Application of an end-to-end biomarker discovery platform to identify target engagement markers in cerebrospinal fluid by high resolution differential mass spectrometry.
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.
Paweletz
Cloud P
CP
Proteomics, Merck Research Laboratories, 33 Louis Pasteur Avenue, Boston, Massachusetts 02115, USA.
Wiener
Matthew C
MC
Bondarenko
Andrey Y
AY
Yates
Nathan A
NA
Song
Qinghua
Q
Liaw
Andy
A
Lee
Anita Y H
AY
Hunt
Brandon T
BT
Henle
Ernst S
ES
Meng
Fanyu
F
Sleph
Holly Funk
HF
Holahan
Marie
M
Sankaranarayanan
Sethu
S
Simon
Adam J
AJ
Settlage
Robert E
RE
Sachs
Jeffrey R
JR
Shearman
Mark
M
Sachs
Alan B
AB
Cook
Jacquelynn J
JJ
Hendrickson
Ronald C
RC
D000465:Algorithms; D000595:Amino Acid Sequence; D053829:Amyloid Precursor Protein Secretases / Q000037:antagonists & inhibitors / Q000378:metabolism; D016229:Amyloid beta-Peptides / Q000032:analysis / Q000378:metabolism; D000704:Analysis of Variance; D000818:Animals; D019540:Area Under Curve; D015415:Biomarkers / Q000134:cerebrospinal fluid; D002556:Cerebrospinal Fluid Proteins / Q000032:analysis* / Q000378:metabolism; D008253:Macaca mulatta; D013058:Mass Spectrometry / Q000379:methods*; D008969:Molecular Sequence Data; D009842:Oligopeptides / Q000493:pharmacokinetics; D010446:Peptide Fragments / Q000032:analysis / Q000378:metabolism; D040901:Proteomics / Q000379:methods*
D016229:Amyloid beta-Peptides; D015415:Biomarkers; D002556:Cerebrospinal Fluid Proteins; D009842:Oligopeptides; D010446:Peptide Fragments; C075222:amyloid beta-protein (1-42); C523462:benzyloxycarbonyl-leucyl-leucyl-norleucinal; D053829:Amyloid Precursor Protein Secretases