Comprehensive serum profiling for the discovery of epithelial ovarian cancer biomarkers

FDA-cleared ovarian cancer biomarkers are limited to CA-125 and HE4 for monitoring and recurrence and OVA1, a multivariate panel consisting of CA-125 and four additional biomarkers, for referring patients to a specialist. Due to relatively poor performance of these tests, more accurate and broadly applicable biomarkers are needed. We evaluated the dysregulation of 259 candidate cancer markers in serum samples from 499 patients. Sera were collected prospectively at 11 monitored sites under a single well-defined protocol. All stages of ovarian cancer and common benign gynecological conditions were represented. To ensure consistency and comparability of biomarker comparisons, all measurements were performed on a single platform, at a single site, using a panel of rigorously calibrated, qualified, high-throughput, multiplexed immunoassays and all analyses were conducted using the same software. Each marker was evaluated independently for its ability to differentiate ovarian cancer from benign conditions. A total of 175 markers were dysregulated in the cancer samples. HE4 (AUC=0.933) and CA-125 (AUC=0.907) were the most informative biomarkers, followed by IL-2 receptor α, α1-antitrypsin, C-reactive protein, YKL-40, cellular fibronectin, CA-72-4 and prostasin (AUC>0.800). To improve the discrimination between cancer and benign conditions, a simple multivariate combination of markers was explored using logistic regression. When combined into a single panel, the nine most informative individual biomarkers yielded an AUC value of 0.950, significantly higher than obtained when combining the markers in the OVA1 panel (AUC 0.912). Additionally, at a threshold sensitivity of 90%, the combination of the top 9 markers gave 88.9% specificity compared to 63.4% specificity for the OVA1 markers. Although a blinded validation study has not yet been performed, these results indicate that alternative biomarker combinations might lead to significant improvements in the detection of ovarian cancer.     

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.     

Profiling of human serum glycans associated with liver cancer and cirrhosis by IMS-MS

Aberrant glycosylation of human glycoproteins is related to various physiological states, including the onset of diseases such as cancer. Consequently, the search for glycans that could be markers of diseases or targets of therapeutic drugs has been intensive. Here, we describe a high-throughput ion mobility spectrometry/mass spectrometry analysis of N-linked glycans from human serum. Distributions of glycans are assigned according to their m/z values, while ion mobility distributions provide information about glycan conformational and isomeric composition. Statistical analysis of data from 22 apparently healthy control patients and 39 individuals with known diseases (20 with cirrhosis of the liver and 19 with liver cancer) shows that ion mobility distributions for individual m/z ions appear to be sufficient to distinguish patients with liver cancer or cirrhosis. Measurements of glycan conformational and isomeric distributions by IMS-MS may provide insight that is valuable for detecting and characterizing disease states.     

Characterization of serum biomarkers for detection of early stage ovarian cancer

We have previously reported the identification of three ovarian cancer biomarker panels comprised of SELDI-TOF-MS peaks representing 14 differentially expressed serum proteins for the diagnosis of ovarian cancer. Using micro-LC-MS/MS, we identified five m/z peaks as transthyretin (TTR 13.9 kDa, TTR fragment 12.9 kDa), beta-hemoglobin (Hb, 15.9 kDa), apolipoprotein AI (ApoAI, 28 kDa) and transferrin (TF, 79 kDa). Western and/or ELISA methods confirmed the differential expression of TTR, Hb, and TF, and multivariate analyses resulted in improving the detection of early stage ovarian tumors (low malignant potential and malignant; receiver operating characteristic, ROC 0.933) as compared to cancer antigen CA125 alone (ROC 0.833). Interestingly, when CA125 was included with our markers in the multivariate analysis, the ROC increased to 0.959. Furthermore, multivariate analysis with only the mucinous subtype of early stage ovarian tumors, showed our markers to greatly improve the detection of disease (ROC 0.959) as compared to CA125 alone (ROC 0.613). Interestingly, the combination of CA125 with our markers did not seem to further improve the detection of mucinous tumors (ROC 0.955). We conclude that TTR, Hb, ApoAI and TF, when combined with CA125 should significantly improve the detection of early stage ovarian cancer.     

O-glycan profiling of serum glycan for potential renal cancer biomarkers

Serum was obtained from 25 male renal cell carcinoma (RCC) patients and 21 healthy males. O-glycans were released by a β-elimination reaction and purified by graphitized carbon cartridge solid phase extraction, then profiled by matrix-assisted laser desorption/ionisation-time of flight mass spectrometry. After noise removal and peak alignment, 1372 peaks were extracted from 200000 data points. Feature peaks were analyzed by calculation of differential sensitivity and specificity. The combination of two feature peaks was chosen as a biomarker and could clearly differentiate RCC and normal samples in our study group.     

Soluble MUC1 and serum MUC1-specific antibodies are potential prognostic biomarkers for platinum-resistant ovarian cancer

MUC1 (CA15-3) and MUC16 (CA125) tumor-associated antigens are upregulated in ovarian cancer and can be detected in patients’ sera by standardized tests. We postulated that increased MUC1 and MUC16 antigens augment antibody responses in platinum-resistant ovarian cancer patients and that the frequency and intensity of these responses can be used as immune biomarkers of treatment response and disease outcome. We measured MUC1 and MUC16 tumor expression by immunohistochemistry (IHC), assessed serum antigenic levels and quantitated circulating antibodies by ELISA in a cohort of 28 ovarian cancer patients with platinum-resistant or platinum-refractory ovarian cancer, and treated with intraperitoneal (IP) interleukin-2 (IL-2). MUC1 and MUC16 were overexpressed in tumor samples and showed differential distribution profiles. Serum MUC1 (CA15-3) measurements were elevated in all patients and significantly correlated with increased risk of death (P = 0.003). MUC1-specific IgM and IgG anitbodies were found in 92 and 50% of cases, respectively. Patients with progressive disease had higher mean anti-MUC1 IgG than responders at both early (P = 0.025) and late (P = 0.022) time points during IP IL-2 treatment. Anti-MUC1 IgM antibodies inversely correlated with overall survival at both early (P = 0.052) and late (P = 0.009) time points. In contrast to MUC1, neither soluble MUC16 nor MUC16-specific antibodies were significantly associated with clinical response or overall survival in this study. Increased serum MUC1 and high anti-MUC1 antibody levels are prognostic for poor clinical response and reduced overall survival in platinum-resistant or platinum-refractory ovarian cancer.     

Secretome proteomics for discovery of cancer biomarkers

“Secretome” is referred to as the rich, complex set of molecules secreted from living cells. In a less strict definition frequently followed in “secretome” studies, the term also includes molecules shed from the surface of living cells. Proteins of secretome (will be referred to as secreted) play a key role in cell signaling, communication and migration. The need for developing more effective cancer biomarkers and therapeutic modalities has led to the study of cancer cell secretome as a means to identify and characterize diagnostic and prognostic markers and potential drug and therapeutic targets. Significant technological advances in the field of proteomics during the last two decades have greatly facilitated research towards this direction. Nevertheless, secretome analysis still faces some difficulties mainly related to sample collection and preparation. The goal of this article is to provide an overview of the main findings from the analysis of cancer cell secretome. Specifically, we focus on the presentation of main methodological approaches that have been developed for the study of secreted proteins and the results thereof from the analysis of secretome in different types of malignancies; special emphasis is given on correlation of findings with protein expression in body fluids.     

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 serum biomarkers for premature ovarian failure

Premature ovarian failure (POF) is defined when a female achieves menopause before the age of 40. Although many conditions are known to be causative for POF, the most common one is idiopathic. This study was undertaken to investigate the pathogenesis of POF using proteomic tools. Two-dimensional electrophoresis (2-DE) analysis was performed to screen for proteins differentially expressed in patients with POF. Using liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS), we identified 11 significant proteins differentially expressed in the serum of POF patients: 5 proteins with expression increased more than two folds, 5 proteins with expression decreased more than two folds, and 1 protein expressed specifically in the serum of patients with POF. The results of the 2-DE analysis were further validated by Western blotting and ELISA analyses, which 5 reproductive system-related proteins (Ceruloplasmin, Complement C3, Fibrinogen α, Fibrinogen β, and SHBG) were selected. The different expression levels for these proteins were confirmed and demonstrated the possibility of using them as biomarkers to screen POF. These pre-clinical data provide plausible translational implications for targeting the pathogenesis of POF for each protein.     

Evaluation of serum estrogen-DNA adducts as potential biomarkers for breast cancer risk

This study was conducted to determine whether the ratio of estrogen-DNA adducts to their respective metabolites and conjugates in serum differed between women with early-onset breast cancer and those with average or high risk of developing breast cancer. Serum samples from women at average risk (n=63) or high risk (n=80) for breast cancer (using Gail model) and women newly diagnosed with early breast cancer (n=79) were analyzed using UPLC-MS/MS. Adduct ratios were statistically compared among the three groups, and the Area Under the Receiver Operating Characteristic Curve (AUC) was used to identify a diagnostic cut-off point. The median adduct ratio in the average-risk group was significantly lower than that of both the high-risk group and the breast cancer group (p values<0.0001), and provided good discrimination between those at average versus high risk of breast cancer (AUC=0.84, 95% CI 0.77-0.90). Sensitivity and specificity were maximized at an adduct ratio of 77. For women in the same age and BMI group, the odds of being at high risk for breast cancer was 8.03 (95% CI 3.46-18.7) times higher for those with a ratio of at least 77 compared to those with a ratio less than 77. The likelihood of being at high risk for breast cancer was significantly increased for those with a high adduct ratio relative to those with a low adduct ratio. These findings suggest that estrogen-DNA adducts deserve further study as potential biomarkers for risk of developing breast cancer.     

Profiling plasma peptides for the identification of potential ageing biomarkers in Chinese Han adults

Advancing age is associated with cardiovascular disease, diabetes mellitus and cancer, and shows significant inter-individual variability. To identify ageing-related biomarkers we performed a proteomic analysis on 1890 Chinese Han individuals, 1136 males and 754 females, aged 18 to 82 years, using weak cation exchange magnetic bead based MALDI-TOF-MS analysis. The study identified 44 peptides which varied in concentration in different age groups. In particular, apolipoprotein A-I (ApoA1) concentration gradually increased between 18 to 50 years of age, the levels of fibrinogen alpha (FGA) decreased over the same age span, while albumin (ALB) was significantly degraded in middle-aged individuals. In addition, the plasma peptide profiles of FGA and four other unidentified proteins were found to be gender-dependent. Plasma proteins such as FGA, ALB and ApoA1 are significantly correlated with age in the Chinese Han population and could be employed as indicative ageing-related biomarkers.     

Proximal fluid proteomics for the discovery of digestive cancer biomarkers

Most digestive malignancies have asymptomatic course, often progressing to poor outcome stages. Surgical resection usually represents the only potentially curative option but a prior assumption of the malignant nature of the lesion is mandatory to avoid exposing patients to unnecessary risks. Unfortunately, currently available diagnostic tools lack accuracy in many cases, consequently more reliable markers are needed to improve detection of malignant lesions. In this challenging context, fluids surrounding digestive malignancies represent a valuable source for the search of new potential biomarkers and proteomic tools offer the opportunity to achieve this goal. The new field of proximal fluid proteomics is thus emerging in the arena of digestive cancer biomarker discovery.     

Proteomic tracking of serum protein isoforms as screening biomarkers of ovarian cancer

Epithelial ovarian cancer is the fourth leading cause of cancer death among women. Due to the asymptomatic nature and poor survival characteristic of the disease, screening for specific biomarkers for ovarian cancer is a major health priority. Differentially expressed proteins in the serum of ovarian cancer patients have the potential to be used as cancer-specific biomarkers. In this study, proteomic methods were used to screen 24 serum samples from women with high-grade ovarian cancer and compared to a control group of 11 healthy women. Affigel-Blue treated serum samples were processed either by linear (pH 4-7) or narrow range (pH 5.5-6.7) IEF strips for the first dimension. Proteins separated in first dimension were resolved by 8-16% gradient SDS-PAGE. Protein spots were visualized by SYPRO Ruby staining, imaged by FX-imager and compared and analyzed by PDQuest software. Twenty-two protein spots were consistently differentially expressed between normal and ovarian cancer patients by resolving proteins in a linear pH strip of 4-7 for the first dimension. Six of the protein spots, significantly up-regulated in grade 3 ovarian cancer patients (p < 0.05), were identified by MALDI-TOF MS and Western blotting as the isoforms of haptoglobin precursor. When serum proteins were resolved on narrow pH range strips (5.5-6.7), 23 spots were consistently differentially expressed between normal and grade 3 ovarian cancer patients. Of these, 4 protein spots significantly down regulated in grade 3 ovarian cancer patients (p < 0.05) were identified by MALDI-TOF MS and Western blotting, as isoforms of transferrin precursor. Increased expression of serum haptoglobin and transferrin was also identified in peritoneal tumor fluid obtained from women diagnosed with grade 2/3 ovarian cancer (n = 7). Changes in the expression of haptoglobin and transferrin in the serum of women with different pathological grades of ovarian cancer was examined by one-dimensional Western blotting method. Serum samples collected from women suffering from benign, borderline, grade 1, grade 2 and grade 3 cancer (n = 4 for haptoglobin and n = 5 for transferrin in each group) were analyzed and compared to the serum of normal healthy women. The mean serum haptoglobin expression in grade 3 ovarian cancer patients was fourfold higher than in the control subjects (p < 0.05). On the other hand, transferrin expression in grade 3 ovarian cancer patients was decreased by twofold than in normal healthy women (p < 0.05). Haptoglobin expression in the serum of cancer patients (n = 7) decreased following chemotherapy (six cycles of taxol/carboplatin). Concomitant with the decrease of haptoglobin, transferrin expression remained constant in four patients, but increased in three out of seven patients included in the study. Changes in serum expression of haptoglobin correlated with the change of CA 125 levels before and after chemotherapy. In conclusion, proteomic profiling of differentially expressed proteins in the sera of normal women compared to women with ovarian cancer can greatly facilitate the discovery of a panel of biomarkers that may aid in the detection of ovarian cancer with greater specificity.     

A multiplex platform for the identification of ovarian cancer biomarkers

Background

Currently, there are no FDA approved screening tools for detecting early stage ovarian cancer in the general population. Development of a biomarker-based assay for early detection would significantly improve the survival of ovarian cancer patients.

Methods

We used a multiplex approach to identify protein biomarkers for detecting early stage ovarian cancer. This new technology (Proseek^® Multiplex Oncology Plates) can simultaneously measure the expression of 92 proteins in serum based on a proximity extension assay. We analyzed serum samples from 81 women representing healthy, benign pathology, early, and advanced stage serous ovarian cancer patients.

Results

Principle component analysis and unsupervised hierarchical clustering separated patients into cancer versus non-cancer subgroups. Data from the Proseek^® plate for CA125 levels exhibited a strong correlation with current clinical assays for CA125 (correlation coefficient of 0.89, 95% CI 0.83, 0.93). CA125 and HE4 were present at very low levels in healthy controls and benign cases, while higher levels were found in early stage cases, with highest levels found in the advanced stage cases. Overall, significant trends were observed for 38 of the 92 proteins (p < 0.001), many of which are novel candidate serum biomarkers for ovarian cancer. The area under the ROC curve (AUC) for CA125 was 0.98 and the AUC for HE4 was 0.85 when comparing early stage ovarian cancer versus healthy controls. In total, 23 proteins had an estimated AUC of 0.7 or greater. Using a naïve Bayes classifier that combined 12 proteins, we improved the sensitivity corresponding to 95% specificity from 93 to 95% when compared to CA125 alone. Although small, a 2% increase would have a significant effect on the number of women correctly identified when screening a large population.

Conclusions

These data demonstrate that the Proseek^® technology can replicate the results established by conventional clinical assays for known biomarkers, identify new candidate biomarkers, and improve the sensitivity and specificity of CA125 alone. Additional studies using a larger cohort of patients will allow for validation of these biomarkers and lead to the development of a screening tool for detecting early stage ovarian cancer in the general population.

     

Screening for potential serum biomarkers in rat mesangial proliferative nephritis

Mesangial proliferative nephritis (MesPGN) is a common kidney disease worldwide. The main feature of the disease is mesangial cell proliferation‐induced injury to kidney function. In this study, we explored serum biomarkers for kidney function injury in anti‐Thy1 nephritis. We found that mesangial proliferation were increased on days 5 and 7, and recovered by day 14 in anti‐Thy1 nephritis. 24‐h urine protein, the ratio of urine protein to urine creatine, serum creatine, and blood urea nitrogen, were increased at days 5 and 7 in the model. We found that TXN, BET1, PrRP, VGF, and NPS differed strongly from controls on days 5 and, associated with kidney injury when detected by SELDI‐TOF MS. Moreover, we applied LC‐MS to detect differential protein expression and found A2M, C3, ITIH4, ITIH3, VDBP, AFM, and SERPINF2 to be upregulated, and ES1, HPX, SERPINC1, SERPINA1F, SERPINA4, SERPINA3K, SPI, TF, VNN3, SERPINF1, and PON1 to be downregulated, on days 5 and 7, associated with kidney injury. The levels of VNN3 and VDBP were validated by Western blotting. Overall, this study explored a group of candidate biomarkers of mesangial proliferation inducing kidney injury, to provide the basis of an assessment model for MesPGN in the future.     

Newer potential biomarkers in prostate cancer

Prostate-specific antigen (PSA) screening has led to a significant rise in the number of men diagnosed with prostate cancer and an associated increase in biopsies performed. Despite its limitations, including a positive predictive value of only 25%-40%, PSA remains the only generally accepted biomarker for prostate cancer. There is a need for better tools to not only identify men with prostate cancer, but also to recognize those with potentially lethal disease who will benefit from intervention. A great deal of work has been done worldwide to improve our knowledge of the genetics behind prostate cancer and the specificity of PSA by developing assays for different PSA isoforms. Common genetic alterations in prostate cancer patients have been identified, including CpG hypermethylation of GSPT1 and TMPRSS2:ERG gene fusion. Serum and urine detection of RNA biomarkers (eg, PCA3) and prostate cancer tissue protein antibodies (eg, EPCA) are being evaluated for detection and prognostic tools. This article reviews some of the promising developments in biomarkers.     

Large-scale proteomics analysis of human ovarian cancer for biomarkers

Ovarian cancer is usually found at a late stage when the prognosis is often bad. Relative survival rates decrease with tumor stage or grade, and the 5-year survival rate for women with carcinoma is only 38%. Thus, there is a great need to find biomarkers that can be used to carry out routine screening, especially in high-risk patient groups. Here, we present a large-scale study of 64 tissue samples taken from patients at all stages and show that we can identify statistically valid markers using nonsupervised methods that distinguish between normal, benign, borderline, and malignant tissue. We have identified 217 of the significantly changing protein spots. We are expressing and raising antibodies to 35 of these. Currently, we have validated 5 of these antibodies for use in immunohistochemical analysis using tissue microarrays of healthy and diseased ovarian, as well as other, human tissues.     

Lectin capture strategies combined with mass spectrometry for the discovery of serum glycoprotein biomarkers

The application of mass spectrometry to identify disease biomarkers in clinical fluids like serum using high throughput protein expression profiling continues to evolve as technology development, clinical study design, and bioinformatics improve. Previous protein expression profiling studies have offered needed insight into issues of technical reproducibility, instrument calibration, sample preparation, study design, and supervised bioinformatic data analysis. In this overview, new strategies to increase the utility of protein expression profiling for clinical biomarker assay development are discussed with an emphasis on utilizing differential lectin-based glycoprotein capture and targeted immunoassays. The carbohydrate binding specificities of different lectins offer a biological affinity approach that complements existing mass spectrometer capabilities and retains automated throughput options. Specific examples using serum samples from prostate cancer and hepatocellular carcinoma subjects are provided along with suggested experimental strategies for integration of lectin-based methods into clinical fluid expression profiling strategies. Our example workflow incorporates the necessity of early validation in biomarker discovery using an immunoaffinity-based targeted analytical approach that integrates well with upstream discovery technologies.