Molecular Classification of Epithelial Ovarian Cancer Based on Methylation Profiling: Evidence for Survival Heterogeneity

Molecular Pathology of Ovarian Epithelial Neoplasms: Predictive, Prognostic, and Emerging Biomarkers

This review focuses on the diagnostic, prognostic, and predictive molecular biomarkers in ovarian epithelial neoplasms in the context of their morphologic classifications. Currently, most clinically actionable molecular findings are reported in high-grade serous carcinomas; however, the data on less common tumor types are rapidly accelerating. Overall, the advances in genomic knowledge over the last decade highlight the significance of integrating molecular findings with morphology in ovarian epithelial tumors for a wide-range of clinical applications, from assistance in diagnosis to predicting response to therapy.

Current data and future perspectives on DNA methylation in ovarian cancer (Review)

Ovarian cancer (OC) represents the most prevalent malignancy of the female reproductive system. Its distinguishing features include a high aggressiveness, substantial morbidity and mortality, and a lack of apparent symptoms, which collectively pose significant challenges for early detection. Given that aberrant DNA methylation events leading to altered gene expression are characteristic of numerous tumor types, there has been extensive research into epigenetic mechanisms, particularly DNA methylation, in human cancers. In the context of OC, DNA methylation is often associated with the regulation of critical genes, such as BRCA1/2 and Ras‑association domain family 1A. Methylation modifications within the promoter regions of these genes not only contribute to the pathogenesis of OC, but also induce medication resistance and influence the prognosis of patients with OC. As such, a more in‑depth understanding of DNA methylation underpinning carcinogenesis could potentially facilitate the development of more effective therapeutic approaches for this intricate disease. The present review focuses on classical tumor suppressor genes, oncogenes, signaling pathways and associated microRNAs in an aim to elucidate the influence of DNA methylation on the development and progression of OC. The advantages and limitations of employing DNA methylation in the diagnosis, treatment and prevention of OC are also discussed. On the whole, the present literature review indicates that the DNA methylation of specific genes could potentially serve as a prognostic biomarker for OC and a therapeutic target for personalized treatment strategies. Further investigations in this field may yield more efficacious diagnostic and therapeutic alternatives for patients with OC.

Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities

We performed a deep proteogenomic analysis of bulk tumor and laser microdissection enriched tumor cell populations from high-grade serous ovarian cancer (HGSOC) tissue specimens spanning a broad spectrum of purity. We identified patients with longer progression-free survival had increased immune-related signatures and validated proteins correlating with tumor-infiltrating lymphocytes in 65 tumors from an independent cohort of HGSOC patients, as well as with overall survival in an additional 126 HGSOC patient cohort. We identified that homologous recombination deficient (HRD) tumors are enriched in pathways associated with metabolism and oxidative phosphorylation that we validated in independent patient cohorts. We further identified that polycomb complex protein BMI-1 is elevated in HR proficient (HRP) tumors, that elevated BMI-1 correlates with poor overall survival in HRP but not HRD HGSOC patients, and that HRP HGSOC cells are uniquely sensitive to BMI-1 inhibition.

Advances in nanotechnology-enabled drug delivery for combining PARP inhibitors and immunotherapy in advanced ovarian cancer

Advanced ovarian cancer is a malignancy that spreads beyond the ovaries to the pelvis, abdomen, lungs, or lymph nodes. Effective treatment options are available to improve survival rates in patients with advanced ovarian cancer. These include radiation, surgery, chemotherapy, immunotherapy, and targeted therapy. Drug resistance, however, remains a significant challenge in pharmacotherapeutic interventions, leading to reduced efficacy and unfavorable patient outcomes. Combination therapy, which involves using multiple drugs with different mechanisms of action at their optimal dose, is a promising approach to circumvent this challenge and it involves using multiple drugs with different mechanisms of action at their optimal dose. In recent years, nanotechnology has emerged as a valuable alternative for enhancing drug delivery precision and minimize toxicity. Nanoparticles can deliver drugs to specific cancer cells, resulting in higher drug concentrations at the tumor site, and reducing overall drug toxicity. Nanotechnology-based drug delivery systems have the potential to improve the therapeutic effects of anti-cancer drugs, reduce drug resistance, and improve outcomes for patients with advanced ovarian cancer. This literature review aims to examine the current understanding of combining poly (ADP-ribose) polymerase (PARP) inhibitors and immunotherapy in treating advanced ovarian cancer and the potential impact of nanotechnology on drug delivery.

An Aptamer-Based Nanoflow Cytometry Method for the Molecular Detection and Classification of Ovarian Cancers through Profiling of Tumor Markers on Small Extracellular Vesicles

Molecular profiling of protein markers on small extracellular vesicles (sEVs) is a promising strategy for the precise detection and classification of ovarian cancers. However, this strategy is challenging owing to the lack of simple and practical detection methods. In this work, using an aptamer-based nanoflow cytometry (nFCM) detection strategy, a simple and rapid method for the molecular profiling of multiple protein markers on sEVs was developed. The protein markers can be easily labeled with aptamer probes and then rapidly profiled by nFCM. Seven cancer-associated protein markers, including CA125, STIP1, CD24, EpCAM, EGFR, MUC1, and HER2, on plasma sEVs were profiled for the molecular detection and classification of ovarian cancers. Profiling these seven protein markers enabled the precise detection of ovarian cancer with a high accuracy of 94.2 %. In addition, combined with machine learning algorithms, such as linear discriminant analysis (LDA) and random forest (RF), the molecular classifications of ovarian cancer cell lines and subtypes were achieved with overall accuracies of 82.9 % and 55.4 %, respectively. Therefore, this simple, rapid, and non-invasive method exhibited considerable potential for the auxiliary diagnosis and molecular classification of ovarian cancers in clinical practice.

Cell State of Origin Impacts Development of Distinct Endometriosis-Related Ovarian Carcinoma Histotypes

Clear cell ovarian carcinoma (CCOC) and endometrioid ovarian carcinoma (ENOC) are ovarian carcinoma histotypes, which are both thought to arise from ectopic endometrial (or endometrial-like) cells through an endometriosis intermediate. How the same cell type of origin gives rise to two morphologically and biologically different histotypes has been perplexing, particularly given that recurrent genetic mutations are common to both and present in nonmalignant precursors. We used RNA transcription analysis to show that the expression profiles of CCOC and ENOC resemble those of normal endometrium at secretory and proliferative phases of the menstrual cycle, respectively. DNA methylation at the promoter of the estrogen receptor (ER) gene (ESR1) was enriched in CCOC, which could potentially lock the cells in the secretory state. Compared with normal secretory-type endometrium, CCOC was further defined by increased expression of cysteine and glutathione synthesis pathway genes and downregulation of the iron antiporter, suggesting iron addiction and highlighting ferroptosis as a potential therapeutic target. Overall, these findings suggest that while CCOC and ENOC arise from the same cell type, these histotypes likely originate from different cell states. This "cell state of origin" model may help to explain the presence of histologic and molecular cancer subtypes arising in other organs.

SIGNIFICANCE

Two cancer histotypes diverge from a common cell of origin epigenetically locked in different cell states, highlighting the importance of considering cell state to better understand the cell of origin of cancer.

Heterogeneity and treatment landscape of ovarian carcinoma

Ovarian carcinoma is characterized by heterogeneity at the molecular, cellular and anatomical levels, both spatially and temporally. This heterogeneity affects response to surgery and/or systemic therapy, and also facilitates inherent and acquired drug resistance. As a consequence, this tumour type is often aggressive and frequently lethal. Ovarian carcinoma is not a single disease entity and comprises various subtypes, each with distinct complex molecular landscapes that change during progression and therapy. The interactions of cancer and stromal cells within the tumour microenvironment further affects disease evolution and response to therapy. In past decades, researchers have characterized the cellular, molecular, microenvironmental and immunological heterogeneity of ovarian carcinoma. Traditional treatment approaches have considered ovarian carcinoma as a single entity. This landscape is slowly changing with the increasing appreciation of heterogeneity and the recognition that delivering ineffective therapies can delay the development of effective personalized approaches as well as potentially change the molecular and cellular characteristics of the tumour, which might lead to additional resistance to subsequent therapy. In this Review we discuss the heterogeneity of ovarian carcinoma, outline the current treatment landscape for this malignancy and highlight potentially effective therapeutic strategies in development.

Molecular cluster mining of high-grade serous ovarian cancer via multi-omics data analysis aids precise medicine

PURPOSE

HGSOC is a kind of gynecological cancer with high mortality and strong heterogeneity. The study used multi-omics and multiple algorithms to identify novel molecular subtypes, which can help patients obtain more personalized treatments.

METHODS

Firstly, the consensus clustering result was obtained using a consensus ensemble of ten classical clustering algorithms, based on mRNA, lncRNA, DNA methylation, and mutation data. The difference in signaling pathways was evaluated using the single-sample gene set enrichment analysis (ssGSEA). Meanwhile, the relationship between genetic alteration, response to immunotherapy, drug sensitivity, prognosis, and subtypes was further analyzed. Finally, the reliability of the new subtype was verified in three external datasets.

RESULTS

Three molecular subtypes were identified. Immune desert subtype (CS1) had little enrichment in the immune microenvironment and metabolic pathways. Immune/non-stromal subtype (CS2) was enriched in the immune microenvironment and metabolism of polyamines. Immune/stromal subtype (CS3) not only enriched anti-tumor immune microenvironment characteristics but also enriched pro-tumor stroma characteristics, glycosaminoglycan metabolism, and sphingolipid metabolism. The CS2 had the best overall survival and the highest response rate to immunotherapy. The CS3 had the worst prognosis and the lowest response rate to immunotherapy but was more sensitive to PARP and VEGFR molecular-targeted therapy. The similar differences among three subtypes were successfully validated in three external cohorts.

CONCLUSION

We used ten clustering algorithms to comprehensively analyze four types of omics data, identified three biologically significant subtypes of HGSOC patients, and provided personalized treatment recommendations for each subtype. Our findings provided novel views into the HGSOC subtypes and could provide potential clinical treatment strategies.

Methylation Signature Implicated in Immuno-Suppressive Activities in Tubo-Ovarian High-Grade Serous Carcinoma

BACKGROUND

Better understanding of prognostic factors in tubo-ovarian high-grade serous carcinoma (HGSC) is critical, as diagnosis confers an aggressive disease course. Variation in tumor DNA methylation shows promise predicting outcome, yet prior studies were largely platform-specific and unable to evaluate multiple molecular features.

METHODS

We analyzed genome-wide DNA methylation in 1,040 frozen HGSC, including 325 previously reported upon, seeking a multi-platform quantitative methylation signature that we evaluated in relation to clinical features, tumor characteristics, time to recurrence/death, extent of CD8+ tumor-infiltrating lymphocytes (TIL), gene expression molecular subtypes, and gene expression of the ATP-binding cassette transporter TAP1.

RESULTS

Methylation signature was associated with shorter time to recurrence, independent of clinical factors (N = 715 new set, hazard ratio (HR), 1.65; 95% confidence interval (CI), 1.10-2.46; P = 0.015; N = 325 published set HR, 2.87; 95% CI, 2.17-3.81; P = 2.2 × 10-13) and remained prognostic after adjustment for gene expression molecular subtype and TAP1 expression (N = 599; HR, 2.22; 95% CI, 1.66-2.95; P = 4.1 × 10-8). Methylation signature was inversely related to CD8+ TIL levels (P = 2.4 × 10-7) and TAP1 expression (P = 0.0011) and was associated with gene expression molecular subtype (P = 5.9 × 10-4) in covariate-adjusted analysis.

CONCLUSIONS

Multi-center analysis identified a novel quantitative tumor methylation signature of HGSC applicable to numerous commercially available platforms indicative of shorter time to recurrence/death, adjusting for other factors. Along with immune cell composition analysis, these results suggest a role for DNA methylation in the immunosuppressive microenvironment.

IMPACT

This work aids in identification of targetable epigenome processes and stratification of patients for whom tailored treatment may be most beneficial.

PCDH17 gene promoter methylation status in a cohort of Egyptian women with epithelial ovarian cancer

BACKGROUND AND OBJECTIVE

Ovarian cancer is a leading cause of female mortality. Epigenetic changes occur in early stages of carcinogenesis and represent a marker for cancer diagnosis. Protocadherin 17 (PCDH17) is a tumor suppressor gene involved in cell adhesion and apoptosis. The methylation of PCDH17 gene promoter has been described in several cancers including ovarian cancer. The aim of the study was to compare the methylation status of PCDH17 gene promoter between females diagnosed with epithelial ovarian cancer and a control group composed of normal and benign ovarian lesions.

METHODS

Fifty female subjects were included in our study (25 ovarian cancer patients and 25 controls). DNA was extracted from Formalin-Fixed Paraffin-Embedded (FFPE) tissues of the subjects. Methylation levels for six CpG sites in the PCDH17 gene promoter were assessed by pyrosequencing.

RESULTS

The methylation levels at five out of six sites were significantly higher in females with epithelial ovarian cancer compared to the control group. Moreover, the same applies for the mean methylation level with p value 0.018.

CONCLUSION

Methylation of PCDH17 gene promoter plays a role in ovarian carcinogenesis and can be used for diagnosis and early detection.

Genome wide DNA methylation analysis identifies novel molecular subgroups and predicts survival in neuroblastoma

BACKGROUND

Neuroblastoma is the most common malignancy in infancy, accounting for 15% of childhood cancer deaths. Outcome for the high-risk disease remains poor. DNA-methylation patterns are significantly altered in all cancer types and can be utilised for disease stratification.

METHODS

Genome-wide DNA methylation (n = 223), gene expression (n = 130), genetic/clinical data (n = 213), whole-exome sequencing (n = 130) was derived from the TARGET study. Methylation data were derived from HumanMethylation450 BeadChip arrays. t-SNE was used for the segregation of molecular subgroups. A separate validation cohort of 105 cases was studied.

RESULTS

Five distinct neuroblastoma molecular subgroups were identified, based on genome-wide DNA-methylation patterns, with unique features in each, including three subgroups associated with known prognostic features and two novel subgroups. As expected, Cluster-4 (infant diagnosis) had significantly better 5-year progression-free survival (PFS) than the four other clusters. However, in addition, the molecular subgrouping identified multiple patient subsets with highly increased risk, most notably infant patients that do not map to Cluster-4 (PFS 50% vs 80% for Cluster-4 infants, P = 0.005), and allowed identification of subgroup-specific methylation differences that may reflect important biological differences within neuroblastoma.

CONCLUSIONS

Methylation-based clustering of neuroblastoma reveals novel molecular subgroups, with distinct molecular/clinical characteristics and identifies a subgroup of higher-risk infant patients.

Blood-based DNA methylation profiling for the detection of ovarian cancer

OBJECTIVES

Ovarian cancer is a fatal gynecological cancer due to the lack of effective screening strategies at early stage. This study explored the utility of DNA methylation profiling of blood samples for the detection of ovarian cancer.

METHODS

Targeted bisulfite sequencing was performed on tissue (n = 152) and blood samples (n = 373) obtained from healthy women, women with benign ovarian tumors, or malignant epithelial ovarian tumors. Based on the tissue-derived differentially-methylated regions, a supervised machine learning algorithm was implemented and cross-validated using the blood-derived DNA methylation profiles of the training cohort (n = 178) to predict and classify each blood sample as malignant or non-malignant. The model was further evaluated using an independent test cohort (n = 184).

RESULTS

Comparison of the DNA methylation profiles of normal/benign and malignant tumor samples identified 1272 differentially-methylated regions, with 49.4% hypermethylated regions and 50.6% hypomethylated regions. Five-fold cross-validation of the model using the training dataset yielded an area under the curve of 0.94. Using the test dataset, the model accurately predicted non-malignancy in 96.2% of healthy women (n = 53) and 93.5% of women with benign tumors (n = 46). For patients with malignant tumors, the model accurately predicted malignancy in 44.4% of stage I-II (n = 9), 86.4% of stage III (n = 59), 100.0% of stage IV tumors (n = 6), and 81.8% of tumors with unknown stage (n = 11). Overall, the model yielded a predictive accuracy of 89.5%.

CONCLUSIONS

Our study demonstrates the potential clinical application of blood-based DNA methylation profiling for the detection of ovarian cancer.

A novel subtype to predict prognosis and treatment response with DNA driver methylation-transcription in ovarian cancer

Aims: To identify a novel subtype with DNA driver methylation-transcriptomic multiomics and predict prognosis and therapy response in serous ovarian cancer (SOC). Methods: SOC cohorts with both mRNA and methylation were collected, and DNA driver methylation (DNAme) was identified with the MithSig method. A novel prognostic subtype was developed by integrating the information on DNAme and prognosis-regulated DNAme-associated mRNA by similarity network fusion. Results: 43 overlapped DNAme were identified in three independent cohorts. SOC patients were categorized into three distinct subtypes by integrated multiomics. There were differences in prognosis, tumor microenvironment and response to therapy among the subtypes. Conclusion: This study identified 43 DNAmes and proposes a novel subtype toward personalized chemotherapy and immunotherapy for SOC patients based on multiomics.

Human RNASET2: A Highly Pleiotropic and Evolutionary Conserved Tumor Suppressor Gene Involved in the Control of Ovarian Cancer Pathogenesis

Ovarian cancer represents one of the most malignant gynecological cancers worldwide, with an overall 5-year survival rate, being locked in the 25-30% range in the last decade. Cancer immunotherapy is currently one of the most intensively investigated and promising therapeutic strategy and as such, is expected to provide in the incoming years significant benefits for ovarian cancer treatment as well. Here, we provide a detailed survey on the highly pleiotropic oncosuppressive roles played by the human RNASET2 gene, whose protein product has been consistently reported to establish a functional crosstalk between ovarian cancer cells and key cellular effectors of the innate immune system (the monocyte/macrophages lineage), which is in turn able to promote the recruitment to the cancer tissue of M1-polarized, antitumoral macrophages. This feature, coupled with the ability of T2 ribonucleases to negatively affect several cancer-related parameters in a cell-autonomous manner on a wide range of ovarian cancer experimental models, makes human RNASET2 a very promising candidate to develop a "multitasking" therapeutic approach for innovative future applications for ovarian cancer treatment.

Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review

With a 5-year survival rate of less than 50%, ovarian high-grade serous carcinoma (HGSC) is one of the most highly aggressive gynecological malignancies affecting women today. The high mortality rate of HGSC is largely attributable to delays in diagnosis, as most patients remain undiagnosed until the late stages of -disease. There are currently no recommended screening tests for ovarian cancer and there thus remains an urgent need for new diagnostic methods, particularly those that can detect the disease at early stages when clinical intervention remains effective. While diagnostics for ovarian cancer share many of the same technical hurdles as for other cancer types, the low prevalence of the disease in the general population, coupled with a notable lack of sensitive and specific biomarkers, have made the development of a clinically useful screening strategy particularly challenging. Here, we present a detailed review of the overall landscape of ovarian cancer diagnostics, with emphasis on emerging methods that employ novel protein, genetic, epigenetic and imaging-based biomarkers and/or advanced diagnostic technologies for the noninvasive detection of HGSC, particularly in women at high risk due to germline mutations such as BRCA1/2. Lastly, we discuss the translational potential of these approaches for achieving a clinically implementable solution for screening and diagnostics of early-stage ovarian cancer as a means of ultimately improving patient outcomes in both the general and high-risk populations.

Methylated DNA markers for plasma detection of ovarian cancer: Discovery, validation, and clinical feasibility

OBJECTIVE

Aberrant DNA methylation is an early event in carcinogenesis which could be leveraged to detect ovarian cancer (OC) in plasma.

METHODS

DNA from frozen OC tissues, benign fallopian tube epithelium (FTE), and buffy coats from cancer-free women underwent reduced representation bisulfite sequencing (RRBS) to identify OC MDMs. Candidate MDM selection was based on receiver operating characteristic (ROC) discrimination, methylation fold change, and low background methylation among controls. Blinded biological validation was performed using methylated specific PCR on DNA extracted from independent OC and FTE FFPE tissues. MDMs were tested using Target Enrichment Long-probe Quantitative Amplified Signal (TELQAS) assays in pre-treatment plasma from women newly diagnosed with OC and population-sampled healthy women. A random forest modeling analysis was performed to generate predictive probability of disease; results were 500-fold in silico cross-validated.

RESULTS

Thirty-three MDMs showed marked methylation fold changes (10 to >1000) across all OC subtypes vs FTE. Eleven MDMs (GPRIN1, CDO1, SRC, SIM2, AGRN, FAIM2, CELF2, RIPPLY3, GYPC, CAPN2, BCAT1) were tested on plasma from 91 women with OC (73 (80%) high-grade serous (HGS)) and 91 without OC; the cross-validated 11-MDM panel highly discriminated OC from controls (96% (95% CI, 89-99%) specificity; 79% (69-87%) sensitivity, and AUC 0.91 (0.86-0.96)). Among the 5 stage I/II HGS OCs included, all were correctly identified.

CONCLUSIONS

Whole methylome sequencing, stringent filtering criteria, and biological validation yielded candidate MDMs for OC that performed with high sensitivity and specificity in plasma. Larger plasma-based OC MDM studies, including testing of pre-diagnostic specimens, are warranted.

Homologous recombination deficiency and molecular subtype are associated with immunogenicity in ovarian cancer

Aim: There is an unmet need for predictive biomarkers for immune checkpoint blockade in ovarian cancer. Homologous recombination deficiency (HRD) and immunoreactive molecular subtype may be associated with determinants of immunogenicity. Materials & methods: Neoantigen load, tumor inflammation signature (TIS), immune cell infiltrates and individual immune checkpoints were assessed based on HRD status and molecular subtype. Results: Tumors with HRD demonstrated significantly higher expression of neoantigens and multiple immune check points, but not higher TIS scores or increased immune cell infiltrates. Immunoreactive tumors had significantly higher neoantigen expression, TIS scores, immune cell infiltrate and immune checkpoint expression compared with other subtypes. Conclusion: HRD and the immunoreactive molecular subtype signature were associated with multiple determinants of immunogenicity and deserve further exploration as predictive biomarkers.

Immune-and Metabolism-Associated Molecular Classification of Ovarian Cancer

Ovarian cancer (OV) is a complex gynecological disease, and its molecular characteristics are not clear. In this study, the molecular characteristics of OV subtypes based on metabolic genes were explored through the comprehensive analysis of genomic data. A set of transcriptome data of 2752 known metabolic genes was used as a seed for performing non negative matrix factorization (NMF) clustering. Three subtypes of OV (C1, C2 and C3) were found in analysis. The proportion of various immune cells in C1 was higher than that in C2 and C3 subtypes. The expression level of immune checkpoint genes TNFRSF9 in C1 was higher than that of other subtypes. The activation scores of cell cycle, RTK-RAS, Wnt and angiogenesis pathway and ESTIMATE immune scores in C1 group were higher than those in C2 and C3 groups. In the validation set, grade was significantly correlated with OV subtype C1. Functional analysis showed that the extracellular matrix related items in C1 subtype were significantly different from other subtypes. Drug sensitivity analysis showed that C2 subtype was more sensitive to immunotherapy. Survival analysis of differential genes showed that the expression of PXDN and CXCL11 was significantly correlated with survival. The results of tissue microarray immunohistochemistry showed that the expression of PXDN was significantly correlated with tumor size and pathological grade. Based on the genomics of metabolic genes, a new OV typing method was developed, which improved our understanding of the molecular characteristics of human OV.

Epigenomic Profiling of Epithelial Ovarian Cancer Stem-Cell Differentiation Reveals GPD1 Associated Immune Suppressive Microenvironment and Poor Prognosis

Intraperitoneal metastasis is a challenging clinical scenario in epithelial ovarian cancer (EOC). As they are distinct from hematogenous metastasizing tumors, epithelial ovarian cancer cells primarily disseminate within the peritoneal cavity to form superficially invasive carcinomas. Unfavorable pharmacokinetics for peritoneal tumors and gut toxicity collectively lead to a narrow therapeutic window and therefore limit the opportunities for a favorable clinical outcome. New insights into tumor metastasis in the peritoneal microenvironment are keenly awaited to develop new therapeutic strategies. Epithelial ovarian cancer stem cell (OCSC) seeding is considered to be a critical component of the peritoneal spread. Using a unique and stepwise process of the OCSC differentiation model may provide insight into the intraperitoneal metastasis. The transcriptome and epigenome of OCSC differentiation were characterized by expression array and MethylCap-Seq. The TCGA, AOCS, and KM-Plotter databases were used to evaluate the association between survival outcomes and the methylation/expression levels of candidate genes in the EOC datasets. The STRING database was used to investigate the protein–protein interaction (PPI) for candidates and their associated genes. The infiltration level of immune cells in EOC patients and the association between clinical outcome and OCSCs differentiation genes were estimated using the TIDE and TIME2.0 algorithms. We established an EOC differentiation model using OCSCs. After an integrated transcriptomics and methylomics analysis of OCSCs differentiation, we revealed that the genes associated with earlier OCSC differentiation were better able to reflect the patient’s outcome. The OCSC differentiation genes were involved in regulating metabolism shift and the suppressive immune microenvironment. High GPD1 expression with high pro-tumorigenic immune cells (M2 macrophage, and cancer associated fibroblast) had worst survival. Moreover, we developed a methylation signature, constituted by GNPDA1, GPD1, GRASP, HOXC11, and MSLN, that may be useful for prognostic prediction in EOC. Our results revealed a novel role of epigenetic plasticity OCSC differentiation and suggested metabolic and immune intervention as a new therapeutic strategy.

The Long Non-Coding RNA SNHG12 as a Mediator of Carboplatin Resistance in Ovarian Cancer via Epigenetic Mechanisms

Simple Summary

Epithelial ovarian cancer is a lethal malignancy in which recurrence and therapy resistance are the major causes of death. We investigated the transcriptome and DNA methylation profile of ovarian cancer cell lines sensitive and resistant to carboplatin, aiming to identify genes associated with therapy resistance. We focused on long non-coding RNAs (lncRNAs), known as epigenetic regulators of several cellular and biological processes. We found 11 lncRNAs associated with carboplatin resistance, including SNHG12 (small nucleolar RNA host gene 12), also confirmed in an external dataset (The Cancer Genome Atlas). SNHG12 gene silencing increased the sensitivity to carboplatin, giving evidence that this lncRNA contributes to resistance to carboplatin in ovarian cancer cell lines. We also demonstrated that SNHG12 could control the expression of nearby genes probably by altering epigenetic markers and modifying the transcript levels.

Abstract

Genetic and epigenetic changes contribute to intratumor heterogeneity and chemotherapy resistance in several tumor types. LncRNAs have been implicated, directly or indirectly, in the epigenetic regulation of gene expression. We investigated lncRNAs that potentially mediate carboplatin-resistance of cell subpopulations, influencing the progression of ovarian cancer (OC). Four carboplatin-sensitive OC cell lines (IGROV1, OVCAR3, OVCAR4, and OVCAR5), their derivative resistant cells, and two inherently carboplatin-resistant cell lines (OVCAR8 and Ovc316) were subjected to RNA sequencing and global DNA methylation analysis. Integrative and cross-validation analyses were performed using external (The Cancer Genome Atlas, TCGA dataset, n = 111 OC samples) and internal datasets (n = 39 OC samples) to identify lncRNA candidates. A total of 4255 differentially expressed genes (DEGs) and 14529 differentially methylated CpG positions (DMPs) were identified comparing sensitive and resistant OC cell lines. The comparison of DEGs between OC cell lines and TCGA-OC dataset revealed 570 genes, including 50 lncRNAs, associated with carboplatin resistance. Eleven lncRNAs showed DMPs, including the SNHG12. Knockdown of SNHG12 in Ovc316 and OVCAR8 cells increased their sensitivity to carboplatin. The results suggest that the lncRNA SNHG12 contributes to carboplatin resistance in OC and is a potential therapeutic target. We demonstrated that SNHG12 is functionally related to epigenetic mechanisms.

Integrated analysis of ovarian juvenile granulosa cell tumors reveals distinct epigenetic signatures and recurrent TERT rearrangements

PURPOSE

Adult granulosa cell tumor (AGCT) is characterized by the somatic FOXL2 p.C134W mutation, and recurrences have been associated with TERT promoter and KMT2D-truncating mutations. Conversely, the molecular underpinnings of the rare juvenile granulosa cell tumor (JGCT) have not been well elucidated. To this end, we applied a tumor-only integrated approach to investigate the genomic, transcriptomic, and epigenomic landscape of 31 JGCTs to identify putative oncogenic drivers.

EXPERIMENTAL DESIGN

Multipronged analyses of 31 JGCTs were performed utilizing a clinically validated next-generation sequencing (NGS)-panel targeting 580 cancer-related genes for genomic interrogation, in addition to targeted RNA NGS for transcriptomic exploration. Genome-wide DNA methylation profiling was conducted using an Infinium Methylation EPIC array targeting 866,562 CpG methylation sites.

RESULTS

We identified frequent KMT2C-truncating mutations along with other mutated genes implicated in the switch/sucrose nonfermentable (SWI/SNF) chromatin remodeling complex, in addition to previously reported hotspot AKT1 and DICER1 mutations. Targeted transcriptome sequencing revealed recurrent TERT rearrangements (13%) involving partners CLPTM1L or DROSHA, and differential gene expression analysis showed FGFR1 upregulation in the TERT non-rearranged JGCTs under direct promoter control. Genome-wide DNA methylation rendered a clear delineation between AGCTs and JGCTs at the epigenomic level further supporting its diagnostic utility in distinguishing among these tumors.

CONCLUSIONS

This is the largest comprehensive molecular study of JGCTs, where we further expand our current understanding of JGCT pathogenesis and demonstrate putative oncogenic drivers and TERT rearrangements in a subset of tumors. Our findings further offer insights into possible targeted therapies in a rare entity.

DNA Methylation Profiles of Ovarian Clear Cell Carcinoma

BACKGROUND

Ovarian clear cell carcinoma (OCCC) is a rare ovarian cancer histotype that tends to be resistant to standard platinum-based chemotherapeutics. We sought to better understand the role of DNA methylation in clinical and biological subclassification of OCCC.

METHODS

We interrogated genome-wide methylation using DNA from fresh frozen tumors from 271 cases, applied nonsmooth nonnegative matrix factorization (nsNMF) clustering, and evaluated clinical associations and biological pathways.

RESULTS

Two approximately equally sized clusters that associated with several clinical features were identified. Compared with Cluster 2 (N = 137), Cluster 1 cases (N = 134) presented at a more advanced stage, were less likely to be of Asian ancestry, and tended to have poorer outcomes including macroscopic residual disease following primary debulking surgery (P < 0.10). Subset analyses of targeted tumor sequencing and IHC data revealed that Cluster 1 tumors showed TP53 mutation and abnormal p53 expression, and Cluster 2 tumors showed aneuploidy and ARID1A/PIK3CA mutation (P < 0.05). Cluster-defining CpGs included 1,388 CpGs residing within 200 bp of the transcription start sites of 977 genes; 38% of these genes (N = 369 genes) were differentially expressed across cluster in transcriptomic subset analysis (P < 10-4). Differentially expressed genes were enriched for six immune-related pathways, including IFNα and IFNγ responses (P < 10-6).

CONCLUSIONS

DNA methylation clusters in OCCC correlate with disease features and gene expression patterns among immune pathways.

IMPACT

This work serves as a foundation for integrative analyses that better understand the complex biology of OCCC in an effort to improve potential for development of targeted therapeutics.

Unraveling Autocrine Signaling Pathways through Metabolic Fingerprinting in Serous Ovarian Cancer Cells

Focusing on defining metabolite-based inter-tumoral heterogeneity in ovarian cancer, we investigated the metabolic diversity of a panel of high-grade serous ovarian carcinoma (HGSOC) cell-lines using a metabolomics platform that interrogate 731 compounds. Metabolic fingerprinting followed by 2-dimensional and 3-dimensional principal component analysis established the heterogeneity of the HGSOC cells by clustering them into five distinct metabolic groups compared to the fallopian tube epithelial cell line control. An overall increase in the metabolites associated with aerobic glycolysis and phospholipid metabolism were observed in the majority of the cancer cells. A preponderant increase in the levels of metabolites involved in trans-sulphuration and glutathione synthesis was also observed. More significantly, subsets of HGSOC cells showed an increase in the levels of 5-Hydroxytryptamine, γ-aminobutyrate, or glutamate. Additionally, 5-hydroxytryptamin synthesis inhibitor as well as antagonists of γ-aminobutyrate and glutamate receptors prohibited the proliferation of HGSOC cells, pointing to their potential roles as oncometabolites and ligands for receptor-mediated autocrine signaling in cancer cells. Consistent with this role, 5-Hydroxytryptamine synthesis inhibitor as well as receptor antagonists of γ-aminobutyrate and Glutamate-receptors inhibited the proliferation of HGSOC cells. These antagonists also inhibited the three-dimensional spheroid growth of TYKNU cells, a representative HGSOC cell-line. These results identify 5-HT, GABA, and Glutamate as putative oncometabolites in ovarian cancer metabolic sub-type and point to them as therapeutic targets in a metabolomic fingerprinting-based therapeutic strategy.

DNA methylation-based profiling reveals distinct clusters with survival heterogeneity in high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSOC) is the most common type of epigenetically heterogeneous ovarian cancer. Methylation typing has previously been used in many tumour types but not in HGSOC. Methylation typing in HGSOC may promote the development of personalized care. The present study used DNA methylation data from The Cancer Genome Atlas database and identified four unique methylation subtypes of HGSOC. With the poorest prognosis and high frequency of residual tumours, cluster 4 featured hypermethylation of a panel of genes, which indicates that demethylation agents may be tested in this group and that neoadjuvant chemotherapy may be used to reduce the possibility of residual lesions. Cluster 1 and cluster 2 were significantly associated with metastasis genes and metabolic disorders, respectively. Two feature CpG sites, cg24673765 and cg25574024, were obtained through Cox proportional hazards model analysis of the CpG sites. Based on the methylation level of the two CpG sites, the samples were classified into high- and low-risk groups to identify the prognostic information. Similar results were obtained in the validation set. Taken together, these results explain the epigenetic heterogeneity of HGSOC and provide guidance to clinicians for the prognosis of HGSOC based on DNA methylation sites.

Molecular Pathology of Ovarian Epithelial Neoplasms: Predictive, Prognostic, and Emerging Biomarkers

This review focuses on the diagnostic, prognostic, and predictive molecular biomarkers in ovarian epithelial neoplasms in the context of their morphologic classifications. Currently, most clinically actionable molecular findings are reported in high-grade serous carcinomas; however, the data on less common tumor types are rapidly accelerating. Overall, the advances in genomic knowledge over the last decade highlight the significance of integrating molecular findings with morphology in ovarian epithelial tumors for a wide-range of clinical applications, from assistance in diagnosis to predicting response to therapy.

Joint IARC/NCI International Cancer Seminar Series Report: expert consensus on future directions for ovarian carcinoma research

Recently, ovarian cancer research has evolved considerably because of the emerging recognition that rather than a single disease, ovarian carcinomas comprise several different histotypes that vary by etiologic origin, risk factors, molecular profiles, therapeutic approaches and clinical outcome. Despite significant progress in our understanding of the etiologic heterogeneity of ovarian cancer, as well as important clinical advances, it remains the eighth most frequently diagnosed cancer in women worldwide and the most fatal gynecologic cancer. The International Agency for Research on Cancer and the United States National Cancer Institute jointly convened an expert panel on ovarian carcinoma to develop consensus research priorities based on evolving scientific discoveries. Expertise ranged from etiology, prevention, early detection, pathology, model systems, molecular characterization and treatment/clinical management. This report summarizes the current state of knowledge and highlights expert consensus on future directions to continue advancing etiologic, epidemiologic and prognostic research on ovarian carcinoma.

Methylation profile of imprinted genes provides evidence for teratomatous origin of a subset of mucinous ovarian tumours

Mucinous ovarian tumours are sometimes associated with mature teratomas. It is suggested that the mucinous tumours in this setting are derived from teratomas, but there remains the possibility of collision or metastasis from extra-ovarian sites. Because mature ovarian teratomas are considered to be parthenogenetic tumours that arise from a single oocyte/ovum, they have only a maternal genome and therefore show maternal genome imprinting. If mucinous ovarian tumours originate from teratomas, their genome imprinting is theoretically maternal. One of the most important mechanisms of genome imprinting is DNA methylation. In the present study, we analysed a total of 28 mucinous ovarian tumours (7 with teratomas, 21 without teratomas; 14 malignant, 14 borderline) to clarify the methylation profiles of their imprinted genes using methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) of 21 imprinting control regions (ICRs) of nine imprinted genes/gene clusters using formalin-fixed, paraffin-embedded samples. All cases lacked evidence of an extra-ovarian primary mucinous tumour. In all seven mucinous tumours with teratomas, the overall methylation profile of mucinous tumours was comparable to that of teratomas, although some ICRs showed aberrant methylation. In contrast, all but one of the mucinous tumours without teratomas showed somatic or irregular methylation patterns. Morphologically, there was little teratomatous tissue in some mucinous tumours carrying teratoma-type methylation profiles, suggesting that mucinous tumours overwhelmed ancestral teratomas. In conclusion, the methylation profile of imprinted genes provides evidence that a subset of mucinous ovarian tumours originated from mature teratomas. Genome imprinting-based analysis is a promising strategy to verify the teratomatous origin of human tumours. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Follow-Up Study Confirms the Presence of Gastric Cancer DNA Methylation Hallmarks in High-Risk Precursor Lesions

To adopt prevention strategies in gastric cancer, it is imperative to develop robust biomarkers with acceptable costs and feasibility in clinical practice to stratified populations according to risk scores. With this aim, we applied an unbiased genome-wide CpG methylation approach to a discovery cohort composed of gastric cancer (n = 24), and non-malignant precursor lesions (n = 64). Then, candidate-methylation approaches were performed in a validation cohort of precursor lesions obtained from an observational longitudinal study (n = 264), with a 12-year follow-up to identify repression or progression cases. H. pylori stratification and histology were considered to determine their influence on the methylation dynamics. As a result, we ascertained that intestinal metaplasia partially recapitulates patterns of aberrant methylation of intestinal type of gastric cancer, independently of the H. pylori status. Two epigenetically regulated genes in cancer, RPRM and ZNF793, consistently showed increased methylation in intestinal metaplasia with respect to earlier precursor lesions. In summary, our result supports the need to investigate the practical utilities of the quantification of DNA methylation in candidate genes as a marker for disease progression. In addition, the H. pylori-dependent methylation in intestinal metaplasia suggests that pharmacological treatments aimed at H. pylori eradication in the late stages of precursor lesions do not prevent epigenome reprogramming toward a cancer signature.

Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer

PURPOSE

To develop a precision tissue sampling technique that uses computed tomography (CT)-based radiomic tumour habitats for ultrasound (US)-guided targeted biopsies that can be integrated in the clinical workflow of patients with high-grade serous ovarian cancer (HGSOC).

METHODS

Six patients with suspected HGSOC scheduled for US-guided biopsy before starting neoadjuvant chemotherapy were included in this prospective study from September 2019 to February 2020. The tumour segmentation was performed manually on the pre-biopsy contrast-enhanced CT scan. Spatial radiomic maps were used to identify tumour areas with similar or distinct radiomic patterns, and tumour habitats were identified using the Gaussian mixture modelling. CT images with superimposed habitat maps were co-registered with US images by means of a landmark-based rigid registration method for US-guided targeted biopsies. The dice similarity coefficient (DSC) was used to assess the tumour-specific CT/US fusion accuracy.

RESULTS

We successfully co-registered CT-based radiomic tumour habitats with US images in all patients. The median time between CT scan and biopsy was 21 days (range 7-30 days). The median DSC for tumour-specific CT/US fusion accuracy was 0.53 (range 0.79 to 0.37). The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76-0.79) while it was lower for the smaller omental metastases (DSC: 0.37-0.53).

CONCLUSION

We developed a precision tissue sampling technique that uses radiomic habitats to guide in vivo biopsies using CT/US fusion and that can be seamlessly integrated in the clinical routine for patients with HGSOC.

KEY POINTS

• We developed a prevision tissue sampling technique that co-registers CT-based radiomics-based tumour habitats with US images. • The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76-0.79) while it was lower for the smaller omental metastases (DSC: 0.37-0.53).

Construction autophagy-related prognostic risk signature to facilitate survival prediction, individual treatment and biomarker excavation of epithelial ovarian cancer patients

Background

Existing clinical methods for prognosis evaluating for Epithelial Ovarian Cancer (EOC) patients had defects of invasive, unsystematic and subjective and little data are available for individualizing treatment, therefore, to identify potential prognostic markers and new therapeutic targets for EOC is urgently required.

Results

Expression of 232 autophagy-related genes (ARGs) in 354 EOC and 56 human ovarian surface epithelial specimens from 7 independent laboratories were analyzed, 31 mRNAs were identified as DEARGs. We did functional and pathway enrichment analysis and constructed protein–protein interaction network for all DEARGs. To screen out candidate DEARGs related to EOC patients’ survival and construct an autophagy-related prognostic risk signature, univariate and multivariate Cox proportional hazards models were established separately. Finally, 5 optimal independent prognostic DEARGs (PEX3, DNAJB9, RB1, HSP90AB1 and CXCR4) were confirmed and the autophagy-related risk model was established by the 5 prognostic DEARGs. The accuracy and robustness of the prognostic risk model for survival prediction were evaluated and verified by analyzing the correlation between EOC patients’ survival status, clinicopathological features and risk scores.

Conclusions

The autophagy-related prognostic risk model can be independently used to predict overall survival in EOC patients, it can also potentially assist in individualizing treatment and biomarker development.

Histone Modifying Enzymes in Gynaecological Cancers

Simple Summary

Epigenetics is a process that allows genetic control, without the involvement of sequence changes to DNA or genes. In cancer, epigenetics is a key event in tumour development that can alter the expression of cancer driver genes and result in genomic instability. Due to the critical role of epigenetics in malignant transformation, therapies that target these processes have been developed to treat cancer. Here, we provide a summary of the epigenetic changes that have been described in a variety of gynaecological cancers. We then highlight how these changes are being targeted in preclinical models and clinical trials for gynaecological cancers.

Abstract

Genetic and epigenetic factors contribute to the development of cancer. Epigenetic dysregulation is common in gynaecological cancers and includes altered methylation at CpG islands in gene promoter regions, global demethylation that leads to genome instability and histone modifications. Histones are a major determinant of chromosomal conformation and stability, and unlike DNA methylation, which is generally associated with gene silencing, are amenable to post-translational modifications that induce facultative chromatin regions, or condensed transcriptionally silent regions that decondense resulting in global alteration of gene expression. In comparison, other components, crucial to the manipulation of chromatin dynamics, such as histone modifying enzymes, are not as well-studied. Inhibitors targeting DNA modifying enzymes, particularly histone modifying enzymes represent a potential cancer treatment. Due to the ability of epigenetic therapies to target multiple pathways simultaneously, tumours with complex mutational landscapes affected by multiple driver mutations may be most amenable to this type of inhibitor. Interrogation of the actionable landscape of different gynaecological cancer types has revealed that some patients have biomarkers which indicate potential sensitivity to epigenetic inhibitors. In this review we describe the role of epigenetics in gynaecological cancers and highlight how it may exploited for treatment.

Sphingolipids as multifaceted mediators in ovarian cancer

Ovarian cancer is the most lethal gynaecological malignancy. It is commonly diagnosed at advanced stage when it has metastasised to the abdominal cavity and treatment becomes very challenging. While current standard therapy involving debulking surgery and platinum + taxane-based chemotherapy is associated with high response rates initially, the large majority of patients relapse and ultimately succumb to chemotherapy-resistant disease. In order to improve survival novel strategies for early detection and therapeutics against treatment-refractory disease are urgently needed. A promising new target against ovarian cancer is the sphingolipid pathway which is commonly hijacked in cancer to support cell proliferation and survival and has been shown to promote chemoresistance and metastasis in a wide range of malignant neoplasms. In particular, the sphingosine kinase 1-sphingosine 1-phosphate receptor 1 axis has been shown to be altered in ovarian cancer in multiple ways and therefore represents an attractive therapeutic target. Here we review the roles of sphingolipids in ovarian cancer progression, metastasis and chemoresistance, highlighting novel strategies to target this pathway that represent potential avenues to improve patient survival.

Glycoproteomics-based signatures for tumor subtyping and clinical outcome prediction of high-grade serous ovarian cancer

Inter-tumor heterogeneity is a result of genomic, transcriptional, translational, and post-translational molecular features. To investigate the roles of protein glycosylation in the heterogeneity of high-grade serous ovarian carcinoma (HGSC), we perform mass spectrometry-based glycoproteomic characterization of 119 TCGA HGSC tissues. Cluster analysis of intact glycoproteomic profiles delineates 3 major tumor clusters and 5 groups of intact glycopeptides. It also shows a strong relationship between N-glycan structures and tumor molecular subtypes, one example of which being the association of fucosylation with mesenchymal subtype. Further survival analysis reveals that intact glycopeptide signatures of mesenchymal subtype are associated with a poor clinical outcome of HGSC. In addition, we study the expression of mRNAs, proteins, glycosites, and intact glycopeptides, as well as the expression levels of glycosylation enzymes involved in glycoprotein biosynthesis pathways in each tumor. The results show that glycoprotein levels are mainly controlled by the expression of their individual proteins, and, furthermore, that the glycoprotein-modifying glycans correspond to the protein levels of glycosylation enzymes. The variation in glycan types further shows coordination to the tumor heterogeneity. Deeper understanding of the glycosylation process and glycosylation production in different subtypes of HGSC may provide important clues for precision medicine and tumor-targeted therapy.

Altered protein glycosylation is increasingly recognized as a hallmark of cancer. Here, the authors profile the glycoproteome of 119 high-grade serous ovarian carcinoma tissues, showing that glycosylation patterns correlate with tumor molecular subtypes and clinical outcomes.

LncRNA MALAT1 Promotes Survival of Epithelial Ovarian Cancer Cells by Downregulating miR-145-5p

Purpose

This paper was aimed at investigating the regulatory mechanism of long non-coding RNA metastasis-associated lung adenocarcinoma transcript-1 (MALAT1) in epithelial ovarian cancer (EOC).

Materials and Methods

MALAT1 and miR-145-5p expression in the tissues, serum, and EOC cell lines (TOV-112D, TOV-21G) of patients with EOC were detected. The two genes were transfected into the cells via upregulating or downregulating their expression. Levels of apoptosis-related proteins (Caspase-3, Bax, Bcl-2) were analyzed. Mechanisms of cell proliferation, invasion, and apoptosis were studied.

Results

MALAT1 was high expressed in EOC tissues, while miR-145-5p was poorly expressed in them. The areas under the curves (AUCs) of the two genes for diagnosing EOC were greater than 0.850, and the two had a significantly negative correlation. According to multivariate Cox regression analysis, high MALAT1 expression, tumor size, degree of differentiation, case staging, and lymph node metastasis were the independent risk factors affecting prognosis. The 5-year overall survival rate (OSR) of patients with low MALAT1 expression was remarkably higher than that of those with high expression. Overexpressing miR-145-5p and silencing MALAT1 could inhibit EOC cells from proliferating and invading, increase their apoptotic rate, and improve levels of the apoptosis-related proteins. After co-transfection with MALAT1-inhibitor + miR-145-5p-inhibitor, the proliferation and invasion of TOV-112D and TOV-21G cells were inhibited and the apoptotic rate rose more obviously. Inhibiting MALAT1 could increase miR-145-5p expression, thus inhibiting EOC cells from proliferating and invading and thereby increasing their apoptotic rate.

Conclusion

MALAT1 promotes EOC cells’ survival by downregulating miR-145-5p so it may become a new direction for EOC diagnosis and gene therapy.

Genomic profiling of platinum-resistant ovarian cancer: The road into druggable targets

Ovarian cancer is the most lethal gynecologic cancer. High-grade serous carcinoma (HGSC) is the most frequent histologic subtype and while it is a highly platinum-sensitive cancer at initial treatment, nearly 90 % of stage IIIC patients recur in 5 years and eventually become resistant to platinum treatment. Historically, the definition of platinum-resistant disease is based on the time interval between last platinum therapy and recurrence shorter than 6 months. Nowadays the use of sophisticated imaging techniques and serum markers to detect recurrence makes the accuracy of this clinical definition less clear and even more debatable as we begin to better understand the molecular landscape of HGSC and markers of platinum resistance and sensitivity. HGSC is characterized by a low frequency of recurrent mutations, great genomic instability with widespread copy number variations, universal TP53 mutations, and homologous recombination deficiency in more than 50 % of cases. Platinum agents form DNA adducts and intra- and inter-strand cross-links in the DNA. Most of DNA repair pathways are involved at some point in the repair of platinum induced DNA damaging, most notably homologous recombination, Fanconi Anemia, and nucleotide excision repair pathways. Mechanisms of platinum resistance are related mostly to the limitation of platinum-DNA adduct formation by changing cellular pharmacology, and to the prevention of cell death after DNA damage due to alterations in DNA repair pathways and cell cycle regulation. Understanding these mechanisms of sensitivity and resistance may help to define the utility of platinum re-challenge in each situation and guide new therapeutic opportunities. Moreover, the discovery of mechanisms of synthetic lethality related to alterations in DNA repair and cell cycle regulation pathways has opened up a new avenue for drug therapy in the last decade. In the present article, we review pathways involved in platinum-induced DNA damage repair and their relationship with genomic alterations present in HGSC. Moreover, we report new treatment strategies that are underway to target these alterations.

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

Background

Identifying 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.

Results

In 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.

Conclusions

The 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.

Integrative genomics approach identifies molecular features associated with early-stage ovarian carcinoma histotypes

Ovarian cancer comprises multiple subtypes (clear-cell (CCC), endometrioid (EC), high-grade serous (HGSC), low-grade serous (LGSC), and mucinous carcinomas (MC)) with differing molecular and clinical behavior. However, robust histotype-specific biomarkers for clinical use have yet to be identified. Here, we utilized a multi-omics approach to identify novel histotype-specific genetic markers associated with ovarian carcinoma histotypes (CCC, EC, HGSC, and MC) using DNA methylation, DNA copy number alteration and RNA sequencing data for 96 primary invasive early-stage (stage I and II) ovarian carcinomas. More specifically, the DNA methylation analysis revealed hypermethylation for CCC in comparison with the other histotypes. Moreover, copy number imbalances and novel chromothripsis-like rearrangements (n = 64) were identified in ovarian carcinoma, with the highest number of chromothripsis-like patterns in HGSC. For the 1000 most variable transcripts, underexpression was most prominent for all histotypes in comparison with normal ovarian samples. Overall, the integrative approach identified 46 putative oncogenes (overexpressed, hypomethylated and DNA gain) and three putative tumor suppressor genes (underexpressed, hypermethylated and DNA loss) when comparing the different histotypes. In conclusion, the current study provides novel insights into molecular features associated with early-stage ovarian carcinoma that may improve patient stratification and subclassification of the histotypes.

MicroRNA-145-5p regulates the proliferation of epithelial ovarian cancer cells via targeting SMAD4

BACKGROUND

Epithelial ovarian cancer (EOC) is one of the most prevalent malignancies affecting females worldwide; however, its etiology mechanism remains unclear. In various malignancies, miR-145-5p is a widely accepted and versatile miRNA. Therefore, our research focused on exploring the activity and etiology of miR-145-5p in the modulation of metastasis, migration, and proliferation of EOC cells. The direct reactions between the 3'UTRs of SMAD4 mRNA and miR-145-5p were verified using dual luciferase reporter test. SKOV-3 cells were subsequently transfected using miR-145-5p mimics. Cell migration, death, and proliferation were evaluated using MTT, flow cytometry, and Transwell test. In addition, SMAD4 transcription and translation were evaluated using qRT-PCR and Western blot.

RESULTS

We found that miR-145-5p expression was repressed prevalently in EOC tissues, apart from SMAD4 upregulation. Excessive miR-145-5p expression remarkably reinforced EOC cell death and repressed EOC cell proliferation. Furthermore, upregulated miR-145-5p expression noticeably repressed migration via MMP-2 and MMP-9 downregulation. Moreover, SMAD4 was downregulated via miR-145-5p transfection. The dual luciferase test revealed that miR-145-5p directly targeted SMAD4.

CONCLUSIONS

Our research suggests that miR-145-5p serves as a malignancy repressor and exerts an essential impact on inhibiting malignancy generation and reinforcing EOC death via targeting SMAD4. MiR-145-5p application could serve as a promising strategy to treat EOC.

Role of microRNAs as Clinical Cancer Biomarkers for Ovarian Cancer: A Short Overview

Ovarian cancer has the highest mortality rate among gynecological cancers. Early clinical signs are missing and there is an urgent need to establish early diagnosis biomarkers. MicroRNAs are promising biomarkers in this respect. In this paper, we review the most recent advances regarding the alterations of microRNAs in ovarian cancer. We have briefly described the contribution of miRNAs in the mechanisms of ovarian cancer invasion, metastasis, and chemotherapy sensitivity. We have also summarized the alterations underwent by microRNAs in solid ovarian tumors, in animal models for ovarian cancer, and in various ovarian cancer cell lines as compared to previous reviews that were only focused the circulating microRNAs as biomarkers. In this context, we consider that the biomarker screening should not be limited to circulating microRNAs per se, but rather to the simultaneous detection of the same microRNA alteration in solid tumors, in order to understand the differences between the detection of nucleic acids in early vs. late stages of cancer. Moreover, in vitro and in vivo models should also validate these microRNAs, which could be very helpful as preclinical testing platforms for pharmacological and/or molecular genetic approaches targeting microRNAs. The enormous quantity of data produced by preclinical and clinical studies regarding the role of microRNAs that act synergistically in tumorigenesis mechanisms that are associated with ovarian cancer subtypes, should be gathered, integrated, and compared by adequate methods, including molecular clustering. In this respect, molecular clustering analysis should contribute to the discovery of best biomarkers-based microRNAs assays that will enable rapid, efficient, and cost-effective detection of ovarian cancer in early stages. In conclusion, identifying the appropriate microRNAs as clinical biomarkers in ovarian cancer might improve the life quality of patients.

DNA methylation-based profiling of uterine neoplasms: a novel tool to improve gynecologic cancer diagnostics

PURPOSE

Uterine neoplasms comprise a broad spectrum of lesions, some of which may pose a diagnostic challenge even to experienced pathologists. Recently, genome-wide DNA methylation-based classification of central nervous system tumors has been shown to increase diagnostic precision in clinical practice when combined with standard histopathology. In this study, we describe DNA methylation patterns of a diverse set of uterine neoplasms and test the applicability of array-based DNA methylation profiling.

METHODS

A multicenter cohort including prototypical epithelial and mesenchymal uterine neoplasms was collected. Tumors were subject to pathology review and array-based DNA methylation profiling (Illumina Infinium HumanMethylation450 or EPIC [850k] BeadChip). Methylation data were analyzed by unsupervised hierarchical clustering and t-SNE analysis.

RESULTS

After sample retrieval and pathology review the study cohort consisted of 49 endometrial carcinomas (EC), 5 carcinosarcomas (MMMT), 8 uterine leiomyomas (ULMO), 7 uterine leiomyosarcomas (ULMS), 15 uterine tumor resembling ovarian sex cord tumors (UTROSCT), 17 low-grade endometrial stromal sarcomas (LGESS) and 9 high-grade endometrial stromal sarcomas (HGESS). Analysis of methylation data identified distinct methylation clusters, which correlated with established diagnostic categories of uterine neoplasms. MMMT clustered together with EC, while ULMO, ULMS and UTROSCT each formed distinct clusters. The LGESS cluster differed from that of HGESS, and within the branch of HGESS, we observed a notable subgrouping of YWHAE- and BCOR-rearranged tumors.

CONCLUSION

Herein, we describe distinct DNA methylation signatures in uterine neoplasms and show that array-based DNA methylation analysis holds promise as an ancillary tool to further characterize uterine neoplasms, especially in cases which are diagnostically challenging by conventional techniques.