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  • 11
    Keywords: brain ; GENE-EXPRESSION ; HYBRIDIZATION ; TUMORS ; UNITED-STATES ; GLIOMAS ; MULTIFORME ; temozolomide ; CODON 132 MUTATION ; IDH2 MUTATIONS
    Abstract: The prognosis of glioblastoma, the most malignant type of glioma, is still poor, with only a minority of patients showing long-term survival of more than three years after diagnosis. To elucidate the molecular aberrations in glioblastomas of long-term survivors, we performed genome- and/or transcriptome-wide molecular profiling of glioblastoma samples from 94 patients, including 28 long-term survivors with 〉36 months overall survival (OS), 20 short-term survivors with 〈12 months OS and 46 patients with intermediate OS. Integrative bioinformatic analyses were used to characterize molecular aberrations in the distinct survival groups considering established molecular markers such as isocitrate dehydrogenase 1 or 2 (IDH1/2) mutations, and O(6) -methylguanine DNA methyltransferase (MGMT) promoter methylation. Patients with long-term survival were younger and more often had IDH1/2-mutant and MGMT-methylated tumors. Gene expression profiling revealed over-representation of a distinct (proneural-like) expression signature in long-term survivors that was linked to IDH1/2 mutation. However, IDH1/2-wildtype glioblastomas from long-term survivors did not show distinct gene expression profiles and included proneural, classical and mesenchymal glioblastoma subtypes. Genomic imbalances also differed between IDH1/2-mutant and IDH1/2-wildtype tumors, but not between survival groups of IDH1/2-wildtype patients. Thus, our data support an important role for MGMT promoter methylation and IDH1/2 mutation in glioblastoma long-term survival and corroborate the association of IDH1/2 mutation with distinct genomic and transcriptional profiles. Importantly, however, IDH1/2-wildtype glioblastomas in our cohort of long-term survivors lacked distinctive DNA copy number changes and gene expression signatures, indicating that other factors might have been responsible for long survival in this particular subgroup of patients.(c) 2014 UICC.
    Type of Publication: Journal article published
    PubMed ID: 24615357
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  • 12
    Keywords: DISEASE ; IMPACT ; EUROPE ; COMPLICATIONS ; quality of life ; EUROPEAN COUNTRIES ; Parkinson's disease ; DYSKINESIA ; LEVODOPA ; Motor complications ; Motor fluctuations ; PDQ-39
    Abstract: BACKGROUND: Little is known about the relationship between specific subtypes of treatment-associated motor complications and different domains of health-related Quality of Life (QoL) in patients with Parkinson's disease (PD). Larger studies that investigate these aspects within a cross-cultural setting are scarce. OBJECTIVE: To assess QoL and its association with on-off fluctuations, peak-dose dyskinesias, biphasic dyskinesias, and off-dystonias in PD patients from five European countries. METHODS: Data from 817 PD patients were collected cross-sectionally in France, Germany, Italy, Spain, and the UK. QoL was measured with the generic EuroQoL 5-Dimension questionnaire (EQ-5D) and the disease-specific Parkinson's Disease Questionnaire-39 (PDQ-39). Multivariable linear regression analyses were performed to test the associations of motor complication subtypes with QoL. RESULTS: Thirty-three percent of the patients (varying from 23% in Italy to 58% in France) suffered from motor complications, either a single subtype or a combination of different subtypes. On-off fluctuations were associated with a 7.1 percentage point decrease in the EQ-5D (p 〈 0.001) and a 3.6 percentage point deterioration in the PDQ-39 (p = 0.01). Dyskinesias were not seen to affect global QoL scores, but had detrimental effects on the PDQ-39 dimensions activities of daily living, cognitions, stigma, and bodily discomfort. Patients from Spain, Italy, and France had lower global QoL scores in the multivariable analyses than patients from Germany and the UK. CONCLUSION: Motor complications, primarily on-off fluctuations, may impact QoL in PD patients. This substantiates the importance of clinical strategies targeting the prevention, delay of onset, and management of motor complications in PD patients.
    Type of Publication: Journal article published
    PubMed ID: 24953743
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  • 13
    Keywords: GENE-EXPRESSION ; MALIGNANT GLIOMAS ; GLIOBLASTOMA ; ASTROCYTIC TUMORS ; PHASE-III TRIAL ; IDH1 mutation ; ANAPLASTIC OLIGODENDROGLIOMA ; MGMT PROMOTER METHYLATION ; FREQUENT ATRX ; ADJUVANT PROCARBAZINE
    Abstract: Cerebral gliomas of World Health Organization (WHO) grade II and III represent a major challenge in terms of histological classification and clinical management. Here, we asked whether large-scale genomic and transcriptomic profiling improves the definition of prognostically distinct entities. We performed microarray-based genome- and transcriptome-wide analyses of primary tumor samples from a prospective German Glioma Network cohort of 137 patients with cerebral gliomas, including 61 WHO grade II and 76 WHO grade III tumors. Integrative bioinformatic analyses were employed to define molecular subgroups, which were then related to histology, molecular biomarkers, including isocitrate dehydrogenase 1 or 2 (IDH1/2) mutation, 1p/19q co-deletion and telomerase reverse transcriptase (TERT) promoter mutations, and patient outcome. Genomic profiling identified five distinct glioma groups, including three IDH1/2 mutant and two IDH1/2 wild-type groups. Expression profiling revealed evidence for eight transcriptionally different groups (five IDH1/2 mutant, three IDH1/2 wild type), which were only partially linked to the genomic groups. Correlation of DNA-based molecular stratification with clinical outcome allowed to define three major prognostic groups with characteristic genomic aberrations. The best prognosis was found in patients with IDH1/2 mutant and 1p/19q co-deleted tumors. Patients with IDH1/2 wild-type gliomas and glioblastoma-like genomic alterations, including gain on chromosome arm 7q (+7q), loss on chromosome arm 10q (-10q), TERT promoter mutation and oncogene amplification, displayed the worst outcome. Intermediate survival was seen in patients with IDH1/2 mutant, but 1p/19q intact, mostly astrocytic gliomas, and in patients with IDH1/2 wild-type gliomas lacking the +7q/-10q genotype and TERT promoter mutation. This molecular subgrouping stratified patients into prognostically distinct groups better than histological classification. Addition of gene expression data to this genomic classifier did not further improve prognostic stratification. In summary, DNA-based molecular profiling of WHO grade II and III gliomas distinguishes biologically distinct tumor groups and provides prognostically relevant information beyond histological classification as well as IDH1/2 mutation and 1p/19q co-deletion status.
    Type of Publication: Journal article published
    PubMed ID: 25783747
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  • 14
    Abstract: Clinical cohorts with time-to-event endpoints are increasingly characterized by measurements of a number of single nucleotide polymorphisms that is by a magnitude larger than the number of measurements typically considered at the gene level. At the same time, the size of clinical cohorts often is still limited, calling for novel analysis strategies for identifying potentially prognostic SNPs that can help to better characterize disease processes. We propose such a strategy, drawing on univariate testing ideas from epidemiological case-controls studies on the one hand, and multivariable regression techniques as developed for gene expression data on the other hand. In particular, we focus on stable selection of a small set of SNPs and corresponding genes for subsequent validation. For univariate analysis, a permutation-based approach is proposed to test at the gene level. We use regularized multivariable regression models for considering all SNPs simultaneously and selecting a small set of potentially important prognostic SNPs. Stability is judged according to resampling inclusion frequencies for both the univariate and the multivariable approach. The overall strategy is illustrated with data from a cohort of acute myeloid leukemia patients and explored in a simulation study. The multivariable approach is seen to automatically focus on a smaller set of SNPs compared to the univariate approach, roughly in line with blocks of correlated SNPs. This more targeted extraction of SNPs results in more stable selection at the SNP as well as at the gene level. Thus, the multivariable regression approach with resampling provides a perspective in the proposed analysis strategy for SNP data in clinical cohorts highlighting what can be added by regularized regression techniques compared to univariate analyses.
    Type of Publication: Journal article published
    PubMed ID: 27159447
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  • 15
    Abstract: BACKGROUND: High-throughput technology allows for genome-wide measurements at different molecular levels for the same patient, e.g. single nucleotide polymorphisms (SNPs) and gene expression. Correspondingly, it might be beneficial to also integrate complementary information from different molecular levels when building multivariable risk prediction models for a clinical endpoint, such as treatment response or survival. Unfortunately, such a high-dimensional modeling task will often be complicated by a limited overlap of molecular measurements at different levels between patients, i.e. measurements from all molecular levels are available only for a smaller proportion of patients. RESULTS: We propose a sequential strategy for building clinical risk prediction models that integrate genome-wide measurements from two molecular levels in a complementary way. To deal with partial overlap, we develop an imputation approach that allows us to use all available data. This approach is investigated in two acute myeloid leukemia applications combining gene expression with either SNP or DNA methylation data. After obtaining a sparse risk prediction signature e.g. from SNP data, an automatically selected set of prognostic SNPs, by componentwise likelihood-based boosting, imputation is performed for the corresponding linear predictor by a linking model that incorporates e.g. gene expression measurements. The imputed linear predictor is then used for adjustment when building a prognostic signature from the gene expression data. For evaluation, we consider stability, as quantified by inclusion frequencies across resampling data sets. Despite an extremely small overlap in the application example with gene expression and SNPs, several genes are seen to be more stably identified when taking the (imputed) linear predictor from the SNP data into account. In the application with gene expression and DNA methylation, prediction performance with respect to survival also indicates that the proposed approach might work well. CONCLUSIONS: We consider imputation of linear predictor values to be a feasible and sensible approach for dealing with partial overlap in complementary integrative analysis of molecular measurements at different levels. More generally, these results indicate that a complementary strategy for integrating different molecular levels can result in more stable risk prediction signatures, potentially providing a more reliable insight into the underlying biology.
    Type of Publication: Journal article published
    PubMed ID: 27578050
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  • 16
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    German Medical Science GMS Publishing House; Düsseldorf
    In:  16. Deutscher Kongress für Versorgungsforschung (DKVF); 20171004-20171006; Berlin; DOCP233a /20170926/
    Publication Date: 2017-09-26
    Keywords: ddc: 610
    Language: German
    Type: conferenceObject
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  • 17
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    German Medical Science GMS Publishing House; Düsseldorf
    In:  45. Jahrestagung der Deutschen Gesellschaft der Plastischen, Rekonstruktiven und Ästhetischen Chirurgen (DGPRÄC), 19. Jahrestagung der Vereinigung der Deutschen Ästhetisch-Plastischen Chirurgen (VDÄPC), 52. Jahrestagung der Österreichischen Gesellschaft für Plastische, Ästhetische und Rekonstruktive Chirurgie (ÖGPRÄC); 20140911-20140913; München; DOC147 /20140903/
    Publication Date: 2014-09-04
    Keywords: ddc: 610
    Language: German
    Type: conferenceObject
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  • 18
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    German Medical Science GMS Publishing House; Düsseldorf
    In:  GMDS 2013; 58. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS); 20130901-20130905; Lübeck; DOCAbstr.303 /20130827/
    Publication Date: 2013-08-28
    Keywords: ddc: 610
    Language: German
    Type: conferenceObject
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  • 19
    Keywords: CANCER ; CELLS ; EXPRESSION ; MODELS ; PROSTATE ; CLASSIFICATION ; INFORMATION ; COHORT ; DISEASE ; PATIENT ; prognosis ; BIOMARKERS ; BIOLOGY ; BREAST-CANCER ; prostate cancer ; PROSTATE-CANCER ; PREDICTION ; SELECTION ; INTEGRATION ; REGRESSION ; ERROR ; MICRORNA EXPRESSION ; VARIABLE SELECTION ; CROSS-VALIDATION
    Abstract: BACKGROUND: One of the main goals in cancer studies including high-throughput microRNA (miRNA) and mRNA data is to find and assess prognostic signatures capable of predicting clinical outcome. Both mRNA and miRNA expression changes in cancer diseases are described to reflect clinical characteristics like staging and prognosis. Furthermore, miRNA abundance can directly affect target transcripts and translation in tumor cells. Prediction models are trained to identify either mRNA or miRNA signatures for patient stratification. With the increasing number of microarray studies collecting mRNA and miRNA from the same patient cohort there is a need for statistical methods to integrate or fuse both kinds of data into one prediction model in order to find a combined signature that improves the prediction. RESULTS: Here, we propose a new method to fuse miRNA and mRNA data into one prediction model. Since miRNAs are known regulators of mRNAs we used the correlations between them as well as the target prediction information to build a bipartite graph representing the relations between miRNAs and mRNAs. This graph was used to guide the feature selection in order to improve the prediction. The method is illustrated on a prostate cancer data set comprising 98 patient samples with miRNA and mRNA expression data. The biochemical relapse was used as clinical endpoint. It could be shown that the bipartite graph in combination with both data sets could improve prediction performance as well as the stability of the feature selection. CONCLUSIONS: Fusion of mRNA and miRNA expression data into one prediction model improves clinical outcome prediction in terms of prediction error and stable feature selection. The R source code of the proposed method is available in the supplement.
    Type of Publication: Journal article published
    PubMed ID: 22188670
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  • 20
    Abstract: Cancer stem cells may mediate therapy resistance and recurrence in various types of cancer, including glioblastoma. Cancer stemlike cells can be isolated from long-term cancer cell lines, including glioma lines. Using sphere formation as a model for cancer cell stemness in vitro, we derived sphere cultures from SMA-497, SMA-540, SMA-560, and GL-261 glioma cells. Gene expression and proteomics profiling demonstrated that sphere cultures uniformly showed an elevated expression of stemness-associated genes, notably including CD44. Differences in neural lineage marker expression between nonsphere and sphere cultures were heterogeneous except for a uniform reduction of beta-III-tubulin in sphere cultures. All sphere cultures showed slower growth. Self-renewal capacity was influenced by medium conditions but not nonsphere versus sphere culture phenotype. Sphere cultures were more resistant to irradiation, whereas both nonsphere and sphere cultures were highly resistant to temozolomide. Nonsphere cells formed more aggressive tumors in syngeneic mice than sphere cells in all models except SMA-560. There were no major differences in vascularization or infiltration by T cells or microglia/macrophages between nonsphere and sphere cell-derived tumors implanted in syngeneic hosts. Together, these data indicate that mouse glioma cell lines may be induced in vitro to form spheres that acquire features of stemness, but they do not exhibit a uniform biologic phenotype, thereby challenging the view that they represent a superior model system.
    Type of Publication: Journal article published
    PubMed ID: 25289892
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