Head and neck paragangliomas, rare neoplasms of the paraganglia composed of nests of neurosecretory and glial cells embedded in vascular stroma, provide a remarkable example of organoid tumor architecture. To identify genes and pathways commonly deregulated in head and neck paraganglioma, we integrated high-density genome-wide copy number variation (CNV) analysis with microRNA and immunomorphological studies. Gene-centric CNV analysis of 24 cases identified a list of 104 genes most significantly targeted by tumor-associated alterations. The “NOTCH signaling pathway” was the most significantly enriched term in the list (P = 0.002 after Bonferroni or Benjamini correction). Expression of the relevant NOTCH pathway proteins in sustentacular (glial), chief (neuroendocrine) and endothelial cells was confirmed by immunohistochemistry in 47 head and neck paraganglioma cases. There were no relationships between level and pattern of NOTCH1/JAG2 protein expression and germline mutation status in the SDH genes, implicated in paraganglioma predisposition, or the presence/absence of immunostaining for SDHB, a surrogate marker of SDH mutations. Interestingly, NOTCH upregulation was observed also in cases with no evidence of CNVs at NOTCH signaling genes, suggesting altered epigenetic modulation of this pathway. To address this issue we performed microarray-based microRNA expression analyses. Notably 5 microRNAs (miR-200a,b,c and miR-34b,c), including those most downregulated in the tumors, correlated to NOTCH signaling and directly targeted NOTCH1 in in vitro experiments using SH-SY5Y neuroblastoma cells. Furthermore, lentiviral transduction of miR-200s and miR-34s in patient-derived primary tympano-jugular paraganglioma cell cultures was associated with NOTCH1 downregulation and increased levels of markers of cell toxicity and cell death. Taken together, our results provide an integrated view of common molecular alterations associated with head and neck paraganglioma and reveal an essential role of NOTCH pathway deregulation in this tumor type.Electronic supplementary materialThe online version of this article (doi:10.1007/s00401-013-1165-y) contains supplementary material, which is available to authorized users.
BackgroundComplex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones.ResultsWe present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful.ConclusionsBy the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study.
BackgroundMultiple high-throughput molecular profiling by omics technologies can be collected for the same individuals. Combining these data, rather than exploiting them separately, can significantly increase the power of clinically relevant patients subclassifications.ResultsWe propose a multi-view approach in which the information from different data layers (views) is integrated at the levels of the results of each single view clustering iterations. It works by factorizing the membership matrices in a late integration manner. We evaluated the effectiveness and the performance of our method on six multi-view cancer datasets. In all the cases, we found patient sub-classes with statistical significance, identifying novel sub-groups previously not emphasized in literature. Our method performed better as compared to other multi-view clustering algorithms and, unlike other existing methods, it is able to quantify the contribution of single views on the final results.ConclusionOur observations suggest that integration of prior information with genomic features in the subtyping analysis is an effective strategy in identifying disease subgroups. The methodology is implemented in R and the source code is available online at http://neuronelab.unisa.it/a-multi-view-genomic-data-integration-methodology/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0680-3) contains supplementary material, which is available to authorized users.
Process flowsheet optimization of a lignocellulosic biorefinery coproducing levulinic acid, succinic acid, and ethanol was performed to maximize an economic objective function (either the net present value, NPV, or the internal rate of return, IRR, alternatively) by means of mathematical programming methods. Most promising alternative industrial processes were selected to build the superstructure of the biorefinery. A discretization method was applied to obtain a MILP approximation of the resulting MINLP master problem. NPV maximization for a biorefinery with hardwood feedstock provided with an optimal flowsheet with all three products. Larger biomass allocation values resulted for levulinic acid and succinic acid (more than 40% each). A sensitivity analysis highlighted that the optimal flowsheet was significantly dependent on the economic scenario (chemical products selling price, discount rate) and on the plant scale. Finally, alternative maximization of NPV and of IRR provided different optimal flowsheets and biomass allocation to chemical products
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