Molecular Subtypes Of Ovarian Cancer Research Articles

Molecular Subtypes of Ovarian Cancer Based on Lipid Metabolism and Glycolysis Reveals Potential Therapeutic Targets

Background: Ovarian cancer (OC) is one of the most lethal gynecological malignant neoplasms. The aim of this study was to use high-throughput sequencing data to investigate the molecular and clinical characteristics of OC subtypes related to lipid metabolism and glycolysis, thus providing a theoretical basis for clinical decision-making. Methods: Molecular data and clinicopathological characteristics of OC patients were extracted from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), and the Gene Expression Omnibus (GEO). Following analysis of genes involved in lipid metabolism and glycolysis, OC was classified into subtypes by unsupervised clustering. The molecular features and clinical outcomes of these subtypes were then evaluated. Results: OC patients were divided into five subtypes based on the analysis of nine genes of interest. Amongst these, patients in subtype D had longer overall survival and more benign clinical features. Subtypes B and E had shorter overall- and progression-free survival, respectively. Both the B and E subtypes were closely related to lipid metabolism and to the glycolytic process. Subtype D was positively correlated with the infiltration of CD8+ T cells, CD4+ T cells, and macrophages, all of which play essential anti-tumor roles. Several risk models for selected subtypes were also constructed based on the expression of select genes. Conclusions: The present work revealed that irregular metabolism in OC tissues was an indicator of poor clinical outcome and altered homeostasis in cancer-related pathways. Moreover, aberrant gene expression signatures associated with lipid metabolism and glycolysis were also correlated with an immunosuppressive tumor microenvironment. Based on lipid metabolism and glycolysis, we have therefore identified several OC molecular subtypes that may prove useful for the development of potential therapeutic targets.

Genomic, Immunological, and Clinical Characterization of Pyroptosis in Ovarian Cancer.

PurposePyroptosis is a form of lytic programmed cell death that is associated with the pathogenesis of many tumors. However, the potential roles of pyroptosis-related genes (PRGs) in the tumor microenvironment (TME) remain unclear.Materials and MethodsWe systematically described the genetic and transcriptional alterations in PRGs in gynecological cancers. An unsupervised clustering method was used to investigate the molecular subtypes of ovarian cancer (OV) and systematically analyze the TME cell infiltration characteristics. A prognostic signature and nomogram were established to quantify the pyroptosis patterns of individual tumors. We also analyzed the expression levels of eight PRGs in the OV tissues.ResultsTwo distinct molecular subtypes of OV were identified, and these two distinct molecular subtypes could predict clinicopathological features, prognosis, TME stromal activity, immune infiltrating cells, and immune checkpoints. A prognostic signature was established, and its predictive capability was validated. Low risk score, characterized by activation of immunity, upregulation of programmed death-ligand 1 expression, lower tumor immune dysfunction and exclusion scores, lower tumor mutation burden, and favorable prognosis. These findings suggested that low-risk patients with OV may be more sensitive to immunotherapy. In addition, this signature could effectively predict the response to chemotherapy in patients with OV. Furthermore, a prognostic nomogram was generated, which exhibited superior predictive accuracy.ConclusionThis study highlights the crucial role of PRGs in the TME and may help develop immunotherapies and promote individualized therapeutic strategies for patients with OV.

Current ideas about molecular genetic subtypes of ovarian cancer: a personalized approach and a new platform for further research

Highly malignant ovarian cancers are a histopathological diagnosis, but can be multiple diseases at the molecular level. Research aimed at identifying molecular genetic subtypes of ovarian cancer is being conducted to find an answer to the question: can different molecular subgroups influence the choice of treatment? One of the achievements of this direction is the recognition of the dualistic theory of the origin of ovarian carcinomas with their division into High-grade and Low-grade subtypes. However, the data of sequencing of the tumor genome suggest the existence of 6 subtypes of carcinoma, including two LG and four HG subtypes. Patients of subtype C1 are characterized by a high stromal response and have the lowest survival, tumors of C2 and C4 subtypes have a higher rate of intratumoral CD3 + cells, lower stroma gene expression and better survival than C1. The mesenchymal subtype C5 is widely represented by mesenchymal cells, characterized by overexpression of N-cadherins and P-cadherins, low expression of differentiation markers and lower survival than C2 and C4. The use of a consensus algorithm to determine the subtype allows the identification of only a minority of ovarian cancers (approximately 25%). In this regard, the practical significance of this classification still requires additional research, and today it is permissible to talk about the existence of only 2-3 reproducible subtypes. It is thought that it makes sense to randomize tumors into groups with altered expression of angiogenic genes and with overexpression of immune response genes, as in the angiogenic group there is a comparison of the advantage in survival (prescribing bevacizumab improves it, and in the immune group even increases bevacizumab). Molecular subtypes with poorer survival rates (proliferative and mesenchymal) also benefit most from bevacizumab treatment. The review focuses on some advances in understanding molecular, cellular, and genetic changes related to ovarian cancers with the results achieved so far in describing molecular subtypes of ovarian cancer. The available information is the basis for planning further research.

Multiple genetic variants predict the progression-free survival of bevacizumab plus chemotherapy in advanced ovarian cancer: A retrospective study.

Bevacizumab (BV) plus chemotherapy is broadly used in advanced ovarian cancer (OC). However, the efficacy of BV-based regimens for advanced OC patients is not satisfactory. Therefore, it is urgent to explore the predictive genetic biomarkers for BV.Tumor tissues from advanced OC patients receiving BV-based regimens were analyzed with a 150-gene targeted panel for next generation sequencing. The associations between gene alterations or clinicopathology features and progression-free survival (PFS) were analyzed by Kaplan–Meier curves or Cox regression. The association of the genetic alteration in potential predictive genes and expressions of 11 vascular endothelial growth factor-related genes were analyzed in The Cancer Genome Atlas cohort using 292 OC cases.Sixty two Chinese advanced OC patients treated with BV-based therapy were included. The median PFS of was 6.9 months, and objective response rate was 14.5%. In multivariate Cox regression analysis, the status of endothelial growth factor receptor (EGFR) (hazard ratio = 6.39, 95% confidence interval [CI] 2.25–18.13, P < .001) and human epidermal growth factor receptor 2 (HER2) (hazard ratio = 3.58, 95% CI 1.27–10.08, P = .016) were significantly correlated with PFS. MYC Proto-Oncogene amplification seemed to have a positive trend (hazard ratio = 0.21, 95% CI 0.05–1.02, P = .052). Moreover, EGFR and HER2 alterations were not prognostic factors of overall survival for OC in The Cancer Genome Atlas OC cohort. The vascular endothelial growth factor-related signature analysis indicated vascular endothelial factor A expression was upregulated with EGFR alterations (P = .034) which may be involved in BV resistance, and HER2 alterations were associated with hypoxia inducible factor 1 subunit alpha overexpression significantly (P = .029).EGFR or HER2 alterations are negative predictors of PFS for OC patient treated with BV plus chemotherapy. Therefore, the clinicians may consider to use alternative regimens such as anti-EGFR or anti-HER2 targeted therapy instead of BV-based regimens on these patients when standard care fail.

Identification of two molecular subtypes of dysregulated immune lncRNAs in ovarian cancer.

Long non-coding RNA (lncRNA) has increasingly been identified as a key regulator in pathologies such as cancer. Multiple platforms were used for comprehensive analysis of ovarian cancer to identify molecular subgroups. However, lncRNA and its role in mapping the ovarian cancer subpopulation are still largely unknown. RNA-sequencing and clinical characteristics of ovarian cancer were acquired from The Cancer Genome Atlas database (TCGA). A total of 52 lncRNAs were identified as aberrant immune lncRNAs specific to ovarian cancer. We redefined two different molecular subtypes, C1(188) and C2(184 samples), in "iClusterPlus" R package, among which C2 grouped ovarian cancer samples have higher survival probability and longer median survival time (P <0.05) with activated IFN-gamma response, Wound Healing and Cytotoxic lymphocytes signal; 456 differentially expressed genes were acquired in C1 and C2 subtypes using limma (3.40.6) package, among which 419 were up-regulated and 37 were down-regulated, in TCGA dataset. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis revealed that these genes were actively involved in ECM-receptor interaction, PI3K-Akt signaling pathway interaction KEGG pathway. Compared with the existing immune subtype, the Cluster2 sample showed a substantial increase in the proportion of the existing C2 immune subtype, accounting for 81.37%, which was associated with good prognosis. Our C1 subtype contains only 56.49% of the existing immune C1 and C4, which also explains the poor prognosis of C1. Furthermore, 52 immune-related lncRNAs were used to divide the TCGA-endometrial cancer and cervical cancer samples into two categories, and C2 had a good prognosis. The differentially expressed genes were highly correlated with immune-cell-related pathways. Based on lncRNA, two molecular subtypes of ovarian cancer were identified and had significant prognostic differences and immunological characteristics.

Deep learning-based ovarian cancer subtypes identification using multi-omics data

BackgroundIdentifying molecular subtypes of ovarian cancer is important. Compared to identify subtypes using single omics data, the multi-omics data analysis can utilize more information. Autoencoder has been widely used to construct lower dimensional representation for multi-omics feature integration. However, learning in the deep architectures in Autoencoder is difficult for achieving satisfied generalization performance. To solve this problem, we proposed a novel deep learning-based framework to robustly identify ovarian cancer subtypes by using denoising Autoencoder.ResultsIn proposed method, the composite features of multi-omics data in the Cancer Genome Atlas were produced by denoising Autoencoder, and then the generated low-dimensional features were input into k-means for clustering. At last based on the clustering results, we built the light-weighted classification model with L1-penalized logistic regression method. Furthermore, we applied the differential expression analysis and WGCNA analysis to select target genes related to molecular subtypes. We identified 34 biomarkers and 19 KEGG pathways associated with ovarian cancer.ConclusionsThe independent test results in three GEO datasets proved the robustness of our model. The literature reviewing show 19 (56%) biomarkers and 8(42.1%) KEGG pathways identified based on the classification subtypes have been proved to be associated with ovarian cancer. The outcomes indicate that our proposed method is feasible and can provide reliable results.

MOLECULAR AND GENETIC SUBTYPES OF OVARIAN CANCER: PROSPECTS FOR FURTHER STUDIES

High-grade ovarian carcinoma is a histopathological diagnosis, however, at the molecular level, ovarian cancer represents a heterogeneous group of diseases. Studies aimed at identifying molecular genetic subtypes of ovarian cancer are conducted in order to find the answer to the question: can different molecular subgroups influence the choice of treatment? One of the achievements in this trend is the recognition of the dualistic model that categorizes various types of ovarian cancer into two groups designated high-grade (HG) and low-grade (LG) tumors. However, the tumor genome sequencing data suggest the existence of 6 ovarian carcinoma subtypes, including two LG and four HG subtypes. Subtype C1 exhibits a high stromal response and the lowest survival. Subtypes C2 and C4 demonstrate higher number of intratumoral CD3 + cells, lower stromal gene expression and better survival than sybtype C1. Subtype C5 (mesenchymal) is characterized by mesenchymal cells, over-expression of N-cadherin and P-cadherin, low expression of differentiation markers, and lower survival rates than C2 and C4. The use of a consensus algorithm to determine the subtype allows identification of only a minority of ovarian carcinomas (approximately 25%) therefore, the practical importance of this classification requires additional research. There is evidence that it makes sense to randomize tumors into groups with altered expression of angiogenic genes and groups with overexpression of the immune response genes, as in the angiogenic group there is a comparative superiority in terms of survival. The administration of bevacizumab in the angiogenic group improves survival, while the administration of bevacizumab in the immune group even worsens the outcome. Molecular subtypes with worse survival rates (proliferative and mesenchymal) also benefit most from bevacizumab treatment. This review focuses on some of the advances in understanding molecular, cellular, and genetic changes in ovarian carcinomas with the results achieved so far regarding the formulation of molecular subtypes of ovarian cancer, however further studies are needed.

Integrative network biology analysis identifies miR-508-3p as the determinant for the mesenchymal identity and a strong prognostic biomarker of ovarian cancer

Ovarian cancer is a heterogeneous malignancy that poses tremendous clinical challenge. Based on unsupervised classification of whole-genome gene expression profiles, four molecular subtypes of ovarian cancer were recently identified. However, single-driver molecular events specific to these subtypes have not been clearly elucidated. We aim to characterize the regulatory mechanisms underlying the poor prognosis mesenchymal subtype of ovarian cancer using a systems biology approach, involving a variety of molecular modalities including gene and microRNA expression profiles. miR-508-3p emerged as the most powerful determinant that regulates a cascade of dysregulated genes in the mesenchymal subtype, including core genes involved in epithelial–mesenchymal transition (EMT) program. Moreover, miR-508-3p down-regulation, due to promoter hypermethylation, was directly correlated with metastatic behaviors in vitro and in vivo. Taken together, our multidimensional network analysis identified miR-508-3p as a master regulator that defines the mesenchymal subtype and provides a novel prognostic biomarker to improve management of this disease.

Reprogramming of stromal fibroblasts by SNAI2 contributes to tumor desmoplasia and ovarian cancer progression

BackgroundMolecular profiling in ovarian cancer (OC) revealed that the desmoplasia subtype presented the poorest prognosis, highlighting the contribution of stromal fibroblasts in tumor progression. This study aimed to investigate the molecular characteristics of SNAI2 driving the transcriptional reprogramming of fibroblasts within tumors.MethodsSNAI2 expression was evaluated in microdissected profiles of various cancers and in various molecular subtypes of OC. Gene set enrichment analysis (GSEA) and single sample GSEA (ssGSEA) were performed to explore the correlation between SNAI2 and stromal fibroblast activation. The SNAI2 defined signature in the mesenchymal OC subtype was identified through an integrative analysis of the TCGA and the Tothill datasets. The predictive value of this signature was validated in independent datasets. SNAI2 expression alteration influence of tumor growth in primary CAFs was evaluated in 3D organotypic and murine xenograft models.ResultsWe demonstrated that SNAI2 was frequently activated in the tumor stroma, correlated with fibroblast activation and worse patient outcome in OC. SNAI2 transformed normal fibroblasts to a CAF-like state and boosted their tumor–supporting role in 3D organotypic culture and in OC xenograft model. SNAI2 drove a transcriptional signature in the mesenchymal subtype of OC that contributed to tumor desmoplasia, which fed back to increase SNAI2 expression and sustain fibroblast activation.ConclusionsOur results address the role of SNAI2 in reprogramming stromal fibroblasts. The identified SNAI2 mesenchymal signature has both a predictive value and biological relevance and might be a therapeutic target for stroma-oriented therapy against the desmoplasia OC subtype.

981P - Reactive stroma mediates CD8+ T cell spatial distribution and function in ovarian cancer

Background: Close proximity between cytotoxic T cells and tumor cells is key to effective immunotherapy. Ovarian cancer exhibits diverse immune phenotypes with distinct prevalence and spatial localization of CD8+ T cells. This study is aimed to characterize the molecular mechanisms orchestrating the localization and function of CD8+ T Cells in ovarian cancer. Methods: CD8 IHC and RNAseq were performed on 277 ovarian tumor tissues from ICON7 phase 3 trial. CD8 T-cells in tumor vs. stromal area was assessed by digital pathology. A Random Forest regression model was constructed to identify molecular features associated with enumeration or spatial localization of CD8+ T cells. In situ validation was performed by MHCI IHC and FAP RNAish. Functional role of ovarian fibroblasts was characterized by ex vivo T cell function assays. Results: We identified three main immune phenotypes, including T-cell infiltrated, T-cell excluded, and immune desert. The immune phenotypes are highly associated with prognosis and the molecular subtypes of ovarian cancer. The T-cell infiltrated phenotype is denoted by high expression of T-effector signatures and antigen presentation machinery. The T-cell excluded phenotype showed similar expression of T-effector signatures as the T-cell infiltrated phenotype, however, most of the CD8+ T-cells were excluded from the tumor bed. The T-cell excluded phenotype showed high expression of the reactive stroma signatures (i.e. FAP), and low expression of class I antigen presentation genes. Lastly, the immune desert phenotype featured low prevalence of CD8+ T-cells, and high expression of neuroendocrine and metabolic pathways. In situ analysis confirmed the two key molecular features associated with the T-cell excluded phenotype: 1) loss of the MHC I expression in the tumor compartment, and 2) high FAP expression in CAFs. Co-culturing of ovarian fibroblast cells with T-cells resulted in reduced T-cell activation and proliferation. Conclusions: Our study uncovered key molecular mechanisms mediating the interplay between CD8+ T cell localization and function in ovarian cancer. Our findings underscore the potential of targeting reactive stroma as a novel therapeutic strategy to optimize immunotherapy for ovarian cancer patients. Legal entity responsible for the study: Genentech Funding: Genentech Disclosure: All authors have declared no conflicts of interest.

Abstract 208: Proteomic measurements of protein abundance and phosphorylation identify novel kinase-substrate relationships in ovarian cancer

Abstract As part of the Clinical Proteomic Tumor Analysis Consortium (CPTAC), we have recently published the first large-scale proteomic and phosphoproteomic analysis of high-grade serous ovarian tumors. We observed that phosphorylation status was an excellent indicator of pathway activity and could discriminate between patient survival times. This dataset covers tumor samples from 69 patients with deep phosphoproteomic depth (&amp;gt;20,000 phosphopeptides confidently identified). Our continuing analysis of this dataset, reported here, has revealed that the correlation between kinase protein abundance and abundance of phosphorylated target peptides is very low, indicating that kinase abundance is not a good proxy for phosphorylation status overall. However, highly correlated kinase-substrate pairs are significantly more likely to be true relationships (from existing knowledge), demonstrating that this method could be used to predict novel kinase targets in some cases. Using this approach we predicted novel kinase-target relationships and constructed a kinase activity network of ovarian cancer. To better analyze cancer-relevant pathway activity we developed a novel approach that characterizes correlation, differential abundance, and statistical interactions between components to analyze multiple omics types in the context of signaling and functional pathways. We used this approach, called the Layered Enrichment Analysis of Pathways (LEAP), to identify active pathways in molecular subtypes of ovarian cancer, contrasting observations in patients stratified for short versus long overall survival. This analysis resulted in a pre-treatment protein-based signature that is significantly predictive of overall survival. Our results show that integration of multiple omics types has the ability to inform our understanding of novel kinase-substrate interactions, and potentially identify novel interactions associated with patient survival. Note: This abstract was not presented at the meeting. Citation Format: Jason E. McDermott, Tao Liu, Samuel Payne, Vladislav Petyuk, Hui Zhang, Zhen Zhang, Daniel Chan, Richard Smith, Karin Rodland, Clinical Proteomic Tumor Analysis Consortium. Proteomic measurements of protein abundance and phosphorylation identify novel kinase-substrate relationships in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 208. doi:10.1158/1538-7445.AM2017-208

A VEGF-dependent gene signature enriched in mesenchymal ovarian cancer predicts patient prognosis.

We have previously reported surrogate biomarkers of VEGF pathway activities with the potential to provide predictive information for anti-VEGF therapies. The aim of this study was to systematically evaluate a new VEGF-dependent gene signature (VDGs) in relation to molecular subtypes of ovarian cancer and patient prognosis. Using microarray profiling and cross-species analysis, we identified 140-gene mouse VDGs and corresponding 139-gene human VDGs, which displayed enrichment of vasculature and basement membrane genes. In patients who received bevacizumab therapy and showed partial response, the expressions of VDGs (summarized to yield VDGs scores) were markedly decreased in post-treatment biopsies compared with pre-treatment baselines. In contrast, VDGs scores were not significantly altered following bevacizumab treatment in patients with stable or progressive disease. Analysis of VDGs in ovarian cancer showed that VDGs as a prognostic signature was able to predict patient outcome. Correlation estimation of VDGs scores and molecular features revealed that VDGs was overrepresented in mesenchymal subtype and BRCA mutation carriers. These findings highlighted the prognostic role of VEGF-mediated angiogenesis in ovarian cancer, and proposed a VEGF-dependent gene signature as a molecular basis for developing novel diagnostic strategies to aid patient selection for VEGF-targeted agents.

Sex Steroid Hormone Receptor Expression Affects Ovarian Cancer Survival

Background and Aims: Although most ovarian cancers express estrogen (ER), progesterone (PR), and androgen (AR) receptors, they are currently not applied in clinical decision making. We explored the prognostic impact of sex steroid hormone receptor protein and mRNA expression on survival in epithelial ovarian cancer. Methods: Immunohistochemical stainings for ERα, ERβ, PR, and AR were assessed in relation to survival in 118 serous and endometrioid ovarian cancers. Expression of the genes encoding the four receptors was studied in relation to prognosis in the molecular subtypes of ovarian cancer in an independent data set, hypothesizing that the expression levels and prognostic impact may differ between the subtypes. Results: Expression of PR or AR protein was associated with improved 5-year progression-free (P=.001 for both) and overall survival (P<.001 for both, log-rank test). ERα and ERβ did not provide prognostic information. Patients whose tumors coexpressed PR and AR had the most favorable prognosis, and this effect was retained in multivariable analyses. Analyses of the corresponding genes using an independent data set revealed differences among the molecular subtypes, but no clear relationship between high coexpression of PGR and AR and prognosis. Conclusions: A favorable outcome was seen for patients whose tumors coexpressed PR and AR. Gene expression data suggested variable effects in the different molecular subtypes. These findings demonstrate a prognostic role for PR and AR in ovarian cancer and support that tumors should be stratified based on molecular as well as histological subtypes in future studies investigating the role of endocrine treatment in ovarian cancer.

Ovarian cancer cell line panel (OCCP): clinical importance of in vitro morphological subtypes.

Epithelial ovarian cancer is a highly heterogeneous disease and remains the most lethal gynaecological malignancy in the Western world. Therapeutic approaches need to account for inter-patient and intra-tumoural heterogeneity and detailed characterization of in vitro models representing the different histological and molecular ovarian cancer subtypes is critical to enable reliable preclinical testing. There are approximately 100 publicly available ovarian cancer cell lines but their cellular and molecular characteristics are largely undescribed. We have characterized 39 ovarian cancer cell lines under uniform conditions for growth characteristics, mRNA/microRNA expression, exon sequencing, drug response for clinically-relevant therapeutics and collated all available information on the original clinical features and site of origin. We tested for statistical associations between the cellular and molecular features of the lines and clinical features. Of the 39 ovarian cancer cell lines, 14 were assigned as high-grade serous, four serous-type, one low-grade serous and 20 non-serous type. Three morphological subtypes: Epithelial (n = 21), Round (n = 7) and Spindle (n = 12) were identified that showed distinct biological and molecular characteristics, including overexpression of cell movement and migration-associated genes in the Spindle subtype. Comparison with the original clinical data showed association of the spindle-like tumours with metastasis, advanced stage, suboptimal debulking and poor prognosis. In addition, the expression profiles of Spindle, Round and Epithelial morphologies clustered with the previously described C1-stromal, C5-mesenchymal and C4 ovarian subtype expression profiles respectively. Comprehensive profiling of 39 ovarian cancer cell lines under controlled, uniform conditions demonstrates clinically relevant cellular and genomic characteristics. This data provides a rational basis for selecting models to develop specific treatment approaches for histological and molecular subtypes of ovarian cancer.

Molecular subtyping of serous ovarian tumors reveals multiple connections to intrinsic breast cancer subtypes.

ObjectiveTranscriptional profiling of epithelial ovarian cancer has revealed molecular subtypes correlating to biological and clinical features. We aimed to determine gene expression differences between malignant, benign and borderline serous ovarian tumors, and investigate similarities with the well-established intrinsic molecular subtypes of breast cancer.MethodsGlobal gene expression profiling using Illumina's HT12 Bead Arrays was applied to 59 fresh-frozen serous ovarian malignant, benign and borderline tumors. Nearest centroid classification was performed applying previously published gene profiles for the ovarian and breast cancer subtypes. Correlations to gene expression modules representing key biological breast cancer features were also sought. Validation was performed using an independent, publicly available dataset.Results5,944 genes were significantly differentially expressed between benign and malignant serous ovarian tumors, with cell cycle processes enriched in the malignant subgroup. Borderline tumors were split between the two clusters. Significant correlations between the malignant serous tumors and the highly aggressive ovarian cancer signatures, and the basal-like breast cancer subtype were found. The benign and borderline serous tumors together were significantly correlated to the normal-like breast cancer subtype and the ovarian cancer signature derived from borderline tumors. The borderline tumors in the study dataset, in addition, also correlated significantly to the luminal A breast cancer subtype. These findings remained when analyzed in an independent dataset, supporting links between the molecular subtypes of ovarian cancer and breast cancer beyond those recently acknowledged.ConclusionsThese data link the transcriptional profiles of serous ovarian cancer to the intrinsic molecular subtypes of breast cancer, in line with the shared clinical and molecular features between high-grade serous ovarian cancer and basal-like breast cancer, and suggest that biomarkers and targeted therapies may overlap between these tumor subsets. The link between benign and borderline ovarian cancer and luminal breast cancer may indicate endocrine responsiveness in a subset of ovarian cancers.

Abstract B8: Molecular subtyping of epithelial ovarian cancer reveals connections to intrinsic breast cancer subtypes

Abstract Aim: Epithelial ovarian cancer is one of the most lethal female cancers. It is a heterogeneous group of neoplasms and the different histologic subtypes are thought to constitute separate disease entities. Efforts have been made to characterize ovarian cancers further and molecular subgroups that correlate to clinical features have been reported, but are still not in clinical use (Tothill et al., Clin Cancer Res, 2008). The subgroups, called C1-C6, represent serous and endometrioid ovarian tumors with the C1-C2 and C4-C5 subgroups characterizing high-grade serous tumors and correlated to a worse prognosis. The C3 and C6 subtypes represent borderline and low-grade tumors and show a favorable prognosis. We aimed to further outline and refine the genetic differences between malignant, benign and borderline ovarian tumors and to investigate similarities between ovarian cancer and the well described intrinsic subtypes of breast cancer (Perou et al., Nature, 2000). Materials and methods: Global gene expression profiling (Illumina HT-12 bead arrays) was applied to 72 fresh-frozen serous ovarian tumors (37 adenocarcinomas, 17 adenomas, 5 borderline tumors,13 biological replicates) collected in the ovarian tumor tissue biobank at Skane University Hospital, Sweden (2004-2011). We performed nearest centroid classification of our tumors for the molecular subtypes of ovarian cancer (“C-signatures”) as well as for the intrinsic subgroups of breast cancer (luminal A, luminal B, basal-like, normal-like, and HER2) using the genes from Hu et al. (BMC Genomics, 2006). The results were validated by performing nearest centroid classification of the intrinsic breast cancer subtypes and the C-signatures on the ovarian tumors in the Tothill cohort as well. Results: Our malignant tumors correlated significantly to the basal-like breast cancer subtype (p&amp;lt;0.001) and to the ovarian C1, C2 and C4 signatures (p=0.020). The basal-like tumors, in turn, correlated significantly to the C2 and C4 signatures (p=0.019 and p=0.001, respectively), thus supporting a link between molecular subtypes of ovarian and breast cancers. The borderline and benign tumors showed a significant correlation to the normal-like breast cancer subtype and the ovarian C3 signature, and these signatures also correlated significantly with each other (p&amp;lt;0.001). These findings were supported when the breast cancer subtypes were applied to the Tothill cohort; the basal-like subtype correlated significantly to the C2 signature and the normal-like subtype to the C3 signature (p&amp;lt;0.001). Of interest, there was a tendency towards worse prognosis among malignant tumors that correlated to the basal-like breast cancer subtype compared to the other malignant tumors, which is in line with similar findings in breast cancer. All reported p-values are calculated using Fischer's Exact Test. Conclusion: Both epithelial ovarian cancers and breast cancers are heterogeneous diseases, nevertheless they share many similarities such as hormonal influence and varying long-term prognosis. Our novel findings support a link between previously reported molecular subtypes in ovarian cancer and the well-established intrinsic subtypes of breast cancer, which could potentially be of use for further investigations of biomarkers in ovarian cancer and new thinking regarding the use of chemotherapeutic agents as well as targeted treatments. Citation Format: Jenny-Maria Jönsson, Mev Dominguez-Valentin, Ida Johansson, Siker Kimbung, Mats Jönsson, Anna Måsbäck, Susanne Malander, Mef Nilbert. Molecular subtyping of epithelial ovarian cancer reveals connections to intrinsic breast cancer subtypes. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr B8.

Cyclooxygenase‐2 inhibition inhibits PI3K/AKT kinase activity in epithelial ovarian cancer

Cyclooxygenase-2 (COX-2) expression contributes to tumor growth and invasion in epithelial ovarian cancer (EOC). COX-2 inhibitors exhibit important anticarcinogenic potential against EOC, but the molecular mechanisms underlying this effect and relation with PI3-kinase/AKT signaling remain the subject of intense investigations. Therefore, the role of COX-2 in EOC and its cross talk with PI3-kinase/AKT pathway were investigated using a large series of EOC tissues in a tissue micro array (TMA) format followed by in vitro and in vivo studies using EOC cell lines and NUDE mice. Clinically, COX-2 was overexpressed in 60.3% of EOC and was significantly associated with activated AKT (p < 0.0001). Cox-1 expression was seen in 59.9% but did not associate with AKT. Our in vitro data using EOC cell line showed that inhibition of COX-2 by aspirin, selective inhibitor NS398 and gene silencing by COX-2 specific siRNA impaired phosphorylation of AKT resulting decreased downstream signaling leading to cell growth inhibition and induction of apoptosis. Finally, treatment of MDAH2774 cell line xenografts with aspirin resulted in growth inhibition of tumors in NUDE mice via down-regulation of COX-2 and AKT activity. These data identify COX-2 as a potential biomarker and therapeutic target in distinct molecular subtypes of ovarian cancer.

Novel Molecular Subtypes of Serous and Endometrioid Ovarian Cancer Linked to Clinical Outcome

The study aim to identify novel molecular subtypes of ovarian cancer by gene expression profiling with linkage to clinical and pathologic features. Microarray gene expression profiling was done on 285 serous and endometrioid tumors of the ovary, peritoneum, and fallopian tube. K-means clustering was applied to identify robust molecular subtypes. Statistical analysis identified differentially expressed genes, pathways, and gene ontologies. Laser capture microdissection, pathology review, and immunohistochemistry validated the array-based findings. Patient survival within k-means groups was evaluated using Cox proportional hazards models. Class prediction validated k-means groups in an independent dataset. A semisupervised survival analysis of the array data was used to compare against unsupervised clustering results. Optimal clustering of array data identified six molecular subtypes. Two subtypes represented predominantly serous low malignant potential and low-grade endometrioid subtypes, respectively. The remaining four subtypes represented higher grade and advanced stage cancers of serous and endometrioid morphology. A novel subtype of high-grade serous cancers reflected a mesenchymal cell type, characterized by overexpression of N-cadherin and P-cadherin and low expression of differentiation markers, including CA125 and MUC1. A poor prognosis subtype was defined by a reactive stroma gene expression signature, correlating with extensive desmoplasia in such samples. A similar poor prognosis signature could be found using a semisupervised analysis. Each subtype displayed distinct levels and patterns of immune cell infiltration. Class prediction identified similar subtypes in an independent ovarian dataset with similar prognostic trends. Gene expression profiling identified molecular subtypes of ovarian cancer of biological and clinical importance.