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Proteomic profiles differ between bone invasive and noninvasive benign meningiomas of fibrous and meningothelial subtype
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
Umeå University, Faculty of Science and Technology, Department of Chemistry.
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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. Vol. 94, no 3, 321-331 p.
Keyword [en]
fibrous, invasive, marker, meningioma, meningothelial, multivariate statistical analysis, proteomics
National Category
Neurosciences
Identifiers
URN: urn:nbn:se:umu:diva-22131DOI: 10.1007/s11060-009-9865-9PubMedID: 19350207OAI: oai:DiVA.org:umu-22131DiVA: diva2:212823
Available from: 2009-04-24 Created: 2009-04-24 Last updated: 2017-12-13Bibliographically 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.
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1281
Keyword
glioblastoma, meningioma, proteomics, metabolomics, multivariate analyses
Research subject
Oncology
Identifiers
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)
Opponent
Supervisors
Available from: 2009-09-03 Created: 2009-08-27 Last updated: 2010-01-18Bibliographically approved
2. Metabolomics and proteomics studies of brain tumors: a chemometric bioinformatics approach
Open this publication in new window or tab >>Metabolomics and proteomics studies of brain tumors: a chemometric bioinformatics approach
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The WHO classification of brain tumors is based on histological features and the aggressiveness of the tumor is classified from grade I to IV, where grade IV is the most aggressive. Today, the correlation between prognosis and tumor grade is the most important component in tumor classification. High grade gliomas, glioblastomas, are associated with poor prognosis and a median survival of 14 months including all available treatments. Low grade meningiomas, usually benign grade I tumors, are in most cases cured by surgical resection. However despite their benign appearance grade I meningiomas can, without any histopathological signs, in some cases develop bone invasive growth and become lethal. Thus, it is necessary to improve conventional treatment modalities, develop new treatment strategies and improve the knowledge regarding the basic pathophysiology in the classification and treatment of brain tumors.

In this thesis, both proteomics and metabolomics have been applied in the search for biomarkers or biomarker patterns in two different types of brain tumors, gliomas and meningiomas. Proteomic studies were carried out mainly by surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS). In one of the studies, isobaric tags for relative and absolute quantitation (iTRAQ) labeling in combination with high-performance liquid chromatography (HPLC) was used for protein detection and identification. For metabolomics, gas-chromatography time-of-flight mass spectrometry (GC-TOF-MS) has been the main platform used throughout this work for generation of robust global metabolite profiles in tissue, blood and cell cultures. To deal with the complexity of the generated data, and to be able to extract relevant biomarker patters or latent biomarkers, for interpretation, prediction and prognosis, bioinformatic strategies based on chemometrics were applied throughout the studies of the thesis.

In summary, we detected differentiating protein profiles between invasive and non-invasive meningiomas, in both fibrous and meningothelial tumors. Furthermore, in a different study we discovered treatment induce protein pattern changes in a rat glioma model treated with an angiogenesis inhibitor. We identified a cluster of proteins linked to angiogenesis. One of those proteins, HSP90, was found elevated in relation to treatment in tumors, following ELISA validation. An interesting observation in a separate study was that it was possible to detect metabolite pattern changes in the serum metabolome, as an effect of treatment with radiotherapy, and that these pattern changes differed between different patients, highlighting a possibility for monitoring individual treatment response.  In the fourth study of this work, we investigated tissue and serum from glioma patients that revealed differences in the metabolome between glioblastoma and oligodendroglioma, as well as between oligodendroglioma grade II and grade III. In addition, we discovered metabolite patterns associated to survival in both glioblastoma and oligodendroglioma. In our final work, we identified metabolite pattern differences between cell lines from a subgroup of glioblastomas lacking argininosuccinate synthetase (ASS1) expression, (ASS1 negative glioblastomas), making them auxotrophic for arginine, a metabolite required for tumor growth and proliferation, as compared to glioblastomas with normal ASS1 expression (ASS1 positive). From the identified metabolite pattern differences we could verify the hypothesized alterations in the arginine biosynthetic pathway. We also identified additional interesting metabolites that may provide clues for future diagnostics and treatments. Finally, we were able to verify the specific treatment effect of ASS1 negative cells by means of arginine deprivation on a metabolic level.

Place, publisher, year, edition, pages
Umeå: Umeå Universitet, 2015. 60 p.
Keyword
glioblastoma, glioma, meningioma, metabolomics, proteomics, mass-spectrometry
National Category
Other Basic Medicine
Research subject
biological chemistry
Identifiers
urn:nbn:se:umu:diva-111309 (URN)978-91-7601-354-0 (ISBN)
Public defence
2015-12-11, KBC-huset, KB3B1, Umeå universitet, Umeå, 13:00 (Swedish)
Opponent
Supervisors
Available from: 2015-11-20 Created: 2015-11-13 Last updated: 2015-12-02Bibliographically approved

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