Differential exoprotease activities confer tumor-specific serum peptidome patterns
Josep Villanueva, … , Howard I. Scher, Paul Tempst
Josep Villanueva, … , Howard I. Scher, Paul Tempst
Published January 4, 2006
Citation Information: J Clin Invest. 2006;116(1):271-284. https://doi.org/10.1172/JCI26022.
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Research Article Oncology

Differential exoprotease activities confer tumor-specific serum peptidome patterns

Abstract

Recent studies have established distinctive serum polypeptide patterns through mass spectrometry (MS) that reportedly correlate with clinically relevant outcomes. Wider acceptance of these signatures as valid biomarkers for disease may follow sequence characterization of the components and elucidation of the mechanisms by which they are generated. Using a highly optimized peptide extraction and matrix-assisted laser desorption/ionization–time-of-flight (MALDI-TOF) MS–based approach, we now show that a limited subset of serum peptides (a signature) provides accurate class discrimination between patients with 3 types of solid tumors and controls without cancer. Targeted sequence identification of 61 signature peptides revealed that they fall into several tight clusters and that most are generated by exopeptidase activities that confer cancer type–specific differences superimposed on the proteolytic events of the ex vivo coagulation and complement degradation pathways. This small but robust set of marker peptides then enabled highly accurate class prediction for an external validation set of prostate cancer samples. In sum, this study provides a direct link between peptide marker profiles of disease and differential protease activity, and the patterns we describe may have clinical utility as surrogate markers for detection and classification of cancer. Our findings also have important implications for future peptide biomarker discovery efforts.

Authors

Josep Villanueva, David R. Shaffer, John Philip, Carlos A. Chaparro, Hediye Erdjument-Bromage, Adam B. Olshen, Martin Fleisher, Hans Lilja, Edi Brogi, Jeff Boyd, Marta Sanchez-Carbayo, Eric C. Holland, Carlos Cordon-Cardo, Howard I. Scher, Paul Tempst

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Figure 1

Unsupervised hierarchical clustering and principal component analysis of MS-based serum peptide profiling data derived from 3 groups of cancer patients and healthy controls.

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Unsupervised hierarchical clustering and principal component analysis of...
(A) Serum samples from healthy volunteers and patients with advanced prostate, bladder, and breast cancer were prepared following the standard protocol. The 4 groups were randomized before automated solid-phase peptide extraction and MALDI-TOF MS. Spectra were processed and aligned using the Qcealign script (see Supplemental Methods). A peak list containing normalized intensities of 651 m/z values for each of the 106 samples was generated. Numbers indicate the number of patients and controls analyzed in the respective groups. (B) Unsupervised, average-linkage hierarchical clustering using standard correlation as a distance metrics between each cancer group and the control in binary format. The entire peak list (651 × 106) was used. Columns represent samples; rows are m/z peaks (i.e., peptides). Dendrogram colors follow the color coding scheme of A. The heat map scale of normalized ion intensities is from 0 (green) to 200 (red) with the midpoint at 100 (yellow). (C) Hierarchical clustering of the 3 cancer groups plus controls (as in B). (D) Principal component analysis (PCA) of the 3 cancer groups plus controls. Color coding is as in A. The first 3 principal components, which account for most of the variance in the original data set, are shown.