Chapter 2 - University of British Columbia

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5.3.2 Integration of cancer genomic and epigenomic events DNA methylation and genomic instability. Cancer-specific aberrant DNA methylation is associated with reduced genomic stability and subsequent copy number alterations, including preferential loss of certain imprinted alleles (LOI) [178-184]. Mechanistically, this instability may be related to the susceptibility of hypomethylated DNA to undergo inappropriate recombination events [185]. Another mechanism known to negatively impact genomic integrity in lung cancer is the relaxation of transposable element control that is mediated by DNA methylation [186-190]. DNA hypomethylation and DNA amplification. Preliminary evidence of specific demethylation of somatic segmental amplifications (or amplicons) has been put forth in lung cancer, perhaps representing a novel mechanism of aberrant oncogene activation [189, 191]. Further studies using large-scale sequencing of bisulfite treated DNA will help to clarify this phenomenon [12]. Hypomethylation has also been implicated in the formation of specific copy number alterations in glioblastoma multiforme [192]. One potentially interesting application for DNA methylation profiling of cancer amplicons such as these, is in the discrimination between "driver" and "passenger" genes within the amplified sequence. It may be that DNA methylation within the promoters or gene bodies of these genes is responsible for the lack of uniform overexpression of genes residing within amplicons. DNA hypermethylation and copy number loss. The relationship between DNA hypermethylation and allelic loss is well documented. Tumor suppressor genes are frequently found in regions of common LOH, and these same TSGs are frequently found to be hypermethylated, perhaps best exemplified by the FHIT gene on chromosome 3p [193]. Although it is unclear whether loss or hypermethylation occurs first, both are known to be very early events in tumorigenesis preceding any histologic alterations [194-196]. With the advent of high resolution genome-wide technologies it has become possible to comprehensively search for genes that are inactivated by both mechanisms simultaneously [197]. 119
Histone modification states. While DNA methylation and gene dosage profiling technologies have become accessible, technologies for global assays of other key epigenetic marks including histone modifications are not widely available. One of the main challenges to conducting the highest quality studies of genome wide chromatin-immunoprecipitation on microarray (ChIP- chip) or on sequencing platform (ChIP-seq) experiments is the requirement of high quality DNA from pure cells – which essentially means growing cells in culture. It is thus difficult to analyze these dimensions from clinical specimens. However, much has been learned from studies of the relationship between different histone modification states and transcriptional activation or repression in model systems. Such examples utilizing ChIP-chip include: cell or context specific histone modification patterns related to cell or context specific gene expression; histone 3 lysine 27 (H3K27) trimethylation patterns associated with prostate, lung and breast cancers; and H3K9 and H3K79 modification patterns in leukemia [198-204]. Examples utilizing ChIP-seq include: the analysis of the growth inhibition program of the androgen receptor, and the chromatic interaction network of the estrogen receptor [205, 206]. 5.4 Relating genetic and epigenetic events to changes in the transcriptome through integrative analysis Aberrations in individual genetic or epigenetic dimensions are prominent across various cancer types, culminating in changes to the transcriptome. However, for a given gene, most of the events documented previously, such as copy number amplification, homozygous deletion, somatic mutation, or DNA hypermethylation, do not occur in 100% of tumors for a given cancer type. Moreover, it has been observed that the same gene may be activated or inactivated by different mechanisms. Since most of the studies described above analyzed single DNA dimensions, it is likely many genes would be overlooked due to a low frequency of alteration in a single dimension; the same gene may be detected at a high frequency when multiple dimensions are considered. Thus, analysis of more dimensions may reveal higher frequency 120
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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
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mechanisms at the DNA and RNA level
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In this study, three DNA dimensions
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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 and 128: 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
Page 149 and 150: Figure 5.5. Integration of copy num
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
Page 165 and 166: 111. Melcher R, Al-Taie O, Kudlich
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
Page 179 and 180: can identify nearly three times as
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Page 183 and 184: 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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