Chapter 2 - University of British Columbia

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expressed in response to smoke exposure and found a subset of genes that were reversible upon smoking cessation and another subset of genes irreversible upon smoking cessation [35]. Those genes which were irreversibly altered after heavy smoke exposure may have implications in affecting future risk of developing lung cancer. Moreover, these findings also suggest that when trying to identify cancer-specific changes when unmatched control samples are not available, clinical characteristics such as smoking status should be taken into consideration when comparisons are made. 1.12.3 Differential gene expression analysis in lung cancer With the non-malignant, baseline gene expression defined, differential gene expression in early stages of lung cancer and locally invasive squamous cell carcinoma were then assessed [101]. It was found that genes associated with epidermal development were increased in expression and mucociliary function were decreased in expression in carcinoma-in-situ as well as in precancerous stages. Finally, genes associated with tissue re-modelling were also altered in expression in local invasive cancer and also showed altered expression in carcinoma-in-situ, suggesting this function is affected early in cancer development. The Wnt pathway has been shown to be aberrant in many cancer types. At the time the study began, there were two branches of the pathway that were known to exist, canonical and non- canonical, whose activation resulted in different downstream consequences. While the canonical branch was the primary focus of most researchers studying this pathway, we sought to assess the role of the non-canonical branch in lung squamous cell carcinoma using semi- quantitative and quantitative PCR of genes which were a part of the non-canonical branch [102]. From this study, it was found that (i) these non-canonical genes were expressed in the normal lung and (ii) some of these non-canonical genes were differentially expressed in tumors. An important consideration in the analysis of differential gene expression in cancer is the use of suitable reference genes for data normalization. This consideration is critical to both 15
quantitative PCR experiments as well as microarray experiments where relative quantifications are typically used. To address this, using SAGE, where quantification of expression is absolute, genes whose expression was constant across both malignant and non-malignant samples were identified [103]. Those genes demonstrated better constancy than some genes which are typically used as controls for gene expression analysis. 1.12.4 Integration of gene dosage and gene expression in lung cancer The first level of genomic integration that needed to be accomplished was the integration of gene dosage and gene expression. In one study using cancer cell lines, hot spots of DNA amplification were identified throughout the genome. When specifically examining lung cancer cell lines and subsequently coupling this with gene expression data, it was found that 50% of genes in these frequently amplified regions show correlation between gene dosage and gene expression [52]. Moreover, it was also observed that different components of the EGFR signaling pathway were amplified in different cell lines illustrating that for a given pathway, one can underestimate the frequency of pathway disruption when only well known genes in the pathway are assessed. In a second study involving clinical lung tumors, a genomic region which was preferentially amplified in squamous cell carcinomas as compared to adenocarcinomas was identified. Further integration with gene expression data allowed for the identification of the target gene, BRF2, in this amplified region [104]. Moreover, gene dosage and protein expression level assessment of CIS samples for BRF2 showed that amplification and overexpression were present, suggesting that this event is occurring at an early stage of tumorigenesis. 16
Page 1 and 2: DEVELOPMENT AND APPLICATION OF AN I
Page 3 and 4: Table of Contents Abstract ........
Page 5 and 6: 3.3.2 Multi-dimensional analysis (M
Page 7 and 8: List of Tables Table 2.1. Features
Page 9 and 10: Figure 5.3. Overlay of chromosomal
Page 11 and 12: RB1 Retinoblastoma 1 RNA Ribonuclei
Page 13 and 14: Dedication To my family. xiii
Page 15 and 16: 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: 1.12.1 Development of tools for gen
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 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
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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
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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
Page 205:
University of British Columbia - Br
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