Cancer heterogeneity and therapeutic resistance revealed by single cell exome and transcriptome sequencing

다국어 초록 (Multilingual Abstract)

Cancer tissue heterogeneity, as well as the communications between cancer cellular components, may exert critical roles in cancer origination and development, finally influencing the effects of anti-cancer therapeutics. In this study, I established a manual single-cell seperation system, MDA (multiple displacement amplification) based single-cell exom sequencing (scWES) method, and SMART-seq2 (switching mechanism at 5' end of the RNA transcript sequencing version 2) based single-cell transcriptome sequencing (scRNA-seq) method. And I conducted these approaches on cancer cell lines and clinical cancer tissues to charecterize the cancer heterogeneity and its consequence on various therapies. In clinical colorectal and esophageal neoplasma/cancer tissues, with scWES, I found that all the neoplasma/cancer tissues were of monoclonal origin, and each showed a specific spectrum of heterogeneous somatic mutations. Novel driver mutations were also identified that related with the development of colorectal adenoma and cancer, particularly OR1B1 (GPCR signaling pathway) for colorectal adenoma evolution, LAMA1 (PI3K-Akt signaling pathway) and ADCY3 (FGFR signaling pathway) for colorectal cancer evolution. By comparing the cancer tissues before and after irradiation in esophageal squamous cell carcinoma (ESCC) at single-cell level, I identified 74 radiosensitive and radioresistant candidates, which decreased or increased their proportion of cells after irradiation, respectively. Further experimental validations showed that knock-down of the recurrent candidate genes, AHNAK2, EVPL and LAMA5 significantly sensitize the ESCC cells to radiotherapy. In scRNA-seq analyses of esophageal cancer cell lines, I found the dynamics and heterogeneities, which formed the intrinsic and acquired resistance during the chemotherapy with paclitaxel. The expression level of KRT19 was identified that is responsible for the intrinsic paclitaxel resistance, while high expressed proteasomes and low expressed HIF-1 signaling genes showed significant corelations with acquired paclitaxel resistance. Finally, by using scRNA-seq, I revealed the characteristics of various cell types and identified unique gene signatures and crucial cancer-related signaling pathways from both carcinoma and non-carcinoma cells in the esophageal carcinoma, including the ESCC and esophageal adenocarcinoma (EAC) tissues. In high stemness level of cancer cells, signaling pathway of DNA damage repair was significantly enriched in ESCC, while cell cycle signaling pathway was enriched in EAC. Furthermore, NOTCH and WNT signaling pathways were also investigated at single-cell level in ESCC and EAC. Different WNT activation axis were identified in ESCC (WNT4/WNT2b – FZD3/FZD6 – DVLs) other than in EAC (WNT10A – FZD5 – DVLs), while NOTCH signaling was specifically activated in ESCC, but not in EAC. Collectively, single-cell sequencing technologies have been the promising systematic and comprehensive approaches for analyzing the intratumoral heterogeneity, and reconstructing cancer clonality or subpopulations with different treatments, which provides more therapeutic targets for the future drug deveolpment in cancer presicion medicine.

목차 (Table of Contents)

• TABLE OF CONTENTS
• I. GENERAL ABSTRACT 1
• II. GENERAL INTRODUCTION 3
• 2-1 Next-generation sequencing and single-cell sequencing 3
• 2-2 Single-cell isolation 5
• 2-3 Single-cell genome sequencing 6
• 2-4 Single-cell transcriptome sequencing 7
• Chapter 1. Evolution and heterogeneity of non-hereditary colorectal cancer revealed by single-cell exome sequencing 10
• 1. Abstract 11
• 2. Introduction 12
• 3. Materials and methods 14
• 3-1 Patients and samples 14
• 3-2 Isolation and amplification of single-cell genomes 14
• 3-3 Chromosome qPCR for WGA amplification 15
• 3-4 Single-cell exome capture and Next-generation Illumina sequencing 15
• 3-5 Sequence slignment and data processing, and databases annotation 16
• 3-6 Single nucleotide variant detection and heatmap clustering 17
• 3-7 Somatic CNA (sCNA) and aneuploidy detection in single cells and bulk samples 17
• 4. RESULTS 21
• 4-1 Identify cancer-related somatic mutations in CRC patients by bulk WES 21
• 4-2 Intratumor heterogeneity of colorectal polyps and CRC revealed by scWES 27
• 4-3 Monoclonal origin of the adenomatous polyps and CRC 38
• 4-4 CRC development with more subclonal somatic mutations accumulated specifically in GPCR and PI3K-Akt signaling pathways 41
• 5. Discussion 45
• Chapter 2. Single-cell delineation of radiation response genes in esophageal cancer 49
• 1. Abstract 50
• 2. Introduction 51
• 3. Materials and methods 53
• 3-1 Study approval and patients’ samples collection 53
• 3-2 DNA Extraction for bulk cell analysis 54
• 3-3 Cell culture and transfection 54
• 3-4 Quantitative real time PCR assay (qRT-PCR) 55
• 3-5 MDA for scWES 55
• 3-6 Exome capture, library preparation and sequencing 56
• 3-7 Read mapping and variation detection 56
• 3-8 Driver mutation analysis 58
• 3-9 Radiosensitive and radioresistant gene candidates 59
• 3-10 Signaling pathway analysis 59
• 3-11 Clonal evolution 60
• 3-12 Irradiation treatment of cell lines 60
• 3-13 Immunofluorescence assay of γ-H2AX expression 60
• 3-14 Colony formation assay 61
• 3-15 Statistics in the experiments 61
• 4. Results 63
• 4-1 Characterization of somatic mutations in distinct ESCC tissues 63
• 4-2 Distinct cancer heterogenity and evolution of ESCC at single-cell level 69
• 4-3 Mutational spectrums and clonal evolution of dysplastic vs. cancer cells 73
• 4-4 Identifying radioresistant and radiosensitive candidate in pre- and post-IR cancer cells. 78
• 4-5 Clonal response to radiation 83
• 4-6 Bulk WES replication cohort 90
• 4-7 Validation of radioresistant AHNAK2, EVPL and LAMA5 91
• 5. Discussion 94
• Chapter 3. Single-cell transcriptome analyses reveal molecular signals to intrinsic and acquired paclitaxel resistance in esophageal squamous cancer cells 96
• 1. Abstract 97
• 2. Introduction 98
• 3. Materials and methods 101
• 3-1 Cell culture and paclitaxel treatment. 101
• 3-2 Xenograft experiments. 101
• 3-3 Antibodies and chemical inhibitors. 102
• 3-4 Cell viability and drug effect assay. 102
• 3-5 Effect of drug on colony formation. 103
• 3-6 Fluorescence activated cell sorting (FACS). 103
• 3-7 Western blotting analysis. 104
• 3-8 RNA isolation for population RNA-seq. 104
• 3-9 Isolation of single cells and cDNA amplification. 105
• 3-10 RNA-seq library preparation and sequencing. 105
• 3-11 RNA-seq data preprocessing, alignment, and quantification. 106
• 3-12 Single-cell clustering and sub-population (sub-cluster) identification. 107
• 3-13 WGCNA analysis. 108
• 4. Results 109
• 4-1 Induction of paclitaxel resistance of ESCC cell lines and their population-cell level transcriptomic characteristics. 109
• 4-2 Paclitaxel resistance analysis at single-cell level. 113
• 4-3 Gene-network modules underlying diverse paclitaxel resistance identified by WGCNAs. 123
• 4-4 Intrinsic paclitaxel resistance revealed by single-cell RNA-seq in ESCC. 129
• 4-5 Single-cell RNA-seq revealed that low HIF-1 and high proteasome expression were critical for acquired paclitaxel resistance in ESCC. 135
• 4-6 Proteasome activity inhibitor carfilzomib sensitize chemotherapeutic effects of paclitaxel. 137
• 5. Discussion 143
• Chapter 4. Single-cell RNA-seq reveals intratumoral heterogeneity and gene signatures in esophageal carcinoma 148
• 1. Abstract 149
• 2. Introduction 150
• 3. Materials and methods 152
• 3-1 Isolation of single cells and cDNA amplification. 152
• 3-2 RNA-seq library preparation and sequencing 152
• 3-3 RNA-seq data preprocessing, alignment, and quantification 153
• 3-4 Weighted gene co-expression network analysis 153
• 3-5 Immunohistochemical staining 154
• 3-6 Opal 4-Color manual immunofluorescence staining 155
• 4. Results 156
• 4-1 Pathological characterization for patient-derived EAC and ESCC cancer samples. 156
• 4-2 Single-cell transcriptome profiles distinguishing cell types from each other in cancer tissues 160
• 4-3 Single-cell RNA–seq data identify specific markers for EAC and ESCC 168
• 4-4 Single-cell RNA–seq data analysis highlights heterogeneity in NOTCH and WNT signaling pathways between EAC and ESCC 181
• 4-5 Heterogeneity of cancer stemness in single carcinoma cells of EAC and ESCC 194
• 5. Discussion 198
• CONCLUDING REMARKS 202
• REFERENCES 207