Chapter 2 - University of British Columbia

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annotated, number of genes represented in the input dataset, and the total number of genes being assessed in the experiment. A pathway was deemed significant if the p-value of enrichment was ≤ 0.05 (adjusted for multiple comparisons using a Benjamini-Hochberg correction). 3.2.6 Survival and differential gene expression analysis in publicly available datasets For survival analysis, Kaplan-Meier analysis was performed using the statistical toolbox in Matlab (Mathworks). For each gene, the expression data were sorted from lowest to highest expression across the sample set and survival times were compared between the top 1/3 and bottom 1/3 of the samples. Two publicly available gene expression microarray datasets with survival data were utilized for this analysis [4, 31]. For the Sorlie et al dataset, individuals whose cause of death was not breast cancer were excluded from the analysis and missing data due to quality control issues were filled using the knn method in the “impute” package in Bioconductor [32]. Of the 23 genes selected by our MCD analysis (see Results), 17 were represented in either dataset. Survival distributions were compared using a log rank test and two-tailed p- values unadjusted for multiple comparisons were reported. Subsequently, these 17 genes were further evaluated for differential expression in publicly available expression datasets of clinical breast cancer samples using the Oncomine database [33]. 3.3 Results and discussion 3.3.1 Analysis of individual genomic dimensions When examining each genomic dimension alone, we see that many of the common features identified are consistent with the current knowledge of breast cancer genomes, for example, previously reported chromosomal regions of frequent copy number gain, segmental loss and loss of heterozygosity (LOH) / allelic imbalance (AI) (Figure 3.1a) [6, 8, 11, 12, 34]. While 55
many regions of frequent LOH/AI do overlap with regions of copy number change, others are in regions of neutral copy number. Key genes implicated in breast cancer reside in these specific regions and are altered expectedly (Figure 3.1b). 3.3.2 Multi-dimensional analysis (MDA) reveals a higher proportion of intra-sample deregulated gene expression can be explained when more dimensions are analyzed The impact of integrative, multi-dimensional analysis on gene discovery is observed at two levels: (i) within an individual sample as well as (ii) across a set of samples. Within a given sample, we see that by sequentially examining more genomic dimensions at the DNA level, i.e. gene dosage, allelic status, and DNA methylation, we can explain a higher proportion of the differential gene expression changes observed. Interestingly, although this proportion may vary between samples, it always increases with every additional dimension examined (Figure 3.2a). For example, in HCC1395, a single genomic dimension alone can explain as much as 64.4% of overexpression but when using all three DNA based dimensions, whereby gene overexpression can be explained by disruption at the DNA level in at least one dimension, as much as 75.7% of aberrant overexpression can be explained. Similarly, in HCC1937, an increase from 56.9% to 74.7% explainable underexpression is observed when moving from one to three genomic dimensions respectively. Conversely, in HCC2218, we observe 44% and 36% of overexpression and underexpression respectively when using all three DNA dimensions. This suggests that the majority of differential expression in sample HCC2218 is most likely a result of complex gene-gene trans-regulation and consequently, highlights the individual differences between samples. 3.3.3 MDA reveals genes are disrupted at higher frequencies when examining multiple dimensions as compared to any single dimension alone When considering across a sample set, we see that analysis of multiple genomic dimensions leads to the discovery of more disrupted genes than what would be detected using a single dimension of analysis alone. For each identified gene, we gain insight in how multiple 56
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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
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 and 68: Raw gene expression profiles from a
Page 69: screening approach to gene amplific
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
Page 119 and 120: 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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