Gene Expression Patterns That Characterize Advanced Stage Serous Ovarian Cancers

Motility-related actinin alpha-4 is associated with advanced and metastatic ovarian carcinoma

Advanced and metastatic ovarian cancer is a leading cause of death from gynecologic malignancies. A more detailed understanding of the factors controlling invasion and metastasis may lead to novel anti-metastatic therapies. To model cellular interactions that occur during intraperitoneal metastasis, comparative cDNA microarray analysis and confirmatory real-time reverse transcription PCR (RT-PCR) were employed to uncover changes in gene expression that may occur in late stage ovarian cancer in response to microenvironmental cues, particularly native three-dimensional collagen I. Gene expression in human ovarian carcinoma tissues was evaluated on the RNA and protein level using real-time RT-PCR and immunohistochemistry. Cell invasion and migration were evaluated in a collagen invasion assay and a scratch wound assay. Three-dimensional collagen I culture led to differential expression of several genes. The role of actinin alpha-4 (ACTN4), a cytoskeleton-associated protein implicated in the regulation of cell motility, was examined in detail. ACTN4 RNA and protein expression were associated with advanced and metastatic human ovarian carcinoma. This report demonstrates that a cytoskeletal-associated protein ACTN4 is upregulated by three-dimensional collagen culture conditions, leading to increased invasion and motility of ovarian cancer cells. Expression of ACTN4 in human ovarian tumors was found to be associated with advanced-stage disease and peritoneal metastases.

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.

Linking the ovarian cancer transcriptome and immunome

Background

Autoantigens have been reported in a variety of tumors, providing insight into the interplay between malignancies and the immune response, and also giving rise to novel diagnostic and therapeutic concepts. Why certain tumor-associated proteins induce an immune response remains largely elusive.

Results

This paper analyzes the proposed link between increased abundance of a protein in cancerous tissue and the increased potential of the protein for induction of a humoral immune response, using ovarian cancer as an example. Public domain data sources on differential gene expression and on autoantigens associated with this malignancy were extracted and compared, using bioinformatics analysis, on the levels of individual genes and proteins, transcriptional coregulation, joint functional pathways, and shared protein-protein interaction networks. Finally, a selected list of ovarian cancer-associated, differentially regulated proteins was tested experimentally for reactivity with antibodies prevalent in sera of ovarian cancer patients.

Genes reported as showing differential expression in ovarian cancer exhibited only minor overlap with the public domain list of ovarian cancer autoantigens. However, experimental screening for antibodies directed against antigenic determinants from ovarian cancer-associated proteins yielded clear reactions with sera.

Conclusion

A link between tumor protein abundance and the likelihood of induction of a humoral immune response in ovarian cancer appears evident.

Amplification of PVT1 contributes to the pathophysiology of ovarian and breast cancer

PURPOSE

This study was designed to elucidate the role of amplification at 8q24 in the pathophysiology of ovarian and breast cancer because increased copy number at this locus is one of the most frequent genomic abnormalities in these cancers.

EXPERIMENTAL DESIGN

To accomplish this, we assessed the association of amplification at 8q24 with outcome in ovarian cancers using fluorescence in situ hybridization to tissue microarrays and measured responses of ovarian and breast cancer cell lines to specific small interfering RNAs against the oncogene MYC and a putative noncoding RNA, PVT1, both of which map to 8q24.

RESULTS

Amplification of 8q24 was associated with significantly reduced survival duration. In addition, small interfering RNA-mediated reduction in either PVT1 or MYC expression inhibited proliferation in breast and ovarian cancer cell lines in which they were both amplified and overexpressed but not in lines in which they were not amplified/overexpressed. Inhibition of PVT1 expression also induced a strong apoptotic response in cell lines in which it was overexpressed but not in lines in which it was not amplified/overexpressed. Inhibition of MYC, on the other hand, did not induce an apoptotic response in cell lines in which MYC was amplified and overexpressed.

CONCLUSIONS

These results suggest that MYC and PVT1 contribute independently to ovarian and breast pathogenesis when overexpressed because of genomic abnormalities. They also suggest that PVT1-mediated inhibition of apoptosis may explain why amplification of 8q24 is associated with reduced survival duration in patients treated with agents that act through apoptotic mechanisms.

The pathogenesis and treatment of acid sphingomyelinase-deficient Niemann-Pick disease

Patients with types A and B Niemann-Pick disease (NPD) have an inherited deficiency of acid sphingomyelinase (ASM) activity. The clinical spectrum of this disorder ranges from the infantile, neurological form that results in death by 3 years of age (type A NPD) to the non-neurological form (type B NPD) that is compatible with survival into adulthood. Intermediate cases also have been reported, and the disease is best thought of as a single entity with a spectrum of phenotypes. ASM deficiency is panethnic, but appears to be more frequent in individuals of Middle Eastern and North African descent. Current estimates of the disease incidence range from approximately 0.5 to 1 per 100,000 births. However, these approximations likely under estimate the true frequency of the disorder since they are based solely on cases referred to biochemical testing laboratories for enzymatic confirmation. The gene encoding ASM (SMPD1) has been studied extensively; it resides within an imprinted region on chromosome 11, and is preferentially expressed from the maternal chromosome. Over 100 SMPD1 mutations causing ASM-deficient NPD have been described, and some useful genotype-phenotype correlations have been made. Based on these findings, DNA-based carrier screening has been implemented in the Ashkenazi Jewish community. ASM 'knockout' mouse models also have been constructed and used to investigate disease pathogenesis and treatment. Based on these studies in the mouse model, an enzyme replacement therapy clinical trial has recently begun in adult patients with non-neurological ASM-deficient NPD.

Filtering genes to improve sensitivity in oligonucleotide microarray data analysis

Many recent microarrays hold an enormous number of probe sets, thus raising many practical and theoretical problems in controlling the false discovery rate (FDR). Biologically, it is likely that most probe sets are associated with un-expressed genes, so the measured values are simply noise due to non-specific binding; also many probe sets are associated with non-differentially-expressed (non-DE) genes. In an analysis to find DE genes, these probe sets contribute to the false discoveries, so it is desirable to filter out these probe sets prior to analysis. In the methodology proposed here, we first fit a robust linear model for probe-level Affymetrix data that accounts for probe and array effects. We then develop a novel procedure called FLUSH (Filtering Likely Uninformative Sets of Hybridizations), which excludes probe sets that have statistically small array-effects or large residual variance. This filtering procedure was evaluated on a publicly available data set from a controlled spiked-in experiment, as well as on a real experimental data set of a mouse model for retinal degeneration. In both cases, FLUSH filtering improves the sensitivity in the detection of DE genes compared to analyses using unfiltered, presence-filtered, intensity-filtered and variance-filtered data. A freely-available package called FLUSH implements the procedures and graphical displays described in the article.

Large-scale integration of cancer microarray data identifies a robust common cancer signature

BACKGROUND

There is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis. DNA microarrays provide a means for measuring gene expression signatures which can then be used as components of genomic-based diagnostic tests to determine the presence of cancer.

RESULTS

In this study, we collect and integrate ~1500 microarray gene expression profiles from 26 published cancer data sets across 21 major human cancer types. We then apply a statistical method, referred to as the Top-Scoring Pair of Groups (TSPG) classifier, and a repeated random sampling strategy to the integrated training data sets and identify a common cancer signature consisting of 46 genes. These 46 genes are naturally divided into two distinct groups; those in one group are typically expressed less than those in the other group for cancer tissues. Given a new expression profile, the classifier discriminates cancer from normal tissues by ranking the expression values of the 46 genes in the cancer signature and comparing the average ranks of the two groups. This signature is then validated by applying this decision rule to independent test data.

CONCLUSION

By combining the TSPG method and repeated random sampling, a robust common cancer signature has been identified from large-scale microarray data integration. Upon further validation, this signature may be useful as a robust and objective diagnostic test for cancer.

Gene expression signatures that predict radiation exposure in mice and humans

BACKGROUND

The capacity to assess environmental inputs to biological phenotypes is limited by methods that can accurately and quantitatively measure these contributions. One such example can be seen in the context of exposure to ionizing radiation.

METHODS AND FINDINGS

We have made use of gene expression analysis of peripheral blood (PB) mononuclear cells to develop expression profiles that accurately reflect prior radiation exposure. We demonstrate that expression profiles can be developed that not only predict radiation exposure in mice but also distinguish the level of radiation exposure, ranging from 50 cGy to 1,000 cGy. Likewise, a molecular signature of radiation response developed solely from irradiated human patient samples can predict and distinguish irradiated human PB samples from nonirradiated samples with an accuracy of 90%, sensitivity of 85%, and specificity of 94%. We further demonstrate that a radiation profile developed in the mouse can correctly distinguish PB samples from irradiated and nonirradiated human patients with an accuracy of 77%, sensitivity of 82%, and specificity of 75%. Taken together, these data demonstrate that molecular profiles can be generated that are highly predictive of different levels of radiation exposure in mice and humans.

CONCLUSIONS

We suggest that this approach, with additional refinement, could provide a method to assess the effects of various environmental inputs into biological phenotypes as well as providing a more practical application of a rapid molecular screening test for the diagnosis of radiation exposure.

Genetic intra-tumour heterogeneity in epithelial ovarian cancer and its implications for molecular diagnosis of tumours

Genetic analysis of solid tumours using DNA or cDNA expression microarrays may enable individualized treatment based on the profiles of genetic changes that are identified from each patient. This could result in better response to adjuvant chemotherapy and, consequently, improved clinical outcome. So far, most research studies that have tested the efficacy of such an approach have sampled only single areas of neoplastic tissue from tumours; this assumes that the genetic profile within solid tumours is homogeneous throughout. The aim of this study was to evaluate the extent of genetic intra-tumour heterogeneity (ITH) within a series of epithelial ovarian cancers. Several different regions (five to eight regions) of tumour tissue from 16 grade 3, serous epithelial ovarian cancers were analysed for genetic alterations using a combination of microsatellite analysis and single nucleotide polymorphism (SNP) analysis, in order to establish the extent of ITH. Maximum parsimony tree analysis was applied to the genetic data from each tumour to evaluate the clonal relationship between different regions within tumours. Extensive ITH was identified within all ovarian cancers using both microsatellite and SNP analysis. Evolutionary analysis of microsatellite data suggested that the origin of all tumours was monoclonal, but that subsequent clonal divergence created mixed populations of genetically distinct cells within the tumour. SNP analysis suggested that ITH was not restricted to random genetic changes, but affected genes that have an important functional role in ovarian cancer development. The frequent occurrence of ITH within epithelial ovarian cancers may have implications for the interpretation of genetic data generated from emerging technologies such as DNA and mRNA expression microarrays, and their use in the clinical management of patients with ovarian cancer. The basis of genetic ITH and the possible implications for molecular approaches to clinical diagnosis of ovarian cancers may apply to other tumour types.

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.

Emerging roles for PAX8 in ovarian cancer and endosalpingeal development

OBJECTIVES

Epithelial ovarian carcinomas develop from ovarian surface epithelia that undergo complex differentiation to form distinguishable phenotypes resembling those of the epithelia of the female urogenital regions. While previous studies have implicated regulatory developmental homeobox (HOX) genes in this process, other factors responsible for this differentiation are largely unknown. Aberrant transcriptional expression of PAX8 has been reported in epithelial ovarian cancer, prompting us to initiate the molecular characterization of this master regulatory gene in ovarian cancer development.

METHODS

Immunohistochemistry, immunoblotting and RT-PCR were used to investigate the presence of PAX8 and its protein products in epithelial ovarian cancer subtypes, normal ovarian surface epithelia, ovarian inclusion cysts and normal endosalpingeal epithelia.

RESULTS

In this report, we confirm microarray results indicating that the transcription factor, PAX8, is highly expressed in epithelial ovarian cancer but absent from the precursor ovarian surface epithelia of healthy individuals. Furthermore, we report that PAX8 is localized to the nucleus of non-ciliated epithelia in simple ovarian epithelial inclusion cysts and in three epithelial ovarian cancer subtypes (serous, endometrioid and clear cell). We also determined that PAX8 is expressed in the non-ciliated, secretory cells of healthy fallopian tube mucosal linings but not in the adjacent ciliated epithelia.

CONCLUSION

These findings support the hypothesis that PAX8 plays parallel roles in the development of epithelial ovarian cancer and in the developmental differentiation of coelomic epithelia into endosalpingeal epithelia.

Sperm protein 17 influences the tissue-specific malignancy of clear cell adenocarcinoma in human epithelial ovarian cancer

Clear cell adenocarcinoma of the ovary has a poor prognosis due to chemoresistance and early metastasis to the lymph nodes. It also can result in endometriosis and is the second most frequent type of ovarian cancer in Japan. Serous adenocarcinoma of the ovary is another common epithelial cancer tissue subtype in Japan, and it is highly sensitive to chemotherapy. In the current study, we examined the differential expression of genes in these types of ovarian cancer and tried to analyze their functions, especially as they relate to chemoresistance. We used differential display to compare clear cell carcinoma and serous adenocarcinoma of the ovary. We identified sperm protein 17 (SP17) as a candidate gene related to the chemoresistance of clear cell carcinoma. Its differential expression was confirmed by real-time polymerase chain reaction. Because the function of the SP17 gene in ovarian cancer is not known, we examined the effect of small interfering RNA targeting the SP17 gene on the chemoresistance and proliferation of ES-2 ovarian cancer cells to paclitaxel, currently the most effective treatment for ovarian cancer. We found that this treatment decreased the chemoresistance of these cells to paclitaxel. Our results strongly suggest that SP17 plays a role in the resistance of clear cell carcinoma to chemotherapy without influencing their ability to proliferate

High expression of insulin-like growth factor binding protein-2 messenger RNA in epithelial ovarian cancers produces elevated preoperative serum levels

The molecular etiology of epithelial ovarian cancer remains unclear. Using microarray expression analysis, we recently reported that expression of the insulin-like growth factor binding protein-2 (IGFBP-2) gene is elevated in advanced epithelial ovarian cancers. The aim of this study was to further delineate the role of IGFBP-2 in the pathoetiology of epithelial ovarian cancer and determine if elevated ovarian cancer IGFBP-2 gene expression is reflected in serum. Relative IGFBP-2 expression was measured using quantitative real-time polymerase chain reaction in 113 epithelial ovarian cancers and 6 normal ovarian surface epithelial samples. Preoperative serum IGFBP-2 levels were measured by radioimmunoassay in 84 women (42 ovarian cancers, 26 benign gynecological conditions, and 10 healthy female controls). Ovarian cancers demonstrated 38-fold higher mean IGFBP-2 expression than normal ovarian epithelium (P < 0.01). Serum IGFBP-2 levels were elevated in women with early- and advanced-stage ovarian cancer compared to controls and patients with benign gynecological conditions (P = 0.05 and P < 0.01, respectively). Epithelial ovarian cancers express high levels of IGFBP-2 relative to normal ovarian epithelium, and this is associated with elevated serum IGFBP-2 levels compared to both normal controls and patients with benign gynecological disease. Our findings provide further support that the insulin-like growth factor pathway plays a significant role in epithelial ovarian cancer pathogenesis. Further, IGFBP-2 may represent an additional serum biomarker with utility in detection and monitoring of epithelial ovarian cancer.

Asparagine synthetase as a causal, predictive biomarker for L-asparaginase activity in ovarian cancer cells

L-Asparaginase (l-ASP), a bacterial enzyme used since the 1970s to treat acute lymphoblastic leukemia, selectively starves cells that cannot synthesize sufficient asparagine for their own needs. Molecular profiling of the NCI-60 cancer cell lines using five different microarray platforms showed strong negative correlations of asparagine synthetase (ASNS) expression and DNA copy number with sensitivity to l-ASP in the leukemia and ovarian cancer cell subsets. To assess whether the ovarian relationship is causal, we used RNA interference to silence ASNS in three ovarian lines and observed 4- to 5-fold potentiation of sensitivity to l-ASP with two of the lines. For OVCAR-8, the line that expresses the least ASNS, the potentiation was >500-fold. Significantly, that potentiation was >700-fold in the multidrug-resistant derivative OVCAR-8/ADR, showing that the causal relationship between ASNS expression and l-ASP activity survives development of classical multidrug resistance. Tissue microarrays confirmed low ASNS expression in a subset of clinical ovarian cancers as well as other tumor types. Overall, this pharmacogenomic/pharmacoproteomic study suggests the use of l-ASP for treatment of a subset of ovarian cancers (and perhaps other tumor types), with ASNS as a biomarker for patient selection.

New potential ligand-receptor signaling loops in ovarian cancer identified in multiple gene expression studies

Based on the hypothesis that gene products involved in the same biological process would be coupled at transcriptional level, a previous study analyzed the correlation of the gene expression patterns of ligand-receptor (L-R) pairs to discover potential autocrine/paracrine signaling loops in different cancers (Graeber and Eisenberg. Nat Genet 2001; 29:295). By refining the starting database, a list of 511 L-R pairs was compiled, combined to eight data sets from a single pathology, epithelial ovarian cancer, and examined as a proof-of-principle of the statistical and biological validity of the correlation of the L-R gene expression patterns in cancer. Analysis revealed a Bonferroni-corrected significant correlation of 105 L-R pairs in at least one data set and, by systematic analysis, identified 39 more frequently correlated L-R pairs, 7 of which were already biologically confirmed. In four data sets examined for an L-R correlation associated with patient survival time, 15 L-R pairs were significantly correlated in short surviving patients in two of the data sets. Immunohistochemical analysis of one of the newly identified correlated L-R pairs (i.e., EFNB3-EPHB4) revealed the correlated expression of ephrin-B3 and EphB4 proteins in 45 of 55 epithelial ovarian tumor samples (P < 0.0001). Together, these data not only support the validity of cross-comparison analysis of gene expression data because known and expected correlations were confirmed but also point to the promise of such analysis in identifying new L-R signaling loops in cancer.

Expression analysis of stage III serous ovarian adenocarcinoma distinguishes a sub-group of survivors

It is difficult to predict the clinical outcome for patients with ovarian cancer. However, the use of biomarkers as additional prognostic factors may improve the outcome for these patients. In order to find novel candidate biomarkers, differences in gene expressions were analysed in 54 stage III serous ovarian adenocarcinomas with oligonucleotide microarrays containing 27,000 unique probes. The microarray data was verified with quantitative real-time polymerase chain reaction for the genes TACC1, MUC5B and PRAME. Using hierarchical cluster analysis we detected a sub-group that included 60% of the survivors. The gene expressions in tumours from patients in this sub-group of survivors were compared with the remaining tumours, and 204 genes were found to be differently expressed. We conclude that the sub-group of survivors might represent patients with favourable tumour biology and sensitivity to treatment. A selection of the 204 genes might be used as a predictive model to distinguish patients within and outside of this group. Alternative chemotherapy strategies could then be offered as first-line treatment, which may lead to improvements in the clinical outcome for these patients.

Molecular prognostic markers in ovarian cancer: toward patient-tailored therapy

In ovarian cancer the ceiling seems to be reached with chemotherapeutic drugs. Therefore a paradigm shift is needed. Instead of treating all patients according to standard guidelines, individualized molecular targeted treatment should be aimed for. This means that molecular profiles of the distinct ovarian cancer subtypes should be established. Until recently, most studies trying to identify molecular targets were single-marker studies. The prognostic role of key components of apoptotic and prosurvival pathways such as p53, EGFR, and HER2 has been extensively studied because resistance to chemotherapy is often caused by failure of tumor cells to go into apoptosis. However, it is more than likely that different ovarian cancer subtypes with extensive molecular heterogeneity exist. Therefore, exploration of the potential of specific tumor-targeted therapy, based on expression of a prognostic tumor profile, may be of interest. Recently, new profiling techniques, such as DNA and protein microarrays, have enabled high-throughput screening of tumors. In this review an overview of the current status of prognostic marker and molecular targeting research in ovarian cancer, including microarray studies, is presented.

RNA quality and gene expression analysis of ovarian tumor tissue undergoing repeated thaw-freezing

Gene expression profiles evaluated by microarray-based quantization of RNA are used in studies of differential diagnosis and prognosis in cancer. RNA of good quality is mandatory for this evaluation. The RNA most often comes from tumor banks with limited amount of tissue, and the tissue often undergoes repeated thawing and freezing. We evaluated the influence of repeated division of tumor samples at room temperature, on RNA quality and quantity, in addition to the gene expression profile. Sixteen ovarian tumor samples were divided in three aliquots each, undergoing respectively one, two, and three thaw-freeze cycles. RNA from each aliquot was extracted on the day of division, and quantity and quality were evaluated. RNA from all three aliquots of four tumor samples underwent microarray analysis on Affymetrix Human Genome U133A 2.0 arrays. Microarray data were evaluated using both unsupervised, and supervised multivariate statistical methods, reliability analysis, as well as verification using published gene lists in ovarian cancer studies. RNA quality and quantity did not change during the division procedure and microarray data showed insignificant difference in gene expression. Tumor samples from tumor banks can be frozen and thawed at least three times without compromising the RNA integrity and genetic expression profile.

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.

Identification of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-inducible genes in human amniotic epithelial cells

BACKGROUND

Exposure to dioxins results in a broad range of pathophysiological disorders in human fetuses. In order to evaluate the effects of dioxins on the feto-placental tissues, we analyzed the gene expression in 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) treated primary cultures of human amniotic epithelial cells.

METHODS

Human amniotic epithelial cells were dispersed by trypsin from amniotic membranes and cultured in DME/Ham's F12 medium supplemented with 10% FBS. Two weeks after plating, cells were treated with 50 nM TCDD or DMSO (control), further incubated for 48 hrs, and the gene expression was analyzed by DNA microarray technology and quantitative real-time PCR.

RESULTS

Thirty eight TCDD-inducible genes, including cytochromeP4501A1 and cytochromeP4501B1, were identified. One of the remarkable profiles of the gene expression was the prominent up-regulation of interferon-inducible genes. The genes involved in the interferon gene expression and interferon signaling pathways were also up-regulated. Furthermore, the expression of genes related to collagen synthesis or degradation was enhanced by TCDD.

CONCLUSION

Using DNA microarray and quantitative real-time PCR analyses, we identified TCDD-inducible genes, including interferon-inducible genes and genes related to collagen synthesis or degradation, in human amniotic epithelial cells.

Gene expression profiling of primary cultures of ovarian epithelial cells identifies novel molecular classifiers of ovarian cancer

In order to elucidate the biological variance between normal ovarian surface epithelial (NOSE) and epithelial ovarian cancer (EOC) cells, and to build a molecular classifier to discover new markers distinguishing these cells, we analysed gene expression patterns of 65 primary cultures of these tissues by oligonucleotide microarray. Unsupervised clustering highlights three subgroups of tumours: low malignant potential tumours, invasive solid tumours and tumour cells derived from ascites. We selected 18 genes with expression profiles that enable the distinction of NOSE from these three groups of EOC with 92% accuracy. Validation using an independent published data set derived from tissues or primary cultures confirmed a high accuracy (87–96%). The distinctive expression pattern of a subset of genes was validated by quantitative reverse transcription–PCR. An ovarian-specific tissue array representing tissues from NOSE and EOC samples of various subtypes and grades was used to further assess the protein expression patterns of two differentially expressed genes (Msln and BMP-2) by immunohistochemistry. This study highlights the relevance of using primary cultures of epithelial ovarian cells as a model system for gene profiling studies and demonstrates that the statistical analysis of gene expression profiling is a useful approach for selecting novel molecular tumour markers.

Predicting the clinical behavior of ovarian cancer from gene expression profiles

We investigated whether prognostic information is reflected in the expression patterns of ovarian carcinoma samples. RNA obtained from seven FIGO stage I without recurrence, seven platin-sensitive advanced-stage (III or IV), and six platin-resistant advanced-stage ovarian tumors was hybridized on a complementary DNA microarray with 21,372 spotted clones. The results revealed that a considerable number of genes exhibit nonaccidental differential expression between the different tumor classes. Principal component analysis reflected the differences between the three tumor classes and their order of transition. Using a leave-one-out approach together with least squares support vector machines, we obtained an estimated classification test accuracy of 100% for the distinction between stage I and advanced-stage disease and 76.92% for the distinction between platin-resistant versus platin-sensitive disease in FIGO stage III/IV. These results indicate that gene expression patterns could be useful in clinical management of ovarian cancer.

Analysis of gene expression patterns of ovarian cancer cell lines with different metastatic potentials

The objective of this study was to investigate the key genetic changes and molecular mechanisms in the process of invasion and metastasis of human epithelial ovarian cancers. The in vitro invasion assay was used to further testify that the human epithelial ovarian cancer cell line SKOV3.ip1 is more invasive and metastatic compared with its parental line SKOV3. A total of 17,000 human genome complementary DNA microarrays were used to compare the gene expression patterns of the two cell lines. Reverse transcription-polymerase chain reactions and Western blotting were performed to validate the results of the microarray. Totally, 1557 twofold differentially expressed genes were screened out by 17,000 human genome complementary DNA microarrays between the two cell lines, including some important genes such as, nm23, c-erbB-2, and other unknown genes or expressed sequence tags with remarkable fold changes. The results of the microarray experiment were further confirmed by reverse transcription-polymerase chain reactions and Western blotting of the nm23-H2 gene. SKOV3.ip1 is more invasive and metastatic than its parental line. The invasion and metastasis mechanism of epithelial ovarian cancer is a very complex process in which many important genes like nm23, c-erbB-2, as well as other unknown genes or expressed sequence tags were involved.

Expression profiling correlates with treatment response in women with advanced serous epithelial ovarian cancer

The majority of epithelial ovarian carcinomas are of serous subtype, with most women presenting at an advanced stage. Approximately 70% respond to initial chemotherapy but eventually relapse. We aimed to find markers of treatment response that might be suitable for routine use, using the gene expression profile of tumor tissue. Thirty one women with histologically-confirmed late-stage serous ovarian cancer were classified into 3 groups based on response to treatment (nonresponders, responders with relapse less than 12 months and responders with no relapse within 12 months). Gene expression profiles of these specimens were analyzed with respect to treatment response and survival (minimum 36 months follow-up). Patients' clinical features did not correlate with prognosis, or with specific gene expression patterns of their tumors. However women who did not respond to treatment could be distinguished from those who responded with no relapse within 12 months based on 34 gene transcripts (p < 0.02). Poor prognosis was associated with high expression of inhibitor of differentiation-2 (ID2) (p = 0.001). High expression of decorin (DCN) and ID2 together was strongly associated with reduced survival (p = 0.003), with an estimated 7-fold increased risk of dying (95% CI 1.9-29.6; 14 months survival) compared with low expression (44 months). Immunohistochemical analysis revealed both nuclear and cytoplasmic distribution of ID2 in ovarian tumors. High percentage of nuclear staining was associated with poor survival, although not statistically significantly. In conclusion, elevated expression of ID2 and DCN was significantly associated with poor prognosis in a homogeneous group of ovarian cancer patients for whom survival could not be predicted from clinical factors.

Emerging issues of the expression profiling technologies for the study of gynecologic cancer

Evaluation of the prognostic parameters of gynecologic cancer has shown their failure for classification according to the clinical behavior or the prediction of its outcome. This weakness has important implications on prognosis and treatment. The increasing understanding of the complexity of the human genome, coupled with the development of high throughput analysis techniques and bioinformatics tools, has changed our concepts on cancer biology, by shifting our targets to a global analysis of the transcriptome and the proteome, linking genes and their products into functional pathways. These approaches permit the documentation of expression patterns of thousands of genes within a cell. With the use of DNA microarray technology, it is feasible to identify signature patterns of expression in tumor samples that faithfully correlate with its biology, providing accurate prognosis for each cancer patient and thus a rational customized treatment. At this stage, there is a need for systematic studies for the validation of these novel approaches. In this review, we provide a basic background of the concept of the technology, highlight several emerging issues from their applications on gynecologic cancer, discuss a series of important themes and problems regarding their interpretation and relevance for the clinicians, and comment on future areas of research.

Patterns of gene expression that characterize long-term survival in advanced stage serous ovarian cancers

PURPOSE

A better understanding of the underlying biology of invasive serous ovarian cancer is critical for the development of early detection strategies and new therapeutics. The objective of this study was to define gene expression patterns associated with favorable survival.

EXPERIMENTAL DESIGN

RNA from 65 serous ovarian cancers was analyzed using Affymetrix U133A microarrays. This included 54 stage III/IV cases (30 short-term survivors who lived <3 years and 24 long-term survivors who lived >7 years) and 11 stage I/II cases. Genes were screened on the basis of their level of and variability in expression, leaving 7,821 for use in developing a predictive model for survival. A composite predictive model was developed that combines Bayesian classification tree and multivariate discriminant models. Leave-one-out cross-validation was used to select and evaluate models.

RESULTS

Patterns of genes were identified that distinguish short-term and long-term ovarian cancer survivors. The expression model developed for advanced stage disease classified all 11 early-stage ovarian cancers as long-term survivors. The MAL gene, which has been shown to confer resistance to cancer therapy, was most highly overexpressed in short-term survivors (3-fold compared with long-term survivors, and 29-fold compared with early-stage cases). These results suggest that gene expression patterns underlie differences in outcome, and an examination of the genes that provide this discrimination reveals that many are implicated in processes that define the malignant phenotype.

CONCLUSIONS

Differences in survival of advanced ovarian cancers are reflected by distinct patterns of gene expression. This biological distinction is further emphasized by the finding that early-stage cancers share expression patterns with the advanced stage long-term survivors, suggesting a shared favorable biology.

Profiling estrogen-regulated gene expression changes in normal and malignant human ovarian surface epithelial cells

Estrogens regulate normal ovarian surface epithelium (OSE) cell functions but also affect epithelial ovarian cancer (OCa) development. Little is known about how estrogens play such opposing roles. Transcriptional profiling using a cDNA microarray containing 2400 named genes identified 155 genes whose expression was altered by estradiol-17beta (E2) in three immortalized normal human ovarian surface epithelial (HOSE) cell lines and 315 genes whose expression was affected by the hormone in three established OCa (OVCA) cell lines. All but 19 of the genes in these two sets were different. Among the 19 overlapping genes, five were found to show discordant responses between HOSE and OVCA cell lines. The five genes are those that encode clone 5.1 RNA-binding protein (RNPS1), erythrocyte adducin alpha subunit (ADD1), plexin A3 (PLXNA3 or the SEX gene), nuclear protein SkiP (SKIIP), and Rap-2 (rap-2). RNPS1, ADD1, rap-2, and SKIIP were upregulated by E2 in HOSE cells but downregulated by estrogen in OVCA cells, whereas PLXNA3 showed the reverse pattern of regulation. The estrogen effects was observed within 6-18 h of treatment. In silicon analyses revealed presence of estrogen response elements in the proximal promoters of all five genes. RNPS1, ADD1, and PLXNA3 were underexpressed in OVCA cell lines compared to HOSE cell lines, while the opposite was true for rap-2 and SKIIP. Functional studies showed that RNPS1 and ADD1 exerted multiple antitumor actions in OVCA cells, while PLXNA3 only inhibited cell invasiveness. In contrast, rap-2 was found to cause significant oncogenic effects in OVCA cells, while SKIIP promotes only anchorage-independent growth. In sum, gene profiling data reveal that (1) E2 exerts different actions on HOSE cells than on OVCA cells by affecting two distinct transcriptomes with few overlapping genes and (2) among the overlapping genes, a set of putative oncogenes/tumor suppressors have been identified due to their differential responses to E2 between the two cell types. These findings may explain the paradoxical roles of estrogens in regulating normal and malignant OSE cell functions.

Atypical PKCiota contributes to poor prognosis through loss of apical-basal polarity and cyclin E overexpression in ovarian cancer

We show that atypical PKCiota, which plays a critical role in the establishment and maintenance of epithelial cell polarity, is genomically amplified and overexpressed in serous epithelial ovarian cancers. Furthermore, PKCiota protein is markedly increased or mislocalized in all serous ovarian cancers. An increased PKCiota DNA copy number is associated with decreased progression-free survival in serous epithelial ovarian cancers. In a Drosophila in vivo epithelial tissue model, overexpression of persistently active atypical PKC results in defects in apical-basal polarity, increased Cyclin E protein expression, and increased proliferation. Similar to the Drosophila model, increased PKCiota proteins levels are associated with increased Cyclin E protein expression and proliferation in ovarian cancers. In nonserous ovarian cancers, increased PKCiota protein levels, particularly in the presence of Cyclin E, are associated with markedly decreased overall survival. These results implicate PKCiota as a potential oncogene in ovarian cancer regulating epithelial cell polarity and proliferation and suggest that PKCiota is a novel target for therapy.

Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancer

Activation in transformed cells of normal stem cells' self-renewal pathways might contribute to the survival life cycle of cancer stem cells and promote tumor progression. The BMI-1 oncogene-driven gene expression pathway is essential for the self-renewal of hematopoietic and neural stem cells. We applied a mouse/human comparative translational genomics approach to identify an 11-gene signature that consistently displays a stem cell-resembling expression profile in distant metastatic lesions as revealed by the analysis of metastases and primary tumors from a transgenic mouse model of prostate cancer and cancer patients. To further validate these results, we examined the prognostic power of the 11-gene signature in several independent therapy-outcome sets of clinical samples obtained from 1,153 cancer patients diagnosed with 11 different types of cancer, including 5 epithelial malignancies (prostate, breast, lung, ovarian, and bladder cancers) and 5 nonepithelial malignancies (lymphoma, mesothelioma, medulloblastoma, glioma, and acute myeloid leukemia). Kaplan-Meier analysis demonstrated that a stem cell-like expression profile of the 11-gene signature in primary tumors is a consistent powerful predictor of a short interval to disease recurrence, distant metastasis, and death after therapy in cancer patients diagnosed with 11 distinct types of cancer. These data suggest the presence of a conserved BMI-1-driven pathway, which is similarly engaged in both normal stem cells and a highly malignant subset of human cancers diagnosed in a wide range of organs and uniformly exhibiting a marked propensity toward metastatic dissemination as well as a high probability of unfavorable therapy outcome.

In silico chromosomal clustering of genes displaying altered expression patterns in ovarian cancer

Ovarian cancer, the leading cause of death due to gynecological malignancy, is diagnosed in most cases at an advanced stage. Combined with the paucity of symptoms of early-stage disease, the need to develop novel effective markers for the detection of potentially curable, early-stage disease is self-evident. Comprehensive analyses of somatic gene expression patterns in ovarian cancer were reported previously (n=17) and yielded substantial information on somatically altered genes, information that can potentially be useful in developing early detection markers. To further substantiate the role that these genes play in ovarian cancer tumorogenesis, we surveyed these reports and arranged the significantly altered genes from all reported studies by their chromosomal location (in silico chromosomal clustering). Subsequent comparison of this clustering to known genomic somatic alterations at the DNA level from data obtained using comparative genomic hybridization (CGH) was carried out. The major chromosomal regions that displayed overexpressed genes were correlated with the major CGH-detectable DNA amplification areas at 20q (harboring HE4, SLPI, MYBL2, UBE2C, and SDC4) and 1q (harboring MUC1). These genes may provide insights into ovarian cancer pathogenesis and may also prove to be useful as early detection tools.

Discrimination between serous low malignant potential and invasive epithelial ovarian tumors using molecular profiling

Tumors of low malignant potential (LMP) represent 20% of epithelial ovarian cancers (EOCs) and are associated with a better prognosis than the invasive tumors (TOV). Defining the relationship between LMPs and TOVs remains an important goal towards understanding the molecular pathways that contribute to prognosis, as well as providing molecular markers, for these EOCs. To this end, DNA microarray analyses were performed either in a primary culture or a tumor tissue model system and selected candidate genes showing a distinctive expression profile between LMPs and TOVs were identified using a class prediction approach based on three statistical methods of analysis. Both model systems appear relevant as candidate genes identified by either model allowed the proper reclassification of samples as either LMPs or TOVs. Selected candidate genes (CAS, CCNE1, LGALS8, ITGbeta3, ATP1B1, FLIP, KRT7 and KRT19) were validated by real-time quantitative PCR analysis and show differential expression between LMPs and TOVs. Immunohistochemistry analyses showed that the two tumor classes were distinguishable by their expression of CAS, TNFR1A, FLIP, CKS1 and CCNE1. These results define signature patterns for gene expression of LMPs and TOVs and identify gene candidates that warrant further study to deepen our understanding of the biology of EOC.

Gene Expression Profiles Predict Early Relapse in Ovarian Cancer after Platinum-Paclitaxel Chemotherapy

PURPOSE

Women with advanced epithelial ovarian cancer are routinely treated with platinum-paclitaxel chemotherapy following cytoreductive surgery, yet only approximately 20% achieve long-term disease-free survival. We hypothesized that differences in gene expression before treatment could distinguish patients with short versus long time to recurrence after administration of platinum-paclitaxel combination chemotherapy.

EXPERIMENTAL DESIGN

To test this hypothesis, gene expression profiling of 79 primary surgically resected tumors from women with advanced-stage, high-grade epithelial ovarian cancer was done using cDNA microarrays containing 30,721 genes. Supervised learning algorithms were applied in an effort to develop a binary classifier that could discriminate women at risk for early (< or =21 months) versus late (>21 months) relapse after initial chemotherapy.

RESULTS

A 14-gene predictive model was developed using a set of training samples (n = 51) and subsequently tested using an independent set of test samples (n = 28). This model correctly predicted the outcome of 24 of the 28 test samples (86% accuracy) with 95% positive predictive value for early relapse.

CONCLUSIONS

Predictive markers for early recurrence can be identified for platinum-paclitaxel combination chemotherapy in primary ovarian carcinoma. The proposed 14-gene model requires further validation.

High insulin-like growth factor-2 (IGF-2) gene expression is an independent predictor of poor survival for patients with advanced stage serous epithelial ovarian cancer

OBJECTIVE

Epithelial ovarian cancer is the deadliest gynecologic malignancy, yet its molecular etiology remains poorly understood. Evidence is accumulating to support a role for the insulin-like growth factor family in human carcinogenesis, and recently using microarray expression analysis, we demonstrated over-expression of the insulin-like growth factor-2 (IGF-2) gene in advanced stage epithelial ovarian cancers. The purpose of the current study is to further elucidate the role of the IGF-2 gene in ovarian cancer development and progression.

METHODS

Relative expression of IGF-2 was measured in 109 epithelial ovarian cancers and eight normal ovarian surface epithelial (NOSE) samples, using quantitative real-time polymerase chain reaction. Associations with clinicopathological parameters were examined.

RESULTS

Expression of the IGF-2 gene was more than 300-fold higher in ovarian cancers compared with normal ovarian surface epithelium samples (P <0.001). High IGF-2 expression was associated with advanced stage disease at diagnosis (P <0.001), high-grade cancers (P <0.05) and sub-optimal surgical cytoreduction (P = 0.08). In multivariate analysis, relative IGF-2 expression was an independent predictor of poor survival.

CONCLUSIONS

Expression of the IGF-2 gene is significantly higher in ovarian cancers relative to normal ovarian surface epithelium. Further, high IGF-2 gene expression is associated with high grade, advanced stage disease, and is an independent predictor of poor survival in patients with epithelial ovarian cancer. As such, IGF-2 is a molecular marker and potential therapeutic target for the most aggressive epithelial ovarian cancers.

Insulin-like growth factor binding protein 2 promotes ovarian cancer cell invasion

BACKGROUND

Insulin-like growth factor binding protein 2 (IGFBP2) is overexpressed in ovarian malignant tissues and in the serum and cystic fluid of ovarian cancer patients, suggesting an important role of IGFBP2 in the biology of ovarian cancer. The purpose of this study was to assess the role of increased IGFBP2 in ovarian cancer cells.

RESULTS

Using western blotting and tissue microarray analyses, we showed that IGFBP2 was frequently overexpressed in ovarian carcinomas compared with normal ovarian tissues. Furthermore, IGFBP2 was significantly overexpressed in invasive serous ovarian carcinomas compared with borderline serous ovarian tumors. To test whether increased IGFBP2 contributes to the highly invasive nature of ovarian cancer cells, we generated IGFBP2-overexpressing cells from an SKOV3 ovarian cancer cell line, which has a very low level of endogenous IGFBP2. A Matrigel invasion assay showed that these IGFBP2-overexpressing cells were more invasive than the control cells. We then designed small interference RNA (siRNA) molecules that attenuated IGFBP2 expression in PA-1 ovarian cancer cells, which have a high level of endogenous IGFBP2. The Matrigel invasion assay showed that the attenuation of IGFBP2 expression indeed decreased the invasiveness of PA-1 cells.

CONCLUSIONS

We therefore showed that IGFBP2 enhances the invasion capacity of ovarian cancer cells. Blockage of IGFBP2 may thus constitute a viable strategy for targeted cancer therapy.

Identification of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-inducible and -suppressive genes in the rat placenta: induction of interferon-regulated genes with possible inhibitory roles for angiogenesis in the placenta

Exposure to a low dose of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) results in a variety of toxic manifestations, including fetal death. In order to evaluate the effects of low dose TCDD on placental function, pregnant Holtzman rats were given a single oral dose of 1600 ng TCDD/kg body wt or an equivalent volume of vehicle (control) on gestation day 15 (GD15), and changes in the gene expression in the placenta on GD20 were analyzed by two comprehensive methods, representational difference analysis (RDA) and DNA microarray technology. Candidates of TCDD-inducible and -suppressive genes were selected. Quantitative real-time PCR analysis was then performed to verify the induction or suppression levels of the candidate genes. Finally, we identified 81 TCDD-inducible and 21 TCDD-suppressive genes from the placenta of TCDD-treated Holtzman rats on GD20. One of the remarkable profiles of the gene expression was that glucose transporters were strongly up-regulated by the TCDD treatment. Furthermore, many interferon-inducible genes were also up-regulated by the treatment. They included several cytokines such as IP-10 known as a potent angiogenesis inhibitor. In addition, interferon molecules are known to suppress angiogenesis. The above observations suggest that activation of the interferon signaling pathway and the induction of anti-angiogenic factors by TCDD might have a role in causing the inhibition of neovascularization, resulting in the hypoxic state of placenta and increased incidence of fetal death.

Gene expression profiling of epithelial ovarian tumours correlated with malignant potential

Background

Epithelial ovarian tumours exhibit a range of malignant potential, presenting distinct clinical phenotypes. Improved knowledge of gene expression changes and functional pathways associated with these clinical phenotypes may lead to new treatment targets, markers for early detection and a better understanding of disease progression.

Results

Gene expression profiling (Affymetrix, U95Av2) was carried out on 18 ovarian tumours including benign adenomas, borderline adenocarcinomas of low malignant potential and malignant adenocarcinomas. Clustering the expression profiles of samples from patients not treated with chemotherapy prior to surgery effectively classified 92% of samples into their proper histopathological group. Some cancer samples from patients treated with chemotherapy prior to surgery clustered with the benign adenomas. Chemotherapy patients whose tumours exhibited benign-like expression patterns remained disease free for the duration of this study as indicated by continued normal serum CA-125 levels. Statistical analysis identified 163 differentially expressed genes: 61 genes under-expressed in cancer and 102 genes over-expressed in cancer. Profiling the functional categories of co-ordinately expressed genes within this list revealed significant correlation between increased malignant potential and loss of both IGF binding proteins and cell adhesion molecules. Interestingly, in several instances co-ordinately expressed genes sharing biological function also shared chromosomal location.

Conclusion

Our findings indicate that gene expression profiling can reliably distinguish between benign and malignant ovarian tumours. Expression profiles of samples from patients pre-treated with chemotherapy may be useful in predicting disease free survival and the likelihood of recurrence. Loss of expression of IGF binding proteins as well as specific cell adhesion molecules may be a significant mechanism of disease progression in ovarian cancer. Expression levels in borderline tumours were intermediate between benign adenomas and malignant adenocarcinomas for a significant portion of the differentially expressed genes, suggesting that borderline tumours are a transitional state between benign and malignant tumours. Finally, genes displaying coordinated changes in gene expression were often genetically linked, suggesting that changes in expression for these genes are the consequence of regional duplications, deletions or epigenetic events.