Chapter 2 - University of British Columbia

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List of Figures Figure 1.1. Multiple mechanisms of alteration leading to same downstream consequences ..... 17 Figure 2.1. Main structural components of SIGMA2. .................................................................. 34 Figure 2.2. Data structure hierarchy. .......................................................................................... 35 Figure 2.3. Algorithm for integrating between different array platforms ...................................... 36 Figure 2.4. SIGMA2 interface. .................................................................................................... 37 Figure 2.5. Consensus calling and heterogeneous array analysis. ............................................ 38 Figure 2.6. Integrative genetic analysis of HCC2218 .................................................................. 40 Figure 2.7. Two-group two dimensional comparison of 37 NSCLC and 16 SCLC cancer cell lines. ............................................................................................................................................ 41 Figure 2.8. Multi-dimensional perspective of chromosome 17 of the HCC2218 breast cancer cell line. ............................................................................................................................................. 42 Figure 3.1. Genomic profiles of breast cancer cell lines. ............................................................ 65 Figure 3.2. Quantitative and qualitative benefits of integrative analyses. ................................... 66 Figure 3.3. Determination and application of a disruption frequency threshold. ......................... 68 Figure 3.4. Impact of multi-dimensional analysis on low frequency events. ............................... 69 Figure 3.5. Pathway analysis of the 1162 genes identified by multi-dimensional analysis. ........ 70 Figure 3.6. Complex deregulation of the Neuregulin/ERBB2 signaling pathway. ....................... 71 Figure 3.7. Deregulation of PTEN occurs differently between samples. ..................................... 72 Figure 3.8. Multiple concerted disruption (MCD) analysis and its application to triple negative breast cancer. ............................................................................................................................. 73 Figure 4.1. Detection of UPD using allele specific copy number. ............................................... 88 Figure 4.2. Comparison of frequent regions of gain, loss and UPD in the lung adenocarcinoma genome ....................................................................................................................................... 90 Figure 4.3. Venn diagram illustrating the amount of the genome covered by gain, loss, and UPD .................................................................................................................................................... 92 Figure 4.4. Genomic profile of an individual lung adenocarcinoma sample ............................... 93 Figure 4.5. Examination of UPD events at the KRAS and RB1 loci ............................................ 95 Figure 4.6. Relationship of homozygous mutation at oncogenes and genomic alteration .......... 96 Figure 4.7. Identification of E2F3 in a focal region of UPD ......................................................... 97 Figure 5.1. Advances in cancer genomic landscape post Y2K. ................................................ 129 Figure 5.2. SNP array analysis to identify areas of altered copy number and allelic composition in a clinical lung cancer specimen. ........................................................................................... 130 viii
Figure 5.3. Overlay of chromosomal regions of gain, loss and UPD (copy number neutral LOH) inherent to the T47D breast cancer cell line. ............................................................................ 131 Figure 5.4. Integration of copy number, allelic status, DNA methylation, and gene expression for a single lung adenocarcinoma sample. ..................................................................................... 132 Figure 5.5. Integration of copy number, allelic status, DNA methylation, and gene expression for a single lung adenocarcinoma sample. ..................................................................................... 134 Figure 5.6. Identification of multiple disrupted components in a biological pathway. ................ 136 Figure 5.7. Multi-dimensional analysis of the epidermal growth factor receptor signaling pathway. .................................................................................................................................... 137 Figure 5.8. Prevalence of SIRPA underexpression and its relationship with PTPN6 and smoking status. ....................................................................................................................................... 138 Figure 5.9. Kaplan-Meier analysis of SIRPA in four independent microarray datasets. ........... 139 Figure 5.10. Automated detection of selected clonal populations of cells within a cancer biopsy tissue section. ........................................................................................................................... 140 ix
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: List of Tables Table 2.1. Features
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
Page 55 and 56: Figure 2.6 HCC2218 HCC2218 HCC2218B
Page 57 and 58: 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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Chapter 2 - University of British Columbia

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