Chapter 2 - University of British Columbia

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5.2 Genomic alterations 5.2.1 Chromosomal aberrations Chromosomal aberrations and rearrangements, such as translocations and gains/losses of whole or portions of chromosome arms are detected through direct examination using molecular cytogenetic techniques such as G-banding, SKY, fluorescence in situ hybridization (FISH) and CGH [2, 44-48]. The manifestation of such alterations are generally attributed to mitotic errors, where centrosomal aberrations and telomere dysfunction play key causative roles [49-53]. Aberrations such as gains and losses have been further refined using technologies such as microarray CGH (see below). While primarily associated with different types of leukemia and lymphomas, recent genomic studies have identified translocations in epithelial tumors such as prostate and lung cancer [54-61]. A compilation of cumulative cytogenetic data from three main sources - NCI/NCBI SKY/M-FISH & CGH Database, NCI Mitelman Database of Chromosome Aberrations in Cancer, and NCI Recurrent Aberrations in Cancer – are now integrated into NCBI's Entrez system as Cancer Chromosomes [62] (Table 5.2). 5.2.2 Gene dosage, allelic imbalance, mutational status Gene dosage. Genomic DNA copy number alterations are a prominent mechanism of gene disruption that contributes to tumor development [63]. Segmental amplification may lead to an increase in gene and protein expression of oncogenes, while deletions may lead to haploinsufficiency or the loss of expression of tumor suppressor genes. Since its development in the mid 1990s, advances in microarray-based CGH technology have dramatically increased genome coverage and target density, improving both the resolution and sensitivity of detection of copy number alterations [64, 65]. The first genome-wide array CGH analysis utilized cDNA microarrays originally designed for gene expression profiling [66]. Since these first experiments, whole genome tiling path arrays with tens of thousands of bacterial artificial chromosome (BAC) clones, oligonucleotide (25-80 bp nucleotide probes) and single nucleotide 113
polymorphism (SNP) arrays with over one million DNA elements and the essential bioinformatics tools for visualization and analysis of high density array CGH data have been developed (Figure 5.1) [7, 33, 67-71]. These innovations have enabled increasingly precise mapping of the boundaries and magnitude of genetic alterations throughout the genome in a single experiment, greatly increasing our understanding of the cancer genome landscape in the context of DNA copy number [33, 72-76]. While early attempts have been made utilizing sequence-based approaches [77-80], recent studies have begun to illustrate the improvement in detection resolution through the advances in high throughput sequencing technologies [6, 11, 13, 14]. The popularity of genome sequencing will depend on further cost reduction in data generation and major advancements in analysis [81]. Copy number variation. The discovery of a vast abundance of germ line segmental DNA copy number variation (CNV) in the normal human population has not only provided a baseline for interpretation of cancer genome data, but also highlighted the need for comparison against paired normal tissue [18, 19, 31, 32, 82-89]. Moreover, it has been shown that many of the reported CNVs overlap with loci involved with sensory perception and more importantly, disease susceptibility. While the role of CNV in cancer is not well understood, a recent study showed that these regions are more susceptible to genomic rearrangement and may initiate subsequent alterations during tumorigenesis [90]. Moreover, CNV at 1q21.1 was recently shown to be associated with neuroblastoma and implicated NBPF23, a new member of the Neuroblastoma Breakpoint Family, in tumorigenesis [91]. A database of all known CNVs is available at http://projects.tcag.ca/variation [31]. In addition, as copy number profiles of cancer genomes accumulate, hotspots for amplification and deletion are becoming evident, and signature alterations associated with specific diseases and cancer histologic subtypes are emerging [92- 96]. The manifestation of “oncogene addiction” through lineage specific DNA amplification is a case in point [38, 39, 97-100]. 114
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DEVELOPMENT AND APPLICATION OF AN I
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Table of Contents Abstract ........
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3.3.2 Multi-dimensional analysis (M
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List of Tables Table 2.1. Features
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Figure 5.3. Overlay of chromosomal
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RB1 Retinoblastoma 1 RNA Ribonuclei
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Dedication To my family. xiii
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Chapter 5: Chari R, Thu KL, Wilson
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1.1 Lung cancer Lung cancer has the
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expression changes are reactive to
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number of different cancer types. M
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large number of tumors, that a give
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(B) By using an integrative approac
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platforms, with one of the latest s
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1.12.1 Development of tools for gen
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quantitative PCR experiments as wel
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1.13 References 1. Jemal A, Siegel
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33. Garber ME, Troyanskaya OG, Schl
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71. Shivapurkar N, Gazdar AF: DNA M
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Chapter 2: SIGMA 2 : A system for t
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Here, we present SIGMA 2 , a novel
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datasets. To utilize this, the user
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2.3.7 Analysis of data from multipl
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expression (Figure 2.8). ERBB2 has
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Figure 2.1 R -Segmentation -Statist
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Figure 2.3 numSamples
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Figure 2.5. Consensus calling and h
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Figure 2.6 HCC2218 HCC2218 HCC2218B
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Figure 2.8. Multi-dimensional persp
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Table 2.1. Features required for in
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2.6 References 1. Garnis C, Buys TP
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Chapter 3: An integrative multi-dim
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observed gene expression deregulati
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Raw gene expression profiles from a
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screening approach to gene amplific
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many regions of frequent LOH/AI do
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approach to examining the whole gen
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PTEN expression [44]. Using this pa
Page 77 and 78: mechanisms at the DNA and RNA level
Page 79 and 80: In this study, three DNA dimensions
Page 81 and 82: Figure 3.2. Quantitative and qualit
Page 83 and 84: Figure 3.3 a Proportion of genes in
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Page 87 and 88: 72 Figure 3.7 Sample: HCC1008 Copy
Page 89 and 90: Figure 3.8 a b Proportion of random
Page 91 and 92: 16. Chari R, Lockwood WW, Coe BP, C
Page 93 and 94: 50. Giovane A, Pintzas A, Maira SM,
Page 95 and 96: 4.1 Introduction Genetic alteration
Page 97 and 98: 4.2.3 Determining frequent regions
Page 99 and 100: etween loss and UPD (Figure 4.3). S
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Page 103 and 104: Figure 4.1. Detection of UPD using
Page 105 and 106: Figure 4.2. Comparison of frequent
Page 107 and 108: Figure 4.3 Gain Loss 642 441 7 Figu
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Page 111 and 112: Figure 4.6 a b c Percent of cases A
Page 113 and 114: Figure 4.7 a b 6p25.3 Log2 fold cha
Page 115 and 116: Chr BPStart BPEnd # of Chr BPStart
Page 117 and 118: Table 4.3. Overlap of oncogenes in
Page 119 and 120: Table 4.5. Cell lines and oncogene
Page 121 and 122: Table 4.7. RefSeq genes in focal re
Page 123 and 124: 4.6 References 1. Bell DW: Our chan
Page 125 and 126: 33. Garnis C, Coe BP, Lam SL, MacAu
Page 127: 5.1 Introduction In the past decade
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Page 145 and 146: Figure 5.2 # of copies Total copy n
Page 147 and 148: Figure 5.4. Integration of copy num
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Page 151 and 152: Figure 5.6 SHC1 GRB2 SOS2 RRAS ER R
Page 153 and 154: Figure 5.8 a Log2 Fold Change (T vs
Page 155 and 156: Figure 5.10 (a) (b) Figure 5.10. Au
Page 157 and 158: Table 5.2. List of genomic resource
Page 159 and 160: 5.5 References 1. Pinkel D, Segrave
Page 161 and 162: 37. Oliphant A, Barker DL, Stuelpna
Page 163 and 164: 75. Selzer RR, Richmond TA, Pofahl
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Page 167 and 168: 145. Takai D, Yagi Y, Wakazono K, O
Page 169 and 170: 179. Kondo M, Suzuki H, Ueda R, Osa
Page 171 and 172: alterations in human prostate cance
Page 173 and 174: 248. Xi L, Feber A, Gupta V, Wu M,
Page 175 and 176: 281. Fernandez-Ranvier GG, Weng J,
Page 177 and 178: Chapter 6: Conclusions 162
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can identify nearly three times as
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was identified in a small set of sa
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will be done at the pathway level a
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previously described genetic and ep
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16. Zhang K, Li JB, Gao Y, Egli D,
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APPENDIX I: List of publications Th
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This publication is described in se
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This chapter details the technologi
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epigenetic alteration. This led to
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This manuscript describes the curre
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APPENDIX III: Sources of data Sampl
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APPENDIX V: Kaplan-Meier and Oncomi
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APPENDIX VI: Summary of Kaplan-Meie
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University of British Columbia - Br
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