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Metabolomics and proteomics studies of brain tumors: a chemometric bioinformatics approach
Umeå University, Faculty of Science and Technology, Department of Chemistry.
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 [en]
glioblastoma, glioma, meningioma, metabolomics, proteomics, mass-spectrometry
National Category
Other Basic Medicine
Research subject
biological chemistry
Identifiers
URN: urn:nbn:se:umu:diva-111309ISBN: 978-91-7601-354-0 (print)OAI: oai:DiVA.org:umu-111309DiVA: diva2:871808
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
List of papers
1. Metabolomic screening of tumor tissue and serum in glioma patients reveals diagnostic and prognostic information
Open this publication in new window or tab >>Metabolomic screening of tumor tissue and serum in glioma patients reveals diagnostic and prognostic information
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2015 (English)In: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 5, no 3, 502-520 p.Article in journal (Refereed) Published
Abstract [en]

Glioma grading and classification, today based on histological features, is not always easy to interpret and diagnosis partly relies on the personal experience of the neuropathologists. The most important feature of the classification is the aimed correlation between tumor grade and prognosis. However, in the clinical reality, large variations exist in the survival of patients concerning both glioblastomas and low-grade gliomas. Thus, there is a need for biomarkers for a more reliable classification of glioma tumors as well as for prognosis. We analyzed relative metabolite concentrations in serum samples from 96 fasting glioma patients and 81 corresponding tumor samples with different diagnosis (glioblastoma, oligodendroglioma) and grade (World Health Organization (WHO) grade II, III and IV) using gas chromatography-time of flight mass spectrometry (GC-TOFMS). The acquired data was analyzed and evaluated by pattern recognition based on chemometric bioinformatics tools. We detected feature patterns in the metabolomics data in both tumor and serum that distinguished glioblastomas from oligodendrogliomas (p(tumor) = 2.46 × 10(-8), p(serum) = 1.3 × 10(-5)) and oligodendroglioma grade II from oligodendroglioma grade III (p(tumor) = 0.01, p(serum) = 0.0008). Interestingly, we also found patterns in both tumor and serum with individual metabolite features that were both elevated and decreased in patients that lived long after being diagnosed with glioblastoma compared to those who died shortly after diagnosis (p(tum)(o)(r) = 0.006, p(serum) = 0.004; AUROCC(tumor) = 0.846 (0.647-1.000), AUROCC(serum) = 0.958 (0.870-1.000)). Metabolic patterns could also distinguish long and short survival in patients diagnosed with oligodendroglioma (p(tumor) = 0.01, p(serum) = 0.001; AUROCC(tumor) = 1 (1.000-1.000), AUROCC(serum) = 1 (1.000-1.000)). In summary, we found different metabolic feature patterns in tumor tissue and serum for glioma diagnosis, grade and survival, which indicates that, following further verification, metabolomic profiling of glioma tissue as well as serum may be a valuable tool in the search for latent biomarkers for future characterization of malignant glioma.

Place, publisher, year, edition, pages
MDPI, 2015
Keyword
glioma, diagnosis, prognosis, blood, tumor, metabolomics, chemometrics, latent biomarkers
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:umu:diva-111307 (URN)10.3390/metabo5030502 (DOI)000363208200007 ()26389964 (PubMedID)
Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2017-12-01Bibliographically approved
2. 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
Keyword
fibrous, invasive, marker, meningioma, meningothelial, multivariate statistical analysis, proteomics
National Category
Neurosciences
Identifiers
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: 2017-12-13Bibliographically approved

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