Paired comparison of the analytical performance between the Oncomine™ Comprehensive Assay v3 and whole-exome sequencing of ovarian cancer tissue.

BACKGROUND: Next-generation sequencing (NGS) has been implemented in clinical oncology as a personalized medicine tool to identify targetable genetic alterations and to guide treatment decisions. However, the optimal NGS test strategy and target genes for clinical use are still being discussed. The aim was to compare the performance of the Oncomine™ Comprehensive Assay v3 (OCAv3) (targeted gene panel) and whole-exome sequencing (WES) to investigate somatic single and multiple nucleotide variants and small indels in ovarian cancer patients. METHODS AND RESULTS: Genomic DNA was isolated from fresh frozen samples of five high-grade serous (HGSC) and three clear cell ovarian (oCCC) cancer patients. Exome sequencing libraries were prepared by using the Ion AmpliSeq Exome RDY kit, whereas libraries for OCAv3 were prepared using by Ion AmpliSeq™ Library Kit Plus. Sequencing was performed using the Ion S5XL System (Thermo Fisher Scientific). When including only variants classified as pathogenic, likely pathogenic or unknown significance based on ClinVar database verdicts and comparing overlapping regions covered both by the OCAv3 assay and WES, 23 variants were detected by both assays. However, OCAv3 detected additionally two variants: ARID1A: p.Gln563Ter and TP53: p.Ser261ValfsTer84 that have not passed WES filtering criteria due to low coverage. CONCLUSIONS: With the present treatment possibilities, OCAv3 panel testing provided higher diagnostic yield due to better coverage. Our study emphasizes that WES, although offering the potential to identify novel findings in genes not covered by OCAv3, might overlook variants in genes relevant for OC.

Identification of stably expressed microRNAs in plasma from high-grade serous ovarian carcinoma and benign tumor patients.

BACKGROUND: Ovarian cancer is a lethal gynecological cancer and no reliable minimally invasive early diagnosis tools exist. High grade serous ovarian carcinoma (HGSOC) is often diagnosed at advanced stages, resulting in poorer outcome than those diagnosed in early stage. Circulating microRNAs have been investigated for their biomarker potential. However, due to lack of standardization methods for microRNA detection, there is no consensus, which microRNAs should be used as stable endogenous controls. We aimed to identify microRNAs that are stably expressed in plasma of HGSOC and benign ovarian tumor patients. METHODS AND RESULTS: We isolated RNA from plasma samples of 60 HGSOC and 48 benign patients. RT-qPCR was accomplished with a custom panel covering 40 microRNAs and 8 controls. Stability analysis was performed using five algorithms: Normfinder, geNorm, Delta-Ct, BestKeeper and RefFinder using an R-package; RefSeeker developed by our study group [1]. Among 41 analyzed RNAs, 13 were present in all samples and eligible for stability analysis. Differences between stability rankings were observed across algorithms. In HGSOC samples, hsa-miR-126-3p and hsa-miR-23a-3p were identified as the two most stable miRNAs. In benign samples, hsa-miR-191-5p and hsa-miR-27a-3p were most stable. In the combined HGSOC and benign group, hsa-miR-23a-3p and hsa-miR-27a-3p were identified by both the RefFinder and Normfinder analysis as the most stable miRNAs. CONCLUSIONS: Consensus regarding normalization approaches in microRNA studies is needed. The choice of endogenous microRNAs used for normalization depends on the histological content of the cohort. Furthermore, normalization also depends on the algorithms used for stability analysis.

Potential Transcriptomic Biomarkers for Predicting Platinum-based Chemotherapy Resistance in Patients With High-grade Serous Ovarian Cancer.

BACKGROUND/AIM: Due to the absence of screening protocols, high-grade serous ovarian cancer (HGSOC) patients are frequently diagnosed at an advanced stage, which significantly reduces the survival rate. Moreover, relapse occurs in approximately 70% of HGSOC patients after primary treatment. Predicting resistance to primary chemotherapy remains a challenge. In the research setting, transcriptomic analyses have emerged as powerful tools for predicting which HGSOC patients are likely to benefit from primary treatment. The aim of this review was to investigate the literature demonstrating the potential of transcriptomic signatures as biomarkers for assessing the risk of resistance to platinum-based chemotherapy. MATERIALS AND METHODS: We conducted a three-step search process on PubMed to systematically review English-language articles published between 2020 and 2024. From the 123 articles retrieved, we included 11 articles that investigated transcriptomic signatures by RNA sequencing in tissues from chemo-sensitive and -resistant HGSOC patients. RESULTS: We report the clinicopathological data of 727 patients in the experimental cohorts, transcriptomic signatures, and technical aspects. Finally, the review lists 15 publicly available datasets used in the included studies. Furthermore, we investigated the overlap of 167 differentially expressed genes retrieved across the various articles. CONCLUSION: We believe this review might offer valuable insights for further studies focusing on predicting platinum resistance and personalized treatments. In addition to discussing the latest findings and potential candidates, we highlight the challenges of validating biomarkers across studies and publicly available datasets. Transcriptomic signatures represent a potential tool for patient stratification, prognosis, and the potential adoption of long-term therapies, such as poly (ADP-ribose) polymerase inhibitors (PARPis).

CANVAR: A Tool for Clinical Annotation of Variants Using ClinVar Databases.

BACKGROUND: Genomic medicine has transformed clinical genetics by utilizing high-throughput sequencing technologies to analyze genetic variants associated with diseases. Accurate variant classification is crucial for diagnosis and treatment decisions, and various tools and software such as the Ion Reporter Software and the Illumina Nirvana Software often used in a clinical setting utilize information from the ClinVar database/archive to aid in variant interpretation. However, these existing annotation tools may lack access to the latest ClinVar data, necessitating manual variant inspection. AIMS: To address this gap in developing a tool providing the latest ClinVar data for variant annotation in clinical and research settings. MATERIALS AND METHODS: We introduce CANVAR, a Python-based script that efficiently annotates variants identified from next-generation sequencing in a clinical or research context, offering comprehensive information from the latest ClinVar database. RESULTS: CANVAR provides accurate, up-to-date variant annotations, streamlining variant analysis. DISCUSSION: The rise in genomic data requires accurate variant annotation for clinical decision-making. Misclassification poses risks, and current tools may not always access the latest data, challenging variant interpretation. CONCLUSION: CANVAR contributes to enhancing variant annotation by offering comprehensive information from the latest ClinVar database for genetic variants identified through next-generation sequencing.

Validating reference-based algorithms to determine cell-type heterogeneity in ovarian cancer DNA methylation studies.

Information about cell composition in tissue samples is crucial for biomarker discovery and prognosis. Specifically, cancer tissue samples present challenges in deconvolution studies due to mutations and genetic rearrangements. Here, we optimized a robust, DNA methylation-based protocol, to be used for deconvolution of ovarian cancer samples. We compared several state-of-the-art methods (HEpiDISH, MethylCIBERSORT and ARIC) and validated the proposed protocol in an in-silico mixture and in an external dataset containing samples from ovarian cancer patients and controls. The deconvolution protocol we eventually implemented is based on MethylCIBERSORT. Comparing deconvolution methods, we paid close attention to the role of a reference panel. We postulate that a possibly high number of samples (in our case: 247) should be used when building a reference panel to ensure robustness and to compensate for biological and technical variation between samples. Subsequently, we tested the performance of the validated protocol in our own study cohort, consisting of 72 patients with malignant and benign ovarian disease as well as in five external cohorts. In conclusion, we refined and validated a reference-based algorithm to determine cell type composition of ovarian cancer tissue samples to be used in cancer biology studies in larger cohorts.

Expression Stability Analysis of Candidate References for Normalization of RT-qPCR Data Using RefSeeker R package.

When performing expression analysis either for coding RNA (e.g., mRNA) or non-coding RNA (e.g., miRNA), reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is a widely used method. To normalize these data, one or more stable endogenous references must be identified. RefFinder is an online web-based tool using four almost universally used algorithms for assessing candidate endogenous references-delta-Ct, BestKeeper, geNorm, and Normfinder. However, the online interface is presently cumbersome and time consuming. We developed an R package, RefSeeker, which performs easy and straightforward RefFinder analysis by enabling raw data import and calculation of stability from each of the algorithms and provides data output tools to create graphs and tables. This protocol uses RefSeeker R package for fast and simple RefFinder stability analysis. Key features Perform stability analysis using five algorithms: Normfinder, geNorm, delta-Ct, BestKeeper, and RefFinder. Identification of endogenous references for normalization of RT-qPCR data. Create publication-ready graphs and tables output. Step-by-step guide dialog window for novice R users.

Strategies for data normalization and missing data imputation and consequences for potential diagnostic microRNA biomarkers in epithelial ovarian cancer.

MicroRNAs (miRNAs) are small non-coding RNA molecules regulating gene expression with diagnostic potential in different diseases, including epithelial ovarian carcinomas (EOC). As only a few studies have been published on the identification of stable endogenous miRNA in EOC, there is no consensus which miRNAs should be used aiming standardization. Currently, U6-snRNA is widely adopted as a normalization control in RT-qPCR when investigating miRNAs in EOC; despite its variable expression across cancers being reported. Therefore, our goal was to compare different missing data and normalization approaches to investigate their impact on the choice of stable endogenous controls and subsequent survival analysis while performing expression analysis of miRNAs by RT-qPCR in most frequent subtype of EOC: high-grade serous carcinoma (HGSC). 40 miRNAs were included based on their potential as stable endogenous controls or as biomarkers in EOC. Following RNA extraction from formalin-fixed paraffin embedded tissues from 63 HGSC patients, RT-qPCR was performed with a custom panel covering 40 target miRNAs and 8 controls. The raw data was analyzed by applying various strategies regarding choosing stable endogenous controls (geNorm, BestKeeper, NormFinder, the comparative ΔCt method and RefFinder), missing data (single/multiple imputation), and normalization (endogenous miRNA controls, U6-snRNA or global mean). Based on our study, we propose hsa-miR-23a-3p and hsa-miR-193a-5p, but not U6-snRNA as endogenous controls in HGSC patients. Our findings are validated in two external cohorts retrieved from the NCBI Gene Expression Omnibus database. We present that the outcome of stability analysis depends on the histological composition of the cohort, and it might suggest unique pattern of miRNA stability profiles for each subtype of EOC. Moreover, our data demonstrates the challenge of miRNA data analysis by presenting various outcomes from normalization and missing data imputation strategies on survival analysis.

miRNA Expression in Ovarian Cancer in Fresh Frozen, Formalin-fixed Paraffin-embedded and Plasma Samples.

BACKGROUND/AIM: MicroRNAs (miRNAs) are small noncoding RNAs involved in gene expression regulation and have been investigated as potential biomarkers for various diseases, including ovarian cancer (OC). However, lack of standardized protocols regarding e.g., RNA isolation, cDNA synthesis, spike-in controls for experimental steps, and data normalization, impacts cross validation of results across research groups and hinders implementation of miRNAs as clinical biomarkers. MATERIALS AND METHODS: RNA was isolated from matching fresh-frozen tissue (FF), formalin-fixed paraffin embedded (FFPE) tissue, and plasma samples from twenty women diagnosed with OC using three commercial kits: miRNeasy Tissue/Cells, miRNeasy FFPE, and miRNeasy Serum/Plasma (Qiagen, Copenhagen, Denmark). RNA isolation, cDNA synthesis, and PCR performance were tested using miRCURY LNA miRNA Quality Control PCR (QC) Panels (Qiagen). Finally, miRNA stability was assessed using five algorithms: BestKeeper, Normfinder, GeNorm, comparative delta-Ct and comprehensive ranking provided by a web-based RefFinder tool. RESULTS: RNA from FF, FFPE and plasma was extracted using commercially available kits and the differences in yield and purity were examined. We developed a simple method for identifying and potentially excluding samples based on their crossing point values from RT-qPCR data, which could improve existing manufacture guidelines. Moreover, we discussed how assessment of miRNA stability differs between algorithms, possibly leading to inconsistent results. CONCLUSION: We present guidelines for RNA isolation, cDNA synthesis, and data normalization for successful miRNA expression profiling using RT-qPCR in corresponding biological OC specimens. We recommend QC panels in combination with spike-in controls and interplate controls to monitor process efficiencies.

Integrated microRNA and mRNA signatures associated with overall survival in epithelial ovarian cancer.

Ovarian cancer (OC), the eighth-leading cause of cancer-related death among females worldwide, is mainly represented by epithelial OC (EOC) that can be further subdivided into four subtypes: serous (75%), endometrioid (10%), clear cell (10%), and mucinous (3%). Major reasons for high mortality are the poor biological understanding of the OC mechanisms and a lack of reliable markers defining each EOC subtype. MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression primarily by targeting messenger RNA (mRNA) transcripts. Their aberrant expression patterns have been associated with cancer development, including OC. However, the role of miRNAs in tumorigenesis is still to be determined, mainly due to the lack of consensus regarding optimal methodologies for identification and validation of miRNAs and their targets. Several tools for computational target prediction exist, but false interpretations remain a problem. The experimental validation of every potential miRNA-mRNA pair is not feasible, as it is laborious and expensive. In this study, we analyzed the correlation between global miRNA and mRNA expression patterns derived from microarray profiling of 197 EOC patients to identify the signatures of miRNA-mRNA interactions associated with overall survival (OS). The aim was to investigate whether these miRNA-mRNA signatures might have a prognostic value for OS in different subtypes of EOC. The content of our cohort (162 serous carcinomas, 15 endometrioid carcinomas, 11 mucinous carcinomas, and 9 clear cell carcinomas) reflects a real-world scenario of EOC. Several interaction pairs between 6 miRNAs (hsa-miR-126-3p, hsa-miR-223-3p, hsa-miR-23a-5p, hsa-miR-27a-5p, hsa-miR-486-5p, and hsa-miR-506-3p) and 8 mRNAs (ATF3, CH25H, EMP1, HBB, HBEGF, NAMPT, POSTN, and PROCR) were identified and the findings appear to be well supported by the literature. This indicates that our study has a potential to reveal miRNA-mRNA signatures relevant for EOC. Thus, the evaluation on independent cohorts will further evaluate the performance of such findings.

Enrichment of megabase-sized DNA molecules for single-molecule optical mapping and next-generation sequencing.

Next-generation sequencing (NGS) has caused a revolution, yet left a gap: long-range genetic information from native, non-amplified DNA fragments is unavailable. It might be obtained by optical mapping of megabase-sized DNA molecules. Frequently only a specific genomic region is of interest, so here we introduce a method for selection and enrichment of megabase-sized DNA molecules intended for single-molecule optical mapping: DNA from a human cell line is digested by the NotI rare-cutting enzyme and size-selected by pulsed-field gel electrophoresis. For demonstration, more than 600 sub-megabase- to megabase-sized DNA molecules were recovered from the gel and analysed by denaturation-renaturation optical mapping. Size-selected molecules from the same gel were sequenced by NGS. The optically mapped molecules and the NGS reads showed enrichment from regions defined by NotI restriction sites. We demonstrate that the unannotated genome can be characterized in a locus-specific manner via molecules partially overlapping with the annotated genome. The method is a promising tool for investigation of structural variants in enriched human genomic regions for both research and diagnostic purposes. Our enrichment method could potentially work with other genomes or target specified regions by applying other genomic editing tools, such as the CRISPR/Cas9 system.