Chapter 2 - University of British Columbia

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6.1 Summary Lung adenocarcinoma is the most commonly diagnosed form of lung cancer today, with a large percentage of patients exhibiting poor overall survival and prognosis. Genomic analysis has provided much insight into this disease with the identification of specific differentially expressed genes, somatically mutated genes, hyper- and hypomethylated genes and genes which are amplified and deleted at the DNA copy number level. While tools and platforms for whole genome analysis of gene dosage and gene expression are widely accessible and have improved in resolution, only until recently have technologies to assess DNA methylation in a high throughput manner been made available. Hence, the logical step with these vast amounts of data is to integrate the information from these different assays in a parallel manner to gain a better understanding of the biology of lung adenocarcinoma. 6.1.1 Development of the integrative genetic and epigenetic approach In chapter 2, the development of the SIGMA2 software package is discussed [1]. At the time the package was developed, there were no analysis tools for integrative genetic and epigenetic analysis, let alone tools to integrate gene dosage and gene expression, which were two well established high throughput platforms. Moreover, for array CGH data alone, limited number of tools existed. Hence, as a precursor to SIGMA2, SIGMA [2], was developed and used as a framework for SIGMA2. In chapter 3, I demonstrated how when this integrative approach is applied to model systems, that we learn much more using multiple dimensions as compared to when only looking at a single dimension alone. Specifically, we show that we can associate more of the dysregulated gene expression with alterations at the DNA level, with some cell lines illustrating as much as 80% of the observed gene expression changes being able to be associated with DNA level changes. In addition, I also illustrated two key concepts: (i) the “Or” (multiple alternate hit mechanisms) concept where across a sample set and using a fixed frequency of disruption, we 163
can identify nearly three times as many genes when we can account for multiple types of disruption as opposed to accounting for only a single type of alteration and (ii) the “And” (coupled multiple hits) concept where we identify genes which are targeted my multiple mechanisms in the sample and show that these genes can have significant biological and/or clinical relevance. 6.1.2 Identification of a prevalent genetic alteration in lung adenocarcinoma Genetic and epigenetic alterations have been shown to be prominent in lung adenocarcinoma. Within genetic alterations, the majority of documented alterations have involved alterations in gene dosage, somatic mutation, and loss of heterozygosity (LOH) or allelic imbalance (whereby an allele or a portion of the allele is lost or gained in the tumor). In terms of allelic imbalance, the majority of the time this event is captured as a decrease or increase in gene dosage. However, there are also cases where allelic imbalance exists but there is no net change in gene dosage, termed copy neutral LOH or somatic uniparental disomy (UPD). Chapter 4 discusses the unexpected prevalence of UPD in the lung adenocarcinoma genome. Though previous studies were done using SNP arrays on lung adenocarcinoma tumors, the prevalence of UPD was likely underestimated due to a number of reasons. Amongst the reasons include the use of heterogeneous samples with high normal cell contamination due to lack of microdissection, lower resolution of alterations identifiable by previous SNP arrays, use of non patient-matched controls as reference and movement from call-based algorithms to algorithms which use allele specific copy number [3]. In addition to the prevalence of UPD, the other key finding from this chapter is the presence of frequent UPD at both known and novel oncogenes. While UPD has previously been shown to affect tumor suppressor genes such as RB1 [4], the association of UPD at oncogene loci has not been reported as often in solid tumors. Moreover, in the previous studies in hematological malignancies such as leukemias, lymphomas, or myeloid dysplastic syndromes, the observed 164
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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
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16. Chari R, Lockwood WW, Coe BP, C
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50. Giovane A, Pintzas A, Maira SM,
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4.1 Introduction Genetic alteration
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4.2.3 Determining frequent regions
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etween loss and UPD (Figure 4.3). S
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obtained, profiled and used as the
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Figure 4.1. Detection of UPD using
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Figure 4.2. Comparison of frequent
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Figure 4.3 Gain Loss 642 441 7 Figu
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94 Figure 4.4 1 2 3 4 5 6 7 8 9 10
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Figure 4.6 a b c Percent of cases A
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Figure 4.7 a b 6p25.3 Log2 fold cha
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Chr BPStart BPEnd # of Chr BPStart
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Table 4.3. Overlap of oncogenes in
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Table 4.5. Cell lines and oncogene
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Table 4.7. RefSeq genes in focal re
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4.6 References 1. Bell DW: Our chan
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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
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Page 151 and 152: Figure 5.6 SHC1 GRB2 SOS2 RRAS ER R
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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
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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,
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Page 181 and 182: was identified in a small set of sa
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Page 185 and 186: previously described genetic and ep
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Page 189 and 190: APPENDIX I: List of publications Th
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Page 193 and 194: This chapter details the technologi
Page 195 and 196: epigenetic alteration. This led to
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Page 201 and 202: APPENDIX V: Kaplan-Meier and Oncomi
Page 203 and 204: APPENDIX VI: Summary of Kaplan-Meie
Page 205: University of British Columbia - Br
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