Chapter 2 - University of British Columbia

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generated over 500,000 data points per sample, representing a 100-fold increase in information obtained from each experiment [99]. Hence, the base software architecture used in SIGMA 2 was already capable of handling large amounts of data. Aim 2: Demonstration of an integrative approach using model systems Chapter 3 discusses the demonstration of an integrative, multi-dimensional approach on tumor cell line model systems. Using a set of breast cancer cell lines, I examine the gene dosage, allelic composition, DNA methylation, and gene expression profiles in an integrative manner to delineate which genes and pathways would be missed or less significant if such an approach was not used. This demonstrative study was needed to show the key advantages and benefits of an integrative approach. While cell lines are artificial systems and may have acquired alterations that are beneficial to grow in vitro, it is important that a sample source was used where material limitations did not exist. For each of the genetic or epigenetic profiling studies, sufficient amounts of DNA and RNA are needed and when more assays are done in a given sample, more material is required. Moreover, when whole tumor samples are microdissected to ensure high tumor cell purity, this inherently will reduce the amount of usable sample material. As such, it is important that the quantitative and qualitative benefits of utilizing an integrative approach are sufficient to warrant using clinical samples. At the time this study was initiated, SNP array and array CGH profiles were available for breast cancer cell lines and thus, only generation of DNA methylation and gene expression profiles were needed to complete this set. Given the purpose of this study was to demonstrate the effectiveness of the integrative approach, while data from lung cancer cell lines would have been most optimal, the source of data has limited relevance to the purpose of this aim. Aim 3: Characterization of DNA level alterations in lung adenocarcinoma A number of studies have been done to identify gene dosage alterations in lung cancer and in lung adenocarcinoma specifically. These studies were done on a number of different array 11
platforms, with one of the latest studies done using Affymetrix SNP arrays. One of the benefits of Affymetrix SNP arrays is the ability to simultaneously detect changes in gene dosage as well as allelic imbalance. Allelic imbalance, though should be determined using a patient matched non-malignant sample as a control, has also been determined using a pool of unmatched non- malignant samples. While the ability to detect imbalance using unmatched control samples is important when matched control samples are not available, this may falsely score regions as imbalanced but in fact are not, and vice versa. In addition, samples in these different studies were typically not microdissected and thus, tumor cell purity in the samples would be variable. Thus, those samples with low tumor cell content would make it difficult to detect genetic alterations. Moreover, there has been a recent drastic increase in resolution with the newest SNP arrays, with the ability to measure over 4X as many SNPs and over 8X as many spots for gene dosage. Chapter 4 discusses the application of a new SNP array technology to microdissected lung adenocarcinoma specimens with the goal of identifying genetic alterations at the highest resolution currently available. Aim 4: Application of an integrative approach to clinical lung adenocarcinoma specimens With the approach and necessary tools developed and now demonstrated to be beneficial using a model dataset, chapter 5 discusses the application of the integrative approach to lung adenocarcinoma specimens. While the published chapter provides an overview of cancer genome and epigenome landscapes, sections 5.4.3 to 5.4.4 present some of the quantitative and qualitative benefits of integrative analysis specific to the analysis of a lung adenocarcinoma dataset. In addition, sections 5.4.5 to 5.4.6 discusses key findings in terms of genes and pathways that were identified from this integrative analysis. 12
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: (B) By using an integrative approac
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
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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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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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Chapter 2 - University of British Columbia

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