Chapter 2 - University of British Columbia

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UPD at oncogenes was typically accompanied by an acquired homozygous mutation at the locus [3, 5-8]. From our data, though UPD was also observed at mutated KRAS, as shown previously [9], there was also frequent UPD in cases where KRAS was not mutated. This finding suggests that in the cases which UPD occurs without somatic mutation, that the UPD event may in fact be used a mechanism for preferential allele selection. Specifically, this could be preference for the methylated allele for tumor suppressor genes or unmethylated allele for oncogenes [10, 11]. Alternatively, the preference could be for a more transcriptionally active (or inactive) allele as it has been shown that for specific genes, the two alleles may differ in rates of transcription [12-16]. Thus, integration of genetic data with epigenetic and gene expression data would help decipher the target of these frequently observed UPD events. 6.1.3 Application of the integrative approach to lung adenocarcinoma specimens Thus far, I have shown that the integrative genetic and epigenetic approach is beneficial in identifying important genes; both which would have been missed if single assays alone were used and those which have concurrent alteration at multiple levels. Chapter 5 discusses how upon application of this approach to lung adenocarcinoma specimens, we see that this trend also holds true in clinical samples. Specifically, I show that novel canonical signaling pathways are significantly enriched for when multiple DNA-based dimensions are used but are missed (not statistically significant) when a single DNA-based dimension is used. In addition, when we examined the well-documented EGFR signaling pathway, a pathway known to be involved in lung adenocarcinoma, the most frequently disrupted gene was signal-regulatory protein alpha (SIRPA). SIRPA has been shown to be a direct downstream component of EGFR, and has been shown to be suppressed in expression by EGFR activation [17, 18]. In the resting lung, SIRPA has been postulated to control the inflammatory response through SHP-1 and eventually, NFKB [19]. While there are likely multiple components between SIRPA and NFKB, we wanted to assess expression of components directly associated with SIRPA. In addition, since this gene 165
was identified in a small set of samples, we wanted to see if this prevalence of disruption was maintained in an additional, larger set of tumors. Hence, we evaluated expression of SHP-1 and SIRPA in a panel of approximately 60 lung adenocarcinoma tumors and found (i) a high prevalence of underexpression of SIRPA and (ii) a strong correlation between SIRPA and SHP- 1 expression levels. It is interesting to observe this strong relationship between SIRPA and SHP-1 as most cancer studies have focused on SIRPA’s relationship with SHP-2 instead of SHP-1. 6.2 Conclusions I have demonstrated the power of an integrative genetic and epigenetic approach to decipher resultant gene expression changes in lung adenocarcinoma. The development of an application such as SIGMA2 was integral as it represented one of the first academic/research applications with the ability to integrate multiple dimensions of data. To date, there have been a few other applications that have been developed that can perform similar functionalities but most of these have been developed by commercial entities. Moreover, the software still is not out-dated and based on the way it was built, can be extended to handle newer high throughput platforms including sequence-based platforms. In terms of what we learn from both the demonstration dataset (Chapter 3) as well as clinical tumor dataset (Chapter 5), we know that by using an integrative, multi-dimensional approach, we are detecting genes being disrupted at a much higher frequency when multiple dimensions are examined as compared to single dimensions alone. Moreover, at a given detection frequency, a gene may be disrupted by a single dimension at a low frequency but when multiple dimensions are accounted for, the frequency is in fact high. In Figure 5.5, I illustrate how well known lung cancer genes such as RRM2 are altered at both the genetic and epigenetic level and illustrate how more pathways are deemed significant when multiple dimensions are analyzed. The latter finding is likely a result of the fact that within a given pathway, not only can different genes be affected in different samples by one mechanism (e.g. DNA copy number 166
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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
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Figure 4.1. Detection of UPD using
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Figure 4.2. Comparison of frequent
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Figure 4.3 Gain Loss 642 441 7 Figu
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94 Figure 4.4 1 2 3 4 5 6 7 8 9 10
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Figure 4.6 a b c Percent of cases A
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Figure 4.7 a b 6p25.3 Log2 fold cha
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Chr BPStart BPEnd # of Chr BPStart
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Table 4.3. Overlap of oncogenes in
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Table 4.5. Cell lines and oncogene
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Table 4.7. RefSeq genes in focal re
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4.6 References 1. Bell DW: Our chan
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33. Garnis C, Coe BP, Lam SL, MacAu
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5.1 Introduction In the past decade
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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
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
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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,
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Page 179: can identify nearly three times as
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Page 185 and 186: previously described genetic and ep
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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
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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
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