OCaMIR-A Noninvasive, Diagnostic Signature for Early-Stage Ovarian Cancer: A Multi-cohort Retrospective and Prospective Study

The emerging roles of miRNA-mediated autophagy in ovarian cancer

Ovarian cancer is one of the common tumors of the female reproductive organs. It has a high mortality rate, is highly heterogeneous, and early detection and primary prevention are very complex. Autophagy is a cellular process in which cytoplasmic substrates are targeted for degradation in lysosomes through membrane structures called autophagosomes. The periodic elimination of damaged, aged, and redundant cellular molecules or organelles through the sequential translation between amino acids and proteins by two biological processes, protein synthesis, and autophagic protein degradation, helps maintain cellular homeostasis. A growing number of studies have found that autophagy plays a key regulatory role in ovarian cancer. Interestingly, microRNAs regulate gene expression at the posttranscriptional level and thus can regulate the development and progression of ovarian cancer through the regulation of autophagy in ovarian cancer. Certain miRNAs have recently emerged as important regulators of autophagy-related gene expression in cancer cells. Moreover, miRNA analysis studies have now identified a sea of aberrantly expressed miRNAs in ovarian cancer tissues that can affect autophagy in ovarian cancer cells. In addition, miRNAs in plasma and stromal cells in tumor patients can affect the expression of autophagy-related genes and can be used as biomarkers of ovarian cancer progression. This review focuses on the potential significance of miRNA-regulated autophagy in the diagnosis and treatment of ovarian cancer.

A unique circulating microRNA pairs signature serves as a superior tool for early diagnosis of pan-cancer

Cancer remains a major burden globally and the critical role of early diagnosis is self-evident. Although various miRNA-based signatures have been developed in past decades, clinical utilization is limited due to a lack of precise cutoff value. Here, we innovatively developed a signature based on pairwise expression of miRNAs (miRPs) for pan-cancer diagnosis using machine learning approach. We analyzed miRNA spectrum of 15832 patients, who were divided into training, validation, test, and external test sets, with 13 different cancers from 10 cohorts. Five different machine-learning (ML) algorithms (XGBoost, SVM, RandomForest, LASSO, and Logistic) were adopted for signature construction. The best ML algorithm and the optimal number of miRPs included were identified using area under the curve (AUC) and youden index in validation set. The AUC of the best model was compared to previously published 25 signatures. Overall, Random Forest approach including 31 miRPs (31-miRP) was developed, proving highly efficient in cancer diagnosis across different datasets and cancer types (AUC range: 0.980-1.000). Regarding diagnosis of cancers at early stage, 31-miRP also exhibited high capacities, with AUC ranging from 0.961 to 0.998. Moreover, 31-miRP exhibited advantages in differentiating cancers from normal tissues (AUC range: 0.976-0.998) as well as differentiating cancers from corresponding benign lesions. Encouragingly, comparing to previously published 25 different signatures, 31-miRP also demonstrated clear advantages. In conclusion, 31-miRP acts as a powerful model for cancer diagnosis, characterized by high specificity and sensitivity as well as a clear cutoff value, thereby holding potential as a reliable tool for cancer diagnosis at early stage.

Advances in precision therapy of low-grade serous ovarian cancer: A review

Low-grade serous ovarian carcinoma (LGSOC) is a rare subtype of ovarian cancer that accounts for approximately 6% to 10% of serous ovarian cancers. The clinical treatment of LGSOC is similar to that of high-grade serous ovarian carcinoma, however, its clinical and molecular characteristics are different from those of high-grade serous ovarian carcinoma. This article reviews the research on gene diagnosis, surgical treatment, chemotherapy, and biological therapy of LGSOC, providing reference for clinical diagnosis and treatment of LGSOC. Surgery is the cornerstone of LGSOC treatment and maximum effort must be made to achieve R0 removal. Although LGSOC is not sensitive to chemotherapy, postoperative platinum-based combination chemotherapy remains the first-line treatment option for LGSOC. Additional clinical trials are needed to confirm the clinical benefits of chemotherapy and explore new chemotherapy protocols. Hormone and targeted therapies may also play important roles. Some patients, particularly those with residual lesions after treatment, may benefit from hormone maintenance therapy after chemotherapy. Targeted therapies, such as MEKi, show good application prospects and are expected to change the treatment pattern of LGSOC. Continuing to further study the genomics of LGSOC, identify its specific gene changes, and combine traditional treatment methods with precision targeted therapy based on second-generation sequencing may be the direction for LGSOC to overcome the treatment bottleneck. In future clinical work, comprehensive genetic testing should be carried out for LGSOC patients to accumulate data for future scientific research, in order to find more effective methods and drugs for the treatment of LGSOC.

Pan-cancer characterization of cell-free immune-related miRNA identified as a robust biomarker for cancer diagnosis

Minimally invasive testing is essential for early cancer detection, impacting patient survival rates significantly. Our study aimed to establish a pioneering cell-free immune-related miRNAs (cf-IRmiRNAs) signature for early cancer detection. We analyzed circulating miRNA profiles from 15,832 participants, including individuals with 13 types of cancer and control. The data was randomly divided into training, validation, and test sets (7:2:1), with an additional external test set of 684 participants. In the discovery phase, we identified 100 differentially expressed cf-IRmiRNAs between the malignant and non-malignant, retaining 39 using the least absolute shrinkage and selection operator (LASSO) method. Five machine learning algorithms were adopted to construct cf-IRmiRNAs signature, and the diagnostic classifies based on XGBoost algorithm showed the excellent performance for cancer detection in the validation set (AUC: 0.984, CI: 0.980-0.989), determined through 5-fold cross-validation and grid search. Further evaluation in the test and external test sets confirmed the reliability and efficacy of the classifier (AUC: 0.980 to 1.000). The classifier successfully detected early-stage cancers, particularly lung, prostate, and gastric cancers. It also distinguished between benign and malignant tumors. This study represents the largest and most comprehensive pan-cancer analysis on cf-IRmiRNAs, offering a promising non-invasive diagnostic biomarker for early cancer detection and potential impact on clinical practice.

Ultrahigh resolution lipid mass spectrometry imaging of high-grade serous ovarian cancer mouse models

No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering that little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights into how metabolic and signaling alterations accompany its development. Herein, we present a comprehensive study using ultra-high-resolution Fourier transform ion cyclotron resonance matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to investigate the spatial distribution and alterations of lipids in ovarian tissues collected from double knockout (n = 4) and triple mutant mouse models (n = 4) of high-grade serous ovarian cancer (HGSOC). Lipids belonging to a total of 15 different classes were annotated and their abundance changes were compared to those in healthy mouse reproductive tissue (n = 4), mapping onto major lipid pathways involved in OC progression. From intermediate-stage OC to advanced HGSC, we provide direct visualization of lipid distributions and their biological links to inflammatory response, cellular stress, cell proliferation, and other processes. We also show the ability to distinguish tumors at different stages from healthy tissues via a number of highly specific lipid biomarkers, providing targets for future panels that could be useful in diagnosis.

Ultrahigh Resolution Lipid Mass Spectrometry Imaging of High-Grade Serous Ovarian Cancer Mouse Models

No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights on how metabolic and signaling alterations accompany its development. Herein, we present a comprehensive study using ultra-high-resolution Fourier transform ion cyclotron resonance matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to investigate the spatial distribution and alterations of lipids in ovarian tissues collected from double knockout (n = 4) and a triple mutant mouse models (n = 4) of high-grade serous ovarian cancer (HGSC). Lipids belonging to a total of 15 different classes were annotated and their abundance changes compared to those in healthy mouse reproductive tissue (n = 4), mapping onto major lipid pathways involved in OC progression. From intermediate-stage OC to advanced HGSC, we provide a direct visualization of lipid distributions and their biological links to inflammatory response, cellular stress, cell proliferation, and other processes. We also show the ability to distinguish tumors at different stages from healthy tissues via a number of highly specific lipid biomarkers, providing targets for future panels that could be useful in diagnosis.

Mortality impact, risks, and benefits of general population screening for ovarian cancer: the UKCTOCS randomised controlled trial

Background

Ovarian and tubal cancers are lethal gynaecological cancers, with over 50% of the patients diagnosed at advanced stage.

Trial design

Randomised controlled trial involving 27 primary care trusts adjacent to 13 trial centres based at NHS Trusts in England, Wales and Northern Ireland.

Methods

Postmenopausal average-risk women, aged 50-74, with intact ovaries and no previous ovarian or current non-ovarian cancer.

Interventions

One of two annual screening strategies: (1) multimodal screening (MMS) using a longitudinal CA125 algorithm with repeat CA125 testing and transvaginal scan (TVS) as second line test (2) ultrasound screening (USS) using TVS alone with repeat scan to confirm any abnormality. The control (C) group had no screening. Follow-up was through linkage to national registries, postal follow-up questionnaires and direct communication with trial centres and participants.

Objective

To assess comprehensively risks and benefits of ovarian cancer screening in the general population.

Outcome

Primary outcome was death due to ovarian or tubal cancer as assigned by an independent outcomes review committee. Secondary outcomes included incidence and stage at diagnosis of ovarian and tubal cancer, compliance, performance characteristics, harms and cost-effectiveness of the two screening strategies and a bioresource for future research.

Randomisation

The trial management system confirmed eligibility and randomly allocated participants using computer-generated random numbers to MMS, USS and C groups in a 1:1:2 ratio.

Blinding

Investigators and participants were unblinded and outcomes review committee was masked to randomisation group.

Analyses

Primary analyses were by intention to screen, comparing separately MMS and USS with C using the Versatile test.

Results

1,243,282 women were invited and 205,090 attended for recruitment between April 2001 and September 2005.

Randomised

202,638 women: 50,640 MMS, 50,639 USS and 101,359 C group.

Numbers analysed for primary outcome

202,562 (>99.9%): 50,625 (>99.9%) MMS, 50,623 (>99.9%) USS, and 101,314 (>99.9%) C group.

Outcome

Women in MMS and USS groups underwent 345,570 and 327,775 annual screens between randomisation and 31 December 2011. At median follow-up of 16.3 (IQR 15.1-17.3) years, 2055 women developed ovarian or tubal cancer: 522 (1.0% of 50,625) MMS, 517 (1.0% of 50,623) USS, and 1016 (1.0% of 101314) in C group. Compared to the C group, in the MMS group, the incidence of Stage I/II disease was 39.2% (95% CI 16.1 to 66.9) higher and stage III/IV 10.2% (95% CI -21.3 to 2.4) lower. There was no difference in stage in the USS group. 1206 women died of the disease: 296 (0.6%) MMS, 291 (0.6%) USS, and 619 (0.6%) C group. There was no significant reduction in ovarian and tubal cancer deaths in either MMS (p = 0.580) or USS (p = 0.360) groups compared to the C group. Overall compliance with annual screening episode was 80.8% (345,570/420,047) in the MMS and 78.0% (327,775/420,047) in the USS group. For ovarian and tubal cancers diagnosed within one year of the last test in a screening episode, in the MMS group, the sensitivity, specificity and positive predictive values were 83.8% (95% CI 78.7 to 88.1), 99.8% (95% CI 99.8 to 99.9), and 28.8% (95% CI 25.5 to 32.2) and in the USS group, 72.2% (95% CI 65.9 to 78.0), 99.5% (95% CI 99.5 to 99.5), and 9.1% (95% CI 7.8 to 10.5) respectively. The final within-trial cost-effectiveness analysis was not undertaken as there was no mortality reduction. A bioresource (UKCTOCS Longitudinal Women's Cohort) of longitudinal outcome data and over 0.5 million serum samples including serial annual samples in women in the MMS group was established and to date has been used in many new studies, mainly focused on early detection of cancer.

Harms

Both screening tests (venepuncture and TVS) were associated with minor complications with low (8.6/100,000 screens MMS; 18.6/100,000 screens USS) complication rates. Screening itself did not cause anxiety unless more intense repeat testing was required following abnormal screens. In the MMS group, for each screen-detected ovarian or tubal cancer, an additional 2.3 (489 false positives; 212 cancers) women in the MMS group had unnecessary false-positive (benign adnexal pathology or normal adnexa) surgery. Overall, 14 (489/345,572 annual screens) underwent unnecessary surgery per 10,000 screens. In the USS group, for each screen-detected ovarian or tubal cancer, an additional 10 (1630 false positives; 164 cancers) underwent unnecessary false-positive surgery. Overall, 50 (1630/327,775 annual screens) women underwent unnecessary surgery per 10,000 screens.

Conclusions

Population screening for ovarian and tubal cancer for average-risk women using these strategies should not be undertaken. Decreased incidence of Stage III/IV cancers during multimodal screening did not translate to mortality reduction. Researchers should be cautious about using early stage as a surrogate outcome in screening trials. Meanwhile the bioresource provides a unique opportunity to evaluate early cancer detection tests.

Funding

Long-term follow-up UKCTOCS (2015-2020) - National Institute for Health and Care Research (NIHR HTA grant 16/46/01), Cancer Research UK, and The Eve Appeal. UKCTOCS (2001-2014) - Medical Research Council (MRC) (G9901012/G0801228), Cancer Research UK (C1479/A2884), and the UK Department of Health, with additional support from The Eve Appeal. Researchers at UCL were supported by the NIHR UCL Hospitals Biomedical Research Centre and by MRC Clinical Trials Unit at UCL core funding (MR_UU_12023).

Predict ovarian cancer by pairing serum miRNAs: Construct of single sample classifiers

Objective

The accuracy of CA125 or clinical examination in ovarian cancer (OVC) screening is still facing challenges. Serum miRNAs have been considered as promising biomarkers for clinical applications. Here, we propose a single sample classifier (SSC) method based on within-sample relative expression orderings (REOs) of serum miRNAs for OVC diagnosis.

Methods

Based on the stable REOs within 4,965 non-cancer serum samples, we developed the SSC for OVC in the training cohort (GSE106817: OVC = 200, non-cancer = 2,000) by focusing on highly reversed REOs within OVC. The best diagnosis is achieved using a combination of reversed miRNA pairs, considering the largest evaluation index and the lowest number of miRNA pairs possessed according to the voting rule. The SSC was then validated in internal data (GSE106817: OVC = 120, non-cancer = 759) and external data (GSE113486: OVC = 40, non-cancer = 100).

Results

The obtained 13-miRPairs classifier showed high diagnostic accuracy on distinguishing OVC from non-cancer controls in the training set (sensitivity = 98.00%, specificity = 99.60%), which was reproducible in internal data (sensitivity = 98.33%, specificity = 99.21%) and external data (sensitivity = 97.50%, specificity = 100%). Compared with the published models, it stood out in terms of correct positive predictive value (PPV) and negative predictive value (NPV) (PPV = 96.08% and NPV=95.16% in training set, and both above 99% in validation set). In addition, 13-miRPairs demonstrated a classification accuracy of over 97.5% for stage I OVC samples. By integrating other non-OVC serum samples as a control, the obtained 17-miRPairs classifier could distinguish OVC from other cancers (AUC>92% in training and validation set).

Conclusion

The REO-based SSCs performed well in predicting OVC (including early samples) and distinguishing OVC from other cancer types, proving that REOs of serum miRNAs represent a robust and non-invasive biomarker.

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.

Toward More Comprehensive Homologous Recombination Deficiency Assays in Ovarian Cancer, Part 1: Technical Considerations

Simple Summary

High-grade serous ovarian cancer (HGSOC) is the most frequent and lethal form of ovarian cancer and is associated with homologous recombination deficiency (HRD) in 50% of cases. This specific alteration is associated with sensitivity to PARP inhibitors (PARPis). Despite vast prognostic improvements due to PARPis, current molecular assays assessing HRD status suffer from several limitations, and there is an urgent need for a more accurate evaluation. In these companion reviews (Part 1: Technical considerations; Part 2: Medical perspectives), we develop an integrative review to provide physicians and researchers involved in HGSOC management with a holistic perspective, from translational research to clinical applications.

Abstract

High-grade serous ovarian cancer (HGSOC), the most frequent and lethal form of ovarian cancer, exhibits homologous recombination deficiency (HRD) in 50% of cases. In addition to mutations in BRCA1 and BRCA2, which are the best known thus far, defects can also be caused by diverse alterations to homologous recombination-related genes or epigenetic patterns. HRD leads to genomic instability (genomic scars) and is associated with PARP inhibitor (PARPi) sensitivity. HRD is currently assessed through BRCA1/2 analysis, which produces a genomic instability score (GIS). However, despite substantial clinical achievements, FDA-approved companion diagnostics (CDx) based on GISs have important limitations. Indeed, despite the use of GIS in clinical practice, the relevance of such assays remains controversial. Although international guidelines include companion diagnostics as part of HGSOC frontline management, they also underscore the need for more powerful and alternative approaches for assessing patient eligibility to PARP inhibitors. In these companion reviews, we review and present evidence to date regarding HRD definitions, achievements and limitations in HGSOC. Part 1 is dedicated to technical considerations and proposed perspectives that could lead to a more comprehensive and dynamic assessment of HR, while Part 2 provides a more integrated approach for clinicians.

Non-coding RNAs as liquid biopsy biomarkers in cancer

Although non-coding RNAs have long been considered as non-functional "junk" RNAs, accumulating evidence in the past decade indicates that they play a critical role in pathogenesis of various cancers. In addition to their biological significance, the recognition that their expression levels are frequently dysregulated in multiple cancers have fueled the interest for exploiting their clinical potential as cancer biomarkers. In particular, microRNAs (miRNAs), a subclass of small non-coding RNAs that epigenetically modulate gene-transcription, have become one of the most well-studied substrates for the development of liquid biopsy biomarkers for cancer patients. The emergence of high-throughput sequencing technologies has enabled comprehensive molecular characterisation of various non-coding RNA expression profiles in multiple cancers. Furthermore, technological advances for quantifying lowly expressed RNAs in the circulation have facilitated robust identification of previously unrecognised and undetectable biomarkers in cancer patients. Here we summarise the latest progress on the utilisation of non-coding RNAs as non-invasive cancer biomarkers. We evaluated the suitability of multiple non-coding RNA types as blood-based cancer biomarkers and examined the impact of recent technological breakthroughs on the development of non-invasive molecular biomarkers in cancer.

Advances in quantifying circulatory microRNA for early disease detection

Precision preventive healthcare aims to improve patient health by integrating preventive measures with early disease detection for timely intervention with precision medicine. Key to the delivery of preventive healthcare is the clinical adoption of novel assays that enable early disease detection. Such assays, typically based on biomarkers such as microRNAs (miRNAs) from liquid biopsy or excreta, are entering clinical practice after years of clinical development and validation. In this review, we discuss the clinical utility and validation of miRNA-based molecular diagnostics for early disease detection through large-cohort studies and key considerations for developing multi-analyte clinical assays. We also highlight recent advances in the ongoing development of integrated PCR-free miRNA detection systems for point-of-care testing.

The Role of miRNA in Ovarian Cancer: an Overview

Ovarian cancer (OC) is a highly malignant disease that seriously threatens women’s health and poses challenges for clinicians. MicroRNAs (miRNAs) have recently been intensively studied in the field of oncology due to their regulatory roles in gene expressions through RNA degradation and/or translation inhibition. This review summarizes the current studies on miRNAs in OC and introduces the latest updates of miRNAs in the early screening, treatment, and prognostic prediction of OC, thereby demonstrating the clinical significance of miRNAs in OC. Further exploration on potential targets of miRNAs in OC may provide new insights on optimizing the diagnosis and treatment of OC. MiRNAs are important driving factors for the progression of OC and the dysregulation of miRNAs can serve as biomarkers in the diagnosis, treatment and prognosis of OC. Therefore, miRNAs are potential biological targets for early screening, targeted therapy, drug resistance monitoring, and prognosis improvement in malignancies such as OC.