Chapter 2 - University of British Columbia

More documents

Recommendations

Info

gene dosage with gene expression analysis would be useful to discern the target gene(s) of a given copy number alteration. 1.2.3 Loss of heterozygosity (LOH) and allelic imbalance Loss of heterozygosity (LOH) is a common genetic event in cancer [53]. In the normal cell, each somatic chromosome has two copies, with one copy (or allele) originiating from each parent. Subsequently, in the tumor, a specific segment from one of the copies of the chromosome is lost, resulting in loss of heterozygosity. Frequent regions of LOH have also been identified in the lung cancer genome [54-58]. While initial studies involved the use of microsatellite markers placed throughout the genome and thus, the resolution of these changes were limited, the application of SNP arrays were able to refine these areas into specific chromosome arms [45, 46]. In addition to advances in SNP array technology, analysis approaches were also developed that increased the detection sensitivity of regions of LOH / allelic imbalance [59-63]. Although most areas with altered gene dosage will also be detected as LOH (in case of copy number loss) and allelic imbalance (in case of copy number gain), there are also areas in the genome which exhibit LOH but no change in copy number, termed copy neutral LOH or uniparental disomy. However, the role of UPD in lung cancer is not well understood. 1.3 Somatic mutations in lung cancer Somatic mutations have also shown to be important in cancer development. In addition, mutational analysis is also used for screening purposes in high risk populations (e.g. BRCA1/2 and hereditary breast cancer) as well as criteria for receiving targeted chemotherapy (e.g. EGFR mutation and EGFR inhibitors). Many studies have been undertaken to identify mutations in genes involved with important cellular processes pertinent to the cancer phenotype such as DNA repair and cellular proliferation and have successfully identified key genes to a 5
number of different cancer types. Moreover, it can be classified that while oncogenes typically harbour activating mutations, tumor suppressor genes often harbour inactivating mutations. In lung adenocarcinoma, the most well known genes shown to be mutated are EGFR, KRAS, LKB1 (or STK11), TP53 and CDKN2A [30, 54, 64, 65], with some mutations such as EGFR and KRAS showing preferential mutation patterns based on smoking history. A recent study assessing other well known oncogenes and tumor suppressor genes showed there were a number of other genes also observed to be mutated in lung adenocarcinoma [64]. However, due to technological and material limitations at the time, many of these studies only assess small numbers of genes in a given study and thus, genome wide screening for somatic mutations is unfeasible. While high throughput sequencing technologies to assess sequence mutation on a genome scale have become available, challenges associated with cost and data analysis preclude the use in a routine manner. 1.4 Epigenetic alterations in lung cancer 1.4.1 DNA methylation Another DNA level mechanism which can affect gene expression is through the methylation of DNA at gene promoters. DNA methylation is a reversible chemical modification which has shown to have a prominent role in the silencing of tumor suppressor genes. Specifically, this modification targets cytosines whereby a methyl (CH3) is added to the carbon 5 moiety of cytosine. It is thought that in cancer, the majority of the genome loses its methylation but small areas in the gene promoters, known as CpG islands, gain methylation [66-69]. Generally, it is thought that the acquired methylation targets tumor suppressor genes while the areas of lost methylation facilitate the activation of repetitive areas of the genome which can lead to increased genomic instability. In addition, aberrant DNA methylation of critical genes have been 6
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: expression changes are reactive to
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
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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?