (Figure 5.3). For these experiments, we also
101 increased the treatment time to 10 days, which may more accurately detect changes resultant from epigenetic modifications that would not otherwise manifest any measurable response within a 3-day period.
A miR-30c-2-5p B miR-30c-2-3p SKOV-3 , n=3 SKOV-3 , n=3
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Figure 5.2: Treatment effects of DZNeP on expression level of miR-30c-2-5p (A) and miR- 30c-2-3p (B) in SKOV-3 ovarian cancer cells. Cells were treated with 5μM DZNeP. After 9, 24, 48 and 72 hours miRNAs were collected and quantified by quantitative RT-PCR.
A miR-30-2-5p B miR-30c-2-3p Hey A8, n=3 HeyA8, n=3
25 10
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Figure 5.3: Treatment effects of 5-aza-CdR and DZNeP on miR-30c-2-5p (A) and miR-30c-2- 3p (B) in ovarian cancer cells HeyA8. Cells were treated with 5-aza-CdR alone (10nM), DZNeP alone (5μM) or a combination of 5-aza-CdR (10nM) and DZNeP (5μM). After 4, 6, 8 and 10 days of treatment, miRNAs were collected and quantified by qPCR.
In contrast with SKOV-3 cells, in HeyA8 cells, both 5-aza-CdR and DZNep increase miR-30c-2-5p and miR-30c-2-3p either in monotherapy or in combination. However, the combination did not have a synergistic effect on re-activating miR genes as expected,
102 probably due to high cytotoxicity of combined treatment [679, 680]. Overall, the anti- methylation impact of 5-aza-CdR on DNA and DZNeP on histones led to the increase of miR-
30c-2-3p expression. However, even though the increase of miR-30c-2-3p is statistically significant, the absolute Ct values before and after treatment are still very low, close to the negligible threshold (Ct=35) (Table 5.1). This indicates that the bar graph reflects changes due to normalized comparisons and calculations, rather than real epigenetic modifications.
In conclusion, epigenetic mechanisms are likely not a major factor that regulates miR-30c-
2-3p expression in ovarian cancer cells.
Table 5.1: Absolute Ct value comparisons of miR-30c-2-3p in different treatments: absolute Ct values of miR-30c-2-3p in ovarian cancer cells HeyA8 after treatment of 5-aza-CdR alone, DZNeP alone and combination of 5-aza-CdR and DZNeP.
Treatment Time (days) Average Ct values Standard Deviation
5-aza-CdR 34.21 0.38
DZNeP 35.45 0.63 6 Combination 34.27 0.36
Control 36.45 0.91
5-aza-CdR 34.31 0.46
DZNeP 35.57 0.08 8 Combination 34.89 0.64
Control 35.99 0.87
5-aza-CdR 35.76 0.39
DZNeP 35.34 0.60 10 Combination 37.67 0.08
Control 35.54 0.27
103 To explore whether ovarian cancer cells have turned off epigenetic regulations that would otherwise result in an increase of miR-30c-2-3p exrepssion, we examined treatment of 5aza-CdR and DZNeP in IOSE immortalized ovarian surface epithelial cells (Figure 5.4).
miR-30c-2-5p miR-30c-2-3p, A IOSE, n=3 B IOSE, n=3
5 4
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Figure 5.4: Effects of 5-aza-CdR and DZNep treatment on miR-30c-2-5p (A) and miR-30c-2- 3p (B) expression levels in normal ovarian epithelial cells IOSE. Cells were treated with 5- aza-CdR (10nM) alone, DZNeP (5μM) alone or a combination of the two compounds, 5-aza- CdR (10nM) and DZNeP (5μM). After 4, 6 and 8 days, miRNAs were collected and quantified by q-PCR.
Exposure to 5-aza-CdR and DZNeP, either individually or in combination, increases gene expression of both miR-30c-2-5p and miR-30c-2-3p after 6 days (Figure 5.4). The combination of the two reagents did not create a significant synergism as expected, probably due to high cytotoxicity [679]. Overall, the data suggest that DNA methylation and histone modifications do not have a major role in regulating miR-30c-2-3p in normal ovarian epithelial cells. We conclude this based on the data showing that inhibition of these factors does not dramatically re-activate this miRNA gene, indicating that epigenetic mechanisms is not the crucial regulator of miR-30c-2-3p expression.
104 Discussion
Epigenetic modifications are widely regarded as important factors that contribute to the regulation of gene expression among normal and malignant cells. In this study, we examined the impact of DNA methylation and histone methylation on the expression of miR-30c-2-3p and its major strand, miR-30c-2-5p, in ovarian cancer and normal cells. The results on the ovarian cancer cell line SKOV-3 were surprising. 5-aza-CdR, a DNA methyltransferase inhibitor reduced both miR-30c-2-3p and miR-30c-2-5p expression.
Since the inhibition of adding methyl groups to CpG islands is normally believed to clear the promoter space, thus facilitating transcription factor binding for gene expression, this effect in SKOV-3 cells was contradictory to what had been expected. However, DNA methylation is known to have different patterns leading to various final impacts on gene expression, which are not only limited to transcriptional repression [681-683]. For instance, Hon and colleagues reported that global DNA hypomethylation is coupled to the formation of repressive chromosomes, mainly due to histone modifications, and ultimately gene repression in breast cancer [681]. In addition, inhibiting histone methylation by treatment of DZNeP on the same cell line did not significantly change miR-30c-2-5p expression. Indeed, it slightly reduced miR-30c-2-3p expression after 9 hours of exposure, but the effect quickly faded.
On the other hand, in HeyA8 cells, exposure to 5-aza-CdR or DZNep or the combination of the two compounds did increase gene expression of miR-30c-2-3p or miR-
30c-2-5p. Nevertheless, miR-30c-2-3p has very low basal expression, in HeyA8 cells. For that reason, even though inhibition of DNA and histone modifications statistically up- regulates miR expression, the absolute Ct value is still close to negligible indicating very
105 low levels. Interestingly, treatment with 5-aza-CdR, DZNeP either alone or in combination in the normal ovarian epithelial cell line, IOSE, showed the same pattern as the HeyA8 ovarian cancer cell line. Exposure to methyltransferase inhibitors did increase miR-30c-2-
3p and miR-30c-2-5p expression levels, although the magnitude was not as robust as it had been recorded in the HeyA8 cell line.
All results collectively suggest that methylation of CpG islands and histone modifications may not have much affect on miR-30c-2-3p and miR-30c-2-5p expression either directly or indirectly in ovarian cancer and normal ovarian epithelial cells. In other words, these epigenetic regulations do not play a critical role in the regulation and expression of the miR-30c-2 gene. Thus, the data obtained from this project leads to the conclusion that epigenetic modifications may have a complex and/or no affect on miR-30c-
2-3p and miR-30c-2-5p expression. In summary, we believe the evidence collected nullifies the original hypothesis that the major regulator of miR-30c-2-3p is an epigenetic mechanism that regulates DNA structure and accessibility. Therefore, our new hypothesis and future direction will seek alternative mechanisms of gene regulation, such as an independent transcription factor binding site.
106
CHAPTER 6
LYSOPHOSPHATIDIC ACID MEDIATES ACTIVATING TRANSCRIPTION FACTOR 3
EXPRESSION WHICH IS A TARGET FOR POST-TRANSCRIPTIONAL SILENCING BY MIR-30C-
2-3P3
3 Ha T. Nguyen, Wei Jia, Aaron M. Beedle, Eileen J. Kennedy, and Mandi M. Murph (2015), PLoS One, 10(9). Reprint here with permission of the publisher
107 Abstract
Although microRNAs (miRNAs) are small, non-protein-coding entities, they have important roles in post-transcriptional regulation of most of the human genome. These small entities generate fine-tuning adjustments in the expression of mRNA, which can mildly or massively affect the abundance of proteins. Previously, we found that the expression of miR-30c-2-3p is induced by lysophosphatidic acid and has an important role in the regulation of cell proliferation in ovarian cancer cells. The goal here is to confirm that
ATF3 mRNA is a target of miR-30c-2-3p silencing, thereby further establishing the functional role of miR-30c-2-3p. Using a combination of bioinformatics, qRT-PCR, immunoblotting and luciferase assays, we uncovered a regulatory pathway between miR-
30c-2-3p and the expression of the transcription factor, ATF3. Lysophosphatidic acids triggers the expression of both miR-30c-2-3p and ATF3, which peak at 1 h and are absent 8 h post stimulation in SKOV-3 and OVCAR-3 serous ovarian cancer cells. The 3´-untranslated region (3´-UTR) of ATF3 was a predicted, putative target for miR-30c-2-3p, which we confirmed as a bona-fide interaction using a luciferase reporter assay. Specific mutations introduced into the predicted site of interaction between miR-30c-2-3p and the 3´-UTR of
ATF3 alleviated the suppression of the luciferase signal. Furthermore, the presence of anti- miR-30c-2-3p enhanced ATF3 mRNA and protein after lysophosphatidic acid stimulation.
Thus, the data suggest that after the expression of ATF3 and miR-30c-2-3p are elicited by lysophosphatidic acid, subsequently miR-30c-2-3p negatively regulates the expression of
ATF3 through post-transcriptional silencing, which prevents further ATF3-related outcomes as a consequence of lysophosphatidic acid signaling.
108 Introduction
Although microRNAs (miRNAs) are small, non-protein-coding entities, they have important roles in post-transcriptional regulation of most of the human genome. These small entities generate fine-tuning adjustments in the expression of mRNA, which can mildly or massively affect the abundance of proteins. Previously, we found that the expression of miR-30c-2-3p is induced by lysophosphatidic acid and has an important role in the regulation of cell proliferation in ovarian cancer cells. The goal here is to confirm that
ATF3 mRNA is a target of miR-30c-2-3p silencing, thereby further establishing the functional role of miR-30c-2-3p. Using a combination of bioinformatics, qRT-PCR, immunoblotting and luciferase assays, we uncovered a regulatory pathway between miR-
30c-2-3p and the expression of the transcription factor, ATF3. Lysophosphatidic acids triggers the expression of both miR-30c-2-3p and ATF3, which peak at 1 h and are absent 8 h post stimulation in SKOV-3 and OVCAR-3 serous ovarian cancer cells. The 3´-untranslated region (3´-UTR) of ATF3 was a predicted, putative target for miR-30c-2-3p, which we confirmed as a bona-fide interaction using a luciferase reporter assay. Specific mutations introduced into the predicted site of interaction between miR-30c-2-3p and the 3´-UTR of
ATF3 alleviated the suppression of the luciferase signal. Furthermore, the presence of anti- miR-30c-2-3p enhanced ATF3 mRNA and protein after lysophosphatidic acid stimulation.
Thus, the data suggest that after the expression of ATF3 and miR-30c-2-3p are elicited by lysophosphatidic acid, subsequently miR-30c-2-3p negatively regulates the expression of
ATF3 through post-transcriptional silencing, which prevents further ATF3-related outcomes as a consequence of lysophosphatidic acid signaling.
109 MicroRNAs (miRNAs) are small non-protein-coding RNA molecules approximately
20–24 nucleotides in length that post-transcriptionally regulate gene expression. Originally discovered in 1993 in C. elegans, miRNAs modulate gene expression by creating fine-tuning adjustments in mRNA expression through their actions as mRNA co-repressors or co- activators [312, 684]. Via non-canonical base pairing with the 3´UTR region of specific mRNAs, a single miRNA molecule is capable of inhibiting the expression of hundreds of different target genes. Although massive gene repression by miRNAs only occurs occasionally, numerous mRNA targets are usually modestly repressed by miRNAs [522], further substantiating their role as the genome’s fine-tuners of protein expression. In fact, predictions suggest most of the human genome is regulated by miRNAs.
Several observations made miRNAs incredibly relevant to oncology and cancer research, establishing the moniker, ‘oncomirs’. Foremost, studies show a global decrease in miRNA expression among tumor tissue and cancer cell lines when compared to normal tissue, implying miRNAs function as tumor suppressors [249, 684]. Additionally, profiles of human tumor specimens demonstrate that poorly differentiated tumors are far better classified by miRNA profiles, rather than mRNA [249]. Lastly, among human specimens of invasive epithelial ovarian cancer, the levels of RNA-interference proteins Dicer and Drosha are decreased in 60% and 51%, respectively, and this correlates with poor clinical outcome
[423].
We previously identified a miRNA located on chromosome 6q13, miR-30c-2-3p, as a target gene induced by the treatment of ovarian cancer cells with lysophosphatidic acid, which is a lipid mediator abundant in ascites fluid that promotes tumor aggressiveness
[137, 685-688]. MiR-30c-2-3p induction resulting from lysophosphatidic acid signaling
110 facilitates the repression of oncogenic mRNA, such as the BCL9 transcript, within 1 hour after stimulation. This suggests the existence of a miRNA-controlled, negative feedback loop that normally serves to regulate oncogenic signaling. This is noteworthy because lysophosphatidic acid-induced profiles correlate with poor prognosis among epithelial ovarian carcinoma [689].
ATF3 is a molecular hub that belongs to the ATF/CREB (cyclic AMP response element-binding) family of transcription factors; furthermore, it plays a role in the promotion of proliferation in malignant cells [690]. It is expressed at nearly undetectable levels in the cell until it is stimulated via serum, cytokines, genotoxic agents or physiological stresses, whereby its transcriptional mRNA is increased, which parallels its protein levels [691]. Interestingly, a gene expression analysis of primary ovarian tumors from patients with either high or low/no depressive symptoms reveals that ATF3 mRNA is significantly amplified among specimens from patients with high levels of depression (S1
Fig) [692, 693]. This reinforces the role of ATF3 and intimates an important contextual function among patients with ovarian cancer, especially since stress promotes the progression of ovarian cancer [694].
ATF3 is somewhat enigmatic since it functions as a dualistic transcription factor with both anti- and pro-tumorigenic effects, depending on the cancer subtype. For example,
ATF3 suppresses metastasis in bladder cancer but possesses oncogenic effects in cutaneous squamous cell carcinoma [695]. In contrast, among some ovarian cancer cells, ATF3 functions as an apoptosis inducer [696], yet in other ovarian cancer cells it belongs to a profile of worsened outcomes [689].
111 Other studies have shown that miR-30c-2-3p induces the interference and silencing of transcripts such as HIF2A, X-box binding protein 1, Cyclin E1, BCL9 and an adaptor protein of the NF-κB signaling pathway [137, 697-699]. However, the purpose of this study was to confirm the interaction between ATF3 and miR-30c-2-3p, thereby further establishing the role of miR-30c-2-3p after induction by lysophosphatidic acid in ovarian cancer cells. The data suggests that miR-30c-2-3p inhibits ATF3 mRNA after both are expressed after 1 h treatment of lysophosphatidic acid. Although part of the transcription repression for ATF3 occurs through the recruitment of histone deacetylases to the promoter [700] and miR-494 repression [701], these elements do not account for its complete regulation and are likely context and time specific. This finding is not an unprecedented phenomenon as the literature reports many miRNAs that silence transcription factors [702], but prior to our discovery, nothing was reported about the putative interaction between ATF3 and miR-30c-2.
Methods
Cell culture and reagents: Ovarian cancer cell lines, SKOV-3 and OVCAR-3 were obtained from American Type Culture Collection (ATCC, Manassas, VA). All cells were maintained at 37°C, 5% CO2. SKOV-3 cells were cultured in Dulbecco’s Modification of
Eagles’s medium (DMEM) (Mediatech, Inc., Manassas, VA); OVCAR-3 cells were cultured in
RPMI 1640 media (Mediatech). All media were supplemented with 10% fetal bovine serum
(Atlanta biological, Flowery Branch, GA). Immortalized ovarian surface epithelial cells,
IOSE, were maintained in 1:1 mixture of medium 199 and MCDB 105 (Sigma-Aldrich, St.
Louis, MO), supplement with 10% fetal bovine serum (Atlanta Biologicals, Norcross, GA).
Lysophosphatidic acid (18:1, 1 oleoyl-2-hydroxy-sn-glycero-3-phosphate) was purchased
112 from Avanti Polar Lipids Inc. (Alabaster, Alabama). For all treatments with lysophosphatidic acid, cells were serum-starved overnight and then treated with serum free media containing lysophosphatidic acid reconstituted in PBS with 1% charcoal- stripped, fatty-acid free, bovine serum albumin (Sigma-Aldrich).
SiRNA knockdown of ATF3: SKOV-3, OVCAR-3 or IOSE cells were transfected with
Dharmacon SMART pools of siRNA (DharmaconRNAi, Lafayett, CO) targeting the ATF3 mRNA using DharmaFECT reagent (DharmaconRNAi) and following the protocol provided by the manufacturer. Control samples were treated with non-targeted siRNA
(DharmaconRNAi). A final concentration of 100 nM siRNA was used, and the expression level of ATF3 mRNA were measured 48 h after transfection. mRNA isolation and quantitative real-time PCR
Total mRNAs from cells were isolated using Trizol reagent (Thermo Fisher
Scientific/Life Technologies, Carlsbad, CA). Total cDNAs and specific cDNAs from miRNAs were generated using iScriptTM cDNA Synthesis kit (Bio-Rad, Hercules, CA). Quantitative
RT-PCR Taqman primer set for miR-30c-2-3p was purchased from Life Technologies. Real- time PCR was assessed by 7900HT Fast Real-Time PCR system (Applied Biosystems, Foster
City, CA) using Power SYBR Green Real-time PCR Master Mix or Taqman Gene Expression
Master Mix (also Applied Biosystems). The primers used were based on algorithm- generated sequences from either Primer Bank (http://pga.mgh.harvard.edu/primerbank) or NCBI Primer-BLAST. The primer used were as followed: ATF3 (5´-
TCTGCGCTGGAATCAGTCAC-3´ and 5´-GTGGGCCGATGAAGGTTGA-3´), and 18S (5’-
AGAAACGGCTACCACATCCA-3´ and 5´- CCCTCCAATGGATCCTCGTT-3´). The reactions were normalized using 18S as housekeeping gene.
113 Exogenous expression of synthetic ATF3, miR-30c-2-3p or anti-miR-30c-2-3p: OVCAR-
3 or SKOV-3 were plated at approximately 1.5 x 105 cells into 6-well plates and then incubated overnight. Cells were then transfected with miR-30c-2-3p
(CUGGGAGAAGGCUGUUUACUCU), anti-miR-30c-2-3p construct or Negative control (Pre- miR miRNA Precursor Negative Control #1)(Life Technologies) where indicated. The negative control does not target any known mRNA within the human transcriptome. The transfection used DharmaFECT 1 (DharmaconRNAi) according to the manufacturer’s instruction. After 48 h transfection, samples were collected for next step of analysis. For ectopic expression of ATF3, cells were transfected with ATF3 overexpression vector,
Precision LentiORF (DharmaconRNAi), using Xfect transfection system (Clontech, Mountain
View, CA) following manufacturer’s instruction. Approximately 48 h after ATF3 overexpression, cells used in experiments as described.
Luciferase assay: SKOV-3 cells were seeded in a 12-well plate and incubated overnight. Cells were later transfected with miTargetTM 3´-UTR miRNA Target Clones custom vectors (GeneCopoeia, Rockville, MD) using Xfect™ Transfection reagent (Clontech).
The human ATF3 3' UTR sequence was inserted downstream of the secreted Gaussia luciferase (GLuc) reporter gene in a mammalian expression vector with the SV40 promoter and neomycin resistance gene. In the mutated vector, the sequence predicted to interact with miR-30c-2-3p, CTCTTCCGA, in the ATF3 3´-UTR was mutated to GGGGAAAGG and inserted into the vector (GeneCopoeia). After transfection, stable cells were selected with
G418 for several weeks prior to the assessment of luciferase. To measure luciferase secretion, approximately 1x105 of stably expressing cells were seeded in a 6-well plate and incubated overnight prior to transfection with miR-30c-2-3p, anti-miR-30c-2-3p or
114 negative miR. After 48 h, media were collected and secreted luciferase was measured in a
96-well plate using Secrete-PairTM Dual Luminescence Assay kit (GeneCopoeia) with a
Synergy 2 Multi-Mode plate reader (BioTek, Winooski, VT). The data presented is the result of experiments performed at least three times. The average luciferase signal is displayed in a bar graph, normalized to the negative-miR control.
Cell viability and number: SKOV-3 cells were seeded (5 x 103) into a 96-well plate and allowed to attach overnight prior to transfection with anti-miR-30c-2-3p or the ATF3 expression vector where indicated. After 48 h of transfection, cells were fixed with formaldehyde 4% and stained with a Whole Cell Stain (Thermo Fisher Scientific, Waltham,
MA) according to the manufacturer’s protocol. Automated fluorescence microscopic images were captured and analyzed using the Cellomics ArraysScan VTI HCS Reader (Thermo
Fisher Scientific/Cellomics). Graphs and the statistical calculations were generated using
GraphPad Prism (GraphPad Software Inc., La Jolla, CA). Experiments are done with a minimum of quintuplicate samples and at least 5 images per well are taken and incorporated in the data analysis. The experiment was repeated three times.
Approximately 10 fields representing 150–750 cells are averaged in the graphs presented.
In another experiment, SKOV-3 cells were seeded (2 x 103) into a 96-well plate. After overnight incubation, cells were transfected with either the ATF3 expressing or control vector using Xfect™ Transfection reagent (Clontech), following manufacturer’s instruction.
After 48 h, media was refreshed, and 0.16, 0.31, 0.63 or 1.25 μM camptothecin was added where indicated. After 48 h of camptothecin treatment, cell viability was assessed using
CellTiter-Blue® reagent (Promega) and the absorbance signal was measured by
SpectraMax M2 plate reader (Molecular Devices, Sunnyvale, CA).
115 Immunoblotting: Approximately 1x105 SKOV-3 cells were seeded in a 6-well plate prior to transfection. Cells were harvested after 48 h priors to being lysed in radioimmunoprecipitation assay buffer. Proteins were separated by SDS-PAGE, transferred to polyvinylidene difluoride membrane and immunoblotted using a primary antibody against ATF3 (Santa Cruz Biotechnology, Inc., Dallas, TX), GAPDH or Actin (Cell Signaling
Technology, Inc., Danvers, MA), and an HRP-conjugated secondary antibody (GE
Healthcare, Atlanta, GA). The protein bands were then visualized with SuperSignal™ West
Dura Extended Duration Substrate (Thermo Fisher Scientific) using a Fluorchem HD2 chemiluminescent imaging system (Protein Simple, Santa Clara, CA). Protein bands were subsequently quantified using Image J (National Institutes of Health, Bethesda, MD) where indicated. Representative blots are shown and all experiments were repeated three times.
Gene expression of patient specimens: No human subjects were directly involved in this research. The publicly available datasets, GSE9116 and datasets with expression of the
NCI-60 cancer cell lines, were downloaded from the National Center for Biotechnology
Information (NCBI) and mined for information relating to ATF3 or miR-30c-2-3p, respectively. Thus, all human identifiers are removed and the subjects are anonymous. Raw
Affymetrix U133 Array data was plotted as a column scatter graph to visualize the distribution of the transcription factor data. Hierarchical clustering analysis and data visualization of miR-30c-2-3p was performed as previously described [11].
Statistical analysis: GraphPad Prism was utilized to analyze statistical differences using an analysis of variance (ANOVA) test, followed by Bonferroni’s multiple comparison tests between groups. For comparisons of two groups, the Student’s t-test was used. Where
116 it is shown in figures, *p < 0.05 **p < 0.01 and ***p < 0.001 indicate the levels of significance.
Results
To commence our investigation, we added lysophosphatidic acid (5 μM) to SKOV-3 ovarian cancer cells and measured an increase in ATF3 protein levels (Figure 6.1A) and
ATF3 mRNA transcripts (Figure 6.1B), over 8 h. Since ATF3 expression peaked 1 h after lysophosphatidic acid treatment, we also assessed a range of concentrations (1 to 40 μM) at the 1 h time point and found 5–20 μM to be effective in stimulating ATF3 mRNA transcription (Figure 6.1C) and ATF3 protein expression (Figure 6.1D) in SKOV-3 cells.
Thus, we selected 5 μM for the remainder of the study because it is the lowest concentration that produces the response. Taken together, this data suggests that in ovarian cancer cells, lysophosphatidic acid stimulation induces the expression of ATF3.
Figure 6.1: Lysophosphatidic acid induces the expression of ATF3.
Intriguingly, a miR-30c-2-3p binding site was predicted for ATF3 using miRWalk 2.0
(Figure 6.S2). Indeed, lysophosphatidic acid elicits the expression of miR-30c-2-3p and
117 ATF3, which both peak at 1 h (Figures 6.2A and 6.2B). When we introduced miR-30c-2-3p into cells and after 48 h, stimulated with lysophosphatidic acid, there was a significant reduction in ATF3 transcripts at 0, 2 and 4 h, which was absent by 8 h (Figure 6.2C). To confirm, we examined miR-30c-2-3p’s ability to suppress various mRNA expression after
48 h of introduction into cells. We observed no change among unrelated E2F3 or AUKRA transcripts, but did observe a decrease in the expression of the specific targets, ATF3 and LYPLA1P3, the latter which is lysophospholipase I pseudogene 3, a pseudogene located downstream of the miR-30c-2-3p gene on chromosome 6q13 (Figure
6.2D).
Figure 6.2: MiR-30c-2-3p inhibits ATF3 expression.
We therefore wanted to confirm that miR-30c-2-3p could bind to the 3´- untranslated region of ATF3. After stably expressing a luciferase vector in ovarian cancer cells that encoded the ATF3 3´-untranslated region sequence, we observed a significant reduction in luciferase among SKOV-3 cells transfected with miR-30c-2-3p, but not those
118 also transfected with the neutralizing anti-miR-30c-2-3p (Figure 6.2E). However, a vector with specific mutations in the sequence did not reduce luciferase. The data was also repeated in experiments with OVCAR-3 cells (Figure 6.2F). This suggests that miR-30c-2-3p is binding to the 3´-untranslated region of ATF3.
As further confirmation that miR-30c-2-3p targets the mRNA ATF3 transcript, we exogenously introduced anti-miR-30c-2-3p into SKOV-3 cells and then measured ATF3 expression after treatment with lysophosphatidic acid. Compared to transfection with a non-targeting miRNA, anti-miR-30c-2-3p enhanced ATF3 protein levels of at 1, 2 and 4 h after stimulation with lysophosphatidic acid (Figure 6.3A). Even the magnitude of ATF3 mRNA expression with anti-miR-30c-2-3p transfection was altered nearly 5-fold (Figure
6.3B), in comparison with our previous results (Figure 6.1B) and the duration shifted as well, so that the expression peaked at 2 h. Similarly, ATF3 protein was increased after anti- miR-30c-2-3p expression in untreated cells, compared with miR-30c-2-3p expression and a non-targeting control (Figure 6.3C). Taken together, this supports our hypothesis, which suggests that miR-30c-2-3p directly silences ATF3 expression.
Figure 6.3: Anti-miR-30c-2-3p augments ATF3 expression.
119 Treating cells with lysophosphatidic acid shows that the expression of both ATF3 and miR-30c-2-3p is increased (Figure 6.4A). However, we wanted to examine the inverse relationship and thereby determine whether the presence of ATF3 affects the expression of miR-30c-2-3p. Therefore, we suppressed ATF3, using siATF3, or increased ATF3 with an expression vector (Figure 6.4B), and stimulated SKOV-3 and OVCAR-3 cells with lysophosphatidic acid (5 μM for 1 h) for comparison of miR-30c-2-3p levels to cells without
ATF3 suppression. Indeed, when ATF3 is reduced, even cells stimulated with lysophosphatidic acid display significant inhibition of miR-30c-2-3p transcript expression
(Figure 6.4C). In contrast, when cells were transfected with the ATF3 vector to increase the expression/function of the transcription factor, both cell lines increased the expression of miR-30c-2-3p (Figure 6.4D), especially SKOV-3, without requiring lysophosphatidic acid treatment. Taken together, these data suggest that ATF3 affects the expression of the miR-
30c-2-3p transcript.
Figure 6.4: ATF3 modulates miR-30c-2-3p expression.
120 In previous work, we established that miR-30c-2-3p inhibits cell proliferation [6].
Therefore, the expression of ATF3 should have the same effect, if it induces miR-30c-2-3p expression. To test this prediction, we transfected ATF3 into SKOV-3 cells (Figure 6.5A) and observed a striking reduction in the number of cells under fluorescence microscopy using whole cell staining (Figure 6.5B). We quantified this observation using automated,
Figure 6.5: ATF3 expression induces cellular stress on ovarian cancer cells.
121 high-throughput ArrayScan imaging to assess the number of cells among >10 fields
(approximately 150–750 cells total) and detected a significant reduction upon over- expression of ATF3 (Figure 6.5C, *p<0.05). Correspondingly, the effect was blocked by anti-
30c-2-3p. This data suggests a similar functional outcome on proliferation between miR-
30c-2-3p and ATF3. Lastly, since ATF3 can enhance cellular stress, ATF3 was transfected into SKOV-3 cells before treatment with campothecin, wherein we observed a significant reduction in the cell viability (Figure 6.5D). Although this may not be the only mechanism responsible, it does suggest that the ability of ATF3 to negatively affect cell proliferation can occur via miR-30c-2-3p transcription.
Discussion
Based on our experimental data, we propose that in ovarian cancer cells, lysophosphatidic acid mediates the expression of both the transcription factor ATF3 and miR-30c-2-3p, which is a miRNA primarily expressed in ovarian and renal cancers (Figure
6.S3) that we have previously shown is also induced by lysophosphatidic acid [6]. As a result, miR-30c-2-3p binds to the 3´-untranslated region of ATF3 and reduces mRNA and protein expression. Taken together, the data suggests the current working molecular model for this system (Figure 6.6). This pathway ultimately leads to the decline of both molecules, the transcription factor and miRNA, which were initially expressed as a result of lysophosphatidic acid signaling.While the tumorigenic effects of lysophosphatidic acid are well known [32, 688, 689, 703-705], which also involve the highly-researched aspects of G protein-coupled receptor signaling, autotaxin and phosphatase enzymes, the molecular mechanisms beyond the cell surface are less appreciated. To date, there are only a handful of papers examining miRNA mechanisms associated with lysophosphatidic acid;
122 correspondingly, our previous report on miR-30c-2-3p was the first in the literature in this area [137]. Thus, our study herein fills a major gap in the molecular understanding of signaling circuits initiated by lysophosphatidic acid, especially at the level of post- transcriptional silencing regulated by miRNA. After characterizing the miRNA response to lysophosphatidic acid-mediated signaling [137], we now further dissect the molecular mechanism and outcomes through the inhibition of the transcription factor, ATF3.
Figure 6.6: Model of auto-regulatory feedback loop between ATF3 and miR-30c-2-3p.
Although this is a novel mechanism demonstrating that miR-30c-2-3p inhibits ATF3, the literature reports the existence of negative feedback loops between miRNA and their
123 targets. For example, miR-200a, miR-200b and miR-429 comprise a double-negative feedback loop with the transcription repressors ZEB1 and SIP1 in breast cancer cells [706].
Another example of a double-negative feedback loop system occurs with miR-422a and three of its targets, the forkhead box genes, which regulate the development of hepatocellular carcinoma [707]. Positive, auto-regulatory feedback loops also exist, for example, between miR-448 and NF-κB in breast cancer cells [708]. In addition, another study uncovered a regulatory axis in a tightly linked transcriptional system between p55PIIK, p53 and miR-148b in colorectal cancer cells [707]. Thus, our data parallels these studies in that regulatory mechanisms exist between transcription factors and miRNA expression.
Furthermore, the study herein provides experimental data to support the regulation of another gene transcript targeted via miR-30c-2-3p. To date, this list now includes ATF3,
BCL9, HIF2A, X-box binding protein 1, Cyclin E1 and an adaptor protein of the NF-κB signaling pathway [137, 698, 699, 709]. The fact that many of the aforementioned are known oncoproteins and/or involved in cell proliferation is consistent with our data. We show that the overexpression of ATF3 significantly reduces the number of ovarian cancer cells, which suggests an influence on proliferation–a hallmark of cancer [220].
Since epigenetics are involved in the development and progression of ovarian cancer [500, 710], we initially hypothesized that miR-30c-2-3p could be induced by alterations in DNA methylation or histone acetylation. However, we did not observe consistent increases in miR-30c-2-3p expression after treating cells with epigenetic modifiers, which would have indicated a reactivation of silenced genes. For example, treatment with 5-azacytidine and/or 3-Deazaneplanocin A over a period of 1–11 days did
124 not exhibit a similar and/or substantial increase in miR-30c-2-3p that is observed upon stimulation with 5 μM lysophosphatidic acid after 1–2 h (data not shown), which would have emulated our previous results (Figure 6.1A and 1B). Thus, epigenetic mechanisms could not explain the considerable activation of miR-30c-2-3p that would also account for the transient nature observed here.
Both ATF3 and miR-30c-2-3p are detected at negligible levels among quiescent cells and are dramatically increased after 1 h of lysophosphatidic acid stimulation. Furthermore, if miRNAs were globally reduced among cancer cells [249, 684], then this would theoretically indicate the possibility of enhanced activity by ATF3 in such a system, which could exacerbate disproportionate protein expression. However, the fate of miR-30c-2-3p under such a scenario is yet to be determined experimentally. Future studies will focus on this area.
125 Supporting Information
Figure 6.S1: ATF3 gene transcription is increased among primary ovarian cancer tumors among patients with a high level of depression.
The GEO Dataset, GSE9116 [692, 693], was downloaded from the NCBI and mined for the expression of transcription factors that have a relationship with lysophosphatidic acid signaling in ovarian cancer [689]. Although EPAS1, ETV5 and SKIL were not significant, the difference in ATF3 raw gene expression between patients with high and low depression was significant. **p<0.01.
126
Figure 6.S2: Schematics of the interaction.
The sequence of miR-30c-2-3p is shown as well as a schematic representation of the
3´-untranslated region of ATF3 with the predicted target site for miR-30c-2-3p highlighted in yellow. For the luciferase experiments using the mutated vector, the sequence is also presented.
127 Figure 6.S3: MiR-30c-2-3p is predominantly expressed in ovarian and renal cancer cell lines.
Gene expression data from the NCI-60 set of cell lines was downloaded and mined for the expression of miR-30c-2-3p. The highest expression was detected mainly among
128 two cell types: ovarian (OV) and renal (RE). Other abbreviations among the cell lines in the
NCI-60 include breast (BR), central nervous system (CNS), colon (CO), leukemia (LE), lung
(LC), melanoma (ME) and prostate (PR). The range of logarithmic expression is from 2.685
(red) to -1.345 (green).
129
CHAPTER 7
CONCLUSION
A previous publication from the laboratory revealed that treating ovarian cancer cells with LPA causes a significant induction of miR-30c-2-3p expression [137].
Furthermore, these results indicated that miR-30c-2-3p is an antitumor miRNA since its ectopic expression reduced cell proliferation and viability in various ovarian cancer cell lines, including cisplatin sensitive SKOV-3, chemoresistant HeyA8-MDR and OVCAR-3 cells.
The effects are specific for ovarian cancers as it is not observed IOSE immortalized ovarian surface cells, nor in A549 lung cancer cells. In addition, miR-30c-2-3p expression is consistently observed across ovarian and renal cell carcinoma cell lines but not many other types. Since LPA is an important growth factor in ovarian oncogenesis and miR-30c-2-3p has anti-proliferative effects against cancer cells, it is desirable to explore more about the mechanisms that regulate its expression as well as its potential as a therapeutic.
As we observed previously, LPA treatment dramatically upregulated miR-30c-2-3p, the passenger strand, expression; whereas it did not alter miR-30c-2-5p, the corresponding major strand, expression [137]. Dicer is an RNAse known for its critical role in processing mature miRNAs, including both the major strand and passenger strand. Dicer is also mentioned in multiple publications as a factor that impedes ovarian cancer progression
[423]. Our experimental results show that the absence of Dicer has a limited impact on ovarian cancer cells SKOV-3 viability after 6 days. Treatment of LPA on SKOV-3 cells does not affect Dicer mRNA expression. Knocking down LPA receptors 4 and 5 caused an
130 increase in Dicer mRNA expression after LPA treatment. However, due to the relatively low expression of LPA receptors 4 and 5 in SKOV-3 ovarian cancer cells, the possibility that
Dicer is regulated by LPA receptors 4 and 5 is of limited interest. On the other hand, knocking down Dicer reduces expression of pri-miR-30c, miR-30c-2-5p and miR-30c-2-3p.
This suggests an opposite effect, whereby increasing Dicer expression up-regulates miR-
30c-2-3p, a tumor suppressor miRNA in ovarian cancer [136].
Metformin, a well-known drug used in Type II diabetes treatment which induces
AMP-activated protein kinase (AMPK) activation, was recently shown to have antitumor characteristics in gynecological cancers [491, 492, 494, 495]. The drug also has been reported to induce Dicer expression and have an indirect role in let-7 regulation in other ovarian cancer cell lines [471, 497]. In this study, our results show that metformin treatment does not affect SKOV-3 ovarian cancer cell viability. The treatment at certain levels reduces Dicer mRNA expression in this cell line but does not significantly change miR-30c-2-3p expression in HeyA8, another ovarian cancer cell line. This data suggests that metformin may not be an effective treatment in this model of ovarian cancer and further corroborates our results with Dicer.
Since Dicer is not a regulator of miR-30c-2-3p under LPA stimulation and miR-30c-
2-3p induction by LPA is a peripheral event, which only lasts for about one hour, the possible explanations are that miR-30c-2-3p is rapidly secreted into the extracellular matrix, probably via encapsulation in exosomes, and/or the modulations of miR-30c-2-3p by LPA is mediated by other factors. Therefore, we hypothesized that miR-30c-2-3p is secreted out of ovarian cancer cells as a pro-growth mechanism employed by cancer cells to avoid miR-30c-2-3p’s anti-proliferation effects. Thus, we examined the exosomes
131 released in the media of ovarian cancer cells culture alone and in co-culture with adipocytes, a condition mimicking ovarian tumor growth in the omentum. Previous publications have shown that adipocytes surrounding tumors can secrete signaling molecules to stimulate cancer growth and metastasis [711-713]. Exosomes are considered vehicles to carry signaling molecules, such as tumor antigens and RNAs, from cell-to-cell. In other words, exosomes are hypothesized to be extracellular mediators of intercellular signals. Interestingly, one study in breast cancer reports that exosomes secreted from these cancer cells contain miRNAs and the RISC-Loading Complex, allowing them to independently process precursor miRNAs (pre-miRNAs) into mature miRNAs [714].
Surprisingly, the Dicer protein is also found in cancer exosomes and these exosomes can induce tumor formation in a Dicer-dependent manner [714].
Other factors that we initially hypothesized might act as miR-30c-2-3p expression regulators were epigenetic modifications, which are widely recognized as important contributors to the regulation of gene expression, both in normal and malignant cells. To investigate this possibility, we examined the impact of DNA methylation and histone methylation on the expression of miR-30c-2-3p and its major strand, miR-30c-2-5p, in ovarian cancer and normal cells. The results on the ovarian cancer cell line SKOV-3 were surprising. Unexpectedly, 5-aza-CdR, a DNA methyltransferase inhibitor, reduced both miR-
30c-2-3p and miR-30c-2-5p expression. This was contradictory to what had been predicted, since the inhibition of adding methyl groups to CpG islands is normally believed to clear the promoter space, thus facilitating transcription factor binding for gene expression. However, DNA methylation may have different patterns leading to various final impacts on gene expression, not only limited to transcriptional repression [681-683]. For
132 example, Hon and colleagues had reported that global DNA hypomethylation is coupled to the formation of repressive chromosomes, mainly due to histone modifications, and ultimately gene repression in breast cancer [681]. Interestingly, treatment of DZNeP on the same cell line did not significantly change miR-30c-2-5p expression. In fact, it slightly reduced miR-30c-2-3p expression after 9 hours of exposure, but the effect quickly subsided. On the other hand, treatment of 5-aza-CdR or DZNep or the combination of the two compounds did increase gene expression among both miR-30c-2-3p and miR-30c-2-5p in HeyA8 cells. However, for miR-30c-2-3p expression, the basal, unstimulated level is already very low in HeyA8 cells; therefore even though inhibition of DNA and histone modifications statistically up-regulates miR expression, the absolute Ct value is still close to negligible indicating very low levels. All results collectively suggest that epigenetic regulations, particularly methylation of CpG islands and histone modifications, do not have a significant impact on the gene expression of miR-30c-2-3p and they are not key players in this regulation. This is supported by the fact that there is a complete absence of CpG islands upstream of the miR-30c-2 gene, which contains approximately 60-70% A and T bases.
After eliminating epigenetic controls as the major mechanism regulating gene expression, we alternatively hypothesized that the mechanism of miR-30c-2-3p gene regulation was an independent transcription factor binding site. In a publication in 2012,
Byrd and colleagues described miR-30c-2-3p as one link in the unfolded protein response
(UPR) cascade. When mouse embryo fibroblasts (MEFs) were stimulated with thapsigargin, an inhibitor of the ER Ca+ATPase, to induce ER stress, the protein kinase RNA-like ER kinase (PERK) was initiated as one pathway in the UPR cascade to reduce unfold protein pressure in the ER. PERK activates NF-κB, and this transcription factor directly binds to the
133 upstream sequence of miR-30c-2-3p to induce this miRNA gene’s expression [138].
Surprisingly, miR-30c-2-3p comes back to down-regulate NF-κB in an indirect manner. As described in a breast cancer study, miR-30c-2-3p targets tumor necrosis factor 1- associated death domain protein (TRADD), which is an upstream inducer of NF-κB, thus concomitantly dowregulating NF-κB [141]. Interestingly, thapsigargin also stimulates the
Activating Transcription Factor 3 (ATF3) through a mediator, c-Jun N-terminal protein kinase (JNK) [715]. In this study, we explore the hypothesis that ATF3 is an inducer of miR-
30c-2-3p after LPA stimulation. Since the overexpression of miR-30c-2-3p after LPA treatment is peripheral, the miRNA level dramatically increased just one hour after LPA exposure and rapidly decreased to basal levels within two hours. Thus, there is a possibility that miR-30c-2-3p comes back to target its inducer, ATF3, in a negative regulation loop similar to what was referred with NF-κB [138, 141]. We observed that the addition of LPA induces ATF3 expression in both ovarian cancer cell lines SKOV-3 and OVCAR-3. However, this induction did not last long, ATF3 expression levels quickly decrease within 2 hours of
LPA treatment, which is consistent with the miR-30c-2-3p expression profile after LPA exposure. The impact of LPA on miR-30c-2-3p is attenuated when ATF3 was knocked- down from ovarian cancer cells by siRNA, suggesting a mediator role of ATF3 in the LPA- miR-30c-2-3p axis. On the other hand, overexpression of ATF3 significantly amplifies miR-
30c-2-3p level, indicating ATF3 facilitates miR-30c-2-3p expression. Using bioinformatics tools, we identified ATF3 as one direct target of miR-30c-2-3p. The hypothesis was proven experimentally since transfection of miR-30c-2-3p decreases luciferase signals from ovarian cancer cells stably transfected with a luciferase vector encoding the ATF3 3’ un- translated region (3’ UTR). The effect was neutralized by exogenous anti-miR-30c-2-3p.
134 Furthermore, mutations of the complementing region of ATF3 3’UTR with miR-30c-2-3p seed region completely eliminate the influence of miR-30c-2-3p on the luciferase signal, suggesting that miR-30c-2-3p directly down-regulates ATF3 through binding to its 3’ UTR.
Ectopic expression of miR-30c-2-3p reduces ATF3 expression in ovarian cancer cells and interferes with the induction of ATF3 by LPA.
ATF3 is known for its dichotomous roles in cancer, where it can be either anti or pro- tumorigenic, depending on the cancer subtype [170]. Our experimental results in ovarian cancer suggest that ATF3 has a benefit role in this disease. Overexpression of ATF3 significantly reduces ovarian cancer cell number. However, this effect was nullified after anti-miR-30c-2-3p transfection, indicating that ATF3 may have this effect through inducing miR-30c-2-3p. In addition, ovarian cancer cells with a high expression level of ATF3 seem to be more sensitive to chemotherapy with camptothecin than cells with basal ATF3 expression.
In conclusion, based on our experimental data, we propose that LPA mediates the expressions of both the transcription factor ATF3 and miR-30c-2-3p. ATF3 may act as an inducer of miR-30c-2-3p gene expression after LPA stimulation. On the other hand, miR-
30c-2-3p binds to the 3’ UTR of ATF3 and reduces both ATF3 mRNA and protein expression. Consequently, this effect comes back to down-regulate miR-30c-2-3p itself in a negative feedback loop (Figure 7.1). Up to now, there is a limited number of publications describing the correlation between LPA and miRNA, even though both factors play critical roles in normal physiology and pathology, especially in a disease such as ovarian cancer
[54, 80, 137, 710, 716, 717]. In addition, there is only a handful of studies describing the regulation of miRNAs by transcription factors [718-721]. Among them, one study indicates
135 the induction of miR-221 and miR-222 by NF-κB, the same transcription factor that induces miR-30c-2-3p, in prostate carcinoma and glioblastoma cells [721]. Our study unveils a novel mechanism demonstrating miR-30c-2-3p induction by LPA through stimulating transcription factor ATF3. Furthermore, the negative regulation loop that miR-30c-2-3p utilizes to down-regulate itself through targeting its own inducer, ATF3, is described for the first time.
Figure 7.1: A proposed model of miR-30c-2-3p regulation by LPA. LPA binds to LPA receptors on cell membrane and triggers signaling cascades leading to the increased expression level of ATF3, which is an inducer of miR-30c-2-3p expression. MiR-30c-2-3p then directly binds to the 3’UTR of ATF3 and represses the protein post-transcriptionally, resulting in the down-regulation of itself in a negative feedback loop.
136 MiR-30c-2-3p inhibits ovarian cancer cell proliferation in vitro. In this study, we examined miR-30c-2-3p’s impact in an in vivo ovarian cancer model. Our results show that local delivery of miR-30c-2-3p to ovarian xenograft tumors significantly reduces tumor burden within a small pilot study group. Nevertheless, it is expensive to expand the scale of study due to the high price of delivery reagent and synthetic miR-30c-2-3p. To solve this problem, we developed an ovarian cancer cell line with inducible expression of miR-30c-2-
3p. Our data shows that, after removing Doxycyclin (Dox), the binding factor that blocks induction of miR-30c-2-3p gene, there is an increase of miR-30c-2-3p expressed compared to the cells with Dox present. However, the system still produces a significant level of miR-
30c-2-3p even with the presence of Dox, indicating leaky transcription control, which could lead to bias in future studies. Furthermore, the inducible expression of miR-30c-2-3p seems insufficient for effective anti-proliferation. Overall, our experimental results suggest that an inducible system for miR-30c-2-3p expression in ovarian cancer cells is feasible.
Data from our previous study combined with results in the literature propose several direct targets of miR-30c-2-3p, such as BCL-9, XBP-1, HIF2 alpha, cyclin E1, and TRADD
[137, 138, 140, 141]. Using bioinformatics tools, we identified multiple other potential targets of miR-30c-2-3p through complementarity between the miRNA seed region and the
3’ Untranslated regions of these genes. Interestingly, many of these targets involve in the
MAP kinase pathway, which receives signal induction from LPA, and also acts as one upstream promoter of ATF3 under cellular stress signal [167, 715]. To experimentally confirm the data, we overexpressed miR-30c-2-3p in ovarian cancer cells and treated these cells with and without LPA to stimulate mRNA expression. The results show that there are three genes being down-regulated by miR-30c-2-3p, independently without LPA; they are
137 MARSKSL1, DUSP19 and PAK1. Interestingly these proteins have further correlation in the signaling network, such as DUSP family regulates JNK and p38 pathways, which are shown to affect ATF3 activity; furthermore, MARCKS is involved in the regulation of phospholipase
D, an important enzyme in LPA biogenesis [167, 715, 722, 723]. To conclude, our research has revealed part of the regulatory pathway of miR-30c-2-3p as well as its antitumor mechanisms in ovarian cancer cells. Nevertheless, this may be just one part of a bigger picture with multiple correlations needed for future research.
Future directions
Based on the data obtained from this project, we propose several future experiments to further explore the regulation of miR-30c-2-3p as well as its therapeutic impacts on ovarian cancer. First, we will overexpress Dicer protein in ovarian cancer cells and observe if there are changes in miR-30c-2-3p expression. Furthermore, exosomes secreted from ovarian cancer cells with high level of Dicer will be examined for miR-30c-2-
3p concentrations to test the hypothesis that Dicer is involved in the secretion of miR-30c-
2-3p to the extracellular matrix. Secondly, we will continue screening the upstream sequence of miR-30c gene to identify the potential direct binding site where ATF3 binds to and induces the gene expression. Thirdly, we will explore the possibility that ATF3 indirectly transactivates miR-30c-2-3p through an intermediate transcription factor. A pull-down assay to examine the interactions between ATF3 and other transcription factors, especially the ones known to directly induce miR-30c-2-3p, such as NF-ĸB [138], will be helpful. Lastly, a larger size in vivo experiment with proper control groups will be needed to confirm the therapeutic properties of miR-30c-2-3p in ovarian cancer as well as identify its specific in vivo targets.
138
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195
APPENDIX A
MiRNA-30C-2-3P AND OVARIAN CANCER
MiR-30c-2-3p in ovarian cancer in vivo study
MiRNAs have long been recognized for their roles in ovarian cancer development and progression [1, 2]. Recent publications have shown that miRNAs in circulation might also have important roles in cancer signaling [2-4]. Due to their potentials in cancer treatment, researchers have endeavored to target these small RNA molecules. One important target is oncomiRs, which are the miRNAs that downregulate tumor suppressor genes. Therapeutic strategies aim to inhibit them through a miRNA antagonist, which are single-stranded oligonucleotides complementary to miRNAs, or miRNA sponges, which are mRNAs with target sites for a specific miRNA to sequester aberrant miRNAs [5-7]. On the other hand, there are also efforts to artificially increase the level of antitumor miRNAs at targeted sites [8]. Currently, strategies to deliver miRNAs in vivo include systemic delivery and local delivery. Systemic delivery sends miRNAs to tumor sites through blood vessel irrigation. This approach faces several challenges because in order to reach target sites, miRNAs need to travel intact through blood stream, escape from vessels, travel through cell membrane to reach cytoplasm. At the same time, there is a high potential of systemic toxicity due to delivery of miRNA to non-specific sites. Local delivery, on the other hand, can avoid miRNA uptake at non-desired tissues and increase bioavailability at tumor sites.
However, this approach is so far only applied in certain types of cancers, such as ocular tumors, brain tumors and mesotheliomas [9]. To date, several miRNA drugs have been
196 brought to clinical trials. For example, Mirna Therapeutics develops a liposomal form of antitumor miR-34 drug, MRX34, with complete tumor regression in orthotopic mouse models of liver cancer and negligible toxicity to normal tissue.
In this study, we chose atelocollagen, a cationic protein modified from type-1 collagen, for miR-30c-2-3p intratumoral delivery. Atelocollagen is produced by pepsin treatment to remove the immunogenic amino and carboxyl terminal peptides [10]. The protein retains nucleic acid through electrostatic interactions to form a complex for sustained delivery. Furthermore, atelocollagen’s characteristics, staying in liquid form at
4oC and readily turning to gel form at 37oC, facilitate intratumoral injection and prolonged release of miRNAs [10]. Atelocollagen has been studied for delivery miRNAs in several types of cancer models. For example, miR-34a in nanoparticle complex with atelocollagen showed a significant inhibition of colon tumor growth in vivo [11]. miR-516-3p delivered intratumoral with atelocollagen is reported to have potential in suppressing gastric cancer metastasis [12].
Ovarian cancer in vivo model and the delivery of miRNA-30c-2-3p
The effects of miR-30c-2-3p in ovarian cancer were examined by delivering the miRNA into a xenograft model. We ip injected 5x106 HeyA8 ovarian cancer cells embedded in Extracel® to form solid tumors within 7-10 days. Animals displaying palpable tumors of identical sizes after 10 days were randomized into control (n=3) and treatment (n=4) groups. The treatment group received 6 μM miR-30c-2-3p injection. The treatment (Rx) period occurred after ~4.5 days, beginning 10 days after cell injection and ending on day
24. Tumors were measured using calipers every other day and volumes were confirmed by
3 individuals. Controls reached their endpoint of maximum allowable tumor volume at 35
197 days, while the miR-treated animals were euthanized at 40 days to terminate the study and collect further data. The 2-way ANOVA test indicated significant differences in tumor volumes at 32 (**p<0.01) and 35 (***p<0.001) days.
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m 0 Control miR-30c-2-3p 0 10 16 21 23 25 28 30 32 35 Treatment groups Time (days) Figure A.1. In vivo delivery of miR-30c-2-3p. (A). Female nude mice injected with ovarian cancer cells HeyA8. Treatment started after 10 days when solid tumors were formed. After 40 days, animals were sacrificed and tissues were collected for miRNA analysis. (B) Treated animals were injected with miR-30c-2-3p on day 39. On day 40, animals were sacrificed, tissues collected and miRNA extracted from tumors to assess miR-30c-2-3p expression using qRT-PCR and taqman primers for miR-30c-2-3p.
The results showed in Figure A.1B indicated that the group of mice received intratumoral injection of miR-30c-2-3p had significantly lower tumor volume compared to the control group. The present of miR-30c-2-3p in tumor was confirmed in Figure A.1A, which indicated that miR-30c-2-3p level in treated tumors was about 7 folds higher than control tumors. Our in vivo pilot study suggested that miR-30c-2-3p could be a potential therapy for ovarian cancer treatment. Nevertheless, this application needs further studies for more exhaustive understanding. The stability and efficacy of miRNA-30c-2-3p after delivery at the tumor site need more examination. Also, the antitumor mechanisms of miR-
30c-2-3p and its specific targets in vivo required more experiments to identify
The impact of miR-30c-2-3p on ovarian cancer cells have been reported as partially caused by targeting BCL-9 [13]. In fact, one miRNA can have up to thousands targets due to
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Table A.1. Potential targets of miR-30c-2-3p identified by Target Scan software. Target scores were given to each target based on its complementary to miR-30c- 2-3p, the higher the score, the higher its possibility to be regulated by miR-30c-2- 3p. Genes Target Gene descriptions scores
AAK1 90 AP2-associated protein kinase 1: phosphorylates a subunit of the AP-2 complex, triggered by clathrin DUSP19 87 Dual specificity protein phosphatase 19: interact with ASK1 and MAP2K7 ALPK3 87 Alpha kinase 3
PRKCA 86 Protein kinase C alpha: cell adhesion, transformation, cell cycle checkpoint, and cell volume control MAP3K13 86 Mitogen-activated protein kinase kinase kinase 13: JNK signaling pathway (activating MAPK8/JNK, MAP2K7/MKK7) PAK1 85 Serine/threonine-protein kinase PAK 1 : cell motility and morphology
79 MARCKS-like 1: involved in protein kinase C MARCKSL1 signaling
61 Mitogen-activated protein kinase kinase kinase kinase MAP4K1 1 56 MAP3K3 Mitogen-activated protein kinase kinase 6
69 ATF3 Activating transcription factor 3
of the nature of miRNA regulatory [14]. Is BCL-9 the only target through that miR-30c-2-3p exerts its antitumor effects in ovarian cancer? To explore potential targets of miR-30c-2-3p, we utilize multiple bioinformatics tools, such as TargetScan and miRWalk. Based on the
199 data collected, several genes have been identified with high target scores, indicating high complementary of 3’UTR regions with miR-30c-2-3p seed area, and theirs potential roles in cancer signaling pathway. Interestingly, among the genes with highest target scores, multiples encoded proteins are involved in MAP kinase pathways, which play important roles in many cancer progresses, such as proliferation, survival and metastasis (Table A.1).
Figure A.2. Potential miR-30c-2-3p targets in JNK pathway (components with yellow labels).
Figure A.2 presents a scheme of the JNK signaling pathway. Interestingly, multiple factors are involved in this pathway, such as PAK, HPK1, MEKK13, MEKK3, DUSP19 and
MARCKSL1, have high a possibility of being miR-30c-2-3p targets. In other words, miR-30c-
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2-3p may regulate cell biological progresses through manipulating members of JNK signaling pathways. Furthermore since multiple mediators in the MAPK pathways are induced by LPA, this data suggests a potential that the upregulation of miR-30c-2-3p by
LPA is a protective mechanism cells utilize to silence LPA-mediated oncogenic transcription of MAPK pathways.
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To experimentally confirm the bioinformatics data, we transfected SKOV-3 ovarian cancer cells with miR-30c-2-3p and evaluated changes in expressions of its potential targets. The results are shown in Figure A.3. As predicted, after ectopic expression of miR-
30c-2-3p, multiple potential target genes had significant reduction in mRNA expression.
Quantitative RT-PCR screening showed that MAP4K1, MARCKSL1, DUSP19, GPSM3 and
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LYPLA1P3 have the most significant expression changes when miR-30c-2-3p levels increase.
Since many mediators in the MAPK pathways are also modulated by LPA, in order to conclude the specificity of miR-30c-2-3p silencing, we treated SKOV-3 cells with LPA, miR-
30c-2-3p and a combination of LPA and miR-30c-2-3p. The results reflex which genes are targeted by miR-30c-2-3p individually and help clarify those induced by LPA in ovarian cancer cells.
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For miR-30c-2-3p treatment alone, ovarian cancer cells, SKOV-3, were transfected with 50 nM of miR-30c-2-3p for 48 hours. For LPA treatment, SKOV-3 cells were starved overnight and then treated with 5 μM of LPA for 1 hour. For combined treatment, SKOV-3 cells were transfected with 50nM of miR-30c-2-3p, after 47 hours of transfection and 202 overnight serum starved, cells were continued treated with 5 μM of LPA for 1 hour. Total
RNAs were collected from all samples using Trizol reagent. Changes in expression levels of multiple genes compared to control non-treated cells were evaluated by quantitative PCR.
The results are illustrated in Figure A.4, showing that miR-30c-2-3p targets several genes in opposite direction with LPA such as MARCKSL1, DUSP19 and PAK1. PAK1 is known to regulate actin cytoskeleton and promote cell motility [15]. MARCKS plays critical roles in phospholipase D, an important protein in LPA biogenesis [16]. DUSP family members are important regulators of many targets, such as ERK, JNK, and p38 [17].
Evidence that miR-30c-2-3p independently regulates these targets suggests potential mechanisms of miR-30c-2-3p antitumor effects in ovarian cancer cells.
Ovarian cancer cell line inducibly expresses miR-30c-2-3p
Our in vivo pilot study indicates miR-30c-2-3p can be a potential therapy for ovarian cancer treatment in clinics. When considered in a larger size study, using direct intratumor injection of miRs is expensive due to the amount of high-priced reagent and synthetic miRs needed for many animal subjects. To solve this problem, we developed an ovarian cancer cell line with inducible expression of miR-30c-2-3p. The scheme is shown in Figure A.5.
Ovarian cancer cells HeyA8 were first transfected with a pTet-Off vector, which constitutively expresses the tetracycline-controlled transcriptional transactivator. The successfully transfected cells were selected with Geneticin 418. In the next step, using a cloning method, we built specific TRE -based vector expressing miR-30c-2-3p. In the vector, PTight is an inducible promoter that controls transcription of our gene of interest, in this case, miR-30c-2-3p. The PTight composite promoter consists of a modified Tet-
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Responsive Element (TREmod) containing 7 direct repeats of the tet operator sequence, tetO, which is joined to a minimal CMV promoter (PminCMVΔ). PTight lacks binding sites for endogenous mammalian transcription factors, so it is virtually silent in the absence of induction. In the absence of doxycycline (Dox), Tet-Off Advanced binds to the tetracycline response element (TREMod) in PTight, and produces high-level transcription of the downstream miR-30c-2-3p.
Figure A.5. Developing scheme of cell line with inducible expression of gene of interest (Source: Clontech Inc.)
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PTight -based vector was then transfected into selected HeyA8 pTet-Off cells together with hygromycin marker for later selection. Successful transfected cells were resistant to both geneticin and hygromycin. When doxycycline in medium was depleted, the cells would inducibly express miR-30c-2-3p.
The final selected HeyA8 cells were resistant to both geneticin and hygromycin. We tested these cells for miR-30c-2-3p inducible expression. After doxycycline had been removed from media, total RNAs were isolated and miR-30c-2-3p expression was evaluated at 1, 2 and 3 days.
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The q-PCR result shown in figure A.6 indicates that when doxycycline was removed,
Tet-Off inducible HeyA8 cells express significantly higher miR-30c-2-3p compared to when doxycycline was present. However, in doxycycline medium, Tet-Off inducible HeyA8 cells still express higher miR-30c-2-3p than original HeyA8 cells, indicating that the system is leaky and was not able not control the expression of interested genes as tightly as desired.
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Examining Tet-Off inducible HeyA8 cells under the microscope, we observed that after one day of doxycycline removal, cells started to express green fluorescence, which was encoded in the vector for co-transcription with the gene of interest, miR-30c-2-3p, indicating miR-30c-2-3p transcription. However, after three days exposed to media, with and without doxycycline, the cells in both conditions expressed green fluorescence. The numbers of cells indicated by DAPI staining method in two conditions also appeared to be the same (Figure A.7). The results suggest that the Tet-Off inducible system successfully expresses miR-30c-2-3p into cells indicated by green fluorescence signal but is insufficient to affect cell proliferation during a short time of observation (3 days). Also the expression
Figure A.7. Fluorescence images of HeyA8 cells with inducible miR-30c-2-3p expression. DAPI and green fluorescence images of Tet-Off inducibly express miR-30c-2-3p HeyA8 cells. Cells were treated with or without Dox and fixed with 3.7% formaldehyde after 1 or 3 days. Fixed cells were stained with DAPI and examined under microscope for nuclei or endogenous green fluorescence.
of green fluorescence in cells treated with Dox at day 3 indicated the Tet-Off system might not inhibit the expression of interested gene (miR-30c-2-3p) in presence of Dox completely.
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To evaluate the correlation between miR-30c-2-3p and ATF3, total RNAs was isolated after doxycycline removal and tested for ATF3 expression. Quantitative PCR results showed that there was a slight increase in ATF3 expression after one day (Figure
A.8). However, in day 2 and 3, ATF3 expression levels were significantly reduced in comparison to cells with doxycycline
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Overall, Tet-Off inducible system induces the expression of miR-30c-2-3p in ovarian cancer as well as reduces its target ATF3 level. However, there are also limitations needed to be further addressed. The system requires improvements to increase gene expression efficacy. In others words, the level of miR-30c-2-3p expression needs to be increased sufficiently to have an effect on cell proliferation. Also, the system is expected to control miR-30c-2-3p expression more tightly in the presence of Dox.
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miR-30c-2-3p in exosomes
High body-mass index is a major risk of multiple types of cancers and also associated with infertility, breast cancer and cervical cancers [18-21]. Adipocytes surrounding tumors can secret signaling molecules (such as adipokines, proinflamatory cytokines and proangiogenic factors) and facilitate tumor growth and metastasis [22-24].
Obesity is correlated with a rapid expansion of adipocyte tissue, which induces hypoxia and consequently stimulates angiogenesis to overcome a shortage of nutrients and oxygen [25].
Furthermore, cancer cells in the abdominal cavity, with favorable access to adipocytes, can reprogram adipocytes into cancer-associated adipocytes and use their reservoir of lipids as energy sources for cancer growth [22, 26, 27]. In fact, obesity is one of the factors related to poor prognosis in ovarian cancer [28] and participation in vigorous physical activities prior to an ovarian cancer diagnosis is reported to be associated with a lower risk of ovarian cancer mortality [29]. Interestingly, leptin, a hormone produced by adipocytes to regulate food intake and energy balance, inhibits apoptosis and stimulates OVCAR-3 ovarian cancer cell division [30, 31]. Limited publications have described the link between leptin and microRNAs, while there is evidence that leptin stimulates cancer growth through miR-498 in several types of cancers, including ovarian cancer [32].
MiR-30c (or miR-30c-2-5p), the major strand of miR-30c-2-3p, is reported to be a negative regulator in adipogenesis. Specifically, miR-30c targets microsomal triglyceride transfer protein (MTP), leading to reduction of MTP activity and hyperlipidemia impair.
Indeed, an increase of miR-30c reduces lipid biosynthesis in the liver [33, 34]. On the other hand, a study on human multipotent adipose-derived stem cells (hMADS) pointed out that
208 miR-30c levels are increased during adipogenesis and this induces adipocyte marker gene expression as well as triglyceride accumulation [35].
Previous research has provided evidence that adipocytes surrounding tumors can secrete signaling molecules to stimulate cancer growth and metastasis [22-24]. In addition exosomes are indicated as one of vehicles to carry signaling molecules from cells to cells, or in other words, a mediator of intercellular communication. There are data that suggest the pathological conditions, such as cancer, enhances exosome release [36]. Tumor-derived exosomes can carry tumors antigens as well as function proteins and RNAs, such as miRNAs. Large amount of evidence suggests that exosomes are considered emerging biomarkers and targets for ovarian cancer [36]. Interestingly, one study in breast cancer reports that exosomes secreted from these cancer cells contain miRNAs and the RISC-
Loading Complex, allowing them to independently process precursor miRNAs (pre- miRNAs) into mature miRNAs [37]. The Dicer protein is also shown to be expressed in cancer exosomes and these exosomes can induce tumor formation in a Dicer-dependent manner [37].
In this study, we hypothesized that secreting miR-30c-2-3p to the extracellular environment could be one mechanism that ovarian cancer cells use to protect themselves, and this mechanism is further stimulated by the present of adipocytes.
Co-culture and exosome miRNA extraction
Adipose stem cells were cultured in Mesenchymal Stem Cell Basal Medium for
Adipose cells (American Types Cell Culture, Manassas, VA). SKOV-3 cells were added in for co-culture after four days, when adipose stem cells were about 50% confluence. After 4 days in co-culture, media was collected and exosomes were isolated using miRCURY
209
Exosome isolation kit (Exiqon, Woburn, MA) and total mRNAs were extracted using miRCURY RNA isolation kit (Exiqon). Total cDNAs and specific cDNAs from miRNAs were generated using iScriptTM cDNA Synthesis kit (BioRad) and processed to quantitative PCR as described above.
Our experiment on SKOV-3 cells also shows miR-30c-2-3p is present in exosomes.
Exosomes were collected from cell culture media and analyzed for miR-30c-2-3p expression. Quantitative PCR results showed that the ratio of miR-30c-2-3p levels in exosomes and in intact cells is significantly higher than the same ratio of housekeeping gene 18S (Figure A.9). The results suggest that miR-30c-2-3p may be secreted out of cell membrane through encapsulating into exosomes.
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210
Human adipose-derived stem cells (ASCs) expresses relatively higher miR-30c-2-3p in exosomes compared to intact cells while this ratio is completely reversed for 18S (Table
A.2). Examining the correlation between adipose and ovarian cancer cells, our results show that SKOV-3 cells in co-culture with adipose stem cells (ASCs) release more miR-30c-2-3p into exosomes than SKOV-3 cells cultured alone (Figure A.10).
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In conclusion, miR-30c-2-3p has higher expression ratio in exosomes over intact
SKOV-3 ovarian cancer cells compared to the house-keeping mRNA 18S. Moreover, even though adipocyte stem cells generally express very low miR-30c-2-3p, co-culturing SKOV-3 with adipocytes also facilitates more secretion of miR-30c-2-3p into exosomes compared to
SKOV-3 cells alone. Another possibility is the adipocyte-conditioned microenvironment may induce the secretion of antitumor factors out of cancer cells. Secreting miR-30c-2-3p into the extracellular environment could be one mechanism that ovarian cancer cells use to protect themselves, and this mechanism is further stimulated by the present of adipocytes.
211
It is also reported that exosomes contain pre-miRNAs, Dicer and RISC Loading
Complex, sufficient for an independent process to generate mature miRNAs [37]. The data collected from this project, together with our previous data on the Dicer experiments, shows potential for further investigation of the link between miR-30c-2-3p – Dicer and
Table A.2. Absolute Ct values of miR-30c-2-3p and 18S from exosomes and intact adipose stem cells (Data of intact cells were kindly provided by Ali Alshamrani). RNA expressed Exosomes Intact cells miR-30-2-3p 34.85 ± 0.07 Undetermined
18S 33.75 ± 0.53 13.48 ± 0.14
miR-30c-2-3p – adipocytes. More experiments need to be done in order to attain a comprehensive conclusion. For example, it is necessary to overexpress Dicer and examine the effects of miR-30c-2-3p secretion from SKOV-3 cells alone or in co-culture with adipocytes. The new research hypothesis may be “Dicer and miR-30c-2-3p are secreted out of cancer cells through exosomes after LPA treatment”.
In order to further our understanding, the presence of Dicer in exosomes secreted from ovarian cancer cells requires confirmation experimentally. At the same time, the impact of LPA treatment on miR-30c-2-3p secretion from ovarian cancer is needed. Lastly, even though 18S is proved to be a stable housekeeping gene in ovarian cancer cells, another internal control is desired to normalize miRNAs expression in exosomes.
212
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APPENDIX B
ADDITIONAL DATA OF ATF3
ATF3 interaction with miR-30c-2-3p upstream sequence
To identify the specific nucleotide sequence that ATF3 binds to, which we hypothesize is upstream of the miR-30c-2 gene, we designed a gel shift assay to confirm these putative sequences. The purpose of such experiments was to conclusively define the ATF3 binding site upstream of miR-30c-2-3p, which would serve to regulate transcription of this gene and thus the production of the miRNA.
After screening approximately 5k base pairs above the miR-30c gene starting point, we located several putative ATF3 binding regions by using PROMO (ALGGEN
Research Software). We next selected the most promising sequence at 680 bp upstream (5’ ATAACTTAG GAAGCAACTG ACTCCATTCA 3’) for further analysis
(Figure B.1). For this subsequent experiment, we mutated this strand
(5’ATAACTTTAG GCCGCAACGC CGTCGGTGGC 3’) for a series of gel shift experiments comparing the probes.
DNA sequences were synthesized with 3’-bionylated labeled by Integrated
DNA Technology (Coralville, IA). Cells were transfected with an ATF3 overexpression vector Precision LentiORF (DharmaconRNAi, Lafayett, CO, USA)) or control vector, and after 48 hours were either treated with LPA for 1 hour or directly processed to nuclear protein extraction using Nuclear Extraction kit
(Affymetrix, Santa Clara, CA), following the manufacturer’s instruction. For a
216 positive control, pure ATF3 protein was purchased from OriGene (Rockville, MD,
USA). The proteins were then incubated with pre-labeled sequences for 30 minutes at 15°C, following the instruction of the EMSA GelShift kit (Affymetrix, Santa Clara,
CA). Mixtures were separated on non-denaturing TBE-polyacrylamide gels. An ATF3 unlabeled competitive sequence was provided by theEMSA GelShift kit (Affymetrix,
Santa Clara, CA). DNA-protein complexes were transferred to a Biodyne B nylon membrane. Bands were then visualized using a FluorChem HD2 Imaging System
(Alpha Innotech, ProteinSimple, San Leandro, CA, USA). The experiment was repeated three times.
Figure B.1: Location of potential ATF3 binding sequence. After using PROMO software to locate potential binding sequences of ATF3 in the upstream region of miR-30c gene, we choose one sequence at 680 base pairs upstream of the gene to process to gel shift assay in order to confirm direct interaction between ATF3 protein and this DNA sequence.
Result from the gel shift assay shows shifts of DNA bands, indicating existing interactions between protein and DNA (Figure B.2). However, the shifts appear in multiple conditions, including ATF3 overexpressing extracts and control extracts, with or without LPA. Mutation of the DNA sequence eliminating the shifting band, suggesting that the sequence facilitates protein binding. However, the DNA-protein interaction is not specific for the ATF3 protein, nor does it appear to be attenuated by LPA withdrawal. The result from this assay suggests that the chosen DNA sequence may not be the site that ATF3 directly binds to miR-30c-2-3p up-stream to
217
Figure B.2 : Results obtained from gelshift assay. Lane 1: Purified ATF3 + positive ATF3 probe (provided by EMSA kit); Lane 2: Purified ATF3 + our experimental probe; Lane 3: blank; Lane 4: Positive probe; Lane 5: experimental probe; Lane 6: Mutated probe; Lane 7: blank; Lane 8: ATF3 overexpress nuclear extract + LPA + experimental probe; Lane 9: ATF3 overexpress nuclear extract + experimental probe; Lane 10: ATF3 overexpress nuclear extract + LPA + mutated probe; Lane 11: ATF3 overexpress nuclear extract + experimental probe; Lane 12: control nuclear extract + LPA + experimental probe; Lane 13: control nuclear extract + experimental probe; Lane 14: control nuclear extract + LPA + mutated probe; Lane 15: control nuclear extract + mutated probe induce this gene expression. Further experiments using additional DNA sequences at different locations are necessary to determine the exact site of transcription factor binding.
Targets of ATF3 overexpression
In a another experiment, we examined the impact of ATF3 on ovarian cancer cells. In this part of the project, we identified potential targets of ATF3 expression to further understand the broad impact and implication of ATF3 expression in this
218 system. Therefore, ovarian cancer cells SKOV-3 were transfected with ATF3 overexpression vector. After 48 hours, total mRNAs were collected and quantified for mRNA transcript expression. First, we examined the changes in the expression of genes predicted to be targeted by miR-30c-2-3p to confirm the possibilty that ATF3 and miR-30c-2-3p simultaneously target same genes for final effects in ovarian cancer cells, or ATF3 exerts its regulation effects on these genes through modulating miR-30c-2-3p.
SKOV-3, n=3 A B SKOV-3, n=3 2.0 *** 2.0
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*** *** *** 0.0 0.0 l 3 1 1 9 6 3 3 l 1 3 3 o 1 L 1 P P o 2 1 tr K P M tr p B R D K 4 S A P 1 n F B A n 3 P K S K S A F M o P A C U L o G C A D A G P C T G S M R Y T M A L M Target genes Target genes Figure B.3: Expressions of multiple genes 48 hours after ovarian cancer cells were transfected with ATF3 overexpression vector. (A) Genes which are potential miR- 30c-2-3p targets; (B) genes which are indicated previously in literature as ATF3 targets.
The results presented in Figure B.3A shows that overexpression of ATF3 seems to significantly reduce the expression of majority of miR-30c-2-3p potential targets, such as MAP3K13, MAP4K1, MARCKSL1, DUSP19, GSPM3 and LYPLA1P3.
Particularly, only AKAP6 appears to stand out as the only gene in the set being induced by ATF3 overexpression. On the other hand, there are potential targets of
ATF3 described in literature, which are not shared with miR-30c-2-3p, such as p21,
219
TGF beta and SMAD [1-3]. The results illustrated in Figure B.3B suggest that, in
SKOV-3 ovarian cancer cell, ATF3 overexpression does not affect TGFβ and SMAD expression as indicated in other studies. However, it does inhibit the expression of p21, an antitumor factor that interferes cell cycle progression, but induces the expression of TGFβ receptor 3, a seemingly protective factor in ovarian cancer [4-6].
In summary, the overexpression of ATF3 may alter the expressions of multiple genes involved in ovarian cancer pathology, either dependent or independent with its induction effects on miR-30c-2-3p. More experiments are needed in order to confirm the data and provide more details about the mechanisms of ATF3 activities in ovarian cancer.
220
REFERENCES
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[2] Yin X, Wolford CC, Chang YS, McConoughey SJ, Ramsey SA, Aderem A, et al. ATF3, an adaptive-response gene, enhances TGF{beta} signaling and cancer-initiating cell features in breast cancer cells. Journal of cell science. 2010;123:3558-65.
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[4] Pasche B. Role of transforming growth factor beta in cancer. J Cell Physiol. 2001;186:153-68.
[5] Berchuck A, Rodriguez G, Olt G, Whitaker R, Boente MP, Arrick BA, et al. Regulation of Growth of Normal Ovarian Epithelial-Cells and Ovarian-Cancer Cell- Lines by Transforming Growth-Factor-Beta. Am J Obstet Gynecol. 1992;166:676-84.
[6] Berchuck A, Olt GJ, Everitt L, Soisson AP, Bast RC, Boyer CM. The Role of Peptide Growth-Factors in Epithelial Ovarian-Cancer. Obstet Gynecol. 1990;75:255-62.
[7] Saunders DE, Lozon MM, Corombos JD, Brooks SC. Role of porcine endometrial estrogen sulfotransferase in progesterone mediated downregulation of estrogen receptor. J Steroid Biochem. 1989;32:749-57.
221
APPENDIX C
SUPPRESSION OF THE GTPASE-ACTIVATING PROTEIN RGS10 INCREASES RHEB-
GTP AND MTOR SIGNALING IN OVARIAN CANCER CELLS4
Suppression of the GTPase-activating protein RGS10 increases Rheb-GTP and
mTOR signaling in ovarian cancer cells
Methods
Reducing RGS10 expression
SKOV-3 ovarian cancer cells were plated in 6-well dishes at 120,000 cells/well and 100,000 cells/well, respectively. The plated cells were incubated for approximately 18 h at 37 °C in 5% CO2 and then transfected with siGENOME RISC- free control (siRISC) or Dharmacon SmartPools siRNA targeting RGS10 (Thermo
Fisher Scientific, Waltham, MA), following manufacturer's recommended protocol.
The siRISC is chemically modified to impair processing and uptake by RISC, which isolates cellular effects related to transfection, but unrelated to siRGS10. In other experiments requiring transfection in a 96-well plate, 100 nM concentration of siRNA and 0.25 µL of Dharmafect 1 transfection reagent (Thermo Fisher Scientific) were used per well. Transfection medium was replaced with DMEM medium with
10% FBS after 8 h. Transfected cells were incubated for another 30 h and all assays were performed approximately 48 h post-transfection.
4 M. Altman, A. Alshamrani, W. Jia, H. Nguyen, J. Fambrough, S. Tran, M. Patel, P. Hoseinzadeh, A. Beedle, M. Murph (2015), Cancer Letters, 369 (1): 175-183. Reprinted here with permission of publisher.
222
Immunoblotting
At 48 h post-transfection, the cells were lysed in buffer containing protease/phosphatase inhibitor cocktail (Cell Signaling Technology, Danvers, MA) and processed for SDS-PAGE. After transferring the denatured proteins to nitrocellulose membranes, the blots were probed with primary antibodies for either
RGS10 (Santa Cruz Biotechnology, Dallas, TX) or GAPDH (Cell Signaling Technology,
Danvers, MA). For detection of RGS10, membranes were incubated for 1.5 h with goat anti-RGS10 primary antibody diluted in 5% w/v BSA in 1× TBS-T at room temperature. Secondary donkey anti-goat HRP conjugated antibody was diluted in
1% w/v BSA in 1× TBS-T and the membrane was incubated for 1.5 h at room temperature. GAPDH blots were washed with TBS-T and probed with anti-mouse
HRP conjugated secondary antibodies (Amersham) diluted 1:2000 in 2.5% milk
TBS-T and incubated for approximately 2 h. The proteins were detected using a
Fluorchem HD2 chemiluminescent imaging system (Protein Simple, Santa Clara, CA) with SuperSignal™ West Dura Extended Duration Substrate (Thermo Fisher
Scientific). Protein bands were quantified using Image J (National Institutes of
Health, Bethesda, MD). Representative blots are shown and these reflect data that were repeated at least four times.
Rheb activation assay
SKOV-3 cells were plated in 10 cm dishes at a density of approximately
400,000 cells per dish and incubated overnight at 37 °C. The cells were transfected with siRGS10, siRISC, pcDNA, and RGS10 plasmid, where indicated. After 48 h, the culture media were removed and cells were rinsed with ice-cold PBS. Then, 1 mL of
223 ice-cold lysis buffer containing protease and phosphatase inhibitors was added to each dish. Plates were placed on ice for 10–20 min with agitation every 5 min.
Lysates were cleared by centrifugation for 10 min at 12,000 × g at 4 °C. The protein supernatant was collected and stored at −80 °C until quantified by BCA assay. Next,
0.5–1 mL of cell lysate was aliquoted to microcentrifuge tubes and steps were performed according to the manufacturer's protocol for the Rheb Activation Assay
(NewEast Biosciences, Malvern, PA). Briefly, the assay utilizes anti-active Rheb mouse monoclonal antibodies (specifically recognizes Rheb-GTP from vertebrates) during the cell lysate incubation with gentle agitation. Thus, bound and active Rheb is pulled down by protein A/G agarose and precipitated active Rheb is detected using immunoblot analysis with anti-Rheb rabbit polyclonal antibody. For the
GTPγS/GDP protein loading, 20 µL of 0.5 M EDTA (20 mM final concentration) was added to each microcentrifuge tube prior to the addition of 5 µL of 100× GTPγS
(positive control) or GDP (negative control) to the appropriate individual tubes, which were then incubated for 30 min at 30 °C with agitation. Loading was stopped by putting tubes on ice and adding 32.5 µL of 1 M MgCl2. In a similar experiment,
1 µg of purified human full-length Rheb protein (Abcam, Cambridge, MA) was treated with GDP or GTPγS prior to immunoprecipitation with anti-active Rheb and immunoblotting with total Rheb. Following these procedures, electrophoresis was performed using approximately 20 µL/well of the pull-down supernatant loaded onto a polyacrylamide gel (17%) and protein bands were resolved by immunoblotting on a nitrocellulose membrane. Proteins were detected by ECL using
224
SuperSignal West Pico Chemiluminescent Substrate. Results were repeated and observed three times.
Figure C.1: Evaluation of the GAP activity of RGS10 (A) SKOV-3 cells were transfected with either siRISC or siRGS10 prior to immunoprecipitation with an active Rheb monoclonal antibody and immunoblotting with total Rheb rabbit polyclonal antibody. (B) In a separate experiment, cells were treated as indicated above, but GTPγS and GDP were added to the cell extracts in vitro and incubated for 30 min prior to the pull-down of active Rheb and blot for total Rheb (for details see Materials and Methods). (C) In the reciprocal experiment, purified, full-length Rheb protein was incubated with GTPγS or GDP prior to immunoprecipitation with active Rheb and immunoblot for total Rheb. (D) Cells were treated as previously described and also transiently transfected with an RGS10 expression vector prior to immunoprecipitation. Following transfection, cell extracts were probed for RGS10, total Rheb and Rheb. Other extracts were immunoprecipitated with anti-RGS10 goat polyclonal antibody and blotted with anti-total Rheb rabbit polyclonal antibody. Unbound fraction is also shown.
Results
In order to identify the molecular mechanism explaining how RGS10 suppression influences the phosphorylation of 4E-BP1 and other proteins in the mTOR signaling pathway (as described in original article), we assessed direct regulators of mTOR signaling and observed Rheb activity changes. We repeatedly detect an abundance of activated Rheb bound to GTP when RGS10 is suppressed
(Figure C.1A, 1.4 vs 1.0). In comparison to RISC-free control siRNA conditions
225
(siRISC), after suppression of RGS10 and incubation with GTPγS, we observed an increase in the bound GTPγS upon immunoprecipitation of active Rheb (Figure
C.1B, ~ 2.3). Similarly, purified Rheb protein incubated with GTPγS or GDP prior to immunoprecipitation of active GTP-Rheb and assessment of the total protein showed an increase (Figure C.1C). This substantiates the specificity of the Rheb antibodies and the system used, which was also reported by other groups [1] and [2]. To confirm our results, we performed the reciprocal set of experiments using an RGS10-specific antibody. After RGS10 suppression, the association with Rheb is dramatically reduced in comparison to siRISC control and/or increased with RGS10 expression (Figure C.1D).
226
REFERENCE [1] Wang CC, Held RG, Hall BJ. SynGAP Regulates Protein Synthesis and Homeostatic Synaptic Plasticity in Developing Cortical Networks. Plos One. 2013;8.
[2] Lopez-Rivera E, Jayaraman P, Parikh F, Davies MA, Ekmekcioglu S, Izadmehr S, et al. Inducible nitric oxide synthase drives mTOR pathway activation and proliferation of human melanoma by reversible nitrosylation of TSC2. Cancer research. 2014;74:1067-78.
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APPENDIX D
VINYL SULPHONE ANALOGS OF LYSOPHOSPHATIDYLCHOLINE IRREVERSIBLY INHIBIT
AUTOTAXIN AND PREVENT ANGIOGENESIS IN MELANOMA5
Animal model of melanoma
Six-week old female athymic nude mice acclimated to the animal facility for one week prior to the study commencement. Animals were anesthetized before tumor cell injection into their right flank with Glycosan Extracel® (BioTime, Inc, Alameda, CA) containing approximately 1 × 106 MeWo cells per 0.15 mL injection. Extracel® was used in this study over traditional Matrigel to eliminate potential interference of exogenous mouse growth factors which could have affected our in vivo study. In addition, Glycosan Extracel® allows us to conserve resources through the achievement of a 100% tumor efficiency rate, whereby this rate is typically unachievable otherwise. Injected mice were measured for tumor formation, tumor volume, body weight and body conditioning scores. After 3 weeks,
100% of mice displayed tumor formation at which time they were randomized into treatment groups; PBS control (n = 10), DMF control (n = 10), HA-130 at 30 mg/kg (n = 5) and PF-8380 at 30 mg/kg (n = 5) dose per 0.1 mL injection. Mice in each treatment group were anesthetized (2–4% isoflurane) before i.p. treatment injections three times a week over the course of the 65-day study. The animals were euthanized according to the animal use protocol approved by the University of Georgia IACUC committee. The tumor volume
5 M. Murph, G. Jiang, M. Altman, W. Jia, D. Nguyen, J. Fambrough, W. Harman, H. Nguyen, S. Tran, A. Alshamrani, D. Madan, J. Zhang, G. Prestwich (2015), Bioorganic & Medicinal Chemistry, 23:5999-6013. Reprinted here with permission of publisher. 228
(mm3) was calculated using the equation: tumor volume = (width)2 × length/2, and then graphed using GraphPad Prism (La Jolla, CA). The tumor volume for each group and overall significance was plotted.
Figure D.1. The autotaxin inhibitors HA-130 and PF-8380 did not impact tumor progression in a xenograft model of melanoma. Animals with melanoma tumors were treated with the indicated concentrations of either: HA-130 (A), PF-8380 (B) or dimethylformamide (DMF) solvent control. The established pigmented tumors were measured with calipers every other day.
Using the in vivo model of melanoma progression, we tested the efficacy of HA-130 and PF-8380. Surprisingly, neither of these ATX inhibitors reached statistical significance against the continuous growth of tumors from MeWo cell inoculation in mice (Figure
D.1A and B).
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