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

Understanding and Targeting Apoptotic Pathways in Ovarian Cancer

Ovarian cancer cells evade the immune system as well as chemotherapeutic and/or biologic treatments through inherent or acquired mechanisms of survival and drug resistance. Depending on the cell type and the stimuli, this threshold can range from external forces such as blunt trauma to programmed processes such as apoptosis, autophagy, or necroptosis. This review focuses on apoptosis, which is one form of programmed cell death. It highlights the multiple signaling pathways that promote or inhibit apoptosis and reviews current clinical therapies that target apoptotic pathways in ovarian cancer.

Identification of differentially expressed genes using an annealing control primer system in stage III serous ovarian carcinoma

Background

Most patients with ovarian cancer are diagnosed with advanced stage disease (i.e., stage III-IV), which is associated with a poor prognosis. Differentially expressed genes (DEGs) in stage III serous ovarian carcinoma compared to normal tissue were screened by a new differential display method, the annealing control primer (ACP) system. The potential targets for markers that could be used for diagnosis and prognosis, for stage III serous ovarian cancer, were found by cluster and survival analysis.

Methods

The ACP-based reverse transcriptase polymerase chain reaction (RT PCR) technique was used to identify DEGs in patients with stage III serous ovarian carcinoma. The DEGs identified by the ACP system were confirmed by quantitative real-time PCR. Cluster analysis was performed on the basis of the expression profile produced by quantitative real-time PCR and survival analysis was carried out by the Kaplan-Meier method and Cox proportional hazards multivariate model; the results of gene expression were compared between chemo-resistant and chemo-sensitive groups.

Results

A total of 114 DEGs were identified by the ACP-based RT PCR technique among patients with stage III serous ovarian carcinoma. The DEGs associated with an apoptosis inhibitory process tended to be up-regulated clones while the DEGs associated with immune response tended to be down-regulated clones. Cluster analysis of the gene expression profile obtained by quantitative real-time PCR revealed two contrasting groups of DEGs. That is, a group of genes including: SSBP1, IFI6 DDT, IFI27, C11orf92, NFKBIA, TNXB, NEAT1 and TFG were up-regulated while another group of genes consisting of: LAMB2, XRCC6, MEF2C, RBM5, FOXP1, NUDCP2, LGALS3, TMEM185A, and C1S were down-regulated in most patients. Survival analysis revealed that the up-regulated genes such as DDAH2, RNase K and TCEAL2 might be associated with a poor prognosis. Furthermore, the prognosis of patients with chemo-resistance was predicted to be very poor when genes such as RNase K, FOXP1, LAMB2 and MRVI1 were up-regulated.

Conclusion

The DEGs in patients with stage III serous ovarian cancer were successfully and reliably identified by the ACP-based RT PCR technique. The DEGs identified in this study might help predict the prognosis of patients with stage III serous ovarian cancer as well as suggest targets for the development of new treatment regimens.

Immunohistochemical evidence for the over-expression of Glutathione peroxidase 3 in clear cell type ovarian adenocarcinoma

Glutathione peroxidase 3 (GPX3) is a member of glutathione peroxidase family, exerting one of the most important cellular defense mechanisms against stress signals, including oxidative damage. In this study, the expression of GPX3 mRNA and protein was analyzed for ovarian cancer tissues to test its applicability as a biomarker that can distinguish the four major histologic types of epithelial ovarian cancer. A public microarray dataset containing 99 ovarian cancer and 4 normal ovary samples was downloaded, and GPX3 mRNA expression was analyzed. The expression of GPX3 protein was measured by immunohistochemical staining in 40 epithelial ovarian cancer tissues, 10 for each of the serous, endometrioid, mucinous, and clear cell type. Histoscores were made from the immunohistostaining, and analysis of variance (ANOVA) was performed to quantitate the differences in protein level. Analysis of genomic dataset confirms a GPX3 overexpression in clear cell type ovarian adenocarcinoma compared with normal ovary and 3 other subtypes of epithelial ovarian cancer at mRNA level. GPX3 also shows the highest average antibody staining intensities in clear cell type ovarian adenocarcinomas over the other 3 types in immunostaining on tissue arrays. This is the first validation of GPX3 as a clear cell type-specific biomarker in ovarian cancer patients' tissues by immunostaining. GPX3 may serve as an important molecular marker for the diagnosis and molecular understanding of clear cell carcinoma of the ovary.

Molecular basis of chemosensitivity of platinum pre-treated ovarian cancer to chemotherapy

BACKGROUND

Ovarian cancer shows considerable heterogeneity in its sensitivity to chemotherapy both clinically and in vitro. This study tested the hypothesis that the molecular basis of this difference lies within the known resistance mechanisms inherent to these patients' tumours.

METHODS

The chemosensitivity of a series of 31 ovarian tumours, all previously treated with platinum-based chemotherapy, was assessed using the ATP-based tumour chemosensitivity assay (ATP-TCA) and correlated with resistance gene expression measured by quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) in a TaqMan Array following extraction of mRNA from formalin-fixed paraffin-embedded tissue. The results were standardised against a housekeeping gene (PBGD), and assessed by multiple linear regression.

RESULTS

Predictive multiple linear regression models were derived for four single agents (cisplatin, gemcitabine, topotecan, and treosulfan), and for the combinations of cisplatin+gemcitabine and treosulfan+gemcitabine. Particularly strong correlations were obtained for cisplatin, gemcitabine, topotecan, and treosulfan+gemcitabine. No individual gene expression showed direct correlation with activity in the ATP-TCA. Genes involved in DNA repair and apoptosis were strongly represented, with some drug pumps also involved.

CONCLUSION

The chemosensitivity of ovarian cancer to drugs is related to the expression of genes involved in sensitivity and resistance mechanisms.

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

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

Targeting the epidermal growth factor receptor in epithelial ovarian cancer: current knowledge and future challenges

The epidermal growth factor receptor is overexpressed in up to 60% of ovarian epithelial malignancies. EGFR regulates complex cellular events due to the large number of ligands, dimerization partners, and diverse signaling pathways engaged. In ovarian cancer, EGFR activation is associated with increased malignant tumor phenotype and poorer patient outcome. However, unlike some other EGFR-positive solid tumors, treatment of ovarian tumors with anti-EGFR agents has induced minimal response. While the amount of information regarding EGFR-mediated signaling is considerable, current data provides little insight for the lack of efficacy of anti-EGFR agents in ovarian cancer. More comprehensive, systematic, and well-defined approaches are needed to dissect the roles that EGFR plays in the complex signaling processes in ovarian cancer as well as to identify biomarkers that can accurately predict sensitivity toward EGFR-targeted therapeutic agents. This new knowledge could facilitate the development of rational combinatorial therapies to sensitize tumor cells toward EGFR-targeted therapies.

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.

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.

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.

A snapshot of microarray-generated gene expression signatures associated with ovarian carcinoma

It was hypothesized that analysis of global gene expression in ovarian carcinoma can identify dysregulated genes that can serve as molecular markers and provide further insight into carcinogenesis and provide the basis for development of new diagnostic tools as well as new targeted therapy protocols. By applying bioinformatics tools for screening of biomedical databases, a gene expression profile databank, specific for ovarian carcinoma, was constructed with utilizable data sets published in 28 studies that applied different array technology platforms. The data sets were divided into four compartments: (i) genes associated with carcinogenesis: in 14 studies, 1881 genes were extracted, 75 genes were identified in more than one study, and only 4 genes (PRKCBP1, SPON1, TACSTD1, and PTPRM) were identified in three studies. (ii) Genes associated with histologic subtypes: in four studies, 463 genes could be identified, but none of them was identified in more than a single study. (iii) Genes associated with therapy response: in seven studies, 606 genes were identified from which 38 were differentially regulated in at least two studies, 3 genes (TMSB4X, GRN, and TJP1) in three studies, and 1 gene (IFITM1) in four studies. (iv) Genes associated with prognosis and progression: 254 genes were found in seven studies. From these genes, merely three were identified in at least two different studies. This snapshot of available gene expression data not only provides independently described potential diagnostic and therapeutic targets for ovarian carcinoma but also emphasizes the drawbacks of the current state of global gene expression analyses in ovarian cancer.

Multiple alternative splicing markers for ovarian cancer

Intense efforts are currently being directed toward profiling gene expression in the hope of developing better cancer markers and identifying potential drug targets. Here, we present a sensitive new approach for the identification of cancer signatures based on direct high-throughput reverse transcription-PCR validation of alternative splicing events. This layered and integrated system for splicing annotation (LISA) fills a gap between high-throughput microarray studies and high-sensitivity individual gene investigations, and was created to monitor the splicing of 600 cancer-associated genes in 25 normal and 21 serous ovarian cancer tissues. Out of >4,700 alternative splicing events screened, the LISA identified 48 events that were significantly associated with serous ovarian tumor tissues. In a further screen directed at 39 ovarian tissues containing cancer pathologies of various origins, our ovarian cancer splicing signature successfully distinguished all normal tissues from cancer. High-volume identification of cancer-associated splice forms by the LISA paves the way for the use of alternative splicing profiling to diagnose subtypes of cancer.

Tissue handling for genome-wide expression analysis: a review of the issues, evidence, and opportunities

CONTEXT

Molecular diagnostic applications that use microarrays to analyze large numbers of genes simultaneously require high-quality mRNA. As these genome-wide expression assays become more commonly used in medical practice, pathologists and oncologists will benefit from understanding the importance of obtaining high-quality RNA in order to generate reliable diagnostic and prognostic information, especially as these relate to cancer.

OBJECTIVE

To review the effects that different tissue preservation techniques have on RNA quality and to provide practical advice on changes in tissue acquisition and handling that may soon be needed for certain clinical situations.

DATA SOURCES

A review of recent literature on RNA quality, tissue fixation, cancer diagnosis, and gene expression analysis.

CONCLUSIONS

Studies have consistently shown that frozen tissue yields more intact RNA than formalin-fixed, paraffin-embedded tissue. The chemical modification, cross-linking, and fragmentation caused by formalin fixation often render RNA unsuitable for microarray analysis. Thus, when expression analysis involving hundreds or more than 1000 gene markers is contemplated, pathologists should consider freezing a specimen within half an hour (preferably within minutes) of surgical resection and storing it at -80 degrees C or below. In coming years, pathologists will need to work closely with oncologists and other clinicians to determine when saving frozen tissue for microarray expression analysis is both practical and necessary. In select cases, the benefit of implementing a few extra tissue-handling steps may improve diagnostic and prognostic capability.

Novel agents in ovarian cancer

The standard treatment for epithelial ovarian cancer remains surgical debulking and chemotherapy with carboplatin and paclitaxel. However, the majority of patients relapse, and few, if any, achieve a cure. Future advancement in treatment should aim at targeting the biology of the disease, specifically mechanisms critical to tumor initiation and progression. Several Phase I and II clinical trials have identified novel opportunities for therapy. The most promising venues appear to be the antiangiogenic agents and the inhibitors of intracellular signaling. Novel modalities of delivering cytotoxics to tumor cells by exploiting ovarian cancer-specific biomarkers are also being tested, and appear promising. Immunomodulatory agents are being developed for consolidation therapy. Although devoid of the common side effects associated with chemotherapy, the use of targeted agents is associated with specific toxicities, related to the biological processes they block. The main challenge for future successful clinical development will be defining molecular markers predictive of response and judicious patient selection based on the biological features of the tumor. Individualized treatment driven by molecular characteristics will open the door to a new age in anticancer medicine.