Longitudinal changes of serum protein N-Glycan levels for earlier detection of pancreatic cancer in high-risk individuals.

BACKGROUND: Surveillance of individuals at risk of developing pancreatic ductal adenocarcinoma (PDAC) has the potential to improve survival, yet early detection based on solely imaging modalities is challenging. We aimed to identify changes in serum glycosylation levels over time to earlier detect PDAC in high-risk individuals. METHODS: Individuals with a hereditary predisposition to develop PDAC were followed in two surveillance programs. Those, of which at least two consecutive serum samples were available, were included. Mass spectrometry analysis was performed to determine the total N-glycome for each consecutive sample. Potentially discriminating N-glycans were selected based on our previous cross-sectional analysis and relative abundances were calculated for each glycosylation feature. RESULTS: 165 individuals ("FPC-cohort" N = 119; Leiden cohort N = 46) were included. In total, 97 (59%) individuals had a genetic predisposition (77 CDKN2A, 15 BRCA1/2, 5 STK11) and 68 (41%) a family history of PDAC without a known genetic predisposition (>10-fold increased risk of developing PDAC). From each individual, a median number of 3 serum samples (IQR 3) was collected. Ten individuals (6%) developed PDAC during 35 months of follow-up; nine (90%) of these patients carried a CDKN2A germline mutation. In PDAC cases, compared to all controls, glycosylation characteristics were increased (fucosylation, tri- and tetra-antennary structures, specific sialic linkage types), others decreased (complex-type diantennary and bisected glycans). The largest change over time was observed for tri-antennary fucosylated glycans, which were able to differentiate cases from controls with a specificity of 92%, sensitivity of 49% and accuracy of 90%. CONCLUSION: Serum N-glycan monitoring may support early detection in a pancreas surveillance program.

Case-controlled identification of colorectal cancer based on proteomic profiles and the potential for screening.

AIM: Colorectal cancer (CRC) screening programmes detect early cancers but unfortunately have limited sensitivity and specificity. Mass spectrometry-based determination of serum peptide and protein profiles provides a new approach for improved screening. METHOD: Serum samples were obtained from 126 CRC patients before treatment and 277 control individuals. An additional group of samples from 50 CRC patients and 82 controls was used for validation. Peptide and protein enrichments were carried out using reverse-phase C18 and weak-cation exchange magnetic beads in an automated solid-phase extraction and spotting procedure. Profiles were acquired on a matrix-assisted laser desorption/ionization time-of-flight system. Discriminant rules using logistic regression were calibrated for the peptide and protein signatures separately, followed by combining the classifications to obtain double cross-validated predicted class probabilities. Results were validated on an identical patient set. RESULTS: A discriminative power was found for patients with CRC representative for all histopathological stages compared with controls with an area under the curve of 0.95 in the test set (0.93 for the validation set) and with a high specificity (94-95%). CONCLUSION: The study has shown that a serum peptide and protein biomarker signature can be used to distinguish CRC patients from healthy controls with high discriminative power. This relatively simple and cheap test is promising for CRC screening.

Parallel processing of large datasets from NanoLC-FTICR-MS measurements.

A new approach for automatic parallel processing of large mass spectral datasets in a distributed computing environment is demonstrated to significantly decrease the total processing time. The implementation of this novel approach is described and evaluated for large nanoLC-FTICR-MS datasets. The speed benefits are determined by the network speed and file transfer protocols only and allow almost real-time analysis of complex data (e.g., a 3-gigabyte raw dataset is fully processed within 5 min). Key advantages of this approach are not limited to the improved analysis speed, but also include the improved flexibility, reproducibility, and the possibility to share and reuse the pre- and postprocessing strategies. The storage of all raw data combined with the massively parallel processing approach described here allows the scientist to reprocess data with a different set of parameters (e.g., apodization, calibration, noise reduction), as is recommended by the proteomics community. This approach of parallel processing was developed in the Virtual Laboratory for e-Science (VL-e), a science portal that aims at allowing access to users outside the computer research community. As such, this strategy can be applied to all types of serially acquired large mass spectral datasets such as LC-MS, LC-MS/MS, and high-resolution imaging MS results.