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Comprehensive Molecular Profiling of Gastrointestinal Cancers through Multi-omic Approach

  • 김다은 (아주대학교 일반대학원)

초록/요약

The multi-omic approach integrates various types of omics data, such as genomics, and transcriptomics to provide a comprehensive understanding of cancer biology. In this study, I employed multi-omic techniques to profile gastrointestinal tumor samples, offering an in-depth view of the genetic and molecular landscape and identifying novel biomarkers and potential therapeutic targets. Focusing on pancreatic ductal adenocarcinoma (PDAC) and colorectal cancer (CRC), both formidable malignancies, we conducted comprehensive profiling utilizing the multi-omic approach. Genomic analysis revealed that KRAS mutations play a crucial role in tumor initiation and serve as significant predictors of prognosis and recurrence timing. Molecular subtyping, leveraging transcriptomic data, disclosed the adverse clinical outcomes of the squamous subtype. Through cell-type deconvolution analysis, it was unraveled that the identification of the exocrine-like PDAC subtype was distinctly characterized by the presence of acinar cells. Single-cell RNA-seq (scRNA-seq) further analyzed the tumor microenvironment, identifying distinct cancer-associated fibroblast (CAF) subtypes in CRC. From scRNA-seq data, ADAM12 emerged as a marker for targeting CAFs and predicting fibroblast abundance in tumors. Collectively, the integration of omics data enabled the development of a creative feature-based prognostic prediction system and the identification of key tumor markers. Keywords: Multi-omic, Pancreatic ductal adenocarcinoma, Colorectal cancer, KRAS mutant dosage, Cancer-associated fibroblast marker

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목차

I. Introduction 1
A. Gastrointestinal cancers 2
1. Pancreatic ductal adenocarcinoma (PDAC) 2
2. Colorectal cancer (CRC) 2
B. Comprehensive cancer profiling 3
1. Multi-omics cancer profiling 3
2. Single-cell RNA-sequencing (scRNA-seq) 5
3. Cancer-associated fibroblasts 7
C. Translating multi-omics findings into clinical practice · 9
D. The aims of the study 10
II. Integrative genomic and transcriptomic analysis for patients with pancreatic ductal adenocarcinoma 11
A. Introduction 12
B. Materials & Methods 13
1. Pancreatic ductal adenocarcinoma patient cohort 13
2. Clinical data collection 13
3. Generation of whole exome sequencing data 14
4. Somatic variant calling analysis 14
5. Identification of significantly mutated genes 15
6. Copy number variation (CNV) analysis 15
7. Generation of KRAS-targeted sequencing data 16
8. Generation and processing of RNA sequencing data 16
9. Estimation of tumor cellularity 17
10. Bailey subtyping 17
11. Processing of single-cell RNA-seq data 18
12. Cell-type deconvolution analysis 19
13. Statistical analysis 19
C. Results 22
1. Landscape of genomic alterations in PDAC 22
2. Transcriptome profiling of PDAC 30
3. Deconvolution for inferring cell type composition in bulk RNA-seq data 32
D. Discussion 39
III. Multi-omic quantification of KRAS mutant dosage improves preoperative prediction of survival and recurrence in patients with pancreatic ductal adenocarcinoma 41
A. Introduction 42
B. Materials & Methods 44
1. Pancreatic ductal adenocarcinoma patient cohort 44
2. Clinical data collection 44
3. Omics data generation and processing 44
4. Identification of significantly mutated genes 45
5. KRAS mutant dosage calculation 45
6. Network analysis 45
7. Statistical analysis 46
C. Results 47
1. Clinical significance of KRAS mutant levels in PDAC 47
2. Association of KRAS mutant dosage and tumor progression 55
3. Development of a scoring system for predicting survival based on KRAS G12 mutant dosage and clinical variables 62
D. Discussion 73
IV. Single-cell profiling of colorectal cancer identified ADAM12 high cancer-associated fibroblasts associated with clinical outcomes 75
A. Introduction 76
B. Materials & Methods 77
1. Processing of single-cell RNA-seq data 77
2. Fibroblasts sub-clustering 77
3. Colorectal cancer patient cohort 78
4. Clinical data collection 78
5. Generation and processing of RNA sequencing data 79
6. Differentially expressed genes (DEG) analysis 79
7. Statistical analysis 80
C. Results 81
1. Profiling the CRC environment through single-cell transcriptome analysis 81
2. Subclustering and profiling CAFs 84
3. Signature genes of CAF subtypes 91
4. Discovery of novel CAFs targeting genes through integration of omics data and clinical information 98
5. Validation of ADAM12 as a novel marker for CAFs and its clinical implications 109
D. Discussion 120
V. References 124
VI. 국문요약 136

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