A Time-Based and Intratumoral Proteomic Assessment of a Recurrent Glioblastoma Multiforme.

Tumors consist of cells in different stages of transformation with molecular and cellular heterogeneity. By far, heterogeneity is the hallmark of glioblastoma multiforme (GBM), the most malignant and aggressive type of glioma. Most proteomic studies aim in comparing tumors from different patients, but here we dive into exploring the intratumoral proteome diversity of a single GBM. For this, we profiled tumor fragments from the profound region of the same patient's GBM but obtained from two surgeries a year's time apart. Our analysis also included GBM's fragments from different anatomical regions. Our quantitative proteomic strategy employed 4-plex iTRAQ peptide labeling followed by a four-step strong cation chromatographic separation; each fraction was then analyzed by reversed-phase nano-chromatography coupled on-line with an Orbitrap-Velos mass spectrometer. Unsupervised clustering grouped the proteomic profiles into four major distinct groups and showed that most changes were related to the tumor's anatomical region. Nevertheless, we report differentially abundant proteins from GBM's fragments of the same region but obtained 1 year apart. We discuss several key proteins (e.g., S100A9) and enriched pathways linked with GBM such as the Ras pathway, RHO GTPases activate PKNs, and those related to apoptosis, to name a few. As far as we know, this is the only report that compares GBM fragments proteomic profiles from the same patient. Ultimately, our results fuel the forefront of scientific discussion on the importance in exploring the richness of subproteomes within a single tissue sample for a better understanding of the disease, as each tumor is unique.

Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0.

PatternLab for proteomics is an integrated computational environment that unifies several previously published modules for the analysis of shotgun proteomic data. The contained modules allow for formatting of sequence databases, peptide spectrum matching, statistical filtering and data organization, extracting quantitative information from label-free and chemically labeled data, and analyzing statistics for differential proteomics. PatternLab also has modules to perform similarity-driven studies with de novo sequencing data, to evaluate time-course experiments and to highlight the biological significance of data with regard to the Gene Ontology database. The PatternLab for proteomics 4.0 package brings together all of these modules in a self-contained software environment, which allows for complete proteomic data analysis and the display of results in a variety of graphical formats. All updates to PatternLab, including new features, have been previously tested on millions of mass spectra. PatternLab is easy to install, and it is freely available from http://patternlabforproteomics.org.

Are gastric cancer resection margin proteomic profiles more similar to those from controls or tumors?

A strategy for treating cancer is to surgically remove the tumor together with a portion of apparently healthy tissue surrounding it, the so-called "resection margin", to minimize recurrence. Here, we investigate whether the proteomic profiles from biopsies of gastric cancer resection margins are indeed more similar to those from healthy tissue than from cancer biopsies. To this end, we analyzed biopsies using an offline MudPIT shotgun proteomic approach and performed label-free quantitation through a distributed normalized spectral abundance factor approach adapted for extracted ion chromatograms (XICs). A multidimensional scaling analysis revealed that each of those tissue-types is very distinct from each other. The resection margin presented several proteins previously correlated with cancer, but also other overexpressed proteins that may be related to tumor nourishment and metastasis, such as collagen alpha-1, ceruloplasmin, calpastatin, and E-cadherin. We argue that the resection margin plays a key role in Paget's "soil to seed" hypothesis, that is, that cancer cells require a special microenvironment to nourish and that understanding it could ultimately lead to more effective treatments.