Consistency of a two clinical site sample collection: a proteomics study.
OBJECTIVE
We investigated the ability to perform a clinical proteomic study using samples collected at different times from two independent clinical sites.


METHODS
Label-free 2-D-LC-MS proteomic analysis was used to differentially quantify tens of thousands of peptides from human plasma. We have asked whether samples collected from two sites, when analyzed by this type of peptide profiling, reproducibly contain detectable peptide markers that are differentially expressed in the plasma of disease (advanced renal cancer) patients relative to healthy normals.


RESULTS
We have demonstrated that plasma proteins enriched in disease patients are indeed detected reproducibly in both clinical collections. Regression analysis, unsupervised hierarchical clustering and PCA detected no systematic bias in the data related to site of sample collection and processing. Using a genetic algorithm, support vector machine classification method, we were able to correctly classify disease samples at 88% sensitivity and 94% specificity using the second site as an independent validation set.


CONCLUSIONS
We conclude that multiple site collection, when analyzed by label-free 2-D-LC-MS, generates data that are sufficiently reproducible to guide reliable biomarker discovery.
Facet Biotech, Redwood City, CA, USA.
Cedric
C
Wiesner
Charles
C
Hannum
Karen
K
Reckamp
Robert
R
Figlin
Robert
R
Dubridge
Sushmita Mimi
SM
Roy
Shanhua
S
Lin
Christopher H
CH
Becker
Ted
T
Jones
Jordan
J
Hiller
John C
JC
Cheville
Keith
K
Wilson
D015415:Biomarkers / Q000032:analysis* / Q000378:metabolism; D001798:Blood Proteins / Q000032:analysis* / Q000378:metabolism; D002853:Chromatography, Liquid / Q000379:methods; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods; D025341:Principal Component Analysis; D040901:Proteomics / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013048:Specimen Handling / Q000379:methods*; D013997:Time Factors
D015415:Biomarkers; D001798:Blood Proteins