Multiplex assays for biomarker research and clinical application: translational science coming of age

Over the last decade, translational science has come into the focus of academic medicine, and significant intellectual and financial efforts have been made to initiate a multitude of bench-to-bedside projects. The quest for suitable biomarkers that will significantly change clinical practice has become one of the biggest challenges in translational medicine. Quantitative measurement of proteins is a critical step in biomarker discovery. Assessing a large number of potential protein biomarkers in a statistically significant number of samples and controls still constitutes a major technical hurdle. Multiplexed analysis offers significant advantages regarding time, reagent cost, sample requirements and the amount of data that can be generated. The two contemporary approaches in multiplexed and quantitative biomarker validation, antibody-based immunoassays and MS-based multiple (or selected) reaction monitoring, are based on different assay principles and instrument requirements. Both approaches have their own advantages and disadvantages and therefore have complementary roles in the multi-staged biomarker verification and validation process. In this review, we discuss quantitative immunoassay and multiple reaction monitoring/selected reaction monitoring assay principles and development. We also discuss choosing an appropriate platform, judging the performance of assays, obtaining reliable, quantitative results for translational research and clinical applications in the biomarker field.

Saliva/pathogen biomarker signatures and periodontal disease progression

The purpose of this study was to determine the role of saliva-derived biomarkers and periodontal pathogens during periodontal disease progression (PDP). One hundred human participants were recruited into a 12-month investigation. They were seen bi-monthly for saliva and clinical measures and bi-annually for subtraction radiography, serum and plaque biofilm assessments. Saliva and serum were analyzed with protein arrays for 14 pro-inflammatory and bone turnover markers, while qPCR was used for detection of biofilm. A hierarchical clustering algorithm was used to group study participants based on clinical, microbiological, salivary/serum biomarkers, and PDP. Eighty-three individuals completed the six-month monitoring phase, with 39 [corrected] exhibiting PDP, while 44 [corrected] demonstrated stability. Participants assembled into three clusters based on periodontal pathogens, serum and salivary biomarkers. Cluster 1 members displayed high salivary biomarkers and biofilm; 71% [corrected] of these individuals were undergoing PDP. Cluster 2 members displayed low biofilm and biomarker levels; 76% [corrected] of these individuals were stable. Cluster 3 members were not discriminated by PDP status; however, cluster stratification followed groups 1 and 2 based on thresholds of salivary biomarkers and biofilm pathogens. The association of cluster membership to PDP was highly significant (p < 0.0007). [corrected] The use of salivary and biofilm biomarkers offers potential for the identification of PDP or stability (ClinicalTrials.gov number, CT00277745).

Phosphatidylethanol (PEth) as a Biomarker of Alcohol Consumption in HIV-Positive Patients in Sub-Saharan Africa

Background:  Alcohol is heavily consumed in sub-Saharan Africa and affects HIV transmission and treatment and is difficult to measure. Our goal was to examine the test characteristics of a direct metabolite of alcohol consumption, phosphatidylethanol (PEth). Methods:  Persons infected with HIV were recruited from a large HIV clinic in southwestern Uganda. We conducted surveys and breath alcohol concentration (BRAC) testing at 21 daily home or drinking establishment visits, and blood was collected on day 21 (n = 77). PEth in whole blood was compared with prior 7-, 14-, and 21-day alcohol consumption. Results:  (i) The receiver operator characteristic area under the curve (ROC-AUC) was highest for PEth versus any consumption over the prior 21 days (0.92; 95% confidence interval [CI]: 0.86 to 0.97). The sensitivity for any detectable PEth was 88.0% (95% CI: 76.0 to 95.6) and the specificity was 88.5% (95% CI: 69.8 to 97.6). (ii) The ROC-AUC of PEth versus any 21-day alcohol consumption did not vary with age, body mass index, CD4 cell count, hepatitis B virus infection, and antiretroviral therapy status, but was higher for men compared with women (p = 0.03). (iii) PEth measurements were correlated with several measures of alcohol consumption, including number of drinking days in the prior 21 days (Spearman r = 0.74, p < 0.001) and BRAC (r = 0.75, p < 0.001). Conclusions:  The data add support to the body of evidence for PEth as a useful marker of alcohol consumption with high ROC-AUC, sensitivity, and specificity. Future studies should further address the period and level of alcohol consumption for which PEth is detectable.

Two patterns of adipokine and other biomarker dynamics in a long-term weight loss intervention

Long-term dietary intervention frequently induces a rapid weight decline followed by weight stabilization/regain. Here, we sought to identify adipokine biomarkers that may reflect continued beneficial effects of dieting despite partial weight regain.

Radiation metabolomics. 4. UPLC-ESI-QTOFMS-Based metabolomics for urinary biomarker discovery in gamma-irradiated rats

Radiation metabolomics has aided in the identification of a number of biomarkers in cells and mice by ultra-performance liquid chromatography-coupled time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) and in rats by gas chromatography-coupled mass spectrometry (GCMS). These markers have been shown to be both dose- and time-dependent. Here UPLC-ESI-QTOFMS was used to analyze rat urine samples taken from 12 rats over 7 days; they were either sham-irradiated or γ-irradiated with 3 Gy after 4 days of metabolic cage acclimatization. Using multivariate data analysis, nine urinary biomarkers of γ radiation in rats were identified, including a novel mammalian metabolite, N-acetyltaurine. These upregulated urinary biomarkers were confirmed through tandem mass spectrometry and comparisons with authentic standards. They include thymidine, 2'-deoxyuridine, 2'deoxyxanthosine, N(1)-acetylspermidine, N-acetylglucosamine/galactosamine-6-sulfate, N-acetyltaurine, N-hexanoylglycine, taurine and, tentatively, isethionic acid. Of these metabolites, 2'-deoxyuridine and thymidine were previously identified in the rat by GCMS (observed as uridine and thymine) and in the mouse by UPLC-ESI-QTOFMS. 2'Deoxyxanthosine, taurine and N-hexanoylglycine were also seen in the mouse by UPLC-ESI-QTOFMS. These are now unequivocal cross-species biomarkers for ionizing radiation exposure. Downregulated biomarkers were shown to be related to food deprivation and starvation mechanisms. The UPLC-ESI-QTOFMS approach has aided in the advance for finding common biomarkers of ionizing radiation exposure.

Reverse-translational biomarker validation of Abnormal Repetitive Behaviors in mice: an illustration of the 4P's modeling approach

The NIMH's new strategic plan, with its emphasis on the "4P's" (Prediction, Pre-emption, Personalization, and Populations) and biomarker-based medicine requires a radical shift in animal modeling methodology. In particular 4P's models will be non-determinant (i.e. disease severity will depend on secondary environmental and genetic factors); and validated by reverse-translation of animal homologues to human biomarkers. A powerful consequence of the biomarker approach is that different closely related disorders have a unique fingerprint of biomarkers. Animals can be validated as a highly specific model of a single disorder by matching this 'fingerprint'; or as a model of a symptom seen in multiple disorders by matching common biomarkers. Here we illustrate this approach with two Abnormal Repetitive Behaviors (ARBs) in mice: stereotypies and barbering (hair pulling). We developed animal versions of the neuropsychological biomarkers that distinguish human ARBs, and tested the fingerprint of the different mouse ARBs. As predicted, the two mouse ARBs were associated with different biomarkers. Both barbering and stereotypy could be discounted as models of OCD (even though they are widely used as such), due to the absence of limbic biomarkers which are characteristic of OCD and hence are necessary for a valid model. Conversely barbering matched the fingerprint of trichotillomania (i.e. selective deficits in set-shifting), suggesting it may be a highly specific model of this disorder. In contrast stereotypies were correlated only with a biomarker (deficits in response shifting) correlated with stereotypies in multiple disorders, suggesting that animal stereotypies model stereotypies in multiple disorders.

Design, conduct, and analysis of a multicenter, pharmacogenomic, biomarker study in matched patients with severe sepsis treated with or without drotrecogin Alfa (activated)

A genomic biomarker identifying patients likely to benefit from drotrecogin alfa (activated) (DAA) may be clinically useful as a companion diagnostic. This trial was designed to validate biomarkers (improved response polymorphisms (IRPs)). Each IRP (A and B) contains two single nucleotide polymorphisms that were associated with a differential DAA treatment effect.

A Data-Analytic Strategy for Protein-Biomarker Discovery: Profiling of High-Dimensional Proteomic Data for Cancer Detection

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of "signature" protein profiles specific to each pathologic state (e.g., normal vs. cancer) or differential profiles between experimental conditions (e.g., treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data analytic strategy for discovering protein biomarkers based on such high-dimensional mass-spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data analytic strategy takes properties of the SELDI mass-spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After these pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Radiation metabolomics. 3. Biomarker discovery in the urine of gamma-irradiated rats using a simplified metabolomics protocol of gas chromatography-mass spectrometry combined with random forests machine learning algorithm

Abstract Radiation metabolomics employing mass spectral technologies represents a plausible means of high-throughput minimally invasive radiation biodosimetry. A simplified metabolomics protocol is described that employs ubiquitous gas chromatography-mass spectrometry and open source software including random forests machine learning algorithm to uncover latent biomarkers of 3 Gy gamma radiation in rats. Urine was collected from six male Wistar rats and six sham-irradiated controls for 7 days, 4 prior to irradiation and 3 after irradiation. Water and food consumption, urine volume, body weight, and sodium, potassium, calcium, chloride, phosphate and urea excretion showed major effects from exposure to gamma radiation. The metabolomics protocol uncovered several urinary metabolites that were significantly up-regulated (glyoxylate, threonate, thymine, uracil, p-cresol) and down-regulated (citrate, 2-oxoglutarate, adipate, pimelate, suberate, azelaate) as a result of radiation exposure. Thymine and uracil were shown to derive largely from thymidine and 2'-deoxyuridine, which are known radiation biomarkers in the mouse. The radiation metabolomic phenotype in rats appeared to derive from oxidative stress and effects on kidney function. Gas chromatography-mass spectrometry is a promising platform on which to develop the field of radiation metabolomics further and to assist in the design of instrumentation for use in detecting biological consequences of environmental radiation release.

Pathogenic infection confounds induction of the estrogenic biomarker vitellogenin in rainbow trout

To examine the behavior of the estrogenic biomarker vitellogenin (VTG) under the combined impact of estrogens and pathogens, parasite-infected or noninfected rainbow trout were exposed to two doses of 17beta-estradiol (E2). Infected and E2-exposed fish showed significantly lower hepatic VTG mRNA levels than healthy fish. Transcriptome data suggest that this was due to energetic constraints. Reduced responsiveness of the VTG biomarker in parasitized fish might obscure detection of low-level field exposure.

Annexin A1 is a biomarker of T-tubular repair in skeletal muscle of nonmyopathic patients undergoing statin therapy

Skeletal muscle complaints are a common consequence of cholesterol-lowering therapy. Transverse tubular (T-tubular) vacuolations occur in patients having statin-associated myopathy and, to a lesser extent, in statin-treated patients without myopathy. We have investigated quantitative changes in T-tubular morphology and looked for early indicators of T-tubular membrane repair in skeletal muscle biopsy samples from patients receiving cholesterol-lowering therapy who do not have myopathic side effects. Gene expression and protein levels of incipient membrane repair proteins were monitored in patients who tolerated statin treatment without myopathy and in statin-naive subjects. In addition, morphometry of the T-tubular system was performed. Only the gene expression for annexin A1 was up-regulated, whereas the expression of other repair genes remained unchanged. However, annexin A1 and dysferlin protein levels were significantly increased. In statin-treated patients, the volume fraction of the T-tubular system was significantly increased, but the volume fraction of the sarcoplasmic reticulum remained unchanged. A complex surface structure in combination with high mechanical loads makes skeletal muscle plasma membranes susceptible to injury. Ca(2+)-dependent membrane repair proteins such as dysferlin and annexin A1 are deployed at T-tubular sites. The up-regulation of annexin A1 gene expression and protein points to this protein as a biomarker for T-tubular repair.