Chapter 2 - University of British Columbia

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gain and loss. When examining major oncogenes and tumor suppressor genes, while most oncogenes were associated with frequent areas of gain, we found a subset of both known and novel oncogenes that were frequently affected by UPD. Finally, examining oncogenes with homozygous mutation in multiple cancer types, we observe frequent UPD at these genes suggesting this mechanism of oncogene activation is prevalent across multiple cancer types. 4.2 Methods 4.2.1 Genome wide profiling of clinical lung adenocarcinoma specimens Forty-six lung adenocarcinoma cases were obtained from Vancouver General Hospital under approved ethics. Cases were reviewed by a pathologist and tumors were microdissected to ensure maximal tumor cell content (≥ 70%). Five hundred nanograms of genomic DNA were extracted from each tumor and adjacent non-malignant tissue were prepared and hybridized to the Affymetrix Genome-Wide Human SNP 6.0 array platform as per manufacturer's instructions. CEL files, the raw data files generated, were then processed using the Affymetrix Genotyping Console version 3.0.2 to generate .chp files using the birdseed v2 genotyping algorithm. 4.2.2 Determination of regions of uniparental disomy (UPD) in clinical lung tumors CEL files and .chp files were imported into Partek Genomics Suite (PGS) using the software's recommended default settings. First, to determine total copy number, paired copy number intensities were calculated for each sample using the intensity in the tumor vs. it's matched non- malignant sample. Paired copy number intensities were then analyzed using the Genomic Segmentation method in PGS with all parameters run at default except for the number of markers which was set to 50. Subsequently, allele specific copy number (ASCN) analysis was used to determine regions of allelic imbalance. A region was deemed imbalanced if the imbalance proportion was ≥ 0.15 (as recommended by PGS). Finally, a region was called UPD if the region was imbalance and no change in total copy number was present. 81
4.2.3 Determining frequent regions of UPD, gain and loss To determine frequent regions of gain, loss, and UPD, the frequency of each alteration was determined for each SNP probe on the somatic chromosomes. A frequency threshold of 40% was used. To smooth out regions of UPD (and for gain and loss as well), a three step process was performed. First, adjacent probes with frequencies greater than the threshold were merged together. Second, to account for dips in frequency where one region is split into two, if the dip is less than 1 Mb in size, the regions were merged. Finally, smoothed regions of UPD, gain, or loss which were less than 100 probes in size were removed. 4.2.4 Determination of UPD in cancer cell lines Raw SNP 6.0 data (.CEL files) from cancer cell lines were obtained from the Wellcome Trust Sanger CGP database. CEL files were then genotyped similarly as above to generate CHP files. To define a total copy number and allele specific copy number reference, SNP 6.0 data, generated from 72 CEPH HapMap samples, were obtained from Affymetrix and were also genotyped. Unpaired copy number and allele specific copy number analyses were performed, as described above, to determine regions of allelic imbalance without a change in total copy number using Partek Genomics Suite. 4.2.5 Expression analysis of genes in focal regions of UPD For 16 of the 46 tumor/non-malignant tissue pairs, gene expression profiles on a custom Affymetrix chip were generated. The 32 samples were normalized using the RMA algorithm [21] in the Bioconductor software suite in R [22]. To determine overexpression in a given sample pair for a given gene, since expression values are in log2 space, expression values in non-malignant samples were subtracted from expression values in the tumor. A two-fold expression change was deemed significant for this analysis. 82
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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
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: 4.1 Introduction Genetic alteration
Page 99 and 100: etween loss and UPD (Figure 4.3). S
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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
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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
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APPENDIX VI: Summary of Kaplan-Meie
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University of British Columbia - Br
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