Mass spectrometry-based serum proteome pattern analysis in molecular diagnostics of early stage breast cancer

Abstract

BackgroundMass spectrometric analysis of the blood proteome is an emerging method of clinical proteomics. The approach exploiting multi-protein/peptide sets (fingerprints) detected by mass spectrometry that reflect overall features of a specimen's proteome, termed proteome pattern analysis, have been already shown in several studies to have applicability in cancer diagnostics. We aimed to identify serum proteome patterns specific for early stage breast cancer patients using MALDI-ToF mass spectrometry.MethodsBlood samples were collected before the start of therapy in a group of 92 patients diagnosed at stages I and II of the disease, and in a group of age-matched healthy controls (104 women). Serum specimens were purified and the low-molecular-weight proteome fraction was examined using MALDI-ToF mass spectrometry after removal of albumin and other high-molecular-weight serum proteins. Protein ions registered in a mass range between 2,000 and 10,000 Da were analyzed using a new bioinformatic tool created in our group, which included modeling spectra as a sum of Gaussian bell-shaped curves.ResultsWe have identified features of serum proteome patterns that were significantly different between blood samples of healthy individuals and early stage breast cancer patients. The classifier built of three spectral components that differentiated controls and cancer patients had 83% sensitivity and 85% specificity. Spectral components (i.e., protein ions) that were the most frequent in such classifiers had approximate m/z values of 2303, 2866 and 3579 Da (a biomarker built from these three components showed 88% sensitivity and 78% specificity). Of note, we did not find a significant correlation between features of serum proteome patterns and established prognostic or predictive factors like tumor size, nodal involvement, histopathological grade, estrogen and progesterone receptor expression. In addition, we observed a significantly (p = 0.0003) increased level of osteopontin in blood of the group of cancer patients studied (however, the plasma level of osteopontin classified cancer samples with 88% sensitivity but only 28% specificity).ConclusionMALDI-ToF spectrometry of serum has an obvious potential to differentiate samples between early breast cancer patients and healthy controls. Importantly, a classifier built on MS-based serum proteome patterns outperforms available protein biomarkers analyzed in blood by immunoassays.

Serum proteome profiling of primary breast cancer indicates a specific biomarker profile

“…It may be a reliable surrogate tissue for analysis of physiologic or pathologic processes such as cancer. Some studies have addressed the potential of proteomic research to provide serum biomarkers that differentiate breast cancer from benign healthy controls (14)(15)(16)(17)(18)(19). Our results compare well with previously reports by us and others, indicating high sensitivity and specificity when an MS profiling method is used for detecting breast cancer in readily accessible body fluids (20).…”

Radiotherapy-related changes in serum proteome patterns of head and neck cancer patients; the effect of low and medium doses of radiation delivered to large volumes of normal tissue

“…In the second step the spectral components, which reflected [M+H] + peptide ions recorded at defined m/z values, were identified using decomposition of mass spectra into their Gaussian components followed by several post-processing steps as described in more details elsewhere [16,17]. The average spectrum was decomposed into a sum of Gaussian bell-shaped curves by using a variant of the expectation maximization (EM) algorithm and Bayesian Information Criterion (BIC) for model selection [18].…”

Application of classic epidemiological studies and proteomics in research of occupational and environmental exposure to lead, cadmium and arsenic

“…Alterations in proteins abundance, structure, or function, act as useful indicators of pathological abnormalities prior to development of clinical symptoms and such are often useful diagnostic and prognostic biomarkers (Issaq and Blonder, 2009). Proteomic techniques have been wildly applied to the study of cancer (Kumar et al, 2010;Pietrowska et al, 2009;Vydra et al, 2008), cardiovascular disease (Dardé et al, 2010;Yu et al, 2007), and other diseases (Araki et al, 2011;Cho et al, 2011;Kataria et al, 2011;Pešić et al, 2011;Terracciano et al, 2011). The application of proteomic investigations in the study of prostate cancer has aimed at improving the currently available Prostate-Specific Antigen (PSA) screening test.…”