Chapter 2 - University of British Columbia

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position was on the x-axis and the log ratio of the data point was drawn on the y-axis. While this type of visualization can provide a quick genome summary of a single sample, it is difficult to readily link to information such as protein coding genes from this type of visualization. Finally, software developed by microarray manufacturers such as Affymetrix, Agilent or Nimblegen were specifically tailored to handle data from their respective microarray platforms. Thus, aggregate analysis of data emanating from different microarray platforms, but analyzing samples with common characteristics, could not be analyzed in a concerted manner resulting in under-utilization of the increasingly available array CGH data in the public domain. Most importantly, no tools existed to integrate multiple dimensions of data such as global gene dosage and gene expression, let alone integration with DNA methylation. Hence, it is clear that with these apparent challenges, the development of such bioinformatic tools was needed. 1.8 Thesis theme The theme of this thesis is the development and utilization of an integrative genetic and epigenetic approach to identify novel aberrant genes and pathways that may be involved in the tumorigenesis of lung adenocarcinoma. This will be achieved by employing genome wide genetic and epigenetic profiling experiments of lung adenocarcinoma samples and the subsequent integration of this data using novel bioinformatics tools and approaches. 1.9 Objectives and hypothesis The objective of this work is to demonstrate the importance of employing an integrative approach to understand genetic and epigenetic alterations and their consequence on gene expression. The hypothesis can be broken down to three parts: (A) Genes/pathways which are important to tumorigenesis are disrupted by multiple mechanisms in lung cancer. 9
(B) By using an integrative approach, looking at the global genetic and epigenetic regulation of gene expression, changes at the DNA level which have downstream effects at the gene expression level will be identified. (C) This approach will lead to the identification of more genes that are disrupted than previously anticipated and these genes will be enriched in key pathways and functions important to lung tumorigenesis. 1.10 Specific aims and outline of thesis This thesis consists of four manuscripts assembled in a non-chronological order to best address the objectives and hypothesis of this thesis. Aim 1: Development of a platform for multi 'omics data integration and analysis Chapter 2 discusses the development of an integrative analysis software package called a system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes (SIGMA 2 ). The development of this application was necessary prior to the undertaking of the analysis of the vast amount of data generated from the utilized high throughput, genome-wide technologies. As discussed earlier in this chapter, there were very few bioionformatic tools available for the analysis of array CGH data, let alone for integrative analysis of gene dosage and gene expression. Prior to the development of SIGMA 2 , I developed the pre-cursor version of this software SIGMA [95]. SIGMA provided the basic framework in terms of the user interfaces, database communication, data structures and "look and feel" that would be utilized in SIGMA 2 . Moreover, one of the key challenges when SIGMA was developed was the effective visualization and analysis of large datasets generated by newer, high density array CGH platforms. At the time, the majority of data that were generated were on platforms comprised of 3000 measurements per sample but, newer technologies were being developed which 10
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: large number of tumors, that a give
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 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
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PTEN expression [44]. Using this pa
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mechanisms at the DNA and RNA level
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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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