Multiomics profiling of paired primary and recurrent glioblastoma patient tissues. Issue 1 (4th July 2020)

• Record Type: Journal Article
• Authors: Dekker, Lennard J M; Kannegieter, Nynke M; Haerkens, Femke; Toth, Emma; Kros, Johan M; Steenhoff Hov, Dag Are; Fillebeen, Julien; Verschuren, Lars; Leenstra, Sieger; Ressa, Anna; Luider, Theo M
• Is Part Of: Neuro-oncology advances. Volume 2:Issue 1(2020)
• Publication Date: 2020-07-04
• Subjects: glioblastoma -- multiomics -- phosphoproteomics -- transcriptomics -- tumor progression; 616.99481
• DOI: 10.1093/noajnl/vdaa083
• Languages: English
• ISSNs: 2632-2498

Abstract

Abstract: 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.