SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis

Because of its clonal evolution a tumor rarely contains multiple genomic alterations in the same pathway as disrupting the pathway by one gene often is sufficient to confer the complete fitness advantage. As a result, many cancer driver genes display mutual exclusivity across tumors. However, searching for mutually exclusive gene sets requires analyzing all possible combinations of genes, leading to a problem which is typically too computationally complex to be solved without a stringent a priori filtering, restricting the mutations included in the analysis. To overcome this problem, we present SSA-ME, a network-based method to detect cancer driver genes based on independently scoring small subnetworks for mutual exclusivity using a reinforced learning approach. Because of the algorithmic efficiency, no stringent upfront filtering is required. Analysis of TCGA cancer datasets illustrates the added value of SSA-ME: well-known recurrently mutated but also rarely mutated drivers are prioritized. We show that using mutual exclusivity to detect cancer driver genes is complementary to state-of-the art approaches. This framework, in which a large number of small subnetworks are being analyzed in order to solve a computationally complex problem (SSA), can be generically applied to any problem in which local neighborhoods in a network hold useful information

QuaDMutEx: quadratic driver mutation explorer

Background Somatic mutations accumulate in human cells throughout life. Some may have no adverse consequences, but some of them may lead to cancer. A cancer genome is typically unstable, and thus more mutations can accumulate in the DNA of cancer cells. An ongoing problem is to figure out which mutations are drivers - play a role in oncogenesis, and which are passengers - do not play a role. One way of addressing this question is through inspection of somatic mutations in DNA of cancer samples from a cohort of patients and detection of patterns that differentiate driver from passenger mutations. Results We propose QuaDMutEx, a method that incorporates three novel elements: a new gene set penalty that includes non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty, through a combination of heuristic Monte Carlo optimization and exact binary quadratic programming. Compared to existing methods, the proposed algorithm finds sets of putative driver genes that show higher coverage and lower excess coverage in eight sets of cancer samples coming from brain, ovarian, lung, and breast tumors. Conclusions Superior ability to improve on both coverage and excess coverage on different types of cancer shows that QuaDMutEx is a tool that should be part of a state-of-the-art toolbox in the driver gene discovery pipeline. It can detect genes harboring rare driver mutations that may be missed by existing methods. QuaDMutEx is available for download from https://github.com/bokhariy/QuaDMutEx under the GNU GPLv3 license

FGF ligands in Drosophila have distinct activities required to support cell migration and differentiation

Fibroblast growth factor (FGF) signaling controls a vast array of biological processes including cell differentiation and migration, wound healing and malignancy. In vertebrates, FGF signaling is complex, with over 100 predicted FGF ligand-receptor combinations. Drosophila melanogaster presents a simpler model system in which to study FGF signaling, with only three ligands and two FGF receptors (FGFRs) identified. Here we analyze the specificity of FGFR [Heartless (Htl) and Breathless (Btl)] activation by each of the FGF ligands [Pyramus (Pyr), Thisbe (Ths) and Branchless (Bnl)] in Drosophila. We confirm that both Pyr and Ths can activate Htl, and that only Bnl can activate Btl. To examine the role of each ligand in supporting activation of the Htl FGFR, we utilize genetic approaches that focus on the earliest stages of embryonic development. When pyr and ths are equivalently expressed using the Gal4 system, these ligands support qualitatively different FGFR signaling responses. Both Pyr and Ths function in a non-autonomous fashion to support mesoderm spreading during gastrulation, but Pyr exhibits a longer functional range. pyr and ths single mutants exhibit defects in mesoderm spreading during gastrulation, yet only pyr mutants exhibit severe defects in dorsal mesoderm specification. We demonstrate that the Drosophila FGFs have different activities and that cell migration and differentiation have different ligand requirements. Furthermore, these FGF ligands are not regulated solely by differential expression, but the sequences of these linked genes have evolved to serve different functions. We contend that inherent properties of FGF ligands make them suitable to support specific FGF-dependent processes, and that FGF ligands are not always interchangeable

SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering

Background: With the advances in high throughput technologies, increasing amounts of cancer somatic mutation data are being generated and made available. Only a small number of (driver) mutations occur in driver genes and are responsible for carcinogenesis, while the majority of (passenger) mutations do not influence tumour biology. In this study, SomInaClust is introduced, a method that accurately identifies driver genes based on their mutation pattern across tumour samples and then classifies them into oncogenes or tumour suppressor genes respectively. Results: SomInaClust starts from the observation that oncogenes mainly contain mutations that, due to positive selection, cluster at similar positions in a gene across patient samples, whereas tumour suppressor genes contain a high number of protein-truncating mutations throughout the entire gene length. The method was shown to prioritize driver genes in 9 different solid cancers. Furthermore it was found to be complementary to existing similar-purpose methods with the additional advantages that it has a higher sensitivity, also for rare mutations (occurring in less than 1% of all samples), and it accurately classifies candidate driver genes in putative oncogenes and tumour suppressor genes. Pathway enrichment analysis showed that the identified genes belong to known cancer signalling pathways, and that the distinction between oncogenes and tumour suppressor genes is biologically relevant. Conclusions: SomInaClust was shown to detect candidate driver genes based on somatic mutation patterns of inactivation and clustering and to distinguish oncogenes from tumour suppressor genes. The method could be used for the identification of new cancer genes or to filter mutation data for further data-integration purposes

cDNA-RNA subtractive hybridization reveals increased expression of mycocerosic acid synthase in intracellular Mycobacterium bovis BCG.

Identifying genes that are differentially expressed by Mycobacterium bovis BCG after phagocytosis by macrophages will facilitate the understanding of the molecular mechanisms of host cell-intracellular pathogen interactions. To identify such genes a cDNA-total RNA subtractive hybridization strategy has been used that circumvents the problems both of limited availability of bacterial RNA from models of infection and the high rRNA backgrounds in total bacterial RNA. The subtraction products were used to screen a high-density gridded Mycobacterium tuberculosis genomic library. Sequence data were obtained from 19 differential clones, five of which contained overlapping sequences for the gene encoding mycocerosic acid synthase (mas). Mas is an enzyme involved in the synthesis of multi-methylated long-chain fatty acids that are part of phthiocerol dimycocerosate, a major component of the complex mycobacterial cell wall. Northern blotting and primer extension data confirmed up-regulation of mas in intracellular mycobacteria and also revealed a putative extended -10 promoter structure and a long untranslated upstream region 5' of the mas transcripts, containing predicted double-stranded structures. Furthermore, clones containing overlapping sequences for furB, groEL-2, rplE and fadD28 were identified and the up-regulation of these genes was confirmed by Northern blot analysis. The cDNA-RNA subtractive hybridization enrichment and high density gridded library screening, combined with selective extraction of bacterial mRNA represents a valuable approach to the identification of genes expressed during intra-macrophage residence for bacteria such as M. bovis BCG and the pathogenic mycobacterium, M. tuberculosis

Traumatic Brain Injury Induces Genome-Wide Transcriptomic, Methylomic, and Network Perturbations in Brain and Blood Predicting Neurological Disorders.

The complexity of the traumatic brain injury (TBI) pathology, particularly concussive injury, is a serious obstacle for diagnosis, treatment, and long-term prognosis. Here we utilize modern systems biology in a rodent model of concussive injury to gain a thorough view of the impact of TBI on fundamental aspects of gene regulation, which have the potential to drive or alter the course of the TBI pathology. TBI perturbed epigenomic programming, transcriptional activities (expression level and alternative splicing), and the organization of genes in networks centered around genes such as Anax2, Ogn, and Fmod. Transcriptomic signatures in the hippocampus are involved in neuronal signaling, metabolism, inflammation, and blood function, and they overlap with those in leukocytes from peripheral blood. The homology between genomic signatures from blood and brain elicited by TBI provides proof of concept information for development of biomarkers of TBI based on composite genomic patterns. By intersecting with human genome-wide association studies, many TBI signature genes and network regulators identified in our rodent model were causally associated with brain disorders with relevant link to TBI. The overall results show that concussive brain injury reprograms genes which could lead to predisposition to neurological and psychiatric disorders, and that genomic information from peripheral leukocytes has the potential to predict TBI pathogenesis in the brain

Every which way? On predicting tumor evolution using cancer progression models

Successful prediction of the likely paths of tumor progression is valuable for diagnostic, prognostic, and treatment purposes. Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and thus CPMs encode the paths of tumor progression. Here we analyze the performance of four CPMs to examine whether they can be used to predict the true distribution of paths of tumor progression and to estimate evolutionary unpredictability. Employing simulations we show that if fitness landscapes are single peaked (have a single fitness maximum) there is good agreement between true and predicted distributions of paths of tumor progression when sample sizes are large, but performance is poor with the currently common much smaller sample sizes. Under multi-peaked fitness landscapes (i.e., those with multiple fitness maxima), performance is poor and improves only slightly with sample size. In all cases, detection regime (when tumors are sampled) is a key determinant of performance. Estimates of evolutionary unpredictability from the best performing CPM, among the four examined, tend to overestimate the true unpredictability and the bias is affected by detection regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability. Analysis of twenty-two cancer data sets shows low evolutionary unpredictability for several of the data sets. But most of the predictions of paths of tumor progression are very unreliable, and unreliability increases with the number of features analyzed. Our results indicate that CPMs could be valuable tools for predicting cancer progression but that, currently, obtaining useful predictions of paths of tumor progression from CPMs is dubious, and emphasize the need for methodological work that can account for the probably multi-peaked fitness landscapes in cancerWork partially supported by BFU2015- 67302-R (MINECO/FEDER, EU) to RDU. CV supported by PEJD-2016-BMD-2116 from Comunidad de Madrid to RD