Altenbuchinger, Michael

[["dc.bibliographiccitation.firstpage","116"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","British Journal of Haematology"],["dc.bibliographiccitation.lastpage","119"],["dc.bibliographiccitation.volume","179"],["dc.contributor.author","Szczepanowski, Monika"],["dc.contributor.author","Lange, Jonas"],["dc.contributor.author","Kohler, Christian W."],["dc.contributor.author","Masque-Soler, Neus"],["dc.contributor.author","Zimmermann, Martin"],["dc.contributor.author","Aukema, Sietse M."],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Rehberg, Thorsten"],["dc.contributor.author","Mahn, Friederike"],["dc.contributor.author","Siebert, Reiner"],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Burkhardt, Birgit"],["dc.contributor.author","Klapper, Wolfram"],["dc.date.accessioned","2021-09-17T09:47:39Z"],["dc.date.available","2021-09-17T09:47:39Z"],["dc.date.issued","2017"],["dc.description.abstract","We present the largest series of diffuse large B-cell lymphoma (DLBCL) in patients younger than 18 years analysed to date by gene expression profiling using Nanostring technology to identify molecular subtypes and fluorescent in situ hybridization for translocations of MYC. We show that the activated B cell-like subtype of DLBCL is exceedingly rare in children and - in contrast to adults- not associated with outcome. Furthermore, we review the current literature and demonstrate that MYC translocations are not more frequent in paediatric compared to adult DLBCL. A prognostic role of MYC in the paediatric age groups seems unlikely."],["dc.identifier.doi","10.1111/bjh.14812"],["dc.identifier.pmid","28643426"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89645"],["dc.language.iso","en"],["dc.relation.eissn","1365-2141"],["dc.relation.issn","0007-1048"],["dc.title","Cell-of-origin classification by gene expression and MYC-rearrangements in diffuse large B-cell lymphoma of children and adolescents"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","3596"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Journal of Proteome Research"],["dc.bibliographiccitation.lastpage","3605"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Zacharias, Helena U."],["dc.contributor.author","Rehberg, Thorsten"],["dc.contributor.author","Mehrl, Sebastian"],["dc.contributor.author","Richtmann, Daniel"],["dc.contributor.author","Wettig, Tilo"],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Gronwald, Wolfram"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.date.accessioned","2021-09-17T09:47:02Z"],["dc.date.available","2021-09-17T09:47:02Z"],["dc.date.issued","2017"],["dc.description.abstract","Metabolomics data is typically scaled to a common reference like a constant volume of body fluid, a constant creatinine level, or a constant area under the spectrum. Such scaling of the data, however, may affect the selection of biomarkers and the biological interpretation of results in unforeseen ways. Here, we studied how both the outcome of hypothesis tests for differential metabolite concentration and the screening for multivariate metabolite signatures are affected by the choice of scale. To overcome this problem for metabolite signatures and to establish a scale-invariant biomarker discovery algorithm, we extended linear zero-sum regression to the logistic regression framework and showed in two applications to 1H NMR-based metabolomics data how this approach overcomes the scaling problem. Logistic zero-sum regression is available as an R package as well as a high-performance computing implementation that can be downloaded at https://github.com/rehbergT/zeroSum ."],["dc.identifier.arxiv","1703.07724v1"],["dc.identifier.doi","10.1021/acs.jproteome.7b00325"],["dc.identifier.pmid","28825821"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89638"],["dc.language.iso","en"],["dc.relation.eissn","1535-3907"],["dc.relation.issn","1535-3893"],["dc.title","Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","75"],["dc.bibliographiccitation.lastpage","89"],["dc.contributor.author","Görtler, Franziska"],["dc.contributor.author","Solbrig, Stefan"],["dc.contributor.author","Wettig, Tilo"],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.editor","Raphael, Benjamin J."],["dc.date.accessioned","2021-09-17T09:46:30Z"],["dc.date.available","2021-09-17T09:46:30Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1007/978-3-319-89929-9_5"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89631"],["dc.publisher","Springer"],["dc.relation.conference","22nd Annual International Conference, RECOMB 2018"],["dc.relation.eventend","2018-04-24"],["dc.relation.eventlocation","Paris"],["dc.relation.eventstart","2018-04-21"],["dc.relation.isbn","978-3-319-89928-2"],["dc.relation.isbn","978-3-319-89929-9"],["dc.relation.ispartof","Research in Computational Molecular Biology"],["dc.title","Loss-Function Learning for Digital Tissue Deconvolution"],["dc.type","conference_paper"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1796"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Journal of Proteome Research"],["dc.bibliographiccitation.lastpage","1805"],["dc.bibliographiccitation.volume","18"],["dc.contributor.author","Zacharias, Helena U."],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Schultheiss, Ulla T."],["dc.contributor.author","Samol, Claudia"],["dc.contributor.author","Kotsis, Fruzsina"],["dc.contributor.author","Poguntke, Inga"],["dc.contributor.author","Sekula, Peggy"],["dc.contributor.author","Krumsiek, Jan"],["dc.contributor.author","Köttgen, Anna"],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Gronwald, Wolfram"],["dc.date.accessioned","2021-09-17T09:47:06Z"],["dc.date.available","2021-09-17T09:47:06Z"],["dc.date.issued","2019"],["dc.description.abstract","Identification of chronic kidney disease patients at risk of progressing to end-stage renal disease (ESRD) is essential for treatment decision-making and clinical trial design. Here, we explored whether proton nuclear magnetic resonance (NMR) spectroscopy of blood plasma improves the currently best performing kidney failure risk equation, the so-called Tangri score. Our study cohort comprised 4640 participants from the German Chronic Kidney Disease (GCKD) study, of whom 185 (3.99%) progressed over a mean observation time of 3.70 ± 0.88 years to ESRD requiring either dialysis or transplantation. The original four-variable Tangri risk equation yielded a C statistic of 0.863 (95% CI, 0.831-0.900). Upon inclusion of NMR features by state-of-the-art machine learning methods, the C statistic improved to 0.875 (95% CI, 0.850-0.911), thereby outperforming the Tangri score in 94 out of 100 subsampling rounds. Of the 24 NMR features included in the model, creatinine, high-density lipoprotein, valine, acetyl groups of glycoproteins, and Ca2+-EDTA carried the highest weights. In conclusion, proton NMR-based plasma fingerprinting improved markedly the detection of patients at risk of developing ESRD, thus enabling enhanced patient treatment."],["dc.identifier.doi","10.1021/acs.jproteome.8b00983"],["dc.identifier.pmid","30817158"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89639"],["dc.language.iso","en"],["dc.relation.eissn","1535-3907"],["dc.relation.issn","1535-3893"],["dc.title","A Novel Metabolic Signature To Predict the Requirement of Dialysis or Renal Transplantation in Patients with Chronic Kidney Disease"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","543"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Leukemia"],["dc.bibliographiccitation.lastpage","552"],["dc.bibliographiccitation.volume","34"],["dc.contributor.author","Staiger, Annette M."],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Ziepert, Marita"],["dc.contributor.author","Kohler, Christian"],["dc.contributor.author","Horn, Heike"],["dc.contributor.author","Huttner, Michael"],["dc.contributor.author","Hüttl, Katrin S."],["dc.contributor.author","Glehr, Gunther"],["dc.contributor.author","Klapper, Wolfram"],["dc.contributor.author","Szczepanowski, Monika"],["dc.contributor.author","Richter, Julia"],["dc.contributor.author","Stein, Harald"],["dc.contributor.author","Feller, Alfred C."],["dc.contributor.author","Möller, Peter"],["dc.contributor.author","Hansmann, Martin-Leo"],["dc.contributor.author","Poeschel, Viola"],["dc.contributor.author","Held, Gerhard"],["dc.contributor.author","Loeffler, Markus"],["dc.contributor.author","Schmitz, Norbert"],["dc.contributor.author","Trümper, Lorenz"],["dc.contributor.author","Pukrop, Tobias"],["dc.contributor.author","Rosenwald, Andreas"],["dc.contributor.author","Ott, German"],["dc.contributor.author","Spang, Rainer"],["dc.date.accessioned","2020-12-10T18:09:35Z"],["dc.date.available","2020-12-10T18:09:35Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1038/s41375-019-0573-y"],["dc.identifier.eissn","1476-5551"],["dc.identifier.issn","0887-6924"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73698"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","A novel lymphoma-associated macrophage interaction signature (LAMIS) provides robust risk prognostication in diffuse large B-cell lymphoma clinical trial cohorts of the DSHNHL"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","341"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Virchows Archiv"],["dc.bibliographiccitation.lastpage","349"],["dc.bibliographiccitation.volume","473"],["dc.contributor.author","Reinke, Sarah"],["dc.contributor.author","Richter, Julia"],["dc.contributor.author","Fend, Falko"],["dc.contributor.author","Feller, Alfred"],["dc.contributor.author","Hansmann, Martin-Leo"],["dc.contributor.author","Hüttl, Katrin"],["dc.contributor.author","Oschlies, Ilske"],["dc.contributor.author","Ott, German"],["dc.contributor.author","Möller, Peter"],["dc.contributor.author","Rosenwald, Andreas"],["dc.contributor.author","Stein, Harald"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Klapper, Wolfram"],["dc.date.accessioned","2021-09-17T09:46:34Z"],["dc.date.available","2021-09-17T09:46:34Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1007/s00428-018-2367-4"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89632"],["dc.relation.issn","0945-6317"],["dc.relation.issn","1432-2307"],["dc.title","Round-robin test for the cell-of-origin classification of diffuse large B-cell lymphoma—a feasibility study using full slide staining"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","13954"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Zacharias, Helena U."],["dc.contributor.author","Solbrig, Stefan"],["dc.contributor.author","Schäfer, Andreas"],["dc.contributor.author","Büyüközkan, Mustafa"],["dc.contributor.author","Schultheiß, Ulla T."],["dc.contributor.author","Kotsis, Fruzsina"],["dc.contributor.author","Köttgen, Anna"],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Krumsiek, Jan"],["dc.contributor.author","Gronwald, Wolfram"],["dc.date.accessioned","2021-09-17T09:47:11Z"],["dc.date.available","2021-09-17T09:47:11Z"],["dc.date.issued","2019"],["dc.description.abstract","Omics data facilitate the gain of novel insights into the pathophysiology of diseases and, consequently, their diagnosis, treatment, and prevention. To this end, omics data are integrated with other data types, e.g., clinical, phenotypic, and demographic parameters of categorical or continuous nature. We exemplify this data integration issue for a chronic kidney disease (CKD) study, comprising complex clinical, demographic, and one-dimensional 1H nuclear magnetic resonance metabolic variables. Routine analysis screens for associations of single metabolic features with clinical parameters while accounting for confounders typically chosen by expert knowledge. This knowledge can be incomplete or unavailable. We introduce a framework for data integration that intrinsically adjusts for confounding variables. We give its mathematical and algorithmic foundation, provide a state-of-the-art implementation, and evaluate its performance by sanity checks and predictive performance assessment on independent test data. Particularly, we show that discovered associations remain significant after variable adjustment based on expert knowledge. In contrast, we illustrate that associations discovered in routine univariate screening approaches can be biased by incorrect or incomplete expert knowledge. Our data integration approach reveals important associations between CKD comorbidities and metabolites, including novel associations of the plasma metabolite trimethylamine-N-oxide with cardiac arrhythmia and infarction in CKD stage 3 patients."],["dc.identifier.doi","10.1038/s41598-019-50346-2"],["dc.identifier.pmid","31562371"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89640"],["dc.language.iso","en"],["dc.relation.issn","2045-2322"],["dc.title","A multi-source data integration approach reveals novel associations between metabolites and renal outcomes in the German Chronic Kidney Disease study"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1107"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Leukemia & Lymphoma"],["dc.bibliographiccitation.lastpage","1115"],["dc.bibliographiccitation.volume","62"],["dc.contributor.author","Nordmo, Carmen"],["dc.contributor.author","Glehr, Gunther"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Ziepert, Marita"],["dc.contributor.author","Horn, Heike"],["dc.contributor.author","Staiger, Annette M."],["dc.contributor.author","Ott, German"],["dc.contributor.author","Schmitz, Norbert"],["dc.contributor.author","Held, Gerhard"],["dc.contributor.author","Einsele, Hermann"],["dc.contributor.author","Topp, Max"],["dc.contributor.author","Rosenwald, Andreas"],["dc.contributor.author","Rauert-Wunderlich, Hilka"],["dc.date.accessioned","2021-09-17T09:47:24Z"],["dc.date.available","2021-09-17T09:47:24Z"],["dc.date.issued","2021"],["dc.description.abstract","In order to differentiate prognostic subgroups of patients with aggressive B-cell lymphoma, we analyzed the expression of 800 miRNAs with the NanoString nCounter human miRNA assay on a cohort of 228 FFPE samples of patients enrolled in the RICOVER-60 and MegaCHOEP trials. We identified significant miRNA signatures for overall survival (OS) and progression-free survival (PFS) by LASSO-penalized linear Cox-regression. High expression levels of miR-130a-3p and miR-423-5p indicate a better prognosis, whereas high levels of miR-374b-5p, miR-590-5p, miR-186-5p, and miR-106b-5p increase patients' risk levels for OS. Regarding PFS high expression of miR-365a-5p in addition to the other two miRNAs improves the prognosis and high levels of miR374a-5p, miR-106b-5p, and miR-590-5p, connects with increased risk and poor prognosis. We identified miRNA signatures to subdivide patients into two different risk groups. These prognostic models may be used in risk stratification in future clinical trials and help making personalized therapy decisions."],["dc.identifier.doi","10.1080/10428194.2020.1861268"],["dc.identifier.pmid","33353431"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89642"],["dc.language.iso","en"],["dc.relation.eissn","1029-2403"],["dc.relation.issn","1042-8194"],["dc.title","Identification of a miRNA based model to detect prognostic subgroups in patients with aggressive B-cell lymphoma"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","13"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Biological Procedures Online"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Wagner, Marcus"],["dc.contributor.author","Hänsel, René"],["dc.contributor.author","Reinke, Sarah"],["dc.contributor.author","Richter, Julia"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Braumann, Ulf-Dietrich"],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Löffler, Markus"],["dc.contributor.author","Klapper, Wolfram"],["dc.date.accessioned","2021-09-17T09:48:05Z"],["dc.date.available","2021-09-17T09:48:05Z"],["dc.date.issued","2019"],["dc.description.abstract","For analysis of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) tissue samples, it is desirable to obtain information about counts and distribution of different macrophage subtypes. Until now, macrophage counts are mostly inferred from gene expression analysis of whole tissue sections, providing only indirect information. Direct analysis of immunohistochemically (IHC) fluorescence stained tissue samples is confronted with several difficulties, e.g. high variability of shape and size of target macrophages and strongly inhomogeneous intensity of staining. Consequently, application of commercial software is largely restricted to very rough analysis modes, and most macrophage counts are still obtained by manual counting in microarrays or high power fields, thus failing to represent the heterogeneity of tumor microenvironment adequately."],["dc.identifier.doi","10.1186/s12575-019-0098-9"],["dc.identifier.pmid","31303867"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89651"],["dc.language.iso","en"],["dc.relation.issn","1480-9222"],["dc.title","Automated macrophage counting in DLBCL tissue samples: a ROF filter based approach"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","386"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Journal of Computational Biology"],["dc.bibliographiccitation.lastpage","389"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Schön, Marian"],["dc.contributor.author","Simeth, Jakob"],["dc.contributor.author","Heinrich, Paul"],["dc.contributor.author","Görtler, Franziska"],["dc.contributor.author","Solbrig, Stefan"],["dc.contributor.author","Wettig, Tilo"],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Spang, Rainer"],["dc.date.accessioned","2021-09-17T09:47:29Z"],["dc.date.available","2021-09-17T09:47:29Z"],["dc.date.issued","2020"],["dc.description.abstract","Digital tissue deconvolution (DTD) estimates the cellular composition of a tissue from its bulk gene-expression profile. For this, DTD approximates the bulk as a mixture of cell-specific expression profiles. Different tissues have different cellular compositions, with cells in different activation states, and embedded in different environments. Consequently, DTD can profit from tailoring the deconvolution model to a specific tissue context. Loss-function learning adapts DTD to a specific tissue context, such as the deconvolution of blood, or a specific type of tumor tissue. We provide software for loss-function learning, for its validation and visualization, and for applying the DTD models to new data."],["dc.identifier.doi","10.1089/cmb.2019.0469"],["dc.identifier.pmid","31995409"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89643"],["dc.language.iso","en"],["dc.relation.issn","1557-8666"],["dc.title","DTD: An R Package for Digital Tissue Deconvolution"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]