Comparison of peptide cancer signatures identified by mass spectrometry in serum of patients with head and neck, lung and colorectal cancers: Association with tumor progression

Abstract

Abstract. Mass spectrometry-based analyses of the lowmolecular-weight fraction of serum proteome allow identifying proteome profiles (signatures) that are potentially useful in detection and diagnostics of cancer. Here we compared serum proteome profiles of healthy donors and patients with three different types of cancer aiming to identify peptide signatures that were either common for all cancer samples or specific for cancer type. Blood samples were collected before start of the therapy from patients with head and neck squamous cell cancer, colorectal adenocarcinoma and non-small cell lung cancer, and from a corresponding group of healthy volunteers. Mass profiles of the serum proteome were recorded in the range between 2 and 13 kDa using MALDI-ToF spectrometry and 131 identified peptide ions were used for statistical analyses. Similar degrees of overall similarities were observed in all intra-group and inter-group analyses when general features of serum proteome profiles were compared between individual samples. However, classifiers built of selected spectral components allowed differentiation between healthy donors and three groups of cancer patients with 69-74% sensitivity and 82-84% specificity. There were two common peptide species (3766 and 5867 Da) with increased levels in all cancer samples. Several spectral components permitted differentiation between lung cancer samples and either head and neck cancer or colorectal cancer samples, but two latter types of samples could not be properly discriminated. Abundance of spectral components that putatively corresponded to fragments of serum amyloid A (11511 and 11667 Da) was highest in lung cancer samples, yet increased levels of these peptides appeared to generally associate with more advanced cancer cases. We concluded that certain components of serum peptide signatures are common for different cancer signatures and putatively reflect general response of organism to the disease, yet other components of such signatures are more specific and most likely correspond to clinical stage of the malignancy. IntroductionThe low-molecular-weight fraction of the blood proteome appears to be a promising source of novel biomarkers of human diseases. Thus, mass spectrometry (MS) methods, which allow characterization of this particular component of human proteome, emerge as a valuable tool of clinical proteomics and disease diagnostics (1-5). The proteomics approach that takes into consideration characteristic features of the whole proteome but does not rely on particular protein, is called proteome pattern analysis or proteome profiling. In this approach specific proteome signatures are built based on several peptide/protein components, which can be exemplified by ions registered at defined m/z values in the mass spectrum. Such signatures can be used for sample identification and classification even though their particular components may lack differentiating power when analyzed separately (6-9). Matrix-assisted laser desorption/ionization spectrometry (MALDI), and its deri...

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].…”

Partial-Body Irradiation in Patients with Prostate Cancer Treated with IMRT Has Little Effect on the Composition of Serum Proteome

“…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 detail elsewhere [26,27]. 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.…”

Quantitation of endogenous peptides using mass spectrometry based methods

“…The power of label-free quantitation has even been applied to the discovery of personalized immunosuppressive therapies for kidney transplant patients [41]. Likewise, comparative profiling of serum and plasma peptidomes has gained momentum as these samples are widely used as sources of biomarkers of various cancers [42,43] and other diseases [44,45]. The salivary peptidome has also drawn attention recently for the diagnosis and treatment of oral diseases, and as a potential tool for the diagnosis of systemic diseases [46].…”