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Next-generation sequencing of human tumours has refined our understanding of the mutational processes operative in cancer initiation and progression, yet major questions remain regarding the factors that induce driver mutations and the processes that shape mutation selection during tumorigenesis. Here we performed whole-exome sequencing on adenomas from three mouse models of non-small-cell lung cancer, which were induced either by exposure to carcinogens (methyl-nitrosourea (MNU) and urethane) or by genetic activation of Kras ([Kras.sup.LA2]). Although the MNU-induced tumours carried exactly the same initiating mutation in Kras as seen in the [Kras.sup.LA2] model (G12D), MNU tumours had an average of 192 non-synonymous, somatic single-nucleotide variants, compared with only six in tumours from the [Kras.sup.LA2] model. By contrast, the [Kras.sup.LA2] tumours exhibited a significantly higher level of aneuploidy and copy number alterations compared with the carcinogen-induced tumours, suggesting that carcinogen-induced and genetically engineered models lead to tumour development through different routes. The wild-type allele of Kras has been shown to act as a tumour suppressor in mouse models of non-small-cell lung cancer. We demonstrate that urethane-induced tumours from wild-type mice carry mostly (94%) Kras Q61R mutations, whereas those from Kras heterozygous animals carry mostly (92%) Kras Q61L mutations, indicating a major role for germline Kras status in mutation selection during initiation. The exomewide mutation spectra in carcinogen-induced tumours overwhelmingly display signatures of the initiating carcinogen, while adenocarcinomas acquire additional C > T mutations at CpG sites. These data provide a basis for understanding results from human tumour genome sequencing, which has identified two broad categories of tumours based on the relative frequency of single-nucleotide variations and copy number alterations (1), and underline the importance of carcinogen models for understanding the complex mutation spectra seen in human cancers., Sequencing studies of human cancers have identified a number of mutation 'signatures', suggesting that tumours carry an imprint of the environmental agents to which patients were exposed (2-4). At present, [...]

Antiviral responses must be tightly regulated to defend rapidly against infection while minimizing inflammatory damage. Type 1 interferons (IFN-I) are crucial mediators of antiviral responses (1) and their transcription is [...]

Pancreatic ductal adenocarcinoma (PDA) remains a lethal malignancy despite much progress concerning its molecular characterization. PDA tumours harbour four signature somatic mutations (1-4) in addition to numerous lower frequency genetic events of uncertain significance (5). Here we use Sleeping Beauty (SB) transposon-mediated insertional mutagenesis (6,7) in a mouse model of pancreatic ductal preneoplasia (8) to identify genes that cooperate with oncogenic [Kras.sup.G12D] to accelerate tumorigenesis and promote progression. Our screen revealed new candidate genes for PDA and confirmed the importance of many genes and pathways previously implicated in human PDA. The most commonly mutated gene was the X-linked deubiquitinase Usp9x, which was inactivated in over 50% of the tumours. Although previous work had attributed a pro-survival role to USP9X in human neoplasia (9), we found instead that loss of Usp9x enhances transformation and protects pancreatic cancer cells from anoikis. Clinically, low USP9X protein and messenger RNA expression in PDA correlates with poor survival after surgery, and USP9X levels are inversely associated with metastatic burden in advanced disease. Furthermore, chromatin modulation with trichostatin A or 5-aza-2'-deoxycytidine elevates USP9X expression in human PDA cell lines, indicating a clinical approach for certain patients. The conditional deletion of Usp9x cooperated with [Kras.sup.G12D] to accelerate pancreatic tumorigenesis in mice, validating their genetic interaction. We propose that USP9X is a major tumour suppressor gene with prognostic and therapeutic relevance in PDA., The biological sequelae of PDA has been partially attributed to frequent and well characterized mutations in KRAS (>90%), CDKN2A (>90%), TP53 (70%) and SMAD4 (55%) (1-4). Recent genomewide analyses have [...]

Author(s): Mark Gilchrist [1]; Vesteinn Thorsson [1]; Bin Li [1]; Alistair G. Rust [1]; Martin Korb [1]; Kathleen Kennedy [1]; Tsonwin Hai [2]; Hamid Bolouri [1]; Alan Aderem (corresponding author) [...]

Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select truepositive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST. Fisher's method | mixture distribution models

The integration of data from multiple global assays is essential to understanding dynamic spatiotemporal interactions within cells. In a companion paper, we reported a data integration methodology, designated Pointillist, that can handle multiple data types from technologies with different noise characteristics. Here we demonstrate its application to the integration of 18 data sets relating to galactose utilization in yeast. These data include global changes in mRNA and protein abundance, genome-wide protein-DNA interaction data, database information, and computational predictions of protein--DNA and protein--protein interactions. We divided the integration task to determine three network components: key system elements (genes and proteins), protein--protein interactions, and protein-DNA interactions. Results indicate that the reconstructed network efficiently focuses on and recapitulates the known biology of galactose utilization. It also provided new insights, some of which were verified experimentally. The methodology described here, addresses a critical need across all domains of molecular and cell biology, to effectively integrate large and disparate data sets. metabolism | yeast | molecular network model | galactose

Author(s): Mouse Genome Sequencing Consortium ; Genome Sequencing Center: ; Asif T. Chinwalla [1, 47]; Lisa L. Cook [1]; Kimberly D. Delehaunty [1]; Ginger A. Fewell [1]; Lucinda A. Fulton [...]

We present the current form of a provisional DNA sequence-based regulatory gene network that explains in outline how endomesodermal specification in the sea urchin embryo is controlled. The model of the network is in a continuous process of revision and growth as new genes are added and new experimental results become available; see http://www.its.cahech.edu/~mirsky/endomeso.htm (End-mes Gene Network Update) for the latest version. The network contains over 40 genes at present, many newly uncovered in the course of this work, and most encoding DNA-binding transcriptional regulatory factors. The architecture of the network was approached initially by construction of a logic model that integrated the extensive experimental evidence now available on endomesoderm specification. The internal linkages between genes in the network have been determined functionally, by measurement of the effects of regulatory perturbations on the expression of all relevant genes in the network. Five kinds of perturbation have been applied: (1) use of morpholino antisense oligonucleotides targeted to many of the key regulatory genes in the network; (2) transformation of other regulatory factors into dominant repressors by construction of Engrailed repressor domain fusions; (3) ectopic expression of given regulatory factors, from genetic expression constructs and from injected mRNAs; (4) blockade of the [beta]-catenin/Tcf pathway by introduction of mRNA encoding the intracellular domain of cadherin; and (5) blockade of the Notch signaling pathway by introduction of mRNA encoding the extracellular domain of the Notch receptor. The network model predicts the cis-regulatory inputs that link each gene into the network. Therefore, its architecture is testable by cis-regulatory analysis. Strongylocentroms purpuratus and Lytechinus variegatus genomic BAC recombinants that include a large number of the genes in the network have been sequenced and annotated. Tests of the cis-regulatory predictions of the model are greatly facilitated by interspecific computational sequence comparison, which affords a rapid identification of likely cis-regulatory elements in advance of experimental analysis. The network specifies genomically encoded regulatory processes between early cleavage and gastrula stages. These control the specification of the micromere lineage and of the initial [veg.sub.2] endomesodermal domain; the blastula-stage separation of the central [veg.sub.2] mesodermal domain (i.e., the secondary mesenchyme progenitor field) from the peripheral [veg.sub.2] endodermal domain; the stabilization of specification state within these domains; and activation of some downstream differentiation genes. Each of the temporal--spatial phases of specification is represented in a subelement of the network model, that treats regulatory events within the relevant embryonic nuclei at particular stages. Key Words: gene network; sea urchin embryo; gene regulation.

The investigation and modeling of gene regulatory networks requires computational tools specific to the task. We present several locally developed software tools that have been used in support of our ongoing research into the embryogenesis of the sea urchin. These tools are especially well suited to iterative refinement of models through experimental and computational investigation. They include: BioArray, a macroarray spot processing program; SUGAR, a system to display and correlate large-BAC sequence analyses; SeqComp and FamilyRelations, programs for comparative sequence analysis; and NetBuilder, an environment for creating and analyzing models of gene networks. We also present an overview of the process used to build our model of the Strongylocentrotus purpuratus endomesoderm gene network. Several of the tools discussed in this paper are still in active development and some are available as open source. Key Words: sequence annotation; comparative analysis; macroarrays; gene network modeling; computational tools.

Development of the body plan is controlled by large networks of regulatory genes. A gene regulatory network that controls the specification of endoderm and mesoderm in the sea urchin embryo is summarized here. The network was derived from large-scale perturbation analyses, in combination with computational methodologies, genomic data, cis-regulatory analysis, and molecular embryology. The network contains over 40 genes at present, and each node can be directly verified at the DNA sequence level by cis-regulatory analysis. Its architecture reveals specific and general aspects of development, such as how given cells generate their ordained fates in the embryo and why the process moves inexorably forward in developmental time., The mechanism causing cats to beget cats and fish to beget fish is hardwired in the genomic DNA, because the species specificity of the body plan is the cardinal heritable [...]