Introduction

Recent advances in biological sample preparation, automated sample handling, and sensitive mass spectrometry with a variety of sample ionization sources have catalyzed the emergence of these methods as tools for clinical proteomic analyses [1,2]. Concurrently, proteomic data analysis has been aided by the expansion and improvement of publicly available protein and gene databases. Among the methods reported for quantitative comparison of proteins of biological origin, several techniques based on liquid-phase separation of proteins and peptides rather than gel electrophoresis have been optimized and implemented within the last few years. These include stable isotope labeling as well as label-free methods [3-9] . Direct measurement and comparison of unlabeled peptide ion peak intensities has become the standard, particularly for high-throughput applications. Quantitative, label-free mass spectrometry-based methods for protein expression profiling include those based on pattern recognition -10,11] . peptide counting [12-14] . and peptide ion intensity or area -4,5,15-20] . Of these, the latter seems to be the most robust and applicable to clinical studies. The correlation between observed peptide intensity differences and protein abundance differences is the fundamental basis of label-free protein expression profiling. In particular, the rela-

Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Malle Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

tionship between liquid chromatography-mass spectrometry (LC-MS) signals and peptide abundance has been shown to vary linearly with concentration, even in complex samples [5,21-23].

Mass spectrometry-based protein expression profiling, without the use of labels or internal standards, is applicable to a wide range of sample types, including tissues, cells, organelles, and organisms, as well as body fluids such as plasma, cerebrospinal fluid, and urine. It has the additional advantage that comparisons can be made post hoc on a per patient basis, thereby dramatically increasing the granularity of information that can be obtained from the data. Multiple approaches can be taken for the label-free identification of differentially expressed proteins in large sample sets. At Caprion we have developed a specific approach, called CellCarta. which has proven to be very effective and reliable. Features of this platform will be used as an example in describing issues and solutions for biomarker discovery.

The platform has low overall variability [<15% coefficient of variation (CV) in the absence of biological variability] and provides an accurate and quantitative measure of the biological modulation of protein abundance. Thus, statistically significant differences in plasma protein abundance can be detected, making this technology highly applicable to biomarker discovery in clinical samples. Plasma markers of disease, drug reversion of disease, predictive markers of drug response, pharmacodynamic markers of drug action, and early markers of both efficacy and toxicity can be detected using this technology. Studies may be conducted in animal models (preclinical) as well as with human clinical samples [24-26].

One requirement for a useful biomarker is that it can be measured in an easily accessible body fluid. In this regard, blood (serum or plasma) is preferred, and it is used routinely for many clinical assays [27] . However, the discovery of biomarkers in blood brings some technical challenges. Plasma has been estimated to contain anywhere from several hundred to several thousand proteins [28,29], whose concentrations are known to vary by up to 10 orders of magnitude. Many of the proteins with desirable biomarker characteristics are expected to be at the low end of the plasma protein dynamic range of concentration, typically at or below 100ng/mL. Two approaches to this issue are described here. One approach depends on the immunoaffinity - based depletion of some of the most abundant plasma proteins, thus making it possible to see more of the lower-abundance proteins. The second approach is based on identifying and comparing plasma proteins just prior to release from their cells or tissue of origin. While residing in the secretory apparatus, proteins destined for the blood are highly concentrated, thus allowing for a comprehensive picture of tissue - specific markers.

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Project Management Made Easy

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