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.

Motivation: In biomedicine, every molecular measurement is relative to a reference point, like a fixed aliquot of RNA extracted from a tissue, a defined number of blood cells, or a defined volume of biofluid. Reference points are often chosen for practical reasons. For example, we might want to assess the metabolome of a diseased organ but can only measure metabolites in blood or urine. In this case the observable data only indirectly reflects the disease state. The statistical implications of these discrepancies in reference points have not yet been discussed. Results: Here we show that reference point discrepancies compromise the performance of regression models like the LASSO. As an alternative, we suggest zero-sum regression for a reference point insensitive analysis. We show that zero-sum regression is superior to the LASSO in case of a poor choice of reference point both in simulations and in an application that integrates intestinal microbiome analysis with metabolomics. Moreover, we describe a novel coordinate descent based algorithm to fit zero-sum elastic nets. Availability: The R-package "zeroSum" can be downloaded at https://github.com/rehbergT/zeroSum. Moreover, we provide all R-scripts and data used to produce the results of this manuscript as supplementary material.

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 H 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 .

Tipping points in complex systems are structural transitions from one state to another. In financial markets these critical points are connected to systemic risks, which have led to financial crisis in the past. Due to this, researchers are studying tipping points with different methods. This paper introduces a new method which bridges the gap between real-world portfolio management and statistical facts in financial markets in order to give more insight into the mechanics of financial markets.

Macrophages (Mφ) are abundantly present in the tumor microenvironment and may predict outcome in solid tumors and defined lymphoma subtypes. Mφ heterogeneity, the mechanisms of their recruitment, and their differentiation into lymphoma‐promoting, alternatively activated M2‐like phenotypes are still not fully understood. Therefore, further functional studies are required to understand biological mechanisms associated with human tumor‐associated Mφ (TAM). Here, we show that the global mRNA expression and protein abundance of human Mφ differentiated in Hodgkin lymphoma (HL)‐conditioned medium (CM) differ from those of Mφ educated by conditioned media from diffuse large B‐cell lymphoma (DLBCL) cells or, classically, by macrophage colony‐stimulating factor (M‐CSF). Conditioned media from HL cells support TAM differentiation through upregulation of surface antigens such as CD40, CD163, CD206, and PD‐L1. In particular, RNA and cell surface protein expression of mannose receptor 1 (MRC1)/CD206 significantly exceed the levels induced by classical M‐CSF stimulation in M2‐like Mφ; this is regulated by interleukin 13 to a large extent. Functionally, high CD206 enhances mannose‐dependent endocytosis and uptake of type I collagen. Together with high matrix metalloprotease9 secretion, HL‐TAMs appear to be active modulators of the tumor matrix. Preclinical in ovo models show that co‐cultures of HL cells with monocytes or Mφ support dissemination of lymphoma cells via lymphatic vessels, while tumor size and vessel destruction are decreased in comparison with lymphoma‐only tumors. Immunohistology of human HL tissues reveals a fraction of cases feature large numbers of CD206‐positive cells, with high MRC1 expression being characteristic of HL‐stage IV. In summary, the lymphoma‐TAM interaction contributes to matrix‐remodeling and lymphoma cell dissemination.

SummaryWe 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.

MotivationMolecular signatures for treatment recommendations are well researched. Still it is challenging to apply them to data generated by different protocols or technical platforms.ResultsWe analyzed paired data for the same tumors (Burkitt lymphoma, diffuse large B-cell lymphoma) and features that had been generated by different experimental protocols and analytical platforms including the nanoString nCounter and Affymetrix Gene Chip transcriptomics as well as the SWATH and SRM proteomics platforms. A statistical model that assumes independent sample and feature effects accounted for 69–94% of technical variability. We analyzed how variability is propagated through linear signatures possibly affecting predictions and treatment recommendations. Linear signatures with feature weights adding to zero were substantially more robust than unbalanced signatures. They yielded consistent predictions across data from different platforms, both for transcriptomics and proteomics data. Similarly stable were their predictions across data from fresh frozen and matching formalin-fixed paraffin-embedded human tumor tissue.Availability and ImplementationThe R-package ‘zeroSum’ can be downloaded at https://github.com/rehbergT/zeroSum. Complete data and R codes necessary to reproduce all our results can be received from the authors upon request.

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