Genome and transcriptome analysis for the process of cancer progression

Cancer is known as one of the leading causes of death in the world. Recent years have witnessed substantial progress in understanding tumor heterogeneity and the process of tumor progression; however, the entire step-wise process of the transition of tumors from benign to metastatic state remains poorly understood. In this dissertation, I conducted genomeand transcriptome-sequencing analyses for the tumorigenesis process to reveal the entire mechanisms of the tumor progression. In the genome-sequencing study, I performed a prospective analysis by employing an experimental carcinogenesis mouse model to systematically understand the evolutionary process of tumors. Collaborators surgically collected a part of a lesion of each tumor and followed the progression of these tumors in vivo over time. I conducted the comparative time-series analysis of the genomes of tumors with different fates, i.e., those that eventually metastasized and regressed. The results suggested that these tumors acquired and inherited different mutations. Despite the occurrence of an intra-tumor selection event for malignant alteration during the transformation from earlyto late-stage papilloma, the fate determination of tumors might be determined at an even earlier stage. While the genome-sequencing enables us to analyze the genomic mutations, transcriptome-sequencing technology is widely used to comprehensively detect and quantify cellular gene expression. Numerous analytical methods have been proposed for identifying differentially expressed genes (DEGs) between paired samples such as tumor and control specimens, but few studies have reported methods for analyzing differential expression under multiple conditions as my research target, the step-wise process of the tumor progression. In the transcriptome-sequencing study, I propose a novel method, DEclust, for differential expression analysis among more than two matched samples from distinct tissues or conditions. As compared to conventional clustering methods, DEclust more accurately extracts statistically significant gene clusters from multi-conditional transcriptome data. As a result of applying my method to the cancer transcriptome analysis, DEclust extracted 16 gene clusters whose expression patterns were discriminative in the process of the tumor progression; moreover, the genes which were belonging to each of these clusters were significantly enriched for biological functions. Through the genome and transcriptome studies, I gave insights of the evolutional process in the tumors over time and the gene clusters whose expression patterns were discriminative in the process of the tumor progression. Examples of the research that directly analyzes the process of tumor progression are rare; hence, my results are meaningful and I consider that it is necessary to advance such a direct approach to reveal the entire process of cancer progression.