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.