Application of multiple statistical tests to enhance mass spectrometry-based biomarker discovery.

BACKGROUND: Mass spectrometry-based biomarker discovery has long been hampered by the difficulty in reconciling lists of discriminatory peaks identified by different laboratories for the same diseases studied. We describe a multi-statistical analysis procedure that combines several independent computational methods. This approach capitalizes on the strengths of each to analyze the same high-resolution mass spectral data set to discover consensus differential mass peaks that should be robust biomarkers for distinguishing between disease states. RESULTS: The proposed methodology was applied to a pilot narcolepsy study using logistic regression, hierarchical clustering, t-test, and CART. Consensus, differential mass peaks with high predictive power were identified across three of the four statistical platforms. Based on the diagnostic accuracy measures investigated, the performance of the consensus-peak model was a compromise between logistic regression and CART, which produced better models than hierarchical clustering and t-test. However, consensus peaks confer a higher level of confidence in their ability to distinguish between disease states since they do not represent peaks that are a result of biases to a particular statistical algorithm. Instead, they were selected as differential across differing data distribution assumptions, demonstrating their true discriminatory potential. CONCLUSION: The methodology described here is applicable to any high-resolution MALDI mass spectrometry-derived data set with minimal mass drift which is essential for peak-to-peak comparison studies. Four statistical approaches with differing data distribution assumptions were applied to the same raw data set to obtain consensus peaks that were found to be statistically differential between the two groups compared. These consensus peaks demonstrated high diagnostic accuracy when used to form a predictive model as evaluated by receiver operating characteristics curve analysis. They should demonstrate a higher discriminatory ability as they are not biased to a particular algorithm. Thus, they are prime candidates for downstream identification and validation efforts.

High-throughput evaluation of relative cell permeability between peptoids and peptides.

Peptides are limited in their use as drugs due to low cell permeability and vulnerability to proteases. In contrast, peptoids are immune to enzymatic degradation and some peptoids have been shown to be relatively cell permeable. In order to facilitate future design of peptoid libraries for screening experiments, it would be useful to have a high-throughput method to estimate the cell permeability of peptoids containing different residues. In this paper, we report the strengths and limitations of a high-throughput cell-based permeability assay that registers the relative ability of steroid-conjugated peptides and peptoids to enter a cell. A comparative investigation of the physicochemical properties and side chain composition of peptoids and peptides is described to explain the observed higher cell permeability of peptoids over peptides. These data suggest that the conversion of the monomeric residues in peptides to an N-alkylglycine moiety in peptoids reduced the hydrogen-bonding potential of the molecules and is the main contributor to the observed permeability improvement.