Browsing by Author "Spang, Rainer"

Background: The distinction between Burkitt's lymphoma and diffuse large-B-cell lymphoma is unclear. We used transcriptional and genomic profiling to define Burkitt's lymphoma more precisely and to distinguish subgroups in other types of mature aggressive B-cell lymphomas. Methods: We performed gene-expression profiling using Affymetrix U133A GeneChips with RNA from 220 mature aggressive B-cell lymphomas, including a core group of 8 Burkitt's lymphomas that met all World Health Organization (WHO) criteria. A molecular signature for Burkitt's lymphoma was generated, and chromosomal abnormalities were detected with interphase fluorescence in situ hybridization and array-based comparative genomic hybridization. Results: We used the molecular signature for Burkitt's lymphoma to identify 44 cases: 11 had the morphologic features of diffuse large-B-cell lymphomas, 4 were unclassifiable mature aggressive B-cell lymphomas, and 29 had a classic or atypical Burkitt's morphologic appearance. Also, five did not have a detectable IG-myc Burkitt's translocation, whereas the others contained an IG-myc fusion, mostly in simple karyotypes. Of the 176 lymphomas without the molecular signature for Burkitt's lymphoma, 155 were diffuse large-B-cell lymphomas. Of these 155 cases, 21 percent had a chromosomal breakpoint at the myc locus associated with complex chromosomal changes and an unfavorable clinical course. Conclusions: Our molecular definition of Burkitt's lymphoma clarifies and extends the spectrum of the WHO criteria for Burkitt's lymphoma. In mature aggressive B-cell lymphomas without a gene signature for Burkitt's lymphoma, chromosomal breakpoints at the myc locus were associated with an adverse clinical outcome.

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.

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.

The genetic hallmark of Burkitt lymphoma (BL) is the t(8;14)(q24;q32) and its variants leading to activation of the MYC oncogene. It is a matter of debate whether true BL without MYC translocation exists. Here, we identified 59 lymphomas concordantly called BL by 2 gene expression classifiers among 753 B-cell lymphomas. Only2 (3%) of these 59 molecular BL lacked a MYC translocation, which both shared a peculiar pattern of chromosome 11q aberration characterized by interstitial gains including 11q23.2-q23.3 and telomeric losses of 11q24.1-qter. We extended our analysis to 17 MYC-negative high-grade B-cell lymphomas with a similar 11q aberration and showed this aberration to be recurrently associated with morphologic and clinical features of BL. The minimal region of gain was defined by high-level amplifications in 11q23.3 and associated with overexpression of genes including PAFAH1B2 on a transcriptional and protein level. The recurrent region of loss contained a focal homozygous deletion in 11q24.2-q24.3 including the ETS1 gene, which was shown to be mutated in 4 of 16 investigated cases. These findings indicate the existence of a molecularly distinct subset of B-cell lymphomas reminiscent of BL, which is characterized by deregulation of genes in 11q.

Nested effects models (NEMs) are a class of probabilistic models introduced to analyze the effects of gene perturbation screens visible in high-dimensional phenotypes like microarrays or cell morphology. NEMs reverse engineer upstream/downstream relations of cellular signaling cascades. NEMs take as input a set of candidate pathway genes and phenotypic profiles of perturbing these genes. NEMs return a pathway structure explaining the observed perturbation effects. Here, we describe the package nem, an open-source software to efficiently infer NEMs from data. Our software implements several search algorithms for model fitting and is applicable to a wide range of different data types and representations. The methods we present summarize the current state-of-the-art in NEMs.

Motivation: Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. Results: We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines.

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.

Chromosomal translocations affecting the MYC oncogene are the biological hallmark of Burkitt lymphomas but also occur in a subset of other mature B-cell lymphomas. If accompanied by a chromosomal break targeting the BCL2 and/or BCL6 oncogene these MYC translocation-positive (MYC+) lymphomas are called double-hit lymphomas, otherwise the term single-hit lymphomas is applied. In order to characterize the biological features of these MYC+ lymphomas other than Burkitt lymphoma we explored, after exclusion of molecular Burkitt lymphoma as defined by gene expression profiling, the molecular, pathological and clinical aspects of 80 MYC-translocation-positive lymphomas (31 single-hit, 46 double-hit and 3 MYC+-lymphomas with unknown BCL6 status). Comparison of single-hit and double-hit lymphomas revealed no difference in MYC partner (IG/non-IG), genomic complexity, MYC expression or gene expression profile. Double-hit lymphomas more frequently showed a germinal center B-cell-like gene expression profile and had higher IGH and MYC mutation frequencies. Gene expression profiling revealed 130 differentially expressed genes between BCL6(+)/MYC+ and BCL2(+)/MYC+ double-hit lymphomas. BCL2(+)/MYC+ double-hit lymphomas more frequently showed a germinal center B-like gene expression profile. Analysis of all lymphomas according to MYC partner (IG/non-IG) revealed no substantial differences. In this series of lymphomas, in which immunochemotherapy was administered in only a minority of cases, single-hit and double-hit lymphomas had a similar poor outcome in contrast to the outcome of molecular Burkitt lymphoma and lymphomas without the MYC break. Our data suggest that, after excluding molecular Burkitt lymphoma and pediatric cases, MYC+ lymphomas are biologically quite homogeneous with single-hit and double-hit lymphomas as well as IG-MYC and non-IG-MYC+ lymphomas sharing various molecular characteristics.

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.

Burkitt's lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL) are pathologically and clinically distinct subtypes of aggressive non-Hodgkin B-cell lymphoma. To learn more about their biology, we employed metabolomic and proteomic methods to study both established cell lines as well as cryopreserved and formalin-fixed paraffin-embedded (FFPE) tissue sections of BL and DLBCL. Strikingly, NMR analyses revealed DLBCL cell lines to produce and secrete significantly (P-adj = 1.72 X 10(-22)) more pyruvic acid than BL cell lines. This finding could be reproduced by targeted GC/MS analyses of cryopreserved tissue sections of BL and DLBCL cases. Enrichment analysis of an overlapping set of N = 2315 proteins, that had been quantified by nanoLC-SWATH-MS in BL and DLBCL cultured cells and cryosections, supported the observed difference in pyruvic acid content, as glycolysis and pyruvate metabolism were downregulated, while one-carbon metabolism was upregulated in BL compared to DLBCL. Furthermore, 92.1% of the overlapping significant proteins showed the same direction of regulation in cryopreserved and FFPE material. Proteome data are available via ProteomeXchange with identifier PXD004936.

Knowledge of stromal factors that have a role in the transcriptional regulation of metabolic pathways aside from c-Myc is fundamental to improvements in lymphoma therapy. Using a MYC-inducible human B-cell line, we observed the cooperative activation of STAT3 and NF-κB by IL10 and CpG stimulation. We show that IL10 + CpG-mediated cell proliferation of MYClow cells depends on glutaminolysis. By 13C- and 15N-tracing of glutamine metabolism and metabolite rescue experiments, we demonstrate that GOT2 provides aspartate and nucleotides to cells with activated or aberrant Jak/STAT and NF-κB signaling. A model of GOT2 transcriptional regulation is proposed, in which the cooperative phosphorylation of STAT3 and direct joint binding of STAT3 and p65/NF-κB to the proximal GOT2 promoter are important. Furthermore, high aberrant GOT2 expression is prognostic in diffuse large B-cell lymphoma underscoring the current findings and importance of stromal factors in lymphoma biology.

Background Knowledge about the genetic lesions that occur in Burkitt's lymphoma, besides the pathognomonic IG-MYC translocations, is limited. Design and Methods Thirty-nine molecularly-defined Burkitt's lymphomas were analyzed with high-resolution single-nucleotide polymorphism chips for genomic imbalances and uniparental disomy. Imbalances were correlated to expression profiles and selected micro-RNA analysis. Translocations affecting the MYC locus were studied by fluoresence in situ hybridization. Results We detected 528 copy number changes, defining 29 recurrently imbalanced regions. Five hundred and eighteen regions of uniparental disomy were found, but these were rarely recurrent. Combined imbalance mapping and expression profiling revealed a strong correlation between copy number and expression. Several recurrent imbalances affected the MYC pathway: the micro-RNA-supercluster 17-92 was frequently gained and the transcription factor E2F2 was recurrently deleted. Molecular Burkitt's lymphoma lacking MYC translocations showed MYC gains. Amplifications of the polymerase iota gene were associated with increased frequency of positions scored as aberrant. Conclusions The present findings suggest that uniparental disomies do not play a major role in the pathogenesis of Burkitt's lymphoma, whereas some genes may contribute to the development of this lymphoma through gene dosage effects. Amplifications of the polymerase iota gene may be functionally linked with increased genomic alterations in Burkitt's lymphoma. The pattern and rarity of chromosomal changes detectable, even at the high resolution employed here, together with aberrations of genes regulating MYC activity, support the hypothesis that deregulation of the MYC pathway is the major force driving the pathogenesis of Burkitt's lymphoma, but show that this deregulation is more complex than previously known.

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.

Gene expression measurements are typically performed on a fixed-weight aliquot of RNA, which assumes that the total number of transcripts per cell stays nearly constant across all conditions. In cases where this assumption does not hold (e.g., when comparing cell types with different cell sizes) the expression data provide a distorted view of cellular events. Assuming constant numbers of total transcripts, increases in expression of some RNAs must be compensated for by decreases in expression of others. Therefore, we propose calibrating gene expression data to an external reference point, the number of cells in the sample, using whole-cell spike-ins. In a systematic dilution experiment, we mixed varying numbers of human cells with fixed numbers of Drosophila melanogaster cells and scaled the expression levels of the human genes relative to those of the Drosophila genes. This approach restored the original gene expression ratios generated by the dilutions. We then used Drosophila whole-cell spike-ins to uncover non-symmetric gene expression changes, in this case much larger numbers of induced than repressed genes, under perturbations of the human cell line P493-6. Drosophila whole-cell spike-ins are an experimentally and computationally easy and low-priced method to derive mRNA fold changes of absolute abundances from RNA sequencing (RNA-Seq) and quantitative real-time PCR (qPCR) data.