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Recent studies have established distinctive serum peptide patterns through mass spectrometry that 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 an optimized peptide extraction and a MALDI-TOF MS–based approach, we show that a limited subset of serum peptides provides accurate class discrimination between patients with three types of solid tumors (prostate, breast, and bladder) and control individuals without cancer. Sequence analysis of these peptides reveals that they fall into several 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 surrogate marker peptides provides highly accurate class prediction for an external validation set of prostate cancer samples. As direct MALDI-TOF MS–based serum peptide profiling is in fact activity-based proteomics, i.e., monitoring proteome metabolomic products, it is particularly well suited for detection of cancer, as proteases are well-established components of cancer progression and invasiveness. We have developed quantitative blood protease assays to that end, using synthetic peptide substrates and non-degradable reference peptides.