Genome-wide association analyses of ovarian cancer patients undergoing primary debulking surgery identify candidate genes for residual disease.

Survival from ovarian cancer depends on the resection status after primary surgery. We performed genome-wide association analyses for resection status of 7705 ovarian cancer patients, including 4954 with high-grade serous carcinoma (HGSOC), to identify variants associated with residual disease. The most significant association with resection status was observed for rs72845444, upstream of MGMT, in HGSOC (p = 3.9 × 10-8). In gene-based analyses, PPP2R5C was the most strongly associated gene in HGSOC after stage adjustment. In an independent set of 378 ovarian tumours from the AGO-OVAR 11 study, variants near MGMT and PPP2R5C correlated with methylation and transcript levels, and PPP2R5C mRNA levels predicted progression-free survival in patients with residual disease. MGMT encodes a DNA repair enzyme, and PPP2R5C encodes the B56γ subunit of the PP2A tumour suppressor. Our results link heritable variation at these two loci with resection status in HGSOC.

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.

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.

Clinical parameters affecting survival outcomes in patients with low-grade serous ovarian carcinoma: an international multicentre analysis.

BACKGROUND: Women with low-grade ovarian serous carcinoma (LGSC) benefit from surgical treatment; however, the role of chemotherapy is controversial. We examined an international database through the Ovarian Cancer Association Consortium to identify factors that affect survival in LGSC. METHODS: We performed a retrospective cohort analysis of patients with LGSC who had had primary surgery and had overall survival data available. We performed univariate and multivariate analyses of progression-free survival and overall survival, and generated Kaplan-Meier survival curves. RESULTS: Of the 707 patients with LGSC, 680 (96.2%) had available overall survival data. The patients' median age overall was 54 years. Of the 659 patients with International Federation of Obstetrics and Gynecology stage data, 156 (23.7%) had stage I disease, 64 (9.7%) had stage II, 395 (59.9%) had stage III, and 44 (6.7%) had stage IV. Of the 377 patients with surgical data, 200 (53.0%) had no visible residual disease. Of the 361 patients with chemotherapy data, 330 (91.4%) received first-line platinum-based chemotherapy. The median follow-up duration was 5.0 years. The median progression-free survival and overall survival were 43.2 months and 110.4 months, respectively. Multivariate analysis indicated a statistically significant impact of stage and residual disease on progression-free survival and overall survival. Platinum-based chemotherapy was not associated with a survival advantage. CONCLUSION: This multicentre analysis indicates that complete surgical cytoreduction to no visible residual disease has the most impact on improved survival in LGSC. This finding could immediately inform and change practice.

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.

A federated approach to identify women with early-stage cervical cancer at low risk of lymph node metastases.

OBJECTIVE: Lymph node metastases (pN+) in presumed early-stage cervical cancer negatively impact prognosis. Using federated learning, we aimed to develop a tool to identify a group of women at low risk of pN+, to guide the shared decision-making process concerning the extent of lymph node dissection. METHODS: Women with cervical cancer between 2005 and 2020 were identified retrospectively from population-based registries: the Danish Gynaecological Cancer Database, Swedish Quality Registry for Gynaecologic Cancer and Netherlands Cancer Registry. Inclusion criteria were: squamous cell carcinoma, adenocarcinoma or adenosquamous carcinoma; The International Federation of Gynecology and Obstetrics 2009 IA2, IB1 and IIA1; treatment with radical hysterectomy and pelvic lymph node assessment. We applied privacy-preserving federated logistic regression to identify risk factors of pN+. Significant factors were used to stratify the risk of pN+. RESULTS: We included 3606 women (pN+ 11%). The most important risk factors of pN+ were lymphovascular space invasion (LVSI) (odds ratio [OR] 5.16, 95% confidence interval [CI], 4.59-5.79), tumour size 21-40 mm (OR 2.14, 95% CI, 1.89-2.43) and depth of invasion>10 mm (OR 1.81, 95% CI, 1.59-2.08). A group of 1469 women (41%)-with tumours without LVSI, tumour size ≤20 mm, and depth of invasion ≤10 mm-had a very low risk of pN+ (2.4%, 95% CI, 1.7-3.3%). CONCLUSION: Early-stage cervical cancer without LVSI, a tumour size ≤20 mm and depth of invasion ≤10 mm, confers a low risk of pN+. Based on an international privacy-preserving analysis, we developed a useful tool to guide the shared decision-making process regarding lymph node dissection.

Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes.

PURPOSE: Advanced-stage mucinous ovarian carcinoma (MOC) has poor chemotherapy response and prognosis and lacks biomarkers to aid stage I adjuvant treatment. Differentiating primary MOC from gastrointestinal (GI) metastases to the ovary is also challenging due to phenotypic similarities. Clinicopathologic and gene-expression data were analyzed to identify prognostic and diagnostic features. EXPERIMENTAL DESIGN: Discovery analyses selected 19 genes with prognostic/diagnostic potential. Validation was performed through the Ovarian Tumor Tissue Analysis consortium and GI cancer biobanks comprising 604 patients with MOC (n = 333), mucinous borderline ovarian tumors (MBOT, n = 151), and upper GI (n = 65) and lower GI tumors (n = 55). RESULTS: Infiltrative pattern of invasion was associated with decreased overall survival (OS) within 2 years from diagnosis, compared with expansile pattern in stage I MOC [hazard ratio (HR), 2.77; 95% confidence interval (CI), 1.04-7.41, P = 0.042]. Increased expression of THBS2 and TAGLN was associated with shorter OS in MOC patients (HR, 1.25; 95% CI, 1.04-1.51, P = 0.016) and (HR, 1.21; 95% CI, 1.01-1.45, P = 0.043), respectively. ERBB2 (HER2) amplification or high mRNA expression was evident in 64 of 243 (26%) of MOCs, but only 8 of 243 (3%) were also infiltrative (4/39, 10%) or stage III/IV (4/31, 13%). CONCLUSIONS: An infiltrative growth pattern infers poor prognosis within 2 years from diagnosis and may help select stage I patients for adjuvant therapy. High expression of THBS2 and TAGLN in MOC confers an adverse prognosis and is upregulated in the infiltrative subtype, which warrants further investigation. Anti-HER2 therapy should be investigated in a subset of patients. MOC samples clustered with upper GI, yet markers to differentiate these entities remain elusive, suggesting similar underlying biology and shared treatment strategies.

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.

CA-125 Levels Are Predictive of Survival in Low-Grade Serous Ovarian Cancer-A Multicenter Analysis.

OBJECTIVE: Studies on low-grade serous ovarian cancer (LGSC) are limited by a low number of cases. The aim of this study was to define the prognostic significance of age, stage, and CA-125 levels on survival in a multi-institutional cohort of women with pathologically confirmed LGSC. METHODS: Women with LGSC were identified from the collaborative Ovarian Cancer Association Consortium (OCAC). Cases of newly diagnosed primary LGSC were included if peri-operative CA-125 levels were available. Age at diagnosis, FIGO stage, pre- and post-treatment CA-125 levels, residual disease, adjuvant chemotherapy, disease recurrence, and vital status were collected by the participating institutions. Progression-free (PFS) and overall survival (OS) were calculated. Multivariable (MVA) Cox proportional hazard models were used and hazard ratios (HR) calculated. RESULTS: A total of 176 women with LGSC were included in this study; 82% had stage III/IV disease. The median PFS was 2.3 years and the median OS was 6.4 years. Age at diagnosis was not significantly associated with worse PFS (p = 0.23) or OS (p = 0.3) (HR per year: 0.99; 95%CI, 0.96-1.01 and 0.98; 95%CI 0.95-1.01). FIGO stage III/IV was independently associated with PFS (HR 4.26, 95%CI 1.43-12.73) and OS (HR 1.69, 95%CI 0.56-5.05). Elevated CA-125 (≥35 U/mL) at diagnosis was not significantly associated with worse PFS (p = 0.87) or OS (p = 0.78) in MVA. Elevated CA-125 (≥35 U/mL) after completion of primary treatment was independently associated with worse PFS (HR 2.81, 95%CI 1.36-5.81) and OS (HR 6.62, 95%CI 2.45-17.92). In the MVA, residual disease was independently associated with PFS (0.022), but not OS (0.85). CONCLUSION: Advanced LGSC was associated with poor long-term prognosis. FIGO stage and abnormal post-treatment CA-125 level are key prognostic factors inversely associated with PFS and OS. HIGHLIGHTS: 1. Through a multi-center collaborative effort, data from 176 women with low-grade serous ovarian cancer were analyzed. 2. Although low-grade serous ovarian cancer is often considered indolent, the progression-free and overall survival are poor. 3. Elevated post-treatment CA-125 levels are independently associated with poor survival.

MCM3 is a novel proliferation marker associated with longer survival for patients with tubo-ovarian high-grade serous carcinoma.

Tubo-ovarian high-grade serous carcinomas (HGSC) are highly proliferative neoplasms that generally respond well to platinum/taxane chemotherapy. We recently identified minichromosome maintenance complex component 3 (MCM3), which is involved in the initiation of DNA replication and proliferation, as a favorable prognostic marker in HGSC. Our objective was to further validate whether MCM3 mRNA expression and possibly MCM3 protein levels are associated with survival in patients with HGSC. MCM3 mRNA expression was measured using NanoString expression profiling on formalin-fixed and paraffin-embedded tissue (N = 2355 HGSC) and MCM3 protein expression was assessed by immunohistochemistry (N = 522 HGSC) and compared with Ki-67. Kaplan-Meier curves and the Cox proportional hazards model were used to estimate associations with survival. Among chemotherapy-naïve HGSC, higher MCM3 mRNA expression (one standard deviation increase in the score) was associated with longer overall survival (HR = 0.87, 95% CI 0.81-0.92, p < 0.0001, N = 1840) in multivariable analysis. MCM3 mRNA expression was highest in the HGSC C5.PRO molecular subtype, although no interaction was observed between MCM3, survival and molecular subtypes. MCM3 and Ki-67 protein levels were significantly lower after exposure to neoadjuvant chemotherapy compared to chemotherapy-naïve tumors: 37.0% versus 46.4% and 22.9% versus 34.2%, respectively. Among chemotherapy-naïve HGSC, high MCM3 protein levels were also associated with significantly longer disease-specific survival (HR = 0.52, 95% CI 0.36-0.74, p = 0.0003, N = 392) compared to cases with low MCM3 protein levels in multivariable analysis. MCM3 immunohistochemistry is a promising surrogate marker of proliferation in HGSC.

Oncomine™ Comprehensive Assay v3 vs. Oncomine™ Comprehensive Assay Plus.

The usage of next generation sequencing in combination with targeted gene panels has enforced a better understanding of tumor compositions. The identification of key genomic biomarkers underlying a disease are crucial for diagnosis, prognosis, treatment and therapeutic responses. The Oncomine™ Comprehensive Assay v3 (OCAv3) covers 161 cancer-associated genes and is routinely employed to support clinical decision making for a therapeutic course. An improved version, Oncomine™ Comprehensive Assay Plus (OCA-Plus), has been recently developed, covering 501 genes (144 overlapping with OCAv3) in addition to microsatellite instability (MSI) and tumor mutational burden (TMB) assays in one workflow. The validation of MSI and TMB was not addressed in the present study. However, the implementation of new assays must be validated and confirmed across multiple samples before it can be introduced into a clinical setting. Here, we report the comparison of DNA sequencing results from 50 ovarian cancer formalin-fixed, paraffin-embedded samples subjected to OCAv3 and OCA-Plus. A validation assessment of gene mutations identified using OCA-Plus was performed on the 144 overlapping genes and 313,769 intersecting nucleotide positions of the OCAv3 and the OCA-Plus. Our results showed a 91% concordance within variants classified as likely-pathogenic or pathogenic. Moreover, results showed that a region of PTEN is poorly covered by the OCA-Plus assay, hence, we implemented rescue filters for those variants. In conclusion, the OCA-Plus can reflect the mutational profile of genomic variants compared with OCAv3 of 144 overlapping genes, without compromising performance.

Genomic Sub-Classification of Ovarian Clear Cell Carcinoma Revealed by Distinct Mutational Signatures.

Ovarian clear cell carcinoma (OCCC) is characterized by dismal prognosis, partially due to its low sensitivity to standard chemotherapy regimen. It is also well-known for presenting unique molecular features in comparison to other epithelial ovarian cancer subtypes. Here, we aim to identify potential subgroups of patients in order to (1) determine their molecular features and (2) characterize their mutational signature. Furthermore, we sought to perform the investigation based on a potentially clinically relevant setting. To that end, we assessed the mutational profile and genomic instability of 55 patients extracted from the Gynecologic Cancer Database (DGCD) by using a panel comprised of 409 cancer-associated genes and a microsatellite assay, respectively; both are currently used in our routine environment. In accordance with previous findings, ARID1A and PIK3CA were the most prevalent mutations, present in 49.1% and 41.8%, respectively. From those, the co-occurrence of ARID1A and PIK3CA mutations was observed in 36.1% of subjects, indicating that this association might be a common feature of OCCC. The microsatellite instability frequency was low across samples. An unbiased assessment of signatures identified the presence of three subgroups, where "PIK3CA" and "Double hit" (with ARID1A and PIK3CA double mutation) subgroups exhibited unique signatures, whilst "ARID1A" and "Undetermined" (no mutations on ARID1A nor PIK3CA) subgroups showed similar profiles. Those differences were further indicated by COSMIC signatures. Taken together, the current findings suggest that OCCC presents distinct mutational landscapes within its group, which may indicate different therapeutic approaches according to its subgroup. Although encouraging, it is noteworthy that the current results are limited by sample size, and further investigation on a larger group would be crucial to better elucidate them.

Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies candidate susceptibility genes for breast and ovarian cancer.

Familial, sequencing, and genome-wide association studies (GWASs) and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWASs) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through application of a pleiotropy-guided conditional/conjunction false discovery rate (FDR) approach in the setting of a TWASs. This identified 14 candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR < 0.05 that were >1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. The 22 genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk.

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.

Identification of a Locus Near ULK1 Associated With Progression-Free Survival in Ovarian Cancer.

BACKGROUND: Many loci have been found to be associated with risk of epithelial ovarian cancer (EOC). However, although there is considerable variation in progression-free survival (PFS), no loci have been found to be associated with outcome at genome-wide levels of significance. METHODS: We carried out a genome-wide association study (GWAS) of PFS in 2,352 women with EOC who had undergone cytoreductive surgery and standard carboplatin/paclitaxel chemotherapy. RESULTS: We found seven SNPs at 12q24.33 associated with PFS (P < 5 × 10-8), the top SNP being rs10794418 (HR = 1.24; 95% CI, 1.15-1.34; P = 1.47 × 10-8). High expression of a nearby gene, ULK1, is associated with shorter PFS in EOC, and with poor prognosis in other cancers. SNP rs10794418 is also associated with expression of ULK1 in ovarian tumors, with the allele associated with shorter PFS being associated with higher expression, and chromatin interactions were detected between the ULK1 promoter and associated SNPs in serous and endometrioid EOC cell lines. ULK1 knockout ovarian cancer cell lines showed significantly increased sensitivity to carboplatin in vitro. CONCLUSIONS: The locus at 12q24.33 represents one of the first genome-wide significant loci for survival for any cancer. ULK1 is a plausible candidate for the target of this association. IMPACT: This finding provides insight into genetic markers associated with EOC outcome and potential treatment options.See related commentary by Peres and Monteiro, p. 1604.

Next Generation Sequencing Technology in the Clinic and Its Challenges.

Data analysis has become a crucial aspect in clinical oncology to interpret output from next-generation sequencing-based testing. NGS being able to resolve billions of sequencing reactions in a few days has consequently increased the demand for tools to handle and analyze such large data sets. Many tools have been developed since the advent of NGS, featuring their own peculiarities. Increased awareness when interpreting alterations in the genome is therefore of utmost importance, as the same data using different tools can provide diverse outcomes. Hence, it is crucial to evaluate and validate bioinformatic pipelines in clinical settings. Moreover, personalized medicine implies treatment targeting efficacy of biological drugs for specific genomic alterations. Here, we focused on different sequencing technologies, features underlying the genome complexity, and bioinformatic tools that can impact the final annotation. Additionally, we discuss the clinical demand and design for implementing NGS.

Gene expression profile association with poor prognosis in epithelial ovarian cancer patients.

Ovarian cancer (OC) is the eighth most common type of cancer for women worldwide. The current diagnostic and prognostic routine available for OC management either lack specificity or are very costly. Gene expression profiling has shown to be a very effective tool in exploring new molecular markers for patients with OC, although association of such markers with patient survival and clinical outcome is still elusive. Here, we performed gene expression profiling of different subtypes of OC to evaluate its association with patient overall survival (OS) and aggressive forms of the disease. By global mRNA microarray profiling in a total of 196 epithelial OC patients (161 serous, 15 endometrioid, 11 mucinous, and 9 clear cell carcinomas), we found four candidates-HSPA1A, CD99, RAB3A and POM121L9P, which associated with OS and poor clinicopathological features. The overexpression of all combined was correlated with shorter OS and progression-free survival (PFS). Furthermore, the combination of at least two markers were further associated with advanced grade, chemotherapy resistance, and progressive disease. These results indicate that a panel comprised of a few predictors that associates with a more aggressive form of OC may be clinically relevant, presenting a better performance than one marker alone.

Noncoding RNA (ncRNA) Profile Association with Patient Outcome in Epithelial Ovarian Cancer Cases.

Ovarian cancer (OC) is the second most frequent type of gynecological cancers worldwide. In the past decades, the development of novel diagnostic and prognostic biomarkers available for OC has been limited, reflecting by the lack of specificity of such markers or very costly management. Microarray expression profiling has shown very effective results in exploring new molecular markers for patients with OC. Nonetheless, most screenings are focused on mutations or expression of molecules that are translated into proteins, corresponding to only 2% of the total human genome. In order to account for the vast majority of transcripts, in the present exploratory study, we assessed the expression levels of a comprehensive panel of noncoding RNA in different subtypes of OC. We further evaluated their association with patient overall survival (OS) and aggressive forms of the disease, such as tumor type, stage, and chemotherapy resistance. By microarray profiling in a total of 197 epithelial OC patients (162 serous carcinomas, 15 endometrioid carcinomas, 11 mucinous carcinomas, and 9 clear cell carcinomas), we found two candidates, SNORA68 and SNORD74, which associated with OS and poor clinicopathological features. The overexpression of those two targets combined was correlated with shorter OS and progression-free survival. That association was further observed to correlate with a more aggressive form of the disease. Overall, the results indicate that a panel comprised of SNORA68 and SNORD74 may be clinically relevant, where patients could be offered a more individualized, targeted follow-up, given its further validation on future prospective clinical studies.

Diagnostic plasma miRNA-profiles for ovarian cancer in patients with pelvic mass.

BACKGROUND: Ovarian cancer is the fifth most common cancer in women worldwide. Moreover, there are no reliable minimal invasive tests to secure the diagnosis of malignant pelvic masses. Cell-free, circulating microRNAs have the potential as diagnostic biomarkers in cancer. Here, we performed and validated a miRNA panel with the potential to distinguish OC from benign pelvic masses. METHODS: The profile of plasma microRNA was determined with a panel of 46 candidates in a discovery group and a validation group, each consisting of 190 pre-surgery plasma samples from age-matched patients with malignant (n = 95) and benign pelvic mass (n = 95), by real time RT-qPCR. RESULTS: Four up-regulated (miR-200c-3p, miR-221-3p, miR-21-5p, and miR-484) and two down-regulated (miR-195-5p and miR-451a) microRNAs were discovered. From those, miR-200c-3p and miR-221-3p were further confirmed in a validation cohort. A combination of these 2 microRNAs together with CA-125 yielded an overall diagnostic accuracy of AUC = 0.96. CONCLUSIONS: We showed consistent plasma microRNA profiles that provide independent diagnostic information of late stage OC.