Gene Expression Signature With Independent Prognostic Significance in Epithelial Ovarian Cancer

Prognostic significance of YY1 protein expression and mRNA levels by bioinformatics analysis in human cancers: a therapeutic target

Conventional therapeutic treatments for various cancers include chemotherapy, radiotherapy, hormonal therapy and immunotherapy. While such therapies have resulted in clinical responses, they were coupled with non-tumor specificity, toxicity and resistance in a large subset of the treated patients. During the last decade, novel approaches based on scientific knowledge on the biology of cancer were exploited and led to the development of novel targeted therapies, such as specific chemical inhibitors and immune-based therapies. Although these targeted therapies resulted in better responses and less toxicity, there still remains the problem of the inherent or acquired resistance. Hence, current studies are seeking additional novel therapeutic targets that can overcome several mechanisms of resistance. The transcription factor Yin Yang 1 (YY1) is a ubiquitous protein expressed in normal and cancer tissues, though the expression level is much higher in a large number of cancers; hence, YY1 has been considered as a potential novel prognostic biomarker and therapeutic target. YY1 has been reported to be involved in the regulation of drug/immune resistance and also in the regulation of EMT. Several excellent reviews have been published on YY1 and cancer (see below), and, thus, this review will update recently published reports as well as report on the analysis of bioinformatics datasets for YY1 in various cancers and the relationship between reported protein expression and mRNA levels. The potential clinical significance of YY1 is discussed.

Tumor antigens as proteogenomic biomarkers in invasive ductal carcinomas

Background

The majority of genetic biomarkers for human cancers are defined by statistical screening of high-throughput genomics data. While a large number of genetic biomarkers have been proposed for diagnostic and prognostic applications, only a small number have been applied in the clinic. Similarly, the use of proteomics methods for the discovery of cancer biomarkers is increasing. The emerging field of proteogenomics seeks to enrich the value of genomics and proteomics approaches by studying the intersection of genomics and proteomics data. This task is challenging due to the complex nature of transcriptional and translation regulatory mechanisms and the disparities between genomic and proteomic data from the same samples. In this study, we have examined tumor antigens as potential biomarkers for breast cancer using genomics and proteomics data from previously reported laser capture microdissected ER+ tumor samples.

Results

We applied proteogenomic analyses to study the genetic aberrations of 32 tumor antigens determined in the proteomic data. We found that tumor antigens that are aberrantly expressed at the genetic level and expressed at the protein level, are likely involved in perturbing pathways directly linked to the hallmarks of cancer. The results found by proteogenomic analysis of the 32 tumor antigens studied here, capture largely the same pathway irregularities as those elucidated from large-scale screening of genomics analyses, where several thousands of genes are often found to be perturbed.

Conclusion

Tumor antigens are a group of proteins recognized by the cells of the immune system. Specifically, they are recognized in tumor cells where they are present in larger than usual amounts, or are physiochemically altered to a degree at which they no longer resemble native human proteins. This proteogenomic analysis of 32 tumor antigens suggests that tumor antigens have the potential to be highly specific biomarkers for different cancers.

Bioinformatics for cancer immunotherapy target discovery

The mechanisms of immune response to cancer have been studied extensively and great effort has been invested into harnessing the therapeutic potential of the immune system. Immunotherapies have seen significant advances in the past 20 years, but the full potential of protective and therapeutic cancer immunotherapies has yet to be fulfilled. The insufficient efficacy of existing treatments can be attributed to a number of biological and technical issues. In this review, we detail the current limitations of immunotherapy target selection and design, and review computational methods to streamline therapy target discovery in a bioinformatics analysis pipeline. We describe specialized bioinformatics tools and databases for three main bottlenecks in immunotherapy target discovery: the cataloging of potentially antigenic proteins, the identification of potential HLA binders, and the selection epitopes and co-targets for single-epitope and multi-epitope strategies. We provide examples of application to the well-known tumor antigen HER2 and suggest bioinformatics methods to ameliorate therapy resistance and ensure efficient and lasting control of tumors.

MEK1 is associated with carboplatin resistance and is a prognostic biomarker in epithelial ovarian cancer

BACKGROUND

Primary systemic treatment for ovarian cancer is surgery, followed by platinum based chemotherapy. Platinum resistant cancers progress/recur in approximately 25% of cases within six months. We aimed to identify clinically useful biomarkers of platinum resistance.

METHODS

A database of ovarian cancer transcriptomic datasets including treatment and response information was set up by mining the GEO and TCGA repositories. Receiver operator characteristics (ROC) analysis was performed in R for each gene and these were then ranked using their achieved area under the curve (AUC) values. The most significant candidates were selected and in vitro functionally evaluated in four epithelial ovarian cancer cell lines (SKOV-3-, CAOV-3, ES-2 and OVCAR-3), using gene silencing combined with drug treatment in viability and apoptosis assays. We collected 94 tumor samples and the strongest candidate was validated by IHC and qRT-PCR in these.

RESULTS

All together 1,452 eligible patients were identified. Based on the ROC analysis the eight most significant genes were JRK, CNOT8, RTF1, CCT3, NFAT2CIP, MEK1, FUBP1 and CSDE1. Silencing of MEK1, CSDE1, CNOT8 and RTF1, and pharmacological inhibition of MEK1 caused significant sensitization in the cell lines. Of the eight genes, JRK (p = 3.2E-05), MEK1 (p = 0.0078), FUBP1 (p = 0.014) and CNOT8 (p = 0.00022) also correlated to progression free survival. The correlation between the best biomarker candidate MEK1 and survival was validated in two independent cohorts by qRT-PCR (n = 34, HR = 5.8, p = 0.003) and IHC (n = 59, HR = 4.3, p = 0.033).

CONCLUSION

We identified MEK1 as a promising prognostic biomarker candidate correlated to response to platinum based chemotherapy in ovarian cancer.

Association between morphologic CT imaging traits and prognostically relevant gene signatures in women with high-grade serous ovarian cancer: a hypothesis-generating study

PURPOSE

To investigate associations among imaging traits observed on computed tomographic (CT) images, Classification of Ovarian Cancer (CLOVAR) gene signatures, and survival in women with high-grade serous ovarian cancer (HGSOC).

MATERIALS AND METHODS

The institutional review board approved this HIPAA-compliant retrospective study of CT images obtained before cytoreductive surgery in 46 women with HGSOC, whose tumors were subjected to molecular analysis performed by the Cancer Genome Atlas Research Network. Two readers independently evaluated the CT features of the primary ovarian mass and sites of metastatic spread if present, including size, outline, and texture. Fisher exact test was used to examine the relationship between imaging traits and CLOVAR subtypes (CLOVAR differentiated, immunoreactive, mesenchymal, and proliferative). Kaplan-Meier and Cox proportional hazards regression survival analyses were performed.

RESULTS

The presence of mesenteric infiltration and diffuse peritoneal involvement by tumor at CT were significantly associated with CLOVAR subtype (P = .002-.004 for reader 1 and P = .005-.012 for reader 2). Mesenteric infiltration at CT was associated with CLOVAR mesenchymal subtype. Patients with mesenteric infiltration had shorter median progression-free survival than patients without mesenteric involvement (14.7 months vs 25.6 months according to both readers; P = .019 for reader 1 and .015 for reader 2) and overall survival (49.0 vs 58.2 months; P = .014 [reader 1] and 50.0 vs 59.1 months; P = .015 [reader 2]). No other imaging features were significantly associated with CLOVAR subtype or survival.

CONCLUSION

Specific CT imaging traits were associated with the CLOVAR subtypes and survival in patients with HGSOC.

Clinically relevant microRNAs in ovarian cancer

microRNAs (miRNAs/miRs) belong to a class of small noncoding RNAs that can negatively regulate messenger RNA (mRNA) expression of target genes. miRNAs are involved in multiple aspects of ovarian cancer cell dysfunction and the phenotype of ovarian cancer cells can be modified by targeting miRNA expression. miRNA profiling has detected a number of candidate miRNAs with the potential to regulate many important biologic functions in ovarian cancer, but their role still needs to be clarified, given the remarkable heterogeneity among ovarian cancers and the context-dependent role of miRNAs. This review summarizes the data collected from The Cancer Genome Atlas (TCGA) and several other genome-wide projects to identify dysregulated miRNAs in ovarian cancers. Copy number variations (CNVs), epigenetic alterations, and oncogenic mutations are also discussed that affect miRNA levels in ovarian disease. Emphasis is given to the role of particular miRNAs in altering expression of genes in human ovarian cancers with the potential to provide diagnostic, prognostic, and therapeutic targets. Particular attention has been given to TP53, BRCA1/2, CA125 (MUC16), HE4 (WFDC2), and imprinted genes such as ARHI (DIRAS3). A better understanding of the abnormalities in miRNA expression and downstream transcriptional and biologic consequences will provide leads for more effective biomarkers and translational approaches in the management of ovarian cancer.

Ovarian cancer: genomic analysis

OBJECTIVES

Despite improvements in the management of ovarian cancer patients over the last 30 years, there has been only a minimal improvement in overall survival. While targeted therapeutic approaches for the treatment of cancer have evolved, major challenges in ovarian cancer research persist, including the identification of predictive biomarkers with clinical relevance, so that empirical drug selection can be avoided. In this article, we review published genomic analysis studies including data generated in our laboratory and how they have been incorporated into modern clinical trials in a rational and effective way.

METHODS

Multiple published genomic analysis studies were collected for review and discussion with emphasis on their potential clinical applicability.

RESULTS

Genomic analysis has been shown to be a powerful tool to identify dysregulated genes, aberrantly activated pathways and to uncover uniqueness of subclasses of ovarian tumors. The application of this technology has provided a solid molecular basis for different clinical behaviors associated with tumor histology and grade. Genomic signatures have been obtained to predict clinical end points for patients with cancer, including response rates, progression-free survival, and overall survival. In addition, genomic analysis has provided opportunities to identify biomarkers, which either result in a modification of existing clinical management or to stratification of patients to novel therapeutic approaches designed as clinical trials.

CONCLUSIONS

Genomic analyses have accelerated the identification of relevant biomarkers and extended our understanding of the molecular biology of ovarian cancer. This in turn, will hopefully lead to a paradigm shift from empirical, uniform treatment to a more rational, personalized treatment of ovarian cancers. However, validation of potential biomarkers on both the statistical and biological levels is needed to confirm they are of clinical relevance, in order to increase the likelihood that the desired outcome can be predicted and achieved.

Gynecologic biopsy for molecular profiling: a review for the interventional radiologist

The interventional radiologist is often asked to obtain multiple biopsies of gynecological malignancies for genetic profiling. This article reviews the current indications for gynecological biopsy as well as how the information gained contributes to a personalized medicine plan for the individual patient. The specific focus of this review is the current knowledge and practice of molecular profiling for gynecological malignancies.

FAK Inhibition disrupts a β5 integrin signaling axis controlling anchorage-independent ovarian carcinoma growth

Ovarian cancer ascites fluid contains matrix proteins that can impact tumor growth via integrin receptor binding. In human ovarian tumor tissue arrays, we find that activation of the cytoplasmic focal adhesion (FAK) tyrosine kinase parallels increased tumor stage, β5 integrin, and osteopontin matrix staining. Elevated osteopontin, β5 integrin, and FAK mRNA levels are associated with decreased serous ovarian cancer patient survival. FAK remains active within ovarian cancer cells grown as spheroids, and anchorage-independent growth analyses of seven ovarian carcinoma cell lines identified sensitive (HEY, OVCAR8) and resistant (SKOV3-IP, OVCAR10) cells to 0.1 μmol/L FAK inhibitor (VS-4718, formerly PND-1186) treatment. VS-4718 promoted HEY and OVCAR8 G0-G1 cell-cycle arrest followed by cell death, whereas growth of SKOV3-IP and OVCAR10 cells was resistant to 1.0 μmol/L VS-4718. In HEY cells, genetic or pharmacological FAK inhibition prevented tumor growth in mice with corresponding reductions in β5 integrin and osteopontin expression. β5 knockdown reduced HEY cell growth in soft agar, tumor growth in mice, and both FAK Y397 phosphorylation and osteopontin expression in spheroids. FAK inhibitor-resistant (SKOV3-IP, OVCAR10) cells exhibited anchorage-independent Akt S473 phosphorylation, and expression of membrane-targeted and active Akt in sensitive cells (HEY, OVCAR8) increased growth but did not create a FAK inhibitor-resistant phenotype. These results link osteopontin, β5 integrin, and FAK in promoting ovarian tumor progression. β5 integrin expression may serve as a biomarker for serous ovarian carcinoma cells that possess active FAK signaling.

Investigational agents for epithelial ovarian cancer

Ovarian cancer is the leading cause of gynecologic cancer deaths and accounts for 4% of women's cancer diagnoses and 5% of all cancer mortalities. Despite the ability of current chemotherapy and cytoreductive surgery to put patients in remission, most patients with advanced cancer will eventually relapse. Many advances in the treatment of ovarian cancer have been reported in the past several years and a historical background is provided. Attention will then turn to analogs of current chemotherapeutic agents, new cytotoxic drugs, targeted molecular therapy, intraperitoneal therapy and immunotherapy. This review will give a perspective on current drugs, potential agents and upcoming clinical trials.

POSTN/TGFBI-associated stromal signature predicts poor prognosis in serous epithelial ovarian cancer

OBJECTIVE

To identify molecular prognosticators and therapeutic targets for high-grade serous epithelial ovarian cancers (EOCs) using genetic analyses driven by biologic features of EOC pathogenesis.

METHODS

Ovarian tissue samples (n = 172; 122 serous EOCs, 30 other EOCs, 20 normal/benign) collected prospectively from sequential patients undergoing gynecologic surgery were analyzed using RNA expression microarrays. Samples were classified based on expression of genes with potential relevance in ovarian cancer. Gene sets were defined using Rosetta Similarity Search Tool (ROAST) and analysis of variance (ANOVA). Gene copy number variations were identified by array comparative genomic hybridization.

RESULTS

No distinct subgroups of EOC could be identified by unsupervised clustering, however, analyses based on genes correlated with periostin (POSTN) and estrogen receptor-alpha (ESR1) yielded distinct subgroups. When 95 high-grade serous EOCs were grouped by genes based on ANOVA comparing ESR1/WT1 and POSTN/TGFBI samples, overall survival (OS) was significantly shorter for 43 patients with tumors expressing genes associated with POSTN/TGFBI compared to 52 patients with tumors expressing genes associated with ESR1/WT1 (median 30 versus 49 months, respectively; P = 0.022). Several targets with therapeutic potential were identified within each subgroup. BRCA germline mutations were more frequent in the ESR1/WT1 subgroup. Proliferation-associated genes and TP53 status (mutated or wild-type) did not correlate with survival. Findings were validated using independent ovarian cancer datasets.

CONCLUSIONS

Two distinct molecular subgroups of high-grade serous EOCs based on POSTN/TGFBI and ESR1/WT1 expressions were identified with significantly different OS. Specific differentially expressed genes between these subgroups provide potential prognostic and therapeutic targets.

Ovarian cancer biomarker discovery based on genomic approaches

Ovarian cancer presents at an advanced stage in more than 75% of patients. Early detection has great promise to improve clinical outcomes. Although the advancing proteomic technologies led to the discovery of numerous ovarian cancer biomarkers, no screening method has been recommended for early detection of ovarian cancer. Complexity and heterogeneity of ovarian carcinogenesis is a major obstacle to discover biomarkers. As cancer arises due to accumulation of genetic change, understanding the close connection between genetic changes and ovarian carcinogenesis would provide the opportunity to find novel gene-level ovarian cancer biomarkers. In this review, we summarize the various gene-based biomarkers by genomic technologies, including inherited gene mutations, epigenetic changes, and differential gene expression. In addition, we suggest the strategy to discover novel gene-based biomarkers with recently introduced next generation sequencing.

FGF18 as a prognostic and therapeutic biomarker in ovarian cancer

High-throughput genomic technologies have identified biomarkers and potential therapeutic targets for ovarian cancer. Comprehensive functional validation studies of the biological and clinical implications of these biomarkers are needed to advance them toward clinical use. Amplification of chromosomal region 5q31-5q35.3 has been used to predict poor prognosis in patients with advanced stage, high-grade serous ovarian cancer. In this study, we further dissected this large amplicon and identified the overexpression of FGF18 as an independent predictive marker for poor clinical outcome in this patient population. Using cell culture and xenograft models, we show that FGF18 signaling promoted tumor progression by modulating the ovarian tumor aggressiveness and microenvironment. FGF18 controlled migration, invasion, and tumorigenicity of ovarian cancer cells through NF-κB activation, which increased the production of oncogenic cytokines and chemokines. This resulted in a tumor microenvironment characterized by enhanced angiogenesis and augmented tumor-associated macrophage infiltration and M2 polarization. Tumors from ovarian cancer patients had increased FGF18 expression levels with microvessel density and M2 macrophage infiltration, confirming our in vitro results. These findings demonstrate that FGF18 is important for a subset of ovarian cancers and may serve as a therapeutic target.

COL11A1 promotes tumor progression and predicts poor clinical outcome in ovarian cancer

Biomarkers that predict disease progression might assist the development of better therapeutic strategies for aggressive cancers, such as ovarian cancer. Here, we investigated the role of collagen type XI alpha 1 (COL11A1) in cell invasiveness and tumor formation and the prognostic impact of COL11A1 expression in ovarian cancer. Microarray analysis suggested that COL11A1 is a disease progression-associated gene that is linked to ovarian cancer recurrence and poor survival. Small interference RNA-mediated specific reduction in COL11A1 protein levels suppressed the invasive ability and oncogenic potential of ovarian cancer cells and decreased tumor formation and lung colonization in mouse xenografts. A combination of experimental approaches, including real-time RT-PCR, casein zymography and chromatin immunoprecipitation (ChIP) assays, showed that COL11A1 knockdown attenuated MMP3 expression and suppressed binding of Ets-1 to its putative MMP3 promoter-binding site, suggesting that the Ets-1-MMP3 axis is upregulated by COL11A1. Transforming growth factor (TGF)-beta (TGF-β1) treatment triggers the activation of smad2 signaling cascades, leading to activation of COL11A1 and MMP3. Pharmacological inhibition of MMP3 abrogated the TGF-β1-triggered, COL11A1-dependent cell invasiveness. Furthermore, the NF-YA-binding site on the COL11A1 promoter was identified as the major determinant of TGF-β1-dependent COL11A1 activation. Analysis of 88 ovarian cancer patients indicated that high COL11A1 mRNA levels are associated with advanced disease stage. The 5-year recurrence-free and overall survival rates were significantly lower (P=0.006 and P=0.018, respectively) among patients with high expression levels of tissue COL11A1 mRNA compared with those with low expression. We conclude that COL11A1 may promote tumor aggressiveness via the TGF-β1-MMP3 axis and that COL11A1 expression can predict clinical outcome in ovarian cancer patients.

Prediction of cancer outcome using DNA microarray technology: past, present and future

BACKGROUND

The use of DNA microarray technology to predict cancer outcome already has a history of almost a decade. Although many breakthroughs have been made, the promise of individualized therapy is still not fulfilled. In addition, new technologies are emerging that also show promise in outcome prediction of cancer patients.

OBJECTIVE

The impact of DNA microarray and other 'omics' technologies on the outcome prediction of cancer patients was investigated. Whether integration of omics data results in better predictions was also examined.

METHODS

DNA microarray technology was focused on as a starting point because this technology is considered to be the most mature technology from all omics technologies. Next, emerging technologies that may accomplish the same goals but have been less extensively studied are described.

CONCLUSION

Besides DNA microarray technology, other omics technologies have shown promise in predicting the cancer outcome or have potential to replace microarray technology in the near future. Moreover, it is shown that integration of multiple omics data can result in better predictions of cancer outcome; but, owing to the lack of comprehensive studies, validation studies are required to verify which omics has the most information and whether a combination of multiple omics data improves predictive performance.

Cancer gene expression signatures - the rise and fall?

A 'gene expression signature' can be defined as a single or a combined gene expression alteration with validated specificity in terms of diagnosis, prognosis or prediction of therapeutic response. Since the publication of the first signature in the late 90s, high-throughput gene expression analysis has revolutionised genetics over the last 15 years. The scientific community has used this new technology to find responses to these fundamental questions; from understanding tumour biology, to prediction of progression, and treatments to which it will respond. Nevertheless, legitimate excitement about the attractiveness of molecular technologies and the promise of discovery-based research should not overlook adherence to the rules of evidence, otherwise it may result in claims that are not meaningful and lead to disappointment. This review will thus focus on the approaches developed to answer these three fundamental questions and the results evidenced both at biological and clinical level. On looking at this huge amount of data that have become increasingly minute, and at times contradictory, we discuss how gene expression signature improve our understanding of cancer biology, our ability to predict progression and response, and finally, our capacity to treat cancers more efficiently.

Validation of ovarian cancer gene expression signatures for survival and subtype in formalin fixed paraffin embedded tissues

INTRODUCTION

Gene expression signatures have been identified for epithelial ovarian cancer survival (TCGA) and intrinsic subtypes (Tothill et al.). One obstacle to clinical translation is that these signatures were developed using frozen tissue, whereas usually only formalin-fixed, paraffin embedded (FFPE) tissue is available. The aim of this study was to determine if gene expression signatures can be translated to fixed archival tissues.

METHODS

RNA extracted from FFPE sections from 240 primary ovarian cancers was analyzed by DASL on Illumina BeadChip arrays. Concordance of expression at the individual gene level was assessed by comparing array data from the same cancers (30 frozen samples analyzed on Affymetrix arrays versus FFPE DASL).

RESULTS

The correlation between FFPE and frozen survival signature estimates was 0.774. The TCGA signature using DASL was predictive of survival in 106 advanced stage high grade serous ovarian cancers (median survival 33 versus 60 months, estimated hazard ratio for death 2.30, P=0.0007). Similar to Tothill, we found using DASL that most high grade serous ovarian cancers (102/110, 93%) were assigned to subtypes 1, 2, 4 and 5, whereas most endometrioid, clear cell, mucinous and low grade serous cases (39/57, 68%) were assigned to subtypes 3 and 6 (P<10e-15).

CONCLUSIONS

Although individual probe estimates of microarrays may be weakly correlated between FFPE and frozen samples, combinations of probes have robust ability to predict survival and subtype. This suggests that it may be possible to use these signatures for prognostic and predictive purposes as we seek to individualize the treatment of ovarian cancer.

Prognostically relevant gene signatures of high-grade serous ovarian carcinoma

Because of the high risk of recurrence in high-grade serous ovarian carcinoma (HGS-OvCa), the development of outcome predictors could be valuable for patient stratification. Using the catalog of The Cancer Genome Atlas (TCGA), we developed subtype and survival gene expression signatures, which, when combined, provide a prognostic model of HGS-OvCa classification, named "Classification of Ovarian Cancer" (CLOVAR). We validated CLOVAR on an independent dataset consisting of 879 HGS-OvCa expression profiles. The worst outcome group, accounting for 23% of all cases, was associated with a median survival of 23 months and a platinum resistance rate of 63%, versus a median survival of 46 months and platinum resistance rate of 23% in other cases. Associating the outcome prediction model with BRCA1/BRCA2 mutation status, residual disease after surgery, and disease stage further optimized outcome classification. Ovarian cancer is a disease in urgent need of more effective therapies. The spectrum of outcomes observed here and their association with CLOVAR signatures suggests variations in underlying tumor biology. Prospective validation of the CLOVAR model in the context of additional prognostic variables may provide a rationale for optimal combination of patient and treatment regimens.

A network module-based method for identifying cancer prognostic signatures

Discovering robust prognostic gene signatures as biomarkers using genomics data can be challenging. We have developed a simple but efficient method for discovering prognostic biomarkers in cancer gene expression data sets using modules derived from a highly reliable gene functional interaction network. When applied to breast cancer, we discover a novel 31-gene signature associated with patient survival. The signature replicates across 5 independent gene expression studies, and outperforms 48 published gene signatures. When applied to ovarian cancer, the algorithm identifies a 75-gene signature associated with patient survival. A Cytoscape plugin implementation of the signature discovery method is available at http://wiki.reactome.org/index.php/Reactome_FI_Cytoscape_Plugin.

Validated gene targets associated with curatively treated advanced serous ovarian carcinoma

OBJECTIVES

High-grade serous ovarian cancer (HGSOC) mostly presents at an advanced stage and has a low overall survival rate. However, a subgroup of patients are seemingly cured after standard initial therapy. We hypothesize that the molecular profiles of these patients vary from long-term survivors who recur.

METHODS

Patients with advanced HGSOC who underwent primary cytoreductive surgery and platinum-based chemotherapy were identified from The Cancer Genome Atlas (TCGA) and institutional (MSKCC) samples. A curative-intent group was defined by recurrence-free survival of >5years. A long-term recurrent group was composed of patients who recurred but survived >5years. RNA was hybridized to Affymetrix U133A transcription microarrays. The NanoString nCounter gene expression system was used for validation in an independent patient population.

RESULTS

In 30 curative and 84 recurrent patients, class comparison identified twice as many differentially expressed probes between the groups than expected by chance alone. TCGA and MSKCC data sets had 19 overlapping genes. Pathway analyses identified over-represented networks that included nuclear factor kappa B (NFkB) transcription and extracellular signal-regulated kinase (ERK) signaling. External validation was performed in an independent population of 28 curative and 38 recurrent patients. Three genes (CYP4B1, CEPT1, CHMP4A) in common between our original data sets remained differentially expressed in the external validation data.

CONCLUSIONS

There are distinct transcriptional elements in HGSOC from patients likely to be cured by standard primary therapy. Three genes have withstood rigorous validation and are plausible targets for further study, which may provide insight into molecular features associated with long-term survival and chemotherapy resistance mechanisms.

A tumor-stroma targeted oncolytic adenovirus replicated in human ovary cancer samples and inhibited growth of disseminated solid tumors in mice

Targeting the tumor stroma in addition to the malignant cell compartment is of paramount importance to achieve complete tumor regression. In this work, we modified a previously designed tumor stroma-targeted conditionally replicative adenovirus (CRAd) based on the SPARC promoter by introducing a mutated E1A unable to bind pRB and pseudotyped with a chimeric Ad5/3 fiber (Ad F512v1), and assessed its replication/lytic capacity in ovary cancer in vitro and in vivo. AdF512v1 was able to replicate in fresh samples obtained from patients: (i) with primary human ovary cancer; (ii) that underwent neoadjuvant treatment; (iii) with metastatic disease. In addition, we show that four intraperitoneal (i.p.) injections of 5 × 10(10) v.p. eliminated 50% of xenografted human ovary tumors disseminated in nude mice. Moreover, AdF512v1 replication in tumor models was enhanced 15-40-fold when the tumor contained a mix of malignant and SPARC-expressing stromal cells (fibroblasts and endothelial cells). Contrary to the wild-type virus, AdF512v1 was unable to replicate in normal human ovary samples while the wild-type virus can replicate. This study provides evidence on the lytic capacity of this CRAd and highlights the importance of targeting the stromal tissue in addition to the malignant cell compartment to achieve tumor regression.

Combined panel of serum human tissue kallikreins and CA-125 for the detection of epithelial ovarian cancer

Objective

To determine the predictive accuracy of the combined panels of serum human tissue kallikreins (hKs) and CA-125 for the detection of epithelial ovarian cancer.

Methods

Serum specimens collected from 5 Indonesian centers and 1 Vietnamese center were analyzed for CA-125, hK6, and hK10 levels. A total of 375 specimens from patients presenting with ovarian tumors, which include 156 benign cysts, 172 epithelial ovarian cancers (stage I/II, n=72; stage III/IV, n=100), 36 germ cell tumors and 11 borderline tumors, were included in the study analysis. Receiver operating characteristic analysis were performed to determine the cutoffs for age, CA-125, hK6, and hK10. Sensitivity, specificity, negative, and positive predictive values were determined for various combinations of the biomarkers.

Results

The levels of hK6 and hK10 were significantly elevated in ovarian cancer cases compared to benign cysts. Combination of 3 markers, age/CA-125/hk6 or CA-125/hk6/hk10, showed improved specificity (100%) and positive predictive value (100%) for prediction of ovarian cancer, when compared to the performance of single markers having 80-92% specificity and 74-87% positive predictive value. Four-marker combination, age/CA-125/hK6/hK10 also showed 100% specificity and 100% positive predictive value, although it demonstrated low sensitivity (11.9%) and negative predictive value (52.8%).

Conclusion

The combination of human tissue kallikreins and CA-125 showed potential for improving prediction of epithelial ovarian cancer in patients presenting with ovarian tumors.

Gene expression analysis of matched ovarian primary tumors and peritoneal metastasis

Background

Ovarian cancer is the most deadly gynecological cancer due to late diagnosis at advanced stage with major peritoneal involvement. To date most research has focused on primary tumor. However the prognosis is directly related to residual disease at the end of the treatment. Therefore it is mandatory to focus and study the biology of meatastatic disease that is most frequently localized to the peritoneal caivty in ovarian cancer.

Methods

We used high-density gene expression arrays to investigate gene expression changes between matched primary and metastatic (peritoneal) lesions.

Results

Here we show that gene expression profiles in peritoneal metastasis are significantly different than their matched primary tumor and these changes are affected by underlying copy number variation differences among other causes. We show that differentially expressed genes are enriched in specific pathways including JAK/STAT pathway, cytokine signaling and other immune related pathways. We show that underlying copy number variations significantly affect gene expression. Indeed patients with important differences in copy number variation displayed greater gene expression differences between their primary and matched metastatic lesions.

Conclusions

Our analysis shows a very specific targeting at both the genomic and transcriptomic level to upregulate certain pathways in the peritoneal metastasis of ovarian cancer. Moreover, while primary tumors use certain pathways we identify distinct differences with metastatic lesions. The variation between primary and metastatic lesions should be considered in personalized treatment of ovarian cancer.

Multidrug resistance-linked gene signature predicts overall survival of patients with primary ovarian serous carcinoma

PURPOSE

This study assesses the ability of multidrug resistance (MDR)-associated gene expression patterns to predict survival in patients with newly diagnosed carcinoma of the ovary. The scope of this research differs substantially from that of previous reports, as a very large set of genes was evaluated whose expression has been shown to affect response to chemotherapy.

EXPERIMENTAL DESIGN

We applied a customized TaqMan low density array, a highly sensitive and specific assay, to study the expression profiles of 380 MDR-linked genes in 80 tumor specimens collected at initial surgery to debulk primary serous carcinoma. The RNA expression profiles of these drug resistance genes were correlated with clinical outcomes.

RESULTS

Leave-one-out cross-validation was used to estimate the ability of MDR gene expression to predict survival. Although gene expression alone does not predict overall survival (OS; P = 0.06), four covariates (age, stage, CA125 level, and surgical debulking) do (P = 0.03). When gene expression was added to the covariates, we found an 11-gene signature that provides a major improvement in OS prediction (log-rank statistic P < 0.003). The predictive power of this 11-gene signature was confirmed by dividing high- and low-risk patient groups, as defined by their clinical covariates, into four specific risk groups on the basis of expression levels.

CONCLUSION

This study reveals an 11-gene signature that allows a more precise prognosis for patients with serous cancer of the ovary treated with carboplatin- and paclitaxel-based therapy. These 11 new targets offer opportunities for new therapies to improve clinical outcome in ovarian cancer.

A unique histone deacetylase inhibitor alters microRNA expression and signal transduction in chemoresistant ovarian cancer cells

Previously, we demonstrated potent antineoplastic activity of a distinctive histone deacetylase inhibitor (HDACI), AR42, against chemoresistant CP70 ovarian cancer cells in vitro and in vivo. Here, in follow-up to that work, we explored AR42 global mechanisms-of-action by examining drug-associated, genome-wide microRNA and mRNA expression profiles, which differed from those of the well-studied HDACI vorinostat. Expression of microRNA genes in negative correlation with their "target" coding gene (mRNA) transcripts, and transcription factor genes with expression positively correlated with coding genes having their cognate binding sites, were identified and subjected to gene ontology analyses. Those evaluations showed AR42 gene expression patterns to negatively correlate with Wnt signaling (> 18-fold induction of SFRP1), the epithelial-to-mesenchymal transition (40% decreased ATF1), and cell cycle progression (33-fold increased 14-3-3σ). By contrast, AR42 transcriptome alterations correlated positively with extrinsic ("death receptor") apoptosis (> 2.3-fold upregulated DAPK) and favorable ovarian cancer histopathology and prognosis. Inhibition of Wnt signaling was experimentally validated by: (1) > 2.6-fold reduced Wnt reporter activity; and (2) 36% reduction in nuclear, activated β-catenin. Likely AR42 induction of multiple (type I or type II autophagic) cell death cascades was further supported by 57% decreased reliance upon reactive oxygen, increased mitochondrial membrane disruption, and caspase independence, as compared with vorinostat. Taken together, we demonstrate distinct antineoplastic pathway alterations, in aggressive ovarian cancer cells, following treatment with a promising HDACI, AR42. These combined computational and experimental approaches may also represent a straightforward means for mechanistic studies of other promising antineoplastics, and/or the identification of agents that may complement epigenetic therapies.

Identification of differentially expressed genes according to chemosensitivity in advanced ovarian serous adenocarcinomas: expression of GRIA2 predicts better survival

Background:

The purpose of this study was to identify genes that are differentially expressed in chemosensitive serous papillary ovarian carcinomas relative to those expressed in chemoresistant tumours.

Methods:

To identify novel candidate biomarkers, differences in gene expression were analysed in 26 stage IIIC/IV serous ovarian adenocarcinomas (12 chemosensitive tumours and 14 chemoresistant tumours). We subsequently investigated the immunohistochemical expression of GRIA2 in 48 independent sets of advanced ovarian serous carcinomas.

Results:

Microarray analysis revealed a total of 57 genes that were differentially expressed in chemoresistant and chemosensitive tumours. Of the 57 genes, 39 genes were upregulated and 18 genes were downregulated in chemosensitive tumours. Five differentially expressed genes (CD36, LIFR, CHL1, GRIA2, and FCGBP) were validated by quantitative real-time PCR. The expression of GRIA2 was validated at the protein level by immunohistochemistry, and patients with GRIA2 expression showed a longer progression-free and overall survival (P=0.051 and P=0.031 respectively).

Conclusions:

We found 57 differentially expressed genes to distinguish between chemosensitive and chemoresistant tumours. We also demonstrated that the expression of GRIA2 among the differentially expressed genes provides better prognosis of patients with advanced serous papillary ovarian adenocarcinoma.

Integrated analysis of gene expression and tumor nuclear image profiles associated with chemotherapy response in serous ovarian carcinoma

Background

Small sample sizes used in previous studies result in a lack of overlap between the reported gene signatures for prediction of chemotherapy response. Although morphologic features, especially tumor nuclear morphology, are important for cancer grading, little research has been reported on quantitatively correlating cellular morphology with chemotherapy response, especially in a large data set. In this study, we have used a large population of patients to identify molecular and morphologic signatures associated with chemotherapy response in serous ovarian carcinoma.

Methodology/Principal Findings

A gene expression model that predicts response to chemotherapy is developed and validated using a large-scale data set consisting of 493 samples from The Cancer Genome Atlas (TCGA) and 244 samples from an Australian report. An identified 227-gene signature achieves an overall predictive accuracy of greater than 85% with a sensitivity of approximately 95% and specificity of approximately 70%. The gene signature significantly distinguishes between patients with unfavorable versus favorable prognosis, when applied to either an independent data set (P = 0.04) or an external validation set (P<0.0001). In parallel, we present the production of a tumor nuclear image profile generated from 253 sample slides by characterizing patients with nuclear features (such as size, elongation, and roundness) in incremental bins, and we identify a morphologic signature that demonstrates a strong association with chemotherapy response in serous ovarian carcinoma.

Conclusions

A gene signature discovered on a large data set provides robustness in accurately predicting chemotherapy response in serous ovarian carcinoma. The combination of the molecular and morphologic signatures yields a new understanding of potential mechanisms involved in drug resistance.

A DNA repair pathway-focused score for prediction of outcomes in ovarian cancer treated with platinum-based chemotherapy

Background

New tools are needed to predict outcomes of ovarian cancer patients treated with platinum-based chemotherapy. We hypothesized that a molecular score based on expression of genes that are involved in platinum-induced DNA damage repair could provide such prognostic information.

Methods

Gene expression data was extracted from The Cancer Genome Atlas (TCGA) database for 151 DNA repair genes from tumors of serous ovarian cystadenocarcinoma patients (n = 511). A molecular score was generated based on the expression of 23 genes involved in platinum-induced DNA damage repair pathways. Patients were divided into low (scores 0–10) and high (scores 11–20) score groups, and overall survival (OS) was analyzed by Kaplan–Meier method. Results were validated in two gene expression microarray datasets. Association of the score with OS was compared with known clinical factors (age, stage, grade, and extent of surgical debulking) using univariate and multivariable Cox proportional hazards models. Score performance was evaluated by receiver operating characteristic (ROC) curve analysis. Correlations between the score and likelihood of complete response, recurrence-free survival, and progression-free survival were assessed. Statistical tests were two-sided.

Results

Improved survival was associated with being in the high-scoring group (high vs low scores: 5-year OS, 40% vs 17%, P < .001), and results were reproduced in the validation datasets (P < .05). The score was the only pretreatment factor that showed a statistically significant association with OS (high vs low scores, hazard ratio of death = 0.40, 95% confidence interval = 0.32 to 0.66, P < .001). ROC curves indicated that the score outperformed the known clinical factors (score in a validation dataset vs clinical factors, area under the curve = 0.65 vs 0.52). The score positively correlated with complete response rate, recurrence-free survival, and progression-free survival (Pearson correlation coefficient [r²] = 0.60, 0.84, and 0.80, respectively; P < .001 for all).

Conclusion

The DNA repair pathway–focused score can be used to predict outcomes and response to platinum therapy in ovarian cancer patients.

Identifying microRNA/mRNA dysregulations in ovarian cancer

BACKGROUND

MicroRNAs are a class of noncoding RNA molecules that co-regulate the expression of multiple genes via mRNA transcript degradation or translation inhibition. Since they often target entire pathways, they may be better drug targets than genes or proteins. MicroRNAs are known to be dysregulated in many tumours and associated with aggressive or poor prognosis phenotypes. Since they regulate mRNA in a tissue specific manner, their functional mRNA targets are poorly understood. In previous work, we developed a method to identify direct mRNA targets of microRNA using patient matched microRNA/mRNA expression data using an anti-correlation signature. This method, applied to clear cell Renal Cell Carcinoma (ccRCC), revealed many new regulatory pathways compromised in ccRCC. In the present paper, we apply this method to identify dysregulated microRNA/mRNA mechanisms in ovarian cancer using data from The Cancer Genome Atlas (TCGA).

METHODS

TCGA Microarray data was normalized and samples whose class labels (tumour or normal) were ambiguous with respect to consensus ensemble K-Means clustering were removed. Significantly anti-correlated and correlated genes/microRNA differentially expressed between tumour and normal samples were identified. TargetScan was used to identify gene targets of microRNA.

RESULTS

We identified novel microRNA/mRNA mechanisms in ovarian cancer. For example, the expression level of RAD51AP1 was found to be strongly anti-correlated with the expression of hsa-miR-140-3p, which was significantly down-regulated in the tumour samples. The anti-correlation signature was present separately in the tumour and normal samples, suggesting a direct causal dysregulation of RAD51AP1 by hsa-miR-140-3p in the ovary. Other pairs of potentially biological relevance include: hsa-miR-145/E2F3, hsa-miR-139-5p/TOP2A, and hsa-miR-133a/GCLC. We also identified sets of positively correlated microRNA/mRNA pairs that are most likely result from indirect regulatory mechanisms.

CONCLUSIONS

Our findings identify novel microRNA/mRNA relationships that can be verified experimentally. We identify both generic microRNA/mRNA regulation mechanisms in the ovary as well as specific microRNA/mRNA controls which are turned on or off in ovarian tumours. Our results suggest that the disease process uses specific mechanisms which may be significant for their utility as early detection biomarkers or in the development of microRNA therapies in treating ovarian cancers. The positively correlated microRNA/mRNA pairs suggest the existence of novel regulatory mechanisms that proceed via intermediate states (indirect regulation) in ovarian tumorigenesis.

Genome wide DNA copy number analysis of serous type ovarian carcinomas identifies genetic markers predictive of clinical outcome

Ovarian cancer is the fifth leading cause of cancer death in women. Ovarian cancers display a high degree of complex genetic alterations involving many oncogenes and tumor suppressor genes. Analysis of the association between genetic alterations and clinical endpoints such as survival will lead to improved patient management via genetic stratification of patients into clinically relevant subgroups. In this study, we aim to define subgroups of high-grade serous ovarian carcinomas that differ with respect to prognosis and overall survival. Genome-wide DNA copy number alterations (CNAs) were measured in 72 clinically annotated, high-grade serous tumors using high-resolution oligonucleotide arrays. Two clinically annotated, independent cohorts were used for validation. Unsupervised hierarchical clustering of copy number data derived from the 72 patient cohort resulted in two clusters with significant difference in progression free survival (PFS) and a marginal difference in overall survival (OS). GISTIC analysis of the two clusters identified altered regions unique to each cluster. Supervised clustering of two independent large cohorts of high-grade serous tumors using the classification scheme derived from the two initial clusters validated our results and identified 8 genomic regions that are distinctly different among the subgroups. These 8 regions map to 8p21.3, 8p23.2, 12p12.1, 17p11.2, 17p12, 19q12, 20q11.21 and 20q13.12; and harbor potential oncogenes and tumor suppressor genes that are likely to be involved in the pathogenesis of ovarian carcinoma. We have identified a set of genetic alterations that could be used for stratification of high-grade serous tumors into clinically relevant treatment subgroups.

Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer

Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 advanced stage, high grade serous ovarian cancers. We implemented a, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a previously undescribed patient stratification, further supported by micro-RNA expression profiles, and gene set enrichment analysis found strong biological support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. The corresponding "angiogenesis signature" was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival. The subtypes we have defined are of potential translational interest as they may be relevant for identifying patients who may benefit from the addition of anti-angiogenic therapies that are now being tested in clinical trials.

Copy number variation analysis of matched ovarian primary tumors and peritoneal metastasis

Ovarian cancer is the most deadly gynecological cancer. The high rate of mortality is due to the large tumor burden with extensive metastatic lesion of the abdominal cavity. Despite initial chemosensitivity and improved surgical procedures, abdominal recurrence remains an issue and results in patients' poor prognosis. Transcriptomic and genetic studies have revealed significant genome pathologies in the primary tumors and yielded important information regarding carcinogenesis. There are, however, few studies on genetic alterations and their consequences in peritoneal metastatic tumors when compared to their matched ovarian primary tumors. We used high-density SNP arrays to investigate copy number variations in matched primary and metastatic ovarian cancer from 9 patients. Here we show that copy number variations acquired by ovarian tumors are significantly different between matched primary and metastatic tumors and these are likely due to different functional requirements. We show that these copy number variations clearly differentially affect specific pathways including the JAK/STAT and cytokine signaling pathways. While many have shown complex involvement of cytokines in the ovarian cancer environment we provide evidence that ovarian tumors have specific copy number variation differences in many of these genes.

Identification of thrombin-like activity in ovarian cancer associated ascites and modulation of multiple cytokine networks

Blood coagulation cascades can be activated by different mechanisms and to different levels in cancer patients. In a study conducted on the transcriptional profile of epithelial ovarian cancer patients a number of possible links between coagulation and inflammation have been suggested and we and others have reported that, in addition to its central role in blood coagulation and haemostasis, thrombin is a powerful regulator of inflammatory responses. Here, we report that thrombin- like activities were present in the malignant ascites of patients with ovarian carcinoma. Malignant ascites significantly enhanced the release of cytokines/chemokines, which have been previously shown to support tumour progression, such as interleukin (IL)-6, IL-1β, CCL2 and CXCL8, in human peripheral blood mononuclear cells of healthy volunteers. Interestingly, ascites enhanced the release of the anti-inflammatory cytokine IL-10 and inhibited the production of interferon-γ and IL-12. The presence of the anticoagulant antithrombin reversed IL-12 inhibition induced by ascites in human monocytes. Finally, the use of thrombin and of the specific thrombin receptor (PAR) agonist peptides, TFLLRN and AYGPK, suggests that IL-12 inhibition is thrombin-specific and related to PAR-1, but not to PAR-4. These findings underline the tight relationship between the coagulation pathway, where thrombin is the key enzyme, and cytokine modulation, including IL-12 inhibition, which is a critical feature of the tumour microenvironment, and may represent a powerful strategy used by tumours to escape immune surveillance.

Marco Ramoni: an appreciation of academic achievement

We review the scholarly career of our colleague, Marco Ramoni, who died unexpectedly in the summer of 2010. His work mainly explored the development and application of Bayesian techniques to model clinical, public health, and bioinformatics questions. His contributions have led to improvements in our ability to model behavior that evolves in time, to explore systematic relationships among large sets of covariates, and to tease out the meaning of data on the role of genetic variation in the genesis of important diseases.

Keap1 mutations and Nrf2 pathway activation in epithelial ovarian cancer

Resistance to platinum-based chemotherapy develops in the majority of patients with epithelial ovarian cancer (EOC). Platinum compounds form electrophilic intermediates that mediate DNA cross-linking and induce double-strand DNA breaks. Because the cellular response to electrophilic xenobiotics is partly mediated by Keap1-Nrf2 pathway, we evaluated the presence of Kelch-like ECH-associated protein 1 (Keap1) mutations and NF-E2-related factor 2 (Nrf2) pathway activation in EOC and correlated these with platinum resistance and clinical outcome. Nrf2 immunohistochemistry revealed nuclear localization (a surrogate of pathway activation) in over half of EOC patient specimens examined, with more common occurrence in the clear cell EOC subtype. Quantitative real-time PCR revealed that Nrf2 target genes were upregulated in tumors with nuclear positivity for Nrf2. Microarray analysis also showed upregulation of Nrf2 target genes in clear cell EOCs compared with other EOC subtypes. In addition, Keap1 sequence analysis revealed genetic mutations in 29% of clear cell samples and 8% of nonclear cell tumors. RNAi-mediated knockdown of Keap1 was associated with Nrf2 pathway activation and resistance to carboplatin in vitro. Importantly, patients with evidence of Nrf2 pathway activation had fewer complete clinical responses to platinum-based therapy, were enriched for platinum resistance, and had shorter median overall survival compared with those who did not show evidence of Nrf2 pathway activation. Our findings identify Keap1 mutations in EOC and they suggest a previously unrecognized role for the Keap1-Nrf2 pathway in mediating chemotherapeutic responses in this disease.

A seven-gene prognostic model for platinum-treated ovarian carcinomas

BACKGROUND

Prognosis of ovarian carcinoma is poor, heterogeneous, and not accurately predicted by histoclinical features. We analysed gene expression profiles of ovarian carcinomas to identify a multigene expression model associated with survival after platinum-based therapy.

METHODS

Data from 401 ovarian carcinoma samples were analysed. The learning set included 35 cases profiled using whole-genome DNA chips. The validation set included 366 cases from five independent public data sets.

RESULTS

Whole-genome unsupervised analysis could not distinguish poor from good prognosis samples. By supervised analysis, we built a seven-gene optimal prognostic model (OPM) out of 94 genes identified as associated with progression-free survival. Using the OPM, we could classify patients in two groups with different overall survival (OS) not only in the learning set, but also in the validation set. Five-year OS was 57 and 27% for the predicted 'Favourable' and 'Unfavourable' classes, respectively. In multivariate analysis, the OPM outperformed the individual current prognostic factors, both in the learning and the validation sets, and added independent prognostic information.

CONCLUSION

We defined a seven-gene model associated with outcome in 401 ovarian carcinomas. Prospective studies are warranted to confirm its prognostic value, and explore its potential ability for better tailoring systemic therapies in advanced-stage tumours.

Integrative computational biology for cancer research

Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed clinical cancer research. They have revealed novel molecular markers of cancer subtypes, metastasis, and drug sensitivity and resistance. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and great volumes of data are accumulating at a rapid pace. Here we discuss these challenges, and show how integrative computational biology can help diminish them by creating new software tools, analytical methods, and data standards.

Integrated analysis of multiple microarray datasets identifies a reproducible survival predictor in ovarian cancer

BACKGROUND

Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival.

METHODOLOGY/PRINCIPAL FINDINGS

Four microarray datasets from different institutions comprising 265 advanced stage tumors were uniformly reprocessed into a single training dataset, also adjusting for inter-laboratory variation ("batch-effect"). Supervised principal component survival analysis was employed to identify prognostic models. Models were independently validated in a 61-patient cohort using a custom array genechip and a publicly available 229-array dataset. Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping. Previously established molecular phenotypes in the 2(nd) validation set were correlated with high and low-risk outcome groups. Functional representational and pathway analysis was used to explore gene networks associated with high and low risk phenotypes. A 19-gene model showed optimal performance in the training set (median OS 31 and 78 months, p < 0.01), 1(st) validation set (median OS 32 months versus not-yet-reached, p = 0.026) and 2(nd) validation set (median OS 43 versus 61 months, p = 0.013) maintaining independent prognostic power in multivariate analysis. There was strong molecular correspondence of the respective high- and low-risk tumors between training and 1(st) validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2(nd) validation set.

CONCLUSIONS/SIGNIFICANCE

Integration of previously generated cancer microarray datasets may lead to robust and widely applicable survival predictors. These predictors are not simply a compilation of prognostic genes but appear to track true molecular phenotypes of good- and poor-outcome.

A microRNA survival signature (MiSS) for advanced ovarian cancer

OBJECTIVES

MicroRNAs (miRNAs) are a class of small non-coding RNAs that negatively regulate gene expression primarily through post-transcriptional modification. We tested the hypothesis that miRNA expression is associated with overall survival in advanced ovarian cancer.

METHODS

Cases included newly diagnosed patients with stage III or IV serous ovarian cancer. RNA from a training set of 62 cases was hybridized to an miRNA microarray containing 470 mature human transcripts. Cox Regression was performed to identify miRNAs associated with overall survival. External validation was performed using quantitative RT-PCR miRNA assays in an independent test set of 123 samples. MiRNA targets and associated biologic pathways were predicted in silico.

RESULTS

Of all patients, 80% had high-grade, stage IIIC tumors and 64% underwent optimal cytoreduction. The median survival for the entire cohort was 49±4 months. The training set identified 3 miRNAs associated with survival--miR-337, miR-410, and miR-645. An miRNA signature containing miR-410 and miR-645 was most strongly associated with overall survival in the training set (HR=2.96, 95% CI: 1.51-5.78). This miRNA survival signature (MiSS) was validated in the test set (HR=1.71, 95% CI: 1.05-2.78). The MiSS was independent of FIGO stage and surgical debulking.

CONCLUSIONS

The data suggest that an MiSS that contains miR-410 and miR-645 is negatively associated with overall survival in advanced serous ovarian cancer. This signature, when further validated, may be useful in individualizing care for the ovarian cancer patient. Pathway analyses identify biologically plausible mechanisms.

Microarray-Based Gene Expression Studies in Ovarian Cancer

Background

DNA microarray technology is a powerful genomic tool that has the potential to elucidate the relationship between clinical features of cancers and their underlying biological alterations.

Methods

We performed a systemic search in PubMed and Medline databases for recently published articles. The search terms used included “genome-wide,” “microarrays,” “ovarian cancer,” “prognosis” “gene expression profiling,” “molecular marker,” and “molecular biomarker.”

Results

Genome-wide expression profiling using DNA microarray technology has enhanced our understanding of the genes that influence ovarian cancer development, histopathologic subtype, progression, response to therapy, and overall survival.

Conclusions

Gene expression profiling has demonstrated its utility in ovarian cancer research. It is hoped that with technologic, statistical, and bioinformatic advances, the reliability and reproducibility of this technique will increase, spawning clinical applications that may enhance our understanding of the disease and our ability to care for patients in the future.

[DNA microarrays and prediction of clinical outcome in ovarian carcinoma patients]

Despite debulking surgery and taxane/platinum-based chemotherapy, ovarian cancer is the most lethal pelvic gynaecological cancer in western countries, with a 25% 5-years survival. Current histo-clinical prognostic factors are insufficient to capture the heterogeneous clinical outcome of patients. A better molecular characterization of the disease is crucial to refine the prognostic classifications and to identify new therapeutic targets. DNA microarrays, which allow the quantitative measurement of expression level of the whole genome simultaneously in a single tumor sample, have been recently used towards this objective with promising results. Here, we present and discuss the main published studies and the issues to address in the future to allow the expected transfer to clinical practice.

Evaluation of candidate stromal epithelial cross-talk genes identifies association between risk of serous ovarian cancer and TERT, a cancer susceptibility "hot-spot"

We hypothesized that variants in genes expressed as a consequence of interactions between ovarian cancer cells and the host micro-environment could contribute to cancer susceptibility. We therefore used a two-stage approach to evaluate common single nucleotide polymorphisms (SNPs) in 173 genes involved in stromal epithelial interactions in the Ovarian Cancer Association Consortium (OCAC). In the discovery stage, cases with epithelial ovarian cancer (n=675) and controls (n=1,162) were genotyped at 1,536 SNPs using an Illumina GoldenGate assay. Based on Positive Predictive Value estimates, three SNPs-PODXL rs1013368, ITGA6 rs13027811, and MMP3 rs522616-were selected for replication using TaqMan genotyping in up to 3,059 serous invasive cases and 8,905 controls from 16 OCAC case-control studies. An additional 18 SNPs with Pper-allele<0.05 in the discovery stage were selected for replication in a subset of five OCAC studies (n=1,233 serous invasive cases; n=3,364 controls). The discovery stage associations in PODXL, ITGA6, and MMP3 were attenuated in the larger replication set (adj. Pper-allele>or=0.5). However genotypes at TERT rs7726159 were associated with ovarian cancer risk in the smaller, five-study replication study (Pper-allele=0.03). Combined analysis of the discovery and replication sets for this TERT SNP showed an increased risk of serous ovarian cancer among non-Hispanic whites [adj. ORper-allele 1.14 (1.04-1.24) p=0.003]. Our study adds to the growing evidence that, like the 8q24 locus, the telomerase reverse transcriptase locus at 5p15.33, is a general cancer susceptibility locus.

Gene expression profile of BRCAness that correlates with responsiveness to chemotherapy and with outcome in patients with epithelial ovarian cancer

PURPOSE

To define a gene expression profile of BRCAness that correlates with chemotherapy response and outcome in epithelial ovarian cancer (EOC).

METHODS

A publicly available microarray data set including 61 patients with EOC with either sporadic disease or BRCA(1/2) germline mutations was used for development of the BRCAness profile. Correlation with platinum responsiveness was assessed in platinum-sensitive and platinum-resistant tumor biopsy specimens from six patients with BRCA germline mutations. Association with poly-ADP ribose polymerase (PARP) inhibitor responsiveness and with radiation-induced RAD51 foci formation (a surrogate of homologous recombination) was assessed in Capan-1 cell line clones. The BRCAness profile was validated in 70 patients enriched for sporadic disease to assess its association with outcome.

RESULTS

The BRCAness profile accurately predicted platinum responsiveness in eight out of 10 patient-derived tumor specimens, and between PARP-inhibitor sensitivity and resistance in four out of four Capan-1 clones. [corrected] When applied to the 70 patients with sporadic disease, patients with the BRCA-like (BL) profile had improved disease-free survival (34 months v 15 months; log-rank P = .013) and overall survival (72 months v 41 months; log-rank P = .006) compared with patients with a non-BRCA-like (NBL) profile, respectively. The BRCAness profile maintained independent prognostic value in multivariate analysis, which controlled for other known clinical prognostic factors.

CONCLUSION

The BRCAness profile correlates with responsiveness to platinum and PARP inhibitors and identifies a subset of sporadic patients with improved outcome. Additional evaluation of this profile as a predictive tool in patients with sporadic EOC is warranted.

Emerging roles of Kruppel-like factor 6 and Kruppel-like factor 6 splice variant 1 in ovarian cancer progression and treatment

Epithelial ovarian cancer is one of the most lethal gynecologic cancers and the fifth most frequent cause of female cancer deaths in the United States. Despite dramatic treatment successes in other cancers through the use of molecular agents targeted against genetically defined events driving cancer development and progression, very few insights into epithelial ovarian cancer have been translated from the laboratory to the clinic. If advances are to be made in the early diagnosis, prevention, and treatment of this disease, it will be critical to characterize the common and private (personalized) genetic defects underlying the development and spread of epithelial ovarian cancer. The tumor suppressor Kruppel-like factor 6 and its alternatively spliced, oncogenic isoform, Kruppel-like factor 6 splice variant 1, are members of the Kruppel-like zinc finger transcription factor family of proteins, which have diverse roles in cellular differentiation, development, proliferation, growth-related signal transduction, and apoptosis. Inactivation of Kruppel-like factor 6 and overexpression of Kruppel-like factor 6 splice variant 1 have been associated with the progression of a number of human cancers and even with patient survival. This article summarizes our recent findings demonstrating that a majority of epithelial ovarian cancer tumors have Kruppel-like factor 6 allelic loss and decreased expression coupled with increased expression of Kruppel-like factor 6 splice variant 1. The targeted reduction of Kruppel-like factor 6 in ovarian cancer cell lines results in marked increases in cell proliferation, invasion, tumor growth, angiogenesis, and intraperitoneal dissemination in vivo. In contrast, the inhibition of Kruppel-like factor 6 splice variant 1 decreases cellular proliferation, invasion, angiogenesis, and tumorigenicity; this provides the rationale for its potential therapeutic application. These results and our recent demonstration that the inhibition of Kruppel-like factor 6 splice variant 1 can dramatically prolong survival in a preclinical mouse model of ovarian cancer are reviewed and discussed.

Amplification of GNAS may be an independent, qualitative, and reproducible biomarker to predict progression-free survival in epithelial ovarian cancer

OBJECTIVES

The purpose of this study was to identify genes that predict progression-free survival (PFS) in advanced epithelial ovarian cancer (aEOC) receiving standard therapy.

METHODS

We performed microarray analysis on laser microdissected aEOC cells. All cases received staging laparotomy and adjuvant chemotherapy (carboplatin+paclitaxel) as primary therapy.

RESULTS

Microarray analysis identified 50 genes differentially expressed between tumors of patients with no evidence of disease (NED) or evidence of disease (ED) (p<0.001). Six genes (13%) were located at 8q24, and 9 genes (19.6%), at 20q11-13. The ratio of selected gene set/analyzed gene set in chromosomes 8 and 20 are significantly higher than that in other chromosome regions (6/606 vs. 32/13656, p=0.01) and (12/383 vs. 32/13656, p=1.3 x 10(-)(16)). We speculate that the abnormal chromosomal distribution is due to genomic alteration and that these genes may play an important role in aEOC and choose GNAS (GNAS complex locus, NM_000516) on 20q13 based on the p value and fold change. Genomic PCR of aEOC cells also showed that amplification of GNAS was significantly correlated with unfavorable PFS (p=0.011). Real-time quantitative RT-PCR analysis of independent samples revealed that high mRNA expression levels of the GNAS genes, located at chromosome 20q13, was significantly unfavorable indicators of progression-free survival (PFS). Finally, GNAS amplification was an independent prognostic factor for PFS.

CONCLUSIONS

Our results suggest that GNAS gene amplification may be an independent, qualitative, and reproducible biomarker of PFS in aEOC.

Integrating high-throughput technologies in the quest for effective biomarkers for ovarian cancer

Despite widespread interest, few serum biomarkers have been introduced to the clinic over the past 20 years. Each approach to ovarian cancer biomarker discovery has its own advantages and disadvantages and it seems likely that a global biomarker discovery platform that mines all possible sources for biomarkers might be more useful. Such data could be combined with information from relevant microarray data, bioinformatic analyses and literature searches. This proposed integrated systems biology approach has the potential to yield promising ovarian cancer markers for diagnosis, prognosis and monitoring of patients during therapy.

Proteomic identification of overexpressed PRDX 1 and its clinical implications in ovarian carcinoma

Ovarian carcinoma is the most lethal gynecological malignancy in worldwide. The discovery of reliable marker for early detection of ovarian carcinoma is critical for increasing patient's survival because high mortality rate is associated with late diagnosis of this tumor. In the present study, we performed comparative analysis of whole proteomes between serous borderline tumor and serous carcinoma to identify a useful biomarker for the early diagnosis and progression of ovarian carcinoma. Nine proteins were significantly overexpressed in ovarian serous carcinomas compared to borderline tumors. After validation with Western blotting and immunohistochemical analysis, prdx 1 was found to be the strongest overexpressed protein in malignant ovarian tumors among the selected proteins. In addition, the high level of prdx 1 expression (>50% positive cancer cells) was significantly correlated with poor overall survival in ovarian serous carcinomas. On a multivariate cox analysis, the relative risk of death was 8.74 in patients with serous carcinomas showing >50% of prdx 1-positive cancer cells. Our results suggest that prdx 1 may be a useful diagnostic and prognostic biomarker in ovarian carcinoma, especially serous carcinoma.