Chapter 2 - University of British Columbia

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Finally, using publicly microarray datasets with patient survival information [250-252], Kaplan- Meier analysis was performed on each of these datasets based on SIRPA expression levels. The association was deemed significant if the gene had a p-value ≤ 0.05 based on a Mantel- Cox (or log ranks) test. Two of the five datasets showed a statistically significant association between SIRPA expression levels and overall patient survival with an additional two datasets close to significance with p values ≤ 0.18 (Figure 5.9). 5.5 Tracking clonal expansion in spatial dimensions Delineating the clonal relationship between multiple tumors in the same patient is relevant not only to clinical management of disease but also to the understanding of metastasis. Multiple tumors in the same patient may not necessarily share an identical genomic profile. The similarities and differences in genomic landscape between tumors are quantifiable and therefore can be used for delineating relatedness. Whole genome comparison based on array CGH profiles is a new tool for distinguishing metastatic from primary synchronous carcinomas. A multitude of genomic features, for example the boundaries of segmental deletions, are used to delineate the presence and the sequence of events in clonal evolution [253-261]. Furthermore, signature genetic alterations can be used to track clonality in a cell population, putting genetic events in the context of tumor tissue architecture. By assessing the appearance of pre-selected markers in individual nuclei on a tissue section by FISH, the clustering and the expansion of clonally related cells can be delineated by analyzing the marker patterns of neighboring cells (Figure 5.10). 5.6 Evaluating the biological significance of integrative genomics findings The utilization of an integrative genomic, epigenomic and transcriptomic approach will undoubtedly improve our ability to identify gene disruptions and their effects on gene 127
expression. The next challenge is to develop approaches for the determination of functional and phenotypic evidence of the biological relevance of such gene disruptions in a high throughput manner -- for example, functional genomic screens by RNAi, proteomic profiling and metabolite profiling. Forced expression of genes and RNAi knockdown of gene expression are commonly used methods for assessing growth and invasion phenotypes in cell models. Genome wide RNAi screens, comprised of large libraries of short hairpin RNA sequences redundantly targeting thousands of genes, have been used to identify genes essential to tumorigenesis, including tumor suppressor genes as well as cooperative genes with oncogenic mutation in several malignancies [22, 28, 29, 262-270]. Animal models are also instrumental to functional validation of genes singly or in combination, but this topic is beyond the scope of this article. Cross referencing genomic findings with proteomic profiles will determine the functional consequences yielding information on expression levels, post-translational modification, and protein-protein interactions [271-275]. As recent studies have highlighted the importance of the metabolome in cancer, the genomic landscape can also be integrated with metabolome profiles to determine the role of genetic and epigenetic alterations in cellular physiology relevant to cancer development [276-278]. The progress made in the development of technologies and approaches to analyze the genome, epigenome, and transcriptome have allowed for much improved understanding of cancer landscapes. With the increased application of sequence based approaches to analyze genetic and epigenetic dimensions and the additional complexity with the proteome and metabolome to follow, an unprecedented definition of the cancer cell can be achieved. The next key challenge will be the synthesis of this information to better understand fundamental cancer processes such as progression, metastasis and drug resistance. 128
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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
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70 Figure 3.5 -log(pvalue) 5.0 4.0
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72 Figure 3.7 Sample: HCC1008 Copy
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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
Page 131 and 132: substitutions) and UV exposure (C t
Page 133 and 134: developed antibodies and methylated
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
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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
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
Page 187 and 188: 16. Zhang K, Li JB, Gao Y, Egli D,
Page 189 and 190: APPENDIX I: List of publications Th
Page 191 and 192: 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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