Circulating Cell-Free DNA Methylation Profiles in the Early Detection of Ovarian Cancer: A Scoping Review of the Literature

Early Diagnosis of Ovarian Cancer: A Comprehensive Review of the Advances, Challenges, and Future Directions

Ovarian cancer (OC), the seventh most common cancer in women and the most lethal gynecological malignancy, is a significant global health challenge, with >324,000 new cases and >200,000 deaths being reported annually. OC is characterized by late-stage diagnosis, a poor prognosis, and 5-year survival rates ranging from 93% (early stage) to 20% (advanced stage). Despite advances in genomics and proteomics, effective early-stage diagnostic tools and population-wide screening strategies remain elusive, contributing to high mortality rates. The complex pathogenesis of OC involves diverse histological subtypes and genetic predispositions, including BRCA1/2 mutations; notably, a considerable proportion of OC cases have a hereditary component. Current diagnostic modalities, including imaging techniques (transvaginal ultrasound, computed/positron emission tomography, and magnetic resonance imaging) and biomarkers (CA-125 and human epididymis protein 4), with varying degrees of sensitivity and specificity, have limited efficacy in detecting early-stage OC. Emerging technologies, such as liquid biopsy, multiomics, and artificial intelligence (AI)-assisted diagnostics, may enhance early detection. Liquid biopsies using circulating tumor DNA and microRNAs are popular minimally invasive diagnostic tools. Integrated multiomics has advanced biomarker discovery. AI algorithms have improved imaging interpretation and risk prediction. Novel screening methods including organoids and multiplex panels are being explored to overcome current diagnostic limitations. This review highlights the critical need for continued research and innovation to enhance early diagnosis, reduce mortality, and improve patient outcomes in OC and posits personalized medicine, integrated emerging technologies, and targeted global initiatives and collaborative efforts, which address care access disparities and promote cost-effective, scalable screening strategies, as potential tools to combat OC.

Transcorneal electrical stimulation restores DNA methylation changes in retinal degeneration

Background

Retinal degeneration is a major cause of irreversible blindness. Stimulation with controlled low-level electrical fields, such as transcorneal electrical stimulation (TES), has recently been postulated as a therapeutic strategy. With promising results, there is a need for detailed molecular characterization of the therapeutic effects of TES.

Methods

Controlled, non-invasive TES was delivered using a custom contact lens electrode to the retinas of Royal College of Surgeons (RCS) rats, a model of retinal degeneration. DNA methylation in the retina, brain and cell-free DNA in plasma was assessed by reduced representation bisulfite sequencing (RRBS) and gene expression by RNA sequencing.

Results

TES induced DNA methylation and gene expression changes implicated in neuroprotection in the retina of RCS rats. We devised an epigenomic-based retinal health score, derived from DNA methylation changes observed with disease progression in RCS rats, and showed that TES improved the epigenomic health of the retina. TES also induced DNA methylation changes in the superior colliculus: the brain which is involved in integrating visual signaling. Lastly, we demonstrated that TES-induced retinal DNA methylation changes were detectable in cell-free DNA derived from plasma.

Conclusion

TES induced DNA methylation changes with therapeutic effects, which can be measured in circulation. Based on these changes, we were able to devise a liquid biopsy biomarker for retinal health. These findings shed light on the therapeutic potential and molecular underpinnings of TES, and provide a foundation for the further development of TES to improve the retinal health of patients with degenerative eye diseases.

Blood-based DNA methylation markers for lung cancer prediction

Objective

Screening high-risk individuals with low-dose CT reduces mortality from lung cancer, but many lung cancers occur in individuals who are not eligible for screening. Risk biomarkers may be useful to refine risk models and improve screening eligibility criteria. We evaluated if blood-based DNA methylation markers can improve a traditional lung cancer prediction model.

Methods and analysis

This study used four prospective cohorts with blood samples collected prior to lung cancer diagnosis. The study was restricted to participants with a history of smoking, and one control was individually matched to each lung cancer case using incidence density sampling by cohort, sex, date of blood collection, age and smoking status. To train a DNA methylation-based risk score, we used participants from Melbourne Collaborative Cohort Study-Australia (n=648) and Northern Sweden Health and Disease Study-Sweden (n=380) based on five selected CpG sites. The risk discriminative performance of the methylation score was subsequently validated in participants from European Investigation into Cancer and Nutrition-Italy (n=267) and Norwegian Women and Cancer-Norway (n=185) and compared with that of the questionnaire-based PLCOm2012 lung cancer risk model.

Results

The area under the receiver operating characteristic curve (AUC) for the PLCOm2012 model in the validation studies was 0.70 (95% CI: 0.65 to 0.75) compared with 0.73 (95% CI: 0.68 to 0.77) for the methylation score model (P difference=0.07). Incorporating the methylation score with the PLCOm2012 model did not improve the risk discrimination (AUC: 0.73, 95% CI: 0.68 to 0.77, P difference=0.73).

Conclusions

This study suggests that the methylation-based risk prediction score alone provides similar lung cancer risk-discriminatory performance as the questionnaire-based PLCOm2012 risk model.

Computational investigations into structure and function impact of novel mutations identified in targeted exons from ovarian cancer cell lines

The lack of sensitive and specific biomarkers for ovarian cancer leads to late stage diagnosis of the disease in a majority of the cases. Mutation accumulation is the basis for cancer progression, thus identifying mutations is an important step in the disease diagnosis. In the present study, a comprehensive analysis of fifteen Next Generation Sequencing samples from thirteen ovarian cancer cell lines was carried out for the identification of new mutations. The study revealed eight clinically significant novel mutations in six ovarian cancer oncogenes, viz. SMARCA4, ARID1A, PPP2R1A, CTNNB1, DICER1 and PIK3CA. In-depth computational analysis revealed that the mutations affected the structure of the proteins in terms of stability, solvent accessible surface area and molecular dynamics. Moreover, the mutations were present in functionally significant domains of the proteins, thereby adversely affecting the protein functionality. PPI network for SMARCA4, CTNNB1, DICER1, PIK3CA, PPP2R1A and ARID1A showed that these genes were involved in certain significant pathways affecting various hallmarks of cancer. For further validation, in vitro studies were performed that revealed hypermutability of the CTNNB1 gene. Through this study we have identified some key mutations and have analysed their structural and functional impact. The study establishes some key mutations, which can be potentially explored as biomarker and drug target.

OvaPrint-A Cell-free DNA Methylation Liquid Biopsy for the Risk Assessment of High-grade Serous Ovarian Cancer

PURPOSE

High-grade serous ovarian carcinoma (HGSOC) is the most lethal epithelial ovarian cancer (EOC) and is often diagnosed at late stage. In women with a known pelvic mass, surgery followed by pathologic assessment is the most reliable way to diagnose EOC and there are still no effective screening tools in asymptomatic women. In the current study, we developed a cell-free DNA (cfDNA) methylation liquid biopsy for the risk assessment of early-stage HGSOC.

EXPERIMENTAL DESIGN

We performed reduced representation bisulfite sequencing to identify differentially methylated regions (DMR) between HGSOC and normal ovarian and fallopian tube tissue. Next, we performed hybridization probe capture for 1,677 DMRs and constructed a classifier (OvaPrint) on an independent set of cfDNA samples to discriminate HGSOC from benign masses. We also analyzed a series of non-HGSOC EOC, including low-grade and borderline samples to assess the generalizability of OvaPrint. A total of 372 samples (tissue n = 59, plasma n = 313) were analyzed in this study.

RESULTS

OvaPrint achieved a positive predictive value of 95% and a negative predictive value of 88% for discriminating HGSOC from benign masses, surpassing other commercial tests. OvaPrint was less sensitive for non-HGSOC EOC, albeit it may have potential utility for identifying low-grade and borderline tumors with higher malignant potential.

CONCLUSIONS

OvaPrint is a highly sensitive and specific test that can be used for the risk assessment of HGSOC in symptomatic women. Prospective studies are warranted to validate OvaPrint for HGSOC and further develop it for non-HGSOC EOC histotypes in both symptomatic and asymptomatic women with adnexal masses.

Early Detection of Ovarian Cancer

The risk of death from ovarian cancer is highly associated with the clinical stage at diagnosis. Efforts to implement screening for ovarian cancer have been largely unsuccessful, due to the low prevalence of the disease in the general population and the heterogeneity of the various cancer types that fall under the ovarian cancer designation. A practical test for early detection will require both high sensitivity and high specificity to balance reducing the number of cancer deaths with minimizing surgical interventions for false positive screens. The technology must be cost-effective to deliver at scale, widely accessible, and relatively noninvasive. Most importantly, a successful early detection test must be effective not only at diagnosing ovarian cancer but also in reducing ovarian cancer deaths. Stepwise or multimodal approaches among the various areas under investigation will likely be required to make early detection a reality.

Early diagnosis of ovarian cancer based on methylation profiles in peripheral blood cell-free DNA: a systematic review

Patients diagnosed with epithelial ovarian cancer (OC) have a 5-year survival rate of 49%. For early-stage disease, the 5-year survival rate is above 90%. However, advanced-stage disease accounts for most cases as patients with early stages often are asymptomatic or present with unspecific symptoms, highlighting the need for diagnostic tools for early diagnosis. Liquid biopsy is a minimal invasive blood-based approach that utilizes circulating tumor DNA (ctDNA) shed from tumor cells for real-time detection of tumor genetics and epigenetics. Increased DNA methylation of promoter regions is an early event during tumorigenesis, and the methylation can be detected in ctDNA, accentuating the promise of methylated ctDNA as a biomarker for OC diagnosis. Many studies have investigated multiple methylation biomarkers in ctDNA from plasma or serum for discriminating OC patients from patients with benign diseases of the ovaries and/or healthy females. This systematic review summarizes and evaluates the performance of the currently investigated DNA methylation biomarkers in blood-derived ctDNA for early diagnosis of OC. PubMed's MEDLINE and Elsevier's Embase were systematically searched, and essential results such as methylation frequency of OC cases and controls, performance measures, as well as preanalytical factors were extracted. Overall, 29 studies met the inclusion criteria for this systematic review. The most common method used for methylation analysis was methylation-specific PCR, with half of the studies using plasma and the other half using serum. RASSF1A, BRCA1, and OPCML were the most investigated gene-specific methylation biomarkers, with OPCML having the best performance measures. Generally, methylation panels performed better than single gene-specific methylation biomarkers, with one methylation panel of 103,456 distinct regions and 1,116,720 CpGs having better performance in both training and validation cohorts. However, the evidence is still limited, and the promising methylation panels, as well as gene-specific methylation biomarkers highlighted in this review, need validation in large, prospective cohorts with early-stage asymptomatic OC patients to assess the true diagnostic value in a clinical setting.

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.

Computational studies reveal co-occurrence of two mutations in IL7R gene of high-grade serous carcinoma patients

Major cause of mortality in ovarian cancer can be attributed to a lack of specific and sensitive biomarkers for diagnosis and prognosis of the disease. Uncovering the mutations in genes involved in crucial oncogenic pathways is a key step in discovery and development of novel biomarkers. Whole exome sequencing (WES) is a powerful method for the detection of cancer driver mutations. The present work focuses on identifying functionally damaging mutations in patients with high-grade serous ovarian carcinoma (HGSC) through computational analysis of WES. In this study, WES data of HGSC patients was retrieved from the genomic literature available in sequence read archive, the variants were identified and comprehensive structural and functional analysis was performed. Interestingly, I66T and V138I mutations were found to be co-occurring in the IL7R gene in four out of five HGSC patient samples investigated in this study. The V138I mutation was located in the fibronectin type-3 domain and computationally assessed to be causing disruptive effects on the structure and dynamics of IL7R protein. This mutation was found to be co-occurring with the neutral I66T mutation in the same domain which compensated the disruptive effects of V138I variant. These comprehensive studies point to a hitherto unexplored significant role of the IL7R gene in ovarian carcinoma. It is envisaged that the work will lay the foundation for the development of a novel biomarker with potential application in molecular profiling and in estimation of the disease prognosis.Communicated by Ramaswamy H. Sarma.

The application of circulating tumor cell and cell-free DNA liquid biopsies in ovarian cancer

Ovarian cancer is the deadliest gynecological cancer. 70% of the cases are diagnosed at late stages with already developed metastases due to the absence of easily noticeable symptoms. Early-stage ovarian cancer has a good prognosis with a 5-year survival rate reaching 95%, hence the identification of effective biomarkers for early diagnosis is important. Advances in liquid biopsy-based methods can have a significant impact not just on the development of an efficient screening strategy, but also in clinical decision-making with additional molecular profiling and genetic alterations linked to therapy resistance. Despite the well-known advantages of liquid biopsy, there are still challenges that need to be addressed before its routine use in clinical practice. Various liquid biopsy-based biomarkers have been investigated in ovarian cancer; however, in this review, we are concentrating on the current use of cell-free DNA (cfDNA) and circulating tumor cells (CTCs) in disease management, focusing on their emerging importance in clinical practice. We also discuss the technical aspects of these workflows. The analysis of cfDNA is often chosen for the detection of mutations, copy number aberrations, and DNA methylation changes, whereas CTC analysis provides a unique opportunity to study whole cells, thus allowing DNA, RNA, and protein-based molecular profiling as well as in vivo studies. Combined solutions which merge the strengths of cfDNA and CTC approaches should be developed to maximize the potential of liquid biopsy technology.

Precision gynecologic oncology: circulating cell free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancer

Ovarian cancer (OC) is the most lethal gynecologic cancer due primarily to its asymptomatic nature and late stage at diagnosis. The development of non-invasive markers is an urgent priority. We report the accurate detection of epithelial OC using Artificial Intelligence (AI) and genome-wide epigenetic analysis of circulating cell free tumor DNA (cfTDNA). In a prospective study, we performed genome-wide DNA methylation profiling of cytosine (CpG) markers. Both conventional logistic regression and six AI platforms were used for OC detection. Further, we performed Gene Set Enrichment Analysis (GSEA) analysis to elucidate the molecular pathogenesis of OC. A total of 179,238 CpGs were significantly differentially methylated (FDR p-value < 0.05) genome-wide in OC. High OC diagnostic accuracies were achieved. Conventional logistic regression achieved an area under the ROC curve (AUC) [95% CI] 0.99 [± 0.1] with 95% sensitivity and 100% specificity. Multiple AI platforms each achieved high diagnostic accuracies (AUC = 0.99-1.00). For example, for Deep Learning (DL)/AI AUC = 1.00, sensitivity = 100% and 88% specificity. In terms of OC pathogenesis: GSEA analysis identified 'Adipogenesis' and 'retinoblastoma gene in cancer' as the top perturbed molecular pathway in OC. This finding of epigenomic dysregulation of molecular pathways that have been previously linked to cancer adds biological plausibility to our results.

New Trends in the Detection of Gynecological Precancerous Lesions and Early-Stage Cancers

The prevention and early diagnostics of precancerous stages are key aspects of contemporary oncology. In cervical cancer, well-organized screening and vaccination programs, especially in developed countries, are responsible for the dramatic decline of invasive cancer incidence and mortality. Cytological screening has a long and successful history, and the ongoing implementation of HPV triage with increased sensitivity can further decrease mortality. On the other hand, endometrial and ovarian cancers are characterized by a poor accessibility to specimen collection, which represents a major complication for early diagnostics. Therefore, despite relatively promising data from evaluating the combined effects of genetic variants, population screening does not exist, and the implementation of new biomarkers is, thus, necessary. The introduction of various circulating biomarkers is of potential interest due to the considerable heterogeneity of cancer, as highlighted in this review, which focuses exclusively on the most common tumors of the genital tract, namely, cervical, endometrial, and ovarian cancers. However, it is clearly shown that these malignancies represent different entities that evolve in different ways, and it is therefore necessary to use different methods for their diagnosis and treatment.

The Molecular Landscape Influencing Prognoses of Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) is one of the major increasing lethal malignancies of the gynecological tract, mostly due to delayed diagnosis and chemoresistance, as well as its very heterogeneous genetic makeup. Application of high-throughput molecular technologies, gene expression microarrays, and powerful preclinical models has provided a deeper understanding of the molecular characteristics of EOC. Therefore, molecular markers have become a potent tool in EOC management, including prediction of aggressiveness, prognosis, and recurrence, and identification of novel therapeutic targets. In addition, biomarkers derived from genomic/epigenomic alterations (e.g., gene mutations, copy number aberrations, and DNA methylation) enable targeted treatment of affected signaling pathways in advanced EOC, thereby improving the effectiveness of traditional treatments. This review outlines the molecular landscape and discusses the impacts of biomarkers on the detection, diagnosis, surveillance, and therapeutic targets of EOC. These findings focus on the necessity to translate these potential biomarkers into clinical practice.