Chapter 2 - University of British Columbia

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Chapter 2: SIGMA 2 : A system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes 1 .............................................................................. 24 2.1 Introduction ........................................................................................................................ 25 2.2 Implementation .................................................................................................................. 26 2.3 Results and discussion ...................................................................................................... 26 2.3.1 Look and feel of SIGMA 2 ............................................................................................ 26 2.3.2 Description of application scope and functionality ...................................................... 27 2.3.3 Approach to integration between array platforms and assays .................................... 27 2.3.4 Format requirements of input data .............................................................................. 27 2.3.5 Description of user interface ....................................................................................... 28 2.3.6 Analysis of data from a single assay type ................................................................... 29 2.3.7 Analysis of data from multiple assays in a given 'omics dimension ............................ 30 2.3.8 Combinatorial analysis of multiple 'omics dimensions - gene dosage and gene expression ........................................................................................................................... 30 2.3.9 Group comparison analysis - single ‘omics dimension ............................................... 31 2.3.10 Group comparison analysis - integrating multiple 'omics dimensions ....................... 31 2.3.11 Multi-dimensional analysis of a breast cancer genome ............................................ 31 2.3.12 Exporting data and results ........................................................................................ 32 2.4 Conclusions ....................................................................................................................... 32 2.5 Availability and requirements ............................................................................................ 33 2.6 References ........................................................................................................................ 46 Chapter 3: An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer 2 .................................................................................................. 48 3.1 Background ....................................................................................................................... 49 3.2 Methods ............................................................................................................................. 50 3.2.1 Data generation and acquisition ................................................................................. 50 3.2.2 Data processing and normalization ............................................................................ 51 3.2.3 Strategy for integrative analysis .................................................................................. 52 3.2.4 Multiple concerted disruption (MCD) analysis ............................................................ 53 3.2.5 Simulated data analysis .............................................................................................. 54 3.2.6 Pathway enrichment analysis ..................................................................................... 54 3.2.6 Survival and differential gene expression analysis in publicly available datasets....... 55 3.3 Results and discussion ...................................................................................................... 55 3.3.1 Analysis of individual genomic dimensions ................................................................. 55 iv
3.3.2 Multi-dimensional analysis (MDA) reveals a higher proportion of intra-sample deregulated gene expression can be explained when more dimensions are analyzed ....... 56 3.3.3 MDA reveals genes are disrupted at higher frequencies when examining multiple dimensions as compared to any single dimension alone .................................................... 56 3.3.4 MDA identifies significantly enriched cancer related pathways .................................. 58 3.3.5 MDA of the Neuregulin signaling pathway reveals a complex pattern of deregulation ............................................................................................................................................. 59 3.3.6 Genes exhibiting multiple concerted disruption (MCD) - biological and clinical significance .......................................................................................................................... 60 3.3.7 Association of genes exhibiting MCD and triple negative breast cancers (TNBC) ..... 62 3.4 Conclusions ....................................................................................................................... 63 3.5 References ........................................................................................................................ 75 Chapter 4: Uniparental disomy is a prevalent genetic mechanism of oncogene disruption in lung adenocarcinoma 3 ........................................................................................................................ 79 4.1 Introduction ........................................................................................................................ 80 4.2 Methods ............................................................................................................................. 81 4.2.1 Genome wide profiling of clinical lung adenocarcinoma specimens ........................... 81 4.2.2 Determination of regions of uniparental disomy (UPD) in clinical lung tumors ........... 81 4.2.3 Determining frequent regions of UPD, gain and loss .................................................. 82 4.2.4 Determination of UPD in cancer cell lines .................................................................. 82 4.2.5 Expression analysis of genes in focal regions of UPD ............................................... 82 4.3 Results .............................................................................................................................. 83 4.3.1 Detection of UPD using allele specific copy number analysis .................................... 83 4.3.2 UPD is prevalent and non-random in the lung cancer genome with comparable frequencies to gain and loss ................................................................................................ 83 4.3.3 Overlap of major oncogenes and tumor suppressor genes in regions of gain, loss, and UPD ..................................................................................................................................... 84 4.3.4 UPD is prevalent at oncogenes across multiple cancer types .................................... 84 4.3.5 Identification of novel candidate oncogenes using focal regions of UPD ................... 85 4.4 Discussion ......................................................................................................................... 85 4.5 Conclusion ......................................................................................................................... 87 4.6 References ...................................................................................................................... 108 Chapter 5: Integrating the multiple dimensions of genomic and epigenomic landscapes of cancer 4 ...................................................................................................................................... 111 5.1 Introduction ...................................................................................................................... 112 v
Page 1 and 2: DEVELOPMENT AND APPLICATION OF AN I
Page 3: Table of Contents Abstract ........
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 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
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Figure 2.6 HCC2218 HCC2218 HCC2218B
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Figure 2.8. Multi-dimensional persp
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Table 2.1. Features required for in
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2.6 References 1. Garnis C, Buys TP
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Chapter 3: An integrative multi-dim
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observed gene expression deregulati
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Raw gene expression profiles from a
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screening approach to gene amplific
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many regions of frequent LOH/AI do
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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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