Chapter 2 - University of British Columbia

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1.11 Description of high throughput data in this thesis Throughout this thesis, a number of platform technologies were utilized to generate high throughput, genome wide data. Below is a summary of all platforms used in each chapter. In Chapter 3, for nine breast cancer cell lines and one control cell line (MCF10A), the following profiles were generated: Affymetrix SNP 500K for the analysis of allelic status; whole genome tiling path array CGH for the analysis of gene dosage; Illumina Infinium HumanMethylation27 for DNA methylation analysis; and Affymetrix U133 Plus 2.0 for the analysis of gene expression. In Chapter 4, for the 46 tumors and matched non-malignant tissue as well as the cancer cell lines, the Affymetrix SNP 6.0 platform was utilized to measure total copy number and allelic imbalance. For a subset of tumors, gene expression profiles were generated using a custom Affymetrix platform designed by Rosetta Inpharmatics. In Chapter 5, for the ten tumors and matched non-malignant tissue samples, the following profiles were generated: Affymetrix SNP 6.0 for the analysis of allelic status and gene dosage; Illumina Infinium HumanMethylation27 for DNA methylation analysis; and Affymetrix HuEx 1.0 ST array for the analysis of gene expression. Quantitative RT-PCR was performed using the Applied Biosystems TaqMan gene expression assay. 1.12 Other relevant contributions not included as chapters in this thesis In this thesis, I have chosen to include a small portion of my overall work in order to achieve a coherent theme. However, in this section, I have outlined specific contributions, which I’ve either led or participated as 2 nd author, that I’ve deemed are relevant to the theme of lung cancer and genomics. 13
1.12.1 Development of tools for genomic analysis As mentioned earlier, the precursor version of SIGMA2 was SIGMA [95]. This tool was built as an interactive database of cancer cell line array CGH profiles and provided a means for effective visualization of high density array CGH data as well as sharing of data. One of the other problems that arose for high density array CGH data is the availability of efficient analysis algorithms to delineate gains and losses. Most algorithms that were developed for array CGH analysis were developed for arrays with 2000 to 3000 data points and their execution times did not scale up efficiently when the arrays were generating 100,000 to 1,000,000 data points. To address this problem, I contributed to the development of a segmentation and calling algorithm named FACADE [100]. 1.12.2 Baseline gene expression in non-malignant lung tissue Though gene expression studies studying malignant samples are important, it is also critical to define what genes are expressed in non-malignant samples as these are used in reference to determine aberrant gene expression. There were two studies I was involved with which addressed this question. First, we examined gene expression of non-malignant, smoke damaged bronchial epithelium using serial analysis of gene expression (SAGE) [37]. We found that there were specific genes that showed high tissue specificity to the bronchial epithelium with limited representation in other tissues and that there were differences between bronchial epithelial samples and lung parenchyma, which are samples adjacent to tumors typically used as non-malignant controls comprised of a mixture of different cells. In the study described above, bronchial epithelium samples from current and former smokers were grouped together. Hence, the next logical question was to assess the effect of active smoking on the bronchial epithelium. In the second study, a group of never smoker samples were added to the groups of current and former smokers and the gene expression profiles of the three groups were compared. We first identified a set of genes which were differentially 14
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: platforms, with one of the latest s
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
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Page 41 and 42: Here, we present SIGMA 2 , a novel
Page 43 and 44: datasets. To utilize this, the user
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Page 47 and 48: expression (Figure 2.8). ERBB2 has
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Page 55 and 56: Figure 2.6 HCC2218 HCC2218 HCC2218B
Page 57 and 58: Figure 2.8. Multi-dimensional persp
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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
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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
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University of British Columbia - Br
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Chapter 2 - University of British Columbia

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