Differential DNA methylation in high-grade serous ovarian cancer (HGSOC) is associated with tumor behavior

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.

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

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

Comprehensive DNA methylation profiling by MeDIP-NGS identifies potential genes and pathways for epithelial ovarian cancer

Ovarian cancer, among all gynecologic malignancies, exhibits the highest incidence and mortality rate, primarily because it is often presents with non-specific or no symptoms during its early stages. For the advancement of Ovarian Cancer Diagnosis, it is crucial to identify the potential molecular signatures that could significantly differentiate between healthy and ovarian cancerous tissues and can be used further as a diagnostic biomarker for detecting ovarian cancer. In this study, we investigated the genome-wide methylation patterns in ovarian cancer patients using Methylated DNA Immunoprecipitation (MeDIP-Seq) followed by NGS. Identified differentially methylated regions (DMRs) were further validated by targeted bisulfite sequencing for CpG site-specific methylation profiles. Furthermore, expression validation of six genes by Quantitative Reverse Transcriptase-PCR was also performed. Out of total 120 differentially methylated genes (DMGs), 68 genes were hypermethylated, and 52 were hypomethylated in their promoter region. After analysis, we identified the top 6 hub genes, namely POLR3B, PLXND1, GIGYF2, STK4, BMP2 and CRKL. Interestingly we observed Non-CpG site methylation in the case of POLR3B and CRKL which was statistically significant in discriminating ovarian cancer samples from normal controls. The most significant pathways identified were focal adhesion, the MAPK signaling pathway, and the Ras signaling pathway. Expression analysis of hypermethylated genes was correlated with the downregulation of the genes. POLR3B and GIGYF2 turned out to be the novel genes associated with the carcinogenesis of EOC. Our study demonstrated that methylation profiling through MeDIP-sequencing has effectively identified six potential hub genes and pathways that might exacerbate our understanding of underlying molecular mechanisms of ovarian carcinogenesis.

DNA methylation characteristics associated with chemotherapy resistance in epithelial ovarian cancer

Objective

The high mortality rate of epithelial ovarian cancer (EOC) is often attributed to the frequent development of chemoresistance. DNA methylation is a predictive biomarker for chemoresistance.

Methods

This study utilized DNA methylation profiles and relevant information from GEO and TCGA to identify different methylated CpG sites (DMCs) between chemoresistant and chemosensitive patients. Subsequently, we constructed chemoresistance risk models with DMCs. The genes corresponding to candidate DMCs in chemoresistance risk models were further analyzed to identify different methylated gene symbols (DMGs) associated with chemoresistance. The DMGs that showed a strong correlation with the corresponding DMCs were analyzed through immunohistochemistry.

Results

Compared to chemosensitive EOC patients, chemoresistant patients showed 423 hypermethylated CpGs and 1445 hypomethylated CpGs. The chemoresistance risk models based on DMCs have shown the improved predictive ability for chemoresistance in EOC (AUC = 65.0-76.2%). The methylations of cg25510164, cg13154880, cg15362155 and cg08665359 were strongly associated with decreased risk of chemoresistance. Conversely, the methylation of cg08872590 and cg14739437 significantly increased the risk. We identified 13 DMGs, from 47 DMCs corresponding genes, between chemosensitive and chemoresistant samples. Among the DMGs, the expression levels of DDR2 and OPCML exhibited strong correlations with the corresponding DMCs. DDR2 and OPCML both showed enhanced expression in chemoresistant ovarian microarray tissue.

Conclusions

Hypomethylated CpGs may play a significant role in DNA methylation associated with chemoresistance in EOC. The epigenetic modification of DDR2 could have important implications for the development of chemoresistance. Our study provides valuable insights for future research on DNA methylation in the chemoresistance of EOC.

High-grade serous ovarian carcinoma, the "Achiles' hill" for clinicians and molecular biologists: a molecular insight

High-grade serous ovarian carcinoma (HGSOC), the deadliest ovarian cancer, alone accounts for 90% of all its subtypes. Characterized by hallmark mutation of TP53, HGSOC show diverse molecular etiology. HGSOC can arise from both ovarian epithelium as well as the fimbrial epithelium of the fallopian tube. Ovulation induced reactive oxygen species, follicular fluid associated growth factor induced stemness, deregulation of hormone receptors like ER, FSHR, AR and hormones like FSH, LH, prolonged ovulation cycle, use of oral contraceptives are agonists of HGSOC while parity, breastfeeding provide protective effect from HGSOC development. Apart from a generic TP53 mutation, mutation of BRCA1/2, RAD51, BRIP1, PALB2, CHEK2, RAD50 etc., were reportedly associated with development of HGSOC. Epigenetic events like methylation of RASSF1A of RAS signaling pathway,OR51L1, OR51I1, OR51F1 etc. has been reported in HGSOC. Micro-RNAs like miR-1290, miR 27-a-3p miR23a, miR205 were reportedly upregulated in HGSOC. Amongst its cognate subtypes viz. differentiated, immunoreactive, mesenchymal, and proliferative, mesenchymal, and proliferative show worst prognosis. A system biology approach showed five major altered pathways in HGSOC, namely, RB, PI3K/RAS, NOTCH, HRR and FOXM1 signaling. For chemonaive patients, drugs that helps in efflux of reduced glutathione or prevent the redox coupling of GSH-GSSG, like Cisplatin, could be considered as the best therapeutic choice for HGSOC. For patients with BRCA1/2 mutations, PARP inhibitors alone or with Bevacizumab can be effective. Immune checkpoint inhibitors could be effective against immunoreactive subtypes. Identification of genes deregulated in chemoresistance could provide better insights in dealing with the disease.

Overview of Tumor Heterogeneity in High-Grade Serous Ovarian Cancers

Ovarian cancers encompass a group of neoplasms originating from germinal tissues and exhibiting distinct clinical, pathological, and molecular features. Among these, epithelial ovarian cancers (EOCs) are the most prevalent, comprising five distinct tumor histotypes. Notably, high-grade serous ovarian cancers (HGSOCs) represent the majority, accounting for over 70% of EOC cases. Due to their silent and asymptomatic behavior, HGSOCs are generally diagnosed in advanced stages with an evolved and complex genomic state, characterized by high intratumor heterogeneity (ITH) due to chromosomal instability that distinguishes HGSOCs. Histologically, these cancers exhibit significant morphological diversity both within and between tumors. The histologic patterns associated with solid, endometrioid, and transitional (SET) and classic subtypes of HGSOCs offer prognostic insights and may indicate specific molecular profiles. The evolution of HGSOC from primary to metastasis is typically characterized by clonal ITH, involving shared or divergent mutations in neoplastic sub-clones within primary and metastatic sites. Disease progression and therapy resistance are also influenced by non-clonal ITH, related to interactions with the tumor microenvironment and further genomic changes. Notably, significant alterations occur in nonmalignant cells, including cancer-associated fibroblast and immune cells, during tumor progression. This review provides an overview of the complex nature of HGSOC, encompassing its various aspects of intratumor heterogeneity, histological patterns, and its dynamic evolution during progression and therapy resistance.

HSF-1/miR-145-5p transcriptional axis enhances hyperthermic intraperitoneal chemotherapy efficacy on peritoneal ovarian carcinosis

Hyperthermic intraperitoneal administration of chemotherapy (HIPEC) increases local drug concentrations and reduces systemic side effects associated with prolonged adjuvant intraperitoneal exposure in patients affected by either peritoneal malignancies or metastatic diseases originating from gastric, colon, kidney, and ovarian primary tumors. Mechanistically, the anticancer effects of HIPEC have been poorly explored. Herein we documented that HIPEC treatment promoted miR-145-5p expression paired with a significant downregulation of its oncogenic target genes c-MYC, EGFR, OCT4, and MUC1 in a pilot cohort of patients with ovarian peritoneal metastatic lesions. RNA sequencing analyses of ovarian peritoneal metastatic nodules from HIPEC treated patients unveils HSF-1 as a transcriptional regulator factor of miR-145-5p expression. Notably, either depletion of HSF-1 expression or chemical inhibition of its transcriptional activity impaired miR-145-5p tumor suppressor activity and the response to cisplatin in ovarian cancer cell lines incubated at 42 °C. In aggregate, our findings highlight a novel transcriptional network involving HSF-1, miR145-5p, MYC, EGFR, MUC1, and OCT4 whose proper activity contributes to HIPEC anticancer efficacy in the treatment of ovarian metastatic peritoneal lesions.

Promoter hypermethylation of neural-related genes is compatible with stemness in solid cancers

BACKGROUND

DNA hypermethylation is an epigenetic feature that modulates gene expression, and its deregulation is observed in cancer. Previously, we identified a neural-related DNA hypermethylation fingerprint in colon cancer, where most of the top hypermethylated and downregulated genes have known functions in the nervous system. To evaluate the presence of this signature and its relevance to carcinogenesis in general, we considered 16 solid cancer types available in The Cancer Genome Atlas (TCGA).

RESULTS

All tested cancers showed significant enrichment for neural-related genes amongst hypermethylated genes. This signature was already present in two premalignant tissue types and could not be explained by potential confounders such as bivalency status or tumor purity. Further characterization of the neural-related DNA hypermethylation signature in colon cancer showed particular enrichment for genes that are overexpressed during neural differentiation. Lastly, an analysis of upstream regulators identified RE1-Silencing Transcription factor (REST) as a potential mediator of this DNA methylation signature.

CONCLUSION

Our study confirms the presence of a neural-related DNA hypermethylation fingerprint in various cancers, of genes linked to neural differentiation, and points to REST as a possible regulator of this mechanism. We propose that this fingerprint indicates an involvement of DNA hypermethylation in the preservation of neural stemness in cancer cells.

Microbial Communities in Gynecological Cancers and Their Association with Tumor Somatic Variation

There are strong correlations between the microbiome and human disease, including cancer. However, very little is known about potential mechanisms associated with malignant transformation in microbiome-associated gynecological cancer, except for HPV-induced cervical cancer. Our hypothesis is that differences in bacterial communities in upper genital tract epithelium may lead to selection of specific genomic variation at the cellular level of these tissues that may predispose to their malignant transformation. We first assessed differences in the taxonomic composition of microbial communities and genomic variation between gynecologic cancers and normal samples. Then, we performed a correlation analysis to assess whether differences in microbial communities selected for specific single nucleotide variation (SNV) between normal and gynecological cancers. We validated these results in independent datasets. This is a retrospective nested case-control study that used clinical and genomic information to perform all analyses. Our present study confirms a changing landscape in microbial communities as we progress into the upper genital tract, with more diversity in lower levels of the tract. Some of the different genomic variations between cancer and controls strongly correlated with the changing microbial communities. Pathway analyses including these correlated genes may help understand the basis for how changing bacterial landscapes may lead to these cancers. However, one of the most important implications of our findings is the possibility of cancer prevention in women at risk by detecting altered bacterial communities in the upper genital tract epithelium.

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.

New Insights into Ferroptosis Initiating Therapies (FIT) by Targeting the Rewired Lipid Metabolism in Ovarian Cancer Peritoneal Metastases

Ovarian cancer is one of the most lethal gynecological cancers worldwide. The poor prognosis of this malignancy is substantially attributed to the inadequate symptomatic biomarkers for early diagnosis and effective remedies to cure the disease against chemoresistance and metastasis. Ovarian cancer metastasis is often relatively passive, and the single clusters of ovarian cancer cells detached from the primary ovarian tumor are transcoelomic spread by the peritoneal fluid throughout the peritoneum cavity and omentum. Our earlier studies revealed that lipid-enriched ascitic/omental microenvironment enforced metastatic ovarian cancer cells to undertake metabolic reprogramming and utilize free fatty acids as the main energy source for tumor progression and aggression. Intriguingly, cell susceptibility to ferroptosis has been tightly correlated with the dysregulated fatty acid metabolism (FAM), and enhanced iron uptake as the prominent features of ferroptosis are attributed to the strengthened lipid peroxidation and aberrant iron accumulation, suggesting that ferroptosis induction is a targetable vulnerability to prevent cancer metastasis. Therefore, the standpoints about tackling altered FAM in combination with ferroptosis initiation as a dual-targeted therapy against advanced ovarian cancer were highlighted herein. Furthermore, a discussion on the prospect and challenge of inducing ferroptosis as an innovative therapeutic approach for reversing remedial resistance in cancer interventions was included. It is hoped this proof-of-concept review will indicate appropriate directions for speeding up the translational application of ferroptosis-inducing compounds (FINs) to improve the efficacy of ovarian cancer treatment.

DNA methylation and transcriptomic features are preserved throughout disease recurrence and chemoresistance in high grade serous ovarian cancers

Background

Little is known about the role of global DNA methylation in recurrence and chemoresistance of high grade serous ovarian cancer (HGSOC).

Methods

We performed whole genome bisulfite sequencing and transcriptome sequencing in 62 primary and recurrent tumors from 28 patients with stage III/IV HGSOC, of which 11 patients carried germline, pathogenic BRCA1 and/or BRCA2 mutations.

Results

Landscapes of genome-wide methylation (on average 24.2 million CpGs per tumor) and transcriptomes in primary and recurrent tumors showed extensive heterogeneity between patients but were highly preserved in tumors from the same patient. We identified significant differences in the burden of differentially methylated regions (DMRs) in tumors from BRCA1/2 compared to non-BRCA1/2 carriers (mean 659 DMRs and 388 DMRs in paired comparisons respectively). We identified overexpression of immune pathways in BRCA1/2 carriers compared to non-carriers, implicating an increased immune response in improved survival (P = 0.006) in these BRCA1/2 carriers.

Conclusion

These findings indicate methylome and gene expression programs established in the primary tumor are conserved throughout disease progression, even after extensive chemotherapy treatment, and that changes in methylation and gene expression are unlikely to serve as drivers for chemoresistance in HGSOC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02440-z.

Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes

The preoperative diagnosis of pelvic masses has been elusive to date. Methods for characterization such as CA-125 have had limited specificity. We hypothesize that genomic variation can be used to create prediction models which accurately distinguish high grade serous ovarian cancer (HGSC) from benign tissue.

METHODS

In this retrospective, pilot study, we extracted DNA and RNA from HGSC specimens and from benign fallopian tubes. Then, we performed whole exome sequencing and RNA sequencing, and identified single nucleotide variants (SNV), copy number variants (CNV) and structural variants (SV). We used these variants to create prediction models to distinguish cancer from benign tissue. The models were then validated in independent datasets and with a machine learning platform.

RESULTS

The prediction model with SNV had an AUC of 1.00 (95% CI 1.00-1.00). The models with CNV and SV had AUC of 0.87 and 0.73, respectively. Validated models also had excellent performances.

CONCLUSIONS

Genomic variation of HGSC can be used to create prediction models which accurately discriminate cancer from benign tissue. Further refining of these models (early-stage samples, other tumor types) has the potential to lead to detection of ovarian cancer in blood with cell free DNA, even in early stage.

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

Simple Summary

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

Abstract

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

Stage-differentiated ensemble modeling of DNA methylation landscapes uncovers salient biomarkers and prognostic signatures in colorectal cancer progression

Background

Aberrant DNA methylation acts epigenetically to skew the gene transcription rate up or down, contributing to cancer etiology. A gap in our understanding concerns the epigenomics of stagewise cancer progression. In this study, we have developed a comprehensive computational framework for the stage-differentiated modelling of DNA methylation landscapes in colorectal cancer (CRC).

Methods

The methylation β-matrix was derived from the public-domain TCGA data, converted into M-value matrix, annotated with AJCC stages, and analysed for stage-salient genes using an ensemble of approaches involving stage-differentiated modelling of methylation patterns and/or expression patterns. Differentially methylated genes (DMGs) were identified using a contrast against controls (adjusted p-value <0.001 and |log fold-change of M-value| >2), and then filtered using a series of all possible pairwise stage contrasts (p-value <0.05) to obtain stage-salient DMGs. These were then subjected to a consensus analysis, followed by matching with clinical data and performing Kaplan–Meier survival analysis to evaluate the impact of methylation patterns of consensus stage-salient biomarkers on disease prognosis.

Results

We found significant genome-wide changes in methylation patterns in cancer cases relative to controls agnostic of stage. The stage-differentiated models yielded the following consensus salient genes: one stage-I gene (FBN1), one stage-II gene (FOXG1), one stage-III gene (HCN1) and four stage-IV genes (NELL1, ZNF135, FAM123A, LAMA1). All the biomarkers were significantly hypermethylated in the promoter regions, indicating down-regulation of expression and implying a putative CpG island Methylator Phenotype (CIMP) manifestation. A prognostic signature consisting of FBN1 and FOXG1 survived all the analytical filters, and represents a novel early-stage epigenetic biomarker / target.

Conclusions

We have designed and executed a workflow for stage-differentiated epigenomic analysis of colorectal cancer progression, and identified several stage-salient diagnostic biomarkers, and an early-stage prognostic biomarker panel. The study has led to the discovery of an alternative CIMP-like signature in colorectal cancer, reinforcing the role of CIMP drivers in tumor pathophysiology.

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.

Epigenetic Silencing of miR-33b Promotes Peritoneal Metastases of Ovarian Cancer by Modulating the TAK1/FASN/CPT1A/NF-κB Axis

Peritoneal metastases are frequently found in high-grade serous carcinoma (HGSOC) patients and are commonly associated with a poor prognosis. The tumor microenvironment (TME) is a complex milieu that plays a critical role in epigenetic alterations driving tumor development and metastatic progression. However, the impact of epigenetic alterations on metastatic ovarian cancer cells in the harsh peritoneal microenvironment remains incompletely understood. Here, we identified that miR-33b is frequently silenced by promoter hypermethylation in HGSOC cells derived from metastatic omental tumor tissues. Enforced expression of miR-33b abrogates the oncogenic properties of ovarian cancer cells cocultured in omental conditioned medium (OCM), which mimics the ascites microenvironment, and in vivo tumor growth. Of note, restoration of miR-33b inhibited OCM-upregulated de novo lipogenesis and fatty acid β-oxidation in ovarian cancer cells, indicating that miR-33b may play a novel tumor suppressor role in the lipid-mediated oncogenic properties of metastatic ovarian cancer cells found in the omentum. Mechanistic studies demonstrated that miR-33b directly targets transforming growth factor beta-activated kinase 1 (TAK1), thereby suppressing the activities of fatty acid synthase (FASN) and carnitine palmitoyltransferase 1A (CPT1A) in modulating lipid metabolic activities and simultaneously inhibiting the phosphorylation of NF-κB signaling to govern the oncogenic behaviors of ovarian cancer cells. Thus, our data suggest that a lipid-rich microenvironment may cause epigenetic silencing of miR-33b, which negatively modulates ovarian cancer peritoneal metastases, at least in part, by suppressing TAK1/FASN/CPT1A/NF-κB signaling.

A nuclear polymorphism at the 8q24 region is associated with improved survival time and chemo-response in high-grade serous ovarian cancer

The 8q24 chromosomal region is strongly associated with an increased risk of ovarian cancer. One single nucleotide polymorphism that is associated with ovarian cancer in this region is rs6983267, located within the long non-coding RNA colon cancer associated transcript 2 (CCAT2). The aim of the present study was to assess the association between rs6983267 and clinical outcomes in patients with high-grade serous ovarian cancer (HGSOC). The present retrospective genetic association study utilized Sanger sequencing to determine the genotype at the rs6983267 locus (GG, GT, TT) in 98 patients with HGSOC. Survival time and chemotherapy responses between patients were compared with the TT genotype and patients with a genotype containing a G allele (GT, GG). Survival analyses were performed using Cox proportional hazard ratio analysis. Association with chemo-response was performed using a logistic regression. The results revealed that patients with HGSOC and the TT genotype at the rs6983267 locus had improved survival time compared with patients with genotypes containing a G allele [hazard ratio=0.59; 95% confidence interval (CI), 0.36–0.97; P=0.039] and were significantly associated with International Federation of Gynecology and Obstetrics stage [odds ratio (OR)=5.34; 95% CI, 1.50–22.62; P=0.014] and positive chemo-response (OR=4.51; 95% CI, 1.40–18.00; P=0.018). In summary, patients with HGSOC and the TT genotype at the rs6983267 locus had improved survival time compared with those with a G allele, despite being associated with more advanced disease; this was possibly due to an improved response to chemotherapy.

Creation and validation of models to predict response to primary treatment in serous ovarian cancer

Nearly a third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial therapy and have an overall poor prognosis. However, there are no validated tools that accurately predict which patients will not respond. Our objective is to create and validate accurate models of prediction for treatment response in HGSC. This is a retrospective case–control study that integrates comprehensive clinical and genomic data from 88 patients with HGSC from a single institution. Responders were those patients with a progression-free survival of at least 6 months after treatment. Only patients with complete clinical information and frozen specimen at surgery were included. Gene, miRNA, exon, and long non-coding RNA (lncRNA) expression, gene copy number, genomic variation, and fusion-gene determination were extracted from RNA-sequencing data. DNA methylation analysis was performed. Initial selection of informative variables was performed with univariate ANOVA with cross-validation. Significant variables (p < 0.05) were included in multivariate lasso regression prediction models. Initial models included only one variable. Variables were then combined to create complex models. Model performance was measured with area under the curve (AUC). Validation of all models was performed using TCGA HGSC database. By integrating clinical and genomic variables, we achieved prediction performances of over 95% in AUC. Most performances in the validation set did not differ from the training set. Models with DNA methylation or lncRNA underperformed in the validation set. Integrating comprehensive clinical and genomic data from patients with HGSC results in accurate and robust prediction models of treatment response.

Bacterial, Archaea, and Viral Transcripts (BAVT) Expression in Gynecological Cancers and Correlation with Regulatory Regions of the Genome

Simple Summary

Microorganisms are found in all human tissues. Some of them are responsible for cancer formation. In our study we found gene expression from bacteria, archaea, and viruses in the upper female genital tract and this expression was associated with ovarian and endometrial cancer. We also found that the expression from these organisms may be involved in regulatory mechanisms of infection and cancer formation. Some of the processes associated with these organisms may affect cancer heterogeneity and be potential targets for cancer therapy.

Abstract

Bacteria, archaea, and viruses are associated with numerous human cancers. To date, microbiome variations in transcription have not been evaluated relative to upper female genital tract cancer risk. Our aim was to assess differences in bacterial, archaea, and viral transcript (BAVT) expression between different gynecological cancers and normal fallopian tubes. In this case-control study we performed RNA sequencing on 12 normal tubes, 112 serous ovarian cancers (HGSC) and 62 endometrioid endometrial cancers (EEC). We used the centrifuge algorithm to classify resultant transcripts into four indexes: bacterial, archaea, viral, and human genomes. We then compared BAVT expression from normal samples, HGSC and EEC. T-test was used for univariate comparisons (correcting for multiple comparison) and lasso for multivariate modelling. For validation we performed DNA sequencing of normal tubes in comparison to HGSC and EEC BAVTs in the TCGA database. Pathway analyses were carried out to evaluate the function of significant BAVTs. Our results show that BAVT expression levels vary between different gynecological cancers. Finally, we mapped some of these BAVTs to the human genome. Numerous map locations were close to regulatory genes and long non-coding RNAs based on the pathway enrichment analysis. BAVTs may affect gynecological cancer risk and may be part of potential targets for cancer therapy.

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

Long non-coding RNA’s (lncRNA) are RNA sequences that do not encode proteins and are greater than 200 nucleotides in length. They regulate complex cellular mechanisms and have been associated with prognosis in various types of cancer. We aimed to identify lncRNA sequences that are associated with high grade serous ovarian cancer (HGSC) and assess their impact on overall survival. RNA was extracted from 112 HGSC patients and 12 normal fallopian tube samples from our Biobank tissue repository. RNA was sequenced and the Ultrafast and Comprehensive lncRNA detection and quantification pipeline (UClncR) was used for the identification of lncRNA sequences. Univariate logistic and multivariate lasso regression analyses identified lncRNA that was associated with HGSC. Univariate and multivariate Cox proportional hazard ratios were used to evaluate independent predictors of survival. 1943 of 16,325 investigated lncRNA’s were differentially expressed in HGSC as compared to controls (p < 0.001). Nine of these demonstrated association with cancer after multivariate lasso regression. Our multivariate analysis of survival identified four lncRNA’s associated with survival in HGSC. Three out of these four were found to be independently significant after accounting for all clinical covariates. Lastly, seven lncRNAs were independently associated with initial response to chemotherapy; four portended a worse response, while three were associated with improved response. More research is needed, but there is potential for these lncRNAs to be used as biomarkers of HGSC or predictors of treatment outcome in the future.

Tumor specific methylome in Chinese high-grade serous ovarian cancer characterized by gene expression profile and tumor genotype

OBJECTIVE

Extensive genetic and limited epigenetics have been characterized by the Cancer Genome Atlas (TCGA) among Western High-grade serous ovarian cancer (HGSOC). The present study aimed to characterize Chinese HGSOC at genome scale.

METHODS

We used reduced representation bisulfite sequencing to investigate whole-genome and tumor-specific DNA methylation in 21 HGSOC tumors paired with their normal tissues, followed by a replication study involving additional 41 HGSOC patients. Altered methylation patterns in HGSOC were further characterized by gene expression profiles and whole-exome sequencing data.

RESULTS

Comparing HGSOC tumors with normal tissues we observed global hypomethylation but with more specific hypermethylation in gene promoter. Totally, we revealed 159,881 differentially methylated regions (DMRs) and 4060 differentially expressed genes (DEGs). By integrating DNA methylation and mRNA expression data, we identified 153 negative (mainly in the upstream region) and 115 positive (mainly in the CDS regions) DMRs-DEGs correlated pairs, respectively. The negatively correlated DMRs-DEGs underlined Wnt and cell adhesion molecule binding as critical canonical pathways disrupted by DNA methylation. Eleven DMRs (in CAPS, FZD7, CDKN2A, PON3, KLF4, etc.), accompanied with a global DNA methylation marker, were validated in the replication samples. Whole-exome sequencing presented a relatively less dominated TP53 mutation in Chinese HGSOC compared to TCGA dataset. Unsupervised analysis of the three-level omics data identified differential methylation and expression subgroups based on tumor genetics, one of which presented increased DNA methylation and significantly associated with TP53 mutation.

CONCLUSIONS

Our individual and integrated analyses contribute details about the tissue-specific genetic and DNA methylation landscape of Chinese HGSOC.