MicroRNAs as prognostic markers in ovarian cancer.

Molecular and Cellular Endocrinology 390 (2014) 73–84

Contents lists available at ScienceDirect

Molecular and Cellular Endocrinology journal homepage: www.elsevier.com/locate/mce

Review

MicroRNAs as prognostic markers in ovarian cancer Marta Llauradó a,b,1, Blanca Majem b,1, Tatiana Altadill b, Lucia Lanau b, Josep Castellví c, Jose Luis Sánchez-Iglesias d, Silvia Cabrera d, Javier De la Torre d, Berta Díaz-Feijoo d, Asuncion Pérez-Benavente d, Eva Colás b, Mireia Olivan b, Andreas Doll b, Francesc Alameda e, Xavier Matias-Guiu f, Gema Moreno-Bueno g, Mark S Carey h, Josep Maria Del Campo i, Antonio Gil-Moreno d,j, Jaume Reventós b,j,k,l,⇑, Marina Rigau b a

Faculty of Medicine, University of British Columbia, Vancouver, Canada Research Unit in Biomedicine and Translational Oncology, Vall Hebron Research Institute University Hospital, Barcelona, Spain c Department of Pathology, Vall Hebron University Hospital, Barcelona, Spain d Department of Gynecological Oncology, Vall Hebron University Hospital, Barcelona, Spain e Department of Pathology, Hospital del Mar, Universitat Autònoma de Barcelona, Barcelona, Spain f Department of Pathology and Molecular Genetics and Research Laboratory, Hospital Universitari Arnau de Vilanova, University of Lleida, IRBLLEIDA, Lleida, Spain g Departamento de Bioquímica, Universidad Autónoma de Madrid (UAM), Instituto de Investigaciones Biomédicas "Alberto Sols" (CSIC-UAM), IdiPAZ, 28029, Madrid, Spain & Fundación MD Anderson Internacional, 28033 Madrid, Spain h Division of Gynecologic Oncology, University of British Columbia and BC Cancer Agency, Vancouver, BC, Canada i Division of Gynecology and Head and Neck, Department of Oncology, Vall Hebron University Hospital, Barcelona, Spain j Faculty of Medicine, Autonomous University of Barcelona, Barcelona, Spain k Departament de Ciències Bàsiques, Universitat Internacional de Catalunya, Barcelona, Spain l IDIBELL- Bellvitge Biomedical Research Institute, Barcelona, Spain b

a r t i c l e

i n f o

Article history: Received 5 December 2013 Received in revised form 9 February 2014 Accepted 25 March 2014 Available online 18 April 2014 Keywords: Ovarian cancer Chemotherapy Tumor resistance Prediction Prognosis miRNAs

a b s t r a c t Ovarian cancer (OC) is the most lethal gynecological malignancy among women. Over 70% of women with OC are diagnosed in advanced stages and most of these cases are incurable. Although most patients respond well to primary chemotherapy, tumors become resistant to treatment. Mechanisms of chemoresistance in cancer cells may be associated with mutational events and/or alterations of gene expression through epigenetic events. Although focusing on known genes has already yielded new information, previously unknown non-coding RNAs, such as microRNAs (miRNAs), also lead insight into the biology of chemoresistance. In this review we summarize the current evidence examining the role of miRNAs as biomarkers of response and survival to therapy in OC. Beside their clinical implications, we also discuss important differences between studies that may have limited their use as clinical biomarkers and suggest new approaches. Ó 2014 Elsevier Ireland Ltd. All rights reserved.

Contents 1. 2. 3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Molecular events related with poor prognosis in ovarian cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MicroRNAs in ovarian cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. MicroRNAs associated with chemoresistance in ovarian cancer cell lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. MicroRNAs associated with chemoresistance in ovarian tumors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

74 74 76 76 79

Abbreviations: OC, Ovarian cancer; OS, overall survival; miRNAs, microRNAs; HG-SOC, high-grade serous OC; TCGA, The Cancer Genome Atlas; SNPs, single nucleotide polymorphisms; PFS, progression-free survival; FFPE, formalin-fixed paraffin-embedded; EMT, epithelial-to-mesenchymal transition; OSE, ovarian surface epithelium; RNAi, RNA interference; siRNA, small interfering RNA; EVs, extracellular vesicles. ⇑ Corresponding author. Address: Biomedical Research Unit, Vall Hebron Institute of Research, Edifici Collserola, Laboratori 209, Pg. Vall d’Hebron 119-129, 08035 Barcelona, Spain. Tel.: +34 934894052; fax: +34 934894015. E-mail address: [email protected] (J. Reventós). 1 Equally contributing. http://dx.doi.org/10.1016/j.mce.2014.03.006 0303-7207/Ó 2014 Elsevier Ireland Ltd. All rights reserved.

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4. 5. 6.

M. Llauradó et al. / Molecular and Cellular Endocrinology 390 (2014) 73–84

Limitations of microRNA studies in ovarian cancer . . . . . . . . . . . . . Application of prognostic microRNAs in ovarian cancer therapies . Conclusions and future directions. . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Ovarian cancer (OC) is the fifth most common cancer and the most lethal gynecological malignancy among women in the Western world, leading to 22.400 newly diagnosed cancer cases and over 14.300 deaths every year in the US (Siegel et al., 2013). Basically, this poor prognosis is due to (a) the insidious asymptomatic nature of this disease in its early onset, (b) the lack of robust and minimally invasive methods to detect the disease at an early stage, and (c) the acquisition of tumor resistance to chemotherapy. After a suspicious physical examination, CA-125 blood test and transvaginal ultrasound screening methods are performed in order to diagnose OC. Nevertheless, the final diagnosis is still performed at the time of the surgery. At the time that OC is diagnosed when the disease is still confined to the ovaries (stage I), the 5-year survival rate exceeds 90%; unfortunately, only 15% of all patients are diagnosed at early stage. The majority of patients (80%) are diagnosed at stage III or IV presenting distant metastases and, despite high initial responsiveness to chemotherapy are observed among them, survival rates remain poor (Siegel et al., 2013). In advanced OC, chemotherapy resistance develops in most (70%) of patients during their treatment (Markman and Bookman, 2000). The majority of patients relapse within 2–3 years following primary chemotherapy and subsequent treatment after relapse is usually less effective than primary treatment. Platinum-resistant, recurrent or persistent disease patients are treated by a variety of agents including topotecan, doxorubicin, gemcitabine, paclitaxel, and/or docetaxel, or liposomal doxorubicin (Stein et al., 2013; Lawrie et al., 2013; Su et al., 2013; Polyzos et al., 2005). Unfortunately, over the last 30 years, no single agent has demonstrated clear superiority in this setting, and response rates are generally less than 20% (Siegel et al., 2013). One of the greatest impediments to improve response rates, and subsequent outcome for patients with OC, is the incomplete understanding of the molecular underpinnings of OC cell chemosensitivity/chemoresistance and afterwards the correct selection of the best treatment for each group of patients (Fig. 1). Therefore, there is an urgent need to discriminate between patients who will or will not benefit from chemotherapy and this

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79 80 81 82 82

will probably have significant clinical implications in the management of OC. Moreover, identification of the molecular pathways involved in primary and acquired drug resistance will play a prominent role in the establishment of rational therapeutic approaches aimed to circumvent or retard the acquisition of the drug-resistant phenotypes. Recently, epigenetic mechanisms like DNA methylation, histone modification, and microRNA (miRNA) regulation have been associated with the resistance of cancer cells to chemotherapy (Zhang et al., 2008).

2. Molecular events related with poor prognosis in ovarian cancer A considerable number of prognostic factors have shown to predict prognosis in OC. Stage, age, grade, performance status and residual disease remain the most important. As far as residual disease is concerned, the presence or absence of macroscopic residuum is the best discriminator of outcome (Hoskins et al., 1994). Currently, the definition of optimal cytoreductive surgery (or debulking surgery) most widely accepted is the definition of residual disease equal to 0 cm (Vergote et al., 2010). Cytoreductive surgery is an important part of the management of advanced OC either prior to or after combination with chemotherapy, based on platinum/paclitaxel (Vergote et al., 2010). In the last decades there has been a considerable advance in our ability to interrogate the molecular events of cancer. Modern technologies (next generation sequencing, methylation arrays, etc.) have allowed us to better understand changes in gene expression, gene mutation, pathway activation and regulation. With the same goal OC has been classified into four major histologic subtypes (serous, mucinous, endometrioid, and clear cell), with serous being the most common histologic type (70% of total OC cases) (Koonings et al., 1989; Seidman et al., 2004). Current data indicate that each of these histologic types are associated with distinct morphologic features, genetic alterations and prognosis (Bast et al., 2009). Recently, The Cancer Genome Atlas (TCGA) Research Network has analyzed messenger RNA (mRNA) expression, miRNA expression, promoter methylation and DNA copy number in 489

Fig. 1. Ovarian cancer outcome. Classification of ovarian cancer patients depending on their resistance/sensitivity to first-line treatment.

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M. Llauradó et al. / Molecular and Cellular Endocrinology 390 (2014) 73–84 Table 1 Ovarian cancer prognostic signatures derived from studies based on TCGA data. Study

Discovery cohort

Validation cohort

Groups

Signature

Number of molecules

TCGA, Nature (2011)

215 stage II-IV HG-SOC TCGA data (489 HG-SOC) TCGA data (489 HGSOC) 260 HG-SOC Japanese data set A TCGA data (304 HG-SOC) TCGA data (174 HG-SOC) TCGA data (489 HG-SOC) TCGA data (232 HG-SOC) TCGA and MSKCC data sets 13 OCAC studies and TCGA data (4616 HG-SOC) TCGA data (489 HG-SOC)

255 Independent cohort of HG-SOC 226 Independent cohort of HG-SOC 879 Publicly available data of HG-SOC Tothill’s, Bonome’s, Dressman’s, TCGA and Japanese data set B Berchuck and Tothill datasets

< > 60 months OS

193

19.4 months PFS and < > 60 months OS < > 60 months OS

193 Prognostic gene signature 9 Proteins PROVAR signature of PFS and OS 100 Genes CLOVAR algorithm

9

52 months OS (median)

126 Gene signature for OS

126

5-years OS

23-DNA repair genes

23

Platinum response ( 60 months OS

Hypermutation in platinum sensitive group 3 SNPs signature of OS

–

277-Gene signature

277

3 genes signature for PFS

3

3 SNPs of ABCB1 gene signature

3

240 Different OC histologies

Platinum response (< 6 months PFS) Recurrence-free survival >5 years Platinum response ( 60 months OS

166 Gene signature

166

TCGA data (83 HG-SOC)

13 months PFS (median)

Two molecular classifications by CNA and methylations status; 5 gene signatures for OS

–

Yang et al. J Clin Invest. (2013) Verhaak et al. J Clin Invest (2013) Yoshihara et al. Clin Cancer Res (2012) Kang et al. J Natl Cancer Inst (2012) Sohn et al. Gynecologic Oncology (2012) Braun et al. PLoS One (2013) Liu et al. PLoS One (2012) Barlin et al. Gynecologic Oncology (2013) Johnatty et al. Gynecologic Oncology (2013) Sfakianos et al. Gynecologic Oncology (2013) Hsu et al. BMC Genomics (2012)

TCGA data (85 HG-SOC)

– – 244 HG-SOC Australian data set and 261 HG-SOC TCGA data set 66 Independent cohort HG-SOC 4025 Independent HG-SOC

high-grade serous OC (HG-SOC), and the DNA sequences of exons from coding genes in 316 of these tumors. The results of this complete study pointed out a transcriptional signature associated with survival and shed new light on the impact on survival of tumors with BRCA1/2 and CCNE1 mutations (TCGA, 2011). Using a training dataset of gene expression profiles from 215 HG-SOC, a prognostic gene signature for overall survival (OS) comprising 193 genes was defined. The signature was then validated in a cohort of 255 samples. The results of these analyses confirmed that HG-SOC mutational profiles are highly diverse. While p53 is mutated in >90% of HG-SOC tumors, the study disclosed many other low frequency somatic mutations, such as BRCA1, BRCA2, NF1, RB1, CSMD3, GABRA6, FAT3, and CDK12, which were only present in 2–6% of samples. Analyses delineated four OC transcriptional subtypes, three miRNA subtypes, four promoter methylation subtypes and a transcriptional signature associated with survival duration, and shed new light on the impact that tumors with BRCA1/2 and CCNE1 aberrations have on survival. Furthermore, pathway analyses suggested that homologous recombination is defective in about half of the tumors analyzed, and that NOTCH and FOXM1 signaling are involved in HG-SOC pathophysiology. Therefore, these results represent an opportunity to determine how each of the HG-SOC subtype emerges and thereafter how they can be controlled by treatment. The impact that this study has generated in the scientific community is very large, since many studies have already been carried out and many more are about to come. Some examples of that, which have used the public TCGA dataset, related to HG-SOC prognosis, are listed in Table 1. Several groups have generated tools for patient risk stratification and for predicting clinical outcome focusing on OC resistance to treatment. Yang et al. (2013) took protein data from the TCGA study and generated a model called PROtein-derived index of OVARian cancer (PROVAR) to predict progression and time to tumor recurrence. Verhaak et al. (2013) developed a subtype and survival gene expression signatures from TCGA data that, when

100

3

combined, provide a prognostic model, named CLassification of OVARian cancer (CLOVAR), which help to outcome classification. Yoshihara et al. (2012) used gene expression data to establish 126-gene signature risk classification system for predicting clinical outcome. Kang et al. (2012) evaluated gene expression data for 151 DNA repair genes and defined a molecular score based on the expression of platinum-induced DNA damage repair genes. Sohn et al. (2012) analyzed whole exome sequences data regarding platinum response and classified patients by mutation status, finding that patients with somatic hypermutation were more likely to be platinum sensitive. Braun et al. (2013) investigated associations between germline polymorphisms and OC from TCGA data and defined significant loci, which are located near genes previously reported as having a possible relationship with chemoresistance. Liu et al. (2012) described a gene signature which provides robustness in accurately predicting chemotherapy response. The combination of the molecular and morphologic signatures yields a new understanding of potential mechanisms involved in drug resistance. Barlin et al. (2013) used TCGA samples together with new samples in patients who underwent primary cytoreductive surgery and platinum-based chemotherapy. A curative-intent group (recurrence-free survival of >5 years) and a long-term recurrent group (patients who recurred but survived >5 years) were defined. After microarray analysis and validation, three genes (CYP4B1, CEPT1, CHMP4A) were successfully validated and therefore represent plausible targets for further study. More recently, a comprehensive analysis from the OC Association Consortium together with the TCGA studied 21 single nucleotide polimorphisms (SNPs) of ABCB1. This gene encodes a multi-drug efflux pump P-glycoprotein (P-gp) and has been implicated in multi-drug resistance. They concluded that, rs1128503, as well as other SNPs linked to it may have an effect on OS (Johnatty et al., 2013). Finally, Sfakianos et al. (2013) analyzed the RNA extracted from formalinfixed paraffin-embedded (FFPE) sections from 240 primary OC and they found that, similar to Tothill and collegues (Tothill et al., 2008), the most HG-SOC (93%) were assigned to subtypes 1, 2, 4

76


and 5, whereas most endometrioid, clear cell, mucinous and low grade SOC (68%) were assigned to subtypes 3 and 6. Indicating that combinations of probes have robust ability to predict survival and subtype in FFPE tissues. They end up with a 166-gene signature, demonstrating that this survival signature is robust and retains prognostic information in FFPE expression data. It is worth pointing out that the diversity of adjuvant treatments and the variability in the definition of residual disease existing in TCGA patients makes the study of chemotherapeutic response challenging. Trying to overcome these limitations, Hsu et al. (2012) analyzed TCGA data by focusing on the most common chemotherapy combination (paclitaxel and carboplatin) allowing them the identification of two molecular classifications, which exhibit poor progression-free survival (PFS) and might be associated with poor chemotherapy response. Comparing patients with poor or good prognosis SF3A3, GNL2, RRAGC, RFC3, and ENC1 were found to be differentially expressed and possibly useful for predicting platinum-paclitaxel chemotherapy response.

3. MicroRNAs in ovarian cancer The discovery of miRNAs has opened up a new field in cancer research with potential novel applications in diagnostics and therapy (Croce, 2009; Bader et al., 2011). Previously, miRNAs were not considered to be biologically significant as mRNA was. However, they are an abundant class of small non-coding RNAs (with an average length of 22 bp), which are evolutionarily conserved and function as regulators of gene expression. Since their discovery in the early 1990s in Caenorhabditis elegans (Lee et al., 1993), 1872 precursors and 2578 mature human miRNAs have been identified and registered in miRBase Release 20 (June 2013). Moreover, miRNA deregulation has been detected in different human malignancies. Currently, 1872 precursors and 2578 mature human miRNAs are registered in miRBase Release 20 (June 2013). The relatively recent discovery of miRNA has revealed the existence of a new level control of the signaling pathways involved in the regulation of cellular functions (Gangaraju and Lin, 2009; Bushati and Cohen, 2007). Thus, aberrant expression of miRNAs can impact deeply on multiple features of cell biology resulting in complex downstream pathological events, such as cancer. Technically, miRNAs exert their negative regulation either by degrading the target mRNA, when they bind with near-perfect complementarity (Yekta et al., 2004), or by post-transcriptionally repressing target-gene expression when they bind with imperfect complementarity (Esquela-Kerscher and Slack, 2006). It has been recently shown that besides acting as gene repressors, miRNAs can also activate gene expression by interacting with complementary regions found in the promoter, coding region, as well as the 3’UTR of their mRNA targets (Breving and Esquela-Kerscher, 2010). Indeed, each miRNA can regulate a large number of mRNAs, many of which control important cellular activities (EsquelaKerscher and Slack, 2006; Miska, 2005; Kumar et al., 2007). Thus, miRNAs themselves may be better drug targets than genes or proteins. MiRNAs are aberrantly expressed or mutated in cancer, indicating that they may function as oncogenes or tumor suppressor genes (Calin and Croce, 2006; Bartel, 2004; Iorio et al., 2005). In 2002, the first evidence of involvement of miRNAs in human cancer came from molecular studies characterizing a deletion in human chronic lymphocytic leukemia, which highlight the importance of two miRNAs, miR-15a and miR-16-1 (Calin et al., 2002). Since their discovery, many studies have underlined the involvement of noncoding RNAs with several conjectures regarding their possible involvement in the evolution of drug resistance (Helleman et al., 2010; van Jaarsveld et al., 2010; Sorrentino et al., 2008). However,

the underlying mechanism and its contributions to genome-wide transcriptional changes are still largely unknown (EsquelaKerscher and Slack, 2006; Calin and Croce, 2006; Bartel, 2004; Ambros, 2004). 3.1. MicroRNAs associated with chemoresistance in ovarian cancer cell lines Many studies have shown that there are significant differences in the miRNA profiles between chemo-sensitive and resistant OC cell lines and tissue samples (Table 2). In this way, Sorrentino et al. (2008) performed high-throughput analysis of the miRNA profile in a panel of paclitaxel- (A2780TAX, A2780TC1 and A2780TC3) and cisplatin-resistant (A2780CIS) epithelial OC cells by using a microarray platform. Six miRNAs (let-7e, miR-30c, miR-125b, miR-130a and miR-335) were always diversely expressed in all the resistant cell lines. MiR-30c, miR-130a and miR-335 were down-regulated in all resistant cell lines, thereby suggesting a direct involvement in the development of chemoresistance. A downstream target validation for the miR-130a revealed that its downregulation was linked to the translational activation of the M-CSF gene, a known resistance factor for OC. In another study from Boren et al. (2009) the expression of 335 unique miRNAs was measured in 16 different OC cell lines. In parallel, the sensitivity of these cell lines to six commonly used chemotherapeutic agents (cisplatin, doxorubicin, topotecan, paclitaxel, docetaxel, and gemcitabine) was evaluated by in vitro cell proliferation assay. Twenty-seven miRNAs were found to be associated with response to one or more of those six drugs. Predicted targets of these miRNAs included 52 mRNAs, previously reported to be associated with chemoresponsiveness. Finally, they also evaluated two pairs of cisplatin sensitive/resistant, mother/daughter OC cell lines (A2008/C13 and A2780S/A2780CP) and found that miR-340, miR-381 and miR-520f were also represented in the list of seven miRNAs associated with inherent/baseline resistance to platinum in the 16 cell lines studied. Using different cell lines, Kumar et al. (2011) published a study focused on the identification of regulatory miRNAs between cisplatin-sensitive (A2780) and cisplatinresistant (A2780/CP70) cell lines. Their data showed a 11-miRNA signature differentially expressed in cisplatin-resistant cells, which could potentially target many important pathways including MAPK, TGFb signaling, actin cytoskeleton, ubiquitin mediated proteasomal pathway, Wnt signaling, mTOR signaling, NOTCH signaling and apoptosis. In another study, published by Fu et al. (2012) miRNA array and RT-PCR were used to show that miR-93 is significantly up-regulated in cisplatin-resistant OC cells (OVCAR3 and SKOV3). In vitro assays shown that over-expression and knockdown of miR-93 regulate apoptotic activity, and thereby cisplatin chemosensitivity. MiR-93 showed capability to directly target PTEN, and therefore participate in the regulation of the AKT signaling pathway. MiR-93 inversely correlated with PTEN expression in cisplatin-resistant and -sensitive human OC tissues. In the study done by van Jaarsveld et al. (2013) the miRNA expression profiles of similar isogenic cisplatin-sensitive and -resistant OC cell lines (A2780/A2780 DDP) were compared. Over-expression of miR-141 in resistant versus sensitive non-serous OC was discovered. Moreover, they demonstrated that regulation of KEAP1 by miR-141 has an essential role in the cellular response to cisplatin. More recently, Prislei et al. (2013) assessed the expression of mirR-200c, a regulator of TUBB3 associated with drug-resistance and poor prognosis, in a panel of OC cell lines with inherent or acquired drug-resistance. Stable over-expression of miR-200c was obtained in A2780 and Hey cell lines. Moreover, they observed a direct correlation between miR-200c over-expression and OC cells chemoresistance and most importantly, with poor or good outcome depending on the cellular localization of HuR, a RNA-binding protein, in 220 OC

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M. Llauradó et al. / Molecular and Cellular Endocrinology 390 (2014) 73–84 Table 2 MicroRNA signatures of response to chemotherapy in ovarian cancer. Study

Cultured cells Sorrentino et al. Gynecol. Oncol. (2008)

Boren et al. Gynecol. Oncol. (2009)

Discovery cohort

RNA origin

Groups of treatment

Platform

Endogenous normalizer

Human ovarian cancer cell lines (A2780, A2780CIS, A2780TAX, A2780TC1, A2780TC3)

Cultured cells Paclitaxel and Microarray DNMAD cisplatin resistant vs module paclitaxel and cisplatin sensitive

Human ovarian cancer cell lines (OV19, TOV21G, TOV112D, FUOV1, C13, OV2008, A2780CP, A2780S, IGROV1, T8, OVCAR5, IMCC3, A2008, OVCAR3, SKOV3, IOSER)

Cultured cells Paclitaxel response (basal resistance)

Action

miR-30c

Tumor Overexpressed Better supressor prognosis Tumor Overexpressed Better supressor prognosis Tumor Overexpressed Better supressor prognosis

miR-130a miR-335

Microarray -

miR-514 miR-126 miR-99b miR-29c miR-23b

Cisplatin response (basal and adquired resistance)

miR-381 miR-340 miR-520f miR-331 miR-185 miR-106a

Kumar et al. Journal of Human ovarian cancer cell lines Ovarian Research (A2780, A2780/ (2011) CP70)

Cultured cells Cisplatin response

Microarray –

miR-20 miR-300 let-7c

Oncogene Infraexpressed Poor prognosis Oncogene Infraexpressed Poor prognosis Oncogene Infraexpressed Poor prognosis Tumor Overexpressed Better supressor prognosis Oncogene Infraexpressed Poor prognosis Oncogene Infraexpressed Poor prognosis Oncogene Infraexpressed Poor prognosis Oncogene Infraexpressed Poor prognosis Tumor Overexpressed Better supressor prognosis Tumor Overexpressed Better supressor prognosis Tumor Overexpressed Better supressor prognosis Tumor Overexpressed Better supressor prognosis Oncogene Infraexpressed Poor prognosis Tumor Overexpressed Better supressor prognosis

Fu et al. FEBS Lett. (2012)

Human ovarian cancer cell lines (OVCAR3, OVCAR/ CCDP, SKOV3, SKOV3/CCDP)


Microarray –

Jaarsveld van et al. Oncogene (2013)

Human ovarian cancer cell lines (A2780/A2780 DDP)


Microarray Quantile miR-141 normalization

Oncogene Infraexpressed Poor prognosis (poorer in non-serous tumors)

Prislei et al. BMC Cancer (2013)

Human ovarian cancer cell lines (A2780, OVCAR-3, A2780-CIS, A2780ADR, A2780/TC1, OVCAR-EPO10)

Cultured cells Platinum response and frozen human primary tissues

RTqPCR

Tumor Overexpressed Better supressor prognosis (if HuR nuclear) Oncogene Infraexpressed Poor prognosis (if HuR cytoplasmic)

Cai et al. Oncogenesis (2013)

Human ovarian cancer cell lines (A2780, A2780/CP, ES2, SKOV3)


RTqPCR

U6snRNA

let-7e

Tumor Overexpressed Better supressor prognosis

Zhang et al. Acta Biochim. Biophys. Sin. (2013)

Human ovarian cancer cell lines (A2780, A2780/ DDP)

Cultured cells Cisplatin resistance RTqPCR vs cisplatin sensitive

U6snRNA

miR-130a


Chen et al. Oncology Reports (2014)

Cultured cells Paclitaxel resistance Microarray Human ovarian vs paclitaxel cancer cell lines sensitive cells (OVCAR8, OVCAR4)

miR-367

Oncogene Infraexpressed Poor prognosis Tumor Overexpressed Better supressor prognosis

–

miR-93

Status in the sensitive group

Outcome (if expressed)

miRNA

miR-200c

miR-200c

miR-30a-5p

Oncogene Infraexpressed Poor prognosis

(continued on next page)

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Table 2 (continued) Study

Discovery cohort

RNA origin

Groups of treatment

Li et al. BMC Cancer (2013)

Human ovarian cancer cell lines (SKOV3, SKOV3TR30)

Cultured cells and FFPE and frozen human primary tissues

Paclitaxel resistance Microarray vs paclitaxel Regression sensitive cells and algorithm human primary tissues

FFPE tissue samples Yang et al. Cancer Res. (2008a,b)

Platform

Endogenous normalizer

IOSE cells, different types of ovarian tumors and normal ovarian tissues

Cultured cells Cisplatin resistance Microarray – vs cisplatin sensitive and frozen human primary tissues

Yang et al. Cancer Res. (2008a,b)

Stage III- IV ovarian tumors with different histologies

Frozen human primary tissues

Hu et al. Gynecol. Oncol. (2009)

Stage III- IV ovarian tumors with different histologies

FFPE tissues

Eitan et al. Gynecol. Oncol. (2009)

Stage I and stage FFPE tissues III endometrioid and serous ovarian tumors

miRNA

Action

miR-9

Tumor Overexpressed Better supressor prognosis Tumor Overexpressed Better supressor prognosis Oncogene Infraexpressed Poor prognosis

miR-155 miR-22

Oncogene Infraexpressed Poor prognosis

Platinum, platinum- Microarray cyclophosphamide, or (after 1995) platinum-paclitaxel

let-7i


Platinum response

RTqPCR

miR-200a


Platinum-resistance (recurrence < 6 months) vs platinum-sensitive (no recurrence in 6 months)

Microarray -

miR-27a

Oncogene Infraexpressed Very poor prognosis Oncogene Infraexpressed Poor prognosis Tumor Overexpressed Better supressor prognosis Oncogene Poor prognosis Tumor Overexpressed Better supressor prognosis

-

miR-23a miR-449b

miR-24–2a

Lu et al. Gynecol. Oncol. (2011)

Different histologies and stages of ovarian tumors

Frozen human primary tissues

Paclitaxel resistance RTqPCR vs paclitaxel sensitive (complete or partial response) Platinum response with or without paclitaxel

RTqPCR

5S ribosomic RNA

miR-200c


RNU48

let-7a

Overexpressed Better Tumor supressor prognosis (with platinum without paclitaxel) Oncogene Infraexpressed Poor prognosis (with platinum and paclitaxel)

let-7a

Lee et al. World Journal Different of Surgical Oncology histologies and stages of ovarian (2012) tumors Vecchione et al. PNAS (2013)

FFPE tissues

Different stages of FFPE tissues serous ovarian tumors

Platinum response

RTqPCR

RNU6b

miR-30d


complete response, partial response, stable disease, and progressive disease

miR-484 Microarray Global median normalization miR-642

Tumor Overexpressed Better supressor prognosis Tumor Overexpressed Better supressor prognosis Tumor Overexpressed Better supressor prognosis

miR-217 Yang et al. Cancer Cell (2013)

All stages serous ovarian tumors

FFPE tissues

Outcome (if expressed)

miR-214

miR-21

Leskela et al. Endocr. Stage III-IV serous FFPE tissues Relat. Cancer (2011) ovarian tumors

Status in the sensitive group

Platinum response

Microarray miR-16 and miR-191

miR-506


(–) Not available. a IOSE, human immortalized ovarian surface epithelial.

tumors. Furthermore, Cai et al. (2013) demonstrated that let-7e expression was significantly reduced in cisplatin-resistant A2780/ CP cell line compared with parental A2780 cell and decreased in a concentration-dependent manner in cells treated with cisplatin.

Also, over-expression of let-7e could re-sensitize A2780/CP and reduce the expression of cisplatin-resistant-related proteins enhancer of EZH2 and CCND1, whereas let-7e inhibitors increased resistance to cisplatin in parental A2780 cells. Finally, Zhang et al.


(2013) found that miR-130a was significantly down-regulated in cisplatin-resistant OC cells, which can directly target XIAP when is up-regulated, participating in the regulation of apoptosis. More recently, Chen et al. (2014), identified 35 miRNAs associated with in vitro paclitaxel sensitivity by using microarray analyses. Their results indicate that miR-367 and miR-30-a-5p had the highest level of statistical significance in association with sensitivity to paclitaxel. Moreover, they evaluated the expression of these two miRNAs in two OC cell lines (PA1 and OVCAR4) and found overexpression of miR-367 and lower expression of miR-30a-5p in PA1 cell line being more sensitive to paclitaxel. In contrast, OVCAR4 showed a lower expression of miR-367 and overexpression of miR-30a-5p, being more resistant to paclitaxel. Finally, Li et al. (2013) identified several miRNAs differentially expressed in paclitaxel-resistant and sensitive OC cell lines by using miRNA microarray and a panel of six miRNAs associated with paclitaxel-resistant OC was validated in 45 FFPE samples. MiR-320a, 22 and 129–5p were significantly up-regulated in FFPE paclitaxel-resistant tumor samples compared with paclitaxel-sensitive samples, while miR-9, 155 and 640 were significantly down regulated. Furthermore, when classifying the 45 FFPE samples according to the mean values of the 6 miRNAs they found longer PFS and OS (21,25 months and 62,63 months, respectively) in patients with high levels of miR-9 than those with low levels of miR-9 (12,51 months and 30,40 months, respectively). Moreover, higher expression of miR-640 showed significant longer OS (69,38 months) than the rest of patients (35,12 months). 3.2. MicroRNAs associated with chemoresistance in ovarian tumors Different miRNAs have been discovered after comparing tumors from chemosensitive or chemoresistant OC patients (Table 2). On one hand in 2008, Yang et al. (2008a) shown that several miRNAs were altered in OC with the most significantly de-regulated miRNAs being miR-214, miR-199a, miR-200a, miR-100, miR-125b, and let-7 cluster. Further, miR-214 induced cell survival and cisplatin-resistance by targeting the PTEN/Akt pathway. On the other hand, Yang et al. (2008b) reported a significantly reduction of let-7i expression in chemotherapy resistant OC patients strongly associated with shorter PFS. Later on, Hu et al. (2009)) outlined the expression of miRNAs in advanced OC using a novel PCR-based platform and correlated their expression profiles with disease outcome. Data analyses showed that miR-200a, miR-200b and miR429 were significantly associated with cancer recurrence and OS. Additional target analysis indicated that these miRNAs target multiple genes that are involved in cancer development and cell migration. Similarly, Eitan et al. (2009) reported several miRNAs that were differentially expressed in stage I and III ovarian tumors. Moreover, they studied the relation of miRNA expression with patient prognosis. Analysis in the group of 38 stage III OC patients identified seven miRNAs differentially expressed in tumors from platinum-sensitive versus platinum-resistant patients. Five miRNAs (miR-23a, miR-27a, miR-449b, miR-21, miR-24-2) were significantly associated with differences in OS or PFS. High expression of miR-27a identified a sub-group of patients with very poor prognosis. Leskela et al. (2011) quantified the expression of the miR-200 family in a well-characterized series of 72 epithelial OC and examined their contribution to the protein expression of b-tubulin isotypes I, II and III. At the same time, they also investigated the impact of these miRNAs on the patient’s response and survival following paclitaxel-based therapy and suggested that miR-200 down-regulates TUBB3 in ovarian tumors. Furthermore, they also suggested a possible role for the miR-200 family both as a prognostic factor and a marker of treatment failure in OC. Recently, Lu et al. (2011) investigated the effect of let-7a expression on survival outcomes of 178 epithelial OC patients treated with different

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chemotherapy agents. The authors suggested that the beneficial impact of the addition of paclitaxel on epithelial OC survival was significantly linked to let-7a levels and that let-7a may be a useful marker for selection of chemotherapeutic agents in epithelial OC management. Further, Lee et al. (2012) measured the expression of nine miRNAs (miR-181d, miR-30a-3p, miR-30c, miR-30d, miR30e-3p, miR-368, miR-370, miR-493–5p, miR-532–5p) in 171 FFPE ovarian tissue blocks as well as six normal human ovarian surface epithelial (OSE) cell lines. This study revealed that higher expression of miR-181d, miR-30c, miR-30d, and miR-30e-3p was associated with significantly better disease-free or OS. In addition, lower expression of miR-30c, miR-30d, miR-30e-3p and miR-532–5p was significantly associated with over-expression of Her-2/neu. Moreover, Vecchione et al. (2013) profiled miRNA expression in HG-SOC to assess the possibility of a miRNA signature associated with chemoresistance. They analyzed tumor samples from 198 patients (86 patients as a training set and 112 patients as a validation set) for human miRNAs, and a signature of 23 miRNAs associated with chemoresistance was generated by array analysis in the training set. Real time PCR (RT-PCT) in the validation set confirmed that three miRNAs (miR-484, -642, and -217) were able to predict chemoresistance of these tumors. Additional in vitro analysis of miR-484 revealed that the sensitive phenotype is caused by a modulation of tumor vasculature through the regulation of the VEGFB and VEGFR2 pathways. Thus, they present compelling evidence that three miRNAs can classify the response to chemotherapy of OC patients in a large multicenter cohort and that one of these three miRNAs is involved in the control of tumor angiogenesis, indicating an option in the treatment of these patients. Finally, Yang et al. (2013) identified a master miRNA regulatory network for the mesenchymal subtype in serous OC, which is associated with poor OS in 459 cases from TCGA and 560 OC cases from independent public cohorts samples. To do that they performed an integtared genomic analyses by using available data, such as mRNA expression, copy number alterations, DNA methylation and miRNA expression, from those samples. A set of 219 genes was predicted to be targeted by 19 miRNAs, which were used to determine whether the miRNA-associated gene set could be used to further characterize a mesenchymal subtype in OC. Eight out of nineteen miRNAs identified, including, miR-506, miR-141 and miR-200a, were predicted to regulate 89% of targets in this network. Follow-up functional experiments ilustrate that miR-506 increased E-cadherin expression, inhibited cell migration and invasion, and prevent TGFb-induced epithelial-to-mesenchymal transition (EMT) by targeting SNAI2. In addition, miR-506 was correlated with decreased levels of SNAI2 and VIM, elevated levels of E-cadherin and beneficial prognosis in human OC samples. Also, nanoparticle delivery of miR-506 in orthotopic OC mouse models led to E-cadherin induction and reduced tumor growth, which may represent an important strategy of the most aggressive OC.

4. Limitations of microRNA studies in ovarian cancer In the last decade it has become clear that miRNAs have the potential to contribute to the pathogenesis and progression of several human malignancies and to modify drug outcomes, however, the frequency and patho-biological significance of aberrant miRNA expression in human OC have not been fully elucidated. The most frequently de-regulated miRNAs are members of the let-7 and miR-200 families (as shown in the literature and in Table 2, where members of these families are even common across cell line- and tissue-based studies), both involved in EMT. EMT is part of normal OSE physiology, being the key regulator of the post-ovulatory repair process, and failure to undergo EMT may be one of the events leading to transformation. A general down

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modulation of miRNAs expression is observed in epithelial OC compared to normal tissue. As described previously, the let-7a may be a useful marker for selection of chemotherapeutic agents in epithelial OC management; it may help to identify patients who will respond to and benefit from the addition of paclitaxel to platinum-based chemotherapy (those with low levels of let-7a expression) (Lu et al., 2011). The miR-200 family members have shown to be highly expressed in localized tumors and down-regulated in metastases, defining a two-stage model of miR-200 expression (Olson et al., 2009; Gibbons et al., 2009). Also, miR200 family have been associated with early relapse and decreased OS (Helleman et al., 2010; Iorio et al., 2007; Nam et al., 2008) while in other tumors the opposite effect of high miR-200 expression was reported (Hu et al., 2009; Eitan et al., 2009; Leskela et al., 2011; Marchini et al., 2011). The precise reasons underlying such contradictory findings remain unknown (Prislei et al., 2013). The results suggested that the same miRNA, miR-200c, could act either as a suppressor or enhancer of the aggressive phenotype, depending upon the localization of HuR. This result offered a possible explanation for the discrepancies among the clinical reports describing miR-200c as a suppressor or enhancer of aggressiveness in solid malignancies. Data obtained in other studies suggests that the relationship between specific miRNA and therapy response may differ between histological subtypes (van Jaarsveld et al., 2013). MiR-141 expression levels appeared to be higher in six resistant non-serous OC as compared with 18 responsive non-serous carcinomas, although the effect was not significant. This exploratory analysis suggests that the relationship between miR-141 expression levels in primary tumors and therapy response may be different for distinct histological subtypes. In this respect, clear-cell OC tumors respond poorly to platinum-based chemotherapy compared to other OC tumors (van Jaarsveld et al., 2013). The differentially altered miRNAs found between studies may reflect differences in sample size, study design, sample types (tumors, cell lines, etc.), clinical data (post-menopausal women, pre-treatment, residual disease, etc.) and platforms (microarray, sequencing, etc.) (Olson et al., 2009). Moreover, most of the current technologies have benefits and constraints to consider when designing miRNA studies. Results can vary across platforms, requiring accurate and critical evaluation when interpreting findings (Koshiol et al., 2010). Furthermore, there is a lack of better study their role in the whole tumor cell deregulation as well as their impact on tumor heterogeneity. Thus, lots of the findings fail at the validation phase and few may become clinical relevant. 5. Application of prognostic microRNAs in ovarian cancer therapies It is known that miRNA influences mRNA post-transcriptional control and can contribute to human carcinogenesis (Boren et al., 2009). As discussed above, OC drug resistance has been associated with a distinct miRNA fingerprint, and specific miRNA analysis could represent a prognostic tool for monitoring chemotherapy outcomes. Moreover, targeting miRNAs, rather than specific genes or proteins, may be more effective, since they often target entire pathways. MiRNA therapeutics in OC can take different forms: (i) oncogenic miRNAs and (ii) tumor-suppressor miRNAs. Oncogenic miRNAs can be inhibited using several methods, such as antisense oligonucleotides (Garzon et al., 2010). However, as cancer cells may exhibit dysregulation in several miRNAs at the same time, targeting a single miRNA may be not sufficient for treatment. For this reason, multiple-target anti-miRNA has been used in the past (Lu et al., 2009). Tumor suppressor miRNAs can be restored by miRNA replacement therapy (Nishimura et al., 2013).

MiRNAs can be used to sensitize tumors to chemotherapy. The efflux of anticancer drugs by ABC transporters is one of the main reasons that resistance to chemotherapy develops. Some miRNAs, such as miR-9 has been shown to negatively regulate SOX2, which induces the expression of the ABC transporters, ABCC3 and ABCC6, in cell lines. Likewise, resistance to tamoxifen is restored by the over-expression of miR-15 and miR-16. They suppress the antiapoptotic molecule, BCL-2, and sensitize the cells to tamoxifen. Similarly, the use of antagomiRs against miR-21 was found to sensitize cultured cells to the chemotherapeutic agent, 5-Fluorouracil (5-FU) (Zaman et al., 2012). In addition, miRNAs can also be used to classify patients as responsive or non-responsive to a treatment. As we previously described, Vecchione et al. (2013) presented a 3-miRNA signature (miR-484, -642, and -217) that was able to discriminate between responsive and non-responsive patients, all of whom were labeled with either stable or progressive disease, that was down-regulated in the non-responsive group. Although it may seem a good profile for differentiating between both groups of patients, each included different stages, grades, and surgery outcome (optimal vs. suboptimal) of serous epithelial OC. This fact may provide less conclusive results since it is known that the stage and the surgery outcomes are themselves prognostic factors (Hoskins et al., 1994). Recently, Tang et al. (2013) performed an integrative study to investigate the potential roles, clinico-pathological functions and prognostic values of the let-7 miRNA family in HG-SOC. Using microarray and clinical data from 1,170 HG-SOC patients, they developed novel survival prediction and system biology methods to analyze the prognostic values and functional associations of let-7 miRNAs with global transcriptome and clinico-pathological factors. They demonstrated that individual let-7 members exhibited diverse evolutionary history and distinct regulatory characteristics. Statistical tests and network analysis suggested that let-7b could act as a global synergistic interactor and master regulator, controlling hundreds of protein-coding genes. The elevated expression of let-7b was associated with poor survival rates, suggesting that it may play an unfavorable role in the treatment response of HG-SOC patients. A novel let-7b-defined 36-gene prognostic survival signature outperforms many clinico-pathological parameters and stratifies HG-SOC patients into three high-confidence clinical subclasses: low-, intermediate- and high-risk, with 5-year OS rates of 56–71%, 12–29% and 0–10%, respectively. Furthermore, the high-risk and low-risk subclasses exhibit strong mesenchymal and proliferative tumor phenotypes concordant with resistance and sensitivity to primary chemotherapy. In another hand, the classification of patients according to their genetic and epigenetic profiles may aid clinicians in anticipating patient response to the actual chemotherapy, thus facilitating decisions for a more specific plan of treatment for each group of patients. Meanwhile, through their investigation into the implications of the differentially expressed miRNAs between subgroups of patients with different chemotherapeutic response, researchers will gain a better understanding of the biology of the disease, as well as its weaknesses. Today, most researchers working with miRNAs tend to explain their implications through their targets, considering that these targets lead to the phenotypic changes that occur in any given cell. MiRNAs act by targeting a short sequence in the 3’UTR of the targeted mRNA. Numerous computational algorithms have been developed that allow the prediction of mRNA targets. Miranda, PicTar and TargetScan are based on predictive target databases, while others, such as TarBase and TargetScan, use experimental target databases. In addition to miRNA-target genes, they can also be involved in different pathways. Interestingly, when we examined the miRNAs described above using Ingenuity Pathway Analysis (IPA) software, both groups of miRNAs, which characterize the chemosensitive (good) or the chemoresistant


(poor) profiles, pointed to the same top molecular function, which was, as expected, ‘‘cancer.’’ Moreover, the core of the networks created by the interacting molecules identified TP53, MYC, MAPK, Ras and E-cadherin (Fig. 2) as the major actors, thus indicating that those molecules and their signaling pathways may be pivotal in the determination of an OC prognosis. Although, all of these pathways have been extensively studied and associated with OC, progress has been limited, since first-line therapy and 5-year survival rates have largely remained unchanged. Over the years, researchers have centered their explanation of the acquisition of a single malignant trait by a neoplastic cell, such as its invasive capacity or autonomy of proliferation, on a mutation (Spencer et al., 2006; Vogelstein and Kinzler, 1993). Nevertheless, in addition to genomic traits and the epigenetic landscape, which includes regulatory networking genes in the genome, signaling proteins and miRNAs play an important role in cancer development and treatment design that has been partially dismissed (see (Huang, 2013) for a review). Thus, two important issues that must be addressed by the research community are whether miRNAs themselves drive the acquisition of tumorigenic hallmarks and whether miRNAs could be the focus of subsequent investigations for ameliorating cancer treatments. One of the greatest challenges in RNA interference (RNAi) therapy continues to be the delivery method of the therapeutic small interfering RNA (siRNA) or miRNA to the target cells. Future focus should be aimed at developing an efficient delivery system, such as the nanoparticle delivery of therapeutic miRNAs, with the help of strategic image-guided systemic delivery to the tumor. Specifically for OC, as discussed above, Yang et al. (2013) used the nanoparticle delivery of miR-506 in mouse models, which exhibited a reduction in tumor growth. Such delivery systems represent a major contribution to the field of cancer therapeutics, which will help to overcome challenges in miRNA delivery. In recent years, extracellular vesicles (EVs), including exosome and microvesicles, have been

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described as important mediators in cell–cell communication. These EVs, which are delivered to the recipient cells, change the phenotype of cargo lipids, proteins, mRNA and miRNA. Thus, unmodified EVs, as well as engineered EVs are likely to have applications in drug delivery, according to the recent review by Andaloussi et al., 2013). Although the molecular mechanisms of this process are not fully understood, possible methods for the application of EVs as a tool for the cell-specific delivery of signaling molecules are being considered. One approach is based on engineering natural EVs, in order to target certain cell types using EVs loaded with therapeutic compounds. Another approach is based on the essential characteristics of EVs for designing nanoscaled drug delivery systems (van Dommelen et al., 2012). An experimental approach assessing EVs interaction and uptake by the target OC cells has recently been published (Nazarenko et al., 2013). Engineered EVs, such as liposomes, have been used as a paclitaxel cargo in in vitro OC models, showing a greater plasma lifetime than the parental drug (Ceruti et al., 2000). The goal of this novel therapeutic approache is to effectively encourage the reprogramming of miRNA networks in cancer cells, which may ultimately lead to a clinically translatable miRNA-based therapy that will benefit OC patients (Zaman et al., 2012).

6. Conclusions and future directions For women, OC remains one of the leading causes of cancer death. Nowadays, only a few successful therapeutic options exist for patients with recurrent disease. This is due, in part, to an incomplete understanding of the molecular determinants for chemotherapy-response. Chemotherapeutic agents that yield responses in the range of 15–20% and that last a median of approximately 4 months emphasize the great need for novel, effective therapeutic strategies (Vidal et al., 2012). Therefore, there is an

Fig. 2. Network interaction of microRNAs related to chemosensitivity/chemoresistance in ovarian cancer. (A) Network interaction resulting from miRNAs related with chemosensitive ovarian cancer. (B) Network interaction resulting from miRNAs related with chemoresistant ovarian cancer.

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urgent need for the identification of markers that can predict response to standard chemotherapy and relapse. Relatively little is known about the earliest events in OC, mainly due to the limited availability of early-stage tumors and the heterogeneity among them. A second point is its genetic instability, which makes it difficult to determine if an identified genetic alteration plays a role in early carcinogenesis or in its later stages (Landen et al., 2008). Furthermore, with regards to multi-level heterogeneity, the current conceptual framework and scientific methodologies are not suitable to account for it. In particular, popular high-throughput methods use cell lysates to describe a molecular profile enrichment across an entire population of cells (Heng et al., 2011; Pelkmans, 2012), thus providing only limited information about more dominant populations and losing information regarding side-populations, which can be related to tumor formation and resistance (Abdallah et al., 2014). The discovery and application of new technologies, which enable the simultaneous measurement of thousands of genes, have elucidated many important genetic and epigenetic events that may lead to OC development and progression. In this review, we have summarized and focused on the potential role that miRNAs may play as biomarkers of prognosis, as well as in predicting treatment response in OC. Different tools for patient risk stratification and for predicting clinical outcome have also been discussed. Nevertheless, the diversity of samples, and patient inclusion criteria, as well as the technologies included in these studies make it difficult to identify and validate the miRNAs that may be associated with response to treatment. In an effort to overcome these particular study variations, we have compared the miRNAs described across prognostic studies (see Section 3). As such, we have been able to identify miRNA-200c and let-7 as two important miRNAs for further validation. Furthermore, with the intent to delineate the dominant signaling pathways that may be responsible for an OC prognosis, we have explored all of the miRNAs described in these studies (Fig. 2). The results of these analyses indicated that both groups of miRNAs, which characterize good and poor prognoses in OC, pointed to ‘‘cancer’’ as the same top molecular function. Moreover, the linkage of the networks generated through the interaction between molecules identified TP53, MYC, MAPK, Ras and E-cadherin as the core molecules in the determination of the OC prognosis. An interesting strategy for identifying potential prognostic markers in OC, is based on the study of prognostic miRNAs in FFPE tissues. This method may reduce confusing results and obtain more clinically useful markers. Due to their small size, miRNAs are very stable in FFPE samples (Gordanpour et al., 2012), allowing for retrospective studies with long-term follow-ups. Additionally, as OC is a low-incidence and heterogeneous disease, the use of FFPE tumor samples allows access to a greater number of tumor cases. This overcomes sample size limitations when patient selection criteria become highly restrictive, and therefore, allows more valuable studies. Another interesting approach in determining the role of miRNAs in tumor heterogeneity is the use of in situ hybridization techniques, which appear to be helpful in showing miRNA distribution directly across all tissue. Dynamic networks among diagnostic, prognostic, and therapeutic miRNAs and their targets are complex. In vitro studies alone may not be appropriate for estimating miRNA significance as it is related to early detection, progression, recurrence, and treatment response. In light of these findings, more studies should be conducted in order to demonstrate the application of miRNAs in diagnosis, prognosis and the treatment of various diseases. In summary, OC treatment is challenging. As a result of the considerable cellular and molecular heterogeneity of this disease, it can no longer be considered a single disease. Rather, it encompasses different types of cancer with different clinico-pathological characteristics. A better understanding of the molecular mechanisms

involved in carboplatin–paclitaxel resistance and how they are influenced by miRNAs may help treatment decision-making, thus avoiding unnecessary and poor secondary treatments. This could lead to the development of novel cancer treatment strategies that are less toxic, more affordable, and more effective. Furthermore, mutations may mean something depending on whether or not they are counteracted by other genetic, epigenetic or functional alterations. Thus, integration of both the genetic and the epigenetic using systems biology could unravel key molecules for better understanding OC. A more complete understanding of cancer at the molecular and cellular levels, considering also tumor heterogeneity, could help to generate more new and efficient target therapies that will contribute toward a real improvement in OC overall survival. Acknowledgements This work has been supported by Grants from Fundació Santiago Dexeus Font for Clinical Investigation Projects 2012; the Spanish Ministry of Economy and Competitivity (SAF2011-26548); the Asociación Española Contra el Cáncer – Grupos estables (AECC 2011); the RTICC Program (RD12/0036/0035); the Catalan Government (2009SGR00487), all from Spain. Finally, the European Commission, 7th Framework Programme, IRSES (PROTBIOFLUID – 269285) - Belgium. References Abdallah, B.Y., Horne, S.D., Kurkinen, M., Stevens, J.B., Liu, G., Ye, C.J., Barbat, J., Bremer, S.W., Heng, H.H., 2014. Ovarian cancer evolution through stochastic genome alterations: defining the genomic role in ovarian cancer. Syst. Biol. Reprod. Med. 60, 2–13. Ambros, V., 2004. The functions of animal microRNAs. Nature 431, 350–355. Andaloussi, S.E.L., Mager, I., Breakefield, X.O., Wood, M.J., 2013. Extracellular vesicles: biology and emerging therapeutic opportunities. Nat. Rev. Drug Discov. 12, 347–357. Bader, A.G., Brown, D., Stoudemire, J., Lammers, P., 2011. Developing therapeutic microRNAs for cancer. Gene Ther. 18, 1121–1126. Barlin, J.N., Jelinic, P., Olvera, N., Bogomolniy, F., Bisogna, M., Dao, F., Barakat, R.R., Chi, D.S., Levine, D.A., 2013. Validated gene targets associated with curatively treated advanced serous ovarian carcinoma. Gynecol. Oncol. 128, 512–517. Bartel, D.P., 2004. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297. Bast Jr., R.C., Hennessy, B., Mills, G.B., 2009. The biology of ovarian cancer: new opportunities for translation. Nat. Rev. Cancer. 9, 415–428. Boren, T., Xiong, Y., Hakam, A., Wenham, R., Apte, S., Chan, G., Kamath, S.G., Chen, D.T., Dressman, H., Lancaster, J.M., 2009. MicroRNAs and their target messenger RNAs associated with ovarian cancer response to chemotherapy. Gynecol. Oncol. 113, 249–255. Braun, R., Finney, R., Yan, C., Chen, Q.R., Hu, Y., Edmonson, M., Meerzaman, D., Buetow, K., 2013. Discovery analysis of TCGA data reveals association between germline genotype and survival in ovarian cancer patients. PLoS ONE 8, e55037. Breving, K., Esquela-Kerscher, A., 2010. The complexities of microRNA regulation: mirandering around the rules. Int. J. Biochem. Cell Biol. 42, 1316–1329. Bushati, N., Cohen, S.M., 2007. MicroRNA functions. Annu. Rev. Cell Dev. Biol. 23, 175–205. Cai, J., Yang, C., Yang, Q., Ding, H., Jia, J., Guo, J., Wang, J., Wang, Z., 2013. Deregulation of let-7e in epithelial ovarian cancer promotes the development of resistance to cisplatin. Oncogenesis 2, e75. Calin, G.A., Croce, C.M., 2006. MicroRNA signatures in human cancers. Nat. Rev. Cancer 6, 857–866. Calin, G.A., Dumitru, C.D., Shimizu, M., Bichi, R., Zupo, S., Noch, E., Aldler, H., Rattan, S., Keating, M., Rai, K., Rassenti, L., Kipps, T., Negrini, M., Bullrich, F., Croce, C.M., 2002. Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc. Natl. Acad. Sci. USA 99, 15524–15529. Ceruti, M., Crosasso, P., Brusa, P., Arpicco, S., Dosio, F., Cattel, L., 2000. Preparation, characterization, cytotoxicity and pharmacokinetics of liposomes containing water-soluble prodrugs of paclitaxel. J. Control Release 63, 141–153. Chen, N., Chon, H.S., Xiong, Y., Marchion, D.C., Judson, P.L., Hakam, A., GonzalezBosquet, J., Permuth-Wey, J., Wenham, R.M., Apte, S.M., Cheng, J.Q., Sellers, T.A., Lancaster, J.M., 2014. Human cancer cell line microRNAs associated with in vitro sensitivity to paclitaxel. Oncol. Rep. 31, 376–383. Croce, C.M., 2009. Causes and consequences of microRNA dysregulation in cancer. Nat. Rev. Genet. 10, 704–714. Eitan, R., Kushnir, M., Lithwick-Yanai, G., David, M.B., Hoshen, M., Glezerman, M., Hod, M., Sabah, G., Rosenwald, S., Levavi, H., 2009. Tumor microRNA expression patterns associated with resistance to platinum based chemotherapy and survival in ovarian cancer patients. Gynecol. Oncol. 114, 253–259.

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