Integration of ultrasonography and circulating tumor DNA analysis enhances early detection of asymptomatic ovarian cancer.

Early detection and treatment of ovarian cancer: shifting from early stage to minimal volume of disease based on a new model of carcinogenesis

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

Ovarian cancer is a leading cause of death in women. Early detection of ovarian cancer is essential to decrease mortality. However, the early diagnosis of ovarian cancer is difficult due to a lack of clinical symptoms and suitable molecular diagnostic markers. Thus, identification of meaningful tumor biomarkers with potential clinical application is clearly needed. To search for a biomarker for the early detection of ovarian cancer, we identified human anterior gradient 2 (AGR2) from our systematic analysis of paired normal and ovarian tumor tissue cDNA microarray. We noted a marked overexpression of AGR2 mRNA and protein in early stage mucinous ovarian tumors compared to normal ovarian tissues and serous type ovarian tumors by Western blot analysis and immunohistochemistry. To further elucidate the role of AGR2 in ovarian tumorigenesis, stable 2774 human ovarian cancer cell lines overexpressing AGR2 were established. Forced expression of AGR2 in 2774 cells enhanced the growth and migration of ovarian cancer cells. AGR2 protein was detected in the serum of mucinous ovarian cancer patients by Western blot and ELISA analysis. Thus, AGR2 is a potential biomarker for the diagnosis of mucinous ovarian cancer and an ELISA assay may facilitate the early detection of mucinous ovarian cancer using patient serum.

Early detection of ovarian cancer has been a challenge to manage the high mortality rate caused by this deadly disease. The trends in mortality have been reduced by the scientific contributions from the corners across the globe, however accounting for the fifth leading cause of gynecological mortality. The complexities in the clinical presentation, origin of tumor, and gene expression profiles had added to much difficulty in understanding and diagnosis of the disease. Stage 1 diagnosis of ovarian cancer improves the 5-year survival rate to around 92%. Cancer antigen-125 (CA-125) is the gold standard tumor marker found at abnormally high levels in the blood of many women in ovarian cancer. However, many non-cancerous conditions exhibit high levels of CA-125 and several women have normal CA-125 level in the early stage of ovarian cancer, suggesting CA-125 biomarker is not specific enough for the screening of early stage ovarian cancer. In addition, several other biomarkers, including HE4 have been added in the diagnostic field for higher sensitivity and specificity in the diagnosis and progression of ovarian cancer. HE4 is a prospective single serum biomarker which has been approved by the FDA to monitor the disease progression in epithelial ovarian cancer. However, owing to low sensitivity and specificity, combination of a panel of biomarkers has been proposed in the diagnosis of the disease. Based on extensive biomarkers research findings, here we discuss current trends in diagnostic approaches and updated potential several panels of cancer biomarkers for early detection of ovarian cancer. It has been recently reported that CA125 in combinations with two or more biomarkers have outperformed single biomarker assays for early detection of the disease. Moreover, CA-125 with CA 19–9, EGFR, G-CSF, Eotaxin, IL-2R, cVCAM, MIF improved the sensitivity with 98.2 % and specificity of 98.7% in early stage detection of ovarian cancer. Overall, this review demonstrates a panel of biomarkers signature as the potential tool for prototype development in future and other advanced approaches for early diagnosis of ovarian cancer to avoid false-diagnosis and excessive cost.

Paraneoplastic antigens as biomarkers for early diagnosis of ovarian cancer

Ovarian cancer is the sixth most common cancer in women. The overall 5-year survival rate for ovarian cancer is approximately 40% due to the fact that most cases are diagnosed at late stages. Detection of ovarian cancer at the earliest possible stages is a public health priority. The article provides a review of existing scientific data regarding the early detection and screening of ovarian cancer, based on an analysis of publications in international electronic databases. Screening strategies are most effective for detecting diseases in their early stages, but at the moment there is no standard instrumental method that could be recommended as a screening examination for detecting ovarian cancer at an early stage. In this regard, most researchers have switched to the field of biomarkers and their combinations. Currently, more than 200 tumor markers, which are produced with varying intensity in ovarian cancer, have been proposed, but only two of them, CA-125 and HE4, have been tested in clinical experience. The article highlights the role of new tumor markers, multimarker panels, longitudinal algorithms in the early diagnosis of ovarian cancer. Most of the studies on the early diagnosis of ovarian cancer is aimed at searching for new biomarkers or developing multimodal algorithms that include both tumor markers, free deoxyribonucleic acid, and ultrasound of the pelvic organs. However, there are still no convincing data on mortality reduction based on randomized controlled trials, which stops doctors from including one or another strategy for the early diagnosis of ovarian cancer in national protocols and/or recommendations as a screening examination.

Ovarian cancer is the second most common type of female reproductive cancer, and more women die from ovarian cancer than from cervical cancer and uterine cancer combined. Currently, there is no strategy for early detection of ovarian cancer that reduces ovarian cancer mortality. Taking a detailed personal and family history for breast, gynecologic, and colon cancer facilitates categorizing women based on their risk (average risk or high risk) of developing epithelial ovarian cancer. Women with a strong family history of ovarian, breast, or colon cancer may have hereditary breast and ovarian cancer syndrome (BRCA mutation) or hereditary nonpolyposis colorectal cancer (Lynch syndrome), and these women are at increased risk of developing ovarian cancer. Women with these conditions should be referred for formal genetic counseling to better assess their cancer risk, including their risk of ovarian cancer. If appropriate, these women may be offered additional testing for early detection of ovarian cancer. The use of transvaginal ultrasonography and tumor markers (such as cancer antigen 125), alone or in combination, for the early detection of ovarian cancer in average-risk women have not been proved to reduce mortality, and harms exist from invasive diagnostic testing (eg, surgery) resulting from false-positive test results. The patient and her obstetrician-gynecologist should maintain an appropriate level of suspicion when potentially relevant signs and symptoms of ovarian cancer are present.

Ovarian cancer is the second most common type of female reproductive cancer, and more women die from ovarian cancer than from cervical cancer and uterine cancer combined. Currently, there is no strategy for early detection of ovarian cancer that reduces ovarian cancer mortality. Taking a detailed personal and family history for breast, gynecologic, and colon cancer facilitates categorizing women based on their risk (average risk or high risk) of developing epithelial ovarian cancer. Women with a strong family history of ovarian, breast, or colon cancer may have hereditary breast and ovarian cancer syndrome (BRCA mutation) or hereditary nonpolyposis colorectal cancer (Lynch syndrome), and these women are at increased risk of developing ovarian cancer. Women with these conditions should be referred for formal genetic counseling to better assess their cancer risk, including their risk of ovarian cancer. If appropriate, these women may be offered additional testing for early detection of ovarian cancer. The use of transvaginal ultrasonography and tumor markers (such as cancer antigen 125), alone or in combination, for the early detection of ovarian cancer in average-risk women have not been proved to reduce mortality, and harms exist from invasive diagnostic testing (eg, surgery) resulting from false-positive test results. The patient and her obstetrician-gynecologist should maintain an appropriate level of suspicion when potentially relevant signs and symptoms of ovarian cancer are present.

Advances in medical technology have led to potentially useful techniques for the early detection of epithelial ovarian cancer. Early detection of ovarian cancer is crucial for survival as women found to have Stage I or II disease have a 5-year survival of 90% and 70%, respectively, whereas those with advanced disease (Stage III and IV) have a survival of approximately 20%. The circulating tumour marker CA-125 has been extremely useful in following women known to have epithelial ovarian cancers. It has been employed in differentiating benign tumours from malignancies, and is now being tested in a variety of programmes for its role in the early detection of ovarian cancer. The application of endovaginal ultrasound and colour Doppler flow techniques to early detection of ovarian cancer have resulted in several large series identifying ovarian cancer in 1:1000 to 1:2000 postmenopausal women screened. However, a high false positivity rate persists using CA-125 and ultrasound techniques alone or in sequence. Developments in molecular genetics may be extremely useful in evaluating women with inherited susceptibilities for this disease, but this probably represents only about 3% of the population of the women who develop epithelial ovarian cancer. The cost-benefit analysis of isolated screening for epithelial ovarian cancer using CA-125 and ultrasound techniques, even in women at high risk for the disease, would suggest that such screening is not cost-effective at this time.

Ovarian cancer is the eighth most common cause of cancer mortality in women. It is diagnosed in more than 20,000 women in the USA each year and approximately 15,000 women die of the disease annually. The majority of patients are diagnosed with advanced-stage ovarian cancer, as this deadly disease causes minimal and nonspecific symptoms until late in the course of the disease. No standardized screening test exists to reliably detect ovarian cancer. Cancer antigen (CA)-125 is a protein antigen found at abnormally high levels in the blood of many women with ovarian cancer. Most healthy women have CA-125 levels of below 35 units/microl of blood serum. However, a number of noncancerous conditions can cause elevated CA 125 levels, and many women with early-stage ovarian cancer have normal CA-125 levels. Owing to these limitations, this test is not recommended for routine screening in women who are not at high risk or who do not have specific symptoms of the disease. Currently, many researchers are focusing on simultaneous examination of multiple markers to increase sensitivity of the screening test for early detection of ovarian cancer. Analysis of the current literature shows that combining several biomarkers dramatically improves sensitivity of CA-125 in ovarian cancer patients. This article provides a comprehensive overview of existing studies in the area of multimarker panel development for the early detection and monitoring of ovarian cancer. Our literature review demonstrates that a multimarker approach for the generation of a prototype assay for early detection of ovarian cancer has a great potential to lead to the development of a screening test for this disease.

The purpose of this study is to identify a positive correlation between serum myeloperoxidase (MPO) and free iron with ovarian cancer stages, such that they may be utilized as biomarkers for early detection of ovarian cancer (OC). Early-stage OC presents with nonspecific symptoms, and most often diagnosis is not made until after the malignancy has spread beyond the ovaries. Mortality rates for this type of malignancy are high due to the lack of an early-stage OC screening method. The rationale of this work stems from our identification of MPO, a hemeprotein previously recognized to be present only in hematopoietic cells, to be present in epithelial OC tissues, while not in normal tissues. Additionally, we have gathered compelling evidence that under oxidative stress, there is destruction of the MPO heme moiety, which results in the elevation of free iron levels in serum. Serum samples were collected from women with early-stage (stages I & II, n=9) and late-stage OC (stages III & IV, n=8), benign gynecologic disorders (n=7), inflammation (endometriosis, n=5), and healthy controls (n=8). We have utilized the MPO ELISA assay and VITROS Fe Slide methods to measure serum MPO and free iron levels, respectively. Data were analyzed with a one-way ANOVA and post-hoc tests. A p<0.05 was considered significant. Both serum MPO and iron are significantly greater in late-stage as compared to early-stage OC (p<0.01). The mean (SD) serum MPO (ng/ml) was 161.0 (33.8) and 98.4(16.3) while mean (SD) serum iron (μg/dl) was 521.3 (110.2) and 289.1(44.70) for the late and early-stage OC, respectively. For serum MPO (ng/ml), both late and early-stage OC were significantly different than healthy control; 43.1 (27.8) and benign gynecologic condition groups; 63.0 (21.0); (p<0.01). The control and benign condition groups were not significantly different. For serum iron (μg/dl), there was a significant difference between early-stage OC; 289.1 (44.70), the healthy control; 104.0 (27.9); the benign gynecologic; 102.1 (32.4); and inflammation groups; 93.1 (18.8). There was no significant difference between healthy control, benign gynecologic, and inflammation groups. There was no significant difference in serum MPO levels between early-stage OC and the inflammation group. However, when examining serum iron levels (μg/dl) in these groups, a significant difference between early-stage OC; 289.1 (44.7) and the inflammation group; 93.1 (18.8) was observed (p<0.01). Collectively, these findings clearly indicate a potential role for the combination of serum MPO and iron as biomarkers for early detection and prognosis of ovarian cancer. In addition, there is a great potential therapeutic value utilizing MPO inhibitors for the treatment of ovarian cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3192. doi:10.1158/1538-7445.AM2011-3192

Background: Accumulated evidence revealed that aberrant CpG island hypermethylation plays significant roles in carcinogenesis that can serve as a promising target for molecular detection in body fluids. Ovarian cancer remains the most lethal gynecologic malignancy and is characterized by few early symptoms, late stage presentation, and resulting poor survival, which thereby renders challenge to its prompt diagnosis. Despite a myriad of attempts at early diagnosis of ovarian cancer, this clinical aim still remains a major challenge. To date, no single biomarker is able to accurately detect early OC in either tissue or body fluid. Aberrant circulating DNA methylation patterns provide highly specific cancer signals. Methods: To develop a DNA methylation-based screening assay for early diagnosis of ovarian cancer, we quantitatively assessed the promoter methylation of DAPK1, HOXA9, RASSF1A, and SOX1 genes in 105 ovarian cancer and 30 non-neoplastic ovarian specimens by means of a high-throughput quantitative, real-time PCR-based technique (MethyLight) and clonal bisulfite sequencing. The best-performing gene cassette was further evaluated for their methylation status in cell-free DNA from serum of matched 35 ovarian cancer patients and 20 normal controls. Area under the ROC (Receiver-operator characteristic) curve was used to evaluate the discriminatory performance of both individual and combined gene panel. Results: RASSF1A, HOXA9, DAPK1, and SOX1 displayed a methylation frequency of 72.72, 82.67, 68.0, and 80.0%, respectively, in tissue samples. We identified DNA methylation of HOXA9 and SOX1 as the best discriminator between cancer and non-neoplastic tissue. In the multiplex assay, the combined panel of HOXA9 and SOX1 achieved a sensitivity of 88.0% with a specificity of 86.7%, when either or both of the gene promoters showed methylation. These genes appear to have great potential to be evaluated for their methylation level in cell-free DNA as a noninvasive diagnostic marker for early diagnosis of ovarian cancer. In cell-free DNA, this novel panel achieved a sensitivity of 62.86.0% and a specificity of 95% with an area under the ROC curve of 0.81 for discriminating OC samples from normal control. The results of MethyLight were concordant with those of clonal bisulfite sequencing. Conclusion: Our findings revealed the potential of epigenetic biomarker for early detection of OC in cell-free DNA as a minimally invasive tool. Here, we have established that promoter hypermethylation can be detected in serum cell-free DNA from patients with early as well as advanced-stage ovarian cancer. Based on the multiplex MethyLight assay, study will focus on analyzing the clinical utility of HOXA9 and SOX1 as methylation biomarkers. These findings further underline the potential of multiplexing individual markers into a panel that helps to achieve higher sensitivity and specificity than individual marker and provides robustness to a noninvasive early screening test. Citation Format: Alka Singh, Jaydeep Aravindbhai Badarukhiya, Sameer Gupta, Manisha Sachan. DNA methylation of SOX1 and HOXA9 as a biomarker for early detection of ovarian cancer in cell-free DNA [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A13.

Newly diagnosed and relapsed epithelial ovarian carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up

Background: Current screening programs for early detection of high grade epithelial ovarian cancer (HGOvC) among high-risk populations have failed to show improvement in HGOvC mortality, therefore these women are offered risk-reducing bilateral salpingo-oophorectomy (RRBSO) at 35- 40 years. Stratification of high-risk population, especially BRCA mutation carriers, may enable personalized risk counseling and individualization of timing of RRBSO. In most cases, the precursor lesions of HGOvC arise in the epithelium of the fallopian tube (FT) fimbriae rather than intra-peritoneally. It is therefore plausible that proteins, RNA or DNA from early-stage tumor cells may be identifiable in fluid samples obtained from the lumen of the gynecological tract, thus making it possible to identify curable, early stage lesions. Aims: (1) Test the feasibility of uterine lavage as a minimally invasive test for early detection of ovarian cancer, and (2) Identify novel early-detection biomarkers in the uterine lavage fluid (UtLF). Methods: We developed a method for sampling of gynecologic tract fluid termed uterine lavage fluid (UtLF), which is a simple, reproducible, low-cost office procedure that can be performed routinely during gynecologic follow-up visits. We have already collected UtLF from 140 HGOvC patients and control women undergoing gynecologic surgical procedures for non-malignant indications. Deep proteomic profiling of UtLF is performed by isolation of microparticles from body fluids, followed by solubilization, trypsin digestion and high resolution mass spectrometric (MS) analysis (on the Q-Exactive MS). Machine learning algorithms have been used to extract a classifier that can predict the diagnosis of ovarian cancer. Results: Uterine lavage appears to be a feasible, low burden procedure. The MS approach has identified thousands of proteins in each UtLF specimen, in a high throughput manner. The label-free quantification algorithm (MaxQuant) enables a quantitative comparison between samples from cases and controls. We have derived a 20-protein classifier with an area under the curve (AUC) of Receiver Operating Characteristics (ROC) curve of 0.91 at 20% error. The composite biomarker has been applied to an independent validation set with a negative predictive value (NPV) of 92% and positive predictive value (PPV) of 45%. Conclusions: A minimally invasive technique of uterine lavage to collect unique diagnostic samples, coupled with state-of-the-art proteomics methods, results in a highly sensitive and specific composite biomarker which may be developed in to a screening tool for early detection of serous ovarian cancer in high-risk populations. Citation Format: Keren Bahar-Shany, Georgina D. Barnabas, Limor Helpman, Ariella Yakobson-Siton, Tamar Perri, Ram Eitan, Jacob Korach, Tamar Geiger, Keren Levanon. Minimally invasive test and composite biomarker for early detection of serous ovarian carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 721. doi:10.1158/1538-7445.AM2017-721

Background: Ovarian cancer is often diagnosed at advanced stages due to the absence of specific early symptoms, resulting in high mortality rates. This study aims to develop a robust and interpretable machine learning (ML) model for the early detection of ovarian cancer, enhancing its transparency through the use of the Contrastive Explanation Method (CEM), an advanced technique within the field of explainable artificial intelligence (XAI). Methods: An open-access dataset of 349 patients with ovarian cancer or benign ovarian tumors was used. To improve reliability, the dataset was augmented via bootstrap resampling. A three-layer deep neural network was trained on normalized demographic, biochemical, and tumor marker features. Model performance was measured using accuracy, sensitivity, specificity, F1-score, and the Matthews correlation coefficient. CEM was used to explain the model's classification results, showing which factors push the model toward "Cancer" or "No Cancer" decisions. Results: The model achieved high diagnostic performance, with an accuracy of 95%, sensitivity of 96.2%, and specificity of 93.5%. CEM analysis identified lymphocyte count (CEM value: 1.36), red blood cell count (1.18), plateletcrit (0.036), and platelet count (0.384) as the strongest positive contributors to the "Cancer" classification, with lymphocyte count demonstrating the highest positive relevance, underscoring its critical role in cancer detection. In contrast, age (change from -0.13 to +0.23) and HE4 (change from -0.43 to -0.05) emerged as key factors in reversing classifications, requiring substantial hypothetical increases to shift classification toward the "No Cancer" class. Among benign cases, a significant reduction in RBC count emerged as the strongest determinant driving a shift in classification. Overall, CEM effectively explained both the primary features influencing the model's classification results and the magnitude of changes necessary to alter its outputs. Conclusions: Using CEM with ML allowed clear and trustworthy detection of early ovarian cancer. This combined approach shows the promise of XAI in assisting clinicians in making decisions in gynecologic oncology.