Chapter 2 - University of British Columbia

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4.3 Results 4.3.1 Detection of UPD using allele specific copy number analysis To determine regions of UPD, we first determined regions of allelic imbalance using an allele specific copy number based approach. This approach has been shown to identify more regions of imbalance than previous call-based approaches [6, 12]. In the first example, where no UPD is present, we observe a chromosome exhibiting no change in total copy number as compared to its matched control and also no imbalance between the alleles, represented by shift between the blue and red data points (Figure 4.1a). However, in the next two samples, we do observe large shifts between the blue and red data points. Specifically, one example illustrates a region of UPD with a region of gain on chromosome arm 12q (Figure 4.1b) and another example illustrates a whole chromosome UPD event on chromosome 14 (Figure 4.1c). The blue data points in the UPD regions are not completely at zero but slightly above due to cells that do not carry the UPD alteration. 4.3.2 UPD is prevalent and non-random in the lung cancer genome with comparable frequencies to gain and loss With the ability to detect UPD as shown above as well as identifying UPD at the KRAS oncogene from a previous study, we then assessed the prevalence of UPD in the genome. Using a 40% frequency threshold, we determine the regions of the genome affected by UPD at this frequency. In total, 153 regions were identified (Table 4.1). Moreover, when examining areas of frequent gain and loss (at similar frequency thresholds), we observe that the amount of the genome affected by frequent UPD is comparable to that of frequent gain and loss (Figure 4.2). While there was some overlap with the regions of loss and UPD, there was very little overlap between gain and UPD, even though we would expect some level of overlap by random chance. Using megabases of the genome as a metric, we observe 650 Mb affected by gain, 500 Mb by loss and 400 Mb by UPD, with 7 Mb overlap in gain and UPD and 58 Mb overlap 83
etween loss and UPD (Figure 4.3). Strikingly, all three alterations cover over 49% of the genome. It should also be noted that the observation of comparable levels of gain, loss and UPD at the frequency level is also seen when examining samples on an individual basis (Figure 4.4). 4.3.3 Overlap of major oncogenes and tumor suppressor genes in regions of gain, loss, and UPD We then assessed how major oncogenes and tumor suppressor genes associated with the three levels of genetic alteration. Using a list of 112 genes derived from a number of sources [23, 24] (Table 4.2), we found 52 of these genes to overlap with at least one of frequent gain, loss, or UPD. Major oncogenes such as EGFR, MYC, AKT1, MDM2, and ERBB2 are affected frequently by copy number gain, which has been shown previously [25-29] (Table 4.3). Similarly, major tumor suppressor genes such as FHIT, RARB and CDKN2A are affected by frequent copy number loss (Table 4.4). Interestingly, while expected tumor suppressor genes such as BRCA2 and RB1 are affected by UPD, a subset of seven oncogenes were affected by UPD. Specifically, UPD was observed at KRAS, as shown previously, PIK3CA, BCL6 and FLT3. Moreover, examining KRAS (Figure 4.5a) and RB1 (Figure 4.5b) specifically, we see that the UPD events are of different sizes between different samples. 4.3.4 UPD is prevalent at oncogenes across multiple cancer types We observed frequent UPD at oncogenes in lung cancer. We sought to assess the prevalence of UPD at oncogenes across multiple cancer types. For this analysis, we utilized SNP 6.0 array data for over 700 cancer cell lines from the Wellcome Trust Sanger database where somatic mutation data were also available. In total, 67 instances of homozygous mutation at 13 oncogene loci were assessed (Table 4.5). It was found that while copy number gain was the most prevalent genetic alteration, a significant proportion of samples exhibited UPD (Figure 4.6a, Table 4.6). Examining the genes with the most samples harbouring homozygous 84
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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
Page 47 and 48: expression (Figure 2.8). ERBB2 has
Page 49 and 50: Figure 2.1 R -Segmentation -Statist
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Page 55 and 56: Figure 2.6 HCC2218 HCC2218 HCC2218B
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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
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Page 67 and 68: Raw gene expression profiles from a
Page 69 and 70: screening approach to gene amplific
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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
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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
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Page 103 and 104: Figure 4.1. Detection of UPD using
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Page 107 and 108: Figure 4.3 Gain Loss 642 441 7 Figu
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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
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Page 137 and 138: metastasis [226]. High throughput a
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Page 145 and 146: Figure 5.2 # of copies Total copy n
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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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