Ovarian cancer: diagnostic, biological and prognostic ...

Review - CME For reprint orders, please contact: [email protected]

Ovarian cancer: diagnostic, biological and prognostic aspects

Ovarian cancer remains the most lethal gynecologic malignancy, owing to late detection, intrinsic and acquired chemoresistance and remarkable heterogeneity. Despite optimization of surgical and chemotherapy protocols and initiation of clinical trials incorporating targeted therapy, only modest gains have been achieved in prolonging survival in this cancer. This review provides an update of recent developments in our understanding of the etiology, origin, diagnosis, progression and treatment of this malignancy, with emphasis on clinically relevant genetic classification approaches. In the authors’ opinion, focused effort directed at understanding the molecular make-up of recurrent and metastatic ovarian cancer, while keeping in mind the unique molecular character of each of its histological types, is central to our effort to improve patient outcome in this cancer. Keywords: chemotherapy resistance • microenvironment • ovarian cancer • prognosis • progression

Ben Davidson*,1,2 & Claes G Tropé2,3 1 Department of Pathology, Oslo University Hospital, Norwegian Radium Hospital, N-0310 Oslo, Norway 2 University of Oslo, Faculty of Medicine, Institute of Clinical Medicine, N-0310 Oslo, Norway. 3 Department of Gynecologic Oncology, Oslo University Hospital, Norwegian Radium Hospital, N-0310 Oslo, Norway. *Author for correspondence: Tel.: +47 2293 4871 Fax: +47 2250 8554 bend@ medisin.uio.no

Medscape: Continuing Medical Education Online This activity has been planned and implemented in accordance with the Essential Areas and policies of the Accreditation Council for Continuing Medical Education through the joint providership of Medscape, LLC and Future Medicine Ltd. Medscape, LLC is accredited by the ACCME to provide continuing medical education for physicians. Medscape, LLC designates this Journal-based CME activity for a maximum of 1.0 AMAPRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity. All other clinicians completing this activity will be issued a certificate of participation. To participate in this journal CME activity: (1) review the learning objectives and author disclosures; (2) study the education content; (3) take the post-test with a 75% minimum passing score and complete the evaluation at www.medscape.org/journal/whe; (4) view/ print certificate. RELEASE DATE: 22 October 2014; EXPIRATION DATE: 22 October 2015 LEARNING OBJECTIVES Upon completion of this activity, participants will be able to: • Evaluate the causes and classifications of ovarian cancer • Distinguish the strongest prognostic markers for ovarian cancer • Assess different biomarkers in ovarian cancer • Analyze the application of microRNAs to cases of ovarian cancer

10.2217/WHE.14.37 © 2014 Future Medicine Ltd

Womens Health (2014) 10(5), 519–533

part of

ISSN 1745-5057

519

Review Davidson & Tropé

CME

Financial & competing interests disclosure Editor: Laura Dormer, Senior Manager – Commissioning & Journal Development, Future Science Group. Disclosure: Laura Dormer has disclosed no relevant financial relationships. CME author: Charles P Vega, MD, Clinical Professor of Family Medicine, University of California, Irvine. Disclosure: Charles P Vega, MD, served as an advisor or consultant for: McNeil Pharmaceuticals. Author & credentials: Ben Davidson, MD PhD, Department of Pathology, Oslo University Hospital, Norwegian Radium Hospital, Oslo, Norway; Faculty of Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway Disclosure: Ben Davidson, MD, PhD, has disclosed no relevant financial relationships. Claes G Tropé, MD, PhD, Department of Gynecologic Oncology, Oslo University Hospital, Norwegian Radium Hospital, Oslo, Norway; Faculty of Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway Disclosure: Claes G Tropé, MD, PhD, has disclosed no relevant financial relationships. No writing assistance was utilized in the production of this manuscript.

Clinical presentation & treatment Ovarian cancer is the most lethal gynecological cancer, ranking ninth in incidence, but fifth in lethality, among women in the USA [1] . This owes mainly to presentation with advanced-stage disease (International Federation of Gynecology and Obstetrics [FIGO] stage III–IV) due to late symptoms and the lack of effective screening for early disease, as well as increasing chemoresistance along with tumor progression. stage III tumors characteristically metastasize widely within the abdominal cavity, as evidenced by the presence of both solid nodules on the peritoneum and malignant ascites [2,3] . Ovarian cancer is treated by surgery, aimed at tumor debulking to no macroscopic disease, and chemotherapy with combination taxanes and platinum. While such treatment prolongs survival even in the presence of stage IV disease [4] , the 5-year survival rate of patients with advanced-stage disease has remained at less than 30%. The most common site defining FIGO stage IV disease is the pleural cavity [2] . Etiology The etiology of ovarian cancer is not fully established. Nulliparity, early menarche and late menopause are associated with an increased risk of this cancer, whereas oral contraceptive use, pregnancy and lactation are associated with reduced risk [5] . However, the strongest risk factor is history of ovarian cancer in a first-degree relative. Hereditary ovarian cancer, believed to account for 20% of cases, belongs mainly to the hereditary breast and ovarian cancer syndrome, in which mutations occur in the DNA repair genes BRCA1 and BRCA2, or Lynch syndrome (hereditary nonpolyposis colorectal cancer syndrome), in which mutations occur in the mismatch repair genes MLH1, MSH2, MSH6 and PMS2. The lifetime risk of developing ovarian cancer is 40, 20 and 4–11%

520

Womens Health (2014) 10(5)

for patients with BRCA1 mutation, BRCA2 mutation or Lynch syndrome, respectively. Patients with BRCA mutations, particularly in BRCA2, have better outcome than those with wild-type BRCA [6] . Classification, diagnosis & origin Malignant primary ovarian tumors fall into three main groups – epithelial, sex cord/stromal and germ cell tumors – with rare miscellaneous entities outside these groups. Epithelial tumors, that is, ovarian carcinomas (OCs), are by far the most common group, accounting for 90% of cancers. Besides differences in morphology and immunophenotype, recent studies have highlighted the fact that the molecular characteristics of nonepithelial tumors differ from those of OC, as evidenced by the detection of mutations in the FOXL2 and DICER1 genes in granulosa cell tumors and Sertoli-Leydig cell tumors, respectively [7,8] . The following sections of this review focus on OC. It is widely recognized at present that the different histological subtypes of OC are five different diseases, each with its own morphological, immunohistochemical, molecular and clinical characteristics. These include low-grade serous carcinoma (LGSC), high-grade serous carcinoma (HGSC), mucinous carcinoma (MC), endometrioid carcinoma (EC) and clear cell carcinoma (CCC) [9] . While mixed forms occur, ancillary analyses show that they are less frequent than previously thought. Malignant Brenner tumors, undifferentiated carcinomas and malignant mixed mesodermal tumors (carcinosarcomas) are additional entities. LGSCs often harbor KRAS and BRAF mutations, whereas the majority of HGSCs have TP53 mutations and gross genomic aberrations manifested as aneuploidy. Mutations in the ARID1A, PIK3CA, PTEN and KRAS genes characterize CCC, whereas ECs, in common with their uterine counterparts, have mutations in ARID1A, CTNNB1 and PTEN, as well as microsatellite

future science group

CME instability [9] . Molecular analyses are nevertheless not part of the diagnostic workup of OC at present, and the diagnosis is based on morphology and immunohistochemistry (IHC). Several recent studies have demonstrated that trained gynecologic pathologists are able to classify OC with high reproducibility based on morphology and a limited panel of immunomarkers [10–12] . Different IHC panels have been used in the literature. The markers that the authors of this review find to be the most useful in classifying OC subtypes and in excluding metastasis to the ovary include PAX8, WT1, estrogen receptor (ER), HNF1β, p16, CDX2 and CEA. PAX8 is expressed by carcinomas of female genital origin, including OC [13] . The majority of serous carcinomas are WT1-positive, with variable ER expression, whereas EC express ER, but are WT1-negative [14,15] . CCCs stain for HNF1β [9] and are commonly negative for both ER and WT1 [14] . Primary MC with intestinal differentiation and metastatic gastrointestinal adenocarcinomas stain diffusely for CEA, CDX2 and CA19.9, whereas tumors of Müllerian type are negative or focally positive for these markers and tend to be positive for ER and CA 125 [16] . Colon carcinoma metastases to the ovary often, although not always, express CK20 and are only focally positive or negative for CK7, whereas the opposite is true for ovarian MC. There origin of OC has been long debated. Recent data suggest that the origin of HGSC, the most common OC histotype, may be in the Fallopian tube, in particular the fimbriae. This may occur via the development of an in situ lesion in the fimbriae, termed tubal intraepithelial carcinoma (TIC) or by Fallopian tube epithelium implants on the ovarian surface [17] . The Fallopian tube may also be the origin of at least some peritoneal serous carcinomas (SCs), as evidenced by common TP53 mutations [18] . Whether the Fallopian tube is the origin of all HGSCs or merely the majority of these tumors remains to be established. The other histological types of OC appear to have other origins. LGSC develops from borderline tumors, whereas endometriosis is the likely precursor of EC and CCC. The origin of MC and transitional cell tumors is still uncertain. Prognostic & predictive markers The difficulty in OC research

The strongest prognostic markers in OC are disease stage at diagnosis and the volume of residual disease after primary surgery, the latter additionally impacting on chemotherapy response. Older age, histological type and high-volume ascites are additional prognostic factors [19] . Despite this fact, considerable effort has been made with the aim of better stratifying OC patients in terms of outcome. Recent years


Ovarian cancer: diagnostic, biological & prognostic aspects

Review

have consequently seen an exponential increase in the number of publications dealing with the clinical course of this cancer. A PubMed search performed on February 11 2014, using the terms ‘ovarian’ and ‘prognostic’ generated over 4600 hits (almost 3700 for ‘ovarian’ and ‘predictive’), whereas changing the latter term to ‘prognosis’ increases the number of references to almost 20,000. Although remarkably enough, not a single molecule, with the exception of PARP in BRCA-mutated OC, is currently universally accepted as a predictive marker or therapeutic target in this disease. With the possible exception of BRCA status, no prognostic biological markers exist either and, despite numerous reports on molecules that have prognostic value in OC independently of clinical parameters, no such biological marker is universally accepted as such at present. While the yield of predictive or prognostic markers that enter the clinic falls very much short of the number of biomarkers proposed by different research groups in any given cancer type, there are several factors that make the situation still worse in OC. First and foremost are the inclusion criteria. The majority of studies dealing with OC have investigated tumors of different histological type together, a practice that has changed, at least in part, in recent years, as a growing number of papers focus on one histological type. Even more problematic is the inclusion of carcinosarcomas or malignant nonepithelial tumors in some series. Studies in which non-serous tumors are grouped as ‘other’ are similarly difficult to evaluate. Another crucial factor is our evolving ability to diagnose OC more accurately owing to the inclusion of the above-described IHC markers, in particular PAX8, WT1 and CDX2, in routine practice. Many tumors previously diagnosed as undifferentiated carcinomas or poorly differentiated EC have been reclassified as serous OC [20] , whereas many mucinous tumors diagnosed as primary in the ovary have been shown to be metastases from the GI tract [21] . Studies in which tumor classification is not done by trained gynecologic pathologists and studies that include older specimens without proper review of the studied material are, therefore, likely to generate inaccurate data. The clinical relevance of adequate histological typing is highlighted by a recent analysis of 575 optimally debulked OCs, in which histology was reviewed for all cases, and where tumor type was significantly related to survival [10] . Although not unique to OC, changing therapeutic approach is another confounding factor in this cancer. Surgery has become more aggressive, with the acceptable cut-off for residual disease falling from 2 to 1 cm to the current goal of no macroscopic

www.futuremedicine.com

521

Review Davidson & Tropé disease, affecting survival. The addition of paclitaxel to platinum as standard chemotherapy regimen in the 1990s has similarly impacted on disease outcome. Consequently, the inclusion of tumors from patients diagnosed before 1990 presents potential bias. Analysis of specimens obtained post-chemotherapy along with chemo-naive ones is yet another potential source of error that is becoming increasingly relevant, as many OC patients are operated following neoadjuvant chemotherapy. Since chemotherapy changes considerably the genotype and phenotype of tumor cells and selects chemoresistant cell populations, post-chemotherapy tumor characteristics are likely to markedly differ from chemo-naive ones. The following sections discuss some of the recent studies focusing on biomarkers associated with outcome in OC. Keeping the above-mentioned caveats in mind, the papers discussed are those that analyzed large tumor series, preferably of one histotype or several histotypes analyzed separately, with a minimal number of pre-1990 tumors or tumors classified histologically as ‘various’ or ‘unknown’. Receptor tyrosine kinases & intracellular signaling proteins Analysis of 183 HGSC specimens for protein expression of the fibroblast growth factor (FGF) receptor family member FGFR4 showed association between FGFR4 expression and poor overall survival (OS) in both univariate and multivariate analysis. FGFR4 silencing in vitro abrogated signaling via the MAPK, Wnt and NF-κB pathways, resulting in reduced proliferation and survival, increased apoptosis and reduced invasion [22] . Presence of the 388Arg FGFR4 allele was significantly associated with longer OS and progression-free survival (PFS) in both univariate and multivariate analysis, as well as platinum sensitivity, in patients with optimally debulked tumors, in analysis of predominantly serous OC [23] . Expression of the epidermal growth factor (EGF) family receptors EGFR, p-EGFR and HER-2/Neu in a series of 232 OCs of mixed histotype was unrelated to disease outcome, whereas expression of the PI3K negative regulator phosphatase and tensin homolog deleted on chromosome 10 (PTEN ) was significantly related to longer OS and PFS in both univariate and multivariate analysis [24] . HER-2 protein expression was similarly unrelated to survival in an analysis of 90 CCCs [25] , nor was PTEN associated with prognosis in this tumor type [26] . Overexpression of PIK3CA, the catalytic subunit of PI3K, by IHC was associated with longer OS in CCC [26] , whereas no relation to clinical outcome was observed in analysis of PIK3CA mutation status in another CCC series [27] .

522


CME The receptor tyrosine kinase MET was reported to be overexpressed in CCC compared with other OC histotypes and its expression was associated with poor OS [28] . Expression of DKK1, negative regulator of the Wnt pathway, and of the MAPK member JNK-1 was studied in 178 OCs. Expression of both markers, separately or together, was associated with poor survival. DKK1 promoted formation of actin filaments and filopodia in vitro and tumor growth in nude mice [29] . DNA repair proteins & transcriptional regulators Expression of the DNA repair protein PARP1 was associated with poor OS and PFS in a series of 174 HGSCs [30] , a finding in agreement with the targeting of this protein as a potential therapeutic approach in OC [31] . In another study, expression of the E3 ubiquitin ligase EDD was associated with increased risk of recurrence or death irrespective of FIGO stage and debulking status, and siRNA-mediated EDD knockdown partially restored platinum sensitivity in the resistant line A2780-cp70 [32] . Two studies that have assessed BRCA1 protein expression by IHC in OC of mixed histotype reached opposite conclusions with respect to its relationship to clinical role [33,34] . The presence of ERCC1, another DNA repair protein, was observed in 27% of 408 OCs of different histotype. Expression by IHC was unrelated to clinical outcome or to the presence of polymorphisms in the ERCC1 gene [35] . Mutations in the TP53 gene are a hallmark of HGSC, and their almost universal presence in this tumor is expectedly associated with lack of predictive or prognostic relevance [36] . Loss of expression of ARID1A, an accessory subunit of the SWI-SNF chromatin remodeling complex, is often seen in CCC and its loss was associated with poor PFS and with chemoresistance in this tumor in one series [37] , whereas no such relationship was found by another group [27] . Amplification of the ZNF217 gene at chromosome 20q13.2, detected by FISH, was significantly related to poor PFS and OS in CCC and ZNF217 silencing by siRNA resulted in growth inhibition and apoptosis in vitro [38] . Expression of steroid receptor coactivator-3 was significantly related to poor survival in an analysis of 471 OCs of mixed histotype [39] . PAX8 expression, while useful in the diagnostic setting, had no prognostic role in analysis of 148 SCs [40] . Immune response parameters The role of immune response mediators in determining outcome in OC has been investigated in several studies, with the focus on tumor-infiltrating lymphocytes


CME (TILs). Hwang et al. recently published a meta-analysis of 10 studies comprising a total of 1815 patients assessing this parameter. Two of the studies focused exclusively on SC, whereas the remaining eight studied OCs of different histotype. Significant association was observed between the presence of CD3-positive or CD8-positive TILs and longer survival [41] . In the paper by Clarke et al. listed among the 10 above-mentioned studies, the presence of CD8-positive, but not CD3-positive, TILs was a prognostic marker, and this finding was limited to SC, with no such role for TILs in EC or CCC [42] . In another study, the clinical role of multiple immune response-related proteins, including leukocyte CD1a, CD3, CD4, CD8, CD20, CD25, CD45RO, CD56, CD57, CD68, CD208, OX-40, TIA-1, granzyme B, FOXP3 and myeloperoxidase expression and tumor cell COX-2 and MHC class I and II expression, was assessed. The presence of cells positive for CD8, CD3, FoxP3, TIA-1, CD20 and MHC class I and class II was significantly associated with disease-specific survival (DSS) in HGSC from optimally debulked patients, whereas immune infiltrates were less evident and of lesser clinical relevance in other OC histotypes [43] . Other markers Several other biomarkers have been studied for a potential role in survival in OC. Analysis of the protein expression of YB-1, found to be related to cisplatin resistance in breast cancer cell lines, and RPS4X in 192 HGSCs showed significant association between reduced expression of the latter and poor PFS and OS, which was retained in multivariate analysis. RPS4X depletion in OC cells lines in vitro was associated with resistance to cisplatin [44] . Analysis of protein expression of AEG-1 by IHC in 131 stage III–IV SCs showed significant direct association with higher residual disease volume, platinum resistance and poor PFS and OS [45] . Protein expression of Emi1, an anaphase-promoting complex/cyclosome (APC/C) inhibitor inducing genetic instability and mitotic catastrophe, was found in 30% of 129 CCCs and was associated with poor OS. No such association was found for the galactoside-binding protein Galectin-3, p53 or the proliferation marker Ki-67 [46] . Expression of the mitotic spindle protein Aurora-A by IHC was associated with poor disease-free survival and OS in EC, with the opposite finding for BRCA [47] . Histotype-specific analyses of the expression of the RNA-binding protein insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3; IMP3) and the autophagy marker microtubule associated protein 1 light chain 3A (LC3A), identified a role for these markers in detecting patients with poor survival in CCC, but not in HGSC or EC [48,49] .



Review

The clinical role of ER and progesterone receptor (PR) was recently investigated in a series of 2933 OCs, including 1742 HGSCs, 110 LGSCs, 207 MCs, 484 ECs and 390 CCCs. PR expression was significantly related to longer DSS in EC and HGSC and ER expression was associated with longer DSS in EC. No prognostic role was found for ER and PR in the other OC subtypes [50] . Analyses of OCs of mixed histotype have identified an association between high TRAP1 and ERα expression [51] , as well as lower proteasomal subunit MB1 expression [52] and improved outcome. Molecular signatures related to OC outcome The heterogeneity across the different OC histotypes, as well as morphological and molecular differences within each group, has led to efforts to classify OC into groups with different clinical course based on high-throughput analyses. Analysis of 267 OCs, predominantly HGSCs, and 18 serous borderline tumors, identified six molecular subtypes, designated C1–6. C3 and C6 tumors, which were associated with the best prognosis, included predominantly serous borderline and lowgrade early-stage ECs, respectively, and had low levels of genes related to proliferation, as well as overexpression of genes related to the MAPK pathway (C3 tumors) or the β-catenin signaling pathway (C6 tumors). Tumors in the C1, C2, C4 and C5 groups consisted of high-grade and advanced-stage SCs and ECs. C5 tumors had a mesenchymal expression profile, C1 tumors had a stromal signature with greater degree of desmoplasia and C2 and C4 tumors were associated with immune response activation. Prognosis was worse for patients with C1 tumors [53] . This classification was recently validated in an independent series of 240 OCs [54] . In the Cancer Genome Atlas project (TCGA), mRNA and microRNA (miRNA, miR) expression, promoter methylation and DNA copy number were analyzed in 489 HGSCs, of which 316 were additionally studied by DNA exome sequencing. TP53 mutations were commonly found, with much lower frequency of mutations in eight other genes investigated. Patients with BRCA1/2-mutated tumors had longer survival. Recurrent gains and losses were detected in eight and 22 extended chromosome regions, respectively. Promoter methylation of 168 genes was found. mRNA and miRNA profiling identified four and three OC subtypes, respectively, the former designated ‘immunoreactive’, ‘differentiated’, ‘proliferative’ and ‘mesenchymal’. A 193-gene transcriptional signature was associated with survival. The NOTCH and FOXM1 signaling pathways were


523

Review Davidson & Tropé found to be characteristic of HGSC [55] . Analysis of 511 SCs using the TCGA database identified 23 genes involved in platinum-induced DNA damage repair that were significantly associated with chemoresponse and PFS [56] . A prognostic signature of 100 genes for HGSC, termed Classification of Ovarian Cancer (CLOVAR), was recently published [57] . Four other studies focusing predominantly or uniquely on SC have identified different gene signatures related to OC prognosis [58–61] . Comparative genomic hybridization analysis of 50 CCCs identified molecular heterogeneity between tumors and identified areas of high-level gain or amplification at chromosomes 8, 17, 19 and 20, which were associated with poor outcome independent of clinicopathologic parameters [62] . Recent analysis of 1538 OCs, including newlystudied tumors and tumors from several public databases, classified OC into five biologically distinct subgroups, termed Epi-A, Epi-B, Mes, Stem-A and Stem-B. The Mes and Stem-A groups were enriched for HGSC, whereas Stem-B tumors included many non-serous OCs. Patients with tumors classified as Mes and Stem-A had significantly worse survival than those with Epi-A, Epi-B and Stem-B tumors [63] . Meta-analyses of 10,000 OCs analyzed for the presence of gene polymorphisms found no prognostic role for this analysis in OC [64] . miRNAs in OC miRNAs are small (20–22 nucleotides) non-coding RNAs that post-transcriptionally regulate mRNA synthesis. miRNAs may have a tumor-promoting or inhibiting role depending on their target mRNAs. Mutations in cancer-associated miRNAs are rare in OC [65,66] , and single nucleotide polymorphisms in miRNA biosynthesis genes and in the putative miRNA-binding sites are not related to increased risk of ovarian cancer [67] . miRNAs have nevertheless received growing attention in recent years with respect to their diagnostic, biological, predictive and prognostic role in OC. miRNA targets in OC include BRCA1, tumor suppressors (PTEN), receptor tyrosine kinases (MET), molecules involved in cancer stem cell biology and hypoxia (SOX4, HIF1α) and modulators of epithelial-to-mesenchymal transition (HMGA2, Zeb1, Zeb2) [68–72] . A role for miRNAs in modifying chemotherapy response (either negatively or positively) has been documented through regulation of various molecules, including, among others, BRCA1 [73] , MET [74] , PTEN [75] , VEGFB and VEGFR2 [76] , the RNA-binding protein IMP1 [77] and the multidrug resistance protein MDR1 [77] .

524


CME A role for miRNAs in disease progression in OC has been demonstrated in several studies, in which altered miRNA profiles were seen in comparative analyses of primary OC and recurrent tumors [78,79] , as well as comparison between primary OC and OC effusions [80] . The levels of multiple miRNAs have been reported to be related to disease outcome in OC, among which miR-29b [81] , miR-100 [82] , miR-187 [83] , miR-200a [84] , miR-200c [85] , miR-203 [86] , miR-221 [87] , miR-222 [87] , miR-410 [88] and let-7b [89] were independent prognosticators, the latter in meta-analysis. The OC stroma Cancer is increasingly perceived to be a pathologic process involving both tumor cells and their microenvironment, which consists of leukocytes, endothelial cells and stromal myofibroblasts. While all classes of host cells have an important role in the biological makeup and the clinical behavior of malignant tumors, cancer-associated fibroblasts (CAFs) have a particularly important role in this context owing to their ability to produce and secrete an array of tumor-promoting factors and to dynamically modify the composition of the extracellular matrix. These in turn enable tumor cells to invade the stroma and vessels and subsequently metastasize to distant organs. In view of this, attempts have been made to develop therapeutic strategies for targeting stromal myofibroblasts in cancer [90–93] . Studies applying mRNA in situ hybridization have shown that the OC stroma, as its counterpart in other carcinomas, has extensive synthetic capacity, as evidenced by the production of a wide array of cancerassociated molecules, including proteases of different classes, extracellular matrix components, growth factors and angiogenic molecules. The OC stroma additionally expresses transcriptional regulators, molecules that modulate the immune response, cell cycle- and apoptosis-related proteins, hormones and myriad other proteins [122] . More recent studies have applied high-throughput technology to studies of OC fibroblasts, often in conjunction with microdissection, which ensures that genomic profiles are indeed localized to this cellular compartment. Analysis of genome-wide copy number and loss of heterozygosity in OC and breast carcinoma CAFs using single nucleotide polymorphism arrays showed only rare loss of heterozygosity and copy number alterations [94] . Kataoka et al. isolated the stroma of 74 OCs of different histotype, obtained from patients diagnosed at FIGO stages IIC–IV. Microarray analysis of 24 specimens identified 52 candidate genes that were


CME significantly related to PFS. Of these genes, EGR1 and FOSB were validated in the remaining 50 tumors and found to be independent prognostic markers of poor PFS [95] . Exosomes are secreted 30–100 nm lipoprotein vesicles containing proteins, mRNAs and miRNAs, which are present in most circulating body fluids and mediate autocrine and paracrine effects in different cellular systems, including cancer [96] . Proteins found in exosomes are involved in multiple biological pathways, including adhesion, signal transduction, membrane transport and fusion, immune response modulation and cytoskeletal organization (reviewed in [97]). Exosomes are overexpressed in the serum of OC patients compared with controls [97] , and exosomes in the serum of OC patients were reported to contain the gap junction protein claudin-4 [98] , which is considered a target for therapeutic intervention in this cancer [99] , as well as various glycans – molecules whose expression is deregulated in different cancers [100] . Exosomes were also detected in OC ascites and were shown to mediate immune response suppression at this anatomic site [101] . Proteomic analysis of IGROV-1 and OVCAR-3 exosomes identified 2230 proteins related to multiple cellular pathways, of which approximately 50% were common to both lines [102] . SKOV-3 and OVCAR-3 cells were recently reported to have different content of Let-7 and miR-200 [103] . Exosomes from SKOV-3 and OVCAR-3 OC cells induced adipose tissue-derived stem cells to acquire myofibroblast characteristics, with resulting activation of the TGFβ pathway [104] . Inhibition of Gli1, part of the Hedgehog pathway, in the stroma of mice bearing OC xenografts using the cyclopamine derivative IPI-926 was tested in combination with chemotherapy. The clinical role of Gli1 was demonstrated in stroma from human OC, where higher levels of this transcript were associated with worse survival [105] . Downregulation of miR-31 and miR-214 and upregulation of miR-155 was recently found in CAF compared with normal or tumor-adjacent fibroblasts, and experimental manipulation of these miRNAs converted normal fibroblasts to CAF. Reprogrammed fibroblasts and patient-derived CAF were enriched for chemokines, particularly CCL5, and the latter was identified as a target of miR-214 [106] . Metastatic OC The above-discussed studies have provided important data regarding the molecular make-up of primary OC and the clinical relevance of cancer-associated molecules at this anatomic site. However, large series and histotype-specific analyses of metastatic disease are



Review

critically missing in this cancer. This owes in part to the fact that many patients with recurrent disease are not operated on, but additionally reflects the unbalanced allocation of research effort and resources. As the majority of OC patients die of metastatic disease rather than from their primary tumor, it is critical to expand our understanding of disease progression in OC through focusing on disease recurrence and metastasis. Larger studies, in which a minimum of 100 SC metastases or 200 metastatic OCs of mixed histotype have been included, are to date limited to analyses of malignant effusions. The prognostic factors identified by the authors’ group at this anatomic site include the transcription factors NF-κB p65 [107] and HOXB8 [108] and the microtubule-associated protein class III β-tubulin [109] , and the expression of the latter was additionally associated with primary chemoresistance. Analysis of 176 serous OC effusion supernatants by another group identified significant association between higher levels of soluble L1CAM and poor PFS [110] . A separate prognostic signature for prechemotherapy and post-chemotherapy SC effusions at the protein level was recently characterized by the authors [111] . Future perspective In view of the lack of tangible improvement in the outcome of OC patients despite optimized surgery and chemotherapy protocols, efforts are being applied in two directions – prevention and early diagnosis of OC and investigation of the clinical role of targeted therapy agents, particularly as adjuvant to standard therapy. In light of the tubal origin theory for OC development, bilateral salpingectomy, followed by delayed bilateral oophorectomy closer to menopause, is increasingly regarded as a widely accepted modality for reducing the risk of developing OC in high-risk populations, particularly carriers of BRCA mutations [112,113] , and may be applied to women in low-risk populations in the future. The issue of OC screening with the aim of detecting early-stage disease is more contentious. The US Preventive Task Force Services recently reaffirmed its recommendation that women not belonging to populations at high-risk for developing OC should not be screened for this cancer [114] , a view shared by other authors [115,116] and supported by a recent meta-analysis [117] . Detection of 11 OCs by screening a cohort of 1455 women was similarly not interpreted to justify large-scale screening in the DOvE study [118] . By contrast, Lu et al. recently reported that annual serum CA 125 measurement followed by transvaginal ultrasound resulted in excellent


525

Review Davidson & Tropé sensitivity and specificity for detecting gynecological tumors, including OC, in the ROCA study [119] . The debate on this issue is therefore likely to continue. Targeted therapy approaches evaluated in clinical trials include inhibition of angiogenesis using VEGF inhibitors (bevacizumab, aflibercept), tyrosine kinase inhibitors of VEGFR and other angiogenic molecules (sorafenib, sunitinib, nintedanib, pazopanib, cediranib, others) or blocking the interaction between angiopoietins and their Tie receptor on endothelial cells (trebananib); PARP inhibition (olaparib); use of EGFR family inhibitors (erlotinib, gefitinib, lapatinib, canertinib); PDGFR inhibition (imatinib mesylate); SRC inhibition (dasatinib); folate receptor-α blocking; and inhibition of the IGF and PI3K/AKT/mTOR pathways [31,120,121] . Results have by-and-large been disappointing to date, with none of the above agents accepted as standard therapy even for subgroups of patients to date. There are various factors contributing to this outcome. Recurrent OCs are biologically aggressive, as evidenced by resistance against all types of chemotherapy. Cancer cells at this stage are able to activate alternative pathways when a given pathway is blocked, hampering all therapeutic attempts. Additionally, the markedly different genomic, transcriptomic and proteomic profiles of tumors of different histology argue against uniform treatment, as is current practice. Side effects resulting from targeting of molecules that are important for

CME normal physiology are an additional factor that may limit therapeutic options. While some of these limitations are hard to circumvent, it is to be expected that the combination of advanced molecular analyses, particularly next generation sequencing, combined with a focus on large multi-institutional histotype-specific studies, will generate more solid data as a basis to interventional studies in the future. Applying targeted therapy at an earlier stage, as adjunct to primary treatment rather than at later stages of the disease, may prove more effective in clearing residual tumor cell populations, particularly in optimally debulked patients. A focus on understanding the biological make-up of recurrent and/or chemoresistant disease may improve our ability to prolong the life of patients with metastatic OC. Financial & competing interests disclosure This work was supported by the Inger and John Fredriksen Foundation for Ovarian Cancer Research. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.

Executive summary Ovarian carcinoma is a heterogeneous disease • The five main ovarian carcinoma (OC) histotypes are distinct diagnostic entities with unique genotypes and phenotypes. • Changing concepts regarding the origin of OC, particularly of the serous type, may affect early detection strategies, prophylactic measures and tumor staging.

Widely-accepted prognostic markers in OC are missing • Large studies that focus on each histotype separately are becoming more common and may improve consensus regarding the clinical role of biomarkers in OC. • Malignant ovarian tumors that are not carcinomas should not be included in studies of OC. • Genome-wide approaches aimed at defining clinically relevant signatures are underway, but different inclusion criteria and methodology affect results.

miRNAs are key regulators of OC biology • The rapid increase in studies focusing on miRNAs in OC reflects growing recognition of their role in regulating the expression of cancer-associated molecules, with relevance for chemotherapy response and patient survival.

Metastasis & tumor recurrence are underrepresented in OC research • There is an obvious need to invest more efforts in understanding the biology of metastases in OC, ideally in sequential patient-matched specimens. • Targeted therapy approaches at metastatic or recurrent disease need to base treatment on analyses of the extraovarian specimens. • In optimally debulked patients receiving standard chemotherapy, targeted therapy based on the genetic make-up of the tumor may prolong life, while cure requires early detection.

The tumor microenvironment has a critical role in OC progression • Better understanding of the contribution of host cells, particularly cancer-associated fibroblasts, to OC progression may lead to new therapeutic strategies.

526



CME


References

14

Papers of special interest have been highlighted as: • of interest

Soslow RA. Histologic subtypes of ovarian carcinoma: an overview. Int. J. Gynecol. Pathol. 27(2), 161–174 (2008).

15

Bárcena C, Oliva E. WT1 expression in the female genital tract. Adv. Anat. Pathol. 18(6), 454–465 (2011).

16

McCluggage WG. Immunohistochemistry in the distinction between primary and metastatic ovarian mucinous neoplasms. J. Clin. Pathol. 65(7), 596–600 (2012).

17

Nik NN, Vang R, Shih IeM, Kurman RJ. Origin and pathogenesis of pelvic (ovarian, tubal, and primary peritoneal) serous carcinoma. Annu. Rev. Pathol. 9, 27–45 (2014).

18

Kuhn E, Kurman RJ, Vang R et al. TP53 mutations in serous tubal intraepithelial carcinoma and concurrent pelvic highgrade serous carcinoma – evidence supporting the clonal relationship of the two lesions. J. Pathol. 226(3), 421–426 (2012).

19

Tropé CG, Davidson B, Dørum A et al. Epithelial ovarian cancer. In: Gynecologic Oncology. Trope CG, Peltecu GC (Eds). Editura Academiei Romane, Bucharest, Romania, 357–444 (2010).

20

Delair D, Soslow RA. Key features of extrauterine pelvic serous tumours (Fallopian tube, ovary, and peritoneum). Histopathology 61(3), 329–339 (2012).

21

Leen SL, Singh N. Pathology of primary and metastatic mucinous ovarian neoplasms. J. Clin. Pathol. 65(7), 591–595 (2012).

1

Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J. Clin. 62(1), 10–29 (2012).

2

Davidson B. Ovarian and primary peritoneal carcinoma. In: Serous Effusions – Etiology, Diagnosis, Prognosis and Therapy. Davidson B, Firat P, Michael CW (Eds). Springer, London, UK, 47–68, 167–204 (2011).

3

Thibault B, Castells M, Delord JP, Couderc B. Ovarian cancer microenvironment: implications for cancer dissemination and chemoresistance acquisition. Cancer Metastasis Rev. doi:10.1007/s10555-013-9456-9452 (2013) (Epub ahead of print).

•

Comprehensive review of the metastatic process in ovarian cancer.

4

Elstrand MB, Sandstad B, Oksefjell H, Davidson B, Tropé CG. Prognostic significance of residual tumor in patients with epithelial ovarian carcinoma stage IV in a 20 year perspective. Acta Scand. Obstet. Gynecol. 91(3), 308–317 (2012).

5

Berek JS, Bast RC Jr. Ovarian cancer. In: Cancer Medicine (7th Edition). Kufe DW, Bast RC Jr, Hait WM et al. (Eds). BC Decker Inc., Hamilton, Canada, 1543–1568 (2006).

6

Weissman SM, Weiss SM, Newlin AC. Genetic testing by cancer site: ovary. Cancer J. 18(4), 320–327 (2012).

7

Caburet S, Georges A, L’Hôte D, Todeschini AL, Benayoun BA, Veitia RA. The transcription factor FOXL2: at the crossroads of ovarian physiology and pathology. Mol. Cell. Endocrinol. 356(1–2), 55–64 (2012).

22

Zaid TM, Yeung TL, Thompson MS et al. Identification of FGFR4 as a potential therapeutic target for advanced-stage, high-grade serous ovarian cancer. Clin. Cancer Res. 19(4), 809–820 (2013).

8

Heravi-Moussavi A, Anglesio MS, Cheng SW et al. Recurrent somatic DICER1 mutations in nonepithelial ovarian cancers. N. Engl. J. Med. 366(3), 234–242 (2012).

23

9

Prat J. Ovarian carcinomas: five distinct diseases with different origins, genetic alterations, and clinicopathological features. Virchows Arch. 460(3), 237–249 (2012).

Marmé F, Hielscher T, Hug S et al. Fibroblast growth factor receptor 4 gene (FGFR4) 388Arg allele predicts prolonged survival and platinum sensitivity in advanced ovarian cancer. Int. J. Cancer 131(4), e586–e591 (2012).

24

•

Timely review of ovarian pathology with emphasis on tumor heterogeneity and its molecular basis.

de Graeff P, Crijns AP, Ten Hoor KA et al. The ErbB signalling pathway: protein expression and prognostic value in epithelial ovarian cancer. Br. J. Cancer 99(2), 341–349 (2008).

25

10

Gilks CB, Ionescu DN, Kalloger SE et al. Cheryl Brown ovarian cancer outcomes unit of the british columbia cancer agency. Tumor cell type can be reproducibly diagnosed and is of independent prognostic significance in patients with maximally debulked ovarian carcinoma. Hum. Pathol. 39(8), 1239–1251 (2008).

Tanabe H, Nishii H, Sakata A et al. Overexpression of HER-2/neu is not a risk factor in ovarian clear cell adenocarcinoma. Gynecol. Oncol. 94(3), 735–739 (2004).

26

Abe A, Minaguchi T, Ochi H et al. PIK3CA overexpression is a possible prognostic factor for favorable survival in ovarian clear cell carcinoma. Hum. Pathol. 44(2), 199–207 (2013).

27

Yamamoto S, Tsuda H, Takano M, Tamai S, Matsubara O. PIK3CA mutations and loss of ARID1A protein expression are early events in the development of cystic ovarian clear cell adenocarcinoma. Virchows Arch. 460(1), 77–87 (2012).

28

Yamamoto S, Tsuda H, Miyai K, Takano M, Tamai S, Matsubara O. Gene amplification and protein overexpression of MET are common events in ovarian clear-cell adenocarcinoma: their roles in tumor progression and prognostication of the patient. Mod. Pathol. 24(8), 1146–1155 (2011).

11

Köbel M, Kalloger SE, Lee S et al; Ovarian Tumor Tissue Analysis Consortium. Biomarker-based ovarian carcinoma typing: a histologic investigation in the ovarian tumor tissue analysis consortium. Cancer Epidemiol. Biomarkers Prev. 22(10), 1677–1686 (2013).

12

Köbel M, Bak J, Bertelsen BI et al. Ovarian carcinoma histotype determination is highly reproducible, and is improved through the use of immunohistochemistry. Histopathology 64(7), 1004–1013 (2014).

29

13

Ordóñez NG. Value of PAX 8 immunostaining in tumor diagnosis: a review and update. Adv. Anat. Pathol. 19(3), 140–151 (2012).

Wang S, Zhang S. Dickkopf-1 is frequently overexpressed in ovarian serous carcinoma and involved in tumor invasion. Clin. Exp. Metastasis. 28(6), 581–591 (2011).

30

Gan A, Green AR, Nolan CC, Martin S, Deen S. Poly(adenosine diphosphate-ribose) polymerase expression



Review

527

Review Davidson & Tropé in BRCA-proficient ovarian high-grade serous carcinoma; association with patient survival. Hum. Pathol. 44(8), 1638–1647 (2013). 31

528

Banerjee S, Kaye SB. New Strategies in the treatment of ovarian cancer: current clinical perspectives and future potential. Clin. Cancer Res. 19(5), 961–968 (2013).

CME 44

Tsofack SP, Meunier L, Sanchez L et al. Low expression of the X-linked ribosomal protein S4 in human serous epithelial ovarian cancer is associated with a poor prognosis. BMC Cancer 13, 303 (2013).

45

Li C, Li Y, Wang X et al. Elevated expression of astrocyte elevated gene-1 (AEG-1) is correlated with cisplatin-based chemoresistance and shortened outcome in patients with stages III-IV serous ovarian carcinoma. Histopathology 60(6), 953–963 (2012).

32

O’Brien PM, Davies MJ, Scurry JP et al. The E3 ubiquitin ligase EDD is an adverse prognostic factor for serous epithelial ovarian cancer and modulates cisplatin resistance in vitro. Br. J. Cancer 98(6), 1085–1093 (2008).

46

33

Weberpals JI, Tu D, Squire JA et al. Breast cancer 1 (BRCA1) protein expression as a prognostic marker in sporadic epithelial ovarian carcinoma: an NCIC CTG OV.16 correlative study. Ann. Oncol. 22(11), 2403–2410 (2011).

Min KW, Park MH, Hong SR et al.; Gynecologic Pathology Study Group of the Korean Society of Pathologists. Clear cell carcinomas of the ovary: a multi-institutional study of 129 cases in Korea with prognostic significance of Emi1 and Galectin-3. Int. J. Gynecol. Pathol. 32(1), 3–14 (2013).

47

34

Carser JE, Quinn JE, Michie CO et al. BRCA1 is both a prognostic and predictive biomarker of response to chemotherapy in sporadic epithelial ovarian cancer. Gynecol. Oncol. 123(3), 492–498, (2011).

Yang F, Guo X, Yang G, Rosen DG, Liu J. AURKA and BRCA2 expression highly correlate with prognosis of endometrioid ovarian carcinoma. Mod. Pathol. 24(6), 836–845 (2011).

48

35

Rubatt JM, Darcy KM, Tian C et al. Pre-treatment tumor expression of ERCC1 in women with advanced stage epithelial ovarian cancer is not predictive of clinical outcomes: a Gynecologic Oncology Group study. Gynecol. Oncol. 125(2), 421–426 (2012).

Köbel M, Xu H, Bourne PA et al. IGF2BP3 (IMP3) expression is a marker of unfavorable prognosis in ovarian carcinoma of clear cell subtype. Mod. Pathol. 22(3), 469–475 (2009).

49

Spowart JE, Townsend KN, Huwait H et al. The autophagy protein LC3A correlates with hypoxia and is a prognostic marker of patient survival in clear cell ovarian cancer. J. Pathol. 228(4), 437–447 (2012).

50

Sieh W, Köbel M, Longacre TA et al. Hormone-receptor expression and ovarian cancer survival: an Ovarian Tumor Tissue Analysis Consortium study. Lancet Oncol. 14(9), 853–862 (2013).

51

Aust S, Bachmayr-Heyda A, Pateisky P et al. Role of TRAP1 and estrogen receptor alpha in patients with ovarian cancer – study of the OVCAD Consortium. Mol. Cancer 11, 69 (2012).

52

Leffers N, Gooden MJ, Mokhova AA et al. Down-regulation of proteasomal subunit MB1 is an independent predictor of improved survival in ovarian cancer. Gynecol. Oncol. 113(2), 256–263 (2009).

53

Tothill RW, Tinker AV, George J et al. Australian Ovarian Cancer Study Group. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin. Cancer Res. 14(16), 5198–5208 (2008).

54

Sfakianos GP, Iversen ES, Whitaker R et al. Validation of ovarian cancer gene expression signatures for survival and subtype in formalin fixed paraffin embedded tissues. Gynecol. Oncol. 129(1), 159–164 (2013).

55

Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474(7353), 609–615 (2011).

36

Ahmed AA, Etemadmoghadam D, Temple J et al. Driver mutations in TP53 are ubiquitous in high grade serous carcinoma of the ovary. J. Pathol. 221(1), 49–56 (2010).

•

Study demonstrating the central role of TP53 in highgrade serous carcinoma.

37

Katagiri A, Nakayama K, Rahman MT et al. Loss of ARID1A expression is related to shorter progressionfree survival and chemoresistance in ovarian clear cell carcinoma. Mod. Pathol. 25(2), 282–288 (2012).

38

Rahman MT, Nakayama K, Rahman M et al. Prognostic and therapeutic impact of the chromosome 20q13.2 ZNF217 locus amplification in ovarian clear cell carcinoma. Cancer 118(11), 2846–2857 (2012).

39

Palmieri C, Gojis O, Rudraraju B et al. Expression of steroid receptor coactivator 3 in ovarian epithelial cancer is a poor prognostic factor and a marker for platinum resistance. Br. J. Cancer 108(10), 2039–2044 (2013).

40

Mhawech-Fauceglia P, Wang D, Samrao D et al. Pair Box 8 (PAX8) protein expression in high grade, late stage (stages III and IV) ovarian serous carcinoma. Gynecol. Oncol. 127(1), 198–201 (2012).

41

Hwang WT, Adams SF, Tahirovic E, Hagemann IS, Coukos G. Prognostic significance of tumor-infiltrating T cells in ovarian cancer: a meta-analysis. Gynecol. Oncol. 124(2), 192–198 (2012).

42

Clarke B, Tinker AV, Lee CH et al. Intraepithelial T cells and prognosis in ovarian carcinoma: novel associations with stage, tumor type, and BRCA1 loss. Mod. Pathol. 22(3), 393–402 (2009).

56

Kang J, D’Andrea AD, Kozono D. A DNA repair pathwayfocused score for prediction of outcomes in ovarian cancer treated with platinum-based chemotherapy. J. Natl Cancer Inst. 104(9), 670–681 (2012).

43

Milne K, Köbel M, Kalloger SE et al. Systematic analysis of immune infiltrates in high-grade serous ovarian cancer reveals CD20, FoxP3 and TIA-1 as positive prognostic factors. PLoS ONE 4(7), e6412 (2009).

57

Verhaak RG, Tamayo P, Yang JY et al. Cancer Genome Atlas Research Network. Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. J. Clin. Invest. 123(1), 517–525 (2013).



CME 58

59

Konstantinopoulos PA, Cannistra SA, Fountzilas H et al. Integrated analysis of multiple microarray datasets identifies a reproducible survival predictor in ovarian cancer. PLoS ONE 6(3), e18202 (2011).

Sun C, Li N, Yang Z et al. miR-9 regulation of BRCA1 and ovarian cancer sensitivity to cisplatin and PARP inhibition. J. Natl Cancer Inst. 105(22), 1750–1758 (2013).

74

Mitamura T, Watari H, Wang L et al. Downregulation of miRNA-31 induces taxane resistance in ovarian cancer cells through increase of receptor tyrosine kinase MET. Oncogenesis 2, e40 (2013).

75

Fu X, Tian J, Zhang L, Chen Y, Hao Q. Involvement of microRNA-93, a new regulator of PTEN/Akt signaling pathway, in regulation of chemotherapeutic drug cisplatin chemosensitivity in ovarian cancer cells. FEBS Lett. 586(9), 1279–1286 (2012).

76

Vecchione A, Belletti B, Lovat F et al. A microRNA signature defines chemoresistance in ovarian cancer through modulation of angiogenesis. Proc. Natl Acad. Sci. USA 110(24), 9845–9850 (2013).

77

Boyerinas B, Park SM, Murmann AE et al. Let-7 modulates acquired resistance of ovarian cancer to taxanes via IMP1-mediated stabilization of multidrug resistance 1. Int. J. Cancer 130(8), 1787–1797 (2012).

78

Laios A, O’Toole S, Flavin R et al. Potential role of miR-9 and miR-223 in recurrent ovarian cancer. Mol. Cancer 7, 35 (2008).

79

Vang S, Wu HT, Fischer A et al. Identification of ovarian cancer metastatic miRNAs. PLoS ONE 8(3), e58226 (2013).

80

Bearfoot JL, Choong DY, Gorringe KL, Campbell IG. Genetic analysis of cancer-implicated microRNA in ovarian cancer. Clin. Cancer Res. 14(22), 7246–7250 (2008).

Vaksman O, Tuft Stavnes H, Kærn J, Trope CG, Davidson B, Reich R. miRNA profiling along tumor progression in ovarian carcinoma. J. Cell Mol. Med. 15(7), 1593–1602 (2011).

81

Ryland GL, Bearfoot JL, Doyle MA et al. MicroRNA genes and their target 3’-untranslated regions are infrequently somatically mutated in ovarian cancers. PLoS ONE 7(4), e35805 (2012).

Flavin R, Smyth P, Barrett C et al. miR-29b expression is associated with disease-free survival in patients with ovarian serous carcinoma. Int. J. Gynecol. Cancer 19(4), 641–647 (2009).

82

Permuth-Wey J, Chen Z, Tsai YY et al. Ovarian Cancer Association Consortium (OCAC). MicroRNA processing and binding site polymorphisms are not replicated in the Ovarian Cancer Association Consortium. Cancer Epidemiol. Biomarkers Prev. 20(8), 1793–1797 (2011).

Peng DX, Luo M, Qiu LW, He YL, Wang XF. Prognostic implications of microRNA-100 and its functional roles in human epithelial ovarian cancer. Oncol. Rep. 27(4), 1238–1244 (2012).

83

Chao A, Lin CY, Lee YS et al. Regulation of ovarian cancer progression by microRNA-187 through targeting disabled homolog-2. Oncogene 31(6), 764–775 (2012).

84

Hu X, Macdonald DM, Huettner PC et al. A miR-200 microRNA cluster as prognostic marker in advanced ovarian cancer. Gynecol. Oncol. 114(3), 457–464 (2009).

85

Marchini S, Cavalieri D, Fruscio R et al. Association between miR-200c and the survival of patients with stage I epithelial ovarian cancer: a retrospective study of two independent tumour tissue collections. Lancet Oncol. 12(3), 273–285 (2011).

86

Wang S, Zhao X, Wang J et al. Upregulation of microRNA-203 is associated with advanced tumor progression and poor prognosis in epithelial ovarian cancer. Med. Oncol. 30(3), 681 (2013).

87

Wurz K, Garcia RL, Goff BA et al. MiR-221 and MiR-222 alterations in sporadic ovarian carcinoma: relationship to CDKN1B, CDKNIC and overall survival. Genes Chromosomes Cancer 49(7), 577–584 (2010).

Sabatier R, Finetti P, Bonensea J et al. A seven-gene prognostic model for platinum-treated ovarian carcinomas. Br. J. Cancer 105(2), 304–311 (2011). Trinh XB, Tjalma WA, Dirix LY et al. Microarray-based oncogenic pathway profiling in advanced serous papillary ovarian carcinoma. PLoS ONE 6(7), e22469 (2011).

61

Schwede M, Spentzos D, Bentink S et al. Stem cell-like gene expression in ovarian cancer predicts type II subtype and prognosis. PLoS ONE 8(3), e57799 (2013).

62

Tan DS, Iravani M, McCluggage WG et al. Genomic analysis reveals the molecular heterogeneity of ovarian clear cell carcinomas. Clin. Cancer Res. 17(6), 1521–1534 (2011).

•

Genetic mapping of clear cell carcinoma.

63

Tan TZ, Miow QH, Huang RY et al. Functional genomics identifies five distinct molecular subtypes with clinical relevance and pathways for growth control in epithelial ovarian cancer. EMBO Mol. Med. 5(7), 983–998 (2013).

•

Genomic classification of ovarian cancer documenting the central role of epithelial-to-mesenchymal transition and stem cell signatures.

64

White KL, Vierkant RA, Fogarty ZC et al. Analysis of over 10,000 Cases finds no association between previously reported candidate polymorphisms and ovarian cancer outcome. Cancer Epidemiol. Biomarkers Prev. 22(5), 987–992 (2013).

66

67

68

Polytarchou C, Iliopoulos D, Hatziapostolou M et al. Akt2 regulates all Akt isoforms and promotes resistance to hypoxia through induction of miR-21 upon oxygen deprivation. Cancer Res. 71(13), 4720–4731 (2011).

69

Corney DC, Hwang CI, Matoso A et al. Frequent downregulation of miR-34 family in human ovarian cancers. Clin. Cancer Res. 16(4), 1119–1128 (2010).

70

Yeh YM, Chuang CM, Chao KC, Wang LH. MicroRNA-138 suppresses ovarian cancer cell invasion and metastasis by targeting SOX4 and HIF-1α. Int. J. Cancer 133(4), 867–878 (2013).

71

72

Liu Z, Liu J, Segura MF et al. MiR-182 overexpression in tumourigenesis of high-grade serous ovarian carcinoma. J. Pathol. 228(2), 204–215 (2012). Bendoraite A, Knouf EC, Garg KS et al. Regulation of miR-200 family microRNAs and ZEB transcription factors


Review

in ovarian cancer: evidence supporting a mesothelial-toepithelial transition. Gynecol. Oncol. 116(1), 117–125 (2010). 73

60

65



529

Review Davidson & Tropé 88

89

90

Shih KK, Qin LX, Tanner EJ et al. A microRNA survival signature (MiSS) for advanced ovarian cancer. Gynecol. Oncol. 121(3), 444–450 (2011). Tang Z, Ow GS, Thiery JP, Ivshina AV, Kuznetsov VA. Meta-analysis of transcriptome reveals let-7b as an unfavorable prognostic biomarker and predicts molecular and clinical subclasses in high-grade serous ovarian carcinoma. Int. J. Cancer 134(2), 306–318 (2014). Karagiannis GS, Poutahidis T, Erdman SE, Kirsch R, Riddell RH, Diamandis EP. Cancer-associated fibroblasts drive the progression of metastasis through both paracrine and mechanical pressure on cancer tissue. Mol. Cancer Res. 10(11), 1403–1418 (2012).

103 Kobayashi M, Salomon C, Tapia J, Illanes SE, Mitchell MD,

Rice GE. Ovarian cancer cell invasiveness is associated with discordant exosomal sequestration of Let-7 miRNA and miR-200. J. Transl. Med. 12, 4 (2014). 104 Cho JA, Park H, Lim EH et al. Exosomes from ovarian

cancer cells induce adipose tissue-derived mesenchymal stem cells to acquire the physical and functional characteristics of tumor-supporting myofibroblasts. Gynecol. Oncol. 123(2), 379–386 (2011). 105 McCann CK, Growdon WB, Kulkarni-Datar K et al.

Inhibition of Hedgehog signaling antagonizes serous ovarian cancer growth in a primary xenograft model. PLoS ONE 6(11), e28077 (2011).

Marsh T, Pietras K, McAllister SS. Fibroblasts as architects of cancer pathogenesis. Biochim. Biophys. Acta 1832(7), 1070–1078 (2013).

106 Mitra AK, Zillhardt M, Hua Y et al. MicroRNAs reprogram

92

Polanska UM, Orimo A. Carcinoma-associated fibroblasts: non-neoplastic tumour-promoting mesenchymal cells. J. Cell Physiol. 228(8), 1651–1657 (2013).

•

93

Fang H, Declerck YA. Targeting the tumor microenvironment: from understanding pathways to effective clinical trials. Cancer Res. 73(16), 4965–4977 (2013).

91

94

Qiu W, Hu M, Sridhar A et al. No evidence of clonal somatic genetic alterations in cancer-associated fibroblasts from human breast and ovarian carcinomas. Nat. Genet. 40(5), 650–655 (2008).

normal fibroblasts into cancer-associated fibroblasts in ovarian cancer. Cancer Discov. 2(12), 1100–1108 (2012). Insight into the ability of miRNAs to modulate fibroblasts in the tumor microenvironment.

107 Kleinberg L, Dong HP, Holth A et al. Cleaved caspases and

NF-κB p65 are prognostic factors in metastatic ovarian carcinoma. Hum. Pathol. 40(6), 795–806 (2009). 108 Stavnes HT, Holth A, Don T et al. HOXB8 expression in

ovarian serous carcinoma effusions is associated with shorter survival. Gynecol. Oncol. 129(2), 358–363 (2013). 109 Hetland TE, Hellesylt E, Flørenes VA, Tropé CG, Davidson

B, Kærn J. Class III beta-tubulin expression in advancedstage serous ovarian carcinoma effusions is associated with poor survival and primary chemoresistance. Hum. Pathol. 42(7), 1019–1026 (2011).

•

In-depth analysis of clonality in cancer-associated fibroblasts.

95

Kataoka F, Tsuda H, Arao T et al. EGRI and FOSB gene expressions in cancer stroma are independent prognostic indicators for epithelial ovarian cancer receiving standard therapy. Genes Chromosomes Cancer 51(3), 300–312 (2012).

110 Bondong S, Kiefel H, Hielscher T et al. Prognostic

Valadi H, Ekström K, Bossios A, Sjöstrand M, Lee JJ, Lötvall JO. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat. Cell Biol. 9(6), 654–659 (2007).

111 Davidson B, Smith Y, Nesland JM, Kærn J, Reich R, Trope

Beach A, Zhang HG, Ratajczak MZ, Kakar SS. Exosomes: an overview of biogenesis, composition and role in ovarian cancer. J. Ovarian Res. 7(1), 14 (2014).

112 Holman LL, Friedman S, Daniels MS, Sun CC, Lu KH.

96

97

98

99

Li J, Sherman-Baust CA, Tsai-Turton M, Bristow RE, Roden RB, Morin PJ. Claudin-containing exosomes in the peripheral circulation of women with ovarian cancer. BMC Cancer 9, 244 (2009). English DP, Santin AD. Claudins overexpression in ovarian cancer: potential targets for Clostridium perfringens enterotoxin (CPE) based diagnosis and therapy. Int. J. Mol. Sci. 14(5), 10412–10437 (2013).

100 Escrevente C, Grammel N, Kandzia S et al.

Sialoglycoproteins and N-glycans from secreted exosomes of ovarian carcinoma cells. PLoS ONE 8(10), e78631 (2013). 101 Peng P, Yan Y, Keng S. Exosomes in the ascites of ovarian

cancer patients: origin and effects on anti-tumor immunity. Oncol. Rep. 25(3), 749–762 (2011). 102 Liang B, Peng P, Chen S et al. Characterization and

proteomic analysis of ovarian cancer-derived exosomes. J. Proteomics 80C, 171–182 (2013).

530

CME


significance of L1CAM in ovarian cancer and its role in constitutive NF-κB activation. Ann. Oncol. 23(7), 1795–1802 (2012). CG. Defining a prognostic marker panel for patients with ovarian serous carcinoma effusion. Hum. Pathol. 44(11), 2449–2460 (2013). Acceptability of prophylactic salpingectomy with delayed oophorectomy as risk-reducing surgery among BRCA mutation carriers. Gynecol. Oncol. 133(2), 283–286 (2014). 113 Schenberg T, Mitchell G. Prophylactic bilateral

salpingectomy as a prevention strategy in women at high-risk of ovarian cancer: a mini-review. Front. Oncol. 4, 21 (2014). 114 Moyer VA. US Preventive Services Task Force. Screening

for ovarian cancer: US Preventive Services Task Force reaffirmation recommendation statement. Ann. Intern. Med. 157(12), 900–904 (2012). 115 Slomski A. Screening women for ovarian cancer still does

more harm than good. JAMA 307(23), 2474–2475 (2012). 116 Partridge EE, Greenlee RT, Riley TL et al. Assessing the

risk of ovarian malignancy in asymptomatic women with abnormal CA 125 and transvaginal ultrasound scans in the prostate, lung, colorectal, and ovarian screening trial. Obstet. Gynecol. 121(1), 25–31 (2013). 117 Reade CJ, Riva JJ, Busse JW, Goldsmith CH, Elit L. Risks

and benefits of screening asymptomatic women for ovarian


CME cancer: a systematic review and meta-analysis. Gynecol. Oncol. 130(3), 674–681 (2013). 118 Gilbert L, Basso O, Sampalis J, Karp I et al. DOvE Study

Group. Assessment of symptomatic women for early diagnosis of ovarian cancer: results from the prospective DOvE pilot project. Lancet Oncol. 13(3), 285–291 (2012). 119 Lu KH, Skates S, Hernandez MA et al. A 2-stage ovarian

cancer screening strategy using the Risk of Ovarian Cancer Algorithm (ROCA) identifies early-stage incident cancers and demonstrates high positive predictive value. Cancer 119(19), 3454–3461 (2013).



Review

120 Morotti M, Becker CM, Menada MV, Ferrero S. Targeting

tyrosine-kinases in ovarian cancer. Expert Opin. Investig. Drugs 22(10), 1265–1279 (2013). 121 Walters CL, Arend RC, Armstrong DK, Naumann RW,

Alvarez RD. Folate and folate receptor alpha antagonists mechanism of action in ovarian cancer. Gynecol. Oncol. 131(2), 493–498 (2013). 122 Davidson B, Tropé CG, Reich R. The role of the tumor

stroma in ovarian cancer. Front Oncol. 4, 104 (2014).


531

Review Davidson & Tropé

CME

Ovarian cancer: diagnostic, biological and prognostic aspects To obtain credit, you should first read the journal article. After reading the article, you should be able to answer the following, related, multiple-choice questions. To complete the questions (with a minimum 75% passing score) and earn continuing medical education (CME) credit, please go to www.medscape. org/journal/whe. Credit cannot be obtained for tests completed on paper, although you may use the worksheet below to keep a record of your answers. You must be a registered user on Medscape.org. If you are not registered onMedscape.org, please click on the “Register” link on the right hand side of the website. Only one answer is correct for each question. Once you successfully answer all post-test questions you will be able to view and/or print your certifi-cate. For questions regarding the content of this activity, contact the accredited provider, [email protected].

For technical assistance, contact [email protected]. American Medical Association’s Physician’s Recognition Award (AMA PRA) credits are accepted in the US as evidence of participation in CME activities. For further information on this award, please refer to http://www.ama-assn.org/ama/pub/about-ama/ awards/ama-physicians-recognition-award.page. The AMA has determined that physicians not licensed in the US who participate in this CME activity are eligible for AMA PRA Category 1 Credits™. Through agreements that the AMA has made with agencies in some countries, AMA PRA credit may be acceptable as evidence of participation in CME activities. If you are not licensed in the US, please complete the questions online, print the AMA PRA CME credit certificate and present it to your national medical association for review.

Activity evaluation: where 1 is strongly disagree and 5 is strongly agree.

1

2

3

4

5

The activity supported the learning objectives. The material was organized clearly for learning to occur. The content learned from this activity will impact my practice. The activity was presented objectively and free of commercial bias.

1. Which of the following statements regarding the cause and classification of ovarian cancer is most accurate? £ A Epithelial tumors account for 90% of ovarian cancers

£ B Early menopause is associated with a higher risk for ovarian cancer £ C Most cases of ovarian cancer are hereditary £ D The lifetime risk for ovarian cancer associated with the BRCA1 mutation is approximately 10%

2. Which of the following variables is considered the strongest prognostic marker in cases of ovarian cancer? £ A Volume of residual disease after surgery

£ B Fibroblast growth factor receptor 4 expression £ C Poly(adenosine-diphosphate ribose) polymerase-1 expression £ D Astrocyte elevated gene-1 expression 3. Which of the following statements regarding different biomarkers in ovarian cases is most accurate?

£ £ £ £

532

A HER-2 expression is negatively associated with survival time B Mutations in the TP53 gene are particularly useful in evaluating prognosis C Biomarkers have failed to predict resistance to chemotherapy D The presence of CD3-positive lymphocytes in the tumor is associated with improved survival time



CME


Review

4. Which of the following statements regarding microRNAs (miRNAs) in ovarian cancer is most accurate?

£ £ £ £

A Nearly all ovarian cancers contain cancer-associated miRNAs B Single nucleotide polymorphisms in miRNA biosynthesis have been established to promote ovarian cancer C miRNAs appear to modify the response to chemotherapy D miRNAs play no role in disease progression in ovarian cancer



533