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Metabolomic patterns in glioblastoma and changes during radiotherapy: a clinical microdialysis study
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
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2010 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 9, no 6, 2909-2919 p.Article in journal (Refereed) Published
Abstract [en]

We employed stereotactic microdialysis to sample extracellular fluid intracranially from glioblastoma patients, before and during the first five days of conventional radiotherapy treatment. Microdialysis catheters were implanted in the contrast enhancing tumor as well as in the brain adjacent to tumor (BAT). Reference samples were collected subcutaneously from the patients' abdomen. The samples were analyzed by gas chromatography-time-of-flight mass spectrometry (GC-TOF MS), and the acquired data was processed by hierarchical multivariate curve resolution (H-MCR) and analyzed with orthogonal partial least-squares (OPLS). To enable detection of treatment-induced alterations, the data was processed by individual treatment over time (ITOT) normalization. One-hundred fifty-one metabolites were reliably detected, of which 67 were identified. We found distinct metabolic differences between the intracranially collected samples from tumor and the BAT region. There was also a marked difference between the intracranially and the subcutaneously collected samples. Furthermore, we observed systematic metabolic changes induced by radiotherapy treatment among both tumor and BAT samples. The metabolite patterns affected by treatment were different between tumor and BAT, both containing highly discriminating information, ROC values of 0.896 and 0.821, respectively. Our findings contribute to increased molecular knowledge of basic glioblastoma pathophysiology and point to the possibility of detecting metabolic marker patterns associated to early treatment response.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2010. Vol. 9, no 6, 2909-2919 p.
Keyword [en]
chemometrics, gas chromatography-mass spectrometry, glioblastoma, metabolomics, predictive metabolomics, radiotherapy, treatment response
National Category
Chemical Sciences
URN: urn:nbn:se:umu:diva-25668DOI: 10.1021/pr901088rISI: 000278243300011PubMedID: 20302353OAI: diva2:233031
Available from: 2009-08-27 Created: 2009-08-27 Last updated: 2015-11-17Bibliographically approved
In thesis
1. Multivariate analyses of proteomic and metabolomic patterns in brain tumors
Open this publication in new window or tab >>Multivariate analyses of proteomic and metabolomic patterns in brain tumors
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Multivariat analys av proteomik- och metabolomikmönster i hjärntumörer
Abstract [en]

Glioblastoma multiforme (GBM) is the most common primary brain tumor. Given the current standard of care, the prognosis for patients diagnosed with this disease is still poor. There consequently exists a need to improve current treatments, as well as to develop new ones. Many obstacles however need to be overcome to facilitate this effort and one of these involves the development of improved methods to monitor treatment effects. At present, the effects of treatment are typically assessed by radiological means several months after its initiation, which is unsatisfactory for a fast growing tumor like GBM. It is however likely that treatment effects can be detected on a molecular level long before radiological response, especially considering many of the targeted therapies that are currently being developed. Biomarkers for treatment efficacy may be of great importance in the future individualization of brain tumor treatment.

The work presented herein was primarily focused on detecting early effects of GBM treatment. To this end, we designed experiments in the BT4C rat glioma model in which we studied effects of both conventional radiotherapy and an experimental angiogenesis inhibitor, vandetanib. Brain tissue samples were analyzed using a high throughput mass spectrometry (MS) based screening, known as Surface Enhanced Laser Desorption/Ionization - Time of Flight - Mass Spectrometry (SELDI-TOF-MS). The vast amounts of data generated were subsequently analyzed by established multivariate statistical methods, such as Principal Component Analysis (PCA), Partial Least Squares (PLS), and Orthogonal Partial Least Squares (OPLS), developed for analysis of large and complex datasets. In the radiotherapy study we detected a protein spectrum pattern clearly related to tumor progression. We notably observed how this progression pattern was hampered by radiotherapy. The vandetanib study also revealed significant alterations of protein expression following treatment of different durations, both in tumor tissue and in normal brain contralateral to the tumor.

In an effort to further elucidate the pathophysiology of GBM, particularly in relation to treatment, we collected extracellular fluid (ECF) samples from 11 patients diagnosed with inoperable GBM. The samples were collected by means of stereotactic microdialysis, both from within the contrast enhancing tumor and the brain adjacent to tumor (BAT). Samples were collected longitudinally from each patient in a time span of up to two weeks, during which the patient received the first five fractions of radiotherapy. The ECF samples were then analyzed by Gas Chromatography Mass Spectrometry (GC-MS) to screen them with respect to concentrations of low molecular weight compounds (metabolites). Suitable multivariate analysis strategies enabled us to extract patterns of varying metabolite concentrations distinguishing between samples collected at different locations in the brain as well as between samples collected at different time points in relation to treatment.

In a separate study, we also applied SELDI-TOF-MS and multivariate statistical methods to unravel possible differences in protein spectra between invasive and non-invasive WHO grade I meningiomas. This type of tumor can usually be cured by surgical resection however sometimes it grows invasively into the bone, ultimately causing clinical problems. This study revealed the possibility to differentiate between invasive and non-invasive benign meningioma based on the expression pattern of a few proteins.

Our approach, which includes sample analysis and data handling, is applicable to a wide range of screening studies. In this work we demonstrated that the combination of MS screening and multivariate analyses is a powerful tool in the search for patterns related to treatment effects and diagnostics in brain tumors.

Place, publisher, year, edition, pages
Umeå: , 2009. 88 p.
Umeå University medical dissertations, ISSN 0346-6612 ; 1281
glioblastoma, meningioma, proteomics, metabolomics, multivariate analyses
Research subject
urn:nbn:se:umu:diva-25670 (URN)978-91-7264-828-9 (ISBN)
Public defence
2009-09-18, Sal 244 Lionssalen, Byggnad 7, Norrlands Universitetssjukhus, Umeå, 09:00 (Swedish)
Available from: 2009-09-03 Created: 2009-08-27 Last updated: 2010-01-18Bibliographically approved

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Wibom, CarlSurowiec, IzabellaMörén, LinaBergström, PerJohansson, MikaelAntti, HenrikBergenheim, A Tommy
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