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

Deciphering Folate Receptor alphaGene Expression and mRNA Signatures in Ovarian Cancer: Implications for Precision Therapies

Antibody-drug conjugates targeting folate receptor alpha (FRα) are a promising treatment for platinum-resistant ovarian cancer (OC) with high FRα expression. Challenges persist in accurately assessing FRα expression levels. Our study aimed to better elucidate FRα gene expression and identify mRNA signatures in OC. We pooled OC gene expression data from 16 public datasets, encompassing 1832 OC and 30 normal ovarian tissues. Additional data included DNA copy number and methylation data from TCGA and protein data from 363 cancer cell lines from the Broad Institute Cancer Cell Line Encyclopedia. FOLR1 mRNA expression was significantly correlated with protein expression in pan-cancer cell lines and ovarian cancer cell lines. FOLR1 expression was higher in OC samples than in normal ovarian tissues (OR = 3.88, p = 6.97 × 10-12). Patients with high FOLR1 expression were more likely to be diagnosed with serous histology, FIGO stage III-IV, and high-grade tumors; however, nearly similar percentages of patients with low FOLR1 expression were also diagnosed with these features. FOLR1 mRNA expression was not correlated with platinum sensitivity or complete surgery, nor with prognosis. However, we identified a 187-gene signature associated with high FOLR1 expression that was significantly associated with improved survival (HR = 0.71, p = 1.18 × 10-6), independently from clinicopathological features. We identified a gene expression signature correlated to high FRα expression and OC prognosis, which may be used to refine therapeutic strategies targeting FRα in OC. These findings warrant validation in larger cohorts.

The Progression and Prospects of the Gene Expression Profiling in Ovarian Epithelial Cancer

Ovarian cancer is one of the most common cancers with a high mortality rate among females worldwide. The understanding of the pathogenesis of the disease is highly important to provide personalized therapy to the patients. Ovarian cancer is as heterogeneous as colon and breast cancer which makes it difficult to treat. The development of gene signature is the only hope in providing targeted therapy to improve the survival of ovarian cancer patients. Malignant epithelial carcinomas are the most common cancers of the ovary with different histological and molecular subtypes and clinical behavior. The development of precursor lesions of ovarian carcinoma in the tubes and endometrium has provided a new dimension to the origin of ovarian cancers. The clinical utility of various gene signatures may not be logical unless validated. Validated gene signatures can aid the clinician in deciding the appropriate line of treatment.

Glutamine metabolism prognostic index predicts tumour microenvironment characteristics and therapeutic efficacy in ovarian cancer

Mounting evidence has highlighted the multifunctional characteristics of glutamine metabolism (GM) in cancer initiation, progression and therapeutic regimens. However, the overall role of GM in the tumour microenvironment (TME), clinical stratification and therapeutic efficacy in patients with ovarian cancer (OC) has not been fully elucidated. Here, three distinct GM clusters were identified and exhibited different prognostic values, biological functions and immune infiltration in TME. Subsequently, glutamine metabolism prognostic index (GMPI) was constructed as a new scoring model to quantify the GM subtypes and was verified as an independent predictor of OC. Patients with low-GMPI exhibited favourable survival outcomes, lower enrichment of several oncogenic pathways, less immunosuppressive cell infiltration and better immunotherapy responses. Single-cell sequencing analysis revealed a unique evolutionary trajectory of OC cells from high-GMPI to low-GMPI, and OC cells with different GMPI might communicate with distinct cell populations through ligand-receptor interactions. Critically, the therapeutic efficacy of several drug candidates was validated based on patient-derived organoids (PDOs). The proposed GMPI could serve as a reliable signature for predicting patient prognosis and contribute to optimising therapeutic strategies for OC.

Poor-prognosis molecular subtypes in adenocarcinomas of pancreato-biliary and gynecological origin: A systematic review

Pancreato-biliary and gynecological adenocarcinomas need better tools to predict clinical outcome. Potential prognostic mesenchymal(-like) transcriptome-based subtypes have been identified in these cancers. In this systematic review, we include studies into molecular subtyping and summarize biological and clinical features of the subtypes within and across sites of origin, searching for suggestions to improve classification and prognostication. PubMed and Embase were searched for original research articles describing potential mesenchymal(-like) mRNA-based subtypes in pancreato-biliary or gynecological adenocarcinomas. Studies limited to supervised clustering were excluded. Fourty-four studies, discussing cholangiocarcinomas, gallbladder, ampullary, pancreatic, ovarian, and endometrial adenocarcinomas were included. There was overlap in molecular and clinical features in mesenchymal(-like) subtypes across all adenocarcinomas. Approaches including microdissection were more likely to identify prognosis-associating subtypes. To conclude, molecular subtypes in pancreato-biliary and gynecological adenocarcinomas share biological and clinical characteristics. Furthermore, separation of stromal and epithelial signals should be applied in future studies into biliary and gynecological adenocarcinomas.

Epigenetically programmed resistance to chemo- and immuno-therapies

Resistance to cancer treatments remains a major barrier in developing cancer cures. While promising combination chemotherapy treatments and novel immunotherapies have improved patient outcomes, resistance to these treatments remains poorly understood. New insights into the dysregulation of the epigenome show how it promotes tumor growth and resistance to therapy. By altering control of gene expression, tumor cells can evade immune cell recognition, ignore apoptotic cues, and reverse DNA damage induced by chemotherapies. In this chapter, we summarize the data on epigenetic remodeling during cancer progression and treatment that enable cancer cell survival and describe how these epigenetic changes are being targeted clinically to overcome resistance.

Establishment and validation of a novel invasion-related gene signature for predicting the prognosis of ovarian cancer

BACKGROUND

Ovarian cancer (OC) is an invasive gynaecologic cancer with a high cancer-related death rate. The purpose of this study was to establish an invasion-related multigene signature to predict the prognostic risk of OC.

METHODS

We extracted 97 invasion-related genes from The Cancer Genome Atlas (TCGA) database. Then, the ConsensusClusterPlus and limma packages were used to calculate differentially expressed genes (DEGs). To calculate the immune scores of the molecular subtypes, we used ESTIMATE to evaluate the stromal score, immune score and ESTIMATE score. MCP-counter and the GSVA package ssgsea were used to evaluate the types of infiltrating immune cells. Survival and nomogram analyses were performed to explore the prognostic value of the signature. Finally, qPCR, immunohistochemistry staining and functional assays were used to evaluate the expression and biological abilities of the signature genes in OC.

RESULTS

Based on the consistent clustering of invasion-related genes, cases in the OC datasets were divided into two subtypes. A significant difference was observed in prognosis between the two subtypes. Most genes were highly expressed in the C1 group. Based on the C1 group genes, we constructed an invasion-related 6-gene prognostic risk model. Furthermore, to verify the signature, we used the TCGA-test and GSE32062 and GSE17260 chip datasets for testing and finally obtained a good risk prediction effect in those datasets. Moreover, the results of the qPCR and immunohistochemistry staining assays revealed that KIF26B, VSIG4 and COL6A6 were upregulated and that FOXJ1, MXRA5 and CXCL9 were downregulated in OC tissues. The functional study showed that the expression of KIF26B, VSIG4, COL6A6, FOXJ1, MXRA5 and CXCL9 can regulate the migration and invasion abilities of OC cells.

CONCLUSION

We developed a 6-gene prognostic stratification system (FOXJ1, MXRA5, KIF26B, VSIG4, CXCL9 and COL6A6) that is independent of clinical features. These results suggest that the signature could potentially be used to evaluate the prognostic risk of OC patients.

Differential gene expression identifies a transcriptional regulatory network involving ER-alpha and PITX1 in invasive epithelial ovarian cancer

Background

The heterogeneous subtypes and stages of epithelial ovarian cancer (EOC) differ in their biological features, invasiveness, and response to chemotherapy, but the transcriptional regulators causing their differences remain nebulous.

Methods

In this study, we compared high-grade serous ovarian cancers (HGSOCs) to low malignant potential or serous borderline tumors (SBTs). Our aim was to discover new regulatory factors causing distinct biological properties of HGSOCs and SBTs.

Results

In a discovery dataset, we identified 11 differentially expressed genes (DEGs) between SBTs and HGSOCs. Their expression correctly classified 95% of 267 validation samples. Two of the DEGs, TMEM30B and TSPAN1, were significantly associated with worse overall survival in patients with HGSOC. We also identified 17 DEGs that distinguished stage II vs. III HGSOC. In these two DEG promoter sets, we identified significant enrichment of predicted transcription factor binding sites, including those of RARA, FOXF1, BHLHE41, and PITX1. Using published ChIP-seq data acquired from multiple non-ovarian cell types, we showed additional regulatory factors, including AP2-gamma/TFAP2C, FOXA1, and BHLHE40, bound at the majority of DEG promoters. Several of the factors are known to cooperate with and predict the presence of nuclear hormone receptor estrogen receptor alpha (ER-alpha). We experimentally confirmed ER-alpha and PITX1 presence at the DEGs by performing ChIP-seq analysis using the ovarian cancer cell line PEO4. Finally, RNA-seq analysis identified recurrent gene fusion events in our EOC tumor set. Some of these fusions were significantly associated with survival in HGSOC patients; however, the fusion genes are not regulated by the transcription factors identified for the DEGs.

Conclusions

These data implicate an estrogen-responsive regulatory network in the differential gene expression between ovarian cancer subtypes and stages, which includes PITX1. Importantly, the transcription factors associated with our DEG promoters are known to form the MegaTrans complex in breast cancer. This is the first study to implicate the MegaTrans complex in contributing to the distinct biological trajectories of malignant and indolent ovarian cancer subtypes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08276-8.

Identification of a Novel Tumor Microenvironment Prognostic Signature for Advanced-Stage Serous Ovarian Cancer

Simple Summary

The expression of tumor microenvironment-related genes is known to be correlated with ovarian cancer patients’ prognosis. Immunotherapeutic targets are in part located in this complex cluster of cells and soluble factors. In our study, we constructed a prognostic 11-gene signature for advanced serous ovarian cancer from tumor microenvironment-related genes through lasso regression. The established risk score can quantify the prognosis of ovarian cancer patients more accurately and is able to predict the putative biological response of cancer samples to a programmed death ligand 1 blocking immunotherapy. This might empower the role of immunotherapy in ovarian cancer through its usage in future study protocols.

Abstract

(1) Background: The tumor microenvironment is involved in the growth and proliferation of malignant tumors and in the process of resistance towards systemic and targeted therapies. A correlation between the gene expression profile of the tumor microenvironment and the prognosis of ovarian cancer patients is already known. (2) Methods: Based on data from The Cancer Genome Atlas (379 RNA sequencing samples), we constructed a prognostic 11-gene signature (SNRPA1, CCL19, CXCL11, CDC5L, APCDD1, LPAR2, PI3, PLEKHF1, CCDC80, CPXM1 and CTAG2) for Fédération Internationale de Gynécologie et d’Obstétrique stage III and IV serous ovarian cancer through lasso regression. (3) Results: The established risk score was able to predict the 1-, 3- and 5-year prognoses more accurately than previously known models. (4) Conclusions: We were able to confirm the predictive power of this model when we applied it to cervical and urothelial cancer, supporting its pan-cancer usability. We found that immune checkpoint genes correlate negatively with a higher risk score. Based on this information, we used our risk score to predict the biological response of cancer samples to an anti-programmed death ligand 1 immunotherapy, which could be useful for future clinical studies on immunotherapy in ovarian cancer.

Identification and Development of Subtypes With Poor Prognosis in Pan-Gynecological Cancer Based on Gene Expression in the Glycolysis-Cholesterol Synthesis Axis

Objective: Metabolic reprogramming is an important biomarker of cancer. Metabolic adaptation driven by oncogenes allows tumor cells to survive and grow in a complex tumor microenvironment. The heterogeneity of tumor metabolism is related to survival time, somatic cell-driven gene mutations, and tumor subtypes. Using the heterogeneity of different metabolic pathways for the classification of gynecological pan-cancer is of great significance for clinical decision-making and prognosis prediction. Methods: RNA sequencing data for patients with ovarian, cervical, and endometrial cancer were downloaded from The Cancer Genome Atlas database. Genes related to glycolysis and cholesterol were extracted and clustered coherently by using ConsensusClusterPlus. The mutations and copy number variations in different subtypes were compared, and the immune scores of the samples were evaluated. The limma R package was used to identify differentially expressed genes between subtypes, and the WebGestaltR package (V0.4.2) was used to conduct Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology functional enrichment analyses. A risk score model was constructed based on multivariate Cox analysis. Prognostic classification efficiency was analyzed by using timeROC, and internal and external cohorts were used to verify the robustness of the model. Results: Based on the expression of 11 glycolysis-related genes and seven cholesterol-related genes, 1,204 samples were divided into four metabolic subtypes (quiescent, glycolysis, cholesterol, and mixed). Immune infiltration scores showed significant differences among the four subtypes. Survival analysis showed that the prognosis of the cholesterol subtype was better than that of the quiescent subtype. A nine-gene signature was constructed based on differentially expressed genes between the cholesterol and quiescent subtypes, and it was validated by using an independent cohort of the International Cancer Genome Consortium. Compared with existing models, our nine-gene signature had good prediction performance. Conclusion: The metabolic classification of gynecological pan-cancer based on metabolic reprogramming may provide an important basis for clinicians to choose treatment options, predict treatment resistance, and predict patients' clinical outcomes.

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.

Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile

(1) Background: Biomarkers might play a significant role in predicting the clinical outcomes of patients with ovarian cancer. By analyzing lipid metabolism genes, future perspectives may be uncovered; (2) Methods: RNA-seq data for serous ovarian cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The non-negative matrix factorization package in programming language R was used to classify molecular subtypes of lipid metabolism genes and the limma package in R was performed for functional enrichment analysis. Through lasso regression, we constructed a multi-gene prognosis model; (3) Results: Two molecular subtypes were obtained and an 11-gene signature was constructed (PI3, RGS, ADORA3, CH25H, CCDC80, PTGER3, MATK, KLRB1, CCL19, CXCL9 and CXCL10). Our prognostic model shows a good independent prognostic ability in ovarian cancer. In a nomogram, the predictive efficiency was notably superior to that of traditional clinical features. Related to known models in ovarian cancer with a comparable amount of genes, ours has the highest concordance index; (4) Conclusions: We propose an 11-gene signature prognosis prediction model based on lipid metabolism genes in serous ovarian cancer.

Whole transcriptome signature for prognostic prediction (WTSPP): application of whole transcriptome signature for prognostic prediction in cancer

Developing prognostic biomarkers for specific cancer types that accurately predict patient survival is increasingly important in clinical research and practice. Despite the enormous potential of prognostic signatures, proposed models have found limited implementations in routine clinical practice. Herein, we propose a generic, RNA sequencing platform independent, statistical framework named whole transcriptome signature for prognostic prediction to generate prognostic gene signatures. Using ovarian cancer and lung adenocarcinoma as examples, we provide evidence that our prognostic signatures overperform previous reported signatures, capture prognostic features not explained by clinical variables, and expose biologically relevant prognostic pathways, including those involved in the immune system and cell cycle. Our approach demonstrates a robust method for developing prognostic gene expression signatures. In conclusion, our statistical framework can be generally applied to all cancer types for prognostic prediction and might be extended to other human diseases. The proposed method is implemented as an R package (PanCancerSig) and is freely available on GitHub ( https://github.com/Cheng-Lab-GitHub/PanCancer_Signature ).

The identification of nuclear αvβ3 integrin in ovarian cancer: non-paradigmal localization with cancer promoting actions

Nuclear translocation of transmembrane proteins was reported in high-grade serous ovarian cancer (HGSOC), a highly aggressive gynecological malignancy. Although the membrane receptor αvβ3 integrin is amply expressed in HGSOC and involved in disease progression, its nuclear localization was never demonstrated. Nuclear αvβ3 was explored in HGSOC cells (OVCAR3, KURAMOCHI, and JHOS4), nuclear localization signal (NLS) modified β3 OVCAR3, Chinese hamster ovaries (CHO-K1) and human embryonic kidney (HEK293) before/after transfections with β3/β1 integrins. We used the ImageStream technology, Western blots (WB), co immunoprecipitations (Co-IP), confocal immunofluorescence (IF) microscopy, flow cytometry for cell counts and cell cycle, wound healing assays and proteomics analyses. Fresh/archived tumor tissues were collected from nine HGSOC patients and normal ovarian and fallopian tube (FT) tissues from eight nononcological patients and assessed for nuclear αvβ3 by WB, confocal IF microscopy and immunohistochemistry (IHC). We identified nuclear αvβ3 in HGSOC cells and tissues, but not in normal ovaries and FTs. The nuclear integrin was Tyr 759 phosphorylated and functionally active. Nuclear αvβ3 enriched OVCAR3 cells demonstrated induced proliferation and oncogenic signaling, intact colony formation ability and inhibited migration. Proteomics analyses revealed a network of nuclear αvβ3-bound proteins, many of which with key cancer-relevant activities. Identification of atypical nuclear localization of the αvβ3 integrin in HGSOC challenges the prevalent conception that the setting in which this receptor exerts its pleiotropic actions is exclusively at the cell membrane. This discovery proposes αvβ3 moonlighting functions and may improve our understanding of the molecular basis of ovarian cancer pathogenesis.

Clinical Interest of Combining Transcriptomic and Genomic Signatures in High-Grade Serous Ovarian Cancer

High-grade serous ovarian cancer is one of the deadliest gynecological malignancies and remains a clinical challenge. There is a critical need to effectively define patient stratification in a clinical setting. In this study, we address this question and determine the optimal number of molecular subgroups for ovarian cancer patients. By studying several independent patient cohorts, we observed that classifying high-grade serous ovarian tumors into four molecular subgroups using a transcriptomic-based approach did not reproducibly predict patient survival. In contrast, classifying these tumors into only two molecular subgroups, fibrosis and non-fibrosis, could reliably inform on patient survival. In addition, we found complementarity between transcriptomic data and the genomic signature for homologous recombination deficiency (HRD) that helped in defining prognosis of ovarian cancer patients. We also established that the transcriptomic and genomic signatures underlined independent biological processes and defined four different risk populations. Thus, combining genomic and transcriptomic information appears as the most appropriate stratification method to reliably subgroup high-grade serous ovarian cancer patients. This method can easily be transferred into the clinical setting.

Novel Model for Comprehensive Assessment of Robust Prognostic Gene Signature in Ovarian Cancer Across Different Independent Datasets

Different analytical methods or models can often find completely different prognostic biomarkers for the same cancer. In the study of prognostic molecular biomarkers of ovarian cancer (OvCa), different studies have reported a variety of prognostic gene signatures. In the current study, based on geometric concepts, the linearity-clustering phase diagram with integrated P-value (LCP) method was used to comprehensively consider three indicators that are commonly employed to estimate the quality of a prognostic gene signature model. The three indicators, namely, concordance index, area under the curve, and level of the hazard ratio were determined via calculation of the prognostic index of various gene signatures from different datasets. As evaluation objects, we selected 13 gene signature models (Cox regression model) and 16 OvCa genomic datasets (including gene expression information and follow-up data) from published studies. The results of LCP showed that three models were universal and better than other models. In addition, combining the three models into one model showed the best performance in all datasets by LCP calculation. The combination gene signature model provides a more reliable model and could be validated in various datasets of OvCa. Thus, our method and findings can provide more accurate prognostic biomarkers and effective reference for the precise clinical treatment of OvCa.

Large-scale integrated analysis of ovarian cancer tumors and cell lines identifies an individualized gene expression signature for predicting response to platinum-based chemotherapy

Heterogeneity in chemotherapeutic response is directly associated with prognosis and disease recurrence in patients with ovarian cancer (OvCa). Despite the significant clinical need, a credible gene signature for predicting response to platinum-based chemotherapy and for guiding the selection of personalized chemotherapy regimens has not yet been identified. The present study used an integrated approach involving both OvCa tumors and cell lines to identify an individualized gene expression signature, denoted as IndividCRS, consisting of 16 robust chemotherapy-responsive genes for predicting intrinsic or acquired chemotherapy response in the meta-discovery dataset. The robust performance of this signature was subsequently validated in 25 independent tumor datasets comprising 2215 patients and one independent cell line dataset, across different technical platforms. The IndividCRS was significantly correlated with the response to platinum therapy and predicted the improved outcome. Moreover, the IndividCRS correlated with homologous recombination deficiency (HRD) and was also capable of discriminating HR-deficient tumors with or without platinum-sensitivity for guiding HRD-targeted clinical trials. Our results reveal the universality and simplicity of the IndividCRS as a promising individualized genomic tool to rapidly monitor response to chemotherapy and predict the outcome of patients with OvCa.

The rise of genomic profiling in ovarian cancer

INTRODUCTION

Next-generation sequencing and advances in 'omics technology have rapidly increased our understanding of the molecular landscape of epithelial ovarian cancers. Areas covered: Once characterized only by histologic appearance and clinical behavior, we now understand many of the molecular phenotypes that underlie the different ovarian cancer subtypes. While the current approach to treatment involves standard cytotoxic therapies after cytoreductive surgery for all ovarian cancers regardless of histologic or molecular characteristics, focus has shifted beyond a 'one size fits all' approach to ovarian cancer. Expert commentary: Genomic profiling offers potentially 'actionable' opportunities for development of targeted therapies and a more individualized approach to treatment with concomitant improved outcomes and decreased toxicity.

The rise of genomic profiling in ovarian cancer

INTRODUCTION

Next-generation sequencing and advances in 'omics technology have rapidly increased our understanding of the molecular landscape of epithelial ovarian cancers. Areas covered: Once characterized only by histologic appearance and clinical behavior, we now understand many of the molecular phenotypes that underlie the different ovarian cancer subtypes. While the current approach to treatment involves standard cytotoxic therapies after cytoreductive surgery for all ovarian cancers regardless of histologic or molecular characteristics, focus has shifted beyond a 'one size fits all' approach to ovarian cancer. Expert commentary: Genomic profiling offers potentially 'actionable' opportunities for development of targeted therapies and a more individualized approach to treatment with concomitant improved outcomes and decreased toxicity.

The rise of genomic profiling in ovarian cancer

INTRODUCTION

Next-generation sequencing and advances in 'omics technology have rapidly increased our understanding of the molecular landscape of epithelial ovarian cancers. Areas covered: Once characterized only by histologic appearance and clinical behavior, we now understand many of the molecular phenotypes that underlie the different ovarian cancer subtypes. While the current approach to treatment involves standard cytotoxic therapies after cytoreductive surgery for all ovarian cancers regardless of histologic or molecular characteristics, focus has shifted beyond a 'one size fits all' approach to ovarian cancer. Expert commentary: Genomic profiling offers potentially 'actionable' opportunities for development of targeted therapies and a more individualized approach to treatment with concomitant improved outcomes and decreased toxicity.

Frontline treatment of epithelial ovarian cancer

This is a contemporaneous review of the frontline treatment of epithelial ovarian cancer (EOC), specifically on the importance of optimal surgical cytoreductive surgery, the pivotal role of platinum-based adjuvant chemotherapy (which encompasses intraperitoneal and dose-dense regimens) and the emergence of neo-adjuvant chemotherapy. Additionally, the benefit of concurrent and maintenance bevacizumab in the suboptimally debullked stage III and stage IV EOC setting is also reviewed. The article also discusses the increasing importance of prognostic and predictive molecular biomarkers in the future management of EOC.

Identification of Cathepsin K in the Peritoneal Metastasis of Ovarian Carcinoma Using In-silico, Gene Expression Analysis

Ovarian carcinomas (OC) are often found in the advanced stage with wide peritoneal dissemination. Differentially-expressed genes (DEGs) between primary ovarian carcinoma (POC) and peritoneal metastatic ovarian carcinomas (PMOC) may have diagnostic and therapeutic values. In this study, we identified 246 DEGs by in-silico analysis using microarrays for 153 POCs and 57 PMOCs. Pathway analysis shows that many of these genes are associated with lipid metabolism. Microfluidic, card-based, quantitative PCR validated 19 DEGs in PMOCs versus POCs (p<0.05). Immunohistochemistry confirmed overexpression of MMP13, CTSK, FGF1 and GREM1 in PMOCs (p<0.05). ELISA detection indicated that serum CTSK levels were significantly increased in OCs versus controls (p<0.001). CTSK levels discriminated between OCs and healthy controls (ROC 0.739; range 0.685-0.793). Combining CA125 and HE4 with CTSK levels produced an improved specificity in the predictive of OCs (sensitivity 88.3%, specificity 92.0%, Youden's index 80.3%). Our study suggests that CTSK levels may be helpful in the diagnosis of primary, ovarian carcinoma.

Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis

PURPOSE

To discover novel prognostic biomarkers in ovarian serous carcinomas.

METHODS

A meta-analysis of all single genes probes in the TCGA and HAS ovarian cohorts was performed to identify possible biomarkers using Cox regression as a continuous variable for overall survival. Genes were ranked by p-value using Stouffer's method and selected for statistical significance with a false discovery rate (FDR) <.05 using the Benjamini-Hochberg method.

RESULTS

Twelve genes with high mRNA expression were prognostic of poor outcome with an FDR <.05 (AXL, APC, RAB11FIP5, C19orf2, CYBRD1, PINK1, LRRN3, AQP1, DES, XRCC4, BCHE, and ASAP3). Twenty genes with low mRNA expression were prognostic of poor outcome with an FDR <.05 (LRIG1, SLC33A1, NUCB2, POLD3, ESR2, GOLPH3, XBP1, PAXIP1, CYB561, POLA2, CDH1, GMNN, SLC37A4, FAM174B, AGR2, SDR39U1, MAGT1, GJB1, SDF2L1, and C9orf82).

CONCLUSION

A meta-analysis of all single genes identified thirty-two candidate biomarkers for their possible role in ovarian serous carcinoma. These genes can provide insight into the drivers or regulators of ovarian cancer and should be evaluated in future studies. Genes with high expression indicating poor outcome are possible therapeutic targets with known antagonists or inhibitors. Additionally, the genes could be combined into a prognostic multi-gene signature and tested in future ovarian cohorts.

Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics

BACKGROUND

A high-quality risk prediction model is urgently needed for the clinical management of ovarian cancer. However most existing models are solely based on clinical parameters, and molecular classifications in recent reports are still being debated. This study aimed to establish a risk prediction model by using both clinicopathological and molecular factors (the synthetic model) for epithelial ovarian cancer.

METHODS

A retrospective cohort study was conducted in epithelial ovarian cancer patients (n = 161) treated with primary debulking surgery and adjuvant chemotherapy. The expression level of 15 selected molecular markers were measured using immunohistochemistry. A risk model was developed using COX regression analysis with overall survival as the primary outcome. A simplified scoring system for each prognostic factor was based on its coefficient. Independent validation (n = 40) was conducted to evaluate the performance of the model.

RESULTS

A total of 10 out of 15 molecular markers were significantly associated with clinical characteristics and overall survival. The synthetic model performed better than the clinicopathological risk model or the molecular risk model alone, as assessed by analysis of the receiver-operating characteristics curve area and the Youden index. The synthetic model included parity (>3), peritoneal metastasis, stage, tumor type, residual disease, and expression of human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), breast cancer 1 (BRCA1), murine sarcoma viral oncogene homolog B (BRAF) and Kirsten rat sarcoma viral oncogene homolog (KRAS).

CONCLUSIONS

Our synthetic risk model may more accurately predict survival of epithelial ovarian cancer patients than current models.

Ovarian cancer: diagnostic, biological and prognostic aspects

Ovarian cancer remains the most lethal gynecologic malignancy, owing to late detection, intrinsic and acquired chemoresistance and remarkable heterogeneity. Despite optimization of surgical and chemotherapy protocols and initiation of clinical trials incorporating targeted therapy, only modest gains have been achieved in prolonging survival in this cancer. This review provides an update of recent developments in our understanding of the etiology, origin, diagnosis, progression and treatment of this malignancy, with emphasis on clinically relevant genetic classification approaches. In the authors' opinion, focused effort directed at understanding the molecular make-up of recurrent and metastatic ovarian cancer, while keeping in mind the unique molecular character of each of its histological types, is central to our effort to improve patient outcome in this 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.

Copy number changes of 4-gene set may predict early relapse in advanced epithelial ovarian cancer after initial platinum-paclitaxel chemotherapy

For advanced epithelial ovarian cancer (EOC), time to recurrence (TTR) is an important indicator to gauge the therapeutic efficacy of postoperative adjuvant chemotherapy. Our objective was to determine the genes that could potentially distinguish patients with short versus long TTR after initial administration of platinum-paclitaxel combination chemotherapy in advanced EOC. Tumor samples of 159 patients were obtained during the primary cytoreduction. Array comparative genomic hybridization (CGH) was carried with genomic DNA from 17 EOC samples (8 with TTR > 15 months and 9 with TTR ≤ 6 months) to screen candidate gene set, copy-number changes (CNC) of which were significantly different between early and late relapse cases. Seventeen candidate genes were identified by array CGH. The analysis of consistency between real-time PCR and array CGH revealed that 4 genes displayed consistent results, namely GSTT1, ISG20L1, STARD5 and FREM1. In a 142-case validation set, CNC of 4 candidate genes was evaluated and verified by real-time PCR. Sixty five point five percent of the patients were correctly divided into early (TTR ≤ 10 months) and late (TTR > 10 months) recurrent group by CNC of the 4 genes using discriminant analysis. The results showed that CNC of 4-gene set could potentially determine early (TTR ≤ 10 months) or late relapse (TTR > 10 months) after initial platinum-paclitaxel combination chemotherapy in advanced EOC.

Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples

BACKGROUND

Ovarian cancer causes more than 15000 deaths per year in the United States. The survival of patients is quite heterogeneous, and accurate prognostic tools would help with the clinical management of these patients.

METHODS

We developed and validated two gene expression signatures, the first for predicting survival in advanced-stage, serous ovarian cancer and the second for predicting debulking status. We integrated 13 publicly available datasets totaling 1525 subjects. We trained prediction models using a meta-analysis variation on the compound covariable method, tested models by a "leave-one-dataset-out" procedure, and validated models in additional independent datasets. Selected genes from the debulking signature were validated by immunohistochemistry and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) in two further independent cohorts of 179 and 78 patients, respectively. All statistical tests were two-sided.

RESULTS

The survival signature stratified patients into high- and low-risk groups (hazard ratio = 2.19; 95% confidence interval [CI] = 1.84 to 2.61) statistically significantly better than the TCGA signature (P = .04). POSTN, CXCL14, FAP, NUAK1, PTCH1, and TGFBR2 were validated by qRT-PCR (P < .05) and POSTN, CXCL14, and phosphorylated Smad2/3 were validated by immunohistochemistry (P < .001) as independent predictors of debulking status. The sum of immunohistochemistry intensities for these three proteins provided a tool that classified 92.8% of samples correctly in high- and low-risk groups for suboptimal debulking (area under the curve = 0.89; 95% CI = 0.84 to 0.93).

CONCLUSIONS

Our survival signature provides the most accurate and validated prognostic model for early- and advanced-stage high-grade, serous ovarian cancer. The debulking signature accurately predicts the outcome of cytoreductive surgery, potentially allowing for stratification of patients for primary vs secondary cytoreduction.

Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer

BACKGROUND

Ovarian cancer is the fifth most common cause of cancer deaths in women in the United States. Numerous gene signatures of patient prognosis have been proposed, but diverse data and methods make these difficult to compare or use in a clinically meaningful way. We sought to identify successful published prognostic gene signatures through systematic validation using public data.

METHODS

A systematic review identified 14 prognostic models for late-stage ovarian cancer. For each, we evaluated its 1) reimplementation as described by the original study, 2) performance for prognosis of overall survival in independent data, and 3) performance compared with random gene signatures. We compared and ranked models by validation in 10 published datasets comprising 1251 primarily high-grade, late-stage serous ovarian cancer patients. All tests of statistical significance were two-sided.

RESULTS

Twelve published models had 95% confidence intervals of the C-index that did not include the null value of 0.5; eight outperformed 97.5% of signatures including the same number of randomly selected genes and trained on the same data. The four top-ranked models achieved overall validation C-indices of 0.56 to 0.60 and shared anticorrelation with expression of immune response pathways. Most models demonstrated lower accuracy in new datasets than in validation sets presented in their publication.

CONCLUSIONS

This analysis provides definitive support for a handful of prognostic models but also confirms that these require improvement to be of clinical value. This work addresses outstanding controversies in the ovarian cancer literature and provides a reproducible framework for meta-analytic evaluation of gene signatures.

Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer

BACKGROUND

Ovarian cancer is the fifth most common cause of cancer deaths in women in the United States. Numerous gene signatures of patient prognosis have been proposed, but diverse data and methods make these difficult to compare or use in a clinically meaningful way. We sought to identify successful published prognostic gene signatures through systematic validation using public data.

METHODS

A systematic review identified 14 prognostic models for late-stage ovarian cancer. For each, we evaluated its 1) reimplementation as described by the original study, 2) performance for prognosis of overall survival in independent data, and 3) performance compared with random gene signatures. We compared and ranked models by validation in 10 published datasets comprising 1251 primarily high-grade, late-stage serous ovarian cancer patients. All tests of statistical significance were two-sided.

RESULTS

Twelve published models had 95% confidence intervals of the C-index that did not include the null value of 0.5; eight outperformed 97.5% of signatures including the same number of randomly selected genes and trained on the same data. The four top-ranked models achieved overall validation C-indices of 0.56 to 0.60 and shared anticorrelation with expression of immune response pathways. Most models demonstrated lower accuracy in new datasets than in validation sets presented in their publication.

CONCLUSIONS

This analysis provides definitive support for a handful of prognostic models but also confirms that these require improvement to be of clinical value. This work addresses outstanding controversies in the ovarian cancer literature and provides a reproducible framework for meta-analytic evaluation of gene signatures.

Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples

BACKGROUND

Ovarian cancer causes more than 15000 deaths per year in the United States. The survival of patients is quite heterogeneous, and accurate prognostic tools would help with the clinical management of these patients.

METHODS

We developed and validated two gene expression signatures, the first for predicting survival in advanced-stage, serous ovarian cancer and the second for predicting debulking status. We integrated 13 publicly available datasets totaling 1525 subjects. We trained prediction models using a meta-analysis variation on the compound covariable method, tested models by a "leave-one-dataset-out" procedure, and validated models in additional independent datasets. Selected genes from the debulking signature were validated by immunohistochemistry and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) in two further independent cohorts of 179 and 78 patients, respectively. All statistical tests were two-sided.

RESULTS

The survival signature stratified patients into high- and low-risk groups (hazard ratio = 2.19; 95% confidence interval [CI] = 1.84 to 2.61) statistically significantly better than the TCGA signature (P = .04). POSTN, CXCL14, FAP, NUAK1, PTCH1, and TGFBR2 were validated by qRT-PCR (P < .05) and POSTN, CXCL14, and phosphorylated Smad2/3 were validated by immunohistochemistry (P < .001) as independent predictors of debulking status. The sum of immunohistochemistry intensities for these three proteins provided a tool that classified 92.8% of samples correctly in high- and low-risk groups for suboptimal debulking (area under the curve = 0.89; 95% CI = 0.84 to 0.93).

CONCLUSIONS

Our survival signature provides the most accurate and validated prognostic model for early- and advanced-stage high-grade, serous ovarian cancer. The debulking signature accurately predicts the outcome of cytoreductive surgery, potentially allowing for stratification of patients for primary vs secondary cytoreduction.

Prognostic biomarkers in endometrial and ovarian carcinoma

This article reviews the main prognostic and predictive biomarkers of endometrial (EC) and ovarian carcinoma (OC). In EC, prognosis still relies on conventional pathological features such as histological type and grade, as well as myometrial or lymphovascular space invasion. Estrogen receptor, p53, Ki-67, and ploidy analysis are the most promising biomarkers among a long list of molecules that have been proposed. Also, a number of putative predictive biomarkers have been proposed in molecular targeted therapy. In OC, prognosis is predominantly dependent on disease stage at diagnosis and the extent of residual disease at primary operation. Diagnostic markers which aid in establishing histological type in OC are available. However, not a single universally accepted predictive or prognostic marker exists to date. Targeted therapy has been growingly focused at in recent years, in view of the frequent development of chemoresistance at recurrent disease. The present review emphasizes the crucial role of correct pathological classification and stringent selection criteria of the material studied as basis for any evaluation of biological markers. It further emphasizes the promise of targeted therapy in EC and OC, while simultaneously highlighting the difficulties remaining before this can become standard of care.

Ovarian cancer emerging subtypes: role of oxidative stress and fibrosis in tumour development and response to treatment

Epithelial ovarian cancer is a silent disease of usually late diagnosis and poor prognosis. Currently treatment options are limited and mainly consist of surgery followed by taxol- and platinum-based chemotherapy. Patient response to treatment is difficult to predict and there is a serious need for anticipating tumour response and orientating medical choices. In that aim, recent researches have focused on molecular aspects of ovarian tumours that could help patient stratification. We review here published discoveries in that field. We emphasize that signatures, defined by combining miRNA and transcriptomic data, enlighten important aspects of ovarian cancer biology and reliably stratify patients. The miR-200-dependent "Oxidative stress" and "Fibrosis" signatures are promising in patient stratification for defining oriented therapeutic strategies. Indeed, the "Stress" patients survive longer than the "Fibrosis" patients, who exhibit partial debulking and incomplete response to chemotherapy. Interestingly, these two subgroups might benefit from specifically targeted therapeutic approaches, as discussed here.

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.