The association of constitutional chromosome imbalance in patients with intellectual disability with or without related dysmorphism and malformations is well established. The resolution of conventional cytogenetic examination is limited to imbalances of 5-10Mb. Patients with characteristic phenotypes which allude to a specific microdeletion or duplication syndrome may be investigated using locus specific fluorescent in situ hybridisation (FISH). Subtelomere FISH, a recently new improvement for cytogenetics screening, detects subtelomeric rearrangements in around 6% of patients with idiopathic disability. However it is evident that for these patients, most do not have a recurrent pattern of dysmorphism or malformations suggesting imbalance in a particular chromosome region. Array CGH has the potential to detect chromosome imbalances beyond that of current technology allowing the whole genome can be screened in a single hybridisation at a resolution limited only by the genomic distance between the arrayed target clones. The aim of this study was to develop a custom whole genome array and utilize this array to screen a number of diverse patient groups. Rather than immediately begin with the development of a whole genome array a smaller pilot study was initiated, in so enabling the efficacy of the methodology to be tested. A small clinical/ subtelomere array was designed and constructed to screen for cytogenetic imbalances within the first 5Mb of each chromosome end (excluding acrocentric chromosomes) together with the number of known clinically significant regions. This clinical/subtelomere array (chapter 3), was ulilised to map the extent of deletion and/or duplication in patients with previously determined subtelomere abnormalities. This was followed with the screening of a small group of patients with idiopathic intellectual disability (chapter 4). Novel Copy number changes were identified together with a number of changes determined to be non-pathogenic variants. The methodology used in the utilization of this array could determine copy changes in patients, however it became clearly evident that the effective resolution was compromised when a number of clones were shown to map to other sites of the genome or cross hybridise to multiple sites. With this knowledge the next stage of the project, a whole genome array, primarily constructed from a FISH validated clone set, reduced the possibility of mapping discrepancies. These FISH mapped clones did not give a even genomic coverage. All clones from this set were mapped using Ensembl resources. Any identified gaps (greater than 1Mb) were covered using clones from a second clone set (32K), giving a resolution of ~0.2-1Mb. Three diverse groups of patients were screened using the whole genome array. Using a novel scoring system which evaluated degree of intellectual disability/developmental delay, dysmorphism, presence of malformations and the presence of an apparently balanced cytogenetic rearrangement, twenty five patients with idiopathic disability were assessed. Four novel copy number changes were determined of which three were determined likely to contribute to the phenotype of the patient. The fourth patient is under investigation. Chapter 6 saw the application of the whole genome array to a second patient group; medically terminated pregnancies, in utero fetal deaths or newborn infants with one or more facial dysmorphism or fetal malformation. One potentially pathogenic copy number change was ascertained from this study. The third patient group, those with retractable epilepsy (chapter 7) showed two copy number changes both of which will require further follow up and investigation, including screening with a higher density array platform. The screening of more than seventy varied patients has shown the efficacy of these two custom platforms, to detect previously undetermined copy number changes. / Thesis (Ph.D.) -- University of Adelaide, School of Paediatrics and Reproductive Health, 2010

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.

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.

Small Cell Lung Cancer (SCLC) is a very aggressive neuroendocrine tumour of the lung, which demonstrates a 5 year survival of only 10% for extensive stage disease (20-30% for limited stage), with only modest improvement over the last few decades. Identification of new molecular diagnostic and therapeutic targets is thus imperative. Previous efforts in identifying molecular changes in SCLC by gene expression profiling using microarrays have facilitated disease classification but yielded very limited information on SCLC biology. Previous DNA studies have been successful in identifying several loci important to SCLC. However the low resolution of conventional chromosomal Comparative Genomic Hybridization (CGH) has limited the findings to large chromosomal regions with only a few specific candidate genes discovered to date. Thus, to further understand the biological behaviour of SCLC, better methods for studying the genomic alterations in SCLC are necessary. This thesis highlights the development of array CGH technology for the high resolution dissection of aneuploidy in cancer genomes and the application of this new technology to the study of SCLC. I present the development of the first whole genome CGH array which offered unprecedented resolution in the profiling of cancer genomes allowing fine mapping of genes in a single experiment. Through application of DNA based analysis in conjunction with integrated expression analysis and comparison of SCLC to less aggressive non-small cell lung tumours I have identified novel patterns of pathway disruption specific to SCLC. This included alteration to Wnt pathway members and striking patterns of cell cycle activation through predominantly downstream disruption of signalling pathways including direct activation of the E2F transcription factors, which are normally repressed by the Rb gene. Analysis of targets of the E2F/Rb pathway identified EZH2 as being specifically hyper-activated in SCLC, compared to NSCLC. EZH2 is a polycomb group gene involved in the control of many cellular functions including targeted DNA methylation and escape from senescence in hematopoietic stem cells. Taken together these results suggest that in SCLC, downstream disruption may replace multiple upstream alterations leading to activation independent of a specific mitogenic pathway, and that EZH2 represents a potentially important therapeutic target.

Small Cell Lung Cancer (SCLC) is a very aggressive neuroendocrine tumour of the lung, which demonstrates a 5 year survival of only 10% for extensive stage disease (20-30% for limited stage), with only modest improvement over the last few decades. Identification of new molecular diagnostic and therapeutic targets is thus imperative. Previous efforts in identifying molecular changes in SCLC by gene expression profiling using microarrays have facilitated disease classification but yielded very limited information on SCLC biology. Previous DNA studies have been successful in identifying several loci important to SCLC. However the low resolution of conventional chromosomal Comparative Genomic Hybridization (CGH) has limited the findings to large chromosomal regions with only a few specific candidate genes discovered to date. Thus, to further understand the biological behaviour of SCLC, better methods for studying the genomic alterations in SCLC are necessary. This thesis highlights the development of array CGH technology for the high resolution dissection of aneuploidy in cancer genomes and the application of this new technology to the study of SCLC. I present the development of the first whole genome CGH array which offered unprecedented resolution in the profiling of cancer genomes allowing fine mapping of genes in a single experiment. Through application of DNA based analysis in conjunction with integrated expression analysis and comparison of SCLC to less aggressive non-small cell lung tumours I have identified novel patterns of pathway disruption specific to SCLC. This included alteration to Wnt pathway members and striking patterns of cell cycle activation through predominantly downstream disruption of signalling pathways including direct activation of the E2F transcription factors, which are normally repressed by the Rb gene. Analysis of targets of the E2F/Rb pathway identified EZH2 as being specifically hyper-activated in SCLC, compared to NSCLC. EZH2 is a polycomb group gene involved in the control of many cellular functions including targeted DNA methylation and escape from senescence in hematopoietic stem cells. Taken together these results suggest that in SCLC, downstream disruption may replace multiple upstream alterations leading to activation independent of a specific mitogenic pathway, and that EZH2 represents a potentially important therapeutic target.

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

Small Cell Lung Cancer (SCLC) is a very aggressive neuroendocrine tumour of the lung, which demonstrates a 5 year survival of only 10% for extensive stage disease (20-30% for limited stage), with only modest improvement over the last few decades. Identification of new molecular diagnostic and therapeutic targets is thus imperative. Previous efforts in identifying molecular changes in SCLC by gene expression profiling using microarrays have facilitated disease classification but yielded very limited information on SCLC biology. Previous DNA studies have been successful in identifying several loci important to SCLC. However the low resolution of conventional chromosomal Comparative Genomic Hybridization (CGH) has limited the findings to large chromosomal regions with only a few specific candidate genes discovered to date. Thus, to further understand the biological behaviour of SCLC, better methods for studying the genomic alterations in SCLC are necessary. This thesis highlights the development of array CGH technology for the high resolution dissection of aneuploidy in cancer genomes and the application of this new technology to the study of SCLC. I present the development of the first whole genome CGH array which offered unprecedented resolution in the profiling of cancer genomes allowing fine mapping of genes in a single experiment. Through application of DNA based analysis in conjunction with integrated expression analysis and comparison of SCLC to less aggressive non-small cell lung tumours I have identified novel patterns of pathway disruption specific to SCLC. This included alteration to Wnt pathway members and striking patterns of cell cycle activation through predominantly downstream disruption of signalling pathways including direct activation of the E2F transcription factors, which are normally repressed by the Rb gene. Analysis of targets of the E2F/Rb pathway identified EZH2 as being specifically hyper-activated in SCLC, compared to NSCLC. EZH2 is a polycomb group gene involved in the control of many cellular functions including targeted DNA methylation and escape from senescence in hematopoietic stem cells. Taken together these results suggest that in SCLC, downstream disruption may replace multiple upstream alterations leading to activation independent of a specific mitogenic pathway, and that EZH2 represents a potentially important therapeutic target. / Medicine, Faculty of / Pathology and Laboratory Medicine, Department of / Graduate

Array comparative genomic hybridization (aCGH) is a technique for identifying duplications and deletions of DNA at specific locations across a genome. Potential objectives of aCGH analysis are the identification of (1) altered regions for a given subject, (2) altered regions across a set of individuals, and (3) clinically relevant clusters of hybridizations. aCGH analysis can be particularly useful when it identifies previously unknown clusters with clinical relevance. This project focuses on the assessment of existing aCGH clustering methodologies. Three methodologies are considered: hierarchical clustering, weighted clustering of called aCGH data, and clustering based on probabilistic recurrent regions of alteration within subsets of individuals. Assessment is conducted first through the analysis of aCGH data obtained from patients with ovarian cancer and then through simulations. Performance assessment for the data analysis is based on cluster assignment correlation with clinical outcomes (e.g., survival). For each method, 1,000 simulations are summarized with Cohen's kappa coefficient, interpreted as the proportion of correct cluster assignments beyond random chance. Both the data analysis and the simulation results suggest that hierarchical clustering tends to find more clinically relevant clusters when compared to the other methods. Additionally, these clusters are composed of more patients who belong in the clusters to which they are assigned.

No description available.

A translocação recíproca é o rearranjo equilibrado mais comum em humanos. Frequentemente, indivíduos com rearranjos equilibrados não apresentam manifestações clínicas, entretanto, na meiose, o pareamento entre cromossomos translocados forma uma figura quadrivalente em forma de cruz que torna a disjunção cromossômica incerta e dependendo do rearranjo, o individuo pode vir a ser infértil, apresentar um risco aumentado de abortamento espontâneo e/ou da prole apresentar alterações fenotípicas. Neste projeto, investigamos duas famílias de pacientes inférteis, portadores de translocações cromossômicas. O objetivo foi caracterizar as alterações citogenéticas e citogenômicas relacionadas à infertilidade masculina em pacientes portadores de rearranjos aparentemente equilibrados, associando técnicas de citogenética clássica (bandeamento GTG), citogenética molecular (FISH) e citogenômica (array-CGH). Foram estudados sete indivíduos da família 1, sendo diagnosticados três portadores da translocação (X;22), sendo um deles azoospérmico. Nesta família foram ainda detectados dois casos de mosaicismo para síndrome de Turner. A família 2 foi composta por dois irmãos oligozoospérmicos, portadores de translocação (8;13). Com a aplicação da técnica de FISH, definimos o cariótipo final dos portadores dos rearranjos como 46,XX ou 46,XY,t(X;22)(p22.3;q11.2) para a família 1 e 46,XY,t(8;13)(q13;q14)para a família 2. A técnica de array-CGH (plataforma 2x400K, Agilent) detectou alterações no número de cópias de alguns genes candidatos relacionados ao fenótipo de infertilidade, sendo a sequência 132 de piRNAs, os genes DDX11, Jagged 2 e ADAM18 na família 1 e os genes candidatos ADAM18 e POTE nos pacientes da família 2. / Reciprocal translocations are the most common balanced rearrangement in humans. Often individuals with balanced rearrangements show no clinical findings. However, in meiosis, the pairing between translocated chromosomes forms a quadrivalent cross-shaped figure which has the effect of making chromosome disjunction uncertain and, depending on the rearrangement, and on the segregation of the unbalanced chromosomes, the individual can be infertile, can present with an increased risk of spontaneous abortions or can have an offspring with abnormal phenotype. We have studied two families of infertile patients, who were carriers of chromosomal translocations. The objective was to characterize the cytogenetic and cytogenomic alterations related to male infertility in patients with apparently balanced rearrangements using classical cytogenetic techniques (GTG banding), molecular cytogenetics (FISH) and cytogenomics (array-CGH). Seven subjects of the family 1 were studied, including three carriers of translocation (X;22), one azoospermic. Two cases of mosaicism for Turner syndrome were detected in this family. The second family consisted of two oligozoospermic brothers with translocation (8;13). FISH was used to characterize the karyotypes as 46, XX or 46,XY, t(X;22)(p22.3;q11.2) for the members of the family 1 and 46,XY,t(8;13)(q13;q14) for family 2. Array-CGH was also performed using the Agilent platform 2x400K, to detect associated copy number variations of some of the candidate genes that could be related to infertility. In the family 1 the candidate genes were 132 piRNAs sequences and DDX11,Jagged 2 and ADAM18 genes. The candidate genes for the family 2 were ADAM18 and POT.