Chapter 2 - University of British Columbia

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Affymetrix U133 Plus 2.0 platform. Visualization of multi-dimensional data was performed using the SIGMA2 software [27]. To determine the genetic events that caused (or could explain) gene expression status, we first identified a set of overexpressed and underexpressed genes for each cell line sample relative to MCF10A based on differential expression criteria mentioned above. Each cancer sample may have a different number of differentially expressed genes. Second, for each differentially expressed gene in each sample, we examined the copy number status, methylation status, and allelic status. A differential expression was considered "explained" when the observed expression change matched the expected change at the DNA level. If a gene was overexpressed, the causal copy number status would be a gain, DNA methylation status would be hypomethylation, or allelic status would be allelic imbalance. Conversely, if a gene was underexpressed, the causal copy number status would be a loss, DNA methylation status would be hypermethylation, or allelic status would be LOH. From this point forward, when a change in allele status with overexpression is discussed, it will be denoted as allelic imbalance (AI). Conversely, for underexpression, a change in allele status will be denoted as loss of heterozygosity (LOH). While changes in methylation or changes in gene dosage leading to differential expression are more commonly discussed, previous studies have shown that changes in allele status without change in copy number (copy neutral AI or LOH) can also lead to differential gene expression due to preferential allelic expression [28-30]. 3.2.4 Multiple concerted disruption (MCD) analysis To determine what are likely key nodes in pathways and functions, we hypothesize that, in addition to being altered frequently (by one mechanism or multiple mechanisms), these genes also exhibit multiple concerted disruption (MCD) in a given sample. That is, a congruent change in gene copy number (gain or loss) accompanied by allelic imbalance and change in DNA methylation (hypomethylation or hypermethylation) resulting in a change in gene expression (over or underexpression). Moreover, the MCD events would be used as a similar 53
screening approach to gene amplifications (multi-copy increases) or homozygous deletions whereby the expectation is that these events would occur at a lower frequency than disruptions through one mechanism alone and observation of these events would signify importance to the genes in question. In this study, the MCD strategy can be broken down into four sequential steps. First, using a pre-defined frequency threshold, we identify a set of the most frequently differentially expressed genes. Second, we identify the most frequently differentially expressed genes from step 1 whose expression change is frequently associated with concerted change in at least one DNA dimension (either DNA copy number, DNA methylation or allelic status) within the same sample. Next, we further refine this subset of genes from step 2 by selecting those having concerted change in all dimensions in the same sample which we term as MCD. Finally, we introduce an additional level of stringency by requiring a minimum frequency of MCD in the given cohort. At the end of the process, we identify a small subset of genes which exhibit disruption through multiple mechanisms and show consequential change in gene expression. 3.2.5 Simulated data analysis Using the status of DNA alteration and expression for every gene in every sample, data within each sample were shuffled and randomized ten times to create ten simulated datasets. Each dataset was analyzed for overall disruption frequency and MCD and all results were then aggregated to determine the frequency distribution of different thresholds observed in the randomized data analysis. 3.2.6 Pathway enrichment analysis For pathway analysis, Ingenuity Pathway Analysis software was used (Ingenuity Systems, CA, USA). Specifically, the core and comparison analyses were used, with focus on canonical signaling pathways. Briefly, for a given function or pathway, statistical significance of pathway enrichment is calculated using a right-tailed Fisher's exact test based on the number of genes 54
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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
Page 17 and 18: 1.1 Lung cancer Lung cancer has the
Page 19 and 20: expression changes are reactive to
Page 21 and 22: number of different cancer types. M
Page 23 and 24: large number of tumors, that a give
Page 25 and 26: (B) By using an integrative approac
Page 27 and 28: platforms, with one of the latest s
Page 29 and 30: 1.12.1 Development of tools for gen
Page 31 and 32: quantitative PCR experiments as wel
Page 33 and 34: 1.13 References 1. Jemal A, Siegel
Page 35 and 36: 33. Garber ME, Troyanskaya OG, Schl
Page 37 and 38: 71. Shivapurkar N, Gazdar AF: DNA M
Page 39 and 40: Chapter 2: SIGMA 2 : A system for t
Page 41 and 42: Here, we present SIGMA 2 , a novel
Page 43 and 44: datasets. To utilize this, the user
Page 45 and 46: 2.3.7 Analysis of data from multipl
Page 47 and 48: expression (Figure 2.8). ERBB2 has
Page 49 and 50: Figure 2.1 R -Segmentation -Statist
Page 51 and 52: Figure 2.3 numSamples
Page 53 and 54: Figure 2.5. Consensus calling and h
Page 55 and 56: Figure 2.6 HCC2218 HCC2218 HCC2218B
Page 57 and 58: Figure 2.8. Multi-dimensional persp
Page 59 and 60: Table 2.1. Features required for in
Page 61 and 62: 2.6 References 1. Garnis C, Buys TP
Page 63 and 64: Chapter 3: An integrative multi-dim
Page 65 and 66: observed gene expression deregulati
Page 67: Raw gene expression profiles from a
Page 71 and 72: many regions of frequent LOH/AI do
Page 73 and 74: approach to examining the whole gen
Page 75 and 76: PTEN expression [44]. Using this pa
Page 77 and 78: mechanisms at the DNA and RNA level
Page 79 and 80: 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
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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
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5.1 Introduction In the past decade
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polymorphism (SNP) arrays with over
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substitutions) and UV exposure (C t
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developed antibodies and methylated
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Histone modification states. While
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metastasis [226]. High throughput a
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Implications on sample size require
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analyzed using the "minet" package
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expression. The next challenge is t
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Figure 5.2 # of copies Total copy n
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Figure 5.4. Integration of copy num
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Figure 5.5. Integration of copy num
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Figure 5.6 SHC1 GRB2 SOS2 RRAS ER R
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Figure 5.8 a Log2 Fold Change (T vs
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Figure 5.10 (a) (b) Figure 5.10. Au
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Table 5.2. List of genomic resource
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5.5 References 1. Pinkel D, Segrave
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37. Oliphant A, Barker DL, Stuelpna
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75. Selzer RR, Richmond TA, Pofahl
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111. Melcher R, Al-Taie O, Kudlich
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145. Takai D, Yagi Y, Wakazono K, O
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179. Kondo M, Suzuki H, Ueda R, Osa
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alterations in human prostate cance
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248. Xi L, Feber A, Gupta V, Wu M,
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281. Fernandez-Ranvier GG, Weng J,
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Chapter 6: Conclusions 162
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can identify nearly three times as
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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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Chapter 2 - University of British Columbia

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