Precision: The Key to Proteomic / Peptidomic Pattern Recognition

Review of "Correcting Common Errors in Identifying Cancer-Specific Serum Peptide Signature"

Journal of Proteome Research 2005, 4, 1060- 1072
Josep Villanueva, John Philip, Carlos A. Chaparro, Yongbiao Li, Ricardo Toledo-Crow, Lin DeNoyer, Martin Fleisher, Richard J. Robbins, and Paul Tempst

While describing early proteomic pattern recognition papers, the authors stated: “If these early results are as robust and reproducible as they seem, then serum proteomics will undoubtedly attain a prominent and lasting position in the future of cancer diagnostics. Despite initial excitement, skepticism about the methodology and the results is mounting in the scientific community.” In response to this skepticism, this paper addresses a very important part of the application of pattern recognition to proteomics or “peptidomics”, namely the clinical and analytical chemistry variables. I feel the skepticism surrounding the early proteomics pattern recognition work is valid and thus the topic of this paper very important. The clinical and analytical chemistry variables addressed in this paper can be major sources of bias in pattern recogntion. It is important to realize how the everything from blood collection and clotting, to serum storage and handling, automated peptide extraction, crystallization, spectral acquisition, and signal processing affect the measurement. This paper includes a clearly written table and diagram that illustrate the protocol for serum peptide sample preparation from the blood draw to the MALDI plate. This paper also includes a rather detailed recipe for data analysis, including smoothing, baseline correction, normalization, calibration/alignment, and peak labeling.

“In sum, any systematic bias in serum preparation and/or storage between two or more groups of samples can result in a statistically relevant, yet clinically useless diagnostic tool.”

An experiment where the effect of clotting at room temperature for 5 min, 1 h and 5 h are compared is included. In this experiment, some intensity diminished while others increased as clotting time increased. This implies a degradation of the plasma peptides. Also, the effect of freeze-thaw cycles on serum peptide profiling using RP magnetic particles and MALDI TOF MS was shown to be dramatic.

The authors illustrated results that should be of great concern to anyone using bead based RP extraction of serum. In their study different batches of the same extraction media from the same manufacture gave dramatically different results. The change was so pronounced that I personally would avoid bead based extraction, although the authors did defend this method.

One criticism of this paper is that after a very detailed look at sample handling, instrument operation and signal preprocessing they described the final result of patter recognition with terms like “A fairly good, but not perfect, segregation.” I wish that they had taken the work that extra mile and reported quantitative results like sensitivity, specificity or accuracy.

The authors created most of their data analysis software in MATLAB. Although they brand these routines, they do not include information on how one might obtain most of the routines. Multivariate analysis like ANOVA, PCA, hierarchical clustering, K-NN and SVMs was done using GeneSpring (Agilent; Palo Alto, CA).

The vocabulary word for today is sera. Sera is plural for serum.