Chapter 2 - University of British Columbia

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mutation, KRAS and BRAF, the overall trend is consistent with what is observed at these two loci (Figure 4.6a). An example of UPD at KRAS in NCI-H2030 and BRAF in A427 are illustrated in Figure 4.6b. For this analysis, cancer cell lines were utilized as the samples represent a more homogeneous population of cells. In contrast, clinical tumors, even after microdissection, still may contain small amounts of contaminating normal cells. As such, determining if a mutation is homozygous in clinical lung tumors is challenging. With available KRAS mutation data, we assessed the frequency of gain, loss and UPD in KRAS mutant tumors and observe a similar distribution pattern observed in the cell lines (Figure 4.6c) 4.3.5 Identification of novel candidate oncogenes using focal regions of UPD Selecting the more focal regions of UPD within the set of 153 regions, we identified 35 of the regions which contained three or less RefSeq annotated genes. In total, 64 RefSeq genes were identified across all 35 regions (Table 4.7) and amongst these genes was E2F3 (Figure 4.7a). Examining paired gene expression for a subset of the 46 tumor/normal pairs, it was found that 10/16 pairs showed overexpression of E2F3 (Figure 4.7b). E2F3 has previously shown to be overexpressed in lung cancer and also shown to have a role in other cancer types [30, 31]. 4.4 Discussion We have shown the unexpected and wide prevalence of UPD in the lung adenocarcinoma genome and have also observed a large number of both known and novel oncogenes harbored in these regions of frequent UPD. While there have been previous studies utilizing SNP arrays on lung adenocarcinoma tumors [26, 30], there are likely a number of reasons why these frequent regions were likely missed. First, the tumors utilized in this study were microdissected to ensure a high proportion of tumor cells (≥ 70% was required) were analyzed. This is important as previous studies have shown the impact of tissue heterogeneity and the ability to detect alterations [32, 33]. Secondly, for every tumor used, matched non-malignant tissue was 85
obtained, profiled and used as the control. While it has been shown that unmatched references can be used to detect UPD, the resultant UPD may not be called correctly all the time. Finally, the progression from call-based approaches to allele specific copy number-based approaches can also increase the detection of UPD [6, 12]. Taken together, these improvements could explain the observed results. While it is interesting to observe these frequent regions of UPD in the lung adenocarcinoma genome, the larger implications of these findings may not be readily apparent. In the cases of somatically mutated oncogenes or tumor suppressor genes, the existence of UPD in these cases is clear as UPD is used to select the mutated allele to result in a homozygous mutation state. We have previously shown that mutant allelic specific imbalance (MASI), either through allele specific amplification or UPD, is associated with a poorer prognosis [20]. To assess the prevalence of UPD at homozygously mutated oncogene sites, we analyzed cancer cell lines encompassing multiple cancer types for UPD at mutated oncogenes. While the most frequent genomic alteration observed is copy number gain, frequent UPD also occurs. The distribution of alterations observed across all genes is consistent with the most frequently mutated oncogenes, KRAS and BRAF. The result of these UPD events is preferential expression of the mutated allele. It should also be noted that with the amount of frequent UPD detected, there are regions likely selected for reasons other than somatic mutation. For example, like in the cases of imprinted regions, there could be preferential selection of an unmethylated or methylated allele which in turn, could regulate downstream gene expression. Previous studies have assessed the relationship between regions of UPD and DNA methylation patterns in cancer [8, 34, 35]. Alternatively, in order to achieve downstream differential expression, in addition to preferential selection based on methylation, it has also been shown that for a given gene, transcription may involve only one of the alleles [36-39] and thus, selection may be based on transcriptional efficiency. Hence, it is important that the genetic data on UPD be integrated with methylation 86
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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
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 and 70: screening approach to gene amplific
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: etween loss and UPD (Figure 4.3). S
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
Page 141 and 142: analyzed using the "minet" package
Page 143 and 144: expression. The next challenge is t
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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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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