Chapter 2 - University of British Columbia

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exhibited DNA copy number associated overexpression of FGFR2 (Figure 3.8b). Hence, every TNBC line was affected at either FGFR2 or COL1A1 at both the DNA and RNA levels. Interestingly, COL1A1 has been shown to be both prognostic and predictive in multiple cancer types, including breast cancer [3, 5, 62, 63]. 3.4 Conclusions In conclusion, we have demonstrated that a multi-dimensional genomic approach is superior to analysis of one or two genomic dimensions alone. Each additional genomic dimension surveyed increases the amount of aberrant gene expression that can be explained within individual samples. As a by-product, when examining across a sample set, multi-dimensional genomic analysis can identify relevant genes that may be overlooked due to low frequencies of disruption by the individual mechanisms. The increased frequency of gene disruption detected, due to the consideration of multiple mechanisms of disruption, could potentially reduce the sample size of study cohort needed for gene discovery. Secondly, while the increased detection of genes disrupted using multi-dimensional analysis is useful for achieving a more comprehensive identification of deregulated pathways and gene networks, it also presents a challenge in prioritizing which genes are likely key nodes or hubs in the affected pathways and networks. Hence, one way to prioritize is to identify genes with evidence of multiple concerted disruption. The Knudson two-hit hypothesis suggests that tumor suppressor genes require two allelic hits to disrupt gene function. Bi-allelic alteration, such as homozygous deletion, or concerted genetic and epigenetic changes, are well documented causal mechanisms of gene disruption. Likewise, hypomethylation and increased gene dosage are known mechanisms for gene overexpression. The bi-allelic disruption phenomenon (leading to loss or gain of function) provides a means to identify causative genes; hence, parallel analysis of the genome and epigenome in the same tumor is of great benefit. In this study, we have developed a stepwise gene selection strategy to identify multiple concerted disruptions using an integrative genomics approach. 63
In this study, three DNA dimensions, which have current affordable high throughput assays, were examined. However, we envision that new techniques for analysis of additional aspects such as histone modification states and gene mutation status will reveal mechanisms that would explain even more gene expression changes within individual samples. The identification of a number of key cancer-related genes and pathways using a relatively small sample size suggests that limitations in requiring large sample sizes for studies to identify relevant genes and pathways may be circumvented by our comprehensive approach. Consequently, this concept can be projected to current technologies such as high throughput sequencing where it may prove more prudent to perform this analysis in multiple dimensions in a smaller number of samples rather than in one dimension in many more samples at a comparable cost. Finally, observing the same gene in a given pathway being deregulated in a completely different manner between samples highlights one of the shortcomings of group-based analysis and highlights the eventual need to move to systems analysis of tumors as individual entities. 64
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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
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: mechanisms at the DNA and RNA level
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
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polymorphism (SNP) arrays with over
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substitutions) and UV exposure (C t
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developed antibodies and methylated
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Histone modification states. While
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metastasis [226]. High throughput a
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Implications on sample size require
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analyzed using the "minet" package
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expression. The next challenge is t
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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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