Chapter 2 - University of British Columbia

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Figure 5.9 Survival Ratio Survival Ratio 1 0.9 0.8 0.7 0.6 0.5 0.4 Duke H. Lee Mo�tt LowExpression HighExpression 0.3 p = 0.009 0.1 p = 0.009 0.2 0 5 10 15 20 25 30 35 40 45 0 0 20 40 60 80 100 120 Time (months) Time 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 MSKCC LowExpression HighExpression p = 0.150 p = 0.180 0 0 50 100 150 200 250 Time Figure 5.9. Kaplan-Meier analysis of SIRPA in four independent microarray datasets. Using publicly available gene expression microarray data, Kaplan-Meier analysis was performed to assess the association of SIRPA expression levels and overall patient survival. Briefly, for each dataset, samples were sorted based on ascending SIRPA expression and survival distributions of the top 1/3 of samples expressing SIRPA and bottom 1/3 of samples expression SIRPA were compared. In total, five datasets were tested with two of the datsets (Duke, H. Lee Moffitt) showing a stastistically significant association. In an additional two datasets (MSKCC, Michigan), the p-values were close to statistical significance. All expression data were normalized using RMA. P-values were calculated using a Mantel-Cox log rank test. Survival Ratio Survival Ratio 139 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 Michigan LowExpression HighExpression LowExpression HighExpression 0.2 0 20 40 60 80 Time 100 120 140 160
Figure 5.10 (a) (b) Figure 5.10. Automated detection of selected clonal populations of cells within a cancer biopsy tissue section. All nuclei (~150,000 in this example) are detected and FISH probe signal counts are enumerated for each nucleus. FISH signal pattern for each cell is compared against its neighbor in order to define spatial association (or neighborhood). A mathematical model is then applied to determine clonal cell relationships. (a) Mapping cancer cells on a tissue section. A gain or loss of any one of three FISH markers indicates a cancer cell. This image shows the density of cancer cells (so defined) in neighborhoods as a color overlay. Red indicates high fraction of cancer cells, yellow indicates medium fraction of cancer cells and blue indicates low to none (see scale bar). Most of the section is highlighted except for the surrounding normal stromal infiltrates. (b) Mapping clonal cells. The same image data were analyzed for concurrent gains of each of the three of the markers. The two clusters of cells, magnified within the white boxes, are cells harboring gain of all three markers. 140
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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
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
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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
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Page 145 and 146: Figure 5.2 # of copies Total copy n
Page 147 and 148: Figure 5.4. Integration of copy num
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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
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
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