Chapter 2 - University of British Columbia

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gene-specific disruption with corresponding transcriptome aberrations for particular cancer types, as would be expected for genes causative to cancer development. 5.4.1 Multiple mechanisms of gene disruption Expression profiling studies have been instrumental in detecting genes dysregulated in cancer [207-209]. However, aberrant expression of some genes may simply reflect incidental genome instability or secondary dysregulation. Global gene expression profiling alone may not distinguish causal events and bystander changes. One of the first studies to relate gene expression changes with gene dosage status on a global scale was a parallel analysis of DNA and mRNA [66, 210]. The same cDNA microarray platform was used to investigate impact of DNA copy number alterations on the expression of over 6,500 genes. This study determined that 62% of genes located within regions of DNA amplification showed elevated expression in breast cancer. Subsequent studies in other cancer types revealed a broad range in the correlation between increased gene dosage and expression levels for protein coding genes (19% to 62%) [92, 207, 210-213]. Studies integrating gene dosage and gene expression have identified cancer subtype-specific pathway activation and signatures associated with clinical outcome [96, 214-217]. In addition, when examining known disease-relevant pathways, it has been shown that even though individual components of a pathway are disrupted at a low frequency, collectively, these alterations can result in frequent disruption of a given pathway [16, 92]. Similarly, alterations in DNA methylation or histone modification status can also affect gene expression and have subsequent pathway level consequences (see above). 5.4.2 Multiple mechanisms of disrupting non-coding RNA levels Segmental DNA copy number alterations also affect the expression of non-coding RNAs (ncRNA) [218-222]. MicroRNAs (miRNA) have been shown to have a significant role in cancer development with specific miRNAs implicated in a number of different cancer types [26, 223- 225]. Specific miRNA expression signatures are associated with critical steps in tumor initiation and development including cell hyperproliferation, angiogenesis, tumor formation and 121
metastasis [226]. High throughput analysis of microRNAs has been of interest and microarrays have been developed to assess essentially all annotated microRNAs. To date, >700 miRNAs have been annotated in the genome (http://mirdb.org/miRDB/statistics.html, [227]), with more likely to be discovered. For example, we recently demonstrated that a deletion on chromosome 5q leads to the reduced expression of two miRNAs that are abundant in hematopoietic stem/progenitor cells. This study revealed haploinsufficiency and reduced expression of miR- 145 and miR-146a as mediators of a subtype of myelodysplastic syndrome [221]. Although the genomic loss and underexpression implicates a tumor-suppressive role for these specific miRNAs, others undergo activating genomic alterations and elevated expression and hence are thought to be oncogenic [228, 229]. Just as copy number alterations can alter miRNA activity, epigenetic alterations have also been shown to affect miRNA expression [230-232]. Aberrant methylation of miRNAs has been reported in a variety of cancer types, and the disruption of epigenetically-mediated miRNA control has been shown to have oncogenic effects due to downstream gene deregulation [233]. For example, abnormal DNA methylation of miRNAs has been associated with tumor metastasis, leading to the appreciation of a group of metastasis-related miRNAs [229]. 5.4.3 Multi-dimensional integration of genome, epigenome, and transcriptome Large scale initiatives. Since multiple genomic/epigenomic mechanisms can influence gene expression and lead to disruption of a given function, an integrative multi-dimensional analysis is necessary for a more comprehensive understanding of the cancer phenotype (Figure 4). Specific programs and initiatives such as those by The Cancer Genome Atlas (TCGA) project and the cancer Biomedical Informatics Grid (caBIG) enable parallel and multi-dimensional analysis of cancer genomes [8, 16] (Table 5.2). Recently, studies in glioblastoma and osteosarcoma have shown that integrative genomic and epigenomic approaches can indeed reveal the specific genetic pathways involved in different cancers [16, 234]. 122
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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
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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
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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
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
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Page 135: Histone modification states. While
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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
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
Page 183 and 184: will be done at the pathway level a
Page 185 and 186: 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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