Chapter 2 - University of British Columbia

More documents

Recommendations

Info

utilized for early detection purposes as well as a target for therapeutic intervention [70-72], emphasizing its key role in cancer. In lung cancer, a number of specific genes such as CDKN2A (or p16), RASSF1A, and MGMT have shown to harbour increased promoter methylation [73]. While many of these methylation events were discovered using single locus assays, recent advances have allowed for the high throughput analysis of 1000s of genes in a single experiment [74-79]. As such, applications of these high throughput approaches in lung cancer are likely to identify novel methylated genes. Similar to array CGH analysis, though many methylated genes are likely to be identified, it will be important to validate if these alterations affect downstream gene expression. 1.5 Current level of integrative analysis At the time this thesis started, there were a small number of whole genome integrative studies which primarily focused on the integration of gene dosage and gene expression. In fact, the majority of the integrative analysis would be done at single locus level such as the examination of gene dosage and expression of HER2 (ERBB2) oncogene in breast cancer [80]. Moreover, there were a limited number of gene dosage or gene expression studies in lung cancer. However, from recent studies involving multiple cancer types, including lung cancer, it has been shown that anywhere between 20% and 60% of genes in regions of copy number change also exhibit a concerted change in gene expression [52, 81-84]. Conversely, when the proportion of differential expression associated with gene dosage alteration was examined, it was found that only 11% of the observed differential expression could be attributed to high level DNA copy number change [83]. Thus, it is clear that gene dosage alterations are responsible for only a part of the overall dysregulated gene expression and that other mechanisms are likely involved. 1.6 Need for an integrative approach to study lung cancer As discussed earlier, a gene such as CDKN2A has been shown to be inactivated by both gene dosage loss and increased promoter methylation. Thus, it is very likely that when examining a 7
large number of tumors, that a given gene may be affected by one mechanism in tumor (e.g. gene dosage increase) and another mechanism in a different tumor (e.g. DNA hypomethylation), but both leading to the same net effect (Figure 1.1). In addition, if the specific event (e.g. gene dosage increase) occurs at a low frequency, but cumulatively, the deregulation occurs at a high frequency, then examining only gene dosage or DNA methylation would preclude the identification of such potentially important genes. Hence, it should be apparent that an integrative, multi-dimensional genetic and epigenetic approach is needed to identify novel genes which would have escaped previous, single dimensional analyses. 1.7 Bioinformatic tools for genomic analysis While many software packages exist for the analysis of high throughput gene expression data [85-88], at the start of my thesis project, software packages for the visualization and analysis of DNA copy number data were very limited [89-95]. A summary of array CGH analysis methodologies and software packages is provided in this review [96]. Moreover, three of the key challenges at the time were (i) the increase in data generated from a single experiment, (ii) the effective visualization of this data for easy interpretation, and (iii) the microarray platform dependence of the majority of software packages. With respect to the increase in data generation, the first generation of microarrays used for array CGH typically comprised of two to three thousand data points. As such, software for both visualization and analysis were developed to effectively handle this level of data complexity. For example, since array CGH data in fact represents discrete levels of copy number throughout the genome, one of the data analysis steps required is segmentation which effectively smoothes data based on genomic position. The first version of DNACopy [97], one of the first algorithms to segment array CGH data, would need a significant amount of time to execute when applied to arrays with 100,000 data points or greater and eventually, a new version of the program was developed a few years later [98]. Similarly, in terms of visualization, most programs displayed array CGH data in an ordinal manner whereby the relative genomic 8
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: number of different cancer types. M
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
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
Page 89 and 90:
Figure 3.8 a b Proportion of random
Page 91 and 92:
16. Chari R, Lockwood WW, Coe BP, C
Page 93 and 94:
50. Giovane A, Pintzas A, Maira SM,
Page 95 and 96:
4.1 Introduction Genetic alteration
Page 97 and 98:
4.2.3 Determining frequent regions
Page 99 and 100:
etween loss and UPD (Figure 4.3). S
Page 101 and 102:
obtained, profiled and used as the
Page 103 and 104:
Figure 4.1. Detection of UPD using
Page 105 and 106:
Figure 4.2. Comparison of frequent
Page 107 and 108:
Figure 4.3 Gain Loss 642 441 7 Figu
Page 109 and 110:
94 Figure 4.4 1 2 3 4 5 6 7 8 9 10
Page 111 and 112:
Figure 4.6 a b c Percent of cases A
Page 113 and 114:
Figure 4.7 a b 6p25.3 Log2 fold cha
Page 115 and 116:
Chr BPStart BPEnd # of Chr BPStart
Page 117 and 118:
Table 4.3. Overlap of oncogenes in
Page 119 and 120:
Table 4.5. Cell lines and oncogene
Page 121 and 122:
Table 4.7. RefSeq genes in focal re
Page 123 and 124:
4.6 References 1. Bell DW: Our chan
Page 125 and 126:
33. Garnis C, Coe BP, Lam SL, MacAu
Page 127 and 128:
5.1 Introduction In the past decade
Page 129 and 130:
polymorphism (SNP) arrays with over
Page 131 and 132:
substitutions) and UV exposure (C t
Page 133 and 134:
developed antibodies and methylated
Page 135 and 136:
Histone modification states. While
Page 137 and 138:
metastasis [226]. High throughput a
Page 139 and 140:
Implications on sample size require
Page 141 and 142:
analyzed using the "minet" package
Page 143 and 144:
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 and 196:
epigenetic alteration. This led to
Page 197 and 198:
This manuscript describes the curre
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
show all

Chapter 2 - University of British Columbia

Create successful ePaper yourself

Delete template?

Save as template?