An integrated approach for comparative proteomic analysis of human bile reveals overexpressed cancer-associated proteins in malignant biliary stenosis

Proteomics is a key tool in the identification of new bile biomarkers for differentiating malignant and nonmalignant biliary stenoses. Unfortunately, the complexity of bile and the presence of molecules interfering with protein analysis represent an obstacle for quantitative proteomic studies in bile samples. The simultaneous need to introduce purification steps and minimize the use of pre-fractionation methods inevitably leads to protein loss and limited quantifications. This dramatically reduces the chance of identifying new potential biomarkers. In the present study, we included differential centrifugation as a preliminary step in a quantitative proteomic workflow involving iTRAQ labeling, peptide fractionation by OFFGEL electrophoresis and LC-MS/MS, to compare protein expression in bile samples collected from patients with malignant or nonmalignant biliary stenoses. A total of 1267 proteins were identified, including a set of 322 newly described bile proteins, mainly belonging to high-density cellular fractions. The subsequent comparative analysis led to a 5-fold increase in the number of quantified proteins over previously published studies and highlighted 104 proteins overexpressed in malignant samples. Finally, immunoblot verifications performed on a cohort of 8 malignant (pancreatic adenocarcinoma, n = 4; cholangiocarcinoma, n = 4) and 5 nonmalignant samples (chronic pancreatitis, n = 3; biliary stones, n = 2) confirmed the results of proteomic analysis for three proteins: olfactomedin-4, syntenin-2 and Ras-related C3 botulinum toxin substrate 1. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.     

The Role of Biliary Carcinoembryonic Antigen-Related Cellular Adhesion Molecule 6 (CEACAM6) as a Biomarker in Cholangiocarcinoma

Objective

The aim of the present study is to determine if CEACAM6 can be detected in the bile of patients with biliary cancer and can serve as a diagnostic biomarker for cholangiocarcinoma.

Summary Background Data

Distinguishing bile duct carcinoma from other diagnoses is often difficult using endoscopic or percutaneous techniques. The cell surface protein CEACAM6 is over-expressed in many gastrointestinal cancers and may be selectively elevated in biliary adenocarcinoma.

Methods

Bile from patients with benign biliary disease and cholangiocarcinoma (hilar, intrahepatic and distal) was collected at the time of index operation. The concentration of CEACAM6 was quantified by sandwich enzyme-linked immunosorbent assay (ELISA) and correlated to pathologic diagnosis. Diagnostic capability of CEACAM6 was evaluated by Wilcoxon rank-sum, linear regression, multiple regression, and receiver operating characteristic (ROC) curve analysis.

Results

Bile from 83 patients was analyzed: 42 with benign disease and 41 with cholangiocarcinoma. Patients in the benign cohort were younger, predominantly female, and had lower median biliary CEACAM6 levels than patients in the malignant cohort (7.5 ng/ml vs. 40 ng/ml; p = <.001). ROC curve analysis determined CEACAM6 to be a positive predictor cholangiocarcinoma with a CEACAM6 level >14 ng/ml associated with 87.5% sensitivity, 69.1% specificity, and a likelihood ratio of 2.8 (AUC 0.74). Multiple regression analysis suggested elevated alkaline phosphatase and the presence of biliary endoprostheses may influence CEACAM6 levels.

Conclusion

Biliary CEACAM6 can identify patients with extrahepatic cholangiocarcinoma with a high degree of sensitivity and should be investigated further as a potential screening tool.     

Validation of a blood protein signature for non-small cell lung cancer

Background

CT screening for lung cancer is effective in reducing mortality, but there are areas of concern, including a positive predictive value of 4% and development of interval cancers. A blood test that could manage these limitations would be useful, but development of such tests has been impaired by variations in blood collection that may lead to poor reproducibility across populations.

Results

Blood-based proteomic profiles were generated with SOMAscan technology, which measured 1033 proteins. First, preanalytic variability was evaluated with Sample Mapping Vectors (SMV), which are panels of proteins that detect confounders in protein levels related to sample collection. A subset of well collected serum samples not influenced by preanalytic variability was selected for discovery of lung cancer biomarkers. The impact of sample collection variation on these candidate markers was tested in the subset of samples with higher SMV scores so that the most robust markers could be used to create disease classifiers. The discovery sample set (n = 363) was from a multi-center study of 94 non-small cell lung cancer (NSCLC) cases and 269 long-term smokers and benign pulmonary nodule controls. The analysis resulted in a 7-marker panel with an AUC of 0.85 for all cases (68% adenocarcinoma, 32% squamous) and an AUC of 0.93 for squamous cell carcinoma in particular. This panel was validated by making blinded predictions in two independent cohorts (n = 138 in the first validation and n = 135 in the second). The model was recalibrated for a panel format prior to unblinding the second cohort. The AUCs overall were 0.81 and 0.77, and for squamous cell tumors alone were 0.89 and 0.87. The estimated negative predictive value for a 15% disease prevalence was 93% overall and 99% for squamous lung tumors. The proteins in the classifier function in destruction of the extracellular matrix, metabolic homeostasis and inflammation.

Conclusions

Selecting biomarkers resistant to sample processing variation led to robust lung cancer biomarkers that performed consistently in independent validations. They form a sensitive signature for detection of lung cancer, especially squamous cell histology. This non-invasive test could be used to improve the positive predictive value of CT screening, with the potential to avoid invasive evaluation of nonmalignant pulmonary nodules.     

Urinary proteomics to support diagnosis of stroke

Accurate diagnosis in suspected ischaemic stroke can be difficult. We explored the urinary proteome in patients with stroke (n = 69), compared to controls (n = 33), and developed a biomarker model for the diagnosis of stroke. We performed capillary electrophoresis online coupled to micro-time-of-flight mass spectrometry. Potentially disease-specific peptides were identified and a classifier based on these was generated using support vector machine-based software. Candidate biomarkers were sequenced by liquid chromatography-tandem mass spectrometry. We developed two biomarker-based classifiers, employing 14 biomarkers (nominal p-value <0.004) or 35 biomarkers (nominal p-value <0.01). When tested on a blinded test set of 47 independent samples, the classification factor was significantly different between groups; for the 35 biomarker model, median value of the classifier was 0.49 (-0.30 to 1.25) in cases compared to -1.04 (IQR -1.86 to -0.09) in controls, p<0.001. The 35 biomarker classifier gave sensitivity of 56%, specificity was 93% and the AUC on ROC analysis was 0.86. This study supports the potential for urinary proteomic biomarker models to assist with the diagnosis of acute stroke in those with mild symptoms. We now plan to refine further and explore the clinical utility of such a test in large prospective clinical trials.     

Multiparametric Analysis of Cell-Free DNA in Melanoma Patients

Cell-free DNA in blood (cfDNA) represents a promising biomarker for cancer diagnosis. Total cfDNA concentration showed a scarce discriminatory power between patients and controls. A higher specificity in cancer diagnosis can be achieved by detecting tumor specific alterations in cfDNA, such as DNA integrity, genetic and epigenetic modifications.The aim of the present study was to identify a sequential multi-marker panel in cfDNA able to increase the predictive capability in the diagnosis of cutaneous melanoma in comparison with each single marker alone. To this purpose, we tested total cfDNA concentration, cfDNA integrity, BRAF^V600E mutation and RASSF1A promoter methylation associated to cfDNA in a series of 76 melanoma patients and 63 healthy controls. The chosen biomarkers were assayed in cfDNA samples by qPCR. Comparison of biomarkers distribution in cases and controls was performed by a logistic regression model in both univariate and multivariate analysis. The predictive capability of each logistic model was investigated by means of the area under the ROC curve (AUC). To aid the reader to interpret the value of the AUC, values between 0.6 and 0.7, between 0.71 and 0.8 and greater than 0.8 were considered as indicating a weak predictive, satisfactory and good predictive capacity, respectively. The AUC value for each biomarker (univariate logistic model) was weak/satisfactory ranging between 0.64 (BRAF^V600E) to 0.85 (total cfDNA). A good overall predictive capability for the final logistic model was found with an AUC of 0.95. The highest predictive capability was given by total cfDNA (AUC:0.86) followed by integrity index 180/67 (AUC:0.90) and methylated RASSF1A (AUC:0.89).An approach based on the simultaneous determination of three biomarkers (total cfDNA, integrity index 180/67 and methylated RASSF1A) could improve the diagnostic performance in melanoma.     

A step further in the analysis of human bile proteome

Bile was shown to collect proteins known as potential cancer biomarkers. Thorough proteomic analysis of bile is of particular interest to search for new, more sensitive and more specific, biomarkers of cancers affecting the biliary tract and surrounding organs, such as the pancreas and the liver. Therefore, extending the knowledge of the bile proteome is highly relevant, but this has proved technically difficult. In this study, we describe a strategy that circumvents problems related to the biochemical complexity of this sample and the presence of high concentrations of interfering substances. Bile collected from a patient suffering from a biliary stenosis caused by a pancreatic adenocarcinoma was fractionated by a differential centrifugation scheme, involving a stepwise increase in centrifugation speeds. Pellets and the final supernatant were further fractionated by polyacrylamide gel electrophoresis and proteins were in-gel digested prior to LC-MS/MS analysis. This approach allowed the identification of 445 unique proteins with at least two peptides (812 proteins if single-hit proteins were included), which represents a 3-fold increase in the knowledge of bile proteome. The subsequent literature comparison revealed that numerous biliary proteins identified in this sample were related to pancreas cancer. Immunoblot analysis of some known tumor markers revealed that they were preferentially associated with the soluble fraction rather than with pellets containing cellular components.     

Genome-wide methylation profiling reveals Zinc finger protein 516 (ZNF516) and FK-506-binding protein 6 (FKBP6) promoters frequently methylated in cervical neoplasia,associated with HPV status and ethnicity in a Chilean population

Cervical cancer is a major health concern among women in Latin America due to its high incidence and mortality. Therefore, the discovery of molecular markers for cervical cancer screening and triage is imperative. The aim of this study was to use a genome wide DNA methylation approach to identify novel methylation biomarkers in cervical cancer. DNA from normal cervical mucosa and cervical cancer tissue samples from Chile was enriched with Methylated DNA Immunoprecipitation (MeDIP), hybridized to oligonucleotide methylation microarrays and analyzed with a stringent bioinformatics pipeline to identify differentially methylated regions (DMRs) as candidate biomarkers. Quantitative Methylation Specific PCR (qMSP) was used to study promoter methylation of candidate DMRs in clinical samples from two independent cohorts. HPV detection and genotyping were performed by Reverse Line Blot analysis. Bioinformatics analysis revealed GGTLA4, FKBP6, ZNF516, SAP130, and INTS1 to be differentially methylated in cancer and normal tissues in the Discovery cohort. In the Validation cohort FKBP6 promoter methylation had 73% sensitivity and 80% specificity (AUC = 0.80). ZNF516 promoter methylation was the best biomarker, with both sensitivity and specificity of 90% (AUC = 0.92), results subsequently corroborated in a Prevalence cohort. Together, ZNF516 and FKBP6 exhibited a sensitivity of 84% and specificity of 81%, when considering both cohorts. Our genome wide DNA methylation assessment approach (MeDIP-chip) successfully identified novel biomarkers that differentiate between cervical cancer and normal samples, after adjusting for age and HPV status. These biomarkers need to be further explored in case-control and prospective cohorts to validate them as cervical cancer biomarkers.     

Genome-wide methylation profiling reveals Zinc finger protein 516 (ZNF516) and FK-506-binding protein 6 (FKBP6) promoters frequently methylated in cervical neoplasia,associated with HPV status and ethnicity in a Chilean population

Cervical cancer is a major health concern among women in Latin America due to its high incidence and mortality. Therefore, the discovery of molecular markers for cervical cancer screening and triage is imperative. The aim of this study was to use a genome wide DNA methylation approach to identify novel methylation biomarkers in cervical cancer. DNA from normal cervical mucosa and cervical cancer tissue samples from Chile was enriched with Methylated DNA Immunoprecipitation (MeDIP), hybridized to oligonucleotide methylation microarrays and analyzed with a stringent bioinformatics pipeline to identify differentially methylated regions (DMRs) as candidate biomarkers. Quantitative Methylation Specific PCR (qMSP) was used to study promoter methylation of candidate DMRs in clinical samples from two independent cohorts. HPV detection and genotyping were performed by Reverse Line Blot analysis. Bioinformatics analysis revealed GGTLA4, FKBP6, ZNF516, SAP130, and INTS1 to be differentially methylated in cancer and normal tissues in the Discovery cohort. In the Validation cohort FKBP6 promoter methylation had 73% sensitivity and 80% specificity (AUC = 0.80). ZNF516 promoter methylation was the best biomarker, with both sensitivity and specificity of 90% (AUC = 0.92), results subsequently corroborated in a Prevalence cohort. Together, ZNF516 and FKBP6 exhibited a sensitivity of 84% and specificity of 81%, when considering both cohorts. Our genome wide DNA methylation assessment approach (MeDIP-chip) successfully identified novel biomarkers that differentiate between cervical cancer and normal samples, after adjusting for age and HPV status. These biomarkers need to be further explored in case-control and prospective cohorts to validate them as cervical cancer biomarkers.     

Comparative proteomic analysis of human pancreatic juice: methodological study

Pancreatic cancer is the most lethal of all the common malignancies. Markers for early detection of this disease are urgently needed. Here, we optimized and applied a proteome analysis of human pancreatic juice to identify biomarkers for pancreatic cancer. Pancreatic juice samples, devoid of blood or bile contamination, were collected from patients with pancreatic cancer (n = 5), benign pancreatic diseases (n = 6), or cholelithiasis (n = 3) during endoscopic retrograde cholangiopancreatography (ERCP). After ultramembrane centrifugation sample preparation, pancreatic juice proteins were separated by 2-DE and identified by MALDI-TOF-MS. A 2-DE dataset of pancreatic juice from patients with cholelithiasis was established, consisting of 76 protein spots representing 22 different proteins. Disease-associated obstruction of the pancreatic duct strongly effected the protein composition of pancreatic juice. Concurrently, pancreatic juice from patients with pancreatic cancer was compared to nonmalignant controls with comparable obstruction of pancreatic ducts. Seven protein spots were identified that consistently appeared at changed levels in pancreatic juice from patients with pancreatic cancer. In conclusion, comparative proteomic analysis of human pancreatic juice can be used to identify biomarkers of pancreatic cancer.     

Pathological features of primary sclerosing cholangitis identified by bile proteomic analysis

Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease of unknown origin. Previous bile proteomic analyses in patients with PSC have revealed changes in disease activity specific to malignant transformation. In this study, we established a reference bile duct-derived bile proteome for PSC that can be used to evaluate biliary pathophysiology. Samples were collected from patients with PSC or with choledocholithiasis (control) (n = 6 each). Furthermore, patients with PSC-associated cholangiocarcinoma (CC) and with CC without concomitant PSC were analyzed. None of the patients showed signs of inflammation or infection based on clinical and laboratory examinations. Proteins overexpressed in patients with PSC relative to control patients were detected by two-dimensional difference gel electrophoresis and identified by liquid chromatography-tandem mass spectrometry. Functional proteomic analysis was performed using STRING software. A total of 101 proteins were overexpressed in the bile fluid of patients with PSC but not in those of controls; the majority of these were predicted to be intracellular and related to the ribosomal and proteasomal pathways. On the other hand, 91 proteins were found only in the bile fluid of controls; most were derived from the extracellular space and were linked to cell adhesion, the complement system, and the coagulation cascade. In addition, proteins associated with inflammation and the innate immune response—e.g., cluster of differentiation 14, annexin-2, and components of the complement system—were upregulated in PSC. The most prominent pathways in PSC/CC-patients were inflammation associated cytokine and chemokine pathways, whereas in CC-patients the Wnt signaling pathway was upregulated. In PSC/CC-patients DIGE-analysis revealed biliary CD14 and Annexin-4 expression, among others, as the most prominent protein that discriminates between both cohorts.Thus, the bile-duct bile proteome of patients with PSC shows disease-specific changes associated with inflammation and the innate immune response even in the absence of obvious clinical signs of cholangitis, malignancy, or inflammation. This article is part of a Special Issue entitled: Cholangiocytes in Health and Diseaseedited by Jesus Banales, Marco Marzioni and Peter Jansen.     

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.     

Real-time PCR-based analysis of the human bile microRNAome identifies miR-9 as a potential diagnostic biomarker for biliary tract cancer

Biliary tract cancer (BTC) is often difficult to diagnose definitively, even through histological examination. MicroRNAs (miRNAs) regulate a variety of physiological processes. In recent years, it has been suggested that profiles for circulating miRNAs, as well as those for tissue miRNAs, have the potential to be used as diagnostic biomarkers for cancer. The aim of this study was to confirm the existence of miRNAs in human bile and to assess their potential as clinical biomarkers for BTC. We sampled bile from patients who underwent biliary drainage for biliary diseases such as BTC and choledocholithiasis. PCR-based miRNA detection and miRNA cloning were performed to identify bile miRNAs. Using high-throughput real-time PCR-based miRNA microarrays, the expression profiles of 667 miRNAs were compared in patients with malignant disease (n = 9) and age-matched patients with the benign disease choledocholithiasis (n = 9). We subsequently characterized bile miRNAs in terms of stability and localization. Through cloning and using PCR methods, we confirmed that miRNAs exist in bile. Differential analysis of bile miRNAs demonstrated that 10 of the 667 miRNAs were significantly more highly expressed in the malignant group than in the benign group at P<0.0005. Setting the specificity threshold to 100% showed that some miRNAs (miR-9, miR-302c*, miR-199a-3p and miR-222*) had a sensitivity level of 88.9%, and receiver-operating characteristic analysis demonstrated that miR-9 and miR-145* could be useful diagnostic markers for BTC. Moreover, we verified the long-term stability of miRNAs in bile, a characteristic that makes them suitable for diagnostic use in clinical settings. We also confirmed that bile miRNAs are localized to the malignant/benign biliary epithelia. These findings suggest that bile miRNAs could be informative biomarkers for hepatobiliary disease and that some miRNAs, particularly miR-9, may be helpful in the diagnosis and clinical management of BTC.     

Proteomic profiling of cerebrospinal fluid identifies biomarkers for amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is characterized by degeneration of motor neurons. We tested the hypothesis that proteomic analysis will identify protein biomarkers that provide insight into disease pathogenesis and are diagnostically useful. To identify ALS specific biomarkers, we compared the proteomic profile of cerebrospinal fluid (CSF) from ALS and control subjects using surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS). We identified 30 mass ion peaks with statistically significant (p < 0.01) differences between control and ALS subjects. Initial analysis with a rule-learning algorithm yielded biomarker panels with diagnostic predictive value as subsequently assessed using an independent set of coded test subjects. Three biomarkers were identified that are either decreased (transthyretin, cystatin C) or increased (carboxy-terminal fragment of neuroendocrine protein 7B2) in ALS CSF. We validated the SELDI-TOF-MS results for transthyretin and cystatin C by immunoblot and immunohistochemistry using commercially available antibodies. These findings identify a panel of CSF protein biomarkers for ALS.     

胆道系统探查中术中超声的应用及临床价值

目的:探讨胆管系统探查中术中超声(intraoperative ultrasound,IOUS)的应用及临床价值。方法:2007年3月至2014年8月应用术中超声对胆道系统进行探查的病例资料58例,对其术前影像学表现、手术过程、术中超声所见以及术中和术后诊断进行分析,研究术中超声对胆道探查的应用价值。结果:(1)58例应用术中超声病人中,肝内外胆管结石35例、肝门部胆管癌及胆总管癌11例,急性胆囊炎8例,胃癌1例,先天性胆总管囊肿1例,胆总管炎性狭窄1例,胰腺癌1例。术中超声确认取净结石或胆总管未见明显异常34例,定位肝内胆管残余结石6例,发现胆总管内尚有结石2例,术中超声确诊胆管癌2例;另发现胆总管先天性解剖异常2例;(2)在发现胆管结石方面,与术前MRCP无显著性差异(P=0.643);与术前CT、B超比较有显著差异(P0.05),诊断率分别为B超74.3%,MRCP 91.4%,CT 77.1%,IOUS 94.3%。结论:术中超声胆道系统的探查可以在广泛的疾病中得到应用,可以对术前影像学检查起到验证和补充的作用,且在术中引导各种介入操作中起到独特作用。     

Reporting the presence of significant epithelial atypia in pancreaticobiliary brush cytology specimens lacking evidence of obvious carcinoma: impact on performance measures

OBJECTIVE: To assess the clinical impact of recognizing and reporting the presence of significant atypia in brush cytology specimens from the biliary and pancreatic ducts lacking obvious features of carcinoma. STUDY DESIGN: Analysis of 120 pancreaticobiliary brushings from 99 patients over a 4-year period. There were 114 bile duct and 6 pancreatic duct specimens obtained via endoscopic retrograde cholangiopancreatography at a tertiary care center. RESULTS: Overall sensitivity, specificity, accuracy, and positive and negative predictive values for carcinoma were 47%, 99%, 79%, 95% and 76%, respectively. When the presence of "significant epithelial abnormalities," cancer or cellular atypia less than carcinoma, was reported, the overall sensitivity, specificity, accuracy, and positive and negative predictive values were 62%, 93%, 82%, 85% and 80%, respectively. CONCLUSION: Recognizing and reporting the presence of significant epithelial abnormalities in pancreaticobiliary specimens lacking obvious features of malignancy in brush cytology specimens led to a modest improvement in sensitivity for "significant epithelial abnormalities" and cancer, along with a slight decrease in specificity and positive predictive value and slightly increased accuracy and negative predictive value. Maintaining high specificity is essential to avoiding false positive diagnoses on pancreaticobiliary brush cytology.     

Why have so few proteomic biomarkers “survived” validation? (Sample size and independent validation considerations)

Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC‐MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC‐MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.     

Seminal Plasma as a Source of Prostate Cancer Peptide Biomarker Candidates for Detection of Indolent and Advanced Disease

Background

Extensive prostate specific antigen screening for prostate cancer generates a high number of unnecessary biopsies and over-treatment due to insufficient differentiation between indolent and aggressive tumours. We hypothesized that seminal plasma is a robust source of novel prostate cancer (PCa) biomarkers with the potential to improve primary diagnosis of and to distinguish advanced from indolent disease.

Methodology/Principal Findings

In an open-label case/control study 125 patients (70 PCa, 21 benign prostate hyperplasia, 25 chronic prostatitis, 9 healthy controls) were enrolled in 3 centres. Biomarker panels a) for PCa diagnosis (comparison of PCa patients versus benign controls) and b) for advanced disease (comparison of patients with post surgery Gleason score <7 versus Gleason score >7) were sought. Independent cohorts were used for proteomic biomarker discovery and testing the performance of the identified biomarker profiles. Seminal plasma was profiled using capillary electrophoresis mass spectrometry. Pre-analytical stability and analytical precision of the proteome analysis were determined. Support vector machine learning was used for classification. Stepwise application of two biomarker signatures with 21 and 5 biomarkers provided 83% sensitivity and 67% specificity for PCa detection in a test set of samples. A panel of 11 biomarkers for advanced disease discriminated between patients with Gleason score 7 and organ-confined (<pT3a) or advanced (≥pT3a) disease with 80% sensitivity and 82% specificity in a preliminary validation setting. Seminal profiles showed excellent pre-analytical stability. Eight biomarkers were identified as fragments of N-acetyllactosaminide beta-1,3-N-acetylglucosaminyltransferase, prostatic acid phosphatase, stabilin-2, GTPase IMAP family member 6, semenogelin-1 and -2. Restricted sample size was the major limitation of the study.

Conclusions/Significance

Seminal plasma represents a robust source of potential peptide makers for primary PCa diagnosis. Our findings warrant further prospective validation to confirm the diagnostic potential of identified seminal biomarker candidates.     

Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib

Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature — integrin β4 (ITGB4) — was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.