Figure 1.1 a Normal Tumor b Normal Tumor c Normal Tumor Copy Number Loss / Loss <strong>of</strong> heterozygosity (LOH) d e Normal Tumor Normal Tumor Allelic Imbalance AATACGCGCGCGTCGCATCCAGCATGAACAGA TTATGCGCGCGCAGCGTAGGTCGTACTTGTCT AATACGCGCGCGTCGCATCCAGCATGAACAGA TTATGCGCGCGCAGCGTAGGTCGTACTTGTCT DNA Hypermethylation AATACGCGCGCGTCGCATCCAGCATGAACAGA TTATGCGCGCGCAGCGTAGGTCGTACTTGTCT AATACGCGCGCGTCGCATCCAGCATGAACTGA TTATGCGCGCGCAGCGTAGGTCGTACTTGACT Somatic mutation Figure 1.1. Multiple mechanisms <strong>of</strong> alteration leading to the same downstream consequences. (a) Illustration <strong>of</strong> copy number loss. Loss <strong>of</strong> a particular chromosomal region in tumors. (b) Illustration <strong>of</strong> allelic imbalance. While both alleles are present, there is a preferential increase <strong>of</strong> one <strong>of</strong> the alleles. (c) Ilustration <strong>of</strong> uniparental disomy. While overall the total number <strong>of</strong> DNA copies is normal, one part <strong>of</strong> an allele is lost and replaced by a part from the other allele. (d) Promoter hypermethylation in tumors which results in suppression <strong>of</strong> gene transcription. (e) Somatic mutation in the tumor which can lead to the transcription <strong>of</strong> a truncated (possibly non-functional) transcript. Mechanisms shown in (a), (c), (d), and (e) can lead to the same net downstream effect in loss <strong>of</strong> gene and protein expression. For (a), (b), and (c), though whole chormosomes are shown, these events can vary in scale from a focal region <strong>of</strong> change to a whole chormosome arm. The green arrow represents the transcription start site. 17 Uniparental disomy (UPD) Premature stop, truncated transcript
1.13 References 1. Jemal A, Siegel R, Ward E, Hao Y, Xu J, Thun MJ: Cancer statistics, 2009. CA Cancer J Clin 2009, 59(4):225-249. 2. Sun S, Schiller JH, Gazdar AF: Lung cancer in never smokers--a different disease. Nat Rev Cancer 2007, 7(10):778-790. 3. Khuder SA: Effect <strong>of</strong> cigarette smoking on major histological types <strong>of</strong> lung cancer: a meta-analysis. Lung Cancer 2001, 31(2-3):139-148. 4. Detterbeck FC, B<strong>of</strong>fa DJ, Tanoue LT: The new lung cancer staging system. Chest 2009, 136(1):260-271. 5. Herbst RS, Lynch TJ, Sandler AB: Beyond doublet chemotherapy for advanced nonsmall-cell lung cancer: combination <strong>of</strong> targeted agents with first-line chemotherapy. Clin Lung Cancer 2009, 10(1):20-27. 6. Kim KS, Jeong JY, Kim YC, Na KJ, Kim YH, Ahn SJ, Baek SM, Park CS, Park CM, Kim YI et al: Predictors <strong>of</strong> the response to gefitinib in refractory non-small cell lung cancer. Clin Cancer Res 2005, 11(6):2244-2251. 7. Kim TE, Murren JR: Erlotinib OSI/Roche/Genentech. Curr Opin Investig Drugs 2002, 3(9):1385-1395. 8. Miller VA, Kris MG, Shah N, Patel J, Azzoli C, Gomez J, Krug LM, Pao W, Rizvi N, Pizzo B et al: Bronchioloalveolar pathologic subtype and smoking history predict sensitivity to gefitinib in advanced non-small-cell lung cancer. J Clin Oncol 2004, 22(6):1103-1109. 9. Mitsudomi T, Kosaka T, Endoh H, Horio Y, Hida T, Mori S, Hatooka S, Shinoda M, Takahashi T, Yatabe Y: Mutations <strong>of</strong> the epidermal growth factor receptor gene predict prolonged survival after gefitinib treatment in patients with non-small-cell lung cancer with postoperative recurrence. J Clin Oncol 2005, 23(11):2513-2520. 10. Pao W, Miller V, Zakowski M, Doherty J, Politi K, Sarkaria I, Singh B, Heelan R, Rusch V, Fulton L et al: EGF receptor gene mutations are common in lung cancers from "never smokers" and are associated with sensitivity <strong>of</strong> tumors to gefitinib and erlotinib. Proc Natl Acad Sci U S A 2004, 101(36):13306-13311. 11. Sirotnak FM, Zakowski MF, Miller VA, Scher HI, Kris MG: Efficacy <strong>of</strong> cytotoxic agents against human tumor xenografts is markedly enhanced by coadministration <strong>of</strong> ZD1839 (Iressa), an inhibitor <strong>of</strong> EGFR tyrosine kinase. Clin Cancer Res 2000, 6(12):4885-4892. 12. Paez JG, Janne PA, Lee JC, Tracy S, Greulich H, Gabriel S, Herman P, Kaye FJ, Lindeman N, Boggon TJ et al: EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 2004, 304(5676):1497-1500. 13. Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring <strong>of</strong> gene expression patterns with a complementary DNA microarray. Science 1995, 270(5235):467-470. 14. Schena M, Shalon D, Heller R, Chai A, Brown PO, Davis RW: Parallel human genome analysis: microarray-based expression monitoring <strong>of</strong> 1000 genes. Proc Natl Acad Sci U S A 1996, 93(20):10614-10619. 15. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA et al: Molecular classification <strong>of</strong> cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286(5439):531-537. 16. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA et al: Molecular portraits <strong>of</strong> human breast tumours. Nature 2000, 406(6797):747-752. 17. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS et al: Gene expression patterns <strong>of</strong> breast carcinomas 18
- 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 and 22: number of different cancer types. M
- 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: quantitative PCR experiments as wel
- 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