Gene-expression profiling in epithelial ovarian cancer

Targeting tumour markers in ovarian cancer treatment

Ovarian cancers (OC) are the most common, lethal, and stage-dependent cancers at the global level, specifically in female patients. Targeted therapies involve the administration of drugs that specifically target the alterations in tumour cells responsible for their growth, proliferation, and metastasis, with the aim of treating particular patients. Presently, within the realm of gynaecological malignancies, specifically in breast and OCs, there exist various prospective therapeutic targets encompassing tumour-intrinsic signalling pathways, angiogenesis, homologous-recombination deficit, hormone receptors, and immunologic components. Breast cancers are often detected in advanced stages, primarily due to the lack of a reliable screening method. However, various tumour markers have been extensively researched and employed to evaluate the condition, progression, and effectiveness of medication treatments for this ailment. The emergence of recent technological advancements in the domains of bioinformatics, genomics, proteomics, and metabolomics has facilitated the exploration and identification of hitherto unknown biomarkers. The primary objective of this comprehensive review is to meticulously investigate and analyze both established and emerging methodologies employed in the identification of tumour markers associated with OC.

The pivotal role of long non-coding RNAs as potential biomarkers and modulators of chemoresistance in ovarian cancer (OC)

Ovarian cancer (OC) is a fatal gynecological disease that is often diagnosed at later stages due to its asymptomatic nature and the absence of efficient early-stage biomarkers. Previous studies have identified genes with abnormal expression in OC that couldn't be explained by methylation or mutation, indicating alternative mechanisms of gene regulation. Recent advances in human transcriptome studies have led to research on non-coding RNAs (ncRNAs) as regulators of cancer gene expression. Long non-coding RNAs (lncRNAs), a class of ncRNAs with a length greater than 200 nucleotides, have been identified as crucial regulators of physiological processes and human diseases, including cancer. Dysregulated lncRNA expression has also been found to play a crucial role in ovarian carcinogenesis, indicating their potential as novel and non-invasive biomarkers for improving OC management. However, despite the discovery of several thousand lncRNAs, only one has been approved for clinical use as a biomarker in cancer, highlighting the importance of further research in this field. In addition to their potential as biomarkers, lncRNAs have been implicated in modulating chemoresistance, a major problem in OC. Several studies have identified altered lncRNA expression upon drug treatment, further emphasizing their potential to modulate chemoresistance. In this review, we highlight the characteristics of lncRNAs, their function, and their potential to serve as tumor markers in OC. We also discuss a few databases providing detailed information on lncRNAs in various cancer types. Despite the promising potential of lncRNAs, further research is necessary to fully understand their role in cancer and develop effective strategies to combat this devastating disease.

Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types

Detecting cancers at early stages can dramatically reduce mortality rates. Therefore, practical cancer screening at the population level is needed. To develop a comprehensive detection system to classify multiple cancer types, we integrated an artificial intelligence deep learning neural network and noncoding RNA biomarkers selected from massive data. Our system can accurately detect cancer vs. healthy objects with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve), and it surprisingly reaches 78.77% of AUC when validated by real-world raw data from a completely independent data set. Even validating with raw exosome data from blood, our system can reach 72% of AUC. Moreover, our system significantly outperforms conventional machine learning models, such as random forest. Intriguingly, with no more than six biomarkers, our approach can easily discriminate any individual cancer type vs. normal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can simultaneously multi-classify common cancers with a stable 82.15% accuracy rate for heterogeneous cancerous tissues and conditions. This detection system provides a promising practical framework for automatic cancer screening at population level. Key points: (1) We developed a practical cancer screening system, which is simple, accurate, affordable, and easy to operate. (2) Our system binarily classify cancers vs. normal with >96% AUC. (3) In total, 26 individual cancer types can be easily detected by our system with 99 to 100% AUC. (4) The system can detect multiple cancer types simultaneously with >82% accuracy.

Gene expression profile association with poor prognosis in epithelial ovarian cancer patients

Ovarian cancer (OC) is the eighth most common type of cancer for women worldwide. The current diagnostic and prognostic routine available for OC management either lack specificity or are very costly. Gene expression profiling has shown to be a very effective tool in exploring new molecular markers for patients with OC, although association of such markers with patient survival and clinical outcome is still elusive. Here, we performed gene expression profiling of different subtypes of OC to evaluate its association with patient overall survival (OS) and aggressive forms of the disease. By global mRNA microarray profiling in a total of 196 epithelial OC patients (161 serous, 15 endometrioid, 11 mucinous, and 9 clear cell carcinomas), we found four candidates-HSPA1A, CD99, RAB3A and POM121L9P, which associated with OS and poor clinicopathological features. The overexpression of all combined was correlated with shorter OS and progression-free survival (PFS). Furthermore, the combination of at least two markers were further associated with advanced grade, chemotherapy resistance, and progressive disease. These results indicate that a panel comprised of a few predictors that associates with a more aggressive form of OC may be clinically relevant, presenting a better performance than one marker alone.

Expression Patterns of Immune Genes Reveal Heterogeneous Subtypes of High-Risk Neuroblastoma

High risk neuroblastoma (HR-NB) remains difficult to treat, and its overall survival (OS) is still below 50%. Although HR-NB is a heterogeneous disease, HR-NB patients are currently treated in a similar fashion. Through unsupervised biclustering, we further stratified HR-NB patients into two reproducible and clinically distinct subtypes, including an ultra-high risk neuroblastoma (UHR-NB) and high risk neuroblastoma (HR-NB). The UHR-NB subtype consistently had the worst OS in multiple independent cohorts ( P < 0 . 008 ). Out of 283 neuroblastoma-specific immune genes that were used for stratification, 39 of them were differentiated in UHR-NB, including four upregulated and 35 downregulated, as compared to HR-NB. The four UHR-NB upregulated genes (ADAM22, GAL, KLHL13 and TWIST1) were all upregulated in MYCN amplified neuroblastoma in 5 additional cohorts. TWIST1 and ADAM22 were also positively correlated with cancer stage, while GAL was an independent OS predictor in addition to MYCN and age. Furthermore, we identified 26 commonly upregulated and 311 downregulated genes in UHR-NB from all 4723 immune-related genes. While 43 KEGG pathways with molecular functions were enriched in the downregulated immune-related genes, only the P53 signaling pathway was enriched in the upregulated ones, which suggested that UHR-NB was a TP53 related subtype with reduced immune activities.

Development and validation of a prognostic nomogram for overall survival in patients with platinum-resistant ovarian cancer treated with chemotherapy

BACKGROUND

Platinum-resistant ovarian cancer (PROC) is associated with a variable prognosis and unpredictable survival times. We have developed and validated a prognostic nomogram with the objective of improving the prediction of overall survival (OS) in patients treated with chemotherapy.

METHODS

The nomogram was developed using data from a training cohort of patients from two trials, including the chemotherapy-only arm in AURELIA and all randomised patients in CARTAXHY. Multivariable proportional hazards models were generated based on pretreatment characteristics to develop a nomogram that classifies patients based on OS. We subsequently assessed the performance of the nomogram in terms of discrimination and calibration in independent validation patient cohorts: PENELOPE and the bevacizumab-chemotherapy arm of AURELIA.

RESULTS

The nomogram included six significant OS predictors, in order of importance: performance status, ascites, size of the largest tumour, CA-125, platinum-free interval and primary platinum resistance (C-statistic 0.69). In the training cohort, the median OS in the good, intermediate and poor prognosis groups was 25.3, 15.2 and 7.4 months, respectively. In the PENELOPE validation cohort (C-statistic 0.59), the median OS in the good, intermediate and poor prognosis groups was 18.5, 10.3 and 5.8 months, respectively. In the AURELIA bevacizumab-chemotherapy validation cohort (C-statistic 0.67), the median OS in good, intermediate and poor prognosis groups was 26.7, 13.8 and 10.0 months, respectively.

CONCLUSIONS

This nomogram with six pretreatment characteristics allows prediction of OS in PROC and could be used for stratification of patients in clinical trials as well as for counselling patients about prognosis.

Engineered 3D Model of Cancer Stem Cell Enrichment and Chemoresistance

Intraperitoneal dissemination of ovarian cancers is preceded by the development of chemoresistant tumors with malignant ascites. Despite the high levels of chemoresistance and relapse observed in ovarian cancers, there are no in vitro models to understand the development of chemoresistance in situ. Method: We describe a highly integrated approach to establish an in vitro model of chemoresistance and stemness in ovarian cancer, using the 3D hanging drop spheroid platform. The model was established by serially passaging non-adherent spheroids. At each passage, the effectiveness of the model was evaluated via measures of proliferation, response to treatment with cisplatin and a novel ALDH1A inhibitor. Concomitantly, the expression and tumor initiating capacity of cancer stem-like cells (CSCs) was analyzed. RNA-seq was used to establish gene signatures associated with the evolution of tumorigenicity, and chemoresistance. Lastly, a mathematical model was developed to predict the emergence of CSCs during serial passaging of ovarian cancer spheroids. Results: Our serial passage model demonstrated increased cellular proliferation, enriched CSCs, and emergence of a platinum resistant phenotype. In vivo tumor xenograft assays indicated that later passage spheroids were significantly more tumorigenic with higher CSCs, compared to early passage spheroids. RNA-seq revealed several gene signatures supporting the emergence of CSCs, chemoresistance, and malignant phenotypes, with links to poor clinical prognosis. Our mathematical model predicted the emergence of CSC populations within serially passaged spheroids, concurring with experimentally observed data. Conclusion: Our integrated approach illustrates the utility of the serial passage spheroid model for examining the emergence and development of chemoresistance in ovarian cancer in a controllable and reproducible format.

The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine

Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However, biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but also by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis, and the path for such tools to the clinic.

Origins based clinical and molecular complexities of epithelial ovarian cancer

Ovarian cancer is the most lethal of all common gynaecological malignancies in women worldwide. Ovarian cancer comprises of >15 distinct tumor types and subtypes characterized by histopathological features, environmental and genetic risk factors, precursor lesions and molecular events during oncogenesis. Recent studies on gene signature profiling of different subtypes of ovarian cancer have revealed significant genetic heterogeneity between and within each ovarian cancer histological subtype. Thus, an immense interest have shown towards a more personalized medicine for understanding the clinical and molecular complexities of four major types of epithelial ovarian cancer (serous, endometrioid, clear cell, and mucinous). As such, further in depth studies are needed for identification of molecular signalling network complexities associated with effective prognostication and targeted therapies to prevent or treat metastasis. Therefore, understanding the metastatic potential of primary ovarian cancer and therapeutic interventions against lethal ovarian cancer for the development of personalized therapies is very much indispensable. Consequently, in this review we have updated the key dysregulated genes of four major subtypes of epithelial carcinomas. We have also highlighted the recent advances and current challenges in unravelling the complexities of the origin of tumor as well as genetic heterogeneity of ovarian cancer.

Systematic module approach identifies altered genes and pathways in four types of ovarian cancer

The present study aimed to identify altered genes and pathways associated with four histotypes of ovarian cancer, according to the systematic tracking of dysregulated modules of reweighted protein-protein interaction (PPI) networks. Firstly, the PPI network and gene expression data were initially integrated to infer and reweight normal ovarian and four types of ovarian cancer (endometrioid, serous, mucinous and clear cell carcinoma) PPI networks based on Spearman's correlation coefficient. Secondly, modules in the PPI network were mined using a clique-merging algorithm and the differential modules were identified through maximum weight bipartite matching. Finally, the gene compositions in the altered modules were analyzed, and pathway functional enrichment analyses for disrupted module genes were performed. In five conditional-specific networks, universal alterations in gene correlations were revealed, which leads to the differential correlation density among disrupted module pairs. The analyses revealed 28, 133, 139 and 33 altered modules in endometrioid, serous, mucinous and clear cell carcinoma, respectively. Gene composition analyses of the disrupted modules revealed five common genes (mitogen-activated protein kinase 1, phosphoinositide 3-kinase-encoding catalytic 110-KDα, AKT serine/threonine kinase 1, cyclin D1 and tumor protein P53) across the four subtypes of ovarian cancer. In addition, pathway enrichment analysis confirmed one common pathway (pathways in cancer), in the four histotypes. This systematic module approach successfully identified altered genes and pathways in the four types of ovarian cancer. The extensive differences of gene correlations result in dysfunctional modules, and the coordinated disruption of these modules contributes to the development and progression of ovarian cancer.

VEGF gene polymorphisms and outcome of epithelial ovarian cancer patients

AIM

Since VEGF polymorphisms were associated with variable protein production, we analyzed herein their roles in outcome of epithelial ovarian cancer (EOC) patients.

METHODS

Genotypes of 85 patients with primary EOC were identified in DNA by real-time PCR. Progression-free survival and overall survival were analyzed using Kaplan-Meier method, univariate Cox model and bootstrap resampling study.

RESULTS

At 60 months of follow-up, progression-free survival was shorter in patients with VEGF c.-2578 CC genotype compared with others (52.7 vs 82.2%; p = 0.04). Those patients had 2.15 more chance of presenting disease progression than others (p = 0.04); bootstrap study validated the result (p = 0.03).

CONCLUSION

Our data suggest that VEGF c.-2578C>A polymorphism acts as a prognostic factor in EOC.

Quantitative Profiling of Single Formalin Fixed Tumour Sections: proteomics for translational research

Although re-sequencing of gene panels and mRNA expression profiling are now firmly established in clinical laboratories, in-depth proteome analysis has remained a niche technology, better suited for studying model systems rather than challenging materials such as clinical trial samples. To address this limitation, we have developed a novel and optimized platform called SP3-Clinical Tissue Proteomics (SP3-CTP) for in-depth proteome profiling of practical quantities of tumour tissues, including formalin fixed and paraffin embedded (FFPE). Using single 10 μm scrolls of clinical tumour blocks, we performed in-depth quantitative analyses of individual sections from ovarian tumours covering the high-grade serous, clear cell, and endometrioid histotypes. This examination enabled the generation of a novel high-resolution proteome map of ovarian cancer histotypes from clinical tissues. Comparison of the obtained proteome data with large-scale genome and transcriptome analyses validated the observed proteome biology for previously validated hallmarks of this disease, and also identified novel protein features. A tissue microarray analysis validated cystathionine gamma-lyase (CTH) as a novel clear cell carcinoma feature with potential clinical relevance. In addition to providing a milestone in the understanding of ovarian cancer biology, these results show that in-depth proteomic analysis of clinically annotated FFPE materials can be effectively used as a biomarker discovery tool and perhaps ultimately as a diagnostic approach.

Identifying candidate drivers of drug response in heterogeneous cancer by mining high throughput genomics data

BACKGROUND

With advances in technologies, huge amounts of multiple types of high-throughput genomics data are available. These data have tremendous potential to identify new and clinically valuable biomarkers to guide the diagnosis, assessment of prognosis, and treatment of complex diseases, such as cancer. Integrating, analyzing, and interpreting big and noisy genomics data to obtain biologically meaningful results, however, remains highly challenging. Mining genomics datasets by utilizing advanced computational methods can help to address these issues.

RESULTS

To facilitate the identification of a short list of biologically meaningful genes as candidate drivers of anti-cancer drug resistance from an enormous amount of heterogeneous data, we employed statistical machine-learning techniques and integrated genomics datasets. We developed a computational method that integrates gene expression, somatic mutation, and copy number aberration data of sensitive and resistant tumors. In this method, an integrative method based on module network analysis is applied to identify potential driver genes. This is followed by cross-validation and a comparison of the results of sensitive and resistance groups to obtain the final list of candidate biomarkers. We applied this method to the ovarian cancer data from the cancer genome atlas. The final result contains biologically relevant genes, such as COL11A1, which has been reported as a cis-platinum resistant biomarker for epithelial ovarian carcinoma in several recent studies.

CONCLUSIONS

The described method yields a short list of aberrant genes that also control the expression of their co-regulated genes. The results suggest that the unbiased data driven computational method can identify biologically relevant candidate biomarkers. It can be utilized in a wide range of applications that compare two conditions with highly heterogeneous datasets.

KCNN4 and S100A14 act as predictors of recurrence in optimally debulked patients with serous ovarian cancer

Approximately 50-75% of patients with serous ovarian carcinoma (SOC) experience recurrence within 18 months after first-line treatment. Current clinical indicators are inadequate for predicting the risk of recurrence. In this study, we used 7 publicly available microarray datasets to identify gene signatures related to recurrence in optimally debulked SOC patients, and validated their expressions in an independent clinic cohort of 127 patients using immunohistochemistry (IHC). We identified a two-gene signature including KCNN4 and S100A14 which was related to recurrence in optimally debulked SOC patients. Their mRNA expression levels were positively correlated and regulated by DNA copy number alterations (CNA) (KCNN4: p=1.918e-05) and DNA promotermethylation (KCNN4: p=0.0179; S100A14: p=2.787e-13). Recurrence prediction models built in the TCGA dataset based on KCNN4 and S100A14 individually and in combination showed good prediction performance in the other 6 datasets (AUC:0.5442-0.9524). The independent cohort supported the expression difference between SOC recurrences. Also, a KCNN4 and S100A14-centered protein interaction subnetwork was built from the STRING database, and the shortest regulation path between them, called the KCNN4-UBA52-KLF4-S100A14 axis, was identified. This discovery might facilitate individualized treatment of SOC.

Immune signaling-based Cascade Propagation approach re-stratifies HNSCC patients

The availability of high-throughput genomic assays and rich electronic medical records allows us to identify cancer subtypes with greater accuracy and resolution. The integration of multiplatform, heterogenous, and high dimensional data remains an enormous challenge in using big data in bioinformatics research. Previous methods have been developed for patient stratification, however, these approaches did not incorporate prior knowledge and offer limited biology insight. New computational methods are needed to better utilize multiple types of information to identify clinically meaningful subtypes. Recent studies have shown that many immune functional genes are associated with cancer progression, recurrence and prognosis in head and neck squamous cell carcinoma (HNSCC). Therefore, we developed a novel immune signaling based Cascade Propagation (CasP) subtyping approach to stratify HNSCC patients. Unlike previous stratification methods that use only patient genomic data, our approach makes use of prior biological information such as immune signaling and protein-protein interactions, as well as patient survival information. CasP is a multi-step stratification procedure, composed of a dynamic network tree cutting step followed by a mutational stratification step. Using this approach, HNSCC patients were first stratified into clinically relative subgroups with different survival outcomes and distinct immunogenic features. We found that the good outcome of a subgroup of HNSCC patients was due to an enhanced immune response. The gene sets were characterized by a significant activation of T cell receptor signaling pathways, in addition to other important cancer related pathways such as PI3K and JAK/STAT signaling pathways. Further stratification of patients based on somatic mutation profiles detected three survival-distinct subnetworks. Our newly developed CasP subtyping approach allowed us to integrate multiple data types and identify clinically relevant subtypes of HNSCC patients.

An integrated model of clinical information and gene expression for prediction of survival in ovarian cancer patients

Accumulating evidence shows that clinical factors alone are not adequate for predicting the survival of patients with ovarian cancer (OvCa), and many genes have been found to be associated with OvCa prognosis. The objective of this study was to develop a model that integrates clinical information and a gene signature to predict the survival durations of patients diagnosed with OvCa. We constructed mRNA and microRNA expression profiles and gathered the corresponding clinical data of 552 OvCa patients and 8 normal controls from The Cancer Genome Atlas. Using univariate Cox regression followed by a permutation test, elastic net-regulated Cox regression, and ridge regression, we generated a prognosis index consisting of 2 clinical variables, 7 protective mRNAs, 12 risky mRNAs, and 1 protective microRNA. The area under the curve of the receiver operating characteristic of the integrated clinical-and-gene model was 0.756, larger than that of the clinical-alone model (0.686) or the gene-alone model (0.703). OvCa patients in the high-risk group had a significantly shorter overall survival time compared with patients in the low-risk group (hazard ratio = 8.374, 95% confidence interval = 4.444-15.780, P = 4.90 × 10(-11), by the Wald test). The reliability of the gene signature was confirmed by a public external data set from the Gene Expression Omnibus. Our conclusions that we have identified an integrated clinical-and-gene model superior to the traditional clinical-alone model in ascertaining the survival prognosis of patients with OvCa. Our findings may prove valuable for improving the clinical management of OvCa.

EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes

MOTIVATION

A major goal of biomedical research is to identify molecular features associated with a biological or clinical class of interest. Differential expression analysis has long been used for this purpose; however, conventional methods perform poorly when applied to data with high within class heterogeneity.

RESULTS

To address this challenge, we developed EMDomics, a new method that uses the Earth mover's distance to measure the overall difference between the distributions of a gene's expression in two classes of samples and uses permutations to obtain q-values for each gene. We applied EMDomics to the challenging problem of identifying genes associated with drug resistance in ovarian cancer. We also used simulated data to evaluate the performance of EMDomics, in terms of sensitivity and specificity for identifying differentially expressed gene in classes with high within class heterogeneity. In both the simulated and real biological data, EMDomics outperformed competing approaches for the identification of differentially expressed genes, and EMDomics was significantly more powerful than conventional methods for the identification of drug resistance-associated gene sets. EMDomics represents a new approach for the identification of genes differentially expressed between heterogeneous classes and has utility in a wide range of complex biomedical conditions in which sample classes show within class heterogeneity.

AVAILABILITY AND IMPLEMENTATION

The R package is available at http://www.bioconductor.org/packages/release/bioc/html/EMDomics.html.

Proteomics of ovarian cancer: functional insights and clinical applications

In the past decade, there has been an increasing interest in applying proteomics to assist in understanding the pathogenesis of ovarian cancer, elucidating the mechanism of drug resistance, and in the development of biomarkers for early detection of ovarian cancer. Although ovarian cancer is a spectrum of different diseases, the strategies for diagnosis and treatment with surgery and adjuvant therapy are similar across ovarian cancer types, increasing the general applicability of discoveries made through proteomics research. While proteomic experiments face many difficulties which slow the pace of clinical applications, recent advances in proteomic technology contribute significantly to the identification of aberrant proteins and networks which can serve as targets for biomarker development and individualized therapies. This review provides a summary of the literature on proteomics' contributions to ovarian cancer research and highlights the current issues, future directions, and challenges. We propose that protein-level characterization of primary lesion in ovarian cancer can decipher the mystery of this disease, improve diagnostic tools, and lead to more effective screening programs.

Network-based stratification analysis of 13 major cancer types using mutations in panels of cancer genes

Background

Cancers are complex diseases with heterogeneous genetic causes and clinical outcomes. It is critical to classify patients into subtypes and associate the subtypes with clinical outcomes for better prognosis and treatment. Large-scale studies have comprehensively identified somatic mutations across multiple tumor types, providing rich datasets for classifying patients based on genomic mutations. One challenge associated with this task is that mutations are rarely shared across patients. Network-based stratification (NBS) approaches have been proposed to overcome this challenge and used to classify tumors based on exome-level mutations. In routine research and clinical applications, however, usually only a small panel of pre-selected genes is screened for mutations. It is unknown whether such small panels are effective in classifying patients into clinically meaningful subtypes.

Results

In this study, we applied NBS to 13 major cancer types with exome-level mutation data and compared the classification based on the full exome data with those focusing only on small sets of genes. Specifically, we investigated three panels, FoundationOne (240 genes), PanCan (127 genes) and TruSeq (48 genes). We showed that small panels not only are effective in clustering tumors but also often outperform full exome data for most cancer types. We further associated subtypes with clinical data and identified 5 tumor types (CRC-Colorectal carcinoma, HNSC-Head and neck squamous cell carcinoma, KIRC-Kidney renal clear cell carcinoma, LUAD-Lung adenocarcinoma and UCEC-Uterine corpus endometrial carcinoma) whose subtypes are significantly associated with overall survival, all based on small panels.

Conclusion

Our analyses indicate that effective patient subtyping can be carried out using mutations detected in smaller gene panels, probably due to the enrichment of clinically important genes in such panels.

Methods: for studying pharmacogenetic profiles of combination chemotherapeutic drugs

INTRODUCTION

Molecular and genetic analysis of tumors and individuals has led to patient-centered therapies, through the discovery and identification of genetic markers predictive of drug efficacy and tolerability. Present therapies often include a combination of synergic drugs, each of them directed against different targets. Therefore, the pharmacogenetic profiling of tumor masses and patients is becoming a challenge, and several questions may arise when planning a translational study.

AREAS COVERED

The review presents the different techniques used to stratify oncology patients and to tailor antineoplastic treatments according to individual pharmacogenetic profiling. The advantages of these methodologies are discussed as well as current limits.

EXPERT OPINION

Facing the rapid technological evolution for genetic analyses, the most pressing issues are the choice of appropriate strategies (i.e., from gene candidate up to next-generation sequencing) and the possibility to replicate study results for their final validation. It is likely that the latter will be the major obstacle in the future. However, the present landscape is opening up new possibilities, overcoming those hurdles that have limited result translation into clinical settings for years.

A quantitative proteomics-based signature of platinum sensitivity in ovarian cancer cell lines

Although DNA encodes the molecular instructions that underlie the control of cell function, it is the proteins that are primarily responsible for implementing those instructions. Therefore quantitative analyses of the proteome would be expected to yield insights into important candidates for the detection and treatment of disease. We present an iTRAQ (isobaric tag for relative and absolute quantification)-based proteomic analysis of ten ovarian cancer cell lines and two normal ovarian surface epithelial cell lines. We profiled the abundance of 2659 cellular proteins of which 1273 were common to all 12 cell lines. Of the 1273, 75 proteins exhibited elevated expression and 164 proteins had diminished expression in the cancerous cells compared with the normal cell lines. The iTRAQ expression profiles allowed us to segregate cell lines based upon sensitivity and resistance to carboplatin. Importantly, we observed no substantial correlation between protein abundance and RNA expression or epigenetic DNA methylation data. Furthermore, we could not discriminate between sensitivity and resistance to carboplatin on the basis of RNA expression and DNA methylation data alone. The present study illustrates the importance of proteomics-based discovery for defining the basis for the carboplatin response in ovarian cancer and highlights candidate proteins, particularly involved in cellular redox regulation, homologous recombination and DNA damage repair, which otherwise could not have been predicted from whole genome and expression data sources alone.

Tumor stratification by a novel graph-regularized bi-clique finding algorithm

Due to involved disease mechanisms, many complex diseases such as cancer, demonstrate significant heterogeneity with varying behaviors, including different survival time, treatment responses, and recurrence rates. The aim of tumor stratification is to identify disease subtypes, which is an important first step towards precision medicine. Recent advances in profiling a large number of molecular variables such as in The Cancer Genome Atlas (TCGA), have enabled researchers to implement computational methods, including traditional clustering and bi-clustering algorithms, to systematically analyze high-throughput molecular measurements to identify tumor subtypes as well as their corresponding associated biomarkers. In this study we discuss critical issues and challenges in existing computational approaches for tumor stratification. We show that the problem can be formulated as finding densely connected sub-graphs (bi-cliques) in a bipartite graph representation of genomic data. We propose a novel algorithm that takes advantage of prior biology knowledge through a gene-gene interaction network to find such sub-graphs, which helps simultaneously identify both tumor subtypes and their corresponding genetic markers. Our experimental results show that our proposed method outperforms current state-of-the-art methods for tumor stratification.

Integrative network analysis of TCGA data for ovarian cancer

Background

Over the past years, tremendous efforts have been made to elucidate the molecular basis of the initiation and progression of ovarian cancer. However, most existing studies have been focused on individual genes or a single type of data, which may lack the power to detect the complex mechanisms of cancer formation by overlooking the interactions of different genetic and epigenetic factors.

Results

We propose an integrative framework to identify genetic and epigenetic features related to ovarian cancer and to quantify the causal relationships among these features using a probabilistic graphical model based on the Cancer Genome Atlas (TCGA) data. In the feature selection, we first defined a set of seed genes by including 48 candidate tumor suppressors or oncogenes and an additional 20 ovarian cancer related genes reported in the literature. The seed genes were then fed into a stepwise correlation-based selector to identify 271 additional features including 177 genes, 82 copy number variation sites, 11 methylation sites and 1 somatic mutation (at gene TP53). We built a Bayesian network model with a logit link function to quantify the causal relationships among these features and discovered a set of 13 hub genes including ARID1A, C19orf53, CSKN2A1 and COL5A2. The directed graph revealed many potential genetic pathways, some of which confirmed the existing results in the literature. Clustering analysis further suggested four gene clusters, three of which correspond to well-defined cellular processes including cell division, tumor invasion and mitochondrial system. In addition, two genes related to glycoprotein synthesis, PSG11 and GALNT10, were found highly predictive for the overall survival time of ovarian cancer patients.

Conclusions

The proposed framework is effective in identifying possible important genetic and epigenetic features that are related to complex cancer diseases. The constructed Bayesian network has identified some new genetic/epigenetic pathways, which may shed new light into the molecular mechanisms of ovarian cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-014-0136-9) contains supplementary material, which is available to authorized users.

Gene expression profile for predicting survival of patients with meningioma

Current staging methods are inadequate for predicting the overall survival of meningioma. DNA microarray technologies improve the understanding of tumour progression. We analysed genome wide expression profiles of 119 meningioma samples from two previous published DNA microarray studies. The Cox proportional hazards regression models were applied to identify overall survival related gene signature. A total of 449 genes (109 upregulated and 340 downregulated) were identified as differentially expressed in meningioma. Among these differentially expressed genes, 37 genes were identified to be related to meningioma overall survival. Our 37-gene signature is closely associated with overall survival among patients with meningioma. This gene expression profile could provide an optimization of the clinical management and development of new therapeutic strategies for meningioma.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Ovarian cancer biomarker discovery based on genomic approaches

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

The 'dnet' approach promotes emerging research on cancer patient survival

We present the 'dnet' package and apply it to the 'TCGA' mutation and clinical data of >3,000 patients. We uncover the existence of an underlying gene network that at least partially controls cancer 'survivalness', with mutations that are significantly correlated with patient survival, yet independent of tumour origin and type. The survivalness network has natural community structure corresponding to tumour hallmarks, and contains genes that are potentially druggable in the clinic. This network has evolutionary roots in Deuterostomia identifying PTK2 and VAV1 as under-valued relative to more studied genes from that era. The 'dnet' R package is available at http://cran.r-project.org/package=dnet.

Discrete mixture modeling to address genetic heterogeneity in time-to-event regression

MOTIVATION

Time-to-event regression models are a critical tool for associating survival time outcomes with molecular data. Despite mounting evidence that genetic subgroups of the same clinical disease exist, little attention has been given to exploring how this heterogeneity affects time-to-event model building and how to accommodate it. Methods able to diagnose and model heterogeneity should be valuable additions to the biomarker discovery toolset.

RESULTS

We propose a mixture of survival functions that classifies subjects with similar relationships to a time-to-event response. This model incorporates multivariate regression and model selection and can be fit with an expectation maximization algorithm, we call Cox-assisted clustering. We illustrate a likely manifestation of genetic heterogeneity and demonstrate how it may affect survival models with little warning. An application to gene expression in ovarian cancer DNA repair pathways illustrates how the model may be used to learn new genetic subsets for risk stratification. We explore the implications of this model for censored observations and the effect on genomic predictors and diagnostic analysis.

AVAILABILITY AND IMPLEMENTATION

R implementation of CAC using standard packages is available at https://gist.github.com/programeng/8620b85146b14b6edf8f Data used in the analysis are publicly available.

Choline kinase-alpha by regulating cell aggressiveness and drug sensitivity is a potential druggable target for ovarian cancer

Background:

Aberrant choline metabolism has been proposed as a novel cancer hallmark. We recently showed that epithelial ovarian cancer (EOC) possesses an altered MRS-choline profile, characterised by increased phosphocholine (PCho) content to which mainly contribute over-expression and activation of choline kinase-alpha (ChoK-alpha).

Methods:

To assess its biological relevance, ChoK-alpha expression was downmodulated by transient RNA interference in EOC in vitro models. Gene expression profiling by microarray analysis and functional analysis was performed to identify the pathway/functions perturbed in ChoK-alpha-silenced cells, then validated by in vitro experiments.

Results:

In silenced cells, compared with control, we observed: (I) a significant reduction of both CHKA transcript and ChoK-alpha protein expression; (II) a dramatic, proportional drop in PCho content ranging from 60 to 71%, as revealed by ¹H-magnetic spectroscopy analysis; (III) a 35–36% of cell growth inhibition, with no evidences of apoptosis or modification of the main cellular survival signalling pathways; (IV) 476 differentially expressed genes, including genes related to lipid metabolism. Ingenuity pathway analysis identified cellular functions related to cell death and cellular proliferation and movement as the most perturbed. Accordingly, CHKA-silenced cells displayed a significant delay in wound repair, a reduced migration and invasion capability were also observed. Furthermore, although CHKA silencing did not directly induce cell death, a significant increase of sensitivity to platinum, paclitaxel and doxorubicin was observed even in a drug-resistant context.

Conclusion:

We showed for the first time in EOC that CHKA downregulation significantly decreased the aggressive EOC cell behaviour also affecting cells' sensitivity to drug treatment. These observations open the way to further analysis for ChoK-alpha validation as a new EOC therapeutic target to be used alone or in combination with conventional drugs.

Network-based stratification of tumor mutations

Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence.

Ovarian cancer evolution through stochastic genome alterations: defining the genomic role in ovarian cancer

Ovarian cancer is the fifth leading cause of death among women worldwide. Characterized by complex etiology and multi-level heterogeneity, its origins are not well understood. Intense research efforts over the last decade have furthered our knowledge by identifying multiple risk factors that are associated with the disease. However, it is still unclear how genetic heterogeneity contributes to tumor formation, and more specifically, how genome-level heterogeneity acts as the key driving force of cancer evolution. Most current genomic approaches are based on 'average molecular profiling.' While effective for data generation, they often fail to effectively address the issue of high level heterogeneity because they mask variation that exists in a cell population. In this synthesis, we hypothesize that genome-mediated cancer evolution can effectively explain diverse factors that contribute to ovarian cancer. In particular, the key contribution of genome replacement can be observed during major transitions of ovarian cancer evolution including cellular immortalization, transformation, and malignancy. First, we briefly review major updates in the literature, and illustrate how current gene-mediated research will offer limited insight into cellular heterogeneity and ovarian cancer evolution. We next explain a holistic framework for genome-based ovarian cancer evolution and apply it to understand the genomic dynamics of a syngeneic ovarian cancer mouse model. Finally, we employ single cell assays to further test our hypothesis, discuss some predictions, and report some recent findings.

Origin of ovarian cancer: molecular profiling

This is a review on the transition from our empirical approach to treat ovarian cancer to a specific treatment based on molecular signature. We have reviewed not only the evidence-based medicine focused on the origin and tumor morphology of ovarian cancer but also the molecular signature era based on molecular phenotyping of the tumor and its microenvironment, which influences the direct targeted therapy. Evidence-based medicine has shown that the targeted therapy studies are mainly biomarker driven, more focused, and hence treat only those patients who have the underlying molecular abnormality. This molecular abnormality is the target of the drug, leading to higher rates of response. These findings will carry important implications for screening, detection, and treatment of ovarian cancer in the future.

WAVE1 gene silencing via RNA interference reduces ovarian cancer cell invasion, migration and proliferation

OBJECTIVE

Wiskott-Aldrich syndrome protein family verprolin-homologous protein 1 (WAVE1) has been implicated in cancer cell migration and invasion. We have previously shown that the overexpression of WAVE1 in epithelial ovarian cancer (EOC) tissues is associated with a poor prognosis. However, the mechanism of WAVE1 regulating the malignant behaviors in EOC remains unclear.

METHODS

In the present study, we knocked down WAVE1 expression in SKOV3 and OVCAR-3 cells through RNA interference to detect the cell biology and molecular biology changes. Moreover, western-blot was used to investigate the underlying mechanism of WAVE1 regulating the proliferative and invasive malignant behaviors in ovarian cancer cells.

RESULTS

The down-regulation of WAVE1 had a significant effect on cell morphological changes. WAVE1 silencing decreased cell migration, cell invasion, cell adhesion, colony formation and cell proliferation in vitro. In addition, we found that down-regulation of WAVE1 inhibited malignant behaviors in vivo. Furthermore, our study also indicated that the PI3K/AKT and p38MAPK signaling pathways might contribute to WAVE1 promotion of ovarian cancer cell proliferation, migration, and invasion.

CONCLUSIONS

WAVE1 might promote the proliferative and invasive malignant behaviors through the activation of the PI3K/AKT and p38MAPK signaling pathways in EOC.

A prognostic nomogram to predict overall survival in patients with platinum-sensitive recurrent ovarian cancer

BACKGROUND

Patients with platinum-sensitive recurrent ovarian cancer have variable prognosis and survival. We extend previous work on prediction of progression-free survival by developing a nomogram to predict overall survival (OS) in these patients treated with platinum-based chemotherapy.

PATIENTS AND METHODS

The nomogram was developed using data from the CAELYX in Platinum-Sensitive Ovarian Patients (CALYPSO) trial. Multivariate proportional hazards models were generated based on pre-treatment characteristics to develop a nomogram that classifies patient prognosis based on OS outcome. We also developed two simpler models with fewer variables and conducted model validations in independent datasets from AGO-OVAR Study 2.5 and ICON 4. We compare the performance of the nomogram with the simpler models by examining the differences in the C-statistics and net reclassification index (NRI).

RESULTS

The nomogram included six significant predictors: interval from last platinum chemotherapy, performance status, size of the largest tumour, CA-125, haemoglobin and the number of organ sites of metastasis (C-statistic 0.67; 95% confidence interval 0.65-0.69). Among the CALPYSO patients, the median OS for good, intermediate and poor prognosis groups was 56.2, 31.0 and 20.8 months, respectively. When CA-125 was not included in the model, the C-statistics were 0.65 (CALYPSO) and 0.64 (AGO-OVAR 2.5). A simpler model (interval from last platinum chemotherapy, performance status and CA-125) produced a significant decrease of the C-statistic (0.63) and NRI (26.4%, P < 0.0001).

CONCLUSIONS

This nomogram with six pre-treatment characteristics improves OS prediction in patients with platinum-sensitive ovarian cancer and is superior to models with fewer prognostic factors or platinum chemotherapy free interval alone. With independent validation, this nomogram could potentially be useful for improved stratification of patients in clinical trials and also for counselling patients.

Systematic analysis and validation of differential gene expression in ovarian serous adenocarcinomas and normal ovary

PURPOSE

Cancer of the ovary confers the worst prognosis among women with gynecological malignancies, primarily because most ovarian cancers are diagnosed at late stage. Hence, there is a substantial need to develop new diagnostic biomarkers to enable detection of ovarian cancer at earlier stages, which would confer better prognosis. In addition, the identification of druggable targets is of substantial interest to find new therapeutic strategies for ovarian cancer.

METHODS

The expression of 22,500 genes in a series of 67 serous papillary carcinomas was compared with 9 crudely enriched normal ovarian tissue samples by RNA hybridization on oligonucleotide microarrays. Multiple genes with near-uniformly expression were elevated in carcinomas of varying grade and malignant potential, including several previously described genes (e.g., MUC-1, CD9, CD24, claudin 3, and mesothelin). We performed immunohistochemical staining with antibodies against several of the proteins encoded by differentially expressed genes in an independent cohort of 71 cases of paraffin-embedded ovarian cancer samples.

RESULTS

We found striking differences in EpCAM (p < 0.005), CD9 (p < 0.001), MUC-1 (p < 0.001), and claudin 3 proteins (p < 0.001) but not for mesothelin (p > 0.05) using the Mann-Whitney U test.

CONCLUSIONS

Protein expression of a majority of the differentially expressed genes tested was found to be elevated in ovarian carcinomas and, as such, define potential new biomarkers or targets.

Molecular classification of gliomas based on whole genome gene expression: a systematic report of 225 samples from the Chinese Glioma Cooperative Group

Defining glioma subtypes based on objective genetic and molecular signatures may allow for a more rational, patient-specific approach to molecularly targeted therapy. However, prior studies attempting to classify glioma subtypes have given conflicting results. We aim to complement and validate the existing molecular classification system on a large number of samples from an East Asian population. A total of 225 samples from Chinese patients was selected for whole genome gene expression profiling. Consensus clustering was applied. Three major groups of gliomas were identified (referred to as G1, G2, and G3). The G1 subgroup correlates with a good clinical outcome, young age, and extremely high frequency of IDH1 mutations. Relative to the G1 subgroup, the G3 subgroup is correlated with a poorer clinical outcome, older age, and a very low rate of mutations in the IDH1 gene. Correlations of the G2 subgroup with respect to clinical outcome, age, and IDH1 mutation fall between the G1 and G3 subgroups. In addition, the G2 subtype was associated with a higher percentage of loss of 1p/19q when compared with G1 and G3 subtypes. Furthermore, our classification scheme was validated on 2 independent datasets derived from the cancer genome atlas (TCGA) and Rembrandt. With use of the TCGA classification system, proneural, neural, and mesenchymal, but not classical subtype, associated gene signatures were clearly defined. In summary, our results reveal that 3 main subtypes stably exist in Chinese patients with glioma. Our classification scheme may reflect the clinical and genetic alterations more clearly. Classical subtype-associated gene signature was not found in our dataset.

LY294002 and metformin cooperatively enhance the inhibition of growth and the induction of apoptosis of ovarian cancer cells

BACKGROUND

The phosphoinositide 3 kinase (PI3K)/v-akt murine thymoma viral oncogene homolog (AKT)/mammalian target of rapamycin (mTOR) pathway is frequently aberrantly activated in ovarian cancer and confers the chemoresistant phenotype of ovarian cancer cells. LY294002 (PI3K inhibitor) and metformin (5'-adenosine monophosphate [AMP]-activated protein kinase [AMPK] activator) are 2 drugs that were known to inhibit mTOR expression through the AKT-dependent and AKT-independent pathways, respectively. In this study, we explored the effectiveness of LY294002 and metformin in combination on inhibition of ovarian cancer cell growth.

METHODS

Western blotting was used to detect the changes of PI3K/AKT/mTOR and AMPK/acetyl-CoA carboxylase (ACC) signaling activities, cell cycle control, and apoptosis. Cell growth was evaluated by cell proliferation, colony formation, and soft agar assays. Flow cytometry was used to study cell cycle distribution and cell death upon drug treatment.

RESULTS

Our study showed that LY294002 and metformin in combination could simultaneously enhance the repression of the PI3K/AKT/mTOR pathway and the activation of the AMPK/ACC pathway. The downstream target of AKT and AMPK, mTOR, was cooperatively repressed when the drugs were used together. The cell cycle regulatory factors, p53, p27, and p21, were up-regulated. On the other hand, caspase 3 and poly (ADP-ribose) polymerase activities involved in apoptosis were also activated. Cell growth assays indicated that LY294002 and metformin could effectively inhibit ovarian cancer cell growth. Flow cytometry analysis showed that the treatment of the 2 drugs mentioned above induced cell cycle arrest at G1 phase and increased sub-G1 apoptotic cells.

CONCLUSION

The combinational use of LY294002 and metformin can enhance inhibition of the growth and induction of the apoptosis of ovarian cancer cells. Our results may provide significant insight into the future therapeutic regimens in ovarian cancer.

Loss-of-heterozygosity on chromosome 19q in early-stage serous ovarian cancer is associated with recurrent disease

BACKGROUND

Ovarian cancer is a heterogeneous disease and prognosis for apparently similar cases of ovarian cancer varies. Recurrence of the disease in early stage (FIGO-stages I-II) serous ovarian cancer results in survival that is comparable to those with recurrent advanced-stage disease. The aim of this study was to investigate if there are specific genomic aberrations that may explain recurrence and clinical outcome.

METHODS

Fifty-one women with early stage serous ovarian cancer were included in the study. DNA was extracted from formalin fixed samples containing tumor cells from ovarian tumors. Tumor samples from thirty-seven patients were analysed for allele-specific copy numbers using OncoScan single nucleotide polymorphism arrays from Affymetrix and the bioinformatic tool Tumor Aberration Prediction Suite. Genomic gains, losses, and loss-of-heterozygosity that associated with recurrent disease were identified.

RESULTS

The most significant differences (p < 0.01) in Loss-of-heterozygosity (LOH) were identified in two relatively small regions of chromosome 19; 8.0-8,8 Mbp (19 genes) and 51.5-53.0 Mbp (37 genes). Thus, 56 genes on chromosome 19 were potential candidate genes associated with clinical outcome. LOH at 19q (51-56 Mbp) was associated with shorter disease-free survival and was an independent prognostic factor for survival in a multivariate Cox regression analysis. In particular LOH on chromosome 19q (51-56 Mbp) was significantly (p < 0.01) associated with loss of TP53 function.

CONCLUSIONS

The results of our study indicate that presence of two aberrations in TP53 on 17p and LOH on 19q in early stage serous ovarian cancer is associated with recurrent disease. Further studies related to the findings of chromosomes 17 and 19 are needed to elucidate the molecular mechanism behind the recurring genomic aberrations and the poor clinical outcome.

CUB and zona pellucida-like domain-containing protein 1 (CUZD1): a novel serological biomarker for ovarian cancer

OBJECTIVES

To measure the levels of serum CUB and zona pellucida-like domain-containing protein 1 (CUZD1) in patients with ovarian cancer (OvCa), benign gynecological conditions and healthy women and in a number of other cancer types (breast, colorectal, lung, prostate and testicular).

DESIGN AND METHODS

Serum CUZD1 levels were measured with a commercial enzyme-linked immunosorbent assay (ELISA). All specimens were analyzed in duplicate. Preliminary verification was performed in serum using 9 healthy women and 20 late stage (III-IV) OvCa patients. An independent cohort of serum samples was used to validate the verification results (18 late stage OvCa, 8 benign gynecological conditions and 8 healthy controls). The following specimens were used for the other cancer types of unknown stage-breast (n=11), colorectal (n=10), lung (n=10), prostate (n=15) and testicular (n=10).

RESULTS

Serum CUZD1 was significantly elevated in ovarian cancer patients (range 95-668 μg/L) as compared to healthy controls (range 0.7-2.5 μg/L). The independent cohort of OvCa samples confirmed the preliminary verification results. CUZD1 was also elevated in breast and lung cancer specimens and not in colorectal, prostate and testicular cancer specimens.

CONCLUSIONS

CUZD1 appears to be a highly promising novel serum biomarker for OvCa diagnosis. Its performance in the 2 independent cohorts examined, and in lung and breast cancer patients warrants further investigation.

The zinc finger gene ZIC2 has features of an oncogene and its overexpression correlates strongly with the clinical course of epithelial ovarian cancer

PURPOSE

Epithelial ovarian tumors (EOT) are among the most lethal of malignancies in women. We have previously identified ZIC2 as expressed at a higher level in samples of a malignant form (MAL) of EOT than in samples of a form with low malignant potential (LMP). We have now investigated the role of ZIC2 in driving tumor growth and its association with clinical outcomes.

EXPERIMENTAL DESIGN

ZIC2 expression levels were analyzed in two independent tumor tissue collections of LMP and MAL. In vitro experiments aimed to test the role of ZIC2 as a transforming gene. Cox models were used to correlate ZIC2 expression with clinical endpoints.

RESULTS

ZIC2 expression was about 40-fold in terms of mRNA and about 17-fold in terms of protein in MAL (n = 193) versus LMP (n = 39) tumors. ZIC2 mRNA levels were high in MAL cell lines but undetectable in LMP cell lines. Overexpression of ZIC2 was localized to the nucleus. ZIC2 overexpression increases the growth rate and foci formation of NIH3T3 cells and stimulates anchorage-independent colony formation; downregulation of ZIC2 decreases the growth rate of MAL cell lines. Zinc finger domains 1 and 2 are required for transforming activity. In stage I MAL, ZIC2 expression was significantly associated with overall survival in both univariate (P = 0.046) and multivariate model (P = 0.049).

CONCLUSIONS

ZIC2, a transcription factor related to the sonic hedgehog pathway, is a strong discriminant between MAL and LMP tumors: it may be a major determinant of outcome of EOTs.

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

Background:

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

Methods:

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

Results:

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

Conclusions:

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

EMT transcription factors snail and slug directly contribute to cisplatin resistance in ovarian cancer

Background

The epithelial to mesenchymal transition (EMT) is a molecular process through which an epithelial cell undergoes transdifferentiation into a mesenchymal phenotype. The role of EMT in embryogenesis is well-characterized and increasing evidence suggests that elements of the transition may be important in other processes, including metastasis and drug resistance in various different cancers.

Methods

Agilent 4 × 44 K whole human genome arrays and selected reaction monitoring mass spectrometry were used to investigate mRNA and protein expression in A2780 cisplatin sensitive and resistant cell lines. Invasion and migration were assessed using Boyden chamber assays. Gene knockdown of snail and slug was done using targeted siRNA. Clinical relevance of the EMT pathway was assessed in a cohort of primary ovarian tumours using data from Affymetrix GeneChip Human Genome U133 plus 2.0 arrays.

Results

Morphological and phenotypic hallmarks of EMT were identified in the chemoresistant cells. Subsequent gene expression profiling revealed upregulation of EMT-related transcription factors including snail, slug, twist2 and zeb2. Proteomic analysis demonstrated up regulation of Snail and Slug as well as the mesenchymal marker Vimentin, and down regulation of E-cadherin, an epithelial marker. By reducing expression of snail and slug, the mesenchymal phenotype was largely reversed and cells were resensitized to cisplatin. Finally, gene expression data from primary tumours mirrored the finding that an EMT-like pathway is activated in resistant tumours relative to sensitive tumours, suggesting that the involvement of this transition may not be limited to in vitro drug effects.

Conclusions

This work strongly suggests that genes associated with EMT may play a significant role in cisplatin resistance in ovarian cancer, therefore potentially leading to the development of predictive biomarkers of drug response or novel therapeutic strategies for overcoming drug resistance.

Adenovirus as a new agent for multiple myeloma therapies: Opportunities and restrictions

Multiple myeloma is a malignancy of B-cells that is characterized by the clonal expansion and accumulation of malignant plasma cells in the bone marrow. This disease remains incurable, and a median survival of 3-5 years has been reported with the use of current treatments. Viral-based therapies offer promising alternatives or possible integration with current therapeutic regimens. Among several gene therapy vectors and oncolytic agents, adenovirus has emerged as a promising agent, and it is already being used for the treatment of solid tumors in humans. The main concern with the clinical use of this vector has been its high immunogenicity; adenovirus is often able to induce a strong immune response in the host. Furthermore, new limitations in the efficacy of this therapy, intrinsic to the nature of tumor cells, have been recently observed. For example, our group showed a strong antiviral phenotype in vitro and in vivo in a subset of tumors, shedding new insights that may explain the partial failure of clinical trials based on this promising new therapy. In this review, we describe novel therapeutic approaches that implement viral-based treatments in hematological malignancies and address the novelty as well as the possible limitations of these new therapies, especially in the context of the use of adenoviral vectors for treating multiple myeloma.

Gene expression profiling reveals differentially expressed genes in ovarian cancer of the hen: support for oviductal origin?

Ovarian cancer has a high mortality rate due, in part, to the lack of early detection and incomplete understanding of the origin of the disease. The hen is the only spontaneous model of ovarian cancer and can therefore aid in the identification and testing of early detection strategies and therapeutics. Our aim was to combine the use of the hen animal model and microarray technology to identify differentially expressed genes in ovarian tissue from normal hens compared with hens with ovarian cancer. We found that the transcripts up-regulated in chicken ovarian tumors were enriched for oviduct-related genes. Quantitative real-time PCR and immunohistochemistry confirmed expression of oviduct-related genes in normal oviduct and in ovaries from hens with early- and late-stage ovarian tumors, but not in normal ovarian surface epithelium. In addition, one of the oviduct-related genes identified in our analysis, paired box 2 has been implicated in human ovarian cancer and may serve as a marker of the disease. Furthermore, estrogen receptor 1 mRNA is over-expressed in early-stage tumors, suggesting that expression of the oviduct-related genes may be regulated by estrogen. We have also identified oviduct-related genes that encode secreted proteins that could represent putative serum biomarkers. The expression of oviduct-related genes in early-stage tumors is similar to what is seen in human ovarian cancer, with tumors resembling normal Müllerian epithelium. These data suggest that chicken ovarian tumors may arise from alternative sites, including the oviduct.

Therapeutic modulation of epigenetic drivers of drug resistance in ovarian cancer

Epigenetic changes in tumours are associated not only with cancer development and progression, but also with resistance to chemotherapy. Aberrant DNA methylation at CpG islands and associated epigenetic silencing are observed during the acquisition of drug resistance. However, it remains unclear whether all of the observed changes are drivers of drug resistance, causally associated with response of tumours to chemotherapy, or are passenger events representing chance DNA methylation changes. Systematic approaches that link DNA methylation and expression with chemosensitivity will be required to identify key drivers. Such drivers will be important prognostic or predicitive biomarkers, both to existing chemotherapies, but also to epigenetic therapies used to modulate drug resistance.

Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance

Although in vitro models have been a cornerstone of anti-cancer drug development, their direct applicability to clinical cancer research has been uncertain. Using a state-of-the-art Taqman-based quantitative RT-PCR assay, we investigated the multidrug resistance (MDR) transcriptome of six cancer types, in established cancer cell lines (grown in monolayer, 3D scaffold, or in xenograft) and clinical samples, either containing >75% tumor cells or microdissected. The MDR transcriptome was determined a priori based on an extensive curation of the literature published during the last three decades, which led to the enumeration of 380 genes. No correlation was found between clinical samples and established cancer cell lines. As expected, we found up-regulation of genes that would facilitate survival across all cultured cancer cell lines evaluated. More troubling, however, were data showing that all of the cell lines, grown either in vitro or in vivo, bear more resemblance to each other, regardless of the tissue of origin, than to the clinical samples they are supposed to model. Although cultured cells can be used to study many aspects of cancer biology and response of cells to drugs, this study emphasizes the necessity for new in vitro cancer models and the use of primary tumor models in which gene expression can be manipulated and small molecules tested in a setting that more closely mimics the in vivo cancer microenvironment so as to avoid radical changes in gene expression profiles brought on by extended periods of cell culture.

Proteomic analysis of ovarian cancer proximal fluids: validation of elevated peroxiredoxin 1 in patient peripheral circulation

Background

Epithelial ovarian cancer (EOC) is the deadliest gynecologic malignancy in the United States. Unfortunately, a validated protein biomarker-screening test to detect early stage disease from peripheral blood has not yet been developed. The present investigation assesses the ability to identify tumor relevant proteins from ovarian cancer proximal fluids, including tissue interstitial fluid (TIF) and corresponding ascites, from patients with papillary serous EOC and translates these findings to targeted blood-based immunoassays.

Methodology/Principal Findings

Paired TIF and ascites collected from four papillary serous EOC patients at the time of surgery underwent immunodepletion, resolution by 1D gel electrophoresis and in-gel digestion for analysis by liquid chromatography-tandem mass spectrometry, which resulted in an aggregate identification of 569 and 171 proteins from TIF and ascites, respectively. Of these, peroxiredoxin I (PRDX1) was selected for validation in serum by ELISA and demonstrated to be present and significantly elevated (p = 0.0188) in 20 EOC patients with a mean level of 26.0 ng/mL (±9.27 SEM) as compared to 4.19 ng/mL (±2.58 SEM) from 16 patients with normal/benign ovarian pathology.

Conclusions/Significance

We have utilized a workflow for harvesting EOC-relevant proximal biofluids, including TIF and ascites, for proteomic analysis. Among the differentially abundant proteins identified from these proximal fluids, PRDX1 was demonstrated to be present in serum and shown by ELISA to be elevated by nearly 6-fold in papillary serous EOC patients relative to normal/benign patients. Our findings demonstrate the facile ability to discover potential EOC-relevant proteins in proximal fluids and confirm their presence in peripheral blood serum. In addition, our finding of elevated levels of PRDX1 in the serum of EOC patients versus normal/benign patients warrants further evaluation as a tumor specific biomarker for EOC.

Cancer testis antigens: novel biomarkers and targetable proteins for ovarian cancer

Ovarian cancer is the fifth leading cause of cancer death in women and the leading cause from gynecological malignancies. Despite the recently improved outcomes of new chemotherapeutical agents in the therapy of ovarian cancer and the increased 5-year survival rate, the mortality of this malignancy disease remains unchanged. Ovarian cancer therapy is often correlated to the stage of the tumor, but the first step is usually surgical treatment. Afterward, various courses of chemotherapy and radiation are suggested. Obviously, the higher the developmental stage of the tumor, the less the probability is in eradicating it surgically, especially in relation to metastasis. It is clear that an early diagnosis of ovarian cancer is important for the survival of these patients. In order to identify ovarian cancer patients in the early stages, a number of studies are focusing on a particular class of antigens called cancer testis antigens. These antigens display high expression in tumors of different histology, but are normally restricted to the testis and have low or no expression in normal tissues. The testes are an immunologically-privileged site due to the presence of tight junctions between adjacent Sertoli cells that constitute the blood-testis barrier, which prevents auto-immune reactions. In the past few years, some of these antigens were demonstrated to be very promising for the early diagnosis and development of vaccines for ovarian cancer. This review aims to underline the most reliable cancer testis antigens under investigation at this moment.