Multiomics profiling of paired primary and recurrent glioblastoma patient tissues.

BACKGROUND: Despite maximal therapy with surgery, chemotherapy, and radiotherapy, glioblastoma (GBM) patients have a median survival of only 15 months. Almost all patients inevitably experience symptomatic tumor recurrence. A hallmark of this tumor type is the large heterogeneity between patients and within tumors itself which relates to the failure of standardized tumor treatment. In this study, tissue samples of paired primary and recurrent GBM tumors were investigated to identify individual factors related to tumor progression. METHODS: Paired primary and recurrent GBM tumor tissues from 8 patients were investigated with a multiomics approach using transcriptomics, proteomics, and phosphoproteomics. RESULTS: In the studied patient cohort, large variations between and within patients are observed for all omics analyses. A few pathways affected at the different omics levels partly overlapped if patients are analyzed at the individual level, such as synaptogenesis (containing the SNARE complex) and cholesterol metabolism. Phosphoproteomics revealed increased STMN1(S38) phosphorylation as part of ERBB4 signaling. A pathway tool has been developed to visualize and compare different omics datasets per patient and showed potential therapeutic drugs, such as abobotulinumtoxinA (synaptogenesis) and afatinib (ERBB4 signaling). Afatinib is currently in clinical trials for GBM. CONCLUSIONS: A large variation on all omics levels exists between and within GBM patients. Therefore, it will be rather unlikely to find a drug treatment that would fit all patients. Instead, a multiomics approach offers the potential to identify affected pathways on the individual patient level and select treatment options.

CellLineMiner: a knowledge portal for human cell lines.

UNLABELLED: Experimental models of human tissues and disease phenotypes frequently rely upon immortalized cell lines, which are easily accessible and simple to use due to their infinite capability of cell division. For decades, cell lines have been used to investigate cellular mechanisms of disease and the efficacy of drugs, most prominently for human cancers. However, the large body of knowledge with respect to human cell lines exists primarily in an unstructured fashion, that is, as free text in the scientific literature. Here we present CellLineMiner, a novel text mining-based web database that provides a comprehensive view of human cell line knowledge. The application offers a simple search in all indexed cell lines, accompanied by a rapid display of all identified literature associations. The CellLineMiner is intended to serve as a knowledge resource companion to the cellular model systems used in biomedical research. AVAILABILITY: CellLineMiner is accessible at http://dev.pubgene.com/cellmine.