Unique Gene Expression Profile Based on Pathologic Response in Epithelial Ovarian Cancer

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.

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.

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.

Integrated genomics of chemotherapy resistant ovarian cancer: a role for extracellular matrix, TGFbeta and regulating microRNAs

Epithelial ovarian cancer is the sixth most common cancer in women worldwide and the most important cause of death from gynaecological cancers in the Western world. Our explorative pathway analysis on seven published gene-sets associated with platinum resistance in ovarian cancer reveals TP53 and transforming growth factor beta as key genes. Furthermore, the extracellular matrix was associated with chemotherapy resistance in ovarian cancer as well as endocrine resistance in breast cancer. Pathway analysis again revealed transforming growth factor beta as a key gene regulating extracellular matrix gene expression. A model is presented based on literature linking transforming growth factor beta, extracellular matrix, integrin signalling, epithelial to mesenchymal transition and regulating microRNAs with a (bivalent) role in chemotherapy response.

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.

A population-based 5-year cohort study including all cases of epithelial ovarian cancer in western Sweden: 10-year survival and prognostic factors

Epithelial ovarian cancer (EOC) is the major gynecologic cancer mortality cause in Sweden. The aim of the present study was to investigate the long-term survival and prognostic factors of a complete population-based 5-year cohort of 682 patients with invasive EOC in western Sweden (population around 1.6 million). Data relating to residual tumor after surgery, FIGO stage, grade, histopathologic subtype, ploidy status, adjuvant chemotherapy (the prepaclitaxel period), and disease state (recurrence and death) were reported to a quality register in a prospectively kept database and were controlled against the Swedish National Cancer Registry for completeness. The median follow-up durations for the prospectively collected data in the Cox analysis and for the survival analysis that was made for all patients were 81 months (range, 52-109 months) and 11.7 years (range, 8.7-14.1 years), respectively. No patient was lost to follow-up. The relative 10-year survival rate was 38.4% (95% confidence interval, 34.5%-42.8%). The median relative survival time was 4.3 years (95% confidence interval, 3.6%-5.2%). In the univariate Cox regression analysis, prognostic significances for age, stage, residual tumor, histopathologic subtype of serous cystadenocarcinoma, grade, CA-125, and ploidy status were seen. In the multivariate analysis, age, stage, residual tumor after surgery, and postoperative CA-125 were of prognostic significance. In conclusion, 4 major prognostic factors were found for EOC in this population-based cohort study that also presents nearly accurate long-term survival owing to the nonselective nature and completeness regarding patients and follow-up of the study.

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.

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.

Ovarian cancer: pathology, biology, and disease models

Epithelial ovarian cancer, which comprises several histologic types and grades, is the most lethal cancer among women in the United States. In this review, we summarize recent progress in understanding the pathology and biology of this disease and in development of models for preclinical research. Our new understanding of this disease suggests new targets for therapeutic intervention and novel markers for early detection of disease.

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.

TP53, BCL-2 and BAX analysis in 199 ovarian cancer patients treated with taxane-platinum regimens

OBJECTIVE

In cell line studies, BCL-2 and BAX proteins interfere with cancer response to taxanes. This issue has not received much attention with regard to taxane-platinum (TP)-treated ovarian cancer patients.

METHODS

We evaluated prognostic/predictive significance of BCL-2 and BAX with regard to TP53 status. Immunohistochemical analysis was performed on 199 ovarian carcinomas FIGO stage IIB-IV treated with TP; the results were analyzed by the Cox and logistic regression models.

RESULTS

Clinicopathological parameters (residual tumor size, FIGO stage and/or tumor grade, but not patient's age) were the only or the strongest predictors of patient's outcome. Platinum highly sensitive response showed a positive association with TP53 accumulation (p=0.045). As in our previously published analysis on platinum-cyclophosphamide-treated group, complete remission showed a borderline negative (paradoxic) association with high BAX expression in the whole group (p=0.058) and with BCL-2 expression in the TP53(-) group (p=0.058).

CONCLUSION

Our results suggest that TP53, BCL-2 and BAX proteins carry some predictive potential in taxane-platinum-treated ovarian cancer patients, auxiliary to clinicopathological factors. We have confirmed on another patient group that clinical importance of BCL-2 may depend on TP53 status.

Gene expression profiling of blood to predict the onset of leukemia

No studies have tested the hypothesis that the onset of a disease can be predicted by gene expression profiling. The AKR/J mouse strain, which spontaneously develops acute T cell lymphatic leukemia, was used to implement a novel strategy to generate global gene expression profiles of WBCs at different time points. The experimental approach was bias free because it was unknown as to which individuals in the mouse population would eventually develop the disease. Our results suggest that profiling WBC gene expression may be an effective means for the very early diagnosis of disease in humans.

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.

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.

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.

An integrative analysis of microRNA and mRNA expression--a case study

BACKGROUND

MicroRNAs are believed to play an important role in gene expression regulation. They have been shown to be involved in cell cycle regulation and cancer. MicroRNA expression profiling became available owing to recent technology advancement. In some studies, both microRNA expression and mRNA expression are measured, which allows an integrated analysis of microRNA and mRNA expression.

RESULTS

We demonstrated three aspects of an integrated analysis of microRNA and mRNA expression, through a case study of human cancer data. We showed that (1) microRNA expression efficiently sorts tumors from normal tissues regardless of tumor type, while gene expression does not; (2) many microRNAs are down-regulated in tumors and these microRNAs can be clustered in two ways: microRNAs similarly affected by cancer and microRNAs similarly interacting with genes; (3) taking let-7f as an example, targets genes can be identified and they can be clustered based on their relationship with let-7f expression.

DISCUSSION

Our findings in this paper were made using novel applications of existing statistical methods: hierarchical clustering was applied with a new distance measure-the co-clustering frequency-to identify sample clusters that are stable; microRNA-gene correlation profiles were subject to hierarchical clustering to identify microRNAs that similarly interact with genes and hence are likely functionally related; the clustering of regression models method was applied to identify microRNAs similarly related to cancer while adjusting for tissue type and genes similarly related to microRNA while adjusting for disease status. These analytic methods are applicable to interrogate multiple types of -omics data in general.

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.

Molecular pathogenesis and therapeutic targets in epithelial ovarian cancer

Ovarian cancer, the most aggressive gynecologic cancer, is the foremost cause of death from gynecologic malignancies in the developed world. Two primary reasons explain its aggressive behavior: most patients present with advanced disease at diagnosis, and die of recurrences from disease that has become resistant to conventional chemotherapies. In this paper on epithelial ovarian cancer (EOC), we will review molecular alterations associated with the few precursor lesions identified to date, followed by the more commonly recognized processes of de novo carcinogenesis, metastasis, and the development of chemoresistance. We will propose a unifying model of ovarian epithelial tumorigenesis that takes into account various hypotheses. We will also review novel approaches to overcome the major problem of chemoresistance in ovarian cancer. Finally, we will discuss advances and new challenges in the development of mouse model systems to investigate EOC precursor lesions, progression, metastasis, and chemoresistance.

Global gene expression analysis of early response to chemotherapy treatment in ovarian cancer spheroids

BACKGROUND

Chemotherapy (CT) resistance in ovarian cancer (OC) is broad and encompasses diverse unrelated drugs, suggesting more than one mechanism of resistance. To better understand the molecular mechanisms controlling the immediate response of OC cells to CT exposure, we have performed gene expression profiling in spheroid cultures derived from six OC cell lines (OVCAR3, SKOV3, TOV-112, TOV-21, OV-90 and TOV-155), following treatment with 10,0 microM cisplatin, 2,5 microM paclitaxel or 5,0 microM topotecan for 72 hours.

RESULTS

Exposure of OC spheroids to these CT drugs resulted in differential expression of genes associated with cell growth and proliferation, cellular assembly and organization, cell death, cell cycle control and cell signaling. Genes, functionally involved in DNA repair, DNA replication and cell cycle arrest were mostly overexpressed, while genes implicated in metabolism (especially lipid metabolism), signal transduction, immune and inflammatory response, transport, transcription regulation and protein biosynthesis, were commonly suppressed following all treatments. Cisplatin and topotecan treatments triggered similar alterations in gene and pathway expression patterns, while paclitaxel action was mainly associated with induction of genes and pathways linked to cellular assembly and organization (including numerous tubulin genes), cell death and protein synthesis. The microarray data were further confirmed by pathway and network analyses.

CONCLUSION

Most alterations in gene expression were directly related to mechanisms of the cytotoxics actions in OC spheroids. However, the induction of genes linked to mechanisms of DNA replication and repair in cisplatin- and topotecan-treated OC spheroids could be associated with immediate adaptive response to treatment. Similarly, overexpression of different tubulin genes upon exposure to paclitaxel could represent an early compensatory effect to this drug action. Finally, multicellular growth conditions that are known to alter gene expression (including cell adhesion and cytoskeleton organization), could substantially contribute in reducing the initial effectiveness of CT drugs in OC spheroids. Results described in this study underscore the potential of the microarray technology for unraveling the complex mechanisms of CT drugs actions in OC spheroids and early cellular response to treatment.

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.

A proteome resource of ovarian cancer ascites: integrated proteomic and bioinformatic analyses to identify putative biomarkers

Epithelial ovarian cancer is the most lethal gynecological malignancy, and disease-specific biomarkers are urgently needed to improve diagnosis, prognosis, and to predict and monitor treatment efficiency. We present an in-depth proteomic analysis of selected biochemical fractions of human ovarian cancer ascites, resulting in the stringent and confident identification of over 2500 proteins. Rigorous filter schemes were applied to objectively minimize the number of false-positive identifications, and we only report proteins with substantial peptide evidence. Integrated computational analysis of the ascites proteome combined with several recently published proteomic data sets of human plasma, urine, 59 ovarian cancer related microarray data sets, and protein-protein interactions from the Interologous Interaction Database I (2)D ( http://ophid.utoronto.ca/i2d) resulted in a short-list of 80 putative biomarkers. The presented proteomics analysis provides a significant resource for ovarian cancer research, and a framework for biomarker discovery.

The extracellular matrix protein TGFBI induces microtubule stabilization and sensitizes ovarian cancers to paclitaxel

The extracellular matrix (ECM) can induce chemotherapy resistance via AKT-mediated inhibition of apoptosis. Here, we show that loss of the ECM protein TGFBI (transforming growth factor beta induced) is sufficient to induce specific resistance to paclitaxel and mitotic spindle abnormalities in ovarian cancer cells. Paclitaxel-resistant cells treated with recombinant TGFBI protein show integrin-dependent restoration of paclitaxel sensitivity via FAK- and Rho-dependent stabilization of microtubules. Immunohistochemical staining for TGFBI in paclitaxel-treated ovarian cancers from a prospective clinical trial showed that morphological changes of paclitaxel-induced cytotoxicity were restricted to areas of strong expression of TGFBI. These data show that ECM can mediate taxane sensitivity by modulating microtubule stability.

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.

Evidence that p53-mediated cell-cycle-arrest inhibits chemotherapeutic treatment of ovarian carcinomas

Gene expression profiles of malignant tumors surgically removed from ovarian cancer patients pre-treated with chemotherapy (neo-adjuvant) prior to surgery group into two distinct clusters. One group clusters with carcinomas from patients not pre-treated with chemotherapy prior to surgery (C-L), while the other clusters with non-malignant adenomas (A-L). We show here that although the C-L cluster is preferentially associated with p53 loss-of-function (LOF) mutations, the C-L cluster cancer patients display a more favorable clinical response to chemotherapy as evidenced by enhanced long-term survivorships. Our results support a model whereby p53 mediated cell-cycle-arrest/DNA repair serves as a barrier to optimal chemotherapeutic treatment of ovarian and perhaps other carcinomas and suggest that inhibition of p53 during chemotherapy may enhance clinical outcome.

Standardizing cancer biomarkers criteria: data elements as a foundation for a database. Inflammatory mediator/M-CSF as model marker

The purpose of this position article was to design a set of criteria (data elements) for a wide range of cancer biomarkers (CBs) in an attempt to standardize biomarkers features through a common language as a foundation for a database. Data elements are described as a set of generic criteria, which should characterize nearly all biomarkers introduced in the literature. Data elements were extracted from the review of prominent features that biomarkers represent within various categories. The extracted characteristics of biomarkers produced a short list of shared and unique generic features such as biological nature and history; stage/phase of study; sensitivity and specificity; modes of action; risk assessment; validation status; technology, and recommendation status for diversified biomarkers. To tailor data elements on specific markers, a cytokine, such as macrophage-colony stimulating factor (M-CSF), which has been proposed as a 'potentially suitable biomarker' for diagnosis of ovarian, lung, breast, pancreatic, and colorectal cancers, was selected as a Model biomarker. Small scale clinical studies suggested the superior usefulness of M-CSF compared with traditional markers for cancer detection. A key criterion for selecting Model marker and tailoring data elements for detection of cancer was the comparison of data on its specificity and sensitivity with traditional markers. The design of data elements for standardizing CBs criteria is considered a Research Tool and a foundation for developing a comprehensive CBs database useful for oncology researchers for a wide range of biomarkers. Validation, integration and proper packaging, data visualization and recommendation of suitability of CBs, by a panel of experts, for technology development are important challenging next steps toward developing a reliable database, which would allow professionals to effectively retrieve and study integrated information on potentially useful markers; identify important knowledge gaps and limitations of data; and assess state of technologies and commercialization of markers at a point of need. Appropriate use of integrated information on biomarkers in clinical practices would eventually account for more cost-effective characteristics of an individual's state of health.

European Organisation for Research and Treatment of Cancer (EORTC) Pathobiology Group standard operating procedure for the preparation of human tumour tissue extracts suited for the quantitative analysis of tissue-associated biomarkers

With the new concept of 'individualized treatment and targeted therapies', tumour tissue-associated biomarkers have been given a new role in selection of cancer patients for treatment and in cancer patient management. Tumour biomarkers can give support to cancer patient stratification and risk assessment, treatment response identification, or to identifying those patients who are expected to respond to certain anticancer drugs. As the field of tumour-associated biomarkers has expanded rapidly over the last years, it has become increasingly apparent that a strong need exists to establish guidelines on how to easily disintegrate the tumour tissue for assessment of the presence of tumour tissue-associated biomarkers. Several mechanical tissue (cell) disruption techniques exist, ranging from bead mill homogenisation and freeze-fracturing through to blade or pestle-type homogenisation, to grinding and ultrasonics. Still, only a few directives have been given on how fresh-frozen tumour tissues should be processed for the extraction and determination of tumour biomarkers. The PathoBiology Group of the European Organisation for Research and Treatment of Cancer therefore has devised a standard operating procedure for the standardised preparation of human tumour tissue extracts which is designed for the quantitative analysis of tumour tissue-associated biomarkers. The easy to follow technical steps involved require 50-300 mg of deep-frozen cancer tissue placed into small size (1.2 ml) cryogenic tubes. These are placed into the shaking flask of a Mikro-Dismembrator S machine (bead mill) to pulverise the tumour tissue in the capped tubes in the deep-frozen state by use of a stainless steel ball, all within 30 s of exposure. RNA is isolated from the pulverised tissue following standard procedures. Proteins are extracted from the still frozen pulverised tissue by addition of Tris-buffered saline to obtain the cytosol fraction of the tumour or by the Tris buffer supplemented with the non-ionic detergent Triton X-100, and, after high-speed centrifugation, are found in the tissue supernatant. The resulting tissue cell debris sediment is a rich source of genomic DNA.

An Integrated Genomic-Based Approach to Individualized Treatment of Patients With Advanced-Stage Ovarian Cancer

Purpose

The purpose of this study was to develop an integrated genomic-based approach to personalized treatment of patients with advanced-stage ovarian cancer. We have used gene expression profiles to identify patients likely to be resistant to primary platinum-based chemotherapy and also to identify alternate targeted therapeutic options for patients with de novo platinum-resistant disease.

Patients and Methods

A gene expression model that predicts response to platinum-based therapy was developed using a training set of 83 advanced-stage serous ovarian cancers and tested on a 36-sample external validation set. In parallel, expression signatures that define the status of oncogenic signaling pathways were evaluated in 119 primary ovarian cancers and 12 ovarian cancer cell lines. In an effort to increase chemotherapy sensitivity, pathways shown to be activated in platinum-resistant cancers were subject to targeted therapy in ovarian cancer cell lines.

Results

Gene expression profiles identified patients with ovarian cancer likely to be resistant to primary platinum-based chemotherapy with greater than 80% accuracy. In patients with platinum-resistant disease, we identified expression signatures consistent with activation of Src and Rb/E2F pathways, components of which were successfully targeted to increase response in ovarian cancer cell lines.

Conclusion

We have defined a strategy for treatment of patients with advanced-stage ovarian cancer that uses therapeutic stratification based on predictions of response to chemotherapy, coupled with prediction of oncogenic pathway deregulation, as a method to direct the use of targeted agents.

Independent prognostic significance of cell cycle regulator proteins p16(INK4a) and pRb in advanced-stage ovarian carcinoma including optimally debulked patients: a translational research subprotocol of a randomised study of the Arbeitsgemeinschaft Gynaekologische Onkologie Ovarian Cancer Study Group

The purpose of the study is to test the hypothesis that expression of cell cycle regulatory proteins p16(INK4a) and pRb is significantly associated with prognosis in ovarian carcinomas. We performed immunohistochemical analysis of p16(INK4a) and pRb expression and correlated with survival in a series of 300 patients with FIGO stage IIb-IV ovarian carcinoma which were enrolled in a randomized prospective trial evaluating two different platinum and paxlitaxel chemotherapy combinations after radical surgery. p16(INK4a) negative tumours (17/300; 6%) had a significantly worse prognosis (univariate analysis, P<0.001; multivariate analysis: odds ratio 2.41, P=0.009). Among p16(INK4a)-positive tumours (283 out of 300; 94%), survival was better for patients with intermediate expression as compared to low or high expression levels (P=0.001). High expression levels of pRb were associated with an incremental deterioration of prognosis (univariate analysis, P=0.004; multivariate analysis: odds ratio 2.98, P=0.002). This observation held also true in the subgroup of optimally debulked patients (n=82), in whom the most important established prognostic factor, postoperative residual tumour cannot be applied. In conclusion p16(INK4a) and pRb are independent prognostic factors in advanced-stage ovarian carcinomas after radical surgery and postoperative chemotherapy. High pRb expression is a significant prognosticator in optimally debulked patients and may hold potential for subgroup stratification in postoperative treatment.

Differential gene expression identifies subgroups of ovarian carcinoma

Papillary serous ovarian carcinoma, the most common type of ovarian cancer, displays different biological behavior in different patients. This heterogeneity cannot be recognized by light microscopy. In this study, gene expression in 29 papillary serous ovarian carcinoma samples (21 invasive tumors and 8 borderline tumors), and 17 nonmalignant tissue types comprising 512 samples, was determined using Affymetrix U_133 oligonucleotide microarrays (Affymetrix, Inc., Santa Clara, Calif) representing approximately 40,000 known genes and expression sequence tags (ESTs). Differences in gene expression were quantified as the fold change in gene expression between the various sets of samples. A set of genes was identified that was over-expressed in the invasive ovarian carcinoma samples compared with the normal ovary samples. Principle component analysis of the set of invasive ovarian carcinomas using this set of genes revealed the existence of 2 major subgroups among the invasive ovarian carcinomas. A series of principle component analyses of the ovarian carcinomas using different gene sets composed of genes involved in different metabolic pathways also revealed the same 2 major subgroups of the invasive ovarian carcinomas. Review of the pathology by a single pathologist in a blinded manner suggested that these 2 subgroups differed in pathologic grade. Genes differentially expressed between the 2 ovarian carcinoma subsets were identified. Examination of gene expression in each ovarian carcinoma subset compared with that in 17 different normal tissue types (512 samples) revealed genes specifically over-expressed in ovarian carcinoma compared with these normal tissues. It is concluded that gene expression patterns may be useful in helping to further classify subtypes of papillary serous ovarian carcinoma that may have clinical significance. In addition, the genes identified as over-expressed in each set of serous ovarian carcinoma compared with normal tissues may represent potential biomarkers and/or targets for therapy.

The role of gene expression profiling in the clinical management of ovarian cancer

Several studies have addressed the clinical value of gene expression profiling in the field of ovarian cancer. This paper reviews the current status of knowledge that can be derived from such studies. Gene expression profiles can be used to reveal sets of genes that can distinguish normal ovarian tissue from invasive ovarian carcinomas. Independent validation of these sets may result in the identification of (a set of) markers valuable for the detection in an early stage. Microarray analysis has shown that different histological subtypes of ovarian cancer might be partly reflected by a different aetiology through the deregulation and activation of different pathways. In addition, this heterogeneity could therefore also lead to different tumour behaviours. Worldwide, the combination of paclitaxel and platinum chemotherapy has been incorporated in the standard protocol for the management of patients with advanced stage ovarian cancer, although the outcome in individual patients is uncertain. Gene expression profiling was found to be a prognostic tool with respect to chemosensitivity and had a predictive performance of 78-86%. With increasing numbers of data from published reports, access to these data for the reproducibility of its results and pooling becomes more and more important and will possibly lead to more individualisation of therapy.

Ovarian clear cell adenocarcinoma: a continuing enigma

Ovarian clear cell adenocarcinomas (OCCAs) account for <5% of all ovarian malignancies. Compared to other epithelial ovarian cancer (EOC) subtypes, when at an advanced stage, they are associated with a poorer prognosis and are relatively resistant to conventional platinum-based chemotherapy. By contrast, early-stage clear cell ovarian cancer carries a relatively good prognosis. Hence, there is a need to improve our understanding of its pathobiology in order to optimise currently available treatments and develop new therapeutic strategies. This review summarises the currently available literature regarding the pathogenesis of OCCA, its molecular genetic features and postulated molecular mechanisms that underlie its chemoresistant phenotype. Marked similarities with clear cell carcinomas of the kidney and endometrium have been noted by some investigators, raising interesting possibilities regarding novel therapeutic approaches. Unfortunately, most studies on OCCA have hitherto been hampered by insufficient sample sizes, leaving many key issues unresolved. It is envisaged that in the future, high-resolution genomic and gene-expression microarray studies incorporating larger sample sizes will lead to the characterisation of the key molecular players in OCCA biology, which may potentially lead to the identification of novel targets for therapeutic development.

Genes regulated by hepatocyte growth factor as targets to sensitize ovarian cancer cells to cisplatin

Advanced ovarian cancers are initially responsive to chemotherapy with platinum drugs but develop drug resistance in most cases. We showed recently that hepatocyte growth factor (HGF) enhances death of human ovarian cancer cell lines treated with cisplatin (CDDP) and that this effect is mediated by the p38 mitogen-activated protein kinase. In this work, we integrated genome-wide expression profiling, in silico data survey, and functional assays to identify transcripts regulated in SK-OV-3 ovarian cancer cells made more responsive to CDDP by HGF. Using oligonucleotide microarrays, we found that HGF pretreatment changes the transcriptional response to CDDP. Quantitative reverse transcription-PCR not only validated all the 15 most differentially expressed genes but also confirmed that they were primarily modulated by the combined treatment with HGF and CDDP and reversed by suppressing p38 mitogen-activated protein kinase activity. Among the differentially expressed genes, we focused functional analysis on two regulatory subunits of the protein phosphatase 2A, which were down-modulated by HGF plus CDDP. Decrease of each subunit by RNA interference made ovarian cancer cells more responsive to CDDP, mimicking the effect of HGF. In conclusion, we show that HGF and CDDP modulate transcription in ovarian cancer cells and that this transcriptional response is involved in apoptosis regulation. We also provide the proof-of-concept that the identified genes might be targeted to either increase the efficacy of chemotherapeutics or revert chemotherapy resistance.

Mismatch repair and treatment resistance in ovarian cancer

BACKGROUND

The treatment of ovarian cancer is hindered by intrinsic or acquired resistance to platinum-based chemotherapy. The aim of this study is to determine the frequency of mismatch repair (MMR) inactivation in ovarian cancer and its association with resistance to platinum-based chemotherapy.

METHODS

We determined, microsatellite instability (MSI) as a marker for MMR inactivation (analysis of BAT25 and BAT26), MLH1 promoter methylation status (methylation specific PCR on bisulfite treated DNA) and mRNA expression of MLH1, MSH2, MSH3, MSH6 and PMS2 (quantitative RT-PCR) in 75 ovarian carcinomas and eight ovarian cancer cell lines

RESULTS

MSI was detected in three of the eight cell lines i.e. A2780 (no MLH1 mRNA expression due to promoter methylation), SKOV3 (no MLH1 mRNA expression) and 2774 (no altered expression of MMR genes). Overall, there was no association between cisplatin response and MMR status in these eight cell lines. Seven of the 75 ovarian carcinomas showed MLH1 promoter methylation, however, none of these showed MSI. Forty-six of these patients received platinum-based chemotherapy (11 non-responders, 34 responders, one unknown response). The resistance seen in the eleven non-responders was not related to MSI and therefore also not to MMR inactivation.

CONCLUSION

No MMR inactivation was detected in 75 ovarian carcinoma specimens and no association was seen between MMR inactivation and resistance in the ovarian cancer cell lines as well as the ovarian carcinomas. In the discussion, the results were compared to that of twenty similar studies in the literature including in total 1315 ovarian cancer patients. Although no association between response and MMR status was seen in the primary tumor the possible role of MMR inactivation in acquired resistance deserves further investigation.

Molecular predictors of response and outcome in ovarian cancer

A major problem in clinical management of patients with epithelial ovarian cancer (EOC) is the largely unpredictable response to first-line treatment and the occurrence of relapse after complete initial response, associated with broad cross-resistance to even structurally dissimilar drugs. During tumor development and progression, multiple genic alterations take place that might contribute specifically to the treatment response and eventually impact on disease outcome. One area of intense research is the identification of molecular markers to accurately assess the prognosis of EOC patients and to define innovative therapeutic strategies. A large survey of recent published data indicates the need to revisit traditional molecular markers with respect to their contribution to the assessment of overall survival in selected populations. Furthermore, recent technological developments that enable simultaneous measurement of many parameters ("omic" approaches) hold the promise of identifying new molecular prognostic and predictive markers.