Associate Professor Ayako SUZUKI
E-mail: asuzuki{at}edu.k.u-tokyo.ac.jp>
[Keywords] Cancer genomics, Multi-omics analysis, High-throughput sequencing
Recent progress of sequencing technologies has enable us to analyze cancer genomes at various angles of genome, epigenome and transcriptome layers. We conduct high-throughput sequencing analysis and data analysis to understand multi-omics features and vulnerabilities of cancers. We also use newly available technologies such as long read, single cell and spatial omics sequencing.
In cancer genomes, various types of genomics mutations, such as point mutations, copy number aberrations and structural variants, are detected. These mutations are potential targets of therapeutic strategies of cancers. However, precise identification and characterization of cancer mutations is still difficult because they occasionally locate in non-coding and repetitive regions of the human genome. By using long read and short read sequencing technologies, we attempt to understand comprehensive structures of cancer genomes and their phase information. Further, we analyze association between genome and different omics layers, such as epigenome, transcriptome and phenotypic consequences.
We conduct multi-omics sequencing analysis of cancer cell lines as a model to classify cancer cells and identify vulnerabilities of cancer cells at a system level. We especially focus on genome, epigenome and transcriptome layers and conduct sequencing analysis using high-throughput sequencing technologies. We integrate the obtained data by computational analysis to characterize omics features of cancer cells. We finally apply the results to clinical samples.
In cancer tissues, there are various types of cells including fibroblasts, immune cells and endothelial cells in addition to tumor cells and they construct complicated microenvironment. Furthermore, genomic statuses of tumor cells are diverse because of mutation accumulation during cancer progression. Such intra-tumor heterogeneity and tumor microenvironment may be associated with treatment resistance, metastasis and relapse of cancers. To understand complicated characteristics of cancer tissues, we also focus on single cell and spatial omics features of cancers in addition to multi-omics statuses.
To achieve these analysis, we need to conduct both experimental and computational analysis covering from basic genomics to cancer biology and clinical oncology.
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