Serum 27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide discovered by metabolomics as potential diagnostic biomarker for epithelium ovarian cancer

The aim of this study was to use a two steps strategy metabolomics to screen/identify and validate novel metabolic biomarker(s) for epithelial ovarian cancer (EOC). In the screening step, serum samples from 27 healthy women, 28 benign ovarian tumors, and 29 EOCs were analyzed by using LC-MS based nontargeted metabolomics. The three groups were separated with OSC filtered PLS-DA model, and six metabolites (27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide (CPG), phenylalanine, glycocholic acid, propionylcarnitine, Phe-Phe and Lyso PC (18:2)) were considered as potential biomarker candidates. In the validation step, the six metabolites were analyzed in targeted metabolomics by LC-selective ion monitoring mass spectrometry in another 685 serum samples with various clinical backgrounds. As a result, CPG was evaluated to be a potential biomarker and its content was elevated in EOC tissues compared with benign ovarian tumor tissues (p = 0.0005). Besides, CPG levels were found to be up-regulated in early stage EOC and in the three types of EOC histological types. Other variables such as nonovarian diseases, medicine consumption, gynecological inflammations, and menopausal state did not interfere in using CPG as diagnosis marker. CPG was found to be complementary to CA125. Our findings suggest that CPG can be considered a statistical relevant biomarker of EOC, ready for early stage detection.     

Predicting ovarian cancer recurrence by plasma metabolic profiles before and after surgery

Background

Previous metabolomic studies have revealed that plasma metabolic signatures may predict epithelial ovarian cancer (EOC) recurrence. However, few studies have performed metabolic profiling of pre- and post-operative specimens to investigate EOC prognostic biomarkers.

Objective

The aims of our study were to compare the predictive performance of pre- and post-operative specimens and to create a better model for recurrence by combining biomarkers from both metabolic signatures.

Methods

Thirty-five paired plasma samples were collected from 35 EOC patients before and after surgery. The patients were followed-up until December, 2016 to obtain recurrence information. Metabolomics using rapid resolution liquid chromatography–mass spectrometry was performed to identify metabolic signatures related to EOC recurrence. The support vector machine model was employed to predict EOC recurrence using identified biomarkers.

Results

Global metabolomic profiles distinguished recurrent from non-recurrent EOC using both pre- and post-operative plasma. Ten common significant biomarkers, hydroxyphenyllactic acid, uric acid, creatinine, lysine, 3-(3,5-diiodo-4-hydroxyphenyl) lactate, phosphohydroxypyruvic acid, carnitine, coproporphyrinogen, l-beta-aspartyl-l-glutamic acid and 24,25-hydroxyvitamin D3, were identified as predictive biomarkers for EOC recurrence. The area under the receiver operating characteristic (AUC) values in pre- and post-operative plasma were 0.815 and 0.909, respectively; the AUC value after combining the two sets reached 0.964.

Conclusion

Plasma metabolomic analysis could be used to predict EOC recurrence. While post-operative biomarkers have a predictive advantage over pre-operative biomarkers, combining pre- and post-operative biomarkers showed the best predictive performance and has great potential for predicting recurrent EOC.

     

Statistical significance analysis of nuclear magnetic resonance-based metabonomics data

Use of nuclear magnetic resonance (NMR)-based metabonomics to search for human disease biomarkers is becoming increasingly common. For many researchers, the ultimate goal is translation from biomarker discovery to clinical application. Studies typically involve investigators from diverse educational and training backgrounds, including physicians, academic researchers, and clinical staff. In evaluating potential biomarkers, clinicians routinely use statistical significance testing language, whereas academicians typically use multivariate statistical analysis techniques that do not perform statistical significance evaluation. In this article, we outline an approach to integrate statistical significance testing with conventional principal components analysis data representation. A decision tree algorithm is introduced to select and apply appropriate statistical tests to loadings plot data, which are then heat map color-coded according to P score, enabling direct visual assessment of statistical significance. A multiple comparisons correction must be applied to determine P scores from which reliable inferences can be made. Knowledge of means and standard deviations of statistically significant buckets enabled computation of effect sizes and study sizes for a given statistical power. Methods were demonstrated using data from a previous study. Integrated metabonomics data assessment methodology should facilitate translation of NMR-based metabonomics discovery of human disease biomarkers to clinical use.     

Circulating Exosomal miR-1290 for Diagnosis of Epithelial Ovarian Cancer

The aim of the study was to develop a new diagnostic biomarker for identifying serum exosomal miRNAs specific to epithelial ovarian cancer (EOC) and to find out target gene of the miRNA for exploring the molecular mechanisms in EOC. A total of 84 cases of ovarian masses and sera were enrolled, comprising EOC (n = 71), benign ovarian neoplasms (n = 13). We detected expression of candidate miRNAs in the serum and tissue of both benign ovarian neoplasm group and EOC group using real-time polymerase chain reaction. Immunohistochemistry were constructed using formalin fixed paraffin embedded (FFPE) tissue to detect expression level of suppressor of cytokine signaling 4 (SOCS4). In the EOC group, miRNA-1290 was significantly overexpressed in serum exosomes and tissues as compared to benign ovarian neoplasm group (fold change ≥ 2, p < 0.05). We observed area under the receiver operating characteristic curve (AUC) for miR-1290, using a cut-off of 0.73, the exosomal miR-1290 from serum had AUC, sensitivity, and specificity values of 0.794, 69.2 and 87.3, respectively. In immunohistochemical study, expression of SOCS4 in EOC was lower than that in benign ovarian neoplasm. Serum exosomal miR-1290 could be considered as a biomarker for differential diagnosis of EOC from benign ovarian neoplasm and SOCS4 might be potential target gene of miR-1290 in EOC.     

Plasma miRNAs as Diagnostic and Prognostic Biomarkers for Ovarian Cancer

Background

Most (70%) epithelial ovarian cancers (EOCs) are diagnosed late. Non-invasive biomarkers that facilitate disease detection and predict outcome are needed. The microRNAs (miRNAs) represent a new class of biomarkers. This study was to identify and validate plasma miRNAs as biomarkers in EOC.

Methodology/Principal Findings

We evaluated plasma samples of 360 EOC patients and 200 healthy controls from two institutions. All samples were grouped into screening, training and validation sets. We scanned the circulating plasma miRNAs by TaqMan low-density array in the screening set and identified/validated miRNA markers by real-time polymerase chain reaction assay in the training set. Receiver operating characteristic and logistic regression analyses established the diagnostic miRNA panel, which were confirmed in the validation sets. We found higher plasma miR-205 and lower let-7f expression in cases than in controls. MiR-205 and let-7f together provided high diagnostic accuracy for EOC, especially in patients with stage I disease. The combination of these two miRNAs and carbohydrate antigen-125 (CA-125) further improved the accuracy of detection. MiR-483-5p expression was elevated in stages III and IV compared with in stages I and II, which was consistent with its expression pattern in tumor tissues. Furthermore, lower levels of let-7f were predictive of poor prognosis in EOC patients.

Conclusions/Significance

Our findings indicate that plasma miR-205 and let-7f are biomarkers for ovarian cancer detection that complement CA-125; let-7f may be predictive of ovarian cancer prognosis.     

Profiling signatures of ovarian cancer tumour suppression using 2D-DIGE and 2D-LC-MS/MS with tandem mass tagging

Epithelial ovarian cancer (EOC) is the most common form of gynaecological malignancy in the developed world and has a poor prognosis due to its late detection. Identifying molecular markers of the disease may provide novel approaches to screening and could enable targeted treatment and the design of novel therapies. Although blood is recognized as a highly important source of disease-related biomarkers, the complexity and dynamic range of protein abundance in body fluids has hampered proteomic biomarker discovery and alternative approaches using cell models may be more successful. Herein, we have utilized two cellular models of EOC, where transfer of normal chromosome 18 material into the EOC cell lines TOV-112D and TOV-21G induced in vitro and in vivo suppression of their tumourigenic phenotype. A combination of quantitative two-dimensional difference gel electrophoresis (2D-DIGE) and two-dimensional-liquid chromatography tandem mass spectrometry (2D-LC-MS/MS) with tandem mass tagging (TMT) was employed to examine the whole cell, secreted and crude membrane proteomes of the parental and hybrid cell models to identify differentially expressed proteins as potential markers of tumour suppression. Protein changes of interest were confirmed by immunoblotting in additional hybrid and revertant cell lines where incorporated chromosome 18 material was lost. One candidate marker was also tested in sera from a set of ovarian cancer cases and controls. We have identified a list of promising candidate biomarkers for further testing and functional characterization.     

A proteome resource of ovarian cancer ascites: integrated proteomic and bioinformatic analyses to identify putative biomarkers

Epithelial ovarian cancer is the most lethal gynecological malignancy, and disease-specific biomarkers are urgently needed to improve diagnosis, prognosis, and to predict and monitor treatment efficiency. We present an in-depth proteomic analysis of selected biochemical fractions of human ovarian cancer ascites, resulting in the stringent and confident identification of over 2500 proteins. Rigorous filter schemes were applied to objectively minimize the number of false-positive identifications, and we only report proteins with substantial peptide evidence. Integrated computational analysis of the ascites proteome combined with several recently published proteomic data sets of human plasma, urine, 59 ovarian cancer related microarray data sets, and protein-protein interactions from the Interologous Interaction Database I (2)D ( http://ophid.utoronto.ca/i2d) resulted in a short-list of 80 putative biomarkers. The presented proteomics analysis provides a significant resource for ovarian cancer research, and a framework for biomarker discovery.     

A Synthetic Lethality Screen Using a Focused siRNA Library to Identify Sensitizers to Dasatinib Therapy for the Treatment of Epithelial Ovarian Cancer

Molecular targeted therapies have been the focus of recent clinical trials for the treatment of patients with recurrent epithelial ovarian cancer (EOC). The majority have not fared well as monotherapies for improving survival of these patients. Poor bioavailability, lack of predictive biomarkers, and the presence of multiple survival pathways can all diminish the success of a targeted agent. Dasatinib is a tyrosine kinase inhibitor of the Src-family kinases (SFK) and in preclinical studies shown to have substantial activity in EOC. However, when evaluated in a phase 2 clinical trial for patients with recurrent or persistent EOC, it was found to have minimal activity. We hypothesized that synthetic lethality screens performed using a cogently designed siRNA library would identify second-site molecular targets that could synergize with SFK inhibition and improve dasatinib efficacy. Using a systematic approach, we performed primary siRNA screening using a library focused on 638 genes corresponding to a network centered on EGFR, HER2, and the SFK-scaffolding proteins BCAR1, NEDD9, and EFS to screen EOC cells in combination with dasatinib. We followed up with validation studies including deconvolution screening, quantitative PCR to confirm effective gene silencing, correlation of gene expression with dasatinib sensitivity, and assessment of the clinical relevance of hits using TCGA ovarian cancer data. A refined list of five candidates (CSNK2A1, DAG1, GRB2, PRKCE, and VAV1) was identified as showing the greatest potential for improving sensitivity to dasatinib in EOC. Of these, CSNK2A1, which codes for the catalytic alpha subunit of protein kinase CK2, was selected for additional evaluation. Synergistic activity of the clinically relevant inhibitor of CK2, CX-4945, with dasatinib in reducing cell proliferation and increasing apoptosis was observed across multiple EOC cell lines. This overall approach to improving drug efficacy can be applied to other targeted agents that have similarly shown poor clinical activity.     

Proteomics in prostate cancer biomarker discovery

Despite advances in molecular medicine, genomics, proteomics and translational research, prostate cancer remains the second most common cause of cancer-related mortality for men in the Western world. Clearly, early detection, targeted treatment and post-treatment monitoring are vital tools to combat this disease. Tumor markers can be useful for diagnosis and early detection of cancer, assessment of prognosis, prediction of therapeutic effect and treatment monitoring. Such tumor markers include prostate-specific antigen (prostate), cancer antigen (CA)15.3 (breast), CA125 (ovarian), CA19.9 (gastrointestinal) and serum α-fetoprotein (testicular cancer). However, all of these biomarkers lack sensitivity and specificity and, therefore, there is a large drive towards proteomic biomarker discovery. Current research efforts are directed towards discovering biosignatures from biological samples using novel proteomic technologies that provide high-throughput, in-depth analysis and quantification of the proteome. Several of these studies have revealed promising biomarkers for use in diagnosis, assessment of prognosis, and targeting treatment of prostate cancer. This review focuses on prostate cancer proteomic biomarker discovery and its future potential.     

Effect of Ovarian Cancer Ascites on Cell Migration and Gene Expression in an Epithelial Ovarian Cancer In Vitro Model

A third of patients with epithelial ovarian cancer (EOC) present ascites. The cellular fraction of ascites often consists of EOC cells, lymphocytes, and mesothelial cells, whereas the acellular fraction contains cytokines and angiogenic factors. Clinically, the presence of ascites correlates with intraperitoneal and retroperitoneal tumor spread. We have used OV-90, a tumorigenic EOC cell line derived from the malignant ascites of a chemonaive ovarian cancer patient, as a model to assess the effect of ascites on migration potential using an in vitro wound-healing assay. A recent report of an invasion assay described the effect of ascites on the invasion potential of the OV-90 cell line. Ascites sampled from 31 ovarian cancer patients were tested and compared with either 5% fetal bovine serum or no serum for their nonstimulatory or stimulatory effect on the migration potential of the OV-90 cell line. A supervised analysis of data generated by the Affymetrix HG-U133A GeneChip identified differentially expressed genes from OV-90 cells exposed to ascites that had either a nonstimulatory or a stimulatory effect on migration. Ten genes (IRS2, CTSD, NRAS, MLXIP, HMGCR, LAMP1, ETS2, NID1, SMARCD1, and CD44) were upregulated in OV-90 cells exposed to ascites, allowing a nonstimulatory effect on cell migration. These findings were validated by quantitative polymerase chain reaction. In addition, the gene expression of IRS2 and MLXIP each correlated with prognosis when their expression was assessed in an independent set of primary cultures established from ovarian ascites. This study revealed novel candidates that may play a role in ovarian cancer cell migration.     

Lipocalin2 Expressions Correlate Significantly With Tumor Differentiation in Epithelial Ovarian Cancer

We recently identified lipocalin2 (LCN2) as being upregulated in ovarian cancer cell lines. The purpose of this study was to validate LCN2 upregulation in ovarian cancers and to investigate its potential as a serum biomarker. We assayed LCN2 expression in ovarian cancers using real-time PCR and IHC. To evaluate the potential of LCN2 as a biomarker, we measured serum LCN2 levels in 54 ovarian cancers, 15 borderline and 53 benign ovarian tumors, and 90 healthy controls. SYBR green PCR and IHC showed LCN2 overexpression in ovarian cancers. LCN2 immunoreactivity was significantly associated with tumor differentiation (p=0.009), as well-differentiated tumors showed the highest LCN2 expression. Serum LCN2 level in ovarian cancer was significantly higher than in the other study groups (p<0.001), and in accordance with IHC results, it also correlated with tumor differentiation, with well-differentiated tumors having the highest value. The sensitivity and specificity of LCN2 in detecting ovarian cancer was 72.2% and 50.4%, respectively. By Cox univariate analysis, LCN2 positivity was an independent prognostic factor for overall survival (hazard ratio = 1.47, p=0.012). In conclusion, LCN2 expressions are upregulated and related to tumor differentiation in ovarian cancers and should be included in future research assessing potential biomarkers for ovarian cancer. (J Histochem Cytochem 57:513–521, 2009)     

Validation of LRG1 as a Potential Biomarker for Detection of Epithelial Ovarian Cancer by a Blinded Study

Background

Leucine-rich alpha-2-glycoprotein (LRG1) was found to be differentially expressed in sera from patients with Epithelial Ovarian Cancer (EOC). The aim of this study is to investigate the performance of LRG1 for detection of EOC, including early stage EOC, and to evaluate if LRG1 can complement CA125 in order to improve EOC detection using two independent blinded sample sets.

Methods and Results

Serum LRG1 and CA125 were measured by immunoassays. All assays were performed blinded to clinical data. Using the two independent sample sets (156 participants for sample set 1, and 233 for sample set 2), LRG1 was differentially expressed in EOC cases as compared to healthy, surgical, and benign controls, and its performance was not affected by the conditions of blood collection. The areas under the ROC curve (AUC) for LRG1 in differentiating EOC cases from non-cases were 0.797 and 0.786 for sample set 1 and 2. For differentiating EOC cases from healthy controls, the AUC values for LRG1 were 0.792 and 0.794. At a fixed specificity of 95%, LRG1 detects 52%, and 53.5% of EOC cases from healthy controls for sample set 1 and 2. When combining LRG1 and CA125, the AUC value increased to 0.927, which was improved compared to CA125 (AUC=0.916) (p=0.008) alone in distinguishing EOC cases from non-cases. More importantly, LRG1 also showed potential performance in differentiating early stage EOC from non-cases with an AUC of 0.715 for sample set 1, and 0.690 for sample set 2. The combination of LRG1 and CA125 resulted in an AUC of 0.838, which outperforms CA125 (AUC=0.785) (p=0.018) in detecting early stage EOC cases from non-cases using the larger sample set.

Conclusions

LRG1 could be a useful biomarker alone or in combination with CA125 for the diagnosis of ovarian cancer.     

Integrative genomic data mining for discovery of potential blood-borne biomarkers for early diagnosis of cancer

Background

With the arrival of the postgenomic era, there is increasing interest in the discovery of biomarkers for the accurate diagnosis, prognosis, and early detection of cancer. Blood-borne cancer markers are favored by clinicians, because blood samples can be obtained and analyzed with relative ease. We have used a combined mining strategy based on an integrated cancer microarray platform, Oncomine, and the biomarker module of the Ingenuity Pathways Analysis (IPA) program to identify potential blood-based markers for six common human cancer types.

Methodology/Principal Findings

In the Oncomine platform, the genes overexpressed in cancer tissues relative to their corresponding normal tissues were filtered by Gene Ontology keywords, with the extracellular environment stipulated and a corrected Q value (false discovery rate) cut-off implemented. The identified genes were imported to the IPA biomarker module to separate out those genes encoding putative secreted or cell-surface proteins as blood-borne (blood/serum/plasma) cancer markers. The filtered potential indicators were ranked and prioritized according to normalized absolute Student t values. The retrieval of numerous marker genes that are already clinically useful or under active investigation confirmed the effectiveness of our mining strategy. To identify the biomarkers that are unique for each cancer type, the upregulated marker genes that are in common between each two tumor types across the six human tumors were also analyzed by the IPA biomarker comparison function.

Conclusion/Significance

The upregulated marker genes shared among the six cancer types may serve as a molecular tool to complement histopathologic examination, and the combination of the commonly upregulated and unique biomarkers may serve as differentiating markers for a specific cancer. This approach will be increasingly useful to discover diagnostic signatures as the mass of microarray data continues to grow in the ‘omics’ era.     

Identification of metabolites in the normal ovary and their transformation in primary and metastatic ovarian cancer

In this study, we characterized the metabolome of the human ovary and identified metabolic alternations that coincide with primary epithelial ovarian cancer (EOC) and metastatic tumors resulting from primary ovarian cancer (MOC) using three analytical platforms: gas chromatography mass spectrometry (GC/MS) and liquid chromatography tandem mass spectrometry (LC/MS/MS) using buffer systems and instrument settings to catalog positive or negative ions. The human ovarian metabolome was found to contain 364 biochemicals and upon transformation of the ovary caused changes in energy utilization, altering metabolites associated with glycolysis and β-oxidation of fatty acids—such as carnitine (1.79 fold in EOC, p<0.001; 1.88 fold in MOC, p<0.001), acetylcarnitine (1.75 fold in EOC, p<0.001; 2.39 fold in MOC, p<0.001), and butyrylcarnitine (3.62 fold, p<0.0094 in EOC; 7.88 fold, p<0.001 in MOC). There were also significant changes in phenylalanine catabolism marked by increases in phenylpyruvate (4.21 fold; p = 0.0098) and phenyllactate (195.45 fold; p<0.0023) in EOC. Ovarian cancer also displayed an enhanced oxidative stress response as indicated by increases in 2-aminobutyrate in EOC (1.46 fold, p = 0.0316) and in MOC (2.25 fold, p<0.001) and several isoforms of tocopherols. We have also identified novel metabolites in the ovary, specifically N-acetylasparate and N-acetyl-aspartyl-glutamate, whose role in ovarian physiology has yet to be determined. These data enhance our understanding of the diverse biochemistry of the human ovary and demonstrate metabolic alterations upon transformation. Furthermore, metabolites with significant changes between groups provide insight into biochemical consequences of transformation and are candidate biomarkers of ovarian oncogenesis. Validation studies are warranted to determine whether these compounds have clinical utility in the diagnosis or clinical management of ovarian cancer patients.     

Proteomics discovery of chemoresistant biomarkers for ovarian cancer therapy

Introduction: Chemoresistance is a major challenge to current ovarian cancer chemotherapy. It is important to identify biomarkers to distinguish chemosensitive and chemoresistant patients.

Areas covered: We review the medical literature, discuss MS-based technologies with respect to chemoresistant ovarian cancer and summarize the promising chemoresistant biomarkers identified. In addition, the challenges and future perspectives of biomarker discovery research are explored. With the employment of mass spectrometry-based (MS-based) proteomics, biomarker discovery of ovarian cancer has made great progress in the last decade. Many potential biomarkers were identified by MS-based proteomics technologies, some of which have been validated for further extensive studies in clinical settings.

Expert commentary: The discovery of chemoresistant biomarkers is a newly developing area and may provide a clue for predicting chemotherapeutic response and discover therapeutic targets for paving the way of personalized medicine. Multiple complementary MS-based proteomics approaches hold promise for finding novel therapeutic targets in ovarian cancer treatment.     

Diagnosis of early stage ovarian cancer by 1H NMR metabonomics of serum explored by use of a microflow NMR probe

We show that (1)H NMR based metabonomicsof serum allows the diagnosis of early stage I/II epithelial ovarian cancer (EOC) required for successful treatment. Because patient specimens are highly precious, we conducted an exploratory study using a microflow probe requiring only 20 μL of serum. By use of logistic regression on principal components (PCs) of the NMR profiles, we built a 4-variable model for early stage EOC prediction (training set: 69 EOC specimens, 84 healthy controls; test set: 40 EOC, 44 controls) with operating characteristics estimated for the test set at 80% specificity [95% confidence interval (CI): 65-90%], 63% sensitivity (95% CI: 46-77%), and an area under the Receiver Operator Characteristic Curve (AUC) of 0.796. Independent validation (50 EOC, 50 controls) of the model yielded 95% specificity (95% CI: 86-99.5%), 68% sensitivity (95% CI: 53-80%) and an AUC of 0.949. A test on cancer type specificity showed that women diseased with renal cell carcinoma were not incorrectly diagnosed with EOC, indicating that metabonomics bears significant potential for cancer type-specific diagnosis. Our model can potentially be applied for women at high risk for EOC, and our study promises to contribute to developing a screening protocol for the general population.     

Biomarkers for the lung cancer diagnosis and their advances in proteomics

Over a last decade, intense interest has been focused on biomarker discovery and their clinical uses. This interest is accelerated by the completion of human genome project and the progress of techniques in proteomics. Especially, cancer biomarker discovery is eminent in this field due to its anticipated critical role in early diagnosis, therapy guidance, and prognosis monitoring of cancers. Among cancers, lung cancer, one of the top three major cancers, is the one showing the highest mortality because of failure in early diagnosis. Numerous potential DNA biomarkers such as hypermethylations of the promoters and mutations in K-ras, p53, and protein biomarkers; carcinoembryonic antigen (CEA), CYFRA21-1, plasma kallikrein B1 (KLKB1), Neuron-specific enolase, etc. have been discovered as lung cancer biomarkers. Despite extensive studies thus far, few are turned out to be useful in clinic. Even those used in clinic do not show enough sensitivity, specificity and reproducibility for general use. This review describes what the cancer biomarkers are for, various types of lung cancer biomarkers discovered at present and predicted future advance in lung cancer biomarker discovery with proteomics technology.