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.

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.

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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.

Somatic Copy Number Alterations (CNAs) are common events in human cancers. Identifying CNAs and Significant Copy number Aberrations (SCAs) in cancer genomes is a critical task in searching for cancer-associated genes. Advanced genome profiling technologies, such as SNP array technology, facilitate copy number study at a genome-wide scale with high resolution. However, due to normal tissue contamination, the observed intensity signals are actually the mixture of copy number signals contributed from both tumor and normal cells. This genetic confounding factor would significantly affect the subsequent copy number analyses. In order to accurately identify significant aberrations in contaminated cancer genome, we develop a Java AISAIC package (Accurate Identification of Significant Aberrations in Cancer) that incorporates recent novel algorithms in the literature, BACOM (Bayesian Analysis of Copy number Mixtures) and SAIC (Significant Aberrations in Cancer). Specifically, BACOM is used to estimate the normal tissue contamination fraction and recover the "true" copy number profiles. And SAIC is used to detect SCAs using large recovered tumor samples. Considering the popularity of modern multi-core computers and clusters, we adopt concurrent computing using Java Fork/Join API to speed up the analysis. We evaluate the performance of the AISAIC package in both empirical family-wise type I error rate and detection power on a large number of simulation data, and get promising results. Finally, we use AISAIC to analyze real cancer data from TCGA portal and detect many SCAs that not only cover majority of reported cancer-associated genes, but also some novel genome regions that may worth further study. / Master of Science

No description available.

Acute myeloid leukaemia (“AML”) is a clonal disease characterised by increased, uncontrolled abnormal white blood cells and the accumulation of leukaemia immature cells in the bone marrow and bloodstream. Chromosomal rearrangements have been detected in almost half of AML cases. It has been proven that the chromosomal rearrangements constitute a marker for the diagnosis and prognosis of AML and have therapeutic consequences. The discovery of these rearrangements has led to a new World Health Organization (“WHO”) classification system. However, small regions of cryptic chromosomal rearrangements have been identified among these cases. Such cryptic rearrangements can be explained by the identification of small regions which cannot be found by conventional chromosome banding techniques. Moreover, approximately 50% of AML cases have been found with normal karyotypes. The improvement of cytogenetic techniques, including fluorescence in situ hybridization (“FISH”) and single nucleotide polymorphism (“SNP”) platforms, have allowed the detection of small rearranged regions (such as copy number changes) both in normal and abnormal karyotype AML. This study identifies: (i) cryptic chromosomal translocations in leukaemia cells of AML patients; (ii) DNA copy number changes in patients with known chromosomal translocations; and (iii) the proliferating state of leukemic cells harbouring chromosomal abnormalities within a series of patients. In the initial study, the FISH technique was performed on 7 AML patient samples to validate a novel three colour probe for the detection of t(7; 12). The results demonstrated that the new three-colour FISH approach used in this study has enabled the detection of a cryptic t(7;12) translocation as part of a complex rearrangement in one patient previously been described as having t(7;16) and ETV6-HLXB9 fusion transcript at the molecular level. To date there are only two cases of a cryptic t(7; 12) translocation reported in the literature. Additionally, the new three-colour FISH approach also enabled identification of t(7; 12) in a new seven year-old AML patient (the first case of childhood leukaemia with an onset after infancy to be found positive for t(7; 12)). In the second study the FISH technique was used to validate three colour probe sets for the detection of 7(q22-q31) and 7(q22-q36.1) regions on several myeloid cell lines. The results indicate that the probes found chromosome 7 rearrangements in myeloid cell lines with complex rearrangements. The three colour probe sets enabled detection of a new rearrangement in the k562 cell line, described as a duplication of 7q36 region, followed by intrachromosomal insertion of long arm material into the short arm of chromosome 7. The intrachromosomal insertion identified in k562 cell line is an uncommon form of chromosomal rearrangement in myeloid leukaemia which has not been previously reported. In the third study, the Illumina BeadArray approach was used to assess copy number alterations (“CNAs”) and copy number loss of hertrozygosity (“CN-LOH”) regions in 22 AML patients samples with inv(16)(p13;q22) and t(8;21)(q22;q22) rearrangements. In order to distinguish between true CNAs and false-positive findings as well as to verify whether CNAs are present in the same clone harbouring inv (16), FISH was used on fixed chromosome and cell suspensions from the same patients. The results showed a low number of copy number losses and copy number gains in 17 (77.27%) out of 22 cases, with an average of 1.86 CNAs per case as well as copy neutral-LOH with an average of 6.7% per patient. Furthermore, interphase FISH was carried out on four cases showing a 7q36.1 deletion, 4q35.1 deletion, 16.13.11 deletion and 8q24.21-q24.3 gain identified by array. The FISH results confirmed CNAs in most cases while CNA was not confirmed in one patient. Moreover, the FISH data analysis showed that the CNAs were found in both cells without inv (16) and cells harbouring the inv (16) rearrangement. In the final study, indirect immunofluorescence (IF) was used to determine the ki67 staining patterns in 8 stimulated and unstimulated peripheral blood samples and k562 cell lines. The results showed a high percentage of ki67 positive staining in the stimulated samples in comparison with unstimulated samples, which showed a low percentage of ki67 positive staining. In addition, a high percentage of proliferating cells were detected in the k562 cell line. ImmunoFISH was performed on five different patient samples and leukaemia cell lines using specific probes in the regions of interest to detect the chromosomal abnormalities and using the ki67 antibody to assess the proliferation state of the cells. The results showed that the proliferation state of the cells carrying chromosomal abnormalities in two patients was higher than the proliferation state of the cells carrying abnormalities in three patients; in other words, most of the cells carrying abnormalities were proliferating in two cases and non-proliferating in three cases.