Correlation network analysis for data integration and biomarker selection.

High-throughput biomolecular profiling techniques such as transcriptomics, proteomics and metabolomics are increasingly being used in in vivo studies to recognize and characterize effects of xenobiotics on organs and systems. Of particular interest are biomarkers of treatment-related effects which are detectable in easily accessible biological fluids such as blood. A fundamental challenge in such biomarker studies is selecting among the plethora of biomolecular changes induced by a compound and revealed by molecular profiling, to identify biomarkers which are exclusively or predominantly due to specific processes. In this work we present a cross-compartment correlation network approach, involving no a priori supervision or design, to integrate proteomic, metabolomic and transcriptomic data for selecting circulating biomarkers. The case study we present is the identification of biomarkers of drug-induced hepatic toxicity effects in a rodent model. Biomolecular profiling of both blood plasma and liver tissue from Wistar Hannover rats administered a toxic compound yielded many hundreds of statistically significant molecular changes. We exploited drug-induced correlations between blood plasma analytes and liver tissue molecules across study animals in order to nominate selected plasma molecules as biomarkers of drug-induced hepatic alterations of lipid metabolism and urea cycle processes.

Quantitative in situ cancer proteomics: molecular pathology comes of age with automated tissue microarray analysis.

Tissue microarrays provide a high-throughput method for assessing a large number of samples by incorporating small cores of tissue into an array that can fit onto one microscope slide. Analyses of tissue microarrays were previously limited by semiquantitative protein expression analysis using brown stain (chromagen-based) methods. These methods are imperfect for protein expression analyses because of a smaller dynamic range and decreased ability for multiplexing many markers, as compared with objective in situ quantitation of protein expression in tumor samples with fluorescence microscopy by a new technology called Automated Quantitative Analysis (AQUA™). By using AQUA analysis, tissue microarrays can serve a unique role as both a discovery tool and as a validation tool for nucleic-acid expression profiling-based target discoveries with results equivalent to enzyme-linked immunosorbent assay quantitation. The identification of novel prognostic markers can identify subsets of patients at high or low risk upon diagnosis, as well as new targets for potential future therapeutic development or metastatic disease treatment decisions. Thus, AQUA provides an unparalleled opportunity to advance personalized medicine through its ability to multiplex, quantitate and localize in situ protein expression.

The role of analytical sciences in medical systems biology.

Medical systems biology has generated widespread interest because of its bold conception and exciting potential, but the field is still in its infancy. Although there has been tremendous progress achieved recently in generating, integrating and analysing data in the medical and pharmaceutical field, many challenges remain, especially with respect to the crucial core technologies required for analytical characterization. This review briefly summarizes these aspects for metabolomics, proteomics, data handling and multivariate biostatistics.