Chapter 2 - University of British Columbia

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26. Buys TPH, Chari R, Lee E, Zhang M, MacAulay C, Lam S, Lam WL, Ling V. (2007) Genetic changes in the evolution of multidrug resistance for cultured human ovarian cancer cells. Genes, Chromosomes and Cancer, 46(12):1069-79. Drug resistance is a common problem for cancer patients treated by chemotherapeutics. One of the mechanisms of resistance is through the multi-drug resistance phenotype which is often associated with the activity of ATP-binding cassette (ABC) transporters. In this study, using an ovarian cancer cell line exposed to increasing concentrations of vincristine to derive drug resistant derivatives, the genetic and gene expression profiles were compared between these resistant derivatives and the original cancer cell line. It was found that in while initial resistant derivatives (lines exposed to lower concentration of drug) harbored copy number and gene expression increase of ABCC1 and ABCC6, latter resistant derivatives (lines exposed to higher concentration of drug) did not have the increase in ABCC1 and ABCC6, but had an increase of ABCB, suggesting the drug resistance phenotype may be a dynamic process. 27. Coe BP, Lockwood WW, Girard L, Chari R, Minna JD, MacAulay C, Lam S, Gazdar AF, Lam WL. (2006) Differential regulation of cell cycle pathways in small cell and non-small cell lung cancer. British Journal of Cancer, (12):1927-35. Small cell lung cancer (SCLC) and non-small cell lung cancer (SCLC) are the two major cell types of lung cancer. While pathologically they can be distinguished, the molecular basis of these two cancer types is not well understood. In this study, a whole genome integrative genetic and gene expression comparison of NSCLC and SCLC was performed and differential regulation of cell cycle pathways was identified. Specifically, NSCLC is primarily deregulated at the receptor level while SCLC is primarily deregulated at the nuclear transcription factor level. Software, analysis approaches, and databases 28. Tsui IFL, Chari R, Buys TPH, Lam WL. (2007) Public databases and software for the pathway analysis of cancer genomes. Cancer Informatics, 3:389-407. 181
This manuscript describes the currently available computational resources for the analysis of pathways in cancer. Specifically, the use of these resources to analyze results from high throughput studies examining genetic, epigenetic or gene expression alterations. 29. Chari R*, Lockwood WW*, Lam WL. (2006) Computational methods for the analysis of array comparative genomic hybridization. Cancer Informatics, (2):48-58. This manuscript describes the most commonly used analysis strategies for array CGH data and compares and contrasts these approaches. In addition, the specific features of currently available software suites are also compared. 182
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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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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
Page 145 and 146: Figure 5.2 # of copies Total copy n
Page 147 and 148: Figure 5.4. Integration of copy num
Page 149 and 150: Figure 5.5. Integration of copy num
Page 151 and 152: Figure 5.6 SHC1 GRB2 SOS2 RRAS ER R
Page 153 and 154: Figure 5.8 a Log2 Fold Change (T vs
Page 155 and 156: Figure 5.10 (a) (b) Figure 5.10. Au
Page 157 and 158: Table 5.2. List of genomic resource
Page 159 and 160: 5.5 References 1. Pinkel D, Segrave
Page 161 and 162: 37. Oliphant A, Barker DL, Stuelpna
Page 163 and 164: 75. Selzer RR, Richmond TA, Pofahl
Page 165 and 166: 111. Melcher R, Al-Taie O, Kudlich
Page 167 and 168: 145. Takai D, Yagi Y, Wakazono K, O
Page 169 and 170: 179. Kondo M, Suzuki H, Ueda R, Osa
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,
Page 177 and 178: Chapter 6: Conclusions 162
Page 179 and 180: can identify nearly three times as
Page 181 and 182: was identified in a small set of sa
Page 183 and 184: will be done at the pathway level a
Page 185 and 186: previously described genetic and ep
Page 187 and 188: 16. Zhang K, Li JB, Gao Y, Egli D,
Page 189 and 190: APPENDIX I: List of publications Th
Page 191 and 192: This publication is described in se
Page 193 and 194: This chapter details the technologi
Page 195: epigenetic alteration. This led to
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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