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.

Human serum contains a complex array of proteolytically derived peptides (serum peptidome) that may provide a correlate of biological events occurring in the entire organism; for instance, as a diagnostic for solid tumors (Petricoin, E. F.; Ardekani, A. M.; Hitt, B. A.; Levine, P. J.; Fusaro, V. A.; Steinberg, S. M.; Mills, G. B.; Simone, C.; Fishman, D. A.; Kohn, E. C.; Liotta, L. Lancet 2002, 359, 572-577). Here, we describe a novel, automated technology platform for the simultaneous measurement of serum peptides that is simple, scalable, and generates highly reproducible patterns. Peptides are captured and concentrated using reversed-phase (RP) batch processing in a magnetic particle-based format, automated on a liquid handling robot, and followed by a MALDI TOF mass spectrometric readout. The protocol is based on a detailed investigation of serum handling, RP ligand and eluant selection, small-volume robotics design, an optimized spectral acquisition program, and consistent peak extraction plus binning across a study set. The improved sensitivity and resolution allowed detection of 400 polypeptides (0.8-15-kDa range) in a single droplet (approximately 50 microL) of serum, and almost 2000 unique peptides in larger sample sets, which can then be analyzed using common microarray data analysis software. A pilot study indicated that sera from brain tumor patients can be distinguished from controls based on a pattern of 274 peptide masses. This, in turn, served to create a learning algorithm that correctly predicted 96.4% of the samples as either normal or diseased.

Abstract'Molecular signatures' are the qualitative and quantitative patterns of groups of biomolecules (e.g., mRNA, proteins, peptides, or metabolites) in a cell, tissue, biological fluid, or an entire organism. To apply this concept to biomarker discovery, the measurements should ideally be noninvasive and performed in a single read-out. We have therefore developed a peptidomics platform that couples magnetics-based, automated solid-phase extraction of small peptides with a high-resolution MALDI-TOF mass spectrometric readout (Villanueva, J.; Philip, J.; Entenberg, D.; Chaparro, C. A.; Tanwar, M. K.; Holland, E. C.; Tempst, P. Anal. Chem. 2004Chem. , 76, 1560Chem. -1570. Since hundreds of peptides can be detected in microliter volumes of serum, it allows to search for disease signatures, for instance in the presence of cancer. We have now evaluated, optimized, and standardized a number of clinical and analytical chemistry variables that are major sources of bias; ranging from blood collection and clotting, to serum storage and handling, automated peptide extraction, crystallization, spectral acquisition, and signal processing. In addition, proper alignment of spectra and user-friendly visualization tools are essential for meaningful, certifiable data mining. We introduce a minimal entropy algorithm, 'Entropycal', that simplifies alignment and subsequent statistical analysis and increases the percentage of the highly distinguishing spectral information being retained after feature selection of the datasets. Using the improved analytical platform and tools, and a commercial statistics program, we found that sera from thyroid cancer patients can be distinguished from healthy controls based on an array of 98 discriminant peptides. With adequate technological and computational methods in place, and using rigorously standardized conditions, potential sources of patient related bias (e.g., gender, age, genetics, environmental, dietary, and other factors) may now be addressed.

We previously identified a polymorphism in the human estrogen receptor (ER) gene, within the coding region for the protein's amino terminal B-domain. In estrogen receptor-positive (ER+) breast tumors, the variant allele was preferentially associated with lower levels of ER, and was clinically correlated with frequent spontaneous abortions. DNA sequencing revealed a point mutation that changes codon 86 from Ala to Val and a silent mutation in codon 87. Because we initially detected the variant allele by analyzing RNA, only those tissues in which the ER gene is actively expressed were suitable for genotype analysis. We now describe an assay that uses genomic DNA as the substrate for determining the ER B genotype, DNA containing the polymorphic region of the ER gene is amplified by the polymerase chain reaction, then the amplified DNA is hybridized with radiolabeled oligonucleotide probes complementary to the wild type and variant ER alleles. This method allowed us to determine the ER B genotype of women with ER+ and ER- tumors, starting with minute amounts of DNA from frozen or paraffin embedded tissues. ER B genotyping was also performed on women without breast cancer using DNA extracted from blood cells. The combined results from analyses of RNA and DNA from 300 breast cancer patients showed that 12% were heterozygotes. In the ER+ group (n = 183), 11.5% carried the variant gene compared to 12.8% in the ER-negative group (n = 117) (chi 2 = 0.11; df = 1; p greater than 0.25).(ABSTRACT TRUNCATED AT 250 WORDS)

We examined the relation between spontaneous abortion and polymorphisms in two genes, glutathione S-transferase (GSTM1) and N-acetyltransferase (NAT2), which are involved in the metabolism of xenobiotics. In a case-control study of 29 women, we found that, among women with the GSTM1 null genotype, the odds ratio (OR) was 3.1 [95% confidence interval (CI) = 1.3-7.0]. There was less evidence of a relation with NAT2 [Mantel-Haenszel adjusted OR (ORMH) = 1.4; 95% CI = 0.45-4.3]. We sought to replicate the GSTM1 finding in an independent case-control study from New York involving 89 cases. We found an inverse association (OR = 0.8; 95% CI = 0.4-2.4). Taken together, these data provide little evidence of an association between GSTM1 or NAT2 genotype and risk of spontaneous abortion.