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Protein expression in experimental malignant glioma varies over time and is altered by radiotherapy treatment
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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2006 (English)In: British Journal of Cancer, ISSN 0007-0920, E-ISSN 1532-1827, Vol. 94, no 12, 1853-1863 p.Article in journal (Refereed) Published
Abstract [en]

Radiotherapy is one of the mainstays of glioblastoma (GBM) treatment. This study aims to investigate and characterise differences in protein expression patterns in brain tumour tissue following radiotherapy, in order to gain a more detailed understanding of the biological effects. Rat BT4C glioma cells were implanted into the brain of two groups of 12 BDIX-rats. One group received radiotherapy (12 Gy single fraction). Protein expression in normal and tumour brain tissue, collected at four different time points after irradiation, were analysed using surface enhanced laser desorption/ionisation - time of flight - mass spectrometry (SELDI-TOF-MS). Mass spectrometric data were analysed by principal component analysis (PCA) and partial least squares (PLS). Using these multivariate projection methods we detected differences between tumours and normal tissue, radiation treatment-induced changes and temporal effects. 77 peaks whose intensity significantly changed after radiotherapy were discovered. The prompt changes in the protein expression following irradiation might help elucidate biological events induced by radiation. The combination of SELDI-TOF-MS with PCA and PLS seems to be well suited for studying these changes. In a further perspective these findings may prove to be useful in the development of new GBM treatment approaches.

Place, publisher, year, edition, pages
2006. Vol. 94, no 12, 1853-1863 p.
Keyword [en]
Glioma, proteomics, SELDI, rat, multivariate analysis
URN: urn:nbn:se:umu:diva-13835DOI: 10.1038/sj.bjc.6603190PubMedID: 16967057OAI: diva2:153506
Available from: 2008-04-09 Created: 2008-04-09 Last updated: 2010-08-19Bibliographically 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, CarlSjöström, MichaelHenriksson, RogerJohansson, MikaelBergenheim, A Tommy
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