Mass Spectrometry in Diagnostic Oncoproteomics

Evaluation of serum diagnosis of pancreatic cancer by using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Proteomic methods have been widely used in disease marker discovery research. The aim of this study was to discover potential biomarkers for pancreatic cancer (PCa) using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Crude serum samples from 132 patients with PCa and 67 healthy controls (HCs) were analyzed in duplicate using SELDI. Support vector machine (SVM) analysis of the spectra was used to generate a predictive algorithm based on proteins that were maximally differentially expressed between patients with PCa and the HCs in the training cohort. This algorithm was tested using leave-one-out cross-validation in the test cohort. From the 4 significant peaks in the training cohort, a classifier for separating patients with PCa from HCs was developed. The classifier was challenged with all samples achieving 96.67% sensitivity and 100% specificity in the training cohort and 93.1% sensitivity and 78.57% specificity in the test cohort. Additionally, the classifier correctly classified 12/12 stage Ia and 13/16 stage IIa PCa cases. The combination of the SELDI panel and CA19-9 was superior to CA19-9 alone in distinguishing individuals with PCa from the healthy subject group. These results suggest that high-throughput proteomic profiling has the capacity to provide new biomarkers for the early detection and diagnosis of PCa.

Quantum dot self-assembly for protein detection with sub-picomolar sensitivity

A novel approach to sensitive and rapid antigen detection is described. In the presence of a specific antigen, quantum dot-antibody conjugates rapidly self-assemble into agglomerates that are typically more than 1 order of magnitude larger than their individual components. The size distribution of the agglomerated colloids depends on, among other things, the relative concentration of quantum dot conjugates and antigen molecules. Quantum dot agglomerates mediated by antigen recognition were characterized by measuring their light scattering and fluorescence characteristics in an unmodified flow cytometer. Protein antigens angiopoietin-2 and mouse IgG were detected to sub-picomolar concentrations using this method. This simple technique enables the potential simultaneous detection of multiple antigenic biomarkers directly from physiological media and could be used for early detection and frequent screening of cancers and other diseases.

Aptamer directly evolved from live cells recognizes membrane bound immunoglobin heavy mu chain in Burkitt's lymphoma cells

The identification of tumor related cell membrane protein targets is important in understanding tumor progression, the development of new diagnostic tools, and potentially for identifying new therapeutic targets. Here we present a novel strategy for identifying proteins that are altered in their expression levels in a diseased cell using cell specific aptamers. Using an intact viable B-cell Burkitt's lymphoma cell line (Ramos cells) as the target, we have selected aptamers that recognize cell membrane proteins with high affinity. Among the selected aptamers that showed different recognition patterns with different cell lines of leukemia, the aptamer TD05 showed binding with Ramos cells. By chemically modifying TD05 to covalently cross-link with its target on Ramos cells to capture and to enrich the target receptors using streptavidin coated magnetic beads followed by mass spectrometry, we were able to identify membrane bound immunoglobin heavy mu chain as the target for TD05 aptamer. Immunoglobin heavy mu chain is a major component of the B-cell antigen receptor, which is expressed in Burkitt's lymphoma cells. This study demonstrates that this two step strategy, the development of high quality aptamer probes and then the identification of their target proteins, can be used to discover new disease related potential markers and thus enhance tumor diagnosis and therapy. The aptamer based strategy will enable effective molecular elucidation of disease related biomarkers and other interesting molecules.

Proteomics in clinical prostate research

The incidence of early prostate cancer (PCa) has increased rapidly in recent years. The majority of newly diagnosed PCa are in early tumor phase. Presently, we do not have adequate biomarkers to assess tumor aggressiveness in individual cases. Consequently, too many patients are given curatively intended treatment. An exploration of the human proteome may provide clinically useful markers. 2-DE has been successfully used for analysis of the protein phenotype using clinical samples. Proteins are separated according to size and charge, gels are compared by image analysis, protein spots of interest are excised, and proteins identified by MS. This method is exploratory and allows protein identification. However, low-abundance proteins are difficult to detect and 2-DE is currently too labor-intensive for routine use. In recent years, nongel based techniques, such as LC-MS, SELDI-MS, and protein arrays have emerged. They require smaller sample sizes and can be more automated than 2-DE. In this review, we describe studies of the protein expression of benign prostatic tissue and PCa, which is likely to serve as the first step in prognostic biomarker discovery. The prostate proteome is still far from a complete mapping which would enhance our understanding of PCa biology.

In-Source Decay Causes Artifacts in SELDI-TOF MS Spectra

SELDI-TOF MS is a mass spectrometric technique which has been extensively used for biomarker discovery. In this study, we show that in-source decay is an important source for the generation of additional spectral peaks with this technique, both for pure proteins and proteins in serum samples. Thus, SELDI-TOF MS could be used to gain sequence information from proteins, but the results also question the uncritical use of SELDI-TOF MS as a general method for the detection of biomarkers.

Serum proteomic patterns for gastric lesions as revealed by SELDI mass spectrometry

SELDI mass spectrometry was used to investigate protein expression in sera of patients with gastric cancer and gastritis compared to normal volunteers. Differences in peak morphology and intensity were observed in regions of 5910 Da, 5084 Da, 6640 Da and 8691 Da. Patients with gastric cancer exhibited an up-regulation of the 5910 Da peak and a down-regulation of the 8691 Da peak compared to the healthy volunteers; there was also some bi-partitioning and tri-partitioning at the 5084 Da peak. When comparing the sera of these cancer patients with those of gastritis, the former had an up-regulation of the 5910 Da peak and a down-regulation of the 6640 Da peak. This is the first report showing that SELDI sera analysis may be useful in the screening of gastric lesions.