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

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.

Unraveling the Transcriptomic Signatures of Homologous Recombination Deficiency in Ovarian Cancers

Homologous recombination deficiency (HRD) is a crucial driver of tumorigenesis by inducing impaired repair of double-stranded DNA breaks. Although HRD possibly triggers the production of numerous tumor neoantigens that sufficiently stimulate and activate various tumor-immune responses, a comprehensive understanding of the HRD-associated tumor microenvironment is elusive. To investigate the effect of HRD on the selective enrichment of transcriptomic signatures, 294 cases from The Cancer Genome Atlas-Ovarian Cancer project with both RNA-sequencing and SNP array data are analyzed. Differentially expressed gene analysis and network analysis are performed to identify HRD-specific signatures. Gene-sets associated with mitochondrial activation, including enhanced oxidative phosphorylation (OxPhos), are significantly enriched in the HRD-high group. Furthermore, a wide range of immune cell activation signatures is enriched in HRD-high cases of high-grade serous ovarian cancer (HGSOC). On further cell-type-specific analysis, M1-like macrophage genes are significantly enriched in HRD-high HGSOC cases, whereas M2-macrophage-related genes are not. The immune-response-associated genomic features, including tumor mutation rate, neoantigens, and tumor mutation burdens, correlated with HRD scores. In conclusion, the results of this study highlight the biological properties of HRD, including enhanced energy metabolism, increased tumor neoantigens and tumor mutation burdens, and consequent exacerbation of immune responses, particularly the enrichment of M1-like macrophages in HGSOC cases.

The EMT-activator ZEB1 is unrelated to platinum drug resistance in ovarian cancer but is predictive of survival

The IGROVCDDP cisplatin-resistant ovarian cancer cell line is an unusual model, as it is also cross-resistant to paclitaxel. IGROVCDDP, therefore, models the resistance phenotype of serous ovarian cancer patients who have failed frontline platinum/taxane chemotherapy. IGROVCDDP has also undergone epithelial-mesenchymal transition (EMT). We aim to determine if alterations in EMT-related genes are related to or independent from the drug-resistance phenotypes. EMT gene and protein markers, invasion, motility and morphology were investigated in IGROVCDDP and its parent drug-sensitive cell line IGROV-1. ZEB1 was investigated by qPCR, Western blotting and siRNA knockdown. ZEB1 was also investigated in publicly available ovarian cancer gene-expression datasets. IGROVCDDP cells have decreased protein levels of epithelial marker E-cadherin (6.18-fold, p = 1.58e-04) and higher levels of mesenchymal markers vimentin (2.47-fold, p = 4.43e-03), N-cadherin (4.35-fold, p = 4.76e-03) and ZEB1 (3.43-fold, p = 0.04). IGROVCDDP have a spindle-like morphology consistent with EMT. Knockdown of ZEB1 in IGROVCDDP does not lead to cisplatin sensitivity but shows a reversal of EMT-gene signalling and an increase in cell circularity. High ZEB1 gene expression (HR = 1.31, n = 2051, p = 1.31e-05) is a marker of poor overall survival in high-grade serous ovarian-cancer patients. In contrast, ZEB1 is not predictive of overall survival in high-grade serous ovarian-cancer patients known to be treated with platinum chemotherapy. The increased expression of ZEB1 in IGROVCDDP appears to be independent of the drug-resistance phenotypes. ZEB1 has the potential to be used as biomarker of overall prognosis in ovarian-cancer patients but not of platinum/taxane chemoresistance.

Novel MicroRNA-Regulated Transcript Networks Are Associated with Chemotherapy Response in Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is a highly lethal gynecologic cancer, in part due to resistance to platinum-based chemotherapy reported among 20% of patients. This study aims to generate novel hypotheses of the biological mechanisms underlying chemotherapy resistance, which remain poorly understood. Differential expression analyses of mRNA- and microRNA-sequencing data from HGSOC patients of The Cancer Genome Atlas identified 21 microRNAs associated with angiogenesis and 196 mRNAs enriched for adaptive immunity and translation. Coexpression network analysis identified three microRNA networks associated with chemotherapy response enriched for lipoprotein transport and oncogenic pathways, as well as two mRNA networks enriched for ubiquitination and lipid metabolism. These network modules were replicated in two independent ovarian cancer cohorts. Moreover, integrative analyses of the mRNA/microRNA sequencing and single-nucleotide polymorphisms (SNPs) revealed potential regulation of significant mRNA transcripts by microRNAs and SNPs (expression quantitative trait loci). Thus, we report novel transcriptional networks and biological pathways associated with resistance to platinum-based chemotherapy in HGSOC patients. These results expand our understanding of the effector networks and regulators of chemotherapy response, which will help to improve the management of ovarian cancer.

Toward More Comprehensive Homologous Recombination Deficiency Assays in Ovarian Cancer, Part 1: Technical Considerations

Simple Summary

High-grade serous ovarian cancer (HGSOC) is the most frequent and lethal form of ovarian cancer and is associated with homologous recombination deficiency (HRD) in 50% of cases. This specific alteration is associated with sensitivity to PARP inhibitors (PARPis). Despite vast prognostic improvements due to PARPis, current molecular assays assessing HRD status suffer from several limitations, and there is an urgent need for a more accurate evaluation. In these companion reviews (Part 1: Technical considerations; Part 2: Medical perspectives), we develop an integrative review to provide physicians and researchers involved in HGSOC management with a holistic perspective, from translational research to clinical applications.

Abstract

High-grade serous ovarian cancer (HGSOC), the most frequent and lethal form of ovarian cancer, exhibits homologous recombination deficiency (HRD) in 50% of cases. In addition to mutations in BRCA1 and BRCA2, which are the best known thus far, defects can also be caused by diverse alterations to homologous recombination-related genes or epigenetic patterns. HRD leads to genomic instability (genomic scars) and is associated with PARP inhibitor (PARPi) sensitivity. HRD is currently assessed through BRCA1/2 analysis, which produces a genomic instability score (GIS). However, despite substantial clinical achievements, FDA-approved companion diagnostics (CDx) based on GISs have important limitations. Indeed, despite the use of GIS in clinical practice, the relevance of such assays remains controversial. Although international guidelines include companion diagnostics as part of HGSOC frontline management, they also underscore the need for more powerful and alternative approaches for assessing patient eligibility to PARP inhibitors. In these companion reviews, we review and present evidence to date regarding HRD definitions, achievements and limitations in HGSOC. Part 1 is dedicated to technical considerations and proposed perspectives that could lead to a more comprehensive and dynamic assessment of HR, while Part 2 provides a more integrated approach for clinicians.

OSov: An Interactive Web Server to Evaluate Prognostic Biomarkers for Ovarian Cancer

Simple Summary

The OSov web server incorporates gene expression profiles with clinical risk factors to estimate the ovarian cancers patients’ survival, and provides a tool for multiple analysis, such as forest-plot, uni/multi-variate survival analysis, Kaplan-Meier plot and nomogram construction.

Abstract

Ovarian cancer is one of the most aggressive and highly lethal gynecological cancers. The purpose of our study is to build a free prognostic web server to help researchers discover potential prognostic biomarkers by integrating gene expression profiling data and clinical follow-up information of ovarian cancer. We construct a prognostic web server OSov (Online consensus Survival analysis for Ovarian cancer) based on RNA expression profiles. OSov is a user-friendly web server which could present a Kaplan–Meier plot, forest plot, nomogram and survival summary table of queried genes in each individual cohort to evaluate the prognostic potency of each queried gene. To assess the performance of OSov web server, 163 previously published prognostic biomarkers of ovarian cancer were tested and 72% of them had their prognostic values confirmed in OSov. It is a free and valuable prognostic web server to screen and assess survival-associated biomarkers for ovarian cancer.

Synthetic lethality-mediated precision oncology via the tumor transcriptome

Precision oncology has made significant advances, mainly by targeting actionable mutations in cancer driver genes. Aiming to expand treatment opportunities, recent studies have begun to explore the utility of tumor transcriptome to guide patient treatment. Here, we introduce SELECT (synthetic lethality and rescue-mediated precision oncology via the transcriptome), a precision oncology framework harnessing genetic interactions to predict patient response to cancer therapy from the tumor transcriptome. SELECT is tested on a broad collection of 35 published targeted and immunotherapy clinical trials from 10 different cancer types. It is predictive of patients' response in 80% of these clinical trials and in the recent multi-arm WINTHER trial. The predictive signatures and the code are made publicly available for academic use, laying a basis for future prospective clinical studies.

Development and validation of prognostic gene signature for basal-like breast cancer and high-grade serous ovarian cancer

PURPOSE

Molecular similarities have been reported between basal-like breast cancer (BLBC) and high-grade serous ovarian cancer (HGSOC). To date, there have been no prognostic biomarkers that can provide risk stratification and inform treatment decisions for both BLBC and HGSOC. In this study, we developed a molecular signature for risk stratification in BLBC and further validated this signature in HGSOC.

METHODS

RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA) project for 190 BLBC and 314 HGSOC patients. Analyses of differentially expressed genes between recurrent vs. non-recurrent cases were performed using different bioinformatics methods. Gene Signature was established using weighted linear combination of gene expression levels. Their prognostic performance was evaluated using survival analysis based on progression-free interval (PFI) and disease-free interval (DFI).

RESULTS

63 genes were differentially expressed between 18 recurrent and 40 non-recurrent BLBC patients by two different methods. The recurrence index (RI) calculated from this 63-gene signature significantly stratified BLBC patients into two risk groups with 38 and 152 patients in the low-risk (RI-Low) and high-risk (RI-High) groups, respectively (p = 0.0004 and 0.0023 for PFI and DFI, respectively). Similar performance was obtained in the HGSOC cohort (p = 0.0131 and 0.004 for PFI and DFI, respectively). Multivariate Cox regression adjusting for age, grade, and stage showed that the 63-gene signature remained statistically significant in stratifying HGSOC patients (p = 0.0005).

CONCLUSION

A gene signature was identified to predict recurrence in BLBC and HGSOC patients. With further validation, this signature may provide an additional prognostic tool for clinicians to better manage BLBC, many of which are triple-negative and HGSOC patients who are currently difficult to treat.

Protein anabolism is key to long-term survival in high-grade serous ovarian cancer

This study aimed to identify the biological processes associated with long-term survival in high-grade serous ovarian cancer (HGSOC). HGSOC cases obtained from The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) database were divided into long-term survivors (LTS) and normal-term survivors (NTS) based on survival cutoffs defined by the HGSOC cohort in the SEER database. Differentially expressed genes (DEGs) were screened using the generalized linear modeling (GLM) method. Gene Ontology (GO) functional and KEGG pathway enrichment analyses were performed using DAVID Bioinformatics Resources. DEG-related protein-protein interactions (PPI) were extracted from the STRING database and hub genes were identified using CytoHubba in the Cytoscape program. In total, 157 DEGs, including 155 upregulated and 2 downregulated genes, were identified. Upregulated genes were statistically enriched in 80 GO terms and 11 KEGG pathways related to energy and substrate metabolism, such as protein absorption, digestion, and metabolism as well as signaling pathways, including chromatin silencing, regulation of ERK1 and ERK2 cascade, and regulation of MAPKKK. ALB and POMC were the common hub genes. These findings reveal that protein anabolism is crucial to long-term survival, regulated by activation of the MAPK/ERK signaling pathway and chromatin silencing. Comprehensive understanding of the molecular mechanisms via further exploration may contribute toward an effective treatment for ovarian cancer.

KLF6 Induces Apoptosis in Human Lens Epithelial Cells Through the ATF4-ATF3-CHOP Axis

Background

Many studies have confirmed that high myopia is related to the high prevalence of cataracts, which results from apoptosis of lens epithelial cells (LECs) due to endoplasmic reticulum stress. Krüppel-like factor 6 (KLF6) is a tumor suppressor that is involved in the regulation of cell proliferation and apoptosis.

Purpose

In this study, our purpose was to find the relationship between KLF6-induced apoptosis in LECs and ATF4 (activating transcription factor 4)-ATF3 (activating transcription factor 3)-CHOP (C/EBP homologous protein) signaling pathway.

Methods

KLF6, ATF4, ATF3, and CHOP were ectopically expressed using cDNAs subcloned into the pCDNA3.1+ vector. ATF4, ATF3, and CHOP knockdown were performed by small interfering RNA (siRNA). Expression of relative gene was tested using QT-PCR and western-blot. Then, accompanied by UVB stimulation, cell viability was measured by CCK-8 assay; The cell damage was examined by live & dead staining; The apoptotic markers Bax and Bcl-2 were detected by immunoblotting; Quantitative apoptotic levels were measured with the Apoptosis Detection Kit; The expression level of reactive oxygen-free radical (ROS) was analyzed by DCFH-DA` probe.

Results

Ectopically expressed ATF4, ATF3, and CHOP-induced apoptosis in cells, whereas ATF4, ATF3, and CHOP knockdown by small interfering RNA (siRNA) blocked KLF6-induced apoptosis. In addition, we determined that ATF4 regulates ATF3 and CHOP expression and that ATF3 silencing reduces CHOP upregulation without changing ATF4 levels; however, ATF4 and ATF3 expression was unaffected by blockade of CHOP, suggesting that KLF6 triggers endoplasmic reticulum stress in LECs by mediating the ATF4-ATF3/CHOP axis. Besides, KLF6 overexpression significantly induced LEC apoptosis under UV radiation, as demonstrated by the elevated Bax/Bcl-2 ratio.

Conclusion

The ATF4-ATF3-CHOP pathway plays an important role in KLF6-induced apoptosis in HLECs. Our results increase our understanding of the mechanisms that regulate LEC apoptosis and contribute to the development of a new preventative strategy for cataract.

Biomarkers for Homologous Recombination Deficiency in Cancer

Defective DNA repair is a common hallmark of cancer. Homologous recombination is a DNA repair pathway of clinical interest due to the sensitivity of homologous recombination-deficient cells to poly-ADP ribose polymerase (PARP) inhibitors. The measurement of homologous recombination deficiency (HRD) in cancer is therefore vital to the appropriate design of clinical trials incorporating PARP inhibitors. However, methods to identify HRD in tumors are varied and controversial. Understanding existing and new methods to measure HRD is important to their appropriate use in clinical trials and practice. The aim of this review is to summarize the biology and clinical validation of current methods to measure HRD, to aid decision-making for patient stratification and translational research in PARP inhibitor trials. We discuss the current clinical development of PARP inhibitors, along with established indicators for HRD such as germline BRCA1/2 mutation status and clinical response to platinum-based therapy. We then examine newer assays undergoing clinical validation, including 1) somatic mutations in homologous recombination genes, 2) "genomic scar" assays using array-based comparative genomic hybridization (aCGH), single nucleotide polymorphism (SNP) analysis or mutational signatures derived from next-generation sequencing, 3) transcriptional profiles of HRD, and 4) phenotypic or functional assays of protein expression and localization. We highlight the strengths and weaknesses of each of these assays, for consideration during the design of studies involving PARP inhibitors.

A novel risk score system for assessment of ovarian cancer based on co-expression network analysis and expression level of five lncRNAs

Background

Ovarian cancer (OC) is the most deadly gynaecological cancer, contributing significantly to female cancer-related deaths worldwide. Improving the outlook for OC patients depends on the identification of more reliable prognostic biomarkers for early diagnosis and survival prediction. The various roles of long non-coding RNAs (lncRNAs) in OC have attracted increasing attention. This study aimed to identify a lncRNA-based signature for survival prediction in OC patients.

Methods

RNA expression data and clinical information from a large number of OC patients were downloaded from a public database. These data were regarded as a training set to construct a weighed gene co-expression network analysis (WGCNA) network, mine stable modules, and screen differentially expressed lncRNAs. The prognostic lncRNAs were screened using univariate Cox regression analysis and the optimal prognosis lncRNA combination was screened using a Cox-PH model. The finalised lncRNA combination was used to construct the risk score system, which was validated and assessed for effectiveness using other independent datasets. Further functional pathway enrichment was performed using gene set enrichment analysis (GSEA).

Results

A co-expression network was constructed and four stable modules with OC-related biological functions were obtained. A total of 19 lncRNAs significantly related to prognosis of ovarian cancer were obtained using univariate Cox regression analysis, and the 5 prognostic signature lncRNAs GAS5, HCP5, PART1, SNHG11, and SNHG5 were used to establish a risk assessment system. The reliability of the prognostic scoring system was further confirmed using validation sets, which indicated that the risk assessment system could be used as an independent prognostic factor. Pathway enrichment analysis revealed that the network modules related to the above five prognostic genes were significantly associated with cell local adhesion, cancer signaling pathways, JAK-STAT signalling, and endogenous cell receptor interaction.

Conclusions

The risk score system established in this study could provide a novel reliable method to identify individuals at high risk of OC. In addition, the five prognostic lncRNAs identified here are promising potential prognostic biomarkers that could help to elucidate the pathogenesis of OC.

Serum C-reactive protein is an important and powerful prognostic biomarker in most adult solid tumors

INTRODUCTION

Prognostication in cancer is challenging and inaccurate. C-Reactive Protein (CRP), a cheap and sensitive marker of inflammation may help. This study investigated the relationship between CRP and prognosis in a large cohort of solid tumors with mixed cancer diagnoses and stages.

METHODS

Electronic medical records of 4931 adults with solid tumors who attended the Taussig Cancer Institute from 2006-2012 were reviewed. Demographic and clinical characteristics were recorded. Maximum CRP (mCRP) was identified for each individual. CRP was analysed as a time-dependent, continuous and categorical variable for association with survival.

RESULTS

Two thirds of patients had a high mCRP. This was consistently associated with shorter survival, even after correction for time from diagnosis, and when analysed as a continuous or a categorical variable. When mCRP values above 10 mg/L were subcategorized, a higher mCRP was always worse. Even among those with normal values, statistically and clinically significant shorter survival was noted at mCRP levels >5 mg/L.

CONCLUSIONS

In a large representative cohort of consecutive solid tumor patients the risk of death was clinically and statistically significantly greater with a high mCRP. This was independent of other variables and regardless of statistical method from both dates of diagnosis and test. CRP appeared to be underutilized. Our results support the routine use of CRP as a universal cost-effective independent prognostic indicator in most solid tumors.

Two protein-coding genes act as a novel clinical signature to predict prognosis in patients with ovarian serous cystadenocarcinoma

Ovarian cancer is the seventh most common type of cancer and the eighth most common cause of cancer-associated mortality among women. A number of studies have hypothesized that the expression status of certain genes may be used to predict prognosis in ovarian cancer. In the present study, the RNA expression data from next-generation sequencing and the clinical information of 413 patients from The Cancer Genome Atlas dataset was downloaded to identify the association between gene-expression level and the survival time of the patients with ovarian serous cystadenocarcinoma. A five-gene model was predicted to be significantly associated with patient survival in ovarian serous cystadenocarcinoma by using random survival forests variable hunting algorithm and Cox analysis. A total of two genes, mesencephalic astrocyte-derived neurotrophic factor and dedicator of cytokinesis 11, of the predicted five genes demonstrated positive expression in the ovarian serous cystadenocarcinoma cancer tissues by polymerase chain reaction analysis. Kaplan-Meier and Receiver Operating Characteristic analysis confirmed that the model of the two genes exhibited high sensitivity and specificity to predict the prognostic survival of patients. In conclusion, the expression of the two genes in the two-gene model was associated with the prognostic outcomes of patients with ovarian serous cystadenocarcinoma; the model demonstrated potential as a novel prognostic indicator, which may have important clinical significance.

Who are the long-term survivors of high grade serous ovarian cancer?

Although the median survival for epithelial ovarian cancer (EOC) is <5years, approximately 15% of patients will survive for >10years. A better understanding of these exceptional responders could reveal opportunities to improve the dismal prognosis of most EOC patients. In this review, we examine the clinical and genomic features that have been associated with long-term survival, which is generally defined as survival of >7-10years after initial diagnosis. Clinical features influencing long-term survival have been best reported in large retrospective population-based studies. These studies find that long-term survival is associated with previously validated prognostic factors, including younger age at diagnosis, earlier clinicopathologic stage, lower grade, non-serous histology, absence of ascites, primary debulking surgery, and optimal cytoreduction at primary surgery. Duration of survival after a recurrence also contributes to long-term survival and depends both on recurrence location and response to subsequent chemotherapy or surgery. Germline BRCA mutations, although associated with short-term chemosensitivity, do not appear to improve long-term survival. Unfortunately, the relative lack of recurrent somatic mutations in EOC has made the identification of genomic signatures associated with long-term survival difficult. Although six independent gene expression analyses of long-term survivors (LTS) have identified signatures associated with prolonged survival, different gene sets are identified in each study. Genes differentially expressed in tumors of LTS are broadly involved in cell proliferation, tumor-stromal interactions, the cytoskeleton, metabolism of nutrients, and immune/stress response. We anticipate that consistent selection of control and LTS groups, combined with the use of emerging transcriptomic, epigenomic, and proteomic platforms, is likely to identify conserved features associated with long-term survival. Further elucidating the factors contributing to long-term survival has the potential to contribute to our understanding of the biology of ovarian cancer, with the goal of improving the survival of all EOC patients.

The convergent roles of the nuclear factor I transcription factors in development and cancer

The nuclear factor I (NFI) transcription factors play important roles during normal development and have been associated with developmental abnormalities in humans. All four family members, NFIA, NFIB, NFIC and NFIX, have a homologous DNA binding domain and function by regulating cell proliferation and differentiation via the transcriptional control of their target genes. More recently, NFI genes have also been implicated in cancer based on genomic analyses and studies of animal models in a variety of tumours across multiple organ systems. However, the association between their functions in development and in cancer is not well described. In this review, we summarise the evidence suggesting a converging role for the NFI genes in development and cancer. Our review includes all cancer types in which the NFI genes are implicated, focusing predominantly on studies demonstrating their oncogenic or tumour-suppressive potential. We conclude by presenting the challenges impeding our understanding of NFI function in cancer biology, and demonstrate how a developmental perspective may contribute towards overcoming such hurdles.

The Prognostic 97 Chemoresponse Gene Signature in Ovarian Cancer

Patient diagnosis and care would be significantly improved by understanding the mechanisms underlying platinum and taxane resistance in ovarian cancer. Here, we aim to establish a gene signature that can identify molecular pathways/transcription factors involved in ovarian cancer progression, poor clinical outcome, and chemotherapy resistance. To validate the robustness of the gene signature, a meta-analysis approach was applied to 1,020 patients from 7 datasets. A 97-gene signature was identified as an independent predictor of patient survival in association with other clinicopathological factors in univariate [hazard ratio (HR): 3.0, 95% Confidence Interval (CI) 1.66–5.44, p = 2.7E-4] and multivariate [HR: 2.88, 95% CI 1.57–5.2, p = 0.001] analyses. Subset analyses demonstrated that the signature could predict patients who would attain complete or partial remission or no-response to first-line chemotherapy. Pathway analyses revealed that the signature was regulated by HIF1α and TP53 and included nine HIF1α-regulated genes, which were highly expressed in non-responders and partial remission patients than in complete remission patients. We present the 97-gene signature as an accurate prognostic predictor of overall survival and chemoresponse. Our signature also provides information on potential candidate target genes for future treatment efforts in ovarian cancer.

Ex vivo tumor culture systems for functional drug testing and therapy response prediction

Optimal patient stratification is of utmost importance in the era of personalized medicine. Prediction of individual treatment responses by functional ex vivo assays requires model systems derived from viable tumor samples, which should closely resemble in vivo tumor characteristics and microenvironment. This review discusses a broad spectrum of model systems, ranging from classic 2D monolayer culture techniques to more experimental ‘cancer-on-chip’ procedures. We mainly focus on organotypic tumor slices that take tumor heterogeneity and tumor–stromal interactions into account. These 3D model systems can be exploited for patient selection as well as for fundamental research. Selection of the right model system for each specific research endeavor is crucial and requires careful balancing of the pros and cons of each technology.

Selection of the right therapy for individual cancer patients is very important with the expanding number of possible treatments. How tumors respond to a therapy can be tested by treating a sample from the tumor outside the body. Various culture methods can be used to maintain this tumor sample. Each of these model systems has its own benefits and disadvantages. In this review, we discuss the advantages and drawbacks of the available model systems and how they can be used to guide personalized medicine.

Association of Somatic Mutations of ADAMTS Genes With Chemotherapy Sensitivity and Survival in High-Grade Serous Ovarian Carcinoma

IMPORTANCE

Chemotherapy response in the majority of patients with ovarian cancer remains unpredictable.

OBJECTIVE

To identify novel molecular markers for predicting chemotherapy response in patients with ovarian cancer.

DESIGN, SETTING, AND PARTICIPANTS

Observational study of genomics and clinical data of high-grade serous ovarian cancer cases with genomic and clinical data made public between 2009 and 2014 via the Cancer Genome Atlas project.

MAIN OUTCOMES AND MEASURES

Chemotherapy response (primary outcome) and overall survival (OS), progression-free survival (PFS), and platinum-free duration (secondary outcome).

RESULTS

In 512 patients with ovarian cancer with available whole-exome sequencing data, mutations from 8 members of the ADAMTS family (ADAMTS mutations) with an overall mutation rate of approximately 10.4% were associated with a significantly higher chemotherapy sensitivity (100% for ADAMTS-mutated vs 64% for ADAMTS wild-type cases; P < .001) and longer platinum-free duration (median platinum-free duration, 21.7 months for ADAMTS-mutated vs 10.1 months for ADAMTS wild-type cases; P = .001). Moreover, ADAMTS mutations were associated with significantly better OS (hazard ratio [HR], 0.54 [95% CI, 0.42-0.89]; P = .01 and median OS, 58.0 months for ADAMTS-mutated vs 41.3 months for ADAMTS wild-type cases) and PFS (HR, 0.42 [95% CI, 0.38-0.70]; P < .001 and median PFS, 31.8 for ADAMTS-mutated vs 15.3 months for ADAMTS wild-type cases). After adjustment by BRCA1 or BRCA2 mutation, surgical stage, residual tumor, and patient age, ADAMTS mutations were significantly associated with better OS (HR, 0.53 [95% CI, 0.32-0.87]; P = .01), PFS (HR, 0.40 [95% CI, 0.25-0.62]; P < .001), and platinum-free survival (HR, 0.45 [95% CI, 0.28-0.73]; P = .001). ADAMTS-mutated cases exhibited a distinct mutation spectrum and were significantly associated with tumors with a higher genome-wide mutation rate than ADAMTS wild-type cases across the whole exome (median mutation number per sample, 121 for ADAMTS-mutated vs 69 for ADAMTS wild-type cases; P < .001).

CONCLUSIONS AND RELEVANCE

ADAMTS mutations may contribute to outcomes in ovarian cancer cases without BRCA1 or BRCA2 mutations and may have important clinical implications.

Expression profile of COL2A1 and the pseudogene SLC6A10P predicts tumor recurrence in high-grade serous ovarian cancer

Tumor recurrence, following initial response to adjuvant chemotherapy, is a major problem in women with high-grade serous ovarian cancer (HGSOC). Microarray analysis of primary tumors has identified genes that may be useful in risk stratification/overall survival, but are of limited value in predicting the >70% rate for tumor recurrence. In this study, we performed RNA-Seq analysis of primary and recurrent HGSOC to first identify unique differentially expressed genes. From this dataset, we selected 21 archetypical coding genes and one noncoding RNA, based on statistically significant differences in their expression profile between tumors, for validation by qPCR in a larger cohort of 110 ovarian tumors (71 primary and 39 recurrent) and for testing association of specific genes with time-to-recurrence (TTR). Kaplan-Meier tests revealed that high expression of collagen type II, alpha 1 (COL2A1) was associated with delayed TTR (HR = 0.47, 95% CI: 0.27-0.82, p = 0.008), whereas low expression of the pseudogene, solute carrier family 6 member 10 (SLC6A10P), was associated with longer TTR (HR = 0.53, 95% CI: 0.30-0.93, p = 0.027). Notably, TTR was significantly delayed for tumors that simultaneously highly expressed COL2A1 and lowly expressed SLC6A10P (HR = 0.21, 95% CI: 0.082-0.54, p = 0.0011), an estimated median of 95 months as compared to an estimated median of 16 months for subjects expressing other levels of COL2A1 and SLC6A10P. Thus, evaluating expression levels of COL2A1 and SLC6A10P at primary surgery could be beneficial for clinically managing recurrence of HGSOC.

Adenocarcinoma of Mullerian origin: review of pathogenesis, molecular biology, and emerging treatment paradigms

Traditionally, epithelial ovarian, tubal, and peritoneal cancers have been viewed as separate entities with disparate origins, pathogenesis, clinical features, and outcomes. Additionally, previous classification systems for ovarian cancer have proposed two primary histologic groups that encompass the standard histologic subtypes. Recent data suggest that these groupings no longer accurately reflect our knowledge surrounding these cancers. In this review, we propose that epithelial ovarian, tubal, and peritoneal carcinomas represent a spectrum of disease that originates in the Mullerian compartment. We will discuss the incidence, classification, origin, molecular determinants, and pathologic analysis of these cancers that support the conclusion they should be collectively referred to as adenocarcinomas of Mullerian origin. As our understanding of the molecular and pathologic profiling of adenocarcinomas of Mullerian origin advances, we anticipate treatment paradigms will shift towards genomic driven therapeutic interventions.

Inhibition of TMEM45A suppresses proliferation, induces cell cycle arrest and reduces cell invasion in human ovarian cancer cells

The association of TMEM45A with various cancers has been recently reported. However, the biological function of TMEM45A in ovarian cancer remains unclear. The present study aimed to elucidate the role of TMEM45A in regulating the biological behavior of ovarian cancer cells. We compared the expression of TMEM45A between ovarian cancer tissues and normal tissues based on RNA-sequencing data of the ovarian cancer cohort from The Cancer Genome Atlas (TCGA) project and our real-time PCR data from 25 pairs of ovarian cancer and their matched non-cancerous tissue samples. The expression of TMEM45A was then suppressed in two ovarian cancer cell lines, HO-8910 and A2780, by RNA interference. Cell proliferation, cell cycle distribution, adhesion and invasive ability were then detected using the Cell Counting Kit-8 assay (CCK-8), propidium iodide (PI) staining, and cell adhesion and Transwell assays, respectively. In addition, the mRNA and protein levels of transforming growth factor-β (TGF-β1 and TGF-β2), Ras homolog family member A (RhoA) and Rho-associated kinase 2 (ROCK2) were detected with real-time PCR and western blotting, respectively. TCGA data and our real-time PCR results demonstrated the overexpression of TMEM45A in ovarian cancer. Silencing of TMEM45A significantly inhibited cell proliferation and significantly increased the cell population in the G1 phase. Moreover, knockdown of TMEM45A also inhibited cell adhesion as well as cell invasion. More importantly, suppression of TMEM45A notably downregulated the expression of TGF-β1, TGF-β2, RhoA and ROCK2. In conclusion, TMEM45A may function as an oncogene for ovarian cancer, and inhibition of TMEM45A may be a therapeutic strategy for ovarian cancer.

Upregulation of Periostin and Reactive Stroma Is Associated with Primary Chemoresistance and Predicts Clinical Outcomes in Epithelial Ovarian Cancer

PURPOSE

Up to one third of ovarian cancer patients are intrinsically resistant to platinum-based treatment. However, predictive and therapeutic strategies are lacking due to a poor understanding of the underlying molecular mechanisms. This study aimed to identify key molecular characteristics that are associated with primary chemoresistance in epithelial ovarian cancers.

EXPERIMENTAL DESIGN

Gene expression profiling was performed on a discovery set of 85 ovarian tumors with clinically well-defined response to chemotherapies as well as on an independent validation dataset containing 138 ovarian patients from the chemotreatment arm of the ICON7 trial.

RESULTS

We identified a distinct "reactive stroma" gene signature that is specifically associated with primary chemoresistant tumors and was further upregulated in posttreatment recurrent tumors. Immunohistochemistry (IHC) and RNA in situ hybridization (RNA ISH) analyses on three of the highest-ranked signature genes (POSTN, LOX, and FAP) confirmed that modulation of the reactive stroma signature genes within the peritumoral stromal compartments was specifically associated with the clinical chemoresistance. Consistent with these findings, chemosensitive ovarian cells grown in the presence of recombinant POSTN promoted resistance to carboplatin and paclitaxel treatment in vitro. Finally, we validated the reactive stroma signature in an independent dataset and demonstrated that a high POSTN expression level predicts shorter progression-free survival following first-line chemotherapy.

CONCLUSIONS

Our findings highlight the important interplay between cancer and the tumor microenvironment in ovarian cancer biology and treatment. The identified reactive stromal components in this study provide a molecular basis to the further development of novel diagnostic and therapeutic strategies for overcoming chemoresistance in ovarian cancer.

Prediction of resistance to chemotherapy in ovarian cancer: a systematic review

Background

Patient response to chemotherapy for ovarian cancer is extremely heterogeneous and there are currently no tools to aid the prediction of sensitivity or resistance to chemotherapy and allow treatment stratification. Such a tool could greatly improve patient survival by identifying the most appropriate treatment on a patient-specific basis.

Methods

PubMed was searched for studies predicting response or resistance to chemotherapy using gene expression measurements of human tissue in ovarian cancer.

Results

42 studies were identified and both the data collection and modelling methods were compared. The majority of studies utilised fresh-frozen or formalin-fixed paraffin-embedded tissue. Modelling techniques varied, the most popular being Cox proportional hazards regression and hierarchical clustering which were used by 17 and 11 studies respectively. The gene signatures identified by the various studies were not consistent, with very few genes being identified by more than two studies. Patient cohorts were often noted to be heterogeneous with respect to chemotherapy treatment undergone by patients.

Conclusions

A clinically applicable gene signature capable of predicting patient response to chemotherapy has not yet been identified. Research into a predictive, as opposed to prognostic, model could be highly beneficial and aid the identification of the most suitable treatment for patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1101-8) contains supplementary material, which is available to authorized users.

OvMark: a user-friendly system for the identification of prognostic biomarkers in publically available ovarian cancer gene expression datasets

BACKGROUND

Ovarian cancer has the lowest survival rate of all gynaecologic cancers and is characterised by a lack of early symptoms and frequent late stage diagnosis. There is a paucity of robust molecular markers that are independent of and complementary to clinical parameters such as disease stage and tumour grade.

METHODS

We have developed a user-friendly, web-based system to evaluate the association of genes/miRNAs with outcome in ovarian cancer. The OvMark algorithm combines data from multiple microarray platforms (including probesets targeting miRNAs) and correlates them with clinical parameters (e.g. tumour grade, stage) and outcomes (disease free survival (DFS), overall survival). In total, OvMark combines 14 datasets from 7 different array platforms measuring the expression of ~17,000 genes and 341 miRNAs across 2,129 ovarian cancer samples.

RESULTS

To demonstrate the utility of the system we confirmed the prognostic ability of 14 genes and 2 miRNAs known to play a role in ovarian cancer. Of these genes, CXCL12 was the most significant predictor of DFS (HR = 1.42, p-value = 2.42x10-6). Surprisingly, those genes found to have the greatest correlation with outcome have not been heavily studied in ovarian cancer, or in some cases in any cancer. For instance, the three genes with the greatest association with survival are SNAI3, VWA3A and DNAH12.

CONCLUSIONS/IMPACT

OvMark is a powerful tool for examining putative gene/miRNA prognostic biomarkers in ovarian cancer (available at http://glados.ucd.ie/OvMark/index.html). The impact of this tool will be in the preliminary assessment of putative biomarkers in ovarian cancer, particularly for research groups with limited bioinformatics facilities.

Image-guided ovarian mass biopsy: efficacy and safety

PURPOSE

Image-guided needle biopsy represents a minimally invasive method for pathologic diagnosis of a mass. This study evaluates the diagnostic yield, accuracy, and safety of ovarian mass biopsy with combined core and fine-needle technique.

MATERIALS AND METHODS

Medical records of all women at least 18 years of age, referred from gynecologic oncology, who underwent image-guided ovarian mass biopsy from 2001 through 2011 were reviewed. Among 27 patients, ultrasound guidance was used in 13 (48%), six transabdominal and seven transvaginal; computed tomography guidance was used in 14 (52%), nine transabdominal and five transgluteal. Biopsy indications were suspected metastasis (n = 15; 56%), suspected ovarian cancer to be treated with neoadjuvant chemotherapy (n = 10; 37%), and relative contraindication to surgery (n = 2; 7%). Mean maximum lesion dimension was 9.9 cm (range, 2-23 cm), with solid composition in nine (33%), cystic in six (22%), and mixed in 12 (44%). Biopsy pathologic findings were compared versus those of the surgical specimen or, for masses that were not resected, versus the stability of benign masses and response to chemotherapy of malignant masses on follow-up.

RESULTS

All biopsies yielded a diagnosis. No biopsy-related complications were noted. Eleven patients (41%) did not undergo lesion resection and were followed for an average of 28.8 months (range, 0.3-118.4 mo). In no patient did malignancy develop during clinical follow-up after a benign biopsy diagnosis. Sensitivity and specificity for diagnosis of malignancy were 100% ± 0 (19 of 19) and 88% ± 26 (seven of eight), respectively, for cancer detection. In nine patients (33%) with final pathologic diagnosis of epithelial ovarian cancer, tumor seeding was not observed during a mean follow-up of 44.6 months (range, 1.3-110.2 mo).

CONCLUSIONS

Image-guided ovarian mass core needle biopsy results in a pathologic diagnosis of benign and malignant masses with high yield, accuracy, and safety.

Gynecologic biopsy for molecular profiling: a review for the interventional radiologist

The interventional radiologist is often asked to obtain multiple biopsies of gynecological malignancies for genetic profiling. This article reviews the current indications for gynecological biopsy as well as how the information gained contributes to a personalized medicine plan for the individual patient. The specific focus of this review is the current knowledge and practice of molecular profiling for gynecological malignancies.

Retrospective analysis of survival improvement by molecular biomarker-based personalized chemotherapy for recurrent ovarian cancer

Aggressive tumors such as epithelial ovarian cancer (EOC) are highly heterogeneous in their therapeutic response, making it difficult to improve overall response by using drugs in unselected patients. The goal of this study was to retrospectively, but independently, examine whether biomarker-based personalized chemotherapy selection could improve survival of EOC patients. Using in vitro drug sensitivity and patient clinical outcome data, we have developed co-expression extrapolation (COXEN) biomarker models for predicting patient response to three standard chemotherapy drugs used to treat advanced EOC: paclitaxel, cyclophosphamide, and topotecan, for which sufficient patient data were available for our modeling and independent validation. Four different cohorts of 783 EOC patients were used in our study, including two cohorts of 499 patients for independent validation. The COXEN predictors for the three drugs independently showed high prediction both for patient short-term therapeutic response and long-term survival for recurrent EOC. We then examined the potential clinical benefit of the simultaneous use of the three drug predictors for a large diverse EOC cohort in a prospective manner, finding that the median overall survival was 21 months longer for recurrent EOC patients who were treated with the predicted most effective chemotherapies. Survival improvement was greater for platinum-sensitive patients if they were treated with the predicted most beneficial drugs. Following the FDA guidelines for diagnostic prediction analysis, our study has retrospectively, yet independently, showed a potential for biomarker-based personalized chemotherapy selection to significantly improve survival of patients in the heterogeneous EOC population when using standard chemotherapies.

POSTN/TGFBI-associated stromal signature predicts poor prognosis in serous epithelial ovarian cancer

OBJECTIVE

To identify molecular prognosticators and therapeutic targets for high-grade serous epithelial ovarian cancers (EOCs) using genetic analyses driven by biologic features of EOC pathogenesis.

METHODS

Ovarian tissue samples (n = 172; 122 serous EOCs, 30 other EOCs, 20 normal/benign) collected prospectively from sequential patients undergoing gynecologic surgery were analyzed using RNA expression microarrays. Samples were classified based on expression of genes with potential relevance in ovarian cancer. Gene sets were defined using Rosetta Similarity Search Tool (ROAST) and analysis of variance (ANOVA). Gene copy number variations were identified by array comparative genomic hybridization.

RESULTS

No distinct subgroups of EOC could be identified by unsupervised clustering, however, analyses based on genes correlated with periostin (POSTN) and estrogen receptor-alpha (ESR1) yielded distinct subgroups. When 95 high-grade serous EOCs were grouped by genes based on ANOVA comparing ESR1/WT1 and POSTN/TGFBI samples, overall survival (OS) was significantly shorter for 43 patients with tumors expressing genes associated with POSTN/TGFBI compared to 52 patients with tumors expressing genes associated with ESR1/WT1 (median 30 versus 49 months, respectively; P = 0.022). Several targets with therapeutic potential were identified within each subgroup. BRCA germline mutations were more frequent in the ESR1/WT1 subgroup. Proliferation-associated genes and TP53 status (mutated or wild-type) did not correlate with survival. Findings were validated using independent ovarian cancer datasets.

CONCLUSIONS

Two distinct molecular subgroups of high-grade serous EOCs based on POSTN/TGFBI and ESR1/WT1 expressions were identified with significantly different OS. Specific differentially expressed genes between these subgroups provide potential prognostic and therapeutic targets.

Stromal responses among common carcinomas correlated with clinicopathologic features

PURPOSE

We have previously characterized a tumor stroma expression signature in a subset of breast tumors that correlates with better clinical outcome. The purpose of this study is to determine whether this stromal signature, termed the "DTF fibroblast" (desmoid-type fibromatosis) signature, is specific to breast cancer or is a common stromal response found in different types of cancer.

EXPERIMENTAL DESIGNS

The DTF fibroblast signature was applied to gene expression profiles from five ovarian, five lung, two colon, and three prostate cancer expression microarray datasets. In addition, two different tissue microarrays of 204 ovarian tumors and 140 colon tumors were examined for the expression of previously characterized protein markers of DTF fibroblast signature. The DTF fibroblast stromal response was then correlated with clinicopathologic features.

RESULTS

The DTF fibroblast signature is robustly present in ovarian, lung, and colon carcinomas. Both expression microarray data and immunohistochemistry show that the subset of ovarian tumors with strong DTF fibroblast signature expression has statistically significant, worse survival outcomes. No reproducible survival differences were found in either the lung or the colon cancers. The prostate cancers failed to show a DTF fibroblast signature. Multivariant analysis showed that DTF fibroblast signature was significantly more prognostic than the proliferation status in ovarian carcinomas.

CONCLUSIONS

Our results suggest that the DTF fibroblast signature is a common tumor stroma signature in different types of cancer, including ovarian, lung, and colon carcinomas. Our findings provide further insight into the DTF fibroblast stromal responses across different types of carcinomas and their potential as prognostic and therapeutic targets.

Characterization of disease progression in ovarian cancer by utilizing 'chemograms' of ovarian cancer stem cells

INTRODUCTION

Ovarian cancer is one of the leading causes of death in women with cancer. First-line chemotherapy with platinum compounds and taxane compounds has been effective, but most patients develop a relapse of the disease due to drug resistance. There is growing evidence that this resistance may be due to the presence of ovarian cancer stem cells.

DISCUSSION

Cells with properties of cancer stem cells have been isolated from the ascitic fluid of ovarian cancer patients. This subset of cells is highly tumourigenic compared to the rest of the cells in the ascitic fluid. They are known to exude harmful chemicals from their cytoplasm and have been found to be resistant to chemotherapeutic agents. This property has been utilized to purify them by fluorescence assisted cytometry to yield a subset of cells which are called 'side population'. These cells exhibit the properties of cancer stem cells and their role in disease progression is being currently investigated. The course of the disease can be potentially characterized at the cellular level by closely studying this cell population. They can also be cultured in different combinations of chemotherapeutic agents at varying concentrations to obtain 'chemograms' which are sensitivity charts. Chemotherapeutic agents which produce the most effective kill curves can then be rationally used as a second-line chemotherapy if the disease relapses. These sensitivity charts can provide insight into emerging patterns of chemoresistance and also help discover surface markers that accurately identify ovarian cancer stem cells.

CONCLUSION

The high rate of disease relapse in patients with ovarian cancer requires a new and different approach utilizing the sensitivity of cancer stem cells. Isolating and characterizing the resistance patterns of ovarian cancer stem cells may provide a rational approach towards an effective and individualized chemotherapeutic regimen.

Interval debulking surgery in patients with Federation of Gynecology and Obstetrics (FIGO) stage IIIC and IV ovarian cancer

BACKGROUND

The feasibility of neoadjuvant chemotherapy (NAC) and the outcome in patients with Federation of Gynecology and Obstetrics (FIGO) IIIC and IV ovarian cancer were assessed.

PATIENTS AND METHODS

67 patients undergoing interval debulking surgery (IDS) and ≥ 4 courses of platinum-based NAC were analyzed for survival, perioperative morbidity and mortality.

RESULTS

The median follow-up was 30 months. The median progression-free survival (PFS) was 17 months, the overall survival (OS) 34 months. The PFS of patients without residual disease (n = 23; 34.3%) was 31 months (p = 0.003), the OS 65 months (p = 0.001). PFS and OS were significantly longer in patients with no residual disease than in patients with 1-10 mm (n = 34; 47.9%) (p = 0.005 and p = 0.0001, respectively) residual disease. No survival benefit was seen for patients with 1-10 mm compared to > 1 cm (n = 12; 16.9%) residual disease (PFS p = 0.518; OS p = 0.077). 1 patient (1.4%) died; 12 patients needed interventional treatment or operation (16.9%) within the first 30 days postoperatively. Out of these, 5 patients (7.0%) had residual or lasting disability.

CONCLUSIONS

NAC and IDS are safe and feasible in this series of patients with unfavorable prognosis. IDS does not change the goal of complete cytoreduction and therefore does not compensate for a less radical surgical approach.

New perspectives on molecular targeted therapy in ovarian clear cell carcinoma

Ovarian clear cell carcinomas (OCCCs) account for about 5–13% of all epithelial ovarian carcinomas in Western populations. It is characterised by resistance to conventional platinum-based chemotherapy, and new therapeutic strategies are urgently required. This article will focus on how recent discoveries have enhanced our understanding of the molecular pathogenesis of OCCCs, leading to new therapeutic opportunities. These include mutations in ARID1A, which provides a link to endometriosis, upregulation of the phosphatidylinositol 3-kinase/AKT pathway, particularly through mutations of PIK3CA and inactivation of PTEN, and increased activity of pathways involved in angiogenesis. Targeting HER2, apoptotic escape mechanisms and mismatch repair defects offer additional opportunities for treating this enigmatic tumour subtype.

Prediction of cancer outcome using DNA microarray technology: past, present and future

BACKGROUND

The use of DNA microarray technology to predict cancer outcome already has a history of almost a decade. Although many breakthroughs have been made, the promise of individualized therapy is still not fulfilled. In addition, new technologies are emerging that also show promise in outcome prediction of cancer patients.

OBJECTIVE

The impact of DNA microarray and other 'omics' technologies on the outcome prediction of cancer patients was investigated. Whether integration of omics data results in better predictions was also examined.

METHODS

DNA microarray technology was focused on as a starting point because this technology is considered to be the most mature technology from all omics technologies. Next, emerging technologies that may accomplish the same goals but have been less extensively studied are described.

CONCLUSION

Besides DNA microarray technology, other omics technologies have shown promise in predicting the cancer outcome or have potential to replace microarray technology in the near future. Moreover, it is shown that integration of multiple omics data can result in better predictions of cancer outcome; but, owing to the lack of comprehensive studies, validation studies are required to verify which omics has the most information and whether a combination of multiple omics data improves predictive performance.

High levels of genomic aberrations in serous ovarian cancers are associated with better survival

Genomic instability and copy number alterations in cancer are generally associated with poor prognosis; however, recent studies have suggested that extreme levels of genomic aberrations may be beneficial for the survival outcome for patients with specific tumour types. We investigated the extent of genomic instability in predominantly high-grade serous ovarian cancers (SOC) using two independent datasets, generated in Norway (n = 74) and Australia (n = 70), respectively. Genomic instability was quantified by the Total Aberration Index (TAI), a measure of the abundance and genomic size of copy number changes in a tumour. In the Norwegian cohort, patients with TAI above the median revealed significantly prolonged overall survival (p<0.001) and progression-free survival (p<0.05). In the Australian cohort, patients with above median TAI showed prolonged overall survival (p<0.05) and moderately, but not significantly, prolonged progression-free survival. Results were confirmed by univariate and multivariate Cox regression analyses with TAI as a continuous variable. Our results provide further evidence supporting an association between high level of genomic instability and prolonged survival of high-grade SOC patients, possibly as disturbed genome integrity may lead to increased sensitivity to chemotherapeutic agents.

Integrated analysis of gene expression profiles associated with response of platinum/paclitaxel-based treatment in epithelial ovarian cancer

Purpose

This study aims to explore gene expression signatures and serum biomarkers to predict intrinsic chemoresistance in epithelial ovarian cancer (EOC).

Patients and Methods

Gene expression profiling data of 322 high-grade EOC cases between 2009 and 2010 in The Cancer Genome Atlas project (TCGA) were used to develop and validate gene expression signatures that could discriminate different responses to first-line platinum/paclitaxel-based treatments. A gene regulation network was then built to further identify hub genes responsible for differential gene expression between the complete response (CR) group and the progressive disease (PD) group. Further, to find more robust serum biomarkers for clinical application, we integrated our gene signatures and gene signatures reported previously to identify secretory protein-encoding genes by searching the DAVID database. In the end, gene-drug interaction network was constructed by searching Comparative Toxicogenomics Database (CTD) and literature.

Results

A 349-gene predictive model and an 18-gene model independent of key clinical features with high accuracy were developed for prediction of chemoresistance in EOC. Among them, ten important hub genes and six critical signaling pathways were identified to have important implications in chemotherapeutic response. Further, ten potential serum biomarkers were identified for predicting chemoresistance in EOC. Finally, we suggested some drugs for individualized treatment.

Conclusion

We have developed the predictive models and serum biomarkers for platinum/paclitaxel response and established the new approach to discover potential serum biomarkers from gene expression profiles. The potential drugs that target hub genes are also suggested.

Validation of ovarian cancer gene expression signatures for survival and subtype in formalin fixed paraffin embedded tissues

INTRODUCTION

Gene expression signatures have been identified for epithelial ovarian cancer survival (TCGA) and intrinsic subtypes (Tothill et al.). One obstacle to clinical translation is that these signatures were developed using frozen tissue, whereas usually only formalin-fixed, paraffin embedded (FFPE) tissue is available. The aim of this study was to determine if gene expression signatures can be translated to fixed archival tissues.

METHODS

RNA extracted from FFPE sections from 240 primary ovarian cancers was analyzed by DASL on Illumina BeadChip arrays. Concordance of expression at the individual gene level was assessed by comparing array data from the same cancers (30 frozen samples analyzed on Affymetrix arrays versus FFPE DASL).

RESULTS

The correlation between FFPE and frozen survival signature estimates was 0.774. The TCGA signature using DASL was predictive of survival in 106 advanced stage high grade serous ovarian cancers (median survival 33 versus 60 months, estimated hazard ratio for death 2.30, P=0.0007). Similar to Tothill, we found using DASL that most high grade serous ovarian cancers (102/110, 93%) were assigned to subtypes 1, 2, 4 and 5, whereas most endometrioid, clear cell, mucinous and low grade serous cases (39/57, 68%) were assigned to subtypes 3 and 6 (P<10e-15).

CONCLUSIONS

Although individual probe estimates of microarrays may be weakly correlated between FFPE and frozen samples, combinations of probes have robust ability to predict survival and subtype. This suggests that it may be possible to use these signatures for prognostic and predictive purposes as we seek to individualize the treatment of ovarian cancer.

High-prevalence and broad spectrum of Cell Adhesion and Extracellular Matrix gene pathway mutations in epithelial ovarian cancer

Background

Ovarian cancer is the most deadly gynecological cancer because of late diagnosis, frequently with diffuse peritoneal metastases. Recent findings have shown that serous epithelial ovarian cancer has a narrow mutational spectrum with TP53 being the most frequently targeted when single genes are considered. It is, however, important to understand which pathways as a whole may be targeted for mutation.

Findings

Previously published mutational data provided by the cancer genome atlas networks findings on ovarian cancer was searched for statistically significant enrichment of genes in pathways. These pathways were then searched in all patients to identify the spectrum of mutations. Statistical significance was further shown through in-silico permutations of exome sequences using empirically observed mutation rates. We detected mutations in the cell adhesion pathway genes in more than 89% of serous epithelial ovarian cancer patients. This level of near universal mutational targeting of the cell adhesion pathway, including the extracellular matrix pathway, is previously unreported in epithelial ovarian cancer.

Conclusions

Taken together with previous studies on the role of cell adhesion and extracellular matrix gene expression in ovarian cancer and metastasis, our results identify pathways for which the mutational prevalence has previously been overlooked using single gene approaches. Analysis of mutations at the pathway level will be critical in studying heterogeneous diseases such as ovarian cancer.

Role of TRAP1 and estrogen receptor alpha in patients with ovarian cancer -a study of the OVCAD consortium

BACKGROUND

The role of the tumor necrosis factor receptor associated protein 1 (TRAP1) - supposed to be involved in protection of cells from apoptosis and oxidative stress - has just started to be investigated in ovarian cancer. TRAP1 has been shown to be estrogen up-regulated in estrogen receptor α (ERα) positive ovarian cancer cells. The clinical impact of TRAP1 is not clear so far and the significance of ERα expression as therapeutic and prognostic marker is still controversial. Therefore, we investigated the importance of TRAP1 together with ERα in regard to clinicopathological parameters, chemotherapy response, and survival.

METHODS AND RESULTS

Expressions of TRAP1 and ERα were evaluated by immunohistochemical staining of tissue microarrays comprised of 208 ovarian cancer samples. TRAP1 was highly expressed in 55% and ERα was expressed in 52% of all cases. High TRAP1 expression correlated significantly with ERα (p<0.001) but high TRAP1 expression was also found in 42% of ERα negative cases. High TRAP1 expression correlated significantly with favorable chemotherapy-response (HR = 0.48; 95%CI 0.24-0.96, p=0.037) and showed a significant impact on overall survival (OS) (HR = 0.65; 95%CI 0.43-0.99, p = 0.044). ERα expression was a favorable prognostic factor for OS in univariate and multivariate analyses. Interestingly, the combined pattern (ERα positive and/or TRAP1-high) revealed the strongest independent and significant positive influence on OS (HR=0.41; 95%CI 0.27-0.64).

CONCLUSION

Immunohistochemical evaluation of TRAP1 together with ERα provides significant prognostic information. TRAP1 alone is significantly associated with chemotherapy response and overall survival, rendering TRAP1 as interesting scientific and therapeutic target.

Integrated analysis of gene expression and tumor nuclear image profiles associated with chemotherapy response in serous ovarian carcinoma

Background

Small sample sizes used in previous studies result in a lack of overlap between the reported gene signatures for prediction of chemotherapy response. Although morphologic features, especially tumor nuclear morphology, are important for cancer grading, little research has been reported on quantitatively correlating cellular morphology with chemotherapy response, especially in a large data set. In this study, we have used a large population of patients to identify molecular and morphologic signatures associated with chemotherapy response in serous ovarian carcinoma.

Methodology/Principal Findings

A gene expression model that predicts response to chemotherapy is developed and validated using a large-scale data set consisting of 493 samples from The Cancer Genome Atlas (TCGA) and 244 samples from an Australian report. An identified 227-gene signature achieves an overall predictive accuracy of greater than 85% with a sensitivity of approximately 95% and specificity of approximately 70%. The gene signature significantly distinguishes between patients with unfavorable versus favorable prognosis, when applied to either an independent data set (P = 0.04) or an external validation set (P<0.0001). In parallel, we present the production of a tumor nuclear image profile generated from 253 sample slides by characterizing patients with nuclear features (such as size, elongation, and roundness) in incremental bins, and we identify a morphologic signature that demonstrates a strong association with chemotherapy response in serous ovarian carcinoma.

Conclusions

A gene signature discovered on a large data set provides robustness in accurately predicting chemotherapy response in serous ovarian carcinoma. The combination of the molecular and morphologic signatures yields a new understanding of potential mechanisms involved in drug resistance.

Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer

Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 advanced stage, high grade serous ovarian cancers. We implemented a, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a previously undescribed patient stratification, further supported by micro-RNA expression profiles, and gene set enrichment analysis found strong biological support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. The corresponding "angiogenesis signature" was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival. The subtypes we have defined are of potential translational interest as they may be relevant for identifying patients who may benefit from the addition of anti-angiogenic therapies that are now being tested in clinical trials.

Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response

BACKGROUND

Genome scale expression profiling of human tumor samples is likely to yield improved cancer treatment decisions. However, identification of clinically predictive or prognostic classifiers can be challenging when a large number of genes are measured in a small number of tumors.

RESULTS

We describe an unsupervised method to extract robust, consistent metagenes from multiple analogous data sets. We applied this method to expression profiles from five "double negative breast cancer" (DNBC) (not expressing ESR1 or HER2) cohorts and derived four metagenes. We assessed these metagenes in four similar but independent cohorts and found strong associations between three of the metagenes and agent-specific response to neoadjuvant therapy. Furthermore, we applied the method to ovarian and early stage lung cancer, two tumor types that lack reliable predictors of outcome, and found that the metagenes yield predictors of survival for both.

CONCLUSIONS

These results suggest that the use of multiple data sets to derive potential biomarkers can filter out data set-specific noise and can increase the efficiency in identifying clinically accurate biomarkers.

HDAC4-regulated STAT1 activation mediates platinum resistance in ovarian cancer

Ovarian cancer frequently acquires resistance to platinum chemotherapy, representing a major challenge for improving patient survival. Recent work suggests that resistant clones exist within a larger drug-sensitive cell population prior to chemotherapy, implying that resistance is selected for rather than generated by treatment. We sought to compare clinically derived, intrapatient paired models of initial platinum response and subsequent resistant relapse to define molecular determinants of evolved resistance. Transcriptional analysis of a matched cell line series from three patients with high-grade serous ovarian cancer before and after development of clinical platinum resistance (PEO1/PEO4/PEO6, PEA1/PEA2, PEO14/PEO23) identified 91 up- and 126 downregulated genes common to acquired resistance. Significantly enhanced apoptotic response to platinum treatment in resistant cells was observed following knockdown of histone deacetylase (HDAC) 4, FOLR2, PIK3R1, or STAT1 (P < 0.05). Interestingly, HDAC4 and STAT1 were found to physically interact. Acetyl-STAT1 was detected in platinum-sensitive cells but not in HDAC4 overexpressing platinum-resistant cells from the same patient. In resistant cells, STAT1 phosphorylation/nuclear translocation was seen following platinum exposure, whereas silencing of HDAC4 increased acetyl-STAT1 levels, prevented platinum-induced STAT1 activation, and restored cisplatin sensitivity. Conversely, matched sensitive cells were refractory to STAT1 phosphorylation on platinum treatment. Analysis of 16 paired tumor biopsies taken before and after development of clinical platinum resistance showed significantly increased HDAC4 expression in resistant tumors [n = 7 of 16 (44%); P = 0.04]. Therefore, clinical selection of HDAC4-overexpressing tumor cells upon exposure to chemotherapy promotes STAT1 deacetylation and cancer cell survival. Together, our findings identify HDAC4 as a novel, therapeutically tractable target to counter platinum resistance in ovarian cancer.

A seven-gene prognostic model for platinum-treated ovarian carcinomas

BACKGROUND

Prognosis of ovarian carcinoma is poor, heterogeneous, and not accurately predicted by histoclinical features. We analysed gene expression profiles of ovarian carcinomas to identify a multigene expression model associated with survival after platinum-based therapy.

METHODS

Data from 401 ovarian carcinoma samples were analysed. The learning set included 35 cases profiled using whole-genome DNA chips. The validation set included 366 cases from five independent public data sets.

RESULTS

Whole-genome unsupervised analysis could not distinguish poor from good prognosis samples. By supervised analysis, we built a seven-gene optimal prognostic model (OPM) out of 94 genes identified as associated with progression-free survival. Using the OPM, we could classify patients in two groups with different overall survival (OS) not only in the learning set, but also in the validation set. Five-year OS was 57 and 27% for the predicted 'Favourable' and 'Unfavourable' classes, respectively. In multivariate analysis, the OPM outperformed the individual current prognostic factors, both in the learning and the validation sets, and added independent prognostic information.

CONCLUSION

We defined a seven-gene model associated with outcome in 401 ovarian carcinomas. Prospective studies are warranted to confirm its prognostic value, and explore its potential ability for better tailoring systemic therapies in advanced-stage tumours.

A microRNA survival signature (MiSS) for advanced ovarian cancer

OBJECTIVES

MicroRNAs (miRNAs) are a class of small non-coding RNAs that negatively regulate gene expression primarily through post-transcriptional modification. We tested the hypothesis that miRNA expression is associated with overall survival in advanced ovarian cancer.

METHODS

Cases included newly diagnosed patients with stage III or IV serous ovarian cancer. RNA from a training set of 62 cases was hybridized to an miRNA microarray containing 470 mature human transcripts. Cox Regression was performed to identify miRNAs associated with overall survival. External validation was performed using quantitative RT-PCR miRNA assays in an independent test set of 123 samples. MiRNA targets and associated biologic pathways were predicted in silico.

RESULTS

Of all patients, 80% had high-grade, stage IIIC tumors and 64% underwent optimal cytoreduction. The median survival for the entire cohort was 49±4 months. The training set identified 3 miRNAs associated with survival--miR-337, miR-410, and miR-645. An miRNA signature containing miR-410 and miR-645 was most strongly associated with overall survival in the training set (HR=2.96, 95% CI: 1.51-5.78). This miRNA survival signature (MiSS) was validated in the test set (HR=1.71, 95% CI: 1.05-2.78). The MiSS was independent of FIGO stage and surgical debulking.

CONCLUSIONS

The data suggest that an MiSS that contains miR-410 and miR-645 is negatively associated with overall survival in advanced serous ovarian cancer. This signature, when further validated, may be useful in individualizing care for the ovarian cancer patient. Pathway analyses identify biologically plausible mechanisms.

Microarray-Based Gene Expression Studies in Ovarian Cancer

Background

DNA microarray technology is a powerful genomic tool that has the potential to elucidate the relationship between clinical features of cancers and their underlying biological alterations.

Methods

We performed a systemic search in PubMed and Medline databases for recently published articles. The search terms used included “genome-wide,” “microarrays,” “ovarian cancer,” “prognosis” “gene expression profiling,” “molecular marker,” and “molecular biomarker.”

Results

Genome-wide expression profiling using DNA microarray technology has enhanced our understanding of the genes that influence ovarian cancer development, histopathologic subtype, progression, response to therapy, and overall survival.

Conclusions

Gene expression profiling has demonstrated its utility in ovarian cancer research. It is hoped that with technologic, statistical, and bioinformatic advances, the reliability and reproducibility of this technique will increase, spawning clinical applications that may enhance our understanding of the disease and our ability to care for patients in the future.

RNAi screening of the kinome identifies modulators of cisplatin response in ovarian cancer cells

OBJECTIVE

Ovarian cancer retains a poor prognosis among the female gynaecological malignancies. It constitutes about 3% of all malignancies in women and accounts for 5% of all female cancer related deaths. A standard treatment is cytoreductive surgery followed by adjuvant chemotherapy, and re-treatment with platinum based chemotherapy at the time of relapse. In order to improve cisplatin response in ovarian cancer cells, we utilized a high-throughput RNAi screening to identify kinase modulators.

METHODS

A high-throughput RNAi screen was performed using a siRNA library targeting 572 kinases to identify potentiators of cisplatin response in the ovarian cancer cell line SKOV3.

RESULTS

RNAi screening identified at least 55 siRNAs that potentiated the growth inhibitory effects of cisplatin in SKOV3 cells. Inhibition of ATR and CHK1 resulted in the greatest modulation of cisplatin response. Drug dose response of cisplatin in the presence of siRNA validated the effects of these target genes. To show that the siRNA data could be successfully translated into potential therapeutic strategies, CHK1 was further targeted with small molecule inhibitor PD 407824 in combination with cisplatin. Results showed that treatment of SKOV3 and OVCAR3 cells with CHK1 inhibitor PD 407824 led to sensitization of ovarian cancer cells to cisplatin.

CONCLUSIONS

Our data provides kinase targets that could be exploited to design better therapeutics for ovarian cancer patients. We also demonstrate the effectiveness of high-throughput RNAi screening as a tool for identifying sensitizing targets to known and established chemotherapeutic agents.

Personalized medicine: marking a new epoch in cancer patient management

Personalized medicine (PM) is defined as "a form of medicine that uses information about a person's genes, proteins, and environment to prevent, diagnose, and treat disease." The promise of PM has been on us for years. The suite of clinical applications of PM in cancer is broad, encompassing screening, diagnosis, prognosis, prediction of treatment efficacy, patient follow-up after surgery for early detection of recurrence, and the stratification of patients into cancer subgroup categories, allowing for individualized therapy. PM aims to eliminate the "one size fits all" model of medicine, which has centered on reaction to disease based on average responses to care. By dividing patients into unique cancer subgroups, treatment and follow-up can be tailored for each individual according to disease aggressiveness and the ability to respond to a certain treatment. PM is also shifting the emphasis of patient management from primary patient care to prevention and early intervention for high-risk individuals. In addition to classic single molecular markers, high-throughput approaches can be used for PM including whole genome sequencing, single-nucleotide polymorphism analysis, microarray analysis, and mass spectrometry. A common trend among these tools is their ability to analyze many targets simultaneously, thus increasing the sensitivity, specificity, and accuracy of biomarker discovery. Certain challenges need to be addressed in our transition to PM including assessment of cost, test standardization, and ethical issues. It is clear that PM will gradually continue to be incorporated into cancer patient management and will have a significant impact on our health care in the future.

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

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