Identification of Novel lncRNAs in Ovarian Cancer and Their Impact on Overall Survival

Identification of long noncoding RNAs downregulated specifically in ovarian high-grade serous carcinoma

Purpose

To investigate whether long noncoding RNAs (lncRNAs) are involved in the development or malignant behavior of ovarian high-grade serous carcinoma (HGSC), we attempted to identify lncRNAs specific to HGSC.

Methods

Total RNAs were isolated from HGSC, normal ovarian, and fallopian tube tissue samples and were subjected to a PCR array that can analyze 84 cancer-associated lncRNAs. The lncRNAs that were upregulated and downregulated in HGSC in comparison to multiple samples of normal ovary and fallopian tube were validated by real-time RT-PCR. To infer the function, ovarian cancer cell lines that overexpress the identified lncRNAs were established, and the activation of cell proliferation, migration, and invasion was analyzed.

Results

Eleven lncRNAs (ACTA2-AS1, ADAMTS9-AS2, CBR3-AS1, HAND2-AS1, IPW, LINC00312, LINC00887, MEG3, NBR2, TSIX, and XIST) were downregulated in HGSC samples. We established the cell lines that overexpress ADAMTS9-AS2, CBR3-AS1, or NBR2. In cell lines overexpressing ADAMTS9-AS2, cell proliferation was suppressed, but migration and invasion were promoted. In cell lines overexpressing CBR3-AS1 or NBR2, cell migration tended to be promoted, although cell proliferation and invasion were unchanged.

Conclusion

We identified eleven lncRNAs that were specifically downregulated in HGSC. Of these, CBR3-AS1, NBR2, and ADAMTS9-AS2 had unique functions in the malignant behaviors of HGSC.

Prediction of Chemoresistance-How Preclinical Data Could Help to Modify Therapeutic Strategy in High-Grade Serous Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is one of the most lethal tumors generally and the most fatal cancer of the female genital tract. The approved standard therapy consists of surgical cytoreduction and platinum/taxane-based chemotherapy, and of targeted therapy in selected patients. The main therapeutic problem is chemoresistance of recurrent and metastatic HGSOC tumors which results in low survival in the group of FIGO III/IV. Therefore, the prediction and monitoring of chemoresistance seems to be of utmost importance for the improvement of HGSOC management. This type of cancer has genetic heterogeneity with several subtypes being characterized by diverse gene signatures and disturbed peculiar epigenetic regulation. HGSOC develops and metastasizes preferentially in the specific intraperitoneal environment composed mainly of fibroblasts, adipocytes, and immune cells. Different HGSOC subtypes could be sensitive to distinct sets of drugs. Moreover, primary, metastatic, and recurrent tumors are characterized by an individual biology, and thus diverse drug responsibility. Without a precise identification of the tumor and its microenvironment, effective treatment seems to be elusive. This paper reviews tumor-derived genomic, mutational, cellular, and epigenetic biomarkers of HGSOC drug resistance, as well as tumor microenvironment-derived biomarkers of chemoresistance, and discusses their possible use in the novel complex approach to ovarian cancer therapy and monitoring.

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.

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case−control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 55 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine-learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone.

Integrated Clinical and Genomic Models to Predict Optimal Cytoreduction in High-Grade Serous Ovarian Cancer

Simple Summary

Approximately 30% of patients with advanced, high-grade serous ovarian cancer who undergo surgery will have a suboptimal result, resulting in decreased overall survival. Improving the ability to predict a successful surgery would improve survival. We aimed to use tumor genomics to create prediction models, which would predict an optimal or complete cytoreduction prior to entering the operating room. We created two sets of models, one for optimal and one for complete cytoreduction. We then validated those models using the TCGA database as well as statistical learning. We developed 21 models for optimal cytoreduction and 37 models for complete cytoreduction, which have the potential to improve our ability to predict these surgical results in patients with ovarian cancer before taking them to the operating room. Improving our pre-operative decision-making will result in more patients having the desired surgical results and, therefore, improved survival.

Abstract

Advanced high-grade serous (HGSC) ovarian cancer is treated with either primary surgery followed by chemotherapy or neoadjuvant chemotherapy followed by interval surgery. The decision to proceed with surgery primarily or after chemotherapy is based on a surgeon’s clinical assessment and prediction of an optimal outcome. Optimal and complete cytoreductive surgery are correlated with improved overall survival. This clinical assessment results in an optimal surgery approximately 70% of the time. We hypothesize that this prediction can be improved by using biological tumor data to predict optimal cytoreduction. With access to a large biobank of ovarian cancer tumors, we obtained genomic data on 83 patients encompassing gene expression, exon expression, long non-coding RNA, micro RNA, single nucleotide variants, copy number variation, DNA methylation, and fusion transcripts. We then used statistical learning methods (lasso regression) to integrate these data with pre-operative clinical information to create predictive models to discriminate which patient would have an optimal or complete cytoreductive outcome. These models were then validated within The Cancer Genome Atlas (TCGA) HGSC database and using machine learning methods (TensorFlow). Of the 124 models created and validated for optimal cytoreduction, 21 performed at least equal to, if not better than, our historical clinical rate of optimal debulking in advanced-stage HGSC as a control. Of the 89 models created to predict complete cytoreduction, 37 have the potential to outperform clinical decision-making. Prospective validation of these models could result in improving our ability to objectively predict which patients will undergo optimal cytoreduction and, therefore, improve our ovarian cancer outcomes.

Identification of an Immune Gene-Based Cisplatin Response Model and CD27 as a Therapeutic Target against Cisplatin Resistance for Ovarian Cancer

Objective. Evidence demonstrates that the immune microenvironment is extensively associated with chemotherapy response of ovarian cancer (OV). Herein, this study is aimed at establishing a cisplatin response prediction model for OV on the basis of immune genes. Methods. The expression profiles of cisplatin-sensitive and cisplatin-resistant OV specimens were integrated from multiple public datasets. The abundance scores of 22 immune cells were estimated with CIBERSORT algorithm. Differentially expressed immune genes (DEGs) were determined between cisplatin-sensitive and cisplatin-resistant groups. Thereafter, a cisplatin response model was constructed based on prognostic DEGs with logistic regression analysis. The prediction performance was validated in independent cohorts. The possible relationships between the model and immunotherapy were then assessed. Results. Treg scores were significantly decreased in cisplatin-resistant than cisplatin-sensitive OV specimens, with the opposite results for naïve B cells and activated dendritic cells. Fourteen prognostic DEGs were identified and used to develop a cisplatin-response model. The response scores, estimated by the model, showed favorable performance in discriminating cisplatin-response and nonresponse samples. The response scores also presented significantly negative correlations with three well-known cisplatin-resistant pathways and a positive correlation with the expression of CD274 (PD-L1). Moreover, the decreased CD27 expression was observed in cisplatin-resistant groups, and OV specimens with higher CD27 expressions were more sensitive to cisplatin treatment. Conclusion. Altogether, our findings proposed a cisplatin response prediction model and identified CD27 that might be involved in cisplatin resistance. Further investigations suggested that CD27 could be a promising immunotherapeutic target for cisplatin-resistant subset of OV.

Silencing of SBF2-AS1 inhibits cell growth and invasion by sponging microRNA-338-3p in serous ovarian carcinoma

Long noncoding RNA SET-binding factor 2 (SBF2) antisense RNA 1 (AS1) is associated with the growth and metastasis of multiple cancer types, but its biological roles in serous ovarian carcinoma (SOC) remain unclear. In this study, the aberrant upregulation of SBF2-AS1 is detected in SOC after analysis of differentially expressed genes between SOC tissues and normal fallopian tubes from the public Gene Expression Omnibus (GEO) database. We determine that knockdown of SBF2-AS1 inhibits SOC cell proliferation and invasion by sponging miR-338-3p. MiR-338-3p acts as a tumor suppressor in SOC, and E26 transformation specific-1 (ETS1) is identified as a potential target of miR-338-3p regulation. Furthermore, SBF2-AS1 could modulate ETS1 by operating as a competing endogenous RNA for miR-338-3p. This finding elucidates a new mechanism for SBF2-AS1 in SOC development and provides a potential target for SOC therapeutic intervention.

Leveraging Genomics, Transcriptomics, and Epigenomics to Understand the Biology and Chemoresistance of Ovarian Cancer

Ovarian cancer is a major cause of fatality due to a gynecological malignancy. This lethality is largely due to the unspecific clinical manifestations of ovarian cancer, which lead to late detection and to high resistance to conventional therapies based on platinum. In recent years, we have advanced our understanding of the mechanisms provoking tumor relapse, and the advent of so-called omics technologies has provided exceptional tools to evaluate molecular mechanisms leading to therapy resistance in ovarian cancer. Here, we review the contribution of genomics, transcriptomics, and epigenomics techniques to our knowledge about the biology and molecular features of ovarian cancers, with a focus on therapy resistance. The use of these technologies to identify molecular markers and mechanisms leading to chemoresistance in these tumors is discussed, as well as potential further applications.