School of Mathematics and Statistics - Research Publications

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    International network of cancer genome projects
    Hudson, TJ ; Anderson, W ; Aretz, A ; Barker, AD ; Bell, C ; Bernabe, RR ; Bhan, MK ; Calvo, F ; Eerola, I ; Gerhard, DS ; Guttmacher, A ; Guyer, M ; Hemsley, FM ; Jennings, JL ; Kerr, D ; Klatt, P ; Kolar, P ; Kusuda, J ; Lane, DP ; Laplace, F ; Lu, Y ; Nettekoven, G ; Ozenberger, B ; Peterson, J ; Rao, TS ; Remacle, J ; Schafer, AJ ; Shibata, T ; Stratton, MR ; Vockley, JG ; Watanabe, K ; Yang, H ; Yuen, MMF ; Knoppers, M ; Bobrow, M ; Cambon-Thomsen, A ; Dressler, LG ; Dyke, SOM ; Joly, Y ; Kato, K ; Kennedy, KL ; Nicolas, P ; Parker, MJ ; Rial-Sebbag, E ; Romeo-Casabona, CM ; Shaw, KM ; Wallace, S ; Wiesner, GL ; Zeps, N ; Lichter, P ; Biankin, AV ; Chabannon, C ; Chin, L ; Clement, B ; de Alava, E ; Degos, F ; Ferguson, ML ; Geary, P ; Hayes, DN ; Johns, AL ; Nakagawa, H ; Penny, R ; Piris, MA ; Sarin, R ; Scarpa, A ; van de Vijver, M ; Futreal, PA ; Aburatani, H ; Bayes, M ; Bowtell, DDL ; Campbell, PJ ; Estivill, X ; Grimmond, SM ; Gut, I ; Hirst, M ; Lopez-Otin, C ; Majumder, P ; Marra, M ; Ning, Z ; Puente, XS ; Ruan, Y ; Stunnenberg, HG ; Swerdlow, H ; Velculescu, VE ; Wilson, RK ; Xue, HH ; Yang, L ; Spellman, PT ; Bader, GD ; Boutros, PC ; Flicek, P ; Getz, G ; Guigo, R ; Guo, G ; Haussler, D ; Heath, S ; Hubbard, TJ ; Jiang, T ; Jones, SM ; Li, Q ; Lopez-Bigas, N ; Luo, R ; Pearson, JV ; Quesada, V ; Raphael, BJ ; Sander, C ; Speed, TP ; Stuart, JM ; Teague, JW ; Totoki, Y ; Tsunoda, T ; Valencia, A ; Wheeler, DA ; Wu, H ; Zhao, S ; Zhou, G ; Stein, LD ; Lathrop, M ; Ouellette, BFF ; Thomas, G ; Yoshida, T ; Axton, M ; Gunter, C ; McPherson, JD ; Miller, LJ ; Kasprzyk, A ; Zhang, J ; Haider, SA ; Wang, J ; Yung, CK ; Cross, A ; Liang, Y ; Gnaneshan, S ; Guberman, J ; Hsu, J ; Chalmers, DRC ; Hasel, KW ; Kaan, TSH ; Knoppers, BM ; Lowrance, WW ; Masui, T ; Rodriguez, LL ; Vergely, C ; Cloonan, N ; Defazio, A ; Eshleman, JR ; Etemadmoghadam, D ; Gardiner, BA ; Kench, JG ; Sutherland, RL ; Tempero, MA ; Waddell, NJ ; Wilson, PJ ; Gallinger, S ; Tsao, M-S ; Shaw, PA ; Petersen, GM ; Mukhopadhyay, D ; DePinho, RA ; Thayer, S ; Muthuswamy, L ; Shazand, K ; Beck, T ; Sam, M ; Timms, L ; Ballin, V ; Ji, J ; Zhang, X ; Chen, F ; Hu, X ; Yang, Q ; Tian, G ; Zhang, L ; Xing, X ; Li, X ; Zhu, Z ; Yu, Y ; Yu, J ; Tost, J ; Brennan, P ; Holcatova, I ; Zaridze, D ; Brazma, A ; Egevad, L ; Prokhortchouk, E ; Banks, RE ; Uhlen, M ; Viksna, J ; Ponten, F ; Skryabin, K ; Birney, E ; Borg, A ; Borresen-Dale, A-L ; Caldas, C ; Foekens, JA ; Martin, S ; Reis-Filho, JS ; Richardson, AL ; Sotiriou, C ; van't Veer, L ; Birnbaum, D ; Blanche, H ; Boucher, P ; Boyault, S ; Masson-Jacquemier, JD ; Pauporte, I ; Pivot, X ; Vincent-Salomon, A ; Tabone, E ; Theillet, C ; Treilleux, I ; Bioulac-Sage, P ; Decaens, T ; Franco, D ; Gut, M ; Samuel, D ; Zucman-Rossi, J ; Eils, R ; Brors, B ; Korbel, JO ; Korshunov, A ; Landgraf, P ; Lehrach, H ; Pfister, S ; Radlwimmer, B ; Reifenberger, G ; Taylor, MD ; von Kalle, C ; Majumder, PP ; Pederzoli, P ; Lawlor, RT ; Delledonne, M ; Bardelli, A ; Gress, T ; Klimstra, D ; Zamboni, G ; Nakamura, Y ; Miyano, S ; Fujimoto, A ; Campo, E ; de Sanjose, S ; Montserrat, E ; Gonzalez-Diaz, M ; Jares, P ; Himmelbaue, H ; Bea, S ; Aparicio, S ; Easton, DF ; Collins, FS ; Compton, CC ; Lander, ES ; Burke, W ; Green, AR ; Hamilton, SR ; Kallioniemi, OP ; Ley, TJ ; Liu, ET ; Wainwright, BJ (NATURE PORTFOLIO, 2010-04-15)
    The International Cancer Genome Consortium (ICGC) was launched to coordinate large-scale cancer genome studies in tumours from 50 different cancer types and/or subtypes that are of clinical and societal importance across the globe. Systematic studies of more than 25,000 cancer genomes at the genomic, epigenomic and transcriptomic levels will reveal the repertoire of oncogenic mutations, uncover traces of the mutagenic influences, define clinically relevant subtypes for prognosis and therapeutic management, and enable the development of new cancer therapies.
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    Genome-Wide Analysis of Glucocorticoid Receptor Binding Regions in Adipocytes Reveal Gene Network Involved in Triglyceride Homeostasis
    Yu, C-Y ; Mayba, O ; Lee, JV ; Tran, J ; Harris, C ; Speed, TP ; Wang, J-C ; Pazin, MJ (PUBLIC LIBRARY SCIENCE, 2010-12-20)
    Glucocorticoids play important roles in the regulation of distinct aspects of adipocyte biology. Excess glucocorticoids in adipocytes are associated with metabolic disorders, including central obesity, insulin resistance and dyslipidemia. To understand the mechanisms underlying the glucocorticoid action in adipocytes, we used chromatin immunoprecipitation sequencing to isolate genome-wide glucocorticoid receptor (GR) binding regions (GBRs) in 3T3-L1 adipocytes. Furthermore, gene expression analyses were used to identify genes that were regulated by glucocorticoids. Overall, 274 glucocorticoid-regulated genes contain or locate nearby GBR. We found that many GBRs were located in or nearby genes involved in triglyceride (TG) synthesis (Scd-1, 2, 3, GPAT3, GPAT4, Agpat2, Lpin1), lipolysis (Lipe, Mgll), lipid transport (Cd36, Lrp-1, Vldlr, Slc27a2) and storage (S3-12). Gene expression analysis showed that except for Scd-3, the other 13 genes were induced in mouse inguinal fat upon 4-day glucocorticoid treatment. Reporter gene assays showed that except Agpat2, the other 12 glucocorticoid-regulated genes contain at least one GBR that can mediate hormone response. In agreement with the fact that glucocorticoids activated genes in both TG biosynthetic and lipolytic pathways, we confirmed that 4-day glucocorticoid treatment increased TG synthesis and lipolysis concomitantly in inguinal fat. Notably, we found that 9 of these 12 genes were induced in transgenic mice that have constant elevated plasma glucocorticoid levels. These results suggested that a similar mechanism was used to regulate TG homeostasis during chronic glucocorticoid treatment. In summary, our studies have identified molecular components in a glucocorticoid-controlled gene network involved in the regulation of TG homeostasis in adipocytes. Understanding the regulation of this gene network should provide important insight for future therapeutic developments for metabolic diseases.
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    Conserved Role of unc-79 in Ethanol Responses in Lightweight Mutant Mice
    Speca, DJ ; Chihara, D ; Ashique, AM ; Bowers, MS ; Pierce-Shimomura, JT ; Lee, J ; Rabbee, N ; Speed, TP ; Gularte, RJ ; Chitwood, J ; Medrano, JF ; Liao, M ; Sonner, JM ; Eger, EI ; Peterson, AS ; McIntire, SL ; Beier, DR (PUBLIC LIBRARY SCIENCE, 2010-08)
    The mechanisms by which ethanol and inhaled anesthetics influence the nervous system are poorly understood. Here we describe the positional cloning and characterization of a new mouse mutation isolated in an N-ethyl-N-nitrosourea (ENU) forward mutagenesis screen for animals with enhanced locomotor activity. This allele, Lightweight (Lwt), disrupts the homolog of the Caenorhabditis elegans (C. elegans) unc-79 gene. While Lwt/Lwt homozygotes are perinatal lethal, Lightweight heterozygotes are dramatically hypersensitive to acute ethanol exposure. Experiments in C. elegans demonstrate a conserved hypersensitivity to ethanol in unc-79 mutants and extend this observation to the related unc-80 mutant and nca-1;nca-2 double mutants. Lightweight heterozygotes also exhibit an altered response to the anesthetic isoflurane, reminiscent of unc-79 invertebrate mutant phenotypes. Consistent with our initial mapping results, Lightweight heterozygotes are mildly hyperactive when exposed to a novel environment and are smaller than wild-type animals. In addition, Lightweight heterozygotes exhibit increased food consumption yet have a leaner body composition. Interestingly, Lightweight heterozygotes voluntarily consume more ethanol than wild-type littermates. The acute hypersensitivity to and increased voluntary consumption of ethanol observed in Lightweight heterozygous mice in combination with the observed hypersensitivity to ethanol in C. elegans unc-79, unc-80, and nca-1;nca-2 double mutants suggests a novel conserved pathway that might influence alcohol-related behaviors in humans.
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    TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays
    Bengtsson, H ; Neuvial, P ; Speed, TP (BMC, 2010-05-12)
    BACKGROUND: High-throughput genotyping microarrays assess both total DNA copy number and allelic composition, which makes them a tool of choice for copy number studies in cancer, including total copy number and loss of heterozygosity (LOH) analyses. Even after state of the art preprocessing methods, allelic signal estimates from genotyping arrays still suffer from systematic effects that make them difficult to use effectively for such downstream analyses. RESULTS: We propose a method, TumorBoost, for normalizing allelic estimates of one tumor sample based on estimates from a single matched normal. The method applies to any paired tumor-normal estimates from any microarray-based technology, combined with any preprocessing method. We demonstrate that it increases the signal-to-noise ratio of allelic signals, making it significantly easier to detect allelic imbalances. CONCLUSIONS: TumorBoost increases the power to detect somatic copy-number events (including copy-neutral LOH) in the tumor from allelic signals of Affymetrix or Illumina origin. We also conclude that high-precision allelic estimates can be obtained from a single pair of tumor-normal hybridizations, if TumorBoost is combined with single-array preprocessing methods such as (allele-specific) CRMA v2 for Affymetrix or BeadStudio's (proprietary) XY-normalization method for Illumina. A bounded-memory implementation is available in the open-source and cross-platform R package aroma.cn, which is part of the Aroma Project (http://www.aroma-project.org/).
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    Identification of Candidate Growth Promoting Genes in Ovarian Cancer through Integrated Copy Number and Expression Analysis
    Ramakrishna, M ; Williams, LH ; Boyle, SE ; Bearfoot, JL ; Sridhar, A ; Speed, TP ; Gorringe, KL ; Campbell, IG ; Tan, P (PUBLIC LIBRARY SCIENCE, 2010-04-08)
    Ovarian cancer is a disease characterised by complex genomic rearrangements but the majority of the genes that are the target of these alterations remain unidentified. Cataloguing these target genes will provide useful insights into the disease etiology and may provide an opportunity to develop novel diagnostic and therapeutic interventions. High resolution genome wide copy number and matching expression data from 68 primary epithelial ovarian carcinomas of various histotypes was integrated to identify genes in regions of most frequent amplification with the strongest correlation with expression and copy number. Regions on chromosomes 3, 7, 8, and 20 were most frequently increased in copy number (> 40% of samples). Within these regions, 703/1370 (51%) unique gene expression probesets were differentially expressed when samples with gain were compared to samples without gain. 30% of these differentially expressed probesets also showed a strong positive correlation (r > or =0.6) between expression and copy number. We also identified 21 regions of high amplitude copy number gain, in which 32 known protein coding genes showed a strong positive correlation between expression and copy number. Overall, our data validates previously known ovarian cancer genes, such as ERBB2, and also identified novel potential drivers such as MYNN, PUF60 and TPX2.