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Multivariate analyses of proteomic and metabolomic patterns in brain tumors
Umeå University, Faculty of Medicine, Radiation Sciences, Oncology. Umeå University, Faculty of Medicine, Pharmacology and Clinical Neuroscience, Neurosurgery. Umeå University, Faculty of Science and Technology, Chemistry.
2009 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Multivariat analys av proteomik- och metabolomikmönster i hjärntumörer (Swedish)
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
Keyword [en]
glioblastoma, meningioma, proteomics, metabolomics, multivariate analyses
Research subject
URN: urn:nbn:se:umu:diva-25670ISBN: 978-91-7264-828-9OAI: diva2:233043
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
List of papers
1. Protein expression in experimental malignant glioma varies over time and is altered by radiotherapy treatment
Open this publication in new window or tab >>Protein expression in experimental malignant glioma varies over time and is altered by radiotherapy treatment
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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.

Glioma, proteomics, SELDI, rat, multivariate analysis
urn:nbn:se:umu:diva-13835 (URN)10.1038/sj.bjc.6603190 (DOI)16967057 (PubMedID)
Available from: 2008-04-09 Created: 2008-04-09 Last updated: 2010-08-19Bibliographically approved
2. Metabolomic patterns in glioblastoma and changes during radiotherapy: a clinical microdialysis study
Open this publication in new window or tab >>Metabolomic patterns in glioblastoma and changes during radiotherapy: a clinical microdialysis study
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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
chemometrics, gas chromatography-mass spectrometry, glioblastoma, metabolomics, predictive metabolomics, radiotherapy, treatment response
National Category
Chemical Sciences
urn:nbn:se:umu:diva-25668 (URN)10.1021/pr901088r (DOI)000278243300011 ()20302353 (PubMedID)
Available from: 2009-08-27 Created: 2009-08-27 Last updated: 2015-11-17Bibliographically approved
3. Vandetanib alters the protein pattern in malignant glioma and normal brain in the BT4C rat glioma model
Open this publication in new window or tab >>Vandetanib alters the protein pattern in malignant glioma and normal brain in the BT4C rat glioma model
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2010 (English)In: International Journal of Cancer, ISSN 0020-7136, E-ISSN 1097-0215, Vol. 37, no 4, 879-890 p.Article in journal (Refereed) Published
Abstract [en]

The treatment of glioblastoma is unsatisfactory. Improved understanding of the biological effects of treatment, together with development of new tools to predict outcome of the initiated treatment are therefore of great need. Vandetanib (ZD6474) is mainly a vascular endothelial growth factor (VEGF) and epidermal growth factor (EGF) receptor tyrosine kinase inhibitor. This study investigated the pattern of protein expression in brain tumor and normal brain tissue, following treatment with vandetanib in a rat glioma model. BT4C-cells were stereotactically implanted into the brain of BD IX rats. The rats were divided into three different experiments. The treatment schedule for experiments one and two consisted of daily, oral doses of vandetanib from day 6 until day 12 or 20 after implantation, respectively. In the third experiment, each animal received a single dose of vandetanib on day 19 after implantation and was then sacrificed 2, 8 or 24 h thereafter. The protein expression profiles were analyzed by SELDI-TOF-MS and evaluated with multivariate statistical methods. Following treatment with vandetanib, we found significantly altered protein expression pattern in malignant glioma and normal brain. Analyzing protein spectra is an interesting option to assess biological effects induced in brain tissue by signal transduction inhibitors such as vandetanib.

urn:nbn:se:umu:diva-25669 (URN)10.3892/ijo_00000739 (DOI)000281920000015 ()20811710 (PubMedID)
Available from: 2009-08-27 Created: 2009-08-27 Last updated: 2011-08-29Bibliographically approved
4. Proteomic profiles differ between bone invasive and noninvasive benign meningiomas of fibrous and meningothelial subtype
Open this publication in new window or tab >>Proteomic profiles differ between bone invasive and noninvasive benign meningiomas of fibrous and meningothelial subtype
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2009 (English)In: Journal of Neuro-Oncology, ISSN 0167-594X, E-ISSN 1573-7373, Vol. 94, no 3, 321-331 p.Article in journal (Refereed) Published
Abstract [en]

Meningiomas of WHO grade I can usually be cured by surgical resection. However, some tumors may, despite their benign appearance, display invasive growth behavior. These tumors constitute a difficult clinical problem to handle. By histology alone, bone invasive meningiomas may be indistinguishable from their noninvasive counterparts. In this study we have examined the protein spectra in a series of meningiomas in search of protein expression patterns that may distinguish between bone invasive and noninvasive meningiomas. Tumor tissue from 13 patients with fibrous (6 invasive and 7 noninvasive) and 29 with meningothelial (10 invasive and 19 noninvasive) grade I meningiomas were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI). Multivariate statistical methods were applied for data analyses. Comparing the protein spectra from invasive and noninvasive fibrous meningioma we found 22 peaks whose intensities were significantly different between the two groups (P < 0.001). Based on the expression pattern of these peaks we were able to perfectly separate the two entities (area under ROC curve = 1.0). In meningothelial meningioma the same comparison yielded six significantly differentially expressed peaks (P < 0.001), which to a large degree separated the invasive from noninvasive tissue (area under ROC curve = 0.873). By analyzing the protein spectra in benign meningiomas we could differentiate between invasive and noninvasive growth behavior in both fibrous and meningothelial meningiomas of grade I. A possibility for early identification of invasive grade I meningiomas may have a strong influence on the follow-up policy and the issue of early or late radiotherapy.

Place, publisher, year, edition, pages
Springer Netherlands, 2009
fibrous, invasive, marker, meningioma, meningothelial, multivariate statistical analysis, proteomics
National Category
urn:nbn:se:umu:diva-22131 (URN)10.1007/s11060-009-9865-9 (DOI)19350207 (PubMedID)
Available from: 2009-04-24 Created: 2009-04-24 Last updated: 2015-11-17Bibliographically approved

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