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6.4 References 1. Chari R, Coe BP, Wedseltoft C, Benetti M, Wilson IM, Vucic EA, MacAulay C, Ng RT, Lam WL: SIGMA2: a system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes. BMC Bioinformatics 2008, 9:422. 2. 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. 3. Sanada M, Suzuki T, Shih LY, Otsu M, Kato M, Yamazaki S, Tamura A, Honda H, Sakata-Yanagimoto M, Kumano K et al: Gain-of-function of mutated C-CBL tumour suppressor in myeloid neoplasms. Nature 2009, 460(7257):904-908. 4. Zhu X, Dunn JM, Goddard AD, Squire JA, Becker A, Phillips RA, Gallie BL: Mechanisms of loss of heterozygosity in retinoblastoma. Cytogenet Cell Genet 1992, 59(4):248-252. 5. Grand FH, Hidalgo-Curtis CE, Ernst T, Zoi K, Zoi C, McGuire C, Kreil S, Jones A, Score J, Metzgeroth G et al: Frequent CBL mutations associated with 11q acquired uniparental disomy in myeloproliferative neoplasms. Blood 2009, 113(24):6182- 6192. 6. Kralovics R, Guan Y, Prchal JT: Acquired uniparental disomy of chromosome 9p is a frequent stem cell defect in polycythemia vera. Exp Hematol 2002, 30(3):229-236. 7. Tiu RV, Gondek LP, O'Keefe CL, Huh J, Sekeres MA, Elson P, McDevitt MA, Wang XF, Levis MJ, Karp JE et al: New lesions detected by single nucleotide polymorphism array-based chromosomal analysis have important clinical impact in acute myeloid leukemia. J Clin Oncol 2009, 27(31):5219-5226. 8. Yamamoto G, Nannya Y, Kato M, Sanada M, Levine RL, Kawamata N, Hangaishi A, Kurokawa M, Chiba S, Gilliland DG et al: Highly sensitive method for genomewide detection of allelic composition in nonpaired, primary tumor specimens by use of affymetrix single-nucleotide-polymorphism genotyping microarrays. Am J Hum Genet 2007, 81(1):114-126. 9. Soh J, Okumura N, Lockwood WW, Yamamoto H, Shigematsu H, Zhang W, Chari R, Shames DS, Tang X, MacAulay C et al: Oncogene mutations, copy number gains and mutant allele specific imbalance (MASI) frequently occur together in tumor cells. PLoS One 2009, 4(10):e7464. 10. Darbary HK, Dutt SS, Sait SJ, Nowak NJ, Heinaman RE, Stoler DL, Anderson GR: Uniparentalism in sporadic colorectal cancer is independent of imprint status, and coordinate for chromosomes 14 and 18. Cancer Genet Cytogenet 2009, 189(2):77- 86. 11. Tuna M, Knuutila S, Mills GB: Uniparental disomy in cancer. Trends Mol Med 2009, 15(3):120-128. 12. Bjornsson HT, Albert TJ, Ladd-Acosta CM, Green RD, Rongione MA, Middle CM, Irizarry RA, Broman KW, Feinberg AP: SNP-specific array-based allele-specific expression analysis. Genome Res 2008, 18(5):771-779. 13. Gimelbrant A, Hutchinson JN, Thompson BR, Chess A: Widespread monoallelic expression on human autosomes. Science 2007, 318(5853):1136-1140. 14. Milani L, Lundmark A, Nordlund J, Kiialainen A, Flaegstad T, Jonmundsson G, Kanerva J, Schmiegelow K, Gunderson KL, Lonnerholm G et al: Allele-specific gene expression patterns in primary leukemic cells reveal regulation of gene expression by CpG site methylation. Genome Res 2009, 19(1):1-11. 15. Palacios R, Gazave E, Goni J, Piedrafita G, Fernando O, Navarro A, Villoslada P: Allele-specific gene expression is widespread across the genome and biological processes. PLoS One 2009, 4(1):e4150. 171
16. Zhang K, Li JB, Gao Y, Egli D, Xie B, Deng J, Li Z, Lee JH, Aach J, Leproust EM et al: Digital RNA allelotyping reveals tissue-specific and allele-specific gene expression in human. Nat Methods 2009, 6(8):613-618. 17. Kapoor GS, Kapitonov D, O'Rourke DM: Transcriptional regulation of signal regulatory protein alpha1 inhibitory receptors by epidermal growth factor receptor signaling. Cancer Res 2004, 64(18):6444-6452. 18. Wu CJ, Chen Z, Ullrich A, Greene MI, O'Rourke DM: Inhibition of EGFR-mediated phosphoinositide-3-OH kinase (PI3-K) signaling and glioblastoma phenotype by signal-regulatory proteins (SIRPs). Oncogene 2000, 19(35):3999-4010. 19. Gardai SJ, Xiao YQ, Dickinson M, Nick JA, Voelker DR, Greene KE, Henson PM: By binding SIRPalpha or calreticulin/CD91, lung collectins act as dual function surveillance molecules to suppress or enhance inflammation. Cell 2003, 115(1):13- 23. 20. Keane MP, Strieter RM, Belperio JA: Mechanisms and mediators of pulmonary fibrosis. Crit Rev Immunol 2005, 25(6):429-463. 21. Geerts A: History, heterogeneity, developmental biology, and functions of quiescent hepatic stellate cells. Semin Liver Dis 2001, 21(3):311-335. 22. Hautekeete ML, Geerts A: The hepatic stellate (Ito) cell: its role in human liver disease. Virchows Arch 1997, 430(3):195-207. 23. Masamune A, Shimosegawa T: Signal transduction in pancreatic stellate cells. J Gastroenterol 2009, 44(4):249-260. 24. Masamune A, Watanabe T, Kikuta K, Shimosegawa T: Roles of pancreatic stellate cells in pancreatic inflammation and fibrosis. Clin Gastroenterol Hepatol 2009, 7(11 Suppl):S48-54. 25. Omary MB, Lugea A, Lowe AW, Pandol SJ: The pancreatic stellate cell: a star on the rise in pancreatic diseases. J Clin Invest 2007, 117(1):50-59. 26. Mahadevan D, Von Hoff DD: Tumor-stroma interactions in pancreatic ductal adenocarcinoma. Mol Cancer Ther 2007, 6(4):1186-1197. 27. Chung KF, Adcock IM: Multifaceted mechanisms in COPD: inflammation, immunity, and tissue repair and destruction. Eur Respir J 2008, 31(6):1334-1356. 28. Ng SB, Buckingham KJ, Lee C, Bigham AW, Tabor HK, Dent KM, Huff CD, Shannon PT, Jabs EW, Nickerson DA et al: Exome sequencing identifies the cause of a mendelian disorder. Nat Genet 2010, 42(1):30-35. 29. Ng SB, Turner EH, Robertson PD, Flygare SD, Bigham AW, Lee C, Shaffer T, Wong M, Bhattacharjee A, Eichler EE et al: Targeted capture and massively parallel sequencing of 12 human exomes. Nature 2009, 461(7261):272-276. 30. Du L, Pertsemlidis A: microRNAs and lung cancer: tumors and 22-mers. Cancer Metastasis Rev 2010. 31. Garofalo M, Di Leva G, Romano G, Nuovo G, Suh SS, Ngankeu A, Taccioli C, Pichiorri F, Alder H, Secchiero P et al: miR-221&222 regulate TRAIL resistance and enhance tumorigenicity through PTEN and TIMP3 downregulation. Cancer Cell 2009, 16(6):498-509. 32. Johnson SM, Grosshans H, Shingara J, Byrom M, Jarvis R, Cheng A, Labourier E, Reinert KL, Brown D, Slack FJ: RAS is regulated by the let-7 microRNA family. Cell 2005, 120(5):635-647. 33. Kumar MS, Erkeland SJ, Pester RE, Chen CY, Ebert MS, Sharp PA, Jacks T: Suppression of non-small cell lung tumor development by the let-7 microRNA family. Proc Natl Acad Sci U S A 2008, 105(10):3903-3908. 34. Talotta F, Cimmino A, Matarazzo MR, Casalino L, De Vita G, D'Esposito M, Di Lauro R, Verde P: An autoregulatory loop mediated by miR-21 and PDCD4 controls the AP-1 activity in RAS transformation. Oncogene 2009, 28(1):73-84. 35. Weiss GJ, Bemis LT, Nakajima E, Sugita M, Birks DK, Robinson WA, Varella-Garcia M, Bunn PA, Jr., Haney J, Helfrich BA et al: EGFR regulation by microRNA in lung 172
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DEVELOPMENT AND APPLICATION OF AN I
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Table of Contents Abstract ........
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3.3.2 Multi-dimensional analysis (M
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List of Tables Table 2.1. Features
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Figure 5.3. Overlay of chromosomal
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RB1 Retinoblastoma 1 RNA Ribonuclei
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Dedication To my family. xiii
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Chapter 5: Chari R, Thu KL, Wilson
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1.1 Lung cancer Lung cancer has the
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expression changes are reactive to
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number of different cancer types. M
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large number of tumors, that a give
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(B) By using an integrative approac
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platforms, with one of the latest s
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1.12.1 Development of tools for gen
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quantitative PCR experiments as wel
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1.13 References 1. Jemal A, Siegel
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33. Garber ME, Troyanskaya OG, Schl
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71. Shivapurkar N, Gazdar AF: DNA M
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Chapter 2: SIGMA 2 : A system for t
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Here, we present SIGMA 2 , a novel
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datasets. To utilize this, the user
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2.3.7 Analysis of data from multipl
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expression (Figure 2.8). ERBB2 has
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Figure 2.1 R -Segmentation -Statist
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Figure 2.3 numSamples
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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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Page 145 and 146: Figure 5.2 # of copies Total copy n
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Page 151 and 152: Figure 5.6 SHC1 GRB2 SOS2 RRAS ER R
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Page 157 and 158: Table 5.2. List of genomic resource
Page 159 and 160: 5.5 References 1. Pinkel D, Segrave
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Page 171 and 172: alterations in human prostate cance
Page 173 and 174: 248. Xi L, Feber A, Gupta V, Wu M,
Page 175 and 176: 281. Fernandez-Ranvier GG, Weng J,
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Page 179 and 180: can identify nearly three times as
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Page 185: previously described genetic and ep
Page 189 and 190: APPENDIX I: List of publications Th
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Page 193 and 194: This chapter details the technologi
Page 195 and 196: epigenetic alteration. This led to
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Page 199 and 200: APPENDIX III: Sources of data Sampl
Page 201 and 202: 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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