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An integrated clinical and genomic information system for cancer precision medicine : 암 정밀의학 구현을 위한 NGS 데이터 분석 기반 임상/유전체 통합 정보 시스템

DC Field Value Language
dc.contributor.advisor김선-
dc.contributor.author장영준-
dc.date.accessioned2018-11-12T00:53:40Z-
dc.date.available2018-11-12T00:53:40Z-
dc.date.issued2018-08-
dc.identifier.other000000151919-
dc.identifier.urihttps://hdl.handle.net/10371/142992-
dc.description학위논문 (박사)-- 서울대학교 대학원 : 자연과학대학 협동과정 생물정보학전공, 2018. 8. 김선.-
dc.description.abstractThe increasing affordability of next-generation sequencing (NGS) has enabled the implementation of genomically-informed personalized cancer therapy as a path to precision oncology. However, the complex nature of genomic information renders it challenging for clinicians to interpret the patient's genomic alterations and select the optimum approved or investigational therapy. Thus, an elaborate and practical information system is urgently needed to support clinical decision and to rapidly test clinical hypotheses.

Molecular alterations in a set of genes, including somatic mutations, copy number variations, and gene expression levels, represent a novel signature in targeted cancer therapy		-
dc.description.tableofcontentsAbstract 1

List of Figures 7

List of Tables 10

Chapter 1. Introduction 11

1.1. Background 14

1.1.1. Cancer precision medicine 14

1.1.2. Bio-markers 16

1.1.3. Pateint stratification 19

1.1.4. Clinical decision support system 21

1.2. Requirements for implementation of cancer precision medicine 24

1.3. System overview 30

1.4. Outline of the thesis 32

Chapter 2. Visual evaluation of bio-markers using omics data based on patient stratification and survival analysis (CaPSSA) 33

2.1. Background 33

2.2. Methods 36

2.2.1. Cancer omics and clinical data 36

2.2.2. Patient stratification 38

2.2.2.1. Patterns of genomic alterations 38

2.2.2.2. Risk estimation based on gene expression 39

2.2.2.3. Gene expression-based hierarchical clustering 40

2.3. Results 42

2.3.1. Exclusivity and co-occurrence of simple nucleotide alterations (SNV) and copy number variations (CNV) 42

2.3.2. Risk groups stratified using gene expression data 45

2.3.3. Hierarchical clustering using gene expression data 49

2.4. Case studies 51

2.4.1. KRAS-mutant lung adenocarcinoma dominated by co-occurring genetic events of CDKN2A/B deletions coupled with low expression of NKX2-1 transcription fator 51

2.4.2. Co-occurrences and mutual exclusiveness of TP53 alterations with amplifications of CCNE1 and SKP2 oncogenes 57

2.5. Comparison with existing tools 63

Chapter 3. An integrated clinical and genomic information system for cancer precision medicine (CGIS) 67

3.1. Background 67

3.2. Implementation 69

3.2.1. Overview of system and features 69

3.2.2. BioDataBank 72

3.2.3. Cohort database and selection of background patients 77

3.3. Results 79

3.3.1. Variant annotation and druggability 79

3.3.2. Patient stratification and survival analysis 85

3.3.2.1. Mutual exclusivity among alterations driving signaling networks 85

3.3.2.2. Patient classification by gene expression signatures 87

3.3.3. Altered key pathways 89

3.4. Comparison with existing tools for clinical decision support 91

Chapter 4. Conclusion 93

4.1. Availability 95

Bibliography 96

국문초록 103		-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subject.ddc574.8732-
dc.titleAn integrated clinical and genomic information system for cancer precision medicine-
dc.title.alternative암 정밀의학 구현을 위한 NGS 데이터 분석 기반 임상/유전체 통합 정보 시스템-
dc.typeThesis-
dc.contributor.AlternativeAuthorYeongjun Jang-
dc.description.degreeDoctor-
dc.contributor.affiliation자연과학대학 협동과정 생물정보학전공-
dc.date.awarded2018-08-
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