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The rapid identification of protein biomarkers in biofluids is important to drug discovery and development. Here, we describe a general proteomic approach for the discovery and identification of proteins that exhibit a statistically significant difference in abundance in cerebrospinal fluid (CSF) before and after pharmacological intervention. This approach, differential mass spectrometry (dMS), is based on the analysis of full scan mass spectrometry data. The dMS workflow does not require complex mixing and pooling strategies, or isotope labeling techniques. Accordingly, clinical samples can be analyzed individually, allowing the use of longitudinal designs and within-subject data analysis in which each subject acts as its own control. As a proof of concept, we performed multifactorial dMS analyses on CSF samples drawn at 6 time points from n = 6 cisterna magna ported (CMP) rhesus monkeys treated with 2 potent gamma secretase inhibitors (GSI) or comparable vehicle in a 3-way crossover study that included a total of 108 individual CSF samples. Using analysis of variance and statistical filtering on the aligned and normalized LC-MS data sets, we detected 26 features that were significantly altered in CSF by drug treatment. Of those 26 features, which belong to 10 distinct isotopic distributions, 20 were identified by MS/MS as 7 peptides from CD99, a cell surface protein. Six features from the remaining 3 isotopic distributions were not identified. A subsequent analysis showed that the relative abundance of these 26 features showed the same temporal profile as the ELISA measured levels of CSF A beta 42 peptide, a known pharmacodynamic marker for gamma-secretase inhibition. These data demonstrate that dMS is a promising approach for the discovery, quantification, and identification of candidate target engagement biomarkers in CSF.

Inhibitors of class 1 and class 2 histone deacetylase (HDAC) enzymes have shown antitumor activity in human clinical trials. More recently, there has been interest in developing subtype-selective HDAC inhibitors designed to retain anticancer activity while reducing potential side effects. Efforts have been initiated to selectively target HDAC1 given its role in tumor proliferation and survival. The development of HDAC1-specific inhibitors will require the identification of HDAC1-selective pharmacodynamic markers that correlate closely with HDAC1-inhibition in vitro and in vivo. Existing histone markers of HDAC target engagement were developed using pan-HDAC inhibitors and do not necessarily represent robust readouts for isoform-specific inhibitors. Therefore, we have initiated a proteomic approach to identify readouts for HDAC1 inhibition. This approach involves the use of differential mass spectrometry (dMS) to identify post-translational changes in histones by profiling histone-enriched cellular fractions treated with various HDAC inhibitors. In this study, we profiled histones isolated from the HCT116 human colon cancer cell line that have been treated with compounds from multiple chemical classes that are specific for HDAC1; HDAC1 and 3; and HDAC1, 3, and 6 enzymes. In two independent experiments, we identified 24 features that correlated with HDAC1-inhibition. Among the peptides modulated by HDAC1-selective inhibitors were Ac-H2B-K5 from histone H2B, and Ac-H3-K18 from histone H3. Commercially available antibodies to specific histone acetyl-lysine residues were used to confirm that these peptides also provide pharmacodynamic readouts for HDAC1-selective inhibitors in vivo and in vitro. These results show the utility of dMS in guiding the identification of specific readouts to aid in the development of HDAC-selective inhibitors.

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 (por= 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.