Multimarker Panel Research Articles

A combined epidemiologic and metabolomic approach improves CKD prediction.

Metabolomic approaches have begun to catalog the metabolic disturbances that accompany CKD, but whether metabolite alterations can predict future CKD is unknown. We performed liquid chromatography/mass spectrometry-based metabolite profiling on plasma from 1434 participants in the Framingham Heart Study (FHS) who did not have CKD at baseline. During the following 8 years, 123 individuals developed CKD, defined by an estimated GFR of <60 ml/min per 1.73 m(2). Numerous metabolites were associated with incident CKD, including 16 that achieved the Bonferroni-adjusted significance threshold of P≤0.00023. To explore how the human kidney modulates these metabolites, we profiled arterial and renal venous plasma from nine individuals. Nine metabolites that predicted CKD in the FHS cohort decreased more than creatinine across the renal circulation, suggesting that they may reflect non-GFR-dependent functions, such as renal metabolism and secretion. Urine isotope dilution studies identified citrulline and choline as markers of renal metabolism and kynurenic acid as a marker of renal secretion. In turn, these analytes remained associated with incident CKD in the FHS cohort, even after adjustment for eGFR, age, sex, diabetes, hypertension, and proteinuria at baseline. Addition of a multimarker metabolite panel to clinical variables significantly increased the c-statistic (0.77-0.83, P<0.0001); net reclassification improvement was 0.78 (95% confidence interval, 0.60 to 0.95; P<0.0001). Thus, the addition of metabolite profiling to clinical data may significantly improve the ability to predict whether an individual will develop CKD by identifying predictors of renal risk that are independent of estimated GFR.

Multimarker Panel for Patients With Chest Pain: Case Closed?

Chest pain is one of the symptoms that most frequently compels patients to consult emergency services. When acute coronary syndrome is suspected, early diagnosis facilitates the immediate implementation of decision and treatment algorithms that favorably affect the prognosis. Thus, patients not showing electrocardiographic changes or pain conclusive for myocardial ischemia in the initial evaluation will remain under observation, awaiting a series of clinical, electrocardiographic, and biochemical evaluations that will help to stratify risk and establish the definitive diagnosis. Diagnosis of acute myocardial infarction currently requires the finding of increased or decreased values of a specific biomarker of myocardial damage or necrosis; due to its high sensitivity and specificity, the biomarker of choice is troponin. An infarction is diagnosed when, in patients with clinical features indicative of or compatible with myocardial ischemia, at least 1 serially performed troponin measurement is above the 99th percentile of the upper reference limit according to the specific assay used in each laboratory. If no troponin measurement is available, the best alternative is to search for a similar alteration in the creatine kinase MB fraction (CK-MB) (determined with the mass assay). Despite the indisputable progress that has enabled troponin to be measured in emergency department laboratories at any given moment, it is well known that many other clinical situations distinct from acute coronary syndromes can present with an increase in troponin and that both its maximum values and its release kinetics can be influenced by diverse factors. The manufacturers of the troponin test kitshave improved their ability to detect minute quantities, which has greatly increased their sensitivity in the diagnosis of small infarctions. Nowadays, the latest generation of highly sensitive or ultrasensitive methods for measuring troponin (usTn) are replacing the conventional methods that have been used thus far in Spanish hospitals. The most important practical consideration is that conventional methods have relatively low or inadequate sensitivity during the first few hours after symptom onset, which can delay diagnosis and treatment administration and prolong patient stay in emergency departments. It is precisely these aspects that have been improved

Abstract 5108: Capture and enumeration of mesenchymal CTCs on the OnQChip platform.

Abstract Circulating tumor cells (CTCs) are cancer cells that disseminate from the primary tumor and enter the blood. While cells in the primary tumor are generally epithelial in nature, as they become migratory and invasive, changes such as loss of epithelial markers (ie: EpCAM and cytokeratin (CK)) and a switch to a mesenchymal phenotype (EMT) often occur. CTC platforms must be flexible in capturing and identifying cells, and not limited to the use of only epithelial markers. Dual modal capture on the OnQChip provides this flexibility, allowing for CTC isolation based on size and independent of surface marker expression in addition to specific antibody mediated capture targeting markers such as EpCAM. For cell lines ranging in EpCAM expression, the OnQChip captures 90% or greater of mid to high EpCAM expressing cells, and 79% or greater of very low EpCAM expressing cells. Due to the OnQChip's gradient design, distinct capture patterns related to EpCAM levels are observed, allowing for capture location to be informative about EpCAM expression. When Hs578T cells, a mesenchymal model with very little EpCAM and CK, are prestained around 79% of spiked cells are enumerated on the OnQChip, but when the same cells are not prestained and only CK staining is used, 0-2% of spiked cells are counted. When two new mesenchymal staining markers are incorporated alongside CK, 60-80% of spiked Hs578T cells are now enumerated, translating to a 75-100% staining efficiency of captured cells. Although very few cells are visualized with CK alone, pooling CK staining with mesenchymal staining shows increased staining intensity compared to mesenchymal staining alone, highlighting benefits of a multi-marker staining panel for visualizing cells with low expression of several different markers, likely the case for subpopulations of cells at different stages of the EMT. On a population of 44 prostate cancer samples, an increase of 28.5, 8.2, 12.7, and 7.6 in the mean number of total, intact, irregular, and fragmented CTCs, respectively, was observed with staining for CK plus the mesenchymal markers as compared to CK staining alone. Specifically, 70.5%, 18.2%, 50%, and 38.6% of patients showed an increase of 5 or more, and 40.9%, 9.1%, 31.8%, and 25% showed an increase of 10 or more, total, intact, irregular, and fragmented CTCs, respectively, when mesenchymal staining was included. Additionally, a few case studies from this population for which serial draws during treatment were obtained indicate changes in the ratio of CTCs enumerated with staining for CK plus mesenchymal markers compared to CK only, suggesting the ratio of mesenchymal to epithelial like CTCs may reflect a clinical outcome. Our data suggests that the OnQChip's ability to capture CTCs based on size independent of surface marker expression and inclusion of two new stains, allow for capture and enumeration of mesenchymal CTCs that would be missed by platforms incorporating only epithelial markers such as EpCAM and CK. Citation Format: Keith D. Merdek, Kam Sprott, Chunsheng Jiang, Michael Stocum, Glenn Bubley, Walter Carney. Capture and enumeration of mesenchymal CTCs on the OnQChip platform. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5108. doi:10.1158/1538-7445.AM2013-5108 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.

Abstract 3501: Identification of plasma metabolites as biomarker candidates for the diagnosis of pancreatic ductal adenocarcinoma (PDAC).

Abstract Background and objective: Pancreatic cancer (PDAC) is characterized by very poor prognosis mostly caused by late diagnosis. Although modern imaging techniques have pushed the detection limit to lesions below 10mm. The combination of diagnostic imaging and biomarkers can distinguish between PDAC and chronic pancreatitis in only 67% (Carriere et al. J Eval Clin Pract. 2009). We have therefore conducted discovery and confirmation studies to identify metabolite plasma biomakers for the detection of pancreatic cancer and the differentiation from chronic pancreatitis. Material and method:The retrospective study was conducted in three phases: An initial pilot study on plasma samples was followed by analysis of a second plasma sample collection and a serum sample collection from pancreatic cancer, chronic pancreatitis and liver cirrhosis patients as well as blood donors. Metabolomic profiles of plasma and serum samples were generated applying a high throughput polar and lipid GC-MS and LC-MS/MS technology (MxP™ Broad Profiling). In addition, targeted platforms for steroids and lipids (MxP™ Steroid, MxP™ Lipids) were applied. Up to 477 metabolites were analyzed semi-quantitatively or quantitatively per study, 90% of them with an identified chemical structure. Statistical data analysis was done by linear models (ANOVA) on log10 transformed data considering age, gender, BMI and sample storage time as fixed effects. A panel of metabolites was selected for the creation of a diagnosis biomarker. The predictive ability of the biomarker was evaluated through the estimation of ROC characteristics and AUC values from Bootstrap-based Cross-Validation. Result within the three consecutive studies, sphingolipids (sphingomyelins and ceramides) were consistently and significantly increased in the pancreatic cancer group relative to the corresponding pancreatitis group whereas certain amino acids, amino acid related metabolites and coenzyme Q9 were consistently and significantly decreased. A multi-marker panel consisted of 10 metabolites and provided an AUC=0.85 when discriminating between pancreatic cancer and pancreatitis. When the CA19-9 data was included in the analysis, an AUC= 0.92 was reached. Conclusions: The results from the metabolomics study indicate that a plasma metabolite biomarker panel can be used to distinguish between pancreatic cancer and chronic pancreatitis with a high degree of accuracy. The most discriminating metabolites showed robustness with respect to transferability of their diagnostic potential from plasma to serum. A multicenter validation study has now been initiated to establish a diagnostic assay with a targeted negative predictive value (NPV) &amp;gt; 95%. Such a test would allow the exclusion of suspicious patients from further more invasive diagnostic procedures. Citation Format: Julia Mayerle, Holger Kalthoff, Regina Reszka, Beate Kamlage, Christian Pilarsky, Robert Grützmann, Bodo Schniewind, F. Ulrich Weiss, Markus M. Lerch. Identification of plasma metabolites as biomarker candidates for the diagnosis of pancreatic ductal adenocarcinoma (PDAC). [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3501. doi:10.1158/1538-7445.AM2013-3501

Putting it into Perspective: Multimarker Panels for Cardiovascular Disease Risk Assessment

Cardiovascular biomarkers started as tools to aid with specific medical diagnoses, but are now being used broadly for screening, prognosis and monitoring of multiple diseases. Novel markers that reflect important pathophysiologic pathways are emerging regularly, although each new set of markers introduced raises many questions on how best to utilize them to improve patient outcomes. One promising approach for getting the most out of cardiovascular biomarkers is to combine multiple markers together into a multimarker panel. When each marker represents a distinct pathophysiologic pathway, the combined panel has advantages over individual biomarkers and may be useful when used in specific clinical scenarios for assessing risk, improving diagnosis or directing individualized therapy. This perspective article highlights several of the most promising biomarkers and strategies for achieving improved cardiovascular risk assessment for primary prevention of cardiovascular disease via multimarker panels.

Verification of Multimarkers for Detection of Early Stage Diabetic Retinopathy Using Multiple Reaction Monitoring

Diabetic retinopathy (DR) is a complication of diabetes and 80% of diabetes mellitus (DM) patients whose DM duration is over 10 years can be expected to suffer with DR. The diagnosis of DR depends on an ophthalmological examination, and no molecular methods of screening DR status exist. Nonproliferative diabetic retinopathy (NPDR) is the early DR which is hard to be noticed in early NPDR, showing significant cause of adult blindness in type 2 diabetes patients. Protein biomarkers have been valuable in the diagnosis of disease and the use of multiple biomarkers has been suggested to overcome the low specificity of single ones. For biomarker development, multiple reaction monitoring (MRM) has been spotlighted as an alternative method to quantify target proteins with no need for immunoassay. In this study, 54 candidate DR marker proteins from a previous study were verified by MRM in plasma samples from NPDR patients in 3 stages (mild, moderate and severe; 15 cases each) and diabetic patients without retinopathy (15 cases) as a control. Notably, 27 candidate markers distinguished moderate NPDR from type 2 diabetic patients with no diabetic retinopathy, generating AUC values (>0.7). Specifically, 28 candidate proteins underwent changes in expression as type 2 diabetic patients with no diabetic retinopathy progressed to mild and moderate NPDR. Further, a combination of 4 markers from these 28 candidates had the improved specificity in distinguishing moderate NPDR from type 2 diabetic patients with no diabetic retinopathy, yielding a merged AUC value of nearly 1.0. We concluded that MRM is a fast, robust approach of multimarker panel determination and an assay platform that provides improved specificity compared with single biomarker assay systems.

Multi-marker network in ST-elevation myocardial infarction patients undergoing primary percutaneous coronary intervention: When and what to measure

BackgroundData on the correlations between biomarkers to suggest cost-effective multi-marker (MM) panels predictive for ST-elevation myocardial infarction (STEMI) patients are lacking. We sought to explore the relationship between cardiac troponin I (cTnI), C-reactive protein (CRP), B-type natriuretic peptide (BNP), and chromogranin A (CgA) accounting for biomarkers' profiles detected within 48h from successful primary percutaneous coronary intervention (PPCI). MethodsIn 73 STEMI patients cTnI, CRP, BNP, and CgA were measured before PPCI and 6, 24, and 48h later. STATIS methods generalizing Principal Component Analysis on three-way data sets were employed to extract information about: 1) similarities between patients, 2) contribution of each time of sampling and 3) correlations between biomarkers' profiles. ResultsSTEMI patients who underwent successful PPCI emerged to have a homogeneous profile tailored on biomarkers' evaluation within 48h. Their measurements at 24h contributed the most variability and information both to patients' and to biomarkers' profiles.BNP and cTnI were highly correlated and explained the 40.1% of the total variance, whereas CgA resulted independent and explained the 26.3% of the total variance. ConclusionsMarkers' measurements at 24h after PPCI contributed most information to the definition of patients' profile. BNP and cTnI resulted interchangeable in a MM panel for reporting about the extent of necrosis.

A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers

BackgroundBiomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is how to create effective proteo-genomic panels. The framework of ensemble classifiers has been applied successfully in various analytical domains to combine classifiers so that the performance of the ensemble exceeds the performance of individual classifiers. Using blood-based diagnosis of acute renal allograft rejection as a case study, we address the following question in this paper: Can acute rejection classification performance be improved by combining individual genomic and proteomic classifiers in an ensemble?ResultsThe first part of the paper presents a computational biomarker development pipeline for genomic and proteomic data. The pipeline begins with data acquisition (e.g., from bio-samples to microarray data), quality control, statistical analysis and mining of the data, and finally various forms of validation. The pipeline ensures that the various classifiers to be combined later in an ensemble are diverse and adequate for clinical use. Five mRNA genomic and five proteomic classifiers were developed independently using single time-point blood samples from 11 acute-rejection and 22 non-rejection renal transplant patients. The second part of the paper examines five ensembles ranging in size from two to 10 individual classifiers. Performance of ensembles is characterized by area under the curve (AUC), sensitivity, and specificity, as derived from the probability of acute rejection for individual classifiers in the ensemble in combination with one of two aggregation methods: (1) Average Probability or (2) Vote Threshold. One ensemble demonstrated superior performance and was able to improve sensitivity and AUC beyond the best values observed for any of the individual classifiers in the ensemble, while staying within the range of observed specificity. The Vote Threshold aggregation method achieved improved sensitivity for all 5 ensembles, but typically at the cost of decreased specificity.ConclusionProteo-genomic biomarker ensemble classifiers show promise in the diagnosis of acute renal allograft rejection and can improve classification performance beyond that of individual genomic or proteomic classifiers alone. Validation of our results in an international multicenter study is currently underway.

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, BRAFV600E 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 (BRAFV600E) 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.

Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma

Metabolomics as one of the most rapidly growing technologies in the “-omics” field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [ {text{volume under ROC surface}};left( {text{VUS}} right) = 0. 8 9 1 { }left( { 9 5,% {text{ CI }}0. 7 9 4- 0. 9 6 8} right)]. We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and—despite all its current limitations—can deliver marker panels with high selectivity even in multi-class settings.

Individual risk assessment in bladder cancer patients based on a multi-marker panel

To investigate whether a combined application of urine cytology (CYT) and molecular markers for bladder cancer (BC) can predict tumor aggressiveness. The study comprised 2,113 patients who underwent urethrocystoscopy and transurethral resection of the bladder. CYT, fluorescence in situ hybridization (FISH), immunocytology (uCyt+) and nuclear matrix protein 22 test (NMP22-ELISA) were performed. Results of the individual tests and of a multi-marker panel were correlated with pT-stages and tumor grades. Five hundred and two of 2,113 (23.8 %) patients had BC. False-negative test rates of CYT (p < 0.001), FISH (p = 0.01) and NMP22-ELISA (p = 0.05) were lower in patients with muscle-invasive BC compared with patients with non-muscle-invasive BC. Furthermore, false-negative rates of CYT (p < 0.001), FISH (p = 0.0002) and NMP22-ELISA (p < 0.001) were lower in patients with G3/CIS compared with patients with G1-G2 BC. In patients with evidence of tumor in urethrocystoscopy, the presence of simultaneously positive CYT and NMP22 was associated with a 20-fold risk for G3/CIS (p < 0.0001). This is the first study investigating the combined use of four urine markers in addition to cystoscopy to predict tumor aggressiveness. Our results indicate that combined application of urine markers as an adjunct to cystoscopy may facilitate identification of patients harboring high-grade tumors.

OS043. Identification and validation of novel markers for the predictionof pre-eclampsia

Currently no test accurately predicts pre-eclampsia (PE) in a healthy nulliparous population. Unbiased protein biomarker discovery has the potential to identify novel markers but multimarker panels are required to achieve clinically relevant prediction of PE. To this purpose, single biomarker performances were obtained and multimarker panels developed in a significant subcohort of the international Screening fOr Pregnancy Endpoints study (SCOPE) study [1]. To identify and validate novel protein markers for PE prediction using chromatographic and mass spectrometric techniques which enable the identification and quantification of plasma proteins present in plasma at sub ng/ml concentration (Pronota, Belgium). Pre-disease plasma samples (22-26 weeks) from women who subsequently developed PE and those with uncomplicated pregnancies [2] were used to generate 30 plasma proteome profiles using the MASStermind™ pipeline. A set of novel protein candidates were validated using an antibody-free mass spectrometry method using multiple reaction monitoring (MASSterclass™) in a subcohort of the SCOPE study (NZ & Aus) [1]. Relative abundance of 40+ proteins was determined in 20week plasma samples from 100 women who developed PE and 200 women who did not develop PE (included women with other pregnancy complications). Multivariate analyses were performed to identify algorithms with predictive performance using combinations confined to a maximum of 6 parameters (protein markers and clinical parameters) to avoid overfitting. Validation of the prediction panels was performed in an independent subcohort of SCOPE (Europe) comprising 50 PE and 150 no PE. From this large scale biomarker discovery effort a number of key results were obtained: a novel protein, i.e., Insulin-like growth factor binding protein, acid labile subunit (IGFALS), was identified. AUC for this marker for the prediction of all PE was 0.71 (CI 0.68-0.75) which was greater than both PlGF and s-Eng (respective AUCs: 0.64 and 0.61). IGFALS was also found to have predictive value for term (AUC 0.70) as well as preterm disease (AUC 0.75). Using multivariate analysis, marker panels were identified that achieved clinically relevant prediction (exemplary panel prediction of all PE cases AUC=0.79; prediction of preterm PE AUC=0.92). These multivariate models were successfully validated in the European SCOPE subcohort. In addition, predictive algorithms based on mass spectrometric read outs were largely invariant to interchanging the IGFALS mass spectrometry quantitation data with IGFALS ELISA data. This study demonstrates the capability of high level LC-MS technologies to discover candidate biomarkers and execute large scale multiplex validation to develop a predictive screening test for preeclampsia.

Persistent tumor cells in bone marrow of non-metastatic breast cancer patients after primary surgery are associated with inferior outcome

BackgroundTo investigate the prognostic significance of disseminated tumor cells (DTCs) in bone marrow (BM) from non-metastatic breast cancer patients before and after surgery.MethodsPatients with non-metastatic breast cancer were consecutively recruited to this project during the years 1998–2000. Real-time RT-PCR quantification of a DTC multimarker panel consisting of cytokeratin 19, mammaglobin A and TWIST1 mRNA was performed in BM samples obtained from 154 patients three weeks (BM2) and/or six months after surgery (BM3). The results were compared to previously published data from pre-operative BM analyses for the same patients.ResultsDTCs were identified in post-operative BM samples (BM2 and/or BM3) from 23 (15%) of the 154 patients investigated. During a median follow-up of 98 months, 10 (44%) of these patients experienced systemic relapse as compared to 16 (12%) of 131 DTC-negative patients. Kaplan-Meier estimates of systemic recurrence-free- and breast-cancer specific survival demonstrated significantly shorter survival for patients with persistent DTCs in BM after surgery (p≤0.001). By multivariate Cox regression analyses, persistent DTCs after surgery was an independent predictor of both systemic recurrence-free- (HR = 5.4, p < 0.001) and breast-cancer specific survival (HR = 5.3, p < 0.001). Furthermore, the prognostic value of DTCs in BM was similar for pre- and post surgery samples. However, patients with DTCs both before and after surgery (BM1 and BM2/3) had a particularly poor prognosis (systemic recurrence-free survival: HR = 7.2, p < 0.0001 and breast-cancer specific survival: HR = 8.0, p < 0.0001).ConclusionsDetection of persistent DTCs in BM samples obtained after surgery identified non-metastatic breast cancer patients at high risk for systemic relapse, and with reduced breast-cancer specific survival. Furthermore, patients with positive DTC status both before and after surgery had a particularly poor prognosis.

Advances in the use of multimarker panels for renal risk stratification

This review discusses novel studies in the past year that have examined the use of combinations of multiple markers to improve risk prediction in the setting of chronic kidney disease (CKD). We will focus on multimarker panels to: improve prediction of CKD onset; improve classification of CKD and risk stratification of persons with CKD; and develop individual-level risk scores for progression to end-stage renal disease (ESRD). One study reported that several novel circulation biomarkers may aid in predicting incident CKD and microalbuminuria. Second, our group has shown that the combination of creatinine, cystatin C, and albuminuria improves detection and risk stratification for death, heart failure, cardiovascular events, and ESRD compared with creatinine alone. Finally, a highly accurate individual risk score was developed to predict progression to ESRD using readily available clinical markers. The combination of multiple markers improves detection and risk stratification in CKD. Future research is needed in understanding the use of a 'renal panel' for detection, classification, and risk stratification in kidney disease in diverse populations. The studies presented here represent the beginning of a paradigm shift to multimarker panels in nephrology.

A New Improved Accelerated Diagnostic Protocol Safely Identifies Low‐risk Patients With Chest Pain in the Emergency Department

To assess whether the accelerated diagnostic protocol (ADP) studied in the Asia Pacific Evaluation of Chest Pain Trial (ASPECT) could be optimized to effectively risk stratify patients with symptoms suggestive of acute coronary syndrome (ACS) and allow early discharge of very-low-risk patients. Patients presenting to the emergency department (ED) with chest pain were prospectively enrolled between November 2007 and April 2010. Blood samples were analyzed at 0 and 2 hours postpresentation with a point-of-care multimarker panel (POC-MMP; troponin I [TnI], creatine kinase myocardial band [CKMB] isoenzyme fraction, and myoglobin) and a high-sensitivity cardiac troponin T assay (hsTnT). Patients received standard care. The original ADP (Thrombolysis in Myocardial Infarction [TIMI] risk score = 0, no ischemic electrocardiogram [ECG] changes, and the multimarker panel negative) was compared with an ADP using the point of care TnI only, hsTnT only, or TIMI risk score = 0 to 1. Primary outcome was ACS within 30 days. Of the 1,000 patients recruited, 362 (36.2%) had ACS. There were 12.3% identified as low risk by the original ADP with a sensitivity for ACS of 99.2% (95% confidence interval [CI] = 97.5% to 99.8%). The ADP with the point of care TnI only or hsTnT had the same sensitivity, but identified more patients for discharge (15.0% vs. 12.3%). Including patients with a TIMI risk score of 1 identified more patients as low risk (19.7%), but with a lower sensitivity (97.0% vs. 99.2%). An ADP consisting of a TIMI risk score of 0, no new ECG changes, and negative troponin at 0 and 2 hours postpresentation safely identifies patients at low risk of ACS, in whom discharge without further evaluation can be considered.

Circulating MicroRNA miR-323-3p as a Biomarker of Ectopic Pregnancy

The use of serum human chorionic gonadotropin (hCG) and progesterone to identify patients with ectopic pregnancy (EP) has been shown to have poor clinical utility. Pregnancy-associated circulating microRNAs (miRNAs) have been proposed as potential biomarkers for the diagnosis of pregnancy-associated complications. This proof-of-concept study examined the diagnostic accuracy of various miRNAs to detect EP in an emergency department (ED) setting. This study was a retrospective case-control analysis of 89 women who presented to the ED with vaginal bleeding and/or abdominal pain/cramping and received a diagnosis of viable intrauterine pregnancy (VIP), spontaneous abortion (SA), or EP. Serum hCG and progesterone concentrations were measured by immunoassays. The serum concentrations of miRNAs miR-323-3p, miR-517a, miR-519d, and miR-525-3p were measured with TaqMan real-time PCR. Statistical analysis was performed to determine the clinical utility of these biomarkers, both as single markers and as multimarker panels for EP. Concentrations of serum hCG, progesterone, miR-517a, miR-519d, and miR-525-3p were significantly lower in EP and SA cases than in VIP cases (P < 0.01). In contrast, the concentration of miR-323-3p was significantly increased in EP cases, compared with SA and VIP cases (P < 0.01). As a single marker, miR-323-3p had the highest sensitivity of 37.0% (at a fixed specificity of 90%). In comparison, the combined panel of hCG, progesterone, and miR-323-3p yielded the highest sensitivity (77.8%, at a fixed specificity of 90%). A stepwise analysis that used hCG first, added progesterone, and then added miR-323-3p yielded a 96.3% sensitivity and a 72.6% specificity. Pregnancy-associated miRNAs, especially miR-323-3p, added substantial diagnostic accuracy to a panel including hCG and progesterone for the diagnosis of EP.

Performance of Survivin mRNA as a Biomarker for Bladder Cancer in the Prospective Study UroScreen

BackgroundUrinary biomarkers have the potential to improve the early detection of bladder cancer. Most of the various known markers, however, have only been evaluated in studies with cross-sectional design. For proper validation a longitudinal design would be preferable. We used the prospective study UroScreen to evaluate survivin, a potential biomarker that has multiple functions in carcinogenesis.Methods/ResultsSurvivin was analyzed in 5,716 urine samples from 1,540 chemical workers previously exposed to aromatic amines. The workers participated in a surveillance program with yearly examinations between 2003 and 2010. RNA was extracted from urinary cells and survivin was determined by Real-Time PCR. During the study, 19 bladder tumors were detected. Multivariate generalized estimation equation (GEE) models showed that β-actin, representing RNA yield and quality, had the strongest influence on survivin positivity. Inflammation, hematuria and smoking did not confound the results. Survivin had a sensitivity of 21.1% for all and 36.4% for high-grade tumors. Specificity was 97.5%, the positive predictive value (PPV) 9.5%, and the negative predictive value (NPV) 99.0%.ConclusionsIn this prospective and so far largest study on survivin, the marker showed a good NPV and specificity but a low PPV and sensitivity. This was partly due to the low number of cases, which limits the validity of the results. Compliance, urine quality, problems with the assay, and mRNA stability influenced the performance of survivin. However, most issues could be addressed with a more reliable assay in the future. One important finding is that survivin was not influenced by confounders like inflammation and exhibited a relatively low number of false-positives. Therefore, despite the low sensitivity, survivin may still be considered as a component of a multimarker panel.

Abstract 4067: Integrated microfluidic bead-based immunosensor for multiplexed determinations of ovarian cancer biomarkers at the point-of-care

Abstract Early detection of ovarian cancer could significantly impact clinical outcome and hence patient survival. Given the biological heterogeneity of ovarian cancer, multi-marker panels will be required. Validation of such panels must utilize pre-clinical serum samples that are available only in small quantities. Diagnostic platforms that are capable of rapid, precise and multiplexed analysis of biomarkers in small quantities of serum should facilitate validation of optimal biomarker panels. Once validated, these platforms could permit rapid screening with finger-stick quantities of blood at point-of-care. The Programmable Bio-Nano-Chip (p-BNC) microfluidic immunosensor provides such a platform. In the p-BNC, serum biomarkers are sequestered and assessed with a fluorescence-based sandwich immunoassay, completed in nano-nets of sensitized agarose microbeads localized in individually addressable wells, housed in a microfluidic module, capable of integrating multiple sample, reagent and biowaste processing and handling steps. Previously, we adapted the p-BNC for the quantification of the CA125 biomarker with low LODs, high precision and a short analysis time of 43 minutes. Here, we adapt the p-BNC to simultaneously assess CA125 and HE4, a promising biomarker panel implicated in early detection. The HE4 immunoassay was developed to perform optimally with the previously established CA125 assay as the rate determining step. Extensive cross reactivity analysis ruled out non-ideal interactions between HE4 and CA125 reagents at the level of capturing antibody, detecting antibody and individual analytes. Wash times were optimized to maximize p-BNC performance. The dose-response curves obtained for the individual analytes (singleplex) were similar to those obtained with multiplex assays. Identical LOD values were noted for the singleplex and multiplex immunoassays with 1.5-fold decrease in precision for the multiplexed assays, demonstrating negligible loss of analytical performance upon multiplexing. For clinical validation, 8 surgically confirmed advanced stage patient sera and 8 age-matched healthy controls were assessed on the multiplexed p-BNC. ROC curve analysis for the CA125-HE4 biomarker combination, interpreted with a Risk of Ovarian Malignancy Algorithm was able to discriminate diseased samples from healthy controls with an AUC of 1.00. The p-BNC indicates strong promise for robust, multiplexed, quantitative measurements of ovarian cancer biomarkers, with high precision and sensitivity to yield clinically pertinent information under 45 minutes. Incorporation of additional biomarkers is underway to establish and validate an optimal panel for early detection of ovarian cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4067. doi:1538-7445.AM2012-4067

Abstract 021: Retinol Binding Protein-4 and Fetuin-A Predict Incident Metabolic Syndrome and Metabolic Tracking Prospectively in Younger Adults: A Multimarker Adipokine Panel Approach from the Framingham Study

Introduction: Recent investigations have highlighted the central role of mediators produced by adipose tissue in the pathogenesis of metabolic dysregulation. We evaluated a multimarker panel of 6 adipokines (adiponectin, leptin, leptin receptor, fetuin-A, adipocyte-fatty acid binding proteins, retinol binding protein-4 [RBP4]) and related them prospectively to the incidence of metabolic syndrome (MetS) and change in metabolic traits on follow-up in a community-based sample of younger adults. We hypothesized that these adipokines would be associated with incidence of MetS and with longitudinal tracking of its components. Methods and Results: We evaluated the adipokine panel measured in 2208 Framingham third generation cohort participants (55% women; mean age 40 years). On follow-up (mean 6 years), 253 individuals developed new-onset MetS. After adjustment for standard clinical risk factors, the multimarker adipokine panel was associated with incident MetS (P = 0.002). Using a backward elimination process, both RBP4 and fetuin-A were significantly associated with development of MetS (multivariable-adjusted OR per 1-SD increment log marker, 1.23 [P= 0.01] and 1.17 [P= 0.03], respectively). Using the group with both the RBP4 and fetuin-A under the median serving as the referent, individuals with levels of both RBP4 and fetuin-A above the median experienced an increased risk for developing MetS (multivariable adjusted OR = 1.7 [95% CI, 1.10 - 2.65], P = 0.01). In multivariable adjusted logistic regression models, both RBP4 and fetuin-A were significantly associated with an increase in systolic BP (P =0.004 and P = 0.045, respectively) and increased fasting glucose. In addition, RBP4 was significantly associated with longitudinal increase in diastolic BP (P = 0.007). Conclusions: Our findings, based on prospective follow-up of a large community-based sample of younger adults, suggest higher levels of RBP4 and fetuin-A are associated with incident MetS and may constitute potential future therapeutic targets.

Multimarker Analysis of Circulating Tumor Cells in Peripheral Blood of Metastatic Breast Cancer Patients: A Step Forward in Personalized Medicine

Aim: To develop an immunomagnetic assay for the isolation of circulating tumor cells (CTCs) followed by the analysis of a multimarker panel, which will enable the characterization of these malignant cells with high accuracy. Patients and Methods: Peripheral blood (PB) was collected from 32 metastatic breast cancer patients and 42 negative controls. The antibodies BM7 and VU1D9 were used for immunomagnetic tumor cell enrichment. A real-time reverse transcription-polymerase chain reaction (RT-PCR) approach for the markers KRT19, SCGB2A2, MUC1, EPCAM, BIRC5 and ERBB2 was used for CTC detection and characterization. Results: The positivity rates for each marker were as follows: 46.9% for KRT19, 25.0% for SCGB2A2, 28.1% for MUC1, 28.1% for EPCAM, 21.9% for BIRC5, and 15.6% for ERBB2. After the creation of individualized cutoffs, the sensitivity and specificity of the combined marker gene panel increased to 56.3% and 100%, respectively. Interestingly, 27.0% of the HER2-negative tumor patients showed ERBB2 mRNA-positive CTCs. Conclusions: The described technique can be used to measure CTCs with great accuracy. The use of a multimarker panel for the characterization of CTCs may provide real-time information and be of great value in therapy monitoring.