Chapter 2 - University of British Columbia

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90. Chen W, Erdogan F, Ropers HH, Lenzner S, Ullmann R: CGHPRO -- a comprehensive data analysis tool for array CGH. BMC Bioinformatics 2005, 6:85. 91. Kim SY, Nam SW, Lee SH, Park WS, Yoo NJ, Lee JY, Chung YJ: ArrayCyGHt: a web application for analysis and visualization of array-CGH data. Bioinformatics 2005, 21(10):2554-2555. 92. Autio R, Hautaniemi S, Kauraniemi P, Yli-Harja O, Astola J, Wolf M, Kallioniemi A: CGH- Plotter: MATLAB toolbox for CGH-data analysis. Bioinformatics 2003, 19(13):1714- 1715. 93. Lingjaerde OC, Baumbusch LO, Liestol K, Glad IK, Borresen-Dale AL: CGH-Explorer: a program for analysis of array-CGH data. Bioinformatics 2005, 21(6):821-822. 94. Chi B, DeLeeuw RJ, Coe BP, MacAulay C, Lam WL: SeeGH--a software tool for visualization of whole genome array comparative genomic hybridization data. BMC Bioinformatics 2004, 5:13. 95. Chari R, Lockwood WW, Coe BP, Chu A, Macey D, Thomson A, Davies JJ, MacAulay C, Lam WL: SIGMA: a system for integrative genomic microarray analysis of cancer genomes. BMC Genomics 2006, 7:324. 96. Chari R, Lockwood WW, Lam WL: Computational methods for the analysis of array comparative genomic hybridization. Cancer Inform 2007, 2:48-58. 97. Olshen AB, Venkatraman ES, Lucito R, Wigler M: Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 2004, 5(4):557-572. 98. Venkatraman ES, Olshen AB: A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 2007, 23(6):657-663. 99. Coe BP, Ylstra B, Carvalho B, Meijer GA, Macaulay C, Lam WL: Resolving the resolution of array CGH. Genomics 2007, 89(5):647-653. 100. Coe BP, Chari R, MacAulay C, Lam WL: FACADE: A fast and sensitive algorithm for the segmentation and calling of high resolution array CGH data. Nucleic Acids Res 2010, Revision. 101. Lonergan KM, Chari R, Coe BP, Wilson IM, Tsao MS, Ng RT, MacAulay C, Lam S, Lam WL: Transcriptome profiles of carcinoma-in-situ and invasive non-small cell lung cancer as revealed by SAGE. PLoS One 2010, Accepted. 102. Lee EHL, Chari R, Lam A, Ng RT, Yee J, English J, Evans KG, MacAulay C, Lam S, Lam WL: Disruption of the non-canonical WNT pathway in lung squamous cell carcinoma. Clinical Medicine: Oncology 2008, 2:169-179. 103. Chari R, Lonergan KM, Pikor LA, Coe BP, Zhu CQ, Chan THW, MacAulay C, Tsao MS, Lam S, Ng RT et al: A sequence-based approach to identify reference genes for gene expression analysis. BMC Medical Genomics 2010, Submitted. 104. Lockwood WW, Chari R, Coe BP, Thu KL, Garnis C, Malloff CA, Campbell J, Williams AC, Hwang D, Zhu CQ et al: BRF2 – A Novel Lineage Specific Oncogene in Lung Squamous Cell Carcinoma. PLoS Med 2010, Revisions. 23
Chapter 2: SIGMA 2 : A system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes 1 1 A version of this chapter has been published. Chari R, Coe BP, Wedseltoft C, Benetti M, Wilson IM, Vucic EA, MacAulay C, Ng RT, Lam WL. (2008) SIGMA2: A system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes. BMC Bioinformatics 9:422. doi:10.1186/1471-2105-9-422. Please see the published version of this chapter for all supplementary materials. 24
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 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: 71. Shivapurkar N, Gazdar AF: DNA M
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
Page 81 and 82: Figure 3.2. Quantitative and qualit
Page 83 and 84: Figure 3.3 a Proportion of genes in
Page 85 and 86: 70 Figure 3.5 -log(pvalue) 5.0 4.0
Page 87 and 88: 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
Page 203 and 204:
APPENDIX VI: Summary of Kaplan-Meie
Page 205:
University of British Columbia - Br
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