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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

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.

Abstract Background CADM1 encodes an immunoglobulin superfamily (IGSF) cell adhesion molecule. Inactivation of CADM1, either by promoter hypermethylation or loss of heterozygosity, has been reported in a wide variety of tumor types, thus it has been postulated as a tumor suppressor gene. Findings We show for the first time that Cadm1 homozygous null mice die significantly faster than wildtype controls due to the spontaneous development of tumors at an earlier age and an increased tumor incidence of predominantly lymphomas, but also some solid tumors. Tumorigenesis was accelerated after irradiation of Cadm1 mice, with the reduced latency in tumor formation suggesting there are genes that collaborate with loss of Cadm1 in tumorigenesis. To identify these co-operating genetic events, we performed a Sleeping Beauty transposon-mediated insertional mutagenesis screen in Cadm1 mice, and identified several common insertion sites (CIS) found specifically on a Cadm1-null background (and not wildtype background). Conclusion We confirm that Cadm1 is indeed a bona fide tumor suppressor gene and provide new insights into genetic partners that co-operate in tumorigenesis when Cadm1-expression is lost.

Abstract Background As part of a National Institute of Allergy and Infectious Diseases funded collaborative project, we have performed over 150 microarray experiments measuring the response of C57/BL6 mouse bone marrow macrophages to toll-like receptor stimuli. These microarray expression profiles are available freely from our project web site http://www.innateImmunity-systemsbiology.org. Here, we report the development of a database of computationally predicted transcription factor binding sites and related genomic features for a set of over 2000 murine immune genes of interest. Our database, which includes microarray co-expression clusters and a host of web-based query, analysis and visualization facilities, is available freely via the internet. It provides a broad resource to the research community, and a stepping stone towards the delineation of the network of transcriptional regulatory interactions underlying the integrated response of macrophages to pathogens. Description We constructed a database indexed on genes and annotations of the immediate surrounding genomic regions. To facilitate both gene-specific and systems biology oriented research, our database provides the means to analyze individual genes or an entire genomic locus. Although our focus to-date has been on mammalian toll-like receptor signaling pathways, our database structure is not limited to this subject, and is intended to be broadly applicable to immunology. By focusing on selected immune-active genes, we were able to perform computationally intensive expression and sequence analyses that would currently be prohibitive if applied to the entire genome. Using six complementary computational algorithms and methodologies, we identified transcription factor binding sites based on the Position Weight Matrices available in TRANSFAC. For one example transcription factor (ATF3) for which experimental data is available, over 50% of our predicted binding sites coincide with genome-wide chromatin immnuopreciptation (ChIP-chip) results. Our database can be interrogated via a web interface. Genomic annotations and binding site predictions can be automatically viewed with a customized version of the Argo genome browser. Conclusion We present the Innate Immune Database (IIDB) as a community resource for immunologists interested in gene regulatory systems underlying innate responses to pathogens. The database website can be freely accessed at http://db.systemsbiology.net/IIDB.

The article discusses the role of lentiviral vector‐based insertional mutagenesis screen in identifying mechanisms of resistance to MAPK inhibitors in melanoma. It mentions that insertional mutagenesis is a forward genetic screening approach that uses specialized vectors which integrate into the genome. It reveals use of a BRAFV600E short‐term melanoma culture that had been characterized for somatic mutations.

Abstract: ARID1A is a tumour suppressor gene that is frequently mutated in clear cell and endometrioid carcinomas of the ovary and endometrium and is an important clinical biomarker for novel treatment approaches for patients with ARID1A defects. However, the accuracy of ARID1A immunohistochemistry (IHC) as a surrogate for mutation status has not fully been established for patient stratification in clinical trials. Here we tested whether ARID1A IHC could reliably predict ARID1A mutations identified by next‐generation sequencing. Three commercially available antibodies – EPR13501 (Abcam), D2A8U (Cell Signaling), and HPA005456 (Sigma) – were optimised for IHC using cell line models and human tissue, and screened across a cohort of 45 gynaecological tumours. IHC was scored independently by three pathologists using an immunoreactive score. ARID1A mutation status was assessed using two independent sequencing platforms and the concordance between ARID1A mutation and protein expression was evaluated using Receiver Operating Characteristic statistics. Overall, 21 ARID1A mutations were identified in 14/43 assessable tumours (33%), the majority of which were predicted to be deleterious. Mutations were identified in 6/17 (35%) ovarian clear cell carcinomas, 5/8 (63%) ovarian endometrioid carcinomas, 2/5 (40%) endometrial carcinomas, and 1/7 (14%) carcinosarcomas. ROC analysis identified greater than 95% concordance between mutation status and IHC using a modified immunoreactive score for all three antibodies allowing a definitive cut‐point for ARID1A mutant status to be calculated. Comprehensive assessment of concordance of ARID1A IHC and mutation status identified EPR13501 as an optimal antibody, with 100% concordance between ARID1A mutation status and protein expression, across different gynaecological histological subtypes. It delivered the best inter‐rater agreement between all pathologists, as well as a clear cost‐benefit advantage. This could allow patients to be accurately stratified based on their ARID1A IHC status into early phase clinical trials. [ABSTRACT FROM AUTHOR]

Prostate cancer (CaP) is the most common adult male cancer in the developed world. The paucity of biomarkers to predict prostate tumor biology makes it important to identify key pathways that confer poor prognosis and guide potential targeted therapy. Using a murine forward mutagenesis screen in a Pten-null background, we identified peroxisome proliferator-activated receptor gamma (Pparg), encoding a ligand-activated transcription factor, as a promoter of metastatic CaP through activation of lipid signaling pathways, including up-regulation of lipid synthesis enzymes [fatty acid synthase (FASN), acetyl-CoA carboxylase (ACC), ATP citrate lyase (ACLY)]. Importantly, inhibition of PPARG suppressed tumor growth in vivo, with down-regulation of the lipid synthesis program. We show that elevated levels of PPARG strongly correlate with elevation of FASN in human CaP and that high levels of PPARG/FASN and PI3K/ pAKT pathway activation confer a poor prognosis. These data suggest that CaP patients could be stratified in terms of PPARG/FASN and PTEN levels to identify patients with aggressive CaP who may respond favorably to PPARG/FASN inhibition. [ABSTRACT FROM AUTHOR]

Mutations in SMAD4 predispose to the development of gastrointestinal cancer, which is the third leading cause of cancer-related deaths. To identify genes driving gastric cancer (GC) development, we performed a Sleeping Beauty (SB) transposon mutagenesis screen in the stomach of Smad4+/- mutant mice. This screen identified 59 candidate GC trunk drivers and a much larger number of candidate GC progression genes. Strikingly, 22 SB-identified trunk drivers are known or candidate cancer genes, whereas four SB-identified trunk drivers, including PTEN, SMAD4, RNF43, and NF1, are known human GC trunk drivers. Similar to human GC, pathway analyses identified WNT, TGF-β, and PI3K-PTEN signaling, ubiquitin- mediated proteolysis, adherens junctions, and RNA degradation in addition to genes involved in chromatin modification and organization as highly deregulated pathways in GC. Comparative oncogenomic filtering of the complete list of SB-identified genes showed that they are highly enriched for genes mutated in human GC and identified many candidate human GC genes. Finally, by comparing our complete list of SB-identified genes against the list of mutated genes identified in five large-scale human GC sequencing studies, we identified LDL receptor-related protein 1B (LRP1B) as a previously unidentified human candidate GC tumor suppressor gene. In LRP1B, 129 mutations were found in 462 human GC samples sequenced, and LRP1B is one of the top 10 most deleted genes identified in a panel of 3,312 human cancers. SB mutagenesis has, thus, helped to catalog the cooperative molecular mechanisms driving SMAD4-induced GC growth and discover genes with potential clinical importance in human GC. [ABSTRACT FROM AUTHOR]

Background: B-cell precursor acute lymphoblastic leukemia (B-ALL) is amongst the leading causes of childhood cancer-related mortality. Its most common chromosomal aberration is the ETV6-RUNX1 fusion gene, with ~25% of ETV6-RUNX1 patients also carrying PAX5 alterations. Methods: We have recreated this mutation background by inter-crossing Etv6-RUNX1 (Etv6RUNX1-SB) and Pax5+/- mice and performed an in vivo analysis to find driver genes using Sleeping Beauty transposon-mediated mutagenesis and also exome sequencing. Results: Combination of Etv6-RUNX1 and Pax5+/- alleles generated a transplantable B220 + CD19+ B-ALL with a significant disease incidence. RNA-seq analysis showed a gene expression pattern consistent with arrest at the pre-B stage. Analysis of the transposon common insertion sites identified genes involved in B-cell development (Zfp423) and the JAK/STAT signaling pathway (Jak1, Stat5 and Il2rb), while exome sequencing revealed somatic hotspot mutations in Jak1 and Jak3 at residues analogous to those mutated in human leukemias, and also mutation of Trp53. Conclusions: Powerful synergies exists in our model suggesting STAT pathway activation and mutation of Trp53 are potent drivers of B-ALL in the context of Etv6-RUNX1;Pax5+/-. [ABSTRACT FROM AUTHOR]

Although nearly half of human melanomas harbor oncogenic BRAFV600E mutations, the genetic events that cooperate with these mutations to drive melanogenesis are still largely unknown. Here we show that Sleeping Beauty (SB) transposon-mediated mutagenesis drives melanoma progression in BrafV600E mutant mice and identify 1,232 recurrently mutated candidate cancer genes (CCGs) from 70 SB-driven melanomas. CCGs are enriched in Wnt, PI3K, MAPK and netrin signaling pathway components and are more highly connected to one another than predicted by chance, indicating that SB targets cooperative genetic networks in melanoma. Human orthologs of >500 CCGs are enriched for mutations in human melanoma or showed statistically significant clinical associations between RNA abundance and survival of patients with metastatic melanoma. We also functionally validate CEP350 as a new tumor-suppressor gene in human melanoma. SB mutagenesis has thus helped to catalog the cooperative molecular mechanisms driving BRAFV600E melanoma and discover new genes with potential clinical importance in human melanoma. [ABSTRACT FROM AUTHOR]

To provide a more comprehensive understanding of the genes and evolutionary forces driving colorectal cancer (CRC) progression, we performed Sleeping Beauty (SB) transposon mutagenesis screens in mice carrying sensitizing mutations in genes that act at different stages of tumor progression. This approach allowed us to identify a set of genes that appear to be highly relevant for CRC and to provide a better understanding of the evolutionary forces and systems properties of CRC. We also identified six genes driving malignant tumor progression and a new human CRC tumor-suppressor gene, ZNF292, that might also function in other types of cancer. Our comprehensive CRC data set provides a resource with which to develop new therapies for treating CRC. [ABSTRACT FROM AUTHOR]

The ability of retroviruses and transposons to insert their genetic material into host DNA makes them widely used tools in molecular biology, cancer research and gene therapy. However, these systems have biases that may strongly affect research outcomes. To address this issue, we generated very large datasets consisting of to unselected integrations in the mouse genome for the Sleeping Beauty (SB) and piggyBac (PB) transposons, and the Mouse Mammary Tumor Virus (MMTV). We analyzed (epi)genomic features to generate bias maps at both local and genome-wide scales. MMTV showed a remarkably uniform distribution of integrations across the genome. More distinct preferences were observed for the two transposons, with PB showing remarkable resemblance to bias profiles of the Murine Leukemia Virus. Furthermore, we present a model where target site selection is directed at multiple scales. At a large scale, target site selection is similar across systems, and defined by domain-oriented features, namely expression of proximal genes, proximity to CpG islands and to genic features, chromatin compaction and replication timing. Notable differences between the systems are mainly observed at smaller scales, and are directed by a diverse range of features. To study the effect of these biases on integration sites occupied under selective pressure, we turned to insertional mutagenesis (IM) screens. In IM screens, putative cancer genes are identified by finding frequently targeted genomic regions, or Common Integration Sites (CISs). Within three recently completed IM screens, we identified 7%–33% putative false positive CISs, which are likely not the result of the oncogenic selection process. Moreover, results indicate that PB, compared to SB, is more suited to tag oncogenes. [ABSTRACT FROM AUTHOR]

Retroviral insertional mutagenesis (RIM) is a powerful tool for cancer genomics that was combined in this study with deep sequencing (RIM/DS) to facilitate a comprehensive analysis of lymphoma progression. Transgenic mice expressing two potent collaborating oncogenes in the germ line (CD2-MYC, -Runx2) develop rapid onset tumours that can be accelerated and rendered polyclonal by neonatal Moloney murine leukaemia virus (MoMLV) infection. RIM/DS analysis of 28 polyclonal lymphomas identified 771 common insertion sites (CISs) defining a ‘progression network’ that encompassed a remarkably large fraction of known MoMLV target genes, with further strong indications of oncogenic selection above the background of MoMLV integration preference. Progression driven by RIM was characterised as a Darwinian process of clonal competition engaging proliferation control networks downstream of cytokine and T-cell receptor signalling. Enhancer mode activation accounted for the most efficiently selected CIS target genes, including Ccr7 as the most prominent of a set of chemokine receptors driving paracrine growth stimulation and lymphoma dissemination. Another large target gene subset including candidate tumour suppressors was disrupted by intragenic insertions. A second RIM/DS screen comparing lymphomas of wild-type and parental transgenics showed that CD2-MYC tumours are virtually dependent on activation of Runx family genes in strong preference to other potent Myc collaborating genes (Gfi1, Notch1). Ikzf1 was identified as a novel collaborating gene for Runx2 and illustrated the interface between integration preference and oncogenic selection. Lymphoma target genes for MoMLV can be classified into (a) a small set of master regulators that confer self-renewal; overcoming p53 and other failsafe pathways and (b) a large group of progression genes that control autonomous proliferation in transformed cells. These findings provide insights into retroviral biology, human cancer genetics and the safety of vector-mediated gene therapy. [ABSTRACT FROM AUTHOR]

A major challenge in cancer genetics is to determine which low-frequency somatic mutations are drivers of tumorigenesis. Here we interrogate the genomes of 7,651 diverse human cancers and find inactivating mutations in the homeodomain transcription factor gene CUX1 (cut-like homeobox 1) in ∼1-5% of various tumors. Meta-analysis of CUX1 mutational status in 2,519 cases of myeloid malignancies reveals disruptive mutations associated with poor survival, highlighting the clinical significance of CUX1 loss. In parallel, we validate CUX1 as a bona fide tumor suppressor using mouse transposon-mediated insertional mutagenesis and Drosophila cancer models. We demonstrate that CUX1 deficiency activates phosphoinositide 3-kinase (PI3K) signaling through direct transcriptional downregulation of the PI3K inhibitor PIK3IP1 (phosphoinositide-3-kinase interacting protein 1), leading to increased tumor growth and susceptibility to PI3K-AKT inhibition. Thus, our complementary approaches identify CUX1 as a pan-driver of tumorigenesis and uncover a potential strategy for treating CUX1-mutant tumors. [ABSTRACT FROM AUTHOR]

The most common risk factor for developing hepatocellular carcinoma (HCC) is chronic infection with hepatitis B virus (HBV). To better understand the evolutionary forces driving HCC, we performed a near-saturating transposon mutagenesis screen in a mouse HBV model of HCC. This screen identified 21 candidate early stage drivers and a very large number (2,860) of candidate later stage drivers that were enriched for genes that are mutated, deregulated or functioning in signaling pathways important for human HCC, with a striking 1,199 genes being linked to cellular metabolic processes. Our study provides a comprehensive overview of the genetic landscape of HCC. [ABSTRACT FROM AUTHOR]

Genetic screens in simple model organisms have identified many of the key components of the conserved signal transduction pathways that are oncogenic when misregulated. Here, we identify H37N21.1 as a gene that regulates vulval induction in let-60(n1046gf), a strain with a gain-of-function mutation in the Caenorhabditis elegans Ras orthologue, and show that somatic deletion of Nrbp1, the mouse orthologue of this gene, results in an intestinal progenitor cell phenotype that leads to profound changes in the proliferation and differentiation of all intestinal cell lineages. We show that Nrbp1 interacts with key components of the ubiquitination machinery and that loss of Nrbp1 in the intestine results in the accumulation of Sall4, a key mediator of stem cell fate, and of Tsc22d2. We also reveal that somatic loss of Nrbp1 results in tumourigenesis, with haematological and intestinal tumours predominating, and that nuclear receptor binding protein 1 (NRBP1) is downregulated in a range of human tumours, where low expression correlates with a poor prognosis. Thus NRBP1 is a conserved regulator of cell fate, that plays an important role in tumour suppression. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is one of the most deadly cancers affecting the Western world. Because the disease is highly metastatic and difficult to diagnosis until late stages, the 5-y survival rate is around 5%. The identification of molecular cancer drivers is critical for furthering our understanding of the disease and development of improved diagnostic tools and therapeutics. We have conducted a mutagenic screen using Sleeping Beauty (SB) in mice to identify new candidate cancer genes in pancreatic cancer. By combining SB with an oncogenic Kras allele, we observed highly metastatic pancreatic adenocarcinomas. Using two independent statistical methods to identify loci commonly mutated by SB in these tumors, we identified 681 loci that comprise 543 candidate cancer genes (CCGs); 75 of these CCGs, including Mll3 and Ptk2, have known mutations in human pancreatic cancer. We identified point mutations in human pancreatic patient samples for another 11 CCGs, including Acvr2a and Map2k4. Importantly, 10% of the CCGs are involved in chromatin remodeling, including Arid4b, Kdm6a, and Nsd3, and all SB tumors have at least one mutated gene involved in this process; 20 CCGs, including Ctnnd1, Fbxo11, and Vgll4, are also significantly associated with poor patient survival. SB mutagenesis provides a rich resource of mutations in potential cancer drivers for cross-comparative analyses with ongoing sequencing efforts in human pancreatic adenocarcinoma. [ABSTRACT FROM AUTHOR]

The evolution of colorectal cancer suggests the involvement of many genes. To identify new drivers of intestinal cancer, we performed insertional mutagenesis using the Sleeping Beauty transposon system in mice carrying germline or somatic Apc mutations. By analyzing common insertion sites (CISs) isolated from 446 tumors, we identified many hundreds of candidate cancer drivers. Comparison to human data sets suggested that 234 CIS-targeted genes are also dysregulated in human colorectal cancers. In addition, we found 183 CIS-containing genes that are candidate Wnt targets and showed that 20 CISs-containing genes are newly discovered modifiers of canonical Wnt signaling. We also identified mutations associated with a subset of tumors containing an expanded number of Paneth cells, a hallmark of deregulated Wnt signaling, and genes associated with more severe dysplasia included those encoding members of the FGF signaling cascade. Some 70 genes had co-occurrence of CIS pairs, clustering into 38 sub-networks that may regulate tumor development. [ABSTRACT FROM AUTHOR]

Currently the best algorithms for predicting transcription factor binding sites in DNA sequences are severely limited in accuracy. There is good reason to believe that predictions from different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets, support vector machines and the Adaboost algorithm to predictions from 12 key real valued algorithms. Furthermore, we use a ‘window’ of consecutive results as the input vector in order to contextualise the neighbouring results. We improve the classification result with the aid of under- and over-sampling techniques. We find that support vector machines and the Adaboost algorithm outperform the original individual algorithms and the other classifiers employed in this work. In particular they give a better tradeoff between recall and precision. [ABSTRACT FROM AUTHOR]

The innate immune system is like a double-edged sword: it is absolutely required for host defense against infection, but when uncontrolled, it can trigger a plethora of inflammatory diseases. Here we use systems-biology approaches to predict and confirm the existence of a gene-regulatory network involving dynamic interaction among the transcription factors NF-κB, C/EBPδ and ATF3 that controls inflammatory responses. We mathematically modeled transcriptional regulation of the genes encoding interleukin 6 and C/EBPδ and experimentally confirmed the prediction that the combination of an initiator (NF-κB), an amplifier (C/EBPδ) and an attenuator (ATF3) forms a regulatory circuit that discriminates between transient and persistent Toll-like receptor 4–induced signals. Our results suggest a mechanism that enables the innate immune system to detect the duration of infection and to respond appropriately. [ABSTRACT FROM AUTHOR]

An important problem in molecular biology is to build a complete understanding of transcriptional regulatory processes in the cell. We have developed a flexible, probabilistic framework to predict TF binding from multiple data sources that differs from the standard hypothesis testing (scanning) methods in several ways. Our probabilistic modeling framework estimates the probability of binding and, thus, naturally reflects our degree of belief in binding. Probabilistic modeling also allows for easy and systematic integration of our binding predictions into other probabilistic modeling methods, such as expression-based gene network inference. The method answers the question of whether the whole analyzed promoter has a binding site, but can also be extended to estimate the binding probability at each nucleotide position. Further, we introduce an extension to model combinatorial regulation by several TFs. Most importantly, the proposed methods can make principled probabilistic inference from multiple evidence sources, such as, multiple statistical models (motifs) of the TFs, evolutionary conservation, regulatory potential, CpG islands, nucleosome positioning, DNase hypersensitive sites, ChIP-chip binding segments and other (prior) sequence-based biological knowledge. We developed both a likelihood and a Bayesian method, where the latter is implemented with a Markov chain Monte Carlo algorithm. Results on a carefully constructed test set from the mouse genome demonstrate that principled data fusion can significantly improve the performance of TF binding prediction methods. We also applied the probabilistic modeling framework to all promoters in the mouse genome and the results indicate a sparse connectivity between transcriptional regulators and their target promoters. To facilitate analysis of other sequences and additional data, we have developed an on-line web tool, ProbTF, which implements our probabilistic TF binding prediction method using multiple data sources. Test data set, a web tool, source codes and supplementary data are available at: http://www.probtf.org. [ABSTRACT FROM AUTHOR]