Gene Expression Signature With Independent Prognostic Significance in Epithelial Ovarian Cancer

Human ovarian cancer stem cells

The isolation and identification of stem-like cells in solid tumors or cancer stem cells (CSCs) have been exciting developments of the last decade, although these rare populations had been earlier identified in leukemia. CSC biology necessitates a detailed delineation of normal stem cell functioning and maintenance of homeostasis within the organ. Ovarian CSC biology has unfortunately not benefited from a pre-established knowledge of stem cell lineage demarcation and functioning in the normal organ. In the absence of such information, some of the classical parameters such as long-term culture-initiating assays to isolate stem cell clones from tumors, screening and evaluation of other epithelial stem cell surface markers, dye efflux, and label retention have been applied toward the putative isolation of CSCs from ovarian tumors. The present review presents an outline of the various approaches developed so far and the various perspectives revealed that are now required to be dealt with toward better disease management.

Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets

Background

Advanced-stage ovarian cancer patients are generally treated with platinum/taxane-based chemotherapy after primary debulking surgery. However, there is a wide range of outcomes for individual patients. Therefore, the clinicopathological factors alone are insufficient for predicting prognosis. Our aim is to identify a progression-free survival (PFS)-related molecular profile for predicting survival of patients with advanced-stage serous ovarian cancer.

Methodology/Principal Findings

Advanced-stage serous ovarian cancer tissues from 110 Japanese patients who underwent primary surgery and platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays. We selected 88 PFS-related genes by a univariate Cox model (p<0.01) and generated the prognostic index based on 88 PFS-related genes after adjustment of regression coefficients of the respective genes by ridge regression Cox model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p<0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008).

Conclusions/Significance

The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.

The molecular genetic basis of ovarian cancer and its roadmap towards a better treatment

OBJECTIVES

Ovarian cancer remains a major health problem for women. Although there is considerable clinico-pathological heterogeneity, the molecular genetic basis of ovarian cancer remains poorly understood. Recently, high-resolution genomic maps generated by genome-wide SNP analyses and novel sequencing technologies, have started to dissect the genetic basis of ovarian cancer.

METHODS

Here, we will describe our first insights on how somatic mutations may contribute to the diagnostic re-classification of ovarian cancer. We will discuss how copy number alterations and epigenetic changes represent promising biomarkers to predict resistance to treatment in ovarian cancer, and will also highlight how some of the recently-discovered microRNAs might represent interesting therapeutic targets for ovarian cancer.

RESULTS AND CONCLUSIONS

Future studies, such as the Cancer Genome Atlas Project, involving a large number of ovarian tumors and combining various high-throughput genetic technologies with sophisticated integrative bioinformatic analyses, will be required and are expected to fine-map the full genetic spectrum of ovarian cancer. It is hoped, however, that once the molecular genetic basis of ovarian cancer is understood, this will lead to better and personalized treatments for ovarian cancer.

Elevated MAL expression is accompanied by promoter hypomethylation and platinum resistance in epithelial ovarian cancer

We previously found that the gene encoding the Myelin and Lymphocyte protein, MAL, was among the most highly expressed genes in serous ovarian cancers from short-term survivors (<3 years) relative to those of long-term survivors (>7 years). In the present study, we have found that this difference in expression is partially attributable to differences in DNA methylation at a specific region within the MAL promoter CpG island. While MAL was largely unmethylated at the transcription start site (Region 1; -48 to +73 bp) in primary serous ovarian cancers, methylation of an upstream region (Region 2; -452 to -266 bp) was inversely correlated with MAL transcription in the primary cancers (R = -0.463) and ovarian cancer cell lines (R = -0.444). Following treatment of the OVCA432 cell line with 5-azacytidine, methylation of Region 2 decreased from 73.3% to 34.7% (p = 0.007) while Region 1 was unaffected. This was accompanied by a 10-fold increase in MAL expression. Since MAL transcripts are elevated in tumors from short-term survivors, all of whom were treated with platinum-based therapy, MAL may have a role in cisplatin response. We therefore determined the 50% growth inhibitory dose of cisplatin in 30 ovarian cancer cell lines and compared this to MAL expression. MAL transcript levels were higher in the resistant ovarian cell lines (p = 0.04). MAL methylation status may therefore serve as a marker of platinum sensitivity while MAL protein may be a target for development of novel therapies aimed at enhancing sensitivity to platinum-based drugs in ovarian cancer.

Gene expression profiles as prognostic markers in women with ovarian cancer

The purpose was to find a gene expression profile that could distinguish short-term from long-term survivors in our collection of serous epithelial ovarian carcinomas. Furthermore, it should be able to stratify in an external validation set. Such a classifier profile will take us a step forward toward investigations for more individualized therapies and the use of gene expression profiles in the clinical practice. RNA from tumor tissue from 43 Danish patients with serous epithelial ovarian carcinoma (11 International Federation of Gynecology and Obstetrics [FIGO] stage I/II, 32 FIGO stage III/IV) was analyzed using Affymetrix U133 plus 2.0 microarrays. A multistep statistical procedure was applied to the data to find the gene set that optimally split the patients into short-term and long-term survivors in a Kaplan-Meier plot. A 14-gene prognostic profile with the ability to distinguish short-term survivors (median overall survival of 32 months) from long-term survivors (median overall survival not yet reached after a median follow-up of 76 months) with a P value of 3.4 x 10 was found. The prognostic gene set was also able to distinguish short-term from long-term survival in patients with advanced disease. Furthermore, its ability to classify in an external validation set was demonstrated. The identified 14-gene prognostic profile was able to predict survival (short- vs long-term survival) with a strength that is better than any other prognostic factor in epithelial ovarian cancer including FIGO stage. This stratification method may form the basis of determinations for new therapeutic approaches, as patients with poor prognosis could obtain the biggest advantage from new treatment modalities.

Mixture classification model based on clinical markers for breast cancer prognosis

OBJECTIVE

Accurate cancer prognosis prediction is critical to cancer treatment. There have been many prognosis models based on clinical markers, but few of them are satisfied in clinical applications. And with the development of microarray technologies, cancer researchers have discovered many genes as new markers from the gene expression data and have further developed powerful prognosis models based on these so-called genetic biomarkers. However, the application of such biomarkers still suffers from some problems. The first one is there are a great number of genes and a few samples in the gene expression data so that it is difficult to select a unified gene set to establish a stable classifier for prognosis. The second one is that, due to the experimental and technical reasons, there are existing noises and redundancies in gene expression data, which may lead to building a prognosis predictor with poor performance. The last but not the least one is the microarray experiments are so expensive currently that it is hard to obtain abundant samples. Therefore, it is practical to develop prognosis methods mainly based on conventional clinical markers in real cancer treatment applications. This paper aims to establish an accurate classification model for cancer prognosis, in order to make full use of the invaluable information in clinical data, especially which is usually ignored by most of the existing methods when they aim for high prediction accuracies.

METHODS

First, this paper gives the formal description of general classification problem, and presents a novel mixture classification model to make full use of the invaluable information in clinical data, which is similar to the traditional ensemble classification models except for putting strict constraints on the construction of mapping functions to avoid voting process. Then, a two-layer instance of the proposed model, named as MRS (Mixture of Rough set and Support vector machine), is constructed by integrating rough set and support vector machine (SVM) classification methods, in which, the rough set classifier acts as the first layer to identify some singular samples in data, and the SVM classifier acts as the second layer to classify the remaining samples. Finally, MRS is used to make prognosis prediction on two open breast cancer datasets. One dataset, denoted as BRC-1 hereafter, is a high quality, publicly available dataset of 97 breast cancer tumors of node-negative patients. The other, denoted as BRC-2 hereafter, uses baseline human primary breast tumor data from LBL breast cancer cell collection containing 174 samples.

RESULTS

We have done two experiments on BRC-1 and BRC-2, respectively. In the first experiment, the BRC-1 dataset is divided into train set with 78 patients (34 ones belonging to poor prognosis group and 44 ones belonging to good prognosis group) and test set with 19 patients (12 ones belonging to poor prognosis group and 7 ones belonging to good prognosis). After trained on the train set, the MRS can correctly classify all the 12 patients with poor prognosis, and 6 of 7 patients with good prognosis in the test set. The results are better than previous researches, even better than the 70-gene based biomarkers. And in the second experiment, we construct the classifiers using BRC-2 dataset, and compare MRS with other representative methods in Weka software by 5-fold cross-validation, and comparison results show that MRS has higher prediction accuracy than those methods.

CONCLUSIONS

The proposed mixture classification model can easily integrate methods with different characteristics. It can overcome the shortcomings of traditional voting-based ensemble models and thus can make full use of the information in clinical data. The experimental results illustrate that our implemented MRS classifier can predict the breast cancer prognosis more accurately than previous prognostic methods.

A gene signature predictive for outcome in advanced ovarian cancer identifies a survival factor: microfibril-associated glycoprotein 2

Advanced stage papillary serous tumors of the ovary are responsible for the majority of ovarian cancer deaths, yet the molecular determinants modulating patient survival are poorly characterized. Here, we identify and validate a prognostic gene expression signature correlating with survival in a series of microdissected serous ovarian tumors. Independent evaluation confirmed the association of a prognostic gene microfibril-associated glycoprotein 2 (MAGP2) with poor prognosis, whereas in vitro mechanistic analyses demonstrated its ability to prolong tumor cell survival and stimulate endothelial cell motility and survival via the alpha(V)beta(3) integrin receptor. Increased MAGP2 expression correlated with microvessel density suggesting a proangiogenic role in vivo. Thus, MAGP2 may serve as a survival-associated target.

Expression profiling identifies genes involved in neoplastic transformation of serous ovarian cancer

Background

The malignant potential of serous ovarian tumors, the most common ovarian tumor subtype, varies from benign to low malignant potential (LMP) tumors to frankly invasive cancers. Given the uncertainty about the relationship between these different forms, we compared their patterns of gene expression.

Methods

Expression profiling was carried out on samples of 7 benign, 7 LMP and 28 invasive (moderate and poorly differentiated) serous tumors and four whole normal ovaries using oligonucleotide microarrays representing over 21,000 genes.

Results

We identified 311 transcripts that distinguished invasive from benign tumors, and 20 transcripts that were significantly differentially expressed between invasive and LMP tumors at p < 0.01 (with multiple testing correction). Five genes that were differentially expressed between invasive and either benign or normal tissues were validated by real time PCR in an independent panel of 46 serous tumors (4 benign, 7 LMP, 35 invasive). Overexpression of SLPI and WNT7A and down-regulation of C6orf31, PDGFRA and GLTSCR2 were measured in invasive and LMP compared with benign and normal tissues. Over-expression of WNT7A in an ovarian cancer cell line led to increased migration and invasive capacity.

Conclusion

These results highlight several genes that may play an important role across the spectrum of serous ovarian tumorigenesis.

Predicting the response of advanced cervical and ovarian tumors to therapy

The management of advanced cervical and ovarian cancers remains a significant challenge as many women fail to respond to recommended therapy, resulting in disease progression and ultimately patient death. Because of tumor heterogeneity, it is rare for all cancers of a particular type to respond to a specific therapy; and, as a result, many patients receive treatment from which they derive little or no benefit, leading to increased morbidity and undue costs. A marker that could rapidly predict or forecast disease outcome would clearly be beneficial in allowing the administration of a tailored regime for each patient while reducing toxicity and cost. Traditional prognostic factors of tumor size, grade, and stage are not ideal for predicting patient outcome, whereas the use of in vitro assays to detect chemosensitivity or resistance has not yet translated into routine clinical practice. Similarly, biomarkers and tumor markers vary in their predictive ability. DNA array technology offers great promise in predicting the response to therapy based on gene expression profiles, and can allow for targeted therapies against specific molecular alterations that cause disease. Imaging techniques, particularly those with the ability to characterize biological tissues and provide functional, structural, and molecular information, have the potential to noninvasively integrate physical and metabolic information. These include F-18-fluorodeoxyglucose positron emission tomography, dynamic contrast-enhanced magnetic resonance imaging, and diffusion weighted magnetic resonance imaging, all techniques that attempt to evaluate and predict therapy response and so influence clinical outcome. This review examines different methods of predicting the response to treatment in advanced cervical and ovarian tumors.

TARGET AUDIENCE

Obstetricians & Gynecologists, Family Physicians.

LEARNING OBJECTIVES

After completion of this article, the reader should be able to describe why prediction of response to therapy for cervical and ovarian cancers is important, describe obstacles to use of in vitro assays to predict outcomes for therapy for ovarian and cervical cancers, and explain potentially new predictive markers.

Gene expression meta-analysis identifies chromosomal regions involved in ovarian cancer survival

Ovarian cancer cells exhibit complex karyotypic alterations causing deregulation of numerous genes. Some of these genes are probably causal for cancer formation and local growth, whereas others are causal for metastasis and recurrence. By using publicly available data sets, we have investigated the relation of gene expression and chromosomal position to identify chromosomal regions of importance for early recurrence of ovarian cancer. By use of *Gene Set Enrichment Analysis*, we have ranked chromosomal regions according to their association to survival. Over-representation analysis including 1-4 consecutive cytogenetic bands identified regions with increased expression for chromosome 5q12-14, and a very large region of chromosome 7 with the strongest signal at 7p15-13 among tumors from short-living patients. Reduced gene expression was identified at 4q26-32, 6p12-q15, 9p21-q32, and 11p14-11. We summarized mutation load in these regions by a combined mutation score that is statistical significantly associated to survival by analysis in the data sets used for identification of the regions. Furthermore, the prognostic value of the combined mutation score was validated in an independent large data set using death (P = 0.015) and recurrence (P = 0.002) as outcome. The combined mutation score is strongly associated to upregulation of several growth factor pathways.

A miR-200 microRNA cluster as prognostic marker in advanced ovarian cancer

OBJECTIVE

Ovarian cancer is one of the most deadly human cancers, resulting in over 15,000 deaths in the US each year. A reliable method that could predict disease outcome would improve care of patients with this disease. The main aim of this study is to identify novel prognostic biomarkers for advanced ovarian cancer.

METHODS

We hypothesized that microRNAs (miRNAs) may predict outcome and have examined the prognostic value of these small RNA molecules on disease outcome prediction. miRNAs are a newly identified family of non-coding RNA genes, and recent studies have shown that miRNAs are extensively involved in the tumor development process. We have profiled the expression of miRNAs in advanced ovarian cancer using a novel PCR-based platform and correlated miRNA expression profiles with disease outcome.

RESULTS

By performing miRNA expression profiling analysis of 55 advanced ovarian tumors, we have shown that three miR-200 miRNAs (miR-200a, miR-200b and miR-429) in the miR-200b-429 cluster are significantly associated with cancer recurrence and overall survival. Further target analysis indicates that these miR-200 miRNAs target multiple genes that are involved in cancer development. In addition, we have also shown that overexpression of this miR-200 cluster inhibits ovarian cancer cell migration.

CONCLUSIONS

miR-200b-429 may be used as a prognostic marker for ovarian cancer outcome, and low-level expression of miR-200 miRNAs in this cluster predicts poor survival. In addition, our study suggests that miR-200 miRNAs could play an important regulatory role in ovarian cancer.

Gene expression profiling of advanced-stage serous ovarian cancers distinguishes novel subclasses and implicates ZEB2 in tumor progression and prognosis

To elucidate the mechanisms of rapid progression of serous ovarian cancer, gene expression profiles from 43 ovarian cancer tissues comprising eight early stage and 35 advanced stage tissues were carried out using oligonucleotide microarrays of 18,716 genes. By non-negative matrix factorization analysis using 178 genes, which were extracted as stage-specific genes, 35 advanced stage cases were classified into two subclasses with superior (n = 17) and poor (n = 18) outcome evaluated by progression-free survival (log rank test, P = 0.03). Of the 178 stage-specific genes, 112 genes were identified as showing different expression between the two subclasses. Of the 48 genes selected for biological function by gene ontology analysis or Ingenuity Pathway Analysis, five genes (ZEB2, CDH1, LTBP2, COL16A1, and ACTA2) were extracted as candidates for prognostic factors associated with progression-free survival. The relationship between high ZEB2 or low CDH1 expression and shorter progression-free survival was validated by real-time RT-PCR experiments of 37 independent advanced stage cancer samples. ZEB2 expression was negatively correlated with CDH1 expression in advanced stage samples, whereas ZEB2 knockdown in ovarian adenocarcinoma SKOV3 cells resulted in an increase in CDH1 expression. Multivariate analysis showed that high ZEB2 expression was independently associated with poor prognosis. Furthermore, the prognostic effect of E-cadherin encoded by CDH1 was verified using immunohistochemical analysis of an independent advanced stage cancer samples set (n = 74). These findings suggest that the expression of epithelial-mesenchymal transition-related genes such as ZEB2 and CDH1 may play important roles in the invasion process of advanced stage serous ovarian cancer.

The chemiluminescence based Ziplex automated workstation focus array reproduces ovarian cancer Affymetrix GeneChip expression profiles

Background

As gene expression signatures may serve as biomarkers, there is a need to develop technologies based on mRNA expression patterns that are adaptable for translational research. Xceed Molecular has recently developed a Ziplex^® technology, that can assay for gene expression of a discrete number of genes as a focused array. The present study has evaluated the reproducibility of the Ziplex system as applied to ovarian cancer research of genes shown to exhibit distinct expression profiles initially assessed by Affymetrix GeneChip^® analyses.

Methods

The new chemiluminescence-based Ziplex^® gene expression array technology was evaluated for the expression of 93 genes selected based on their Affymetrix GeneChip^® profiles as applied to ovarian cancer research. Probe design was based on the Affymetrix target sequence that favors the 3' UTR of transcripts in order to maximize reproducibility across platforms. Gene expression analysis was performed using the Ziplex Automated Workstation. Statistical analyses were performed to evaluate reproducibility of both the magnitude of expression and differences between normal and tumor samples by correlation analyses, fold change differences and statistical significance testing.

Results

Expressions of 82 of 93 (88.2%) genes were highly correlated (p < 0.01) in a comparison of the two platforms. Overall, 75 of 93 (80.6%) genes exhibited consistent results in normal versus tumor tissue comparisons for both platforms (p < 0.001). The fold change differences were concordant for 87 of 93 (94%) genes, where there was agreement between the platforms regarding statistical significance for 71 (76%) of 87 genes. There was a strong agreement between the two platforms as shown by comparisons of log₂ fold differences of gene expression between tumor versus normal samples (R = 0.93) and by Bland-Altman analysis, where greater than 90% of expression values fell within the 95% limits of agreement.

Conclusion

Overall concordance of gene expression patterns based on correlations, statistical significance between tumor and normal ovary data, and fold changes was consistent between the Ziplex and Affymetrix platforms. The reproducibility and ease-of-use of the technology suggests that the Ziplex array is a suitable platform for translational research.

Expression Profiles of Genes Involved in Poor Prognosis of Epithelial Ovarian Carcinoma: A Review

Background:

Epithelial ovarian cancer (EOC) is the commonest cause of gynecological cancer-related mortality. Although the prognosis for patients with advanced cancer is poor, there is a wide range of outcomes for individual patients.

Objective:

The aim of this study was to review molecular factors predictive of poor prognosis of women with EOC by reviewing microarray research identifying gene expression profiles.

Methods:

A systematic search was performed in the electronic databases PubMed and ScienceDirect up to July 2008, combining the keywords "genome-wide," "microarray," "epithelial ovarian cancer" "prognosis," and "epithelial-mesenchymal transition" with specific expression profiles of genes.

Results:

Many genes that participated in cell signaling, growth factors, transcription factors, proteinases, metabolism, cell adhesion, extracellular matrix component, cell proliferation, and anti-apoptosis were overexpressed in patients with poor prognosis. Several important prognosis-related genes overlap with those known to be regulated by epithelial-mesenchymal transition (EMT). This signaling pathway of EMT (E-cadherin, β-catenin, receptor tyrosine kinases, NF-κB, TGF-β, or Wnt signalings) will be discussed, as it provides new insights into a new treatment strategy.

Conclusions:

This review summarizes recent advances in prognosis-related molecular biology. Collectively, molecular changes possibly through EMT are considered to be a major contributor to the poor prognosis of EOC.

Extreme drug resistance assay results do not influence survival in women with epithelial ovarian cancer

OBJECTIVES

Extreme drug resistance (EDR) assays have been used to identify chemotherapy regimens that are least likely to be of clinical benefit in the treatment of epithelial ovarian cancer (EOC). We sought to examine the impact of EDR assay-guided therapy on the outcome of patients with EOC in the primary and recurrent settings.

METHODS

We conducted a retrospective review of demographic, pathologic, EDR assay and clinical outcome data from 377 patients with EOC who had an assay sent at the time of their primary or subsequent cytoreductive surgeries. Multivariate analyses were performed using Cox proportional hazards method to identify and estimate the impact of independent prognostic factors on time to progression (TTP), overall survival (OS) and survival after recurrence (RS).

RESULTS

Increasing age was associated with a worse OS and RS (HR=1.34; 95% CI, 1.14-1.58 and HR=1.14; 95% CI, 1.00-1.31, respectively for each decade increase in age). Surgical outcome in the setting of primary or secondary cytoreduction remained an important predictor of survival. Compared with patients with microscopic residual disease, patients who were left with 0.1 to 1.0 cm and >1.0 cm residual disease had an increased risk of recurrence (HR=1.94; 95% CI, 1.33 to 2.84 and HR=3.61; 95% CI; 2.07 to 6.39, respectively) and death (HR=1.59; 95% CI, 1.03 to 2.45; and HR=2.14; 95% CI, 1.09 to 4.20, respectively). For patients who recurred, those who did not undergo secondary cytoreductive surgery and patients who were left with >1.0 cm residual had an increased risk of death compared to patients with microscopic residual (HR=2.13; 95% CI, 1.28 to 3.54; and HR=2.84; 95% CI, 1.71 to 4.71, respectively). EDR assay results analyzed for single agents or combinations of chemotherapies failed to independently predict patient outcomes no matter if the assay was performed at the time of the primary surgery or recurrence.

CONCLUSION

EDR assay results do not independently predict or alter the outcomes of patients with EOC who are treated with the current standards of primary cytoreductive surgery followed by platinum and taxane combination chemotherapy.

Biomarker discovery in ovarian cancer

Ovarian cancer is a leading cause of gynecologic cancer death among women. Tumors diagnosed early (in stage I) have a cure rate approaching 90%. However, because specific symptoms and screening tools are lacking, most ovarian cancers are very advanced when finally diagnosed. CA125 expression and pelvic ultrasonography are of limited efficacy in screening, and the search for new, complementary ovarian cancer biomarkers continues. New technology and research techniques have allowed the identification of over 100 possible tumor markers, many of which are still being evaluated for clinical relevance and several of which have entered clinical trials. Here, we review the methods of biomarker discovery, address the significance and functions of newly identified ovarian cancer tumor markers, and provide further insight into the future of ovarian cancer biomarkers.

Microarray analysis of early stage serous ovarian cancers shows profiles predictive of favorable outcome

PURPOSE

Although few women with advanced serous ovarian cancer are cured, detection of the disease at an early stage is associated with a much higher likelihood of survival. We previously used gene expression array analysis to distinguish subsets of advanced cancers based on disease outcome. In the present study, we report on gene expression of early-stage cancers and validate our prognostic model for advanced-stage cancers.

EXPERIMENTAL DESIGN

Frozen specimens from 39 stage I/II, 42 stage III/IV, and 20 low malignant potential cancers were obtained from four different sites. A linear discriminant model was used to predict survival based upon array data.

RESULTS

We validated the late-stage survival model and show that three of the most differentially expressed genes continue to be predictive of outcome. Most early-stage cancers (38 of 39 invasive, 15 of 20 low malignant potential) were classified as long-term survivors (median probabilities 0.97 and 0.86). MAL, the most differentially expressed gene, was further validated at the protein level and found to be an independent predictor of poor survival in an unselected group of advanced serous cancers (P = 0.0004).

CONCLUSIONS

These data suggest that serous ovarian cancers detected at an early stage generally have a favorable underlying biology similar to advanced-stage cases that are long-term survivors. Conversely, most late-stage ovarian cancers seem to have a more virulent biology. This insight suggests that if screening approaches are to succeed it will be necessary to develop approaches that are able to detect these virulent cancers at an early stage.

Gene expression profiling and prediction of clinical outcome in ovarian cancer

Epithelial ovarian cancer is the most lethal gynaecological cancer. Despite debulking surgery and platinum/taxane-based chemotherapy, the prognosis remains poor with approximately 25% 5-year survival. Current histo-clinical prognostic factors are insufficient to capture the complex cascade of events that drive the heterogeneous clinical behaviour of the disease. There is a crucial need to identify new prognostic subclasses of disease as well as new therapeutic targets. Today, DNA microarrays allow the simultaneous and quantitative analysis of the mRNA expression levels of thousands of genes in a tumour sample. They have been applied to ovarian cancer research for predicting initial surgical resectability, survival and response to first-line chemotherapy. The first results are promising. In this review, we describe recent applications of DNA microarrays in ovarian cancer research and discuss some issues to address in the near future to allow the technology to reach its full potential in clinical practice.

Yin yang 1 modulates taxane response in epithelial ovarian cancer

Survival of ovarian cancer patients is largely dictated by their response to chemotherapy, which depends on underlying molecular features of the malignancy. We previously identified YIN YANG 1 (YY1) as a gene whose expression is positively correlated with ovarian cancer survival. Herein, we investigated the mechanistic basis of this association. Epigenetic and genetic characteristics of YY1 in serous epithelial ovarian cancer were analyzed along with YY1 mRNA and protein. Patterns of gene expression in primary serous epithelial ovarian cancer and in the NCI60 database were investigated using computational methods. YY1 function and modulation of chemotherapeutic response in vitro was studied using small interfering RNA knockdown. Microarray analysis showed strong positive correlation between expression of YY1 and genes with YY1 and transcription factor E2F binding motifs in ovarian cancer and in the NCI60 cancer cell lines. Clustering of microarray data for these genes revealed that high YY1/E2F3 activity positively correlates with survival of patients treated with the microtubule-stabilizing drug paclitaxel. Increased sensitivity to taxanes, but not to DNA cross-linking platinum agents, was also characteristic of NCI60 cancer cell lines with a high YY1/E2F signature. YY1 knockdown in ovarian cancer cell lines results in inhibition of anchorage-independent growth, motility, and proliferation but also increases resistance to taxanes, with no effect on cisplatin sensitivity. These results, together with the prior demonstration of augmentation of microtubule-related genes by E2F3, suggest that enhanced taxane sensitivity in tumors with high YY1/E2F activity may be mediated by modulation of putative target genes with microtubule function.

Survival-related profile, pathways, and transcription factors in ovarian cancer

BACKGROUND

Ovarian cancer has a poor prognosis due to advanced stage at presentation and either intrinsic or acquired resistance to classic cytotoxic drugs such as platinum and taxoids. Recent large clinical trials with different combinations and sequences of classic cytotoxic drugs indicate that further significant improvement in prognosis by this type of drugs is not to be expected. Currently a large number of drugs, targeting dysregulated molecular pathways in cancer cells have been developed and are introduced in the clinic. A major challenge is to identify those patients who will benefit from drugs targeting these specific dysregulated pathways.The aims of our study were (1) to develop a gene expression profile associated with overall survival in advanced stage serous ovarian cancer, (2) to assess the association of pathways and transcription factors with overall survival, and (3) to validate our identified profile and pathways/transcription factors in an independent set of ovarian cancers.

METHODS AND FINDINGS

According to a randomized design, profiling of 157 advanced stage serous ovarian cancers was performed in duplicate using approximately 35,000 70-mer oligonucleotide microarrays. A continuous predictor of overall survival was built taking into account well-known issues in microarray analysis, such as multiple testing and overfitting. A functional class scoring analysis was utilized to assess pathways/transcription factors for their association with overall survival. The prognostic value of genes that constitute our overall survival profile was validated on a fully independent, publicly available dataset of 118 well-defined primary serous ovarian cancers. Furthermore, functional class scoring analysis was also performed on this independent dataset to assess the similarities with results from our own dataset. An 86-gene overall survival profile discriminated between patients with unfavorable and favorable prognosis (median survival, 19 versus 41 mo, respectively; permutation p-value of log-rank statistic = 0.015) and maintained its independent prognostic value in multivariate analysis. Genes that composed the overall survival profile were also able to discriminate between the two risk groups in the independent dataset. In our dataset 17/167 pathways and 13/111 transcription factors were associated with overall survival, of which 16 and 12, respectively, were confirmed in the independent dataset.

CONCLUSIONS

Our study provides new clues to genes, pathways, and transcription factors that contribute to the clinical outcome of serous ovarian cancer and might be exploited in designing new treatment strategies.

Angiogenesis-related gene expression profile with independent prognostic value in advanced ovarian carcinoma

Background

Ovarian carcinoma is the most important cause of gynecological cancer-related mortality in Western societies. Despite the improved median overall survival in patients receiving chemotherapy regimens such as paclitaxel and carboplatin combination, relapse still occurs in most advanced diseased patients. Increased angiogenesis is associated with rapid recurrence and decreased survival in ovarian cancer. This study was planned to identify an angiogenesis-related gene expression profile with prognostic value in advanced ovarian carcinoma patients.

Methodology/Principal Findings

RNAs were collected from formalin-fixed paraffin-embedded samples of 61 patients with III/IV FIGO stage ovarian cancer who underwent surgical cytoreduction and received a carboplatin plus paclitaxel regimen. Expression levels of 82 angiogenesis related genes were measured by quantitative real-time polymerase chain reaction using TaqMan low-density arrays. A 34-gene-profile which was able to predict the overall survival of ovarian carcinoma patients was identified. After a leave-one-out cross validation, the profile distinguished two groups of patients with different outcomes. Median overall survival and progression-free survival for the high risk group was 28.3 and 15.0 months, respectively, and was not reached by patients in the low risk group at the end of follow-up. Moreover, the profile maintained an independent prognostic value in the multivariate analysis. The hazard ratio for death was 2.3 (95% CI, 1.5 to 3.2; p<0.001).

Conclusions/Significance

It is possible to generate a prognostic model for advanced ovarian carcinoma based on angiogenesis-related genes using formalin-fixed paraffin-embedded samples. The present results are consistent with the increasing weight of angiogenesis genes in the prognosis of ovarian carcinoma.

Ovarian cancer biomarkers: current options and future promise

As more effective, less toxic cancer drugs reach patients, the need for accurate and reliable cancer diagnostics and prognostics has become widely appreciated. Nowhere is this need more dire than in ovarian cancer; here most women are diagnosed late in disease progression. The ability to sensitively and specifically predict the presence of early disease and its status, stage, and associated therapeutic efficacy has the potential to revolutionize ovarian cancer detection and treatment. This article reviews current ovarian cancer diagnostics and prognostics and potential biomarkers that are being studied and validated. Some of the most recent molecular approaches being used to identify genes and proteins are presented, which may represent the next generation of ovarian cancer diagnostics and prognostics.

Carboplatin-induced gene expression changes in vitro are prognostic of survival in epithelial ovarian cancer

Background

We performed a time-course microarray experiment to define the transcriptional response to carboplatin in vitro, and to correlate this with clinical outcome in epithelial ovarian cancer (EOC). RNA was isolated from carboplatin and control-treated 36M2 ovarian cancer cells at several time points, followed by oligonucleotide microarray hybridization. Carboplatin induced changes in gene expression were assessed at the single gene as well as at the pathway level. Clinical validation was performed in publicly available microarray datasets using disease free and overall survival endpoints.

Results

Time-course and pathway analyses identified 317 genes and 40 pathways (designated time-course and pathway signatures) deregulated following carboplatin exposure. Both types of signatures were validated in two separate platinum-treated ovarian and NSCLC cell lines using published microarray data. Expression of time-course and pathway signature genes distinguished between patients with unfavorable and favorable survival in two independent ovarian cancer datasets. Among the pathways most highly induced by carboplatin in vitro, the NRF2, NF-kB, and cytokine and inflammatory response pathways were also found to be upregulated prior to chemotherapy exposure in poor prognosis tumors.

Conclusion

Dynamic assessment of gene expression following carboplatin exposure in vitro can identify both genes and pathways that are correlated with clinical outcome. The functional relevance of this observation for better understanding the mechanisms of drug resistance in EOC will require further evaluation.

C-Fos expression is a molecular predictor of progression and survival in epithelial ovarian carcinoma

Members of the Fos protein family dimerise with Jun proteins to form the AP-1 transcription factor complex. They have a central function in proliferation and differentiation of normal tissue as well as in oncogenic transformation and tumour progression. We analysed the expression of c-Fos, FosB, Fra-1 and Fra-2 to investigate the function of Fos transcription factors in ovarian cancer. A total of 101 patients were included in the study. Expression of Fos proteins was determined by western blot analysis, quantified by densitometry and verified by immunohistochemistry. Reduced c-Fos expression was independently associated with unfavourable progression-free survival (20.6, 31.6 and 51.2 months for patients with low, moderate and high c-Fos expression; P=0.003) as well as overall survival (23.8, 46.0 and 55.5 months for low, moderate and high c-Fos levels; P=0.003). No correlations were observed for FosB, Fra-1 and Fra-2. We conclude that loss of c-Fos expression is associated with tumour progression in ovarian carcinoma and that c-Fos may be a prognostic factor. These results are in contrast to the classic concept of c-Fos as an oncogene, but are supported by the recently discovered tumour-suppressing and proapoptotic function of c-Fos in various cancer types.

Conventional and Proteomic Technologies for the Detection of Early Stage Malignancies: Markers for Ovarian Cancer

Our understanding of the tumor microenvironment continues to evolve and allows for the identification of biomarkers that should detect the presence of early stage malignancies. Recent advances in computational analysis and biomedical technologies have come together to elucidate signatures associated with cancer and that are capable of identifying unique tumor-specific proteins. Within the tumor microenvironment, we continue to characterize the proteophysiology of the different steps associated with tumor progression. The urgent need for biomarkers accurately detecting early-stage epithelial ovarian cancer has prompted us, and others, to engage in a search for specific peptide signatures that may discriminate transformed cells from those of the normal ovarian microenvironment. This endeavor also provides new insights into the biology of the disease, which may not only be applicable to detection but may also help to initiate new therapies and optimize patient care.

Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome

PURPOSE

The study aim to identify novel molecular subtypes of ovarian cancer by gene expression profiling with linkage to clinical and pathologic features.

EXPERIMENTAL DESIGN

Microarray gene expression profiling was done on 285 serous and endometrioid tumors of the ovary, peritoneum, and fallopian tube. K-means clustering was applied to identify robust molecular subtypes. Statistical analysis identified differentially expressed genes, pathways, and gene ontologies. Laser capture microdissection, pathology review, and immunohistochemistry validated the array-based findings. Patient survival within k-means groups was evaluated using Cox proportional hazards models. Class prediction validated k-means groups in an independent dataset. A semisupervised survival analysis of the array data was used to compare against unsupervised clustering results.

RESULTS

Optimal clustering of array data identified six molecular subtypes. Two subtypes represented predominantly serous low malignant potential and low-grade endometrioid subtypes, respectively. The remaining four subtypes represented higher grade and advanced stage cancers of serous and endometrioid morphology. A novel subtype of high-grade serous cancers reflected a mesenchymal cell type, characterized by overexpression of N-cadherin and P-cadherin and low expression of differentiation markers, including CA125 and MUC1. A poor prognosis subtype was defined by a reactive stroma gene expression signature, correlating with extensive desmoplasia in such samples. A similar poor prognosis signature could be found using a semisupervised analysis. Each subtype displayed distinct levels and patterns of immune cell infiltration. Class prediction identified similar subtypes in an independent ovarian dataset with similar prognostic trends.

CONCLUSION

Gene expression profiling identified molecular subtypes of ovarian cancer of biological and clinical importance.

Four potential biomarkers as prognostic factors in stage III serous ovarian adenocarcinomas

The mortality rate for patients with ovarian carcinomas is high and the available prognostic factors are insufficient. The use of biomarkers may contribute to better prediction and survival for these patients. We aimed to study the gene and protein expressions for 7 potential biomarkers, to determine if it is possible to use them as prognostic factors. Genes selected from our previous microarray analysis (2006), CLU, ITGB3, TACC1, MUC5B, CAPG, PRAME and TROAP, were analyzed in 19 of the tumors with quantitative real-time polymerase chain reaction (QPCR). We found that CLU and ITGB3 were more expressed in tumors from survivors and PRAME and CAPG were more expressed in tumors from deceased patients. None of the other 3 genes were significantly differently expressed. The protein expressions of CLU, ITGB3, PRAME and CAPG were analyzed in 43 of the tumors with western blot for semiquantitative analysis. We established that the mRNA and protein expressions correlated and that all 4 proteins were significantly differently expressed. Further, immunohistochemistry (IHC) was used to localize the expression of the proteins in the tumor samples. According to our results, the 4 biomarkers CLU, ITGB3, PRAME and CAPG may be used as prognostic factors for patients with stage III serous ovarian adenocarcinomas.

Imatinib mesylate in combination with docetaxel for the treatment of patients with advanced, platinum-resistant ovarian cancer and primary peritoneal carcinomatosis : a Hoosier Oncology Group trial

BACKGROUND

Ovarian tumors frequently express c-Kit and/or platelet-derived growth factor receptors (PDGFRs). Imatinib mesylate blocks the growth of ovarian cancer cells in vitro and may enhance the activity of chemotherapy. This study was conducted to determine the activity of imatinib in combination with docetaxel in patients with recurrent, platinum-resistant epithelial ovarian cancer (EOC).

METHODS

Eligible patients had recurrent, platinum-resistant, or refractory EOC that expressed PDGFRalpha or c-kit, as determined by immunohistochemistry. Imatinib mesylate at a dose of 600 mg orally once daily was administered continuously with docetaxel at a dose of 30 mg/m(2) given intravenously once weekly in Weeks 1 through 4 of every 6-week cycle. The primary endpoint was objective response rate (ORR) as assessed by the Response Evaluation Criteria in Solid Tumors (RECIST).

RESULTS

Thirty-four patients were screened for PDGFRalpha and c-kit expression to enroll 23 patients between December 2003 and October 2005. Four patients had c-kit-positive/PDGFR-negative tumors, 11 patients had PDGFR-positive/c-kit-negative tumors, and 8 patients had c-kit-positive/PDGFR-positive tumors. The median patient age was 56 years (range, 33-76 years). Patients had received a median of 3 prior treatments. The ORR was 21.7% and included 1 complete and 4 partial responses. An additional 3 patients had stable disease for more than 4 months. Expression of PDGFR, c-kit, phosphatase and tensin homolog (PTEN), and phosphorylated protein kinase B (Akt) did not predict response to therapy. The most common adverse events encountered were fatigue (83%), nausea (74%), diarrhea (61%), anorexia (52%), and edema (65%), and the majority of those events were graded as grade 1 or 2.

CONCLUSIONS

The combination imatinib and docetaxel was tolerated in patients with heavily pretreated EOC that expressed c-kit or PDGFRalpha. Few patients had sustained responses or stable disease.

Image-guided biopsy in patients with suspected ovarian carcinoma: a safe and effective technique?

In patients with suspected advanced ovarian carcinoma, a precise histological diagnosis is required before commencing neo-adjuvant chemotherapy. This study aims to determine the diagnostic accuracy and complication rate of percutaneous biopsies performed under ultrasound or computed tomography guidance. Between 2002 to 2007, 60 consecutive image-guided percutaneous biopsies were performed in patients with suspected ovarian cancer. The following variables were recorded: tissue biopsied, imaging technique, experience of operator, biopsy needle gauge, number of passes, complications, and final histology. Forty-seven patients had omental biopsies, 12 pelvic mass biopsies, and 1 para-aortic lymph node biopsy. Thirty-five biopsies were performed under ultrasound, 25 under computed tomography guidance. Biopsy needle gauges ranged from 14-20 swg with two to five passes for each patient. There were no complications. Histology was obtained in 52 (87%) patients. Percutaneous image-guided biopsy of peritoneal disease or pelvic mass is safe with high diagnostic accuracy. The large-gauge biopsy needle is as safe as the small gauge needle, but has the added value of obtaining tissue samples for immunohistochemistry and genomic studies.

Differential expression of GADD45beta in normal and osteoarthritic cartilage: potential role in homeostasis of articular chondrocytes

OBJECTIVE

Our previous study suggested that growth arrest and DNA damage-inducible protein 45beta (GADD45beta) prolonged the survival of hypertrophic chondrocytes in the developing mouse embryo. This study was undertaken, therefore, to investigate whether GADD45beta plays a role in adult articular cartilage.

METHODS

Gene expression profiles of cartilage from patients with late-stage osteoarthritis (OA) were compared with those from patients with early OA and normal controls in 2 separate microarray analyses. Histologic features of cartilage were graded using the Mankin scale, and GADD45beta was localized by immunohistochemistry. Human chondrocytes were transduced with small interfering RNA (siRNA)-GADD45beta or GADD45beta-FLAG. GADD45beta and COL2A1 messenger RNA (mRNA) levels were analyzed by real-time reverse transcriptase-polymerase chain reaction, and promoter activities were analyzed by transient transfection. Cell death was detected by Hoechst 33342 staining of condensed chromatin.

RESULTS

GADD45beta was expressed at higher levels in cartilage from normal donors and patients with early OA than in cartilage from patients with late-stage OA. All chondrocyte nuclei in normal cartilage immunostained for GADD45beta. In early OA cartilage, GADD45beta was distributed variably in chondrocyte clusters, in middle and deep zone cells, and in osteophytes. In contrast, COL2A1, other collagen genes, and factors associated with skeletal development were up-regulated in late OA, compared with early OA or normal cartilage. In overexpression and knockdown experiments, GADD45beta down-regulated COL2A1 mRNA and promoter activity. NF-kappaB overexpression increased GADD45beta promoter activity, and siRNA-GADD45beta decreased cell survival per se and enhanced tumor necrosis factor alpha-induced cell death in human articular chondrocytes.

CONCLUSION

These observations suggest that GADD45beta might play an important role in regulating chondrocyte homeostasis by modulating collagen gene expression and promoting cell survival in normal adult cartilage and in early OA.

A gene signature predicting for survival in suboptimally debulked patients with ovarian cancer

Despite the existence of morphologically indistinguishable disease, patients with advanced ovarian tumors display a broad range of survival end points. We hypothesize that gene expression profiling can identify a prognostic signature accounting for these distinct clinical outcomes. To resolve survival-associated loci, gene expression profiling was completed for an extensive set of 185 (90 optimal/95 suboptimal) primary ovarian tumors using the Affymetrix human U133A microarray. Cox regression analysis identified probe sets associated with survival in optimally and suboptimally debulked tumor sets at a P value of <0.01. Leave-one-out cross-validation was applied to each tumor cohort and confirmed by a permutation test. External validation was conducted by applying the gene signature to a publicly available array database of expression profiles of advanced stage suboptimally debulked tumors. The prognostic signature successfully classified the tumors according to survival for suboptimally (P = 0.0179) but not optimally debulked (P = 0.144) patients. The suboptimal gene signature was validated using the independent set of tumors (odds ratio, 8.75; P = 0.0146). To elucidate signaling events amenable to therapeutic intervention in suboptimally debulked patients, pathway analysis was completed for the top 57 survival-associated probe sets. For suboptimally debulked patients, confirmation of the predictive gene signature supports the existence of a clinically relevant predictor, as well as the possibility of novel therapeutic opportunities. Ultimately, the prognostic classifier defined for suboptimally debulked tumors may aid in the classification and enhancement of patient outcome for this high-risk population.

Gene-expression profiling in epithelial ovarian cancer

DNA-microarray technology has made it possible to simultaneously analyze the expression of thousands of genes in a small sample of tumor tissue. In epithelial ovarian cancer, gene-expression profiling has been used to provide prognostic information, to predict response to first-line platinum-based chemotherapy, and to discriminate between different histologic subtypes. Furthermore, DNA-microarray technology might permit identification of novel markers for early detection of disease and provide insights into the mechanisms of cancer growth and chemotherapy resistance. In this Review, we summarize the contributions of gene-expression profiling to the diagnosis and management of epithelial ovarian cancer and discuss ways in which this technique could become a useful tool in clinical management.

Serum and tissue biomarkers as predictive and prognostic variables in epithelial ovarian cancer

Tumour stage, residual disease after initial surgery, histological type and tumour grade are the most important clinical-pathological factors related to the clinical outcome of patients with epithelial ovarian cancer. In the last years, several investigations have assessed different biological variables in sera and in tissue samples from patients with this malignancy in order to detect biomarkers able to reflect either the response to chemotherapy or survival. The present paper reviewed the literature data about the predictive or prognostic relevance of serum CA 125, soluble cytokeratin fragments, serum human kallikreins, serum cytokines, serum vascular endothelial growth factor and plasma d-dimer as well as of tissue expression of cell cycle- and apoptosis-regulatory proteins, human telomerase reverse transcriptase, membrane tyrosine kinase receptors and matrix metalloproteinases. A next future microarray technology will hopefully offer interesting perspectives of translational research for the identification of novel predictive and prognostic biomarkers for epithelial ovarian cancer.

A gene signature predicting for survival in suboptimally debulked patients with ovarian cancer

Despite the existence of morphologically indistinguishable disease, patients with advanced ovarian tumors display a broad range of survival end points. We hypothesize that gene expression profiling can identify a prognostic signature accounting for these distinct clinical outcomes. To resolve survival-associated loci, gene expression profiling was completed for an extensive set of 185 (90 optimal/95 suboptimal) primary ovarian tumors using the Affymetrix human U133A microarray. Cox regression analysis identified probe sets associated with survival in optimally and suboptimally debulked tumor sets at a P value of <0.01. Leave-one-out cross-validation was applied to each tumor cohort and confirmed by a permutation test. External validation was conducted by applying the gene signature to a publicly available array database of expression profiles of advanced stage suboptimally debulked tumors. The prognostic signature successfully classified the tumors according to survival for suboptimally (P = 0.0179) but not optimally debulked (P = 0.144) patients. The suboptimal gene signature was validated using the independent set of tumors (odds ratio, 8.75; P = 0.0146). To elucidate signaling events amenable to therapeutic intervention in suboptimally debulked patients, pathway analysis was completed for the top 57 survival-associated probe sets. For suboptimally debulked patients, confirmation of the predictive gene signature supports the existence of a clinically relevant predictor, as well as the possibility of novel therapeutic opportunities. Ultimately, the prognostic classifier defined for suboptimally debulked tumors may aid in the classification and enhancement of patient outcome for this high-risk population.

Genome-wide studies in thyroid neoplasia

There is much interest in the application of genome biology to the field of thyroid neoplasia, despite the relatively low mortality rate associated with thyroid cancer in general. The principal reason for this interest is that the field of thyroid neoplasia stands to benefit from the application of genomic information to address a variety of pathologic and clinical issues. In addition to practical patient care issues, there is an excellent opportunity of expand the basic understanding of thyroid carcinogenesis. In this article, the most relevant genomic work on thyroid tumors performed to date is reviewed along with some general comments about the potential impact of genomic biology on thyroid pathology and the management of patients with thyroid nodules and cancer.

Subcellular localization of activated leukocyte cell adhesion molecule is a molecular predictor of survival in ovarian carcinoma patients

PURPOSE

Currently available clinicopathologic prognostic factors are imperfect predictors of clinical course in advanced-stage epithelial ovarian cancer patients. New molecular predictors are needed to identify patients with higher risk of relapse or death from disease. In a retrospective study, we investigated the prognostic impact of activated leukocyte cell adhesion molecule (ALCAM) expression in epithelial ovarian cancer.

EXPERIMENTAL DESIGN

We analyzed the effect of cell-anchorage loss on ALCAM cellular localization in vitro and assessed ALCAM expression by immunohistochemistry in a series of 109 well-characterized epithelial ovarian cancer patient samples. Chi-square test, Kaplan-Meier method, and Cox proportional hazard analyses were used to relate ALCAM cellular localization to clinical-pathologic parameters and to overall survival (OS) rate.

RESULTS

Loss of epithelial ovarian cancer cell anchorage was associated both in vitro and in vivo with decreased ALCAM membrane expression. In vivo, ALCAM was localized to cell membrane in normal surface ovarian epithelium, whereas in 67% of the epithelial ovarian cancer samples, membrane localization was decreased or even lost, and the molecule was mainly expressed in cytoplasm. Median OS in this group of patients was 58 months, whereas a median OS was not yet reached in patients with ALCAM membrane localization (P = 0.036, hazard ratio [HR] = 2.0, 95% confidence interval [CI] 1.1 to 3.5). In a multivariate Cox regression model including all the available clinicopathologic variables, loss of ALCAM membrane expression was an independent factor of unfavorable prognosis (P = 0.042, HR = 2.15, 95% CI: 1.0 to 4.5).

CONCLUSIONS

Decreased/lost ALCAM membrane expression is a marker of poorer outcome in epithelial ovarian cancer patients and might help to identify patients who could benefit from more frequent follow-up or alternative therapeutic modalities.

A multiparametric panel for ovarian cancer diagnosis, prognosis, and response to chemotherapy

PURPOSE

Our goal was to examine a panel of 11 biochemical variables, measured in cytosolic extracts of ovarian tissues (normal, benign, and malignant) by quantitative ELISAs for their ability to diagnose, prognose, and predict response to chemotherapy of ovarian cancer patients.

EXPERIMENTAL DESIGN

Eleven proteins were measured (9 kallikreins, B7-H4, and CA125) in cytosolic extracts of 259 ovarian tumor tissues, 50 tissues from benign conditions, 35 normal tissues, and 44 tissues from nonovarian tumors that metastasized to the ovary. Odds ratios and hazard ratios and their 95% confidence interval were calculated. Time-dependent receiver operating characteristic curves for censored survival data were used to evaluate the performance of the biomarkers. Resampling was used to validate the performance.

RESULTS

Most biomarkers effectively separated cancer from noncancer groups. A composite marker provided an area under the curve of 0.97 (95% confidence interval, 0.95-0.99) for discriminating normal and cancer groups. Univariately, hK5 and hK6 were positively associated with progression. After adjusting for clinical variables in multivariate analysis, both hK10 and hK11 significantly predicted time to progression. Increasing levels of hK13 were associated with chemotherapy response, and the predictive power of hK13 to chemotherapy response was improved by a panel of five biomarkers.

CONCLUSIONS

The evidence shows that a group of kallikreins and multiparametric combinations with other biomarkers and clinical variables can significantly assist with ovarian cancer classification, prognosis, and response to platinum-based chemotherapy. In particular, we developed a multiparametric strategy for predicting ovarian cancer response to chemotherapy, comprising several biomarkers and clinical features.

Changes in mRNA and protein levels of human HtrA1, HtrA2 and HtrA3 in ovarian cancer

OBJECTIVES

Expression of human HtrA1, HtrA2, HtrA3 and TGF-beta1 genes was examined in ovarian tissue specimens including 19 normal ovaries, 20 benign tumors, 7 borderline tumors, 44 cancers and 8 Krukenberg tumors.

DESIGN AND METHODS

mRNA and protein levels were evaluated by semi-quantitative RT-PCR and Western-blotting methods, respectively.

RESULTS

A statistically significant decrease of HtrA1 and HtrA3 expression in ovarian tumors comparing to normal tissues was observed. A dramatic decrease of HtrA3 mRNA and protein levels in all tumor tissue groups, and a loss of HtrA3 protein in 30% malignant tumors were found. A significant decrease of HtrA1 mRNA, and of HtrA3 mRNA and protein in malignant tumors compared to benign tumors was revealed. HtrA2 expression in tumor tissues was slightly decreased. Expression of TGF-beta1 in tumor tissues was not significantly different compared to control tissues.

CONCLUSIONS

Our results show downregulation of HtrA1 and HtrA3 genes' expression in different types of ovarian tumors and give additional evidence that these genes may function as tumor suppressors.

Identification of pathways associated with invasive behavior by ovarian cancer cells using multidimensional protein identification technology (MudPIT)

Proteomic profiling has emerged as a useful tool for identifying tissue alterations in disease states including malignant transformation. The aim of this study was to reveal expression profiles associated with the highly motile/invasive ovarian cancer cell phenotype. Six ovarian cancer cell lines were subjected to proteomic characterization using multidimensional protein identification technology (MudPIT), and evaluated for their motile/invasive behavior, so that these parameters could be compared. Within whole cell extracts of the ovarian cancer cells, MudPIT identified proteins that mapped to 2245 unique genes. Western blot analysis for selected proteins confirmed the expression profiles revealed by MudPIT, demonstrating the fidelity of this high-throughput analysis. Unsupervised cluster analysis partitioned the cell lines in a manner that reflected their motile/invasive capacity. A comparison of protein expression profiles between cell lines of high (group 1) versus low (group 2) motile/invasive capacity revealed 300 proteins that were differentially expressed, of which 196 proteins were significantly upregulated in group 1. Protein network and KEGG pathway analysis indicated a functional interplay between proteins up-regulated in group 1 cells, with increased expression of several key members of the actin cytoskeleton, extracellular matrix (ECM) and focal adhesion pathways. These proteomic expression profiles can be utilized to distinguish highly motile, aggressive ovarian cancer cells from lesser invasive ones, and could prove to be essential in the development of more effective strategies that target pivotal cell signaling pathways used by cancer cells during local invasion and distant metastasis.

Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluation

Background

In a previously published pilot study we explored the performance of microarrays in predicting clinical behaviour of ovarian tumours. For this purpose we performed microarray analysis on 20 patients and estimated that we could predict advanced stage disease with 100% accuracy and the response to platin-based chemotherapy with 76.92% accuracy using leave-one-out cross validation techniques in combination with Least Squares Support Vector Machines (LS-SVMs).

Methods

In the current study we evaluate whether tumour characteristics in an independent set of 49 patients can be predicted using the pilot data set with principal component analysis or LS-SVMs.

Results

The results of the principal component analysis suggest that the gene expression data from stage I, platin-sensitive advanced stage and platin-resistant advanced stage tumours in the independent data set did not correspond to their respective classes in the pilot study. Additionally, LS-SVM models built using the data from the pilot study – although they only misclassified one of four stage I tumours and correctly classified all 45 advanced stage tumours – were not able to predict resistance to platin-based chemotherapy. Furthermore, models based on the pilot data and on previously published gene sets related to ovarian cancer outcomes, did not perform significantly better than our models.

Conclusion

We discuss possible reasons for failure of the model for predicting response to platin-based chemotherapy and conclude that existing results based on gene expression patterns of ovarian tumours need to be thoroughly scrutinized before these results can be accepted to reflect the true performance of microarray technology.

Bayesian Weibull tree models for survival analysis of clinico-genomic data

An important goal of research involving gene expression data for outcome prediction is to establish the ability of genomic data to define clinically relevant risk factors. Recent studies have demonstrated that microarray data can successfully cluster patients into low- and high-risk categories. However, the need exists for models which examine how genomic predictors interact with existing clinical factors and provide personalized outcome predictions. We have developed clinico-genomic tree models for survival outcomes which use recursive partitioning to subdivide the current data set into homogeneous subgroups of patients, each with a specific Weibull survival distribution. These trees can provide personalized predictive distributions of the probability of survival for individuals of interest. Our strategy is to fit multiple models; within each model we adopt a prior on the Weibull scale parameter and update this prior via Empirical Bayes whenever the sample is split at a given node. The decision to split is based on a Bayes factor criterion. The resulting trees are weighted according to their relative likelihood values and predictions are made by averaging over models. In a pilot study of survival in advanced stage ovarian cancer we demonstrate that clinical and genomic data are complementary sources of information relevant to survival, and we use the exploratory nature of the trees to identify potential genomic biomarkers worthy of further study.

Systems biology and cancer stem cells

The identification, purification and characterization of cancer stem cells (CSCs) holds tremendous promise for improving the treatment of cancer. Mounting evidence is demonstrating that only certain tumour cells (i.e. the CSCs) can give rise to tumours when injected and that these purified cell populations generate heterogeneous tumours. While the cell of origin is still not determined definitively, specific molecular markers for populations containing these CSCs have been found for leukaemia, brain cancer and breast cancer, among others. Systems approaches, particularly molecular profiling, have proven to be of great utility for cancer diagnosis and characterization. These approaches also hold significant promise for identifying distinctive properties of the CSCs, and progress is already being made.

Profiling studies in ovarian cancer: a review

Ovarian cancer is a heterogeneous disease with respect to histopathology, molecular biology, and clinical outcome. In advanced stages, surgery and chemotherapy result in an approximately 25% overall 5-year survival rate, pointing to a strong need to identify subgroups of patients that may benefit from targeted innovative molecular therapy. This review summarizes: (a) microarray research identifying gene-expression profiles in ovarian cancer; (b) the methodological flaws in the available microarray studies; and (c) applications of pathway analysis to define new molecular subgroups. Microarray technology now permits the analysis of expression levels of thousands of genes. So far seven studies have aimed to identify a genetic profile that can predict survival/clinical outcome and/or response to platinum-based therapy. To date, the clinical evidence of prognostic microarray studies has only reached the level of small retrospective studies, and there are other issues that may explain the nonreproducibility among the reported prognostic profiles, such as overfitting, technical platform differences, and accuracy of measurements. We consider pathway analysis a promising new strategy. The accumulation of small differential expressions within a meaningful molecular regulatory network might lead to a critical threshold level, resulting in ovarian cancer. Microarray technologies have already provided valuable expression data for classifying ovarian cancer and the first clues about which molecular changes in ovarian cancer could be exploited in new treatment strategies. Further improvements in technology as well as in study design, combined with pathway analysis, will allow us to detect even more subtle tumor expression differences among subgroups of ovarian cancer patients. Disclosure of potential conflicts of interest is found at the end of this article.

Emmprin and survivin predict response and survival following cisplatin-containing chemotherapy in patients with advanced bladder cancer

PURPOSE

Cisplatin-containing chemotherapy is the standard of care for patients with locally advanced and metastatic transitional cell carcinoma of the urothelium. The response rate is approximately 50% and tumor-derived molecular prognostic markers are desirable for improved estimation of response and survival.

EXPERIMENTAL DESIGN

Affymetrix GeneChip expression profiling was carried out using tumor material from 30 patients. A set of genes with an expression highly correlated to survival time after chemotherapy was identified. Two genes were selected for validation by immunohistochemistry in an independent material of 124 patients receiving cisplatin-containing therapy.

RESULTS

Fifty-five differentially expressed genes correlated significantly to survival time. Two of the protein products (emmprin and survivin) were validated using immunohistochemistry. Multivariate analysis identified emmprin expression (hazard ratio, 2.23; P < 0.0001) and survivin expression (hazard ratio, 2.46; P < 0.0001) as independent prognostic markers for poor outcome, together with the presence of visceral metastases (hazard ratio, 2.62; P < 0.0001). In the clinical good prognostic group of patients without visceral metastases, both markers showed significant discriminating power as supplemental risk factors (P < 0.0001). Within this group of patients, the subgroups of patients with no positive, one positive, or two positive immunohistochemistry scores (emmprin and survivin) had estimated 5-year survival rates of 44.0%, 21.1%, and 0%, respectively. Response to chemotherapy could also be predicted with an odds ratio of 4.41 (95% confidence interval, 1.91-10.1) and 2.48 (95% confidence interval, 1.1-5.5) for emmprin and survivin, respectively.

CONCLUSIONS

Emmprin and survivin proteins were identified as strong independent prognostic factors for response and survival after cisplatin-containing chemotherapy in patients with advanced bladder cancer.