Chapter 2 - University of British Columbia

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Recent studies have shown that UPD events are frequently observed in tumor genomes, with most of the findings reported from hematological malignancies [122-131]. Our genome wide analysis of segmental gain, loss and UPD in the T47D breast cancer cell line genome identified that a significant portion of the genome exhibits UPD, rivaling the proportion of the genome affected by segmental gain and loss, and highlighting the potential of UPD as a prominent mechanism of gene disruption in epithelial cancer (Figure 3). Interestingly, PIK3CA and TP53 mutations in T47D are noted in the Catalogue of Somatic Mutations in Cancer [132]. Integrative analysis at these loci detected copy number increase at PIK3CA and copy number loss at TP53 illustrating the MASI concept described above (Figure 3). Somatic UPD also exists at genes without mutation. The potential significance of this somatic event is not readily apparent, but it raises the intriguing possibility of allelic conversion of epigenetic status [117, 122, 133]. 5.3 Epigenomic alterations 5.3.1 The cancer methylome Abnormal DNA methylation patterns occur in cancer, whereby focal hypermethylation at many CpG islands is evident in a background of global DNA hypomethylation [134-137]. Broad hypomethylation may lead to genomic instability, while hypermethylation of CpG islands silences transcription of specific genes [136, 138-140]. Non-random methylation of multiple CpG islands observed in colon cancer led to the discovery of CpG island methylator phenotype (CIMP), which is causally linked to microsatellite instability via silencing of the mismatch repair gene, MLH1 [141-143]. The determination of DNA methylation status relies on the ability to discriminate between methylated and unmethylated cytosines. This is achieved by exploiting methylation- sensitive/insensitive isoschizomer restriction-enzyme pairs [144-150], chemical conversion of unmethylated cytosine to uracil [151-156], and the affinity for methylated DNA of specially 117
developed antibodies and methylated-DNA binding proteins [24, 157-163]. Several computational methods have been developed for deriving approximations of actual methylation levels from the relative levels generated by most microarray and locus specific sequencing assays [147, 162, 164, 165]. However, it is important to note that CpG targets represented on microarrays may or may not be the only elements controlling gene expression. Recently, it was shown that in the human colon cancer methylome sequences up to 2 kb away from CpG islands, termed CpG shores, exhibited more methylation than CpG islands and had greater influence on gene expression than CpG islands [166]. Furthermore, while excess promoter methylation is typically associated with transcriptional repression, the loss of required methylation within gene bodies, proximal to promoters, can have the same effect [167]. DNA methylation of epigenetic neighborhoods in the megabase size range has also been reported [168]. Validation of methylation-mediated control of gene-specific expression, and evaluation of biological significance, can be achieved via pharmacologic manipulation of DNA methylation, for example by 5-azacytidine treatment, to relieve methylation silencing and invoke re-expression [20, 169]. The first single-base-resolution maps of the human methylome have recently been generated by sequencing of bisulfite converted DNA from human embryonic stem cells and fetal fibroblasts [12, 170]. This landmark study will greatly advance the analysis of DNA methylation by providing whole genome reference maps of methylation in these specific cells. However, it is well known that DNA methylation is tissue specific and that it changes throughout development thus, methylome maps for all tissues at various stages of development may be necessary to provide adequate maps of 'normal' methylation patterns for use in deciphering aberrant methylation patterns characteristic of tumors [171-176]. In recognition of this, the Human Epigenome Project was launched in 2004 to map the methylomes of all major human tissues [177]. 118
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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
Page 81 and 82: Figure 3.2. Quantitative and qualit
Page 83 and 84: Figure 3.3 a Proportion of genes in
Page 85 and 86: 70 Figure 3.5 -log(pvalue) 5.0 4.0
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
Page 101 and 102: obtained, profiled and used as the
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
Page 109 and 110: 94 Figure 4.4 1 2 3 4 5 6 7 8 9 10
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
Page 129 and 130: polymorphism (SNP) arrays with over
Page 131: substitutions) and UV exposure (C t
Page 135 and 136: Histone modification states. While
Page 137 and 138: metastasis [226]. High throughput a
Page 139 and 140: Implications on sample size require
Page 141 and 142: analyzed using the "minet" package
Page 143 and 144: expression. The next challenge is t
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
Page 181 and 182: 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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