Ovarian Cancer Biomarker Discovery Research Articles

Tandem SERS and MS/MS Profiling of Plasma Extracellular Vesicles for Early Ovarian Cancer Biomarker Discovery.

Extracellular vesicles (EVs) are vectors of biomolecular cargo that play essential roles in intercellular communication across a range of cells. Protein, lipid, and nucleic acid cargo harbored within EVs may serve as biomarkers at all stages of disease; however, the choice of methodology may challenge the specificity and reproducibility of discovery. To address these challenges, the integration of rigorous EV purification methods, cutting-edge spectroscopic technologies, and data analysis are critical to uncover diagnostic signatures of disease. Herein, we demonstrate an EV isolation and analysis pipeline using surface-enhanced Raman spectroscopy (SERS) and mass spectrometry (MS) techniques on plasma samples obtained from umbilical cord blood, healthy donor (HD) plasma, and plasma from women with early stage high-grade serous carcinoma (HGSC). Plasma EVs were purified by size exclusion chromatography and analyzed by surface-enhanced Raman spectroscopy (SERS), mass spectrometry (MS), and atomic force microscopy. After determining the fraction of highest EV purity, SERS and MS were used to characterize EVs from HDs, pooled donors with noncancerous gynecological ailments (n = 6), and donors with early stage [FIGO (I/II)] with HGSC. SERS spectra were subjected to different machine learning algorithms such as PCA, logistic regression, support vector machine, naïve Bayes, random forest, neural network, and k nearest neighbors to differentiate healthy, benign, and HGSC EVs. Collectively, we demonstrate a reproducible workflow with the potential to serve as a diagnostic platform for HGSC.

Urinary biomarkers for the detection of ovarian cancer: a systematic review.

Currently, the only definitive method for diagnosing ovarian cancer involves histological examination of tissue obtained at time of surgery or by invasive biopsy. Blood has traditionally been the biofluid of choice in ovarian cancer biomarker discovery; however, there has been a growing interest in exploring urinary biomarkers, particularly as it is non-invasive. In this systematic review, we present the diagnostic accuracy of urinary biomarker candidates for the detection of ovarian cancer. A comprehensive literature search was performed using the MEDLINE/PubMed and EMBASE, up to 1 April 2021. All included studies reported the diagnostic accuracy using sensitivity and/or specificity and/or receiver operating characteristics (ROC) curve. Risk of bias and applicability of included studies were assessed using the QUADAS-2 tool. Twenty-seven studies were included in the narrative synthesis. Protein/peptide biomarkers were most commonly described (n = 18), with seven studies reporting composite scores of multiple protein-based targets. The most frequently described urinary protein biomarker was HE4 (n = 5), with three studies reporting a sensitivity and specificity > 80%. Epigenetic (n = 1) and metabolomic/organic compound biomarkers (n = 8) were less commonly described. Overall, six studies achieved a sensitivity and specificity of >90% and/or an AUC > 0.9. Evaluation of urinary biomarkers for the detection of ovarian cancer is a dynamic and growing field. Currently, the most promising biomarkers are those that interrogate metabolomic pathways and organic compounds, or quantify multiple proteins. Such biomarkers require external validation in large, prospective observational studies before they can be implemented into clinical practice.

Emerging Protein Biomarkers in Epithelial Ovarian Cancer Prognosis: An Aid for Multivariate Indexing.

Epithelial ovarian cancer (EOC) is a chronic and degenerative disease propelled by a mutation in BRCA1/2 genes, familial history, smoking and polycystic ovary syndrome. Although the lifetime risk of ovarian cancer is low, yet it is the fifth leading cause of cancer-related deaths. Surprisingly, EOC represents 90% of all ovarian cancers, out of which 70% of women are diagnosed with the malignancy at its advanced III-IV stages. Early detection may increase the life expectancy up to 5 years. Thus, it has become the need of the hour to attain improvement of clinical outcomes of EOC and improve the life expectancy of patients. A plethora of proteins in different biological fluids may serve as prospective identifiers for the disease. Over the years, accurate identification of proteins secreted by EOC cells has been perfected by in vitro and in silico state-of-theart technologies. Multivariate test, consisting of histo-pathological data in combination with protein biomarker panel has paved way for enhanced and accurate assessment of EOC; still, there is a chance of further improvement. This review encompasses the advances made in ovarian cancer biomarker discovery and demonstrates their potential usefulness for the design of early diagnostics of EOC.

Ovarian Cancer Prevention and Screening.

There has been much progress in ovarian cancer screening and prevention in recent years. Improved tools that combine genetic and epidemiologic factors to predict an individual's ovarian cancer risk are set to become available for tailoring preventive and screening approaches. The increasing evidence on tubal origins of a proportion of ovarian cancer has paved the way to use of opportunistic bilateral salpingectomy at tubal ligation and hysterectomy in the general population. Clinical trials are in progress to estimate the long-term effects on endocrine function. In women at high risk, risk reducing salpingo-oophorectomy remains the standard of care with the current focus on management of resulting noncancer outcomes, especially sexual dysfunction in younger women. This has led to evaluation of early bilateral salpingectomy and delayed oophorectomy in this population. Meanwhile, modeling suggests that BRCA mutation carriers should consider using the oral contraceptive pill for chemoprevention. In the general population, the largest ovarian cancer screening trial to date, the UK Collaborative Trial of Ovarian Cancer Screening reported a stage shift with annual multimodal screening using the longitudinal CA 125 Risk of Ovarian Cancer Algorithm but not with annual transvaginal ultrasound screening. There was no definitive mortality reduction with either screening strategy compared with no screening. Further follow-up until December 2018 in now underway. Stage shift and higher rates of optimal cytoreduction were also reported during 3- to 4-monthly multimodal screening in the United Kingdom and U.S. high-risk screening trials. Although all agree that there is not yet evidence to support general population screening, recommendations for high-risk screening vary between countries. A key finding from the screening trials has been the better performance of longitudinal algorithms compared with a single cutoff for CA 125. A major focus of ovarian cancer biomarker discovery work has been tumor DNA markers in both plasma and novel specimens such as cervical cytology samples.

NGS approach for new ovarian cancer biomarker discovery Ovarian cancer biomarker discovery

NGS approach for new ovarian cancer biomarker discovery Ovarian cancer biomarker discovery

Morph-X-Select: Morphology-based tissue aptamer selection for ovarian cancer biomarker discovery.

High affinity aptamer-based biomarker discovery has the advantage of simultaneously discovering an aptamer affinity reagent and its target biomarker protein. Here, we demonstrate a morphology-based tissue aptamer selection method that enables us to use tissue sections from individual patients and identify high-affinity aptamers and their associated target proteins in a systematic and accurate way. We created a combinatorial DNA aptamer library that has been modified with thiophosphate substitutions of the phosphate ester backbone at selected 5´dA positions for enhanced nuclease resistance and targeting. Based on morphological assessment, we used image-directed laser microdissection (LMD) to dissect regions of interest bound with the thioaptamer (TA) library and further identified target proteins for the selected TAs. We have successfully identified and characterized the lead candidate TA, V5, as a vimentin-specific sequence that has shown specific binding to tumor vasculature of human ovarian tissue and human microvascular endothelial cells. This new Morph-X-Select method allows us to select high-affinity aptamers and their associated target proteins in a specific and accurate way, and could be used for personalized biomarker discovery to improve medical decision-making and to facilitate the development of targeted therapies to achieve more favorable outcomes.

Validation of a Novel Biomarker Panel for the Detection of Ovarian Cancer.

Ovarian cancer is the most lethal gynecological malignancy. Our integrated -omics approach to ovarian cancer biomarker discovery has identified kallikrein 6 (KLK6) and folate-receptor 1 (FOLR1) as promising candidates but these markers require further validation. KLK6, FOLR1, CA125, and HE4 were investigated in three independent serum cohorts with a total of 20 healthy controls, 150 benign controls, and 216 ovarian cancer patients. The serum biomarker levels were determined by ELISA or automated immunoassay. All biomarkers demonstrated elevations in the sera of ovarian cancer patients compared with controls (P < 0.01). Overall, CA125 and HE4 displayed the strongest ability (AUC 0.80 and 0.82, respectively) to identify ovarian cancer patients and the addition of HE4 to CA125 improved the sensitivity from 36% to 67% at a set specificity of 95%. In addition, the combination of HE4 and FOLR1 was a strong predictor of ovarian cancer diagnosis, displaying comparable sensitivity (65%) to the best-performing CA125-based models (67%) at a set specificity of 95%. The markers identified through our integrated -omics approach performed similarly to the clinically approved markers CA125 and HE4. Furthermore, HE4 represents a powerful diagnostic marker for ovarian cancer and should be used more routinely in a clinical setting. The implications of our study are 2-fold: (i) we have demonstrated the strengths of HE4 alone and in combination with CA125, lending credence to increasing its usage in the clinic; and (ii) we have demonstrated the clinical utility of our integrated -omics approach to identifying novel serum markers with comparable performance to clinical markers. Cancer Epidemiol Biomarkers Prev; 25(9); 1333-40. ©2016 AACR.

Ovarian cancer biomarker discovery based on genomic approaches.

Ovarian cancer presents at an advanced stage in more than 75% of patients. Early detection has great promise to improve clinical outcomes. Although the advancing proteomic technologies led to the discovery of numerous ovarian cancer biomarkers, no screening method has been recommended for early detection of ovarian cancer. Complexity and heterogeneity of ovarian carcinogenesis is a major obstacle to discover biomarkers. As cancer arises due to accumulation of genetic change, understanding the close connection between genetic changes and ovarian carcinogenesis would provide the opportunity to find novel gene-level ovarian cancer biomarkers. In this review, we summarize the various gene-based biomarkers by genomic technologies, including inherited gene mutations, epigenetic changes, and differential gene expression. In addition, we suggest the strategy to discover novel gene-based biomarkers with recently introduced next generation sequencing.

Folate-receptor 1 (FOLR1) protein is elevated in the serum of ovarian cancer patients

ObjectivesOvarian cancer is the most lethal gynecological malignancy in North America. Although survival rates are high when the disease is diagnosed at an early stage, this decreases exponentially in late-stage diagnoses. As such, there is a need for novel early detection biomarkers. Through an integrated approach to ovarian cancer biomarker discovery that combines proteomics with transcriptomics and bioinformatics, our laboratory has identified folate-receptor 1 (FOLR1) and Dickkopf-related protein 3 (Dkk-3) as putative biomarkers. The objective of this study was to measure the levels of FOLR1 and Dkk-3 in the serum of patients with ovarian cancer, benign gynecological conditions and healthy women. Design and methodsFOLR1 and Dkk-3 were analyzed in serum of 100 ovarian cancer patients, 100 patients with benign gynecological conditions, and 100 healthy women using enzyme-linked immunosorbent assays (ELISAs). All specimens were analyzed in triplicate. ResultsFOLR1 was significantly elevated in the serum of ovarian cancer patients compared to serum of both healthy controls (P<0.0001) and patients with benign gynecological conditions (P<0.0001). Furthermore, FOLR1 was strongly correlated with CA125 as both were elevated in the serous histotype and in late-stage disease. FOLR1 did not outperform CA125 in receiver operating characteristic curve analysis and there was no significant complementarity between the two markers. Dkk-3 was not significantly different between the three serum cohorts and was not correlated with CA125. ConclusionsFOLR1 is a new biomarker for ovarian cancer which correlates closely with CA125. The role of FOLR1 in the pathogenesis of ovarian cancer warrants further investigation.

From Bench to Bedside: Discovery of Ovarian Cancer Biomarkers Using High-Throughput Technologies in the Past Decade

Ovarian cancer is the most lethal gynecological malignancy and survival of this disease has remained relatively unchanged over the past 30 years. A contributing factor to this has been the lack of reliable biomarkers for the clinical management of ovarian cancer. Rapid advances in high-throughput technologies over the past decade has allowed for new and exciting opportunities for biomarker discovery in the field of ovarian cancer, especially with respect to serum biomarkers that can be used for various clinical applications. This review highlights the major genomic and proteomic studies dedicated to ovarian cancer biomarker discovery over the past decade. An emphasis will be placed on the HE4, Risk of Malignancy Algorithm (ROMA) and OVA1™ serum-based tests/algorithms that have recently been approved by the US FDA as ovarian cancer biomarkers.

From the endometrium physiology to a comprehensive strategy for the discovery of ovarian cancer biomarkers

From the endometrium physiology to a comprehensive strategy for the discovery of ovarian cancer biomarkers

Discovery and validation of ovarian cancer biomarkers utilizing high density antibody microarrays

Here we demonstrate the utility of high-density antibody microarrays for ovarian cancer biomarker discovery. This report describes the technology and how it can be optimized for hypothesis-generating and testing experiments. Our previous results validated the high density antibody array technology platform, the current work expands on it utilizing a second generation array that we tested with a larger set of ovarian case and control serum samples. We then describe our strategies and methods for result validation, including Western immunoblots to confirm antibody specificity. By comparing and combining the current results with our previous study, we solidified the case that the markers found could be used for ovarian cancer diagnosis using this technology. These results set the stage for further validation of these potential biomarkers and the use of this technology in future biomarker discovery studies.

Use of a Single-Chain Antibody Library for Ovarian Cancer Biomarker Discovery

The discovery of novel early detection biomarkers of disease could offer one of the best approaches to decrease the morbidity and mortality of ovarian and other cancers. We report on the use of a single-chain variable fragment antibody library for screening ovarian serum to find novel biomarkers for the detection of cancer. We alternately panned the library with ovarian cancer and disease-free control sera to make a sublibrary of antibodies that bind proteins differentially expressed in cancer. This sublibrary was printed on antibody microarrays that were incubated with labeled serum from multiple sets of cancer patients and controls. The antibodies that performed best at discriminating disease status were selected, and their cognate antigens were identified using a functional protein microarray. Overexpression of some of these antigens was observed in cancer serum, tumor proximal fluid, and cancer tissue via dot blot and immunohistochemical staining. Thus, our use of recombinant antibody microarrays for unbiased discovery found targets for ovarian cancer detection in multiple sample sets, supporting their further study for disease diagnosis.

ULDA-based heuristic feature selection method for proteomic profile analysis and biomarker discovery

Uncorrelated linear discriminant analysis (ULDA)-based heuristic feature selection (ULDA-HFS) method was proposed for sample classification and feature extraction for SELDI-TOF MS ovarian cancer data. The ULDA-HFS method includes 4 steps: (1) noise reduction and normalization; (2) selection of discriminatory bins with CHI2 method; (3) peak detection and alignment for each selected bins; and (4) selection of several peaks as potential biomarkers by means of ULDA. As a result, 7 m/ z locations were selected in this study; they were 245.3, 559.4, 565.6, 704.2, 717.2, 2667 and 4074.4. To evaluate the classification impression, PCA, PLS-DA and ULDA were performed for discriminant analysis and ULDA obtained the perfect separation. Finally, the 7 selected potential biomarkers were evaluated by ULDA, both sensitivity and specificity were 100%. The 7 m/ z values obtained may provide clues for ovarian cancer biomarker discovery. Once the proteins were identified at these m/ z locations, it can be used as specific protein for early detection and diagnosis for ovarian cancer.

Integrating high-throughput technologies in the quest for effective biomarkers for ovarian cancer

Despite widespread interest, few serum biomarkers have been introduced to the clinic over the past 20 years. Each approach to ovarian cancer biomarker discovery has its own advantages and disadvantages and it seems likely that a global biomarker discovery platform that mines all possible sources for biomarkers might be more useful. Such data could be combined with information from relevant microarray data, bioinformatic analyses and literature searches. This proposed integrated systems biology approach has the potential to yield promising ovarian cancer markers for diagnosis, prognosis and monitoring of patients during therapy.

From the endometrium physiology to a comprehensive strategy for the discovery of ovarian cancer biomarkers

The development of comprehensive strategies for biomarker discovery of gynecological cancers is needed. The unique physiology of the female genital track revolves around ovulatory cycles ending by the proteolysis of the endometrium triggered by progesterone decline during the last part of the luteal phase. Building on the known link between incessant ovulation and ovarian cancer, we hypothesize that life-long menstruations could damage neighboring organs such as fallopian tubes, ovaries and peritoneum via endometrial secretions, and thus endometrium neighboring structures may have developed highly efficient protective strategies that could, in turn, be hijacked by cancer cells for survival and invasion. After literature review, we could classify the molecules involved in ovulation and menstruation pathways in three main categories: proteases, proteases inhibitors and cell-surface protectors. Strikingly, all validated biomarkers for ovarian cancers belong to at least one of these categories. We thus propose the development of comprehensive methods for identification of early diagnostic markers for gynecological cancers using systematical mapping and characterization of surface or soluble molecules belonging to physiological pathways linked to menstruation and differently expressed during luteal cycles.

Correction: Integrated Proteomic Analysis of Human Cancer Cells and Plasma from Tumor Bearing Mice for Ovarian Cancer Biomarker Discovery

Correction: Integrated Proteomic Analysis of Human Cancer Cells and Plasma from Tumor Bearing Mice for Ovarian Cancer Biomarker Discovery

Integrated Proteomic Analysis of Human Cancer Cells and Plasma from Tumor Bearing Mice for Ovarian Cancer Biomarker Discovery

BackgroundThe complexity of the human plasma proteome represents a substantial challenge for biomarker discovery. Proteomic analysis of genetically engineered mouse models of cancer and isolated cancer cells and cell lines provide alternative methods for identification of potential cancer markers that would be detectable in human blood using sensitive assays. The goal of this work is to evaluate the utility of an integrative strategy using these two approaches for biomarker discovery.Methodology/Principal FindingsWe investigated a strategy that combined quantitative plasma proteomics of an ovarian cancer mouse model with analysis of proteins secreted or shed by human ovarian cancer cells. Of 106 plasma proteins identified with increased levels in tumor bearing mice, 58 were also secreted or shed from ovarian cancer cells. The remainder consisted primarily of host-response proteins. Of 25 proteins identified in the study that were assayed, 8 mostly secreted proteins common to mouse plasma and human cancer cells were significantly upregulated in a set of plasmas from ovarian cancer patients. Five of the eight proteins were confirmed to be upregulated in a second independent set of ovarian cancer plasmas, including in early stage disease.Conclusions/SignificanceIntegrated proteomic analysis of cancer mouse models and human cancer cell populations provides an effective approach to identify potential circulating protein biomarkers.

Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines

BackgroundThe majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease.ResultsIn this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM.ConclusionThree evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer.

Discovery of ovarian cancer biomarkers in serum using NanoLC electrospray ionization TOF and FT-ICR mass spectrometry.

Treatment of cancer patients is greatly facilitated by detection of the cancer prior to metastasis. One of the obstacles to early cancer detection is the lack of availability of biomarkers with sufficient specificity. With modern differential proteomic techniques, the potential exists to identify high specificity cancer biomarkers. We have delineated a set of protocols for the isolation and identification of serum biomarkers for ovarian cancer that exist in the low molecular weight serum fraction. After isolation of the low molecular weight fraction by ultrafiltration, the potential biomarkers are separated by reversed phase nano liquid chromatography. Detection via TOF or FT-ICR yields a data set for each sample. We compared stage III/IV ovarian cancer serum with postmenopausal age-matched controls. Using bioinformatics tools developed at Mayo, we normalized each sample for intensity and chromatographic alignment. Normalized data sets are subsequently compared and potential biomarkers identified. Several candidate biomarkers were found. One of these contains the sequence of fibrinopeptide-A known to be elevated in many types of cancer including ovarian cancer. The protocols utilized will be examined and would be applicable to a wide variety of cancers or disease states.