Jump to Main Contents
ncc en
HOME > Publication & Reports > Annual Report 2019 > Research Institute

Annual Report 2019

Department of Bioinformatics

Mamoru Kato, Jo Nishino, Daichi Narushima, Eisaku Furukawa, Asmaa M. Elzawahry, Iurii Nagornov, Hanako Ono, Momoko Nagai, Ritsuko Onuki, Nao Futagami

Introduction

 The Department of Bioinformatics mainly focuses on the following three areas: (1) researching bioinformatics and its application to cancer genome medicine, (2) building new theories on cancer biology through big-data analysis and computational methods, and (3) providing bioinformatics support for experimental biology groups at the National Cancer Center (NCC) and other research institutions.

Research activities

1. Development of bioinformatics technology for cancer genome medicine

 We previously developed a clinical sequencing caller, cisCall, which has been used in the NCC Oncopanel. cisCall is optimized for formalin-fixed paraffin-embedded (FFPE) samples obtained from clinical settings of cancer genome medicine and can detect DNA alterations such as single nucleotide variants (SNVs), indels, gene fusions, copy number alterations (CNAs), and known important aberrations. This fiscal year, we improved cisCall as follows:

1) For an application for partial changes to the NCC Oncopanel medical device, we improved the algorithm to detect gene fusion, SNVs/indels and CNAs, and implemented the structural variant (SV) detection algorithm.

2) We released an updated version of cisCall (https://www.ciscall.org/).

3) For further improvement, we are developing a deep learning module of cisCall. We validated the learning accuracy using 187 samples from a clinical sequencing project.

2. Liquid biopsy

 We investigated how cisCall could be applied to detect DNA alterations in cfDNA samples using commercially available products. We confirmed that our tool can be put to practical use if there is enough read depth.

3. Detection of DNA adducts

 We developed a deep learning-based computational tool to detect DNA adducts from time-series current values obtained from the Nanopore sequencer, which is a third-generation single-molecule sequencer. Our computational tool is fast and easy to use, which enables us to directly detect DNA alterations associated with DNA adducts and to reveal relationships between DNA adducts and cancer mutational signatures.

4. Modeling of cancer-cell evolution and intra-tumor heterogeneity

 We developed a mathematical model of cancer-cell evolution and intra-tumor heterogeneity (ITH). We also developed a simulator called tugHall (tumor gene-Hallmark) based on this mathematical model. In tugHall, known cancer hallmarks are set as variables and cancer-cells stochastically transit mutational states. By simulating individual tumor cell behavior, we can better understand cancer cell evolution and generation of ITH. This fiscal year, we published a tugHall paper and released a tugHall program and a website for it.

5. Bioinformatics analysis support

 We provided bioinformatics analysis support for projects as follows:

  • Comprehensive genome analysis of intractable digestive system cancer.
  • SNV/indel and CNA analyses of 127 cases of esophageal cancer from the Japan Clinical Oncology Group (JCOG) including pathway and GO analyses using the tumor mutations.
  • Multi-omics analysis of data released from working groups in Pan Cancer Analysis of Whole Genomes (PCAWG) and comparative analysis of PCAWG data and the data released from The Cancer Genome Atlas (TCGA).
  • Elucidating the mechanisms of carcinogenesis induced by environmental factors (mutational signature analysis of rats and bacteria that were exposed to carcinogens).
  • Single-cell RNA sequencing analysis of central nervous system germ cell tumor (unsupervised clustering using UMAP and identifying the lineage of tumor cell development using pseudo-time course analysis)
  • Gene expression analysis of glioblastoma (classification of samples using NanoString data)

Education

 We have provided bioinformatics education and support for research groups in the NCC and other institutions.

Future prospects

 We will continue to develop core bioinformatics technologies for cancer genome medicine and proceed toward the realization of precision medicine by transferring the technologies. We will also carry out basic research. To achieve these purposes, we will utilize machine-learning and AI technologies and perform large-scale computational analysis using cloud computing platforms.

List of papers published in 2019

Journal

1. ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature, 578:82-93, 2020

2. Takai E, Maeda D, Li Z, Kudo-Asabe Y, Totoki Y, Nakamura H, Nakamura A, Nakamura R, Kirikawa M, Ito Y, Yoshida M, Inoue T, Habuchi T, Ikoma S, Katoh H, Kato M, Shibata T, Ishikawa S, Yachida S, Goto A. Post-mortem Plasma Cell-Free DNA Sequencing: Proof-of-Concept Study for the “Liquid Autopsy”. Sci Rep, 10:2120, 2020

3. Sunami K, Ichikawa H, Kubo T, Kato M, Fujiwara Y, Shimomura A, Koyama T, Kakishima H, Kitami M, Matsushita H, Furukawa E, Narushima D, Nagai M, Taniguchi H, Motoi N, Sekine S, Maeshima A, Mori T, Watanabe R, Yoshida M, Yoshida A, Yoshida H, Satomi K, Sukeda A, Hashimoto T, Shimizu T, Iwasa S, Yonemori K, Kato K, Morizane C, Ogawa C, Tanabe N, Sugano K, Hiraoka N, Tamura K, Yoshida T, Fujiwara Y, Ochiai A, Yamamoto N, Kohno T. Feasibility and utility of a panel testing for 114 cancer-associated genes in a clinical setting: A hospital-based study. Cancer Sci, 110:1480-1490, 2019

4. Totsuka Y, Lin Y, He Y, Ishino K, Sato H, Kato M, Nagai M, Elzawahry A, Totoki Y, Nakamura H, Hosoda F, Shibata T, Matsuda T, Matsushima Y, Song G, Meng F, Li D, Liu J, Qiao Y, Wei W, Inoue M, Kikuchi S, Nakagama H, Shan B. DNA Adductome Analysis Identifies N-Nitrosopiperidine Involved in the Etiology of Esophageal Cancer in Cixian, China. Chem Res Toxicol, 32:1515-1527, 2019