Amplification of IGH/MYC fusion in clinically aggressive IGH/BCL2-positive germinal center B-cell lymphomas

Activation of an oncogene via its juxtaposition to the IGH locus by a chromosomal translocation or, less frequently, by genomic amplification is considered a major mechanism of B-cell lymphomagenesis. However, amplification of an IGH/oncogene fusion, coined a complicon, is a rare event in human cancers and has been associated with poor outcome and resistance to treatment. In this article are descriptions of two cases of germinal-center-derived B-cell lymphomas with IGH/BCL2 fusion that additionally displayed amplification of an IGH/MYC fusion. As shown by fluorescence in situ hybridization, the first case contained a IGH/MYC complicon in double minutes, whereas the second case showed a BCL2/IGH/MYC complicon on a der(8)t(8;14)t(14;18). Additional molecular cytogenetic and mutation analyses revealed that the first case also contained a chromosomal translocation affecting the BCL6 oncogene and a biallelic inactivation of TP53. The second case harbored a duplication of REL and acquired a translocation affecting IGL and a biallelic inactivation of TP53 during progression. Complicons affecting Igh/Myc have been reported previously in lymphomas of mouse models simultaneously deficient in Tp53 and in genes of the nonhomologous end-joining DNA repair pathway. To the best of our knowledge, this is the first time that IGH/MYC complicons have been reported in human lymphomas. Our findings imply that the two mechanisms resulting in MYC deregulation, that is, translocation and amplification, can occur simultaneously

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

Signaling pathways play a key role in complex diseases such as cancer, for which the development of novel therapies is a difficult, expensive and laborious task. Computational models that can predict the effect of a new combination of drugs without having to test it experimentally can help in accelerating this process. In particular, network-based dynamic models of these pathways hold promise to both understand and predict the effect of therapeutics. However, their use is currently hampered by limitations in our knowledge of the underlying biochemistry, as well as in the experimental and computational technologies used for calibrating the models. Thus, the results from such models need to be carefully interpreted and used in order to avoid biased predictions. Here we present a procedure that deals with this uncertainty by using experimental data to build an ensemble of dynamic models. The method incorporates steps to reduce overfitting and maximize predictive capability. We find that by combining the outputs of individual models in an ensemble it is possible to obtain a more robust prediction. We report results obtained with this method, which we call SELDOM (enSEmbLe of Dynamic lOgic-based Models), showing that it improves the predictions previously reported for several challenging problems.JRB and DH acknowledge funding from the EU FP7 project NICHE (ITN Grant number 289384). JRB acknowledges funding from the Spanish MINECO project SYNBIOFACTORY (grant number DPI2014-55276-C5-2-R). AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C postdoctoral fellowship ED481B2014/133-0. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Demographic, clinical and antibody characteristics of patients with digital ulcers in systemic sclerosis: data from the DUO Registry

OBJECTIVES: The Digital Ulcers Outcome (DUO) Registry was designed to describe the clinical and antibody characteristics, disease course and outcomes of patients with digital ulcers associated with systemic sclerosis (SSc). METHODS: The DUO Registry is a European, prospective, multicentre, observational, registry of SSc patients with ongoing digital ulcer disease, irrespective of treatment regimen. Data collected included demographics, SSc duration, SSc subset, internal organ manifestations, autoantibodies, previous and ongoing interventions and complications related to digital ulcers. RESULTS: Up to 19 November 2010 a total of 2439 patients had enrolled into the registry. Most were classified as either limited cutaneous SSc (lcSSc; 52.2%) or diffuse cutaneous SSc (dcSSc; 36.9%). Digital ulcers developed earlier in patients with dcSSc compared with lcSSc. Almost all patients (95.7%) tested positive for antinuclear antibodies, 45.2% for anti-scleroderma-70 and 43.6% for anticentromere antibodies (ACA). The first digital ulcer in the anti-scleroderma-70-positive patient cohort occurred approximately 5 years earlier than the ACA-positive patient group. CONCLUSIONS: This study provides data from a large cohort of SSc patients with a history of digital ulcers. The early occurrence and high frequency of digital ulcer complications are especially seen in patients with dcSSc and/or anti-scleroderma-70 antibodies

SELDOM V1.0.3

SELDOM stands for enSEmbLe of Dynamic lOgic-based Models. SELDOM is framework for building predictive signalling models that combines mutual information, ensemble modelling, logic-based ODEs and optimization. Here we provide a short manual on how to use SELDOM both for reproducing our results and adapting SELDOM to solve a new problem. The code is not distributed with the purpose of being a user friendly software. It requires a LINUX cluster as well as some knowledge in LINUX and R programming

SELDOM workflow.

<p>The experimental data is used to build an adjacency (a dense DDN) matrix based on the mutual information of all pairs of variables. Through a simple sampling scheme, and limiting the maximum in-degree for each node, a set of more sparse DDNs are generated. Each individual DDN is then used as a scaffold for independent model training and model reduction problems. The resulting models are used to form an ensemble which is able to produce predictions for state trajectories under untested experimental conditions.</p

An overview of the characteristics of all case studies approached in this work.

<p>An overview of the characteristics of all case studies approached in this work.</p

MAPK signaling network.

<p>The model by Huang et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005379#pcbi.1005379.ref067" target="_blank">67</a>] was used to generate pseudo-experimental data for two sub-problems. The first (MAPKp) partially observed (MAPK-PP, MAPKK-PP and MAPKKK), and the second fully observed MAPKf.</p