This study presents an integrated analysis of pancreatic ductal adenocarcinomas (PDACs) for identification of putative cancer driver genes in somatic copy number gains (SCNGs). SCNG data on 60 PDAC genomes was extracted to identify 756 genes, mapping to 20 genomic loci that are recurrently gained. Through copy number and gene expression analysis on a panel of 29 human pancreatic cancer cell lines, this gene catalogue was refined to 34 PDAC high-confidence candidate genes. The performance of these genes was assessed in pooled shRNA screens and only ECT2 showed significant essentiality to cell viability in specific PDAC cell lines with genomic gains at the 3q26.3 locus that harbor this gene. Targeted shRNA-mediated interference of ECT2, as well as pharmacological inhibition, are supportive of the pooled shRNA screen findings. These results favor ECT2 as a candidate target gene for further evaluation in the subset of PDACs presenting with 3q26 somatic copy number gains.

Thesis (M.A.)--Florida State University, 2004. / Advisor: Dr. Kartik Pashupati, Florida State University, Collegeof Communication, Dept. of Communication. Title and description from dissertation home page (viewed Sept. 22, 2004). Includes bibliographical references.

The Subtext project seeks to make programming fundamentally easier by altering the nature of programming languages and tools. This paper defines an operational semantics for an essential subset of the Subtext language. It also presents a fresh approach to the problems of mutable state, I/O, and concurrency.Inclusions reify copy & paste edits into persistent relationships that propagate changes from their source into their destination. Inclusions formulate a programming language in which there is no distinction between a program’s representation and its execution. Like spreadsheets, programs are live executions within a persistent runtime, and programming is direct manipulation of these executions via a graphical user interface. There is no need to encode programs into source text.Mutation of state is effected by the computation of hypothetical recursive variants of the state, which can then be lifted into new versions of the state. Transactional concurrency is based upon queued single-threaded execution. Speculative execution of queued hypotheticals provides concurrency as a semantically transparent implementation optimization.

DNA copy number alterations (CNAs) are genetic changes that can produce adverse effects in numerous human diseases, including cancer. CNAs are segments of DNA that have been deleted or amplified and can range in size from one kilobases to whole chromosome arms. Development of array comparative genomic hybridization (aCGH) technology enables CNAs to be measured at sub-megabase resolution using tens of thousands of probes. However, aCGH data are noisy and result in continuous valued measurements of the discrete CNAs. Consequently, the data must be processed through algorithmic and statistical techniques in order to derive meaningful biological insights. We introduce model-based approaches to analysis of aCGH data and develop state-of-the-art solutions to three distinct analytical problems. In the simplest scenario, the task is to infer CNAs from a single aCGH experiment. We apply a hidden Markov model (HMM) to accurately identify CNAs from aCGH data. We show that borrowing statistical strength across chromosomes and explicitly modeling outliers in the data, improves on baseline models. In the second scenario, we wish to identify recurrent CNAs in a set of aCGH data derived from a patient cohort. These are locations in the genome altered in many patients, providing evidence for CNAs that may be playing important molecular roles in the disease. We develop a novel hierarchical HMM profiling method that explicitly models both statistical and biological noise in the data and is capable of producing a representative profile for a set of aCGH experiments. We demonstrate that our method is more accurate than simpler baselines on synthetic data, and show our model produces output that is more interpretable than other methods. Finally, we develop a model based clustering framework to stratify a patient cohort, expected to be composed of a fixed set of molecular subtypes. We introduce a model that jointly infers CNAs, assigns patients to subgroups and infers the profiles that represent each subgroup. We show our model to be more accurate on synthetic data, and show in two patient cohorts how the model discovers putative novel subtypes and clinically relevant subgroups. / Science, Faculty of / Computer Science, Department of / Graduate

Copy number variation (CNV) is a class of genetic variation where large segments of the genome vary in copy number among different individuals. It has become clear in the past decade that CNV affects a significant proportion of the human genome and can play an important role in human disease. With array-based copy number detection and the current generation of sequencing technologies, our ability to discover genetic variants is running far ahead of our ability to interpret their functional impact. One approach to close this gap is to explore statistical association between genetic variants and phenotypes. In contrast to the successes of genome-wide association studies for common disease using common single nucleotide polymorphism (SNP) as markers, the majority of disease CNVs discovered so far have low population frequencies and are mainly involved in rare developmental disorders. Another strategy to improve interpretation of genomic variants is to establish a predictive understanding of their functional impact. Large heterozygous deletions are of particular interest, since (i) loss-of-function (LOF) of coding sequences encompassed by large deletions can be relatively unambiguously ascribed and (ii) haploinsufficiency (HI), wherein only one functional copy of a gene is not sufficient to maintain normal phenotype, is a major cause of dominant diseases. This thesis explored both approaches. Initially, I developed an informatics pipeline for robust discovery of CNVs from large numbers of samples genotyped using the Affymetrix whole-genome SNP array 6.0, to support both the association-based and prediction-based study. For the disease association strategy, I studied the role of both common and rare CNVs in severe early-onset obesity using a case-control design, from which a rare 220kb heterozygous deletion at 16p11.2 that encompasses SH2B1 was found causal for the phenotype and an 8kb common deletion upstream of NEGR1 was found to be significantly associated with the disease, particularly in females. Using the prediction-based approach, I characterized the properties of HI genes by comparing with genes observed to be deleted in apparently healthy individuals and I developed a prediction model to distinguish HI and haplosufficient (HS) genes using the most informative properties identified from these comparisons. An HI-based pathogenicity score was devised to distinguish pathogenic genic CNVs from benign genic CNVs. Finally, I proposed a probabilistic diagnostic framework to incorporate population variation, and integrate other sources of evidence, to enable an improved, and quantitative, identification of causal variants.

No description available.

No description available.

Copy number variations (CNVs) are a major source of inter-individual genetic difference, accounting for a greater proportion of the human genome than other forms of variation. Recently, the identification of benign and pathogenic CNVs has improved due to arrays with increased coverage. Nevertheless, most CNVs have not been studied with great precision and questions persist regarding their exact breakpoint, gene content, frequency and functional impact. This is especially true in cancer, in which a role for CNVs as risk factors is under-explored. Li-Fraumeni syndrome (LFS) is a dominantly inherited disorder with an increased risk of early-onset breast cancer, sarcomas, brain tumors and other neoplasms in individuals harboring germline TP53 mutations. Known genetic determinants of LFS do not fully explain its clinical phenotype. In this thesis we describe the association between CNVs and LFS. First, by examining DNA from a healthy population and an LFS cohort using oligonucleotide arrays, we show that the number of CNVs per genome is well conserved in the healthy population, but remarkably enriched in these cancer-prone individuals. We found a significant increase in CNVs among carriers of germline TP53 mutations with a familial cancer history. Second, we find that ii specific CNVs at 17p13.1 are associated with LFS or developmental delay, depending on the exact breakpoint with respect to TP53. Using a purpose built array with 93.75% accuracy, we fine-mapped these microdeletions and find that they arise by Alu-mediated non-allelic homologous recombination, and contain common genes, whose under-expression distinguishes the two phenotypes. Third, we explore somatic CNVs in choroid plexus carcinoma tumor genomes. We show that this tumor is over-represented in LFS, and the number of somatic CNVs is associated with TP53 mutations and disease progression. These studies represent the first genomic analyses of LFS, and suggest a more generalized association between CNVs and cancer.

Copy number variations (CNVs) are a major source of inter-individual genetic difference, accounting for a greater proportion of the human genome than other forms of variation. Recently, the identification of benign and pathogenic CNVs has improved due to arrays with increased coverage. Nevertheless, most CNVs have not been studied with great precision and questions persist regarding their exact breakpoint, gene content, frequency and functional impact. This is especially true in cancer, in which a role for CNVs as risk factors is under-explored. Li-Fraumeni syndrome (LFS) is a dominantly inherited disorder with an increased risk of early-onset breast cancer, sarcomas, brain tumors and other neoplasms in individuals harboring germline TP53 mutations. Known genetic determinants of LFS do not fully explain its clinical phenotype. In this thesis we describe the association between CNVs and LFS. First, by examining DNA from a healthy population and an LFS cohort using oligonucleotide arrays, we show that the number of CNVs per genome is well conserved in the healthy population, but remarkably enriched in these cancer-prone individuals. We found a significant increase in CNVs among carriers of germline TP53 mutations with a familial cancer history. Second, we find that ii specific CNVs at 17p13.1 are associated with LFS or developmental delay, depending on the exact breakpoint with respect to TP53. Using a purpose built array with 93.75% accuracy, we fine-mapped these microdeletions and find that they arise by Alu-mediated non-allelic homologous recombination, and contain common genes, whose under-expression distinguishes the two phenotypes. Third, we explore somatic CNVs in choroid plexus carcinoma tumor genomes. We show that this tumor is over-represented in LFS, and the number of somatic CNVs is associated with TP53 mutations and disease progression. These studies represent the first genomic analyses of LFS, and suggest a more generalized association between CNVs and cancer.

In the knowledge economy era, a firm¡¦s competitive edge increasingly depends on the possession of knowledge and intellectual capital. Knowledge becomes the key resource for firms, and firms must increase the attention they pay to knowledge management. A very important issue regarding intra-firm knowledge transfer is to identify the model and circumstances in which knowledge can effectively be transferred. The objective of this study is to demonstrate the typology of intra-firm knowledge transfer executed by different semiconductor firms. Multiple case study approach is adopted to identify the pattern of knowledge and technology transfer in this study. In order to analyses knowledge transfer activities, four semiconductor firms were studied with qualitative method. Multiple sources of evidence are gathered, including documents, interviews, and internal reports. Synthesizing the knowledge transfer practices of these firms, this study demonstrates three models for intra-firm knowledge transfer. These knowledge transfer models have important implications for technology and knowledge management. First, deploying these transfer models, replication strategies can apply to the manufacturing sector, including high-tech firms. Manufacturing firms can transfer manufacturing processes between business units and achieve synergy effects or performance consistency. Second, these transfer models validate the contingency theory since various situations induce different transfer models. Third, from these cases we can understand that external benchmarking may not always be the best way to solve problems and maintain competitive advantage. Resource-based theory maintains that firms can retain competitive edge by efficiently and effectively applying practices developed in-house.