Efficiently identifying and quantifying disease- or treatment-related changes in the abundance of proteins is an important area of research for the pharmaceutical industry. Here we describe an automated, label-free method for finding differences in complex mixtures using complete LC-MS data sets, rather than subsets of extracted peaks or features. The method selectively finds statistically significant differences in the intensity of both high-abundance and low-abundance ions, accounting for the variability of measured intensities and the fact that true differences will persist in time. The method was used to compare two complex peptide mixtures with known peptide differences. This controlled experiment allowed us to assess the validity of each difference found and so to analyze the method's sensitivity and specificity. The method detects both presence versus absence and a 2-fold change in peptide concentration near the limit of detection of the instrument used, where chromatographic peaks may not be sufficiently well defined to be detected in individual samples. The method is more sensitive and gives fewer false positives than subtractive methods that ignore signal variability. Differential mass spectrometry combined with targeted MS/MS analysis of only identified differences may save both computation time and human effort compared to shotgun proteomics approaches.

To dissect common human diseases such as obesity and diabetes, a systematic approach is needed to study how genes interact with one another, and with genetic and environmental factors, to determine clinical end points or disease phenotypes. Bayesian networks provide a convenient framework for extracting relationships from noisy data and are frequently applied to large-scale data to derive causal relationships among variables of interest. Given the complexity of molecular networks underlying common human disease traits, and the fact that biological networks can change depending on environmental conditions and genetic factors, large datasets, generally involving multiple perturbations (experiments), are required to reconstruct and reliably extract information from these networks. With limited resources, the balance of coverage of multiple perturbations and multiple subjects in a single perturbation needs to be considered in the experimental design. Increasing the number of experiments, or the number of subjects in an experiment, is an expensive and time-consuming way to improve network reconstruction. Integrating multiple types of data from existing subjects might be more efficient. For example, it has recently been demonstrated that combining genotypic and gene expression data in a segregating population leads to improved network reconstruction, which in turn may lead to better predictions of the effects of experimental perturbations on any given gene. Here we simulate data based on networks reconstructed from biological data collected in a segregating mouse population and quantify the improvement in network reconstruction achieved using genotypic and gene expression data, compared with reconstruction using gene expression data alone. We demonstrate that networks reconstructed using the combined genotypic and gene expression data achieve a level of reconstruction accuracy that exceeds networks reconstructed from expression data alone, and that fewer subjects may be required to achieve this superior reconstruction accuracy. We conclude that this integrative genomics approach to reconstructing networks not only leads to more predictive network models, but also may save time and money by decreasing the amount of data that must be generated under any given condition of interest to construct predictive network models.

One of the key objectives of oncology first-in-human trials has often been to establish the maximum tolerated dose (MTD). However, targeted therapies might not exhibit doselimiting toxicities (DLT) at doses significantly higher than sufficiently active doses, and there is frequently a limited ability to objectively quantify adverse events. Thus, while MTD-based determination of recommended phase II dose may have yielded appropriate dosing for some cytotoxics, targeted therapeutics (including monoclonal antibodies and/or immunotherapies) sometimes need alternative or complementary strategies to help identify dose ranges for a randomized dose-ranging study. One complementary strategy is to define a biologically efficacious dose (BED) using an "effect marker." An effect marker could be a target engagement, pharmacodynamic, or disease progression marker (change in tumor size for solid tumors or bone marrow blast count for some hematologic tumors). Although the concept of BED has been discussed extensively, we review specific examples in which the approach influenced oncology clinical development. Data extracted from the literature and the examples support improving dose selection strategies to benefit patients, providers, and the biopharmaceutical industry. Although the examples illustrate key contributions of effect markers in dose selection, no one-size-fits-all approach to dosing can be justified. Higher-than-optimal dosing can increase toxicity in later trials (and in clinical use), which can have a negative impact on efficacy (via lower adherence or direct sequelae of toxicities). Proper dose selection in oncology should follow a multifactorial decision process leading to a randomized, dose-ranging study instead of a single phase II dose.

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Label-free LC-MS profiling is a powerful quantitative proteomic method to study relative peptide abundances between two or more biological samples. Here we demonstrate the use of a previously described comparative LC-MS method, differential mass spectrometry (dMS), to analyze high-resolution Fourier transform mass spectrometry (FTMS) data for detection and quantification of known peptide differences between two sets of complex mixtures. Six standard peptides were spiked into a processed plasma background at fixed ratios from 1.25:1 to 4:1 to make two sets of samples. The resulting mixtures were analyzed by microcapillary LC-FTMS and dMS. dMS successfully identified five out of the six peptides as statistically significant differences (p Յ 0.005). In this experiment, the smallest fold change reliably detected by our method was 1.5:1, and the errors of estimated ratios of concentrations were less than 20% for peptides spiked at 1.5:1 to 4:1. We conclude that LC-FTMS coupled with dMS is a useful label-free quantitative MS method that can be used to detect subtle yet statistically significant peptide differences in complex protein mixtures, including plasma samples. (J Am Soc Mass Spectrom 2007, 18, 226 -233)