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Annual Report 2020

Division of Bioinformatics

Mamoru Kato, Jo Nishino, Daichi Narushima, Eisaku Furukawa, Iurii Nagornov, Momoko Nagai, Ritsuko Onuki, Yoko Iwahara

Introduction

 The activities of the Division of Bioinformatics are as follows: (1) developing new bioinformatics methods and data analysis for cancer research and medicine, (2) building new mathematical theories in biology through data analysis and computational approaches, and (3) performing bioinformatics analysis for experimental groups at this center and other research institutions.

Research activities

1.  Development of bioinformatics technologies for cancer genome medicine

 We previously developed a clinical sequencing caller, cisCall, which has been used in the NCC Oncopanel. By developing and testing new methods, we improved cisCall this fiscal year as follows:

  • For the NCC Oncopanel, improvement of mutation detection and copy number analysis methods, and implementation of structural variant (SV) detection. These improvements were approved by the government as Partial Changes in the Medical Device Manufacturing Approval.
  • Version update of the public version of cisCall.
  • Performance evaluation of the mutation detection method based on deep learning.
  • Performance evaluation of the mutation detection method with molecular barcode sequencing.
  • Development of the pipeline for the whole genome analysis.
  • Design of cloud infrastructure for accelerating calling calculation.

2.  Development of numerical simulation-based personalized medicine

 We have been developing tugHall, a simulator of cancer cell evolution in the context of personalized medicine. This fiscal year, we developed a new tugHall version that can accelerate the calculations and handles a huge number of tumor cells. We have also published datasets containing ITH information from parallel computing by tugHall.

3.  Bioinformatics analysis support

  • Biomarker analysis using clinical trial data for glioblastoma.
  • Exome sequencing analysis of chemical-induced lung cancer using a mouse model.
  • SNV/indel and CNA analyses of esophageal cancer patients from the Japan Clinical Oncology Group (JCOG), as well as pathway and GO analyses with the tumor mutations.
  • Multiomics analysis of data released from working groups in the Pan Cancer Analysis of Whole Genomes (PCAWG) and comparative analysis of PCAWG data and the data released from The Cancer Genome Atlas (TCGA).
  • ICGC clinico-genomic data analysis in stomach cancer.
  • Mutational signature analysis of rat and bacteria exposed to carcinogens.
  • Whole genome sequencing analysis of germinoma.
  • Gene expression analysis of mouse colitis-derived models.
  • Searching for potentially translated sequences in RNA for various species.
  • Building a collaborative system and developing analysis methods with the Biostatistics Division of the Center for Research Administration and Support toward clinical research involving omics 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 conduct basic research. To achieve these goals, we will use machine-learning and AI technologies and perform large-scale computational analysis using cloud computing platforms.

List of papers published in 2020

Journal

1. Nagornov IS, Nishino J, Kato M. Dataset of tugHall simulations of cell evolution for colorectal cancer. Data Brief, 34:106719, 2021

2. Shiokawa D, Sakai H, Ohata H, Miyazaki T, Kanda Y, Sekine S, Narushima D, Hosokawa M, Kato M, Suzuki Y, Takeyama H, Kambara H, Nakagama H, Okamoto K. Slow-Cycling Cancer Stem Cells Regulate Progression and Chemoresistance in Colon Cancer. Cancer Res, 80:4451-4464, 2020

3. Totsuka Y, Maesako Y, Ono H, Nagai M, Kato M, Gi M, Wanibuchi H, Fukushima S, Shiizaki K, Nakagama H. Comprehensive analysis of DNA adducts (DNA adductome analysis) in the liver of rats treated with 1,4-dioxane. Proc Jpn Acad Ser B Phys Biol Sci, 96:180-187, 2020

4. Nagornov IS, Kato M. tugHall: a simulator of cancer-cell evolution based on the hallmarks of cancer and tumor-related genes. Bioinformatics, 36:3597-3599, 2020

5. Jiao W, Atwal G, Polak P, Karlic R, Cuppen E, Danyi A, de Ridder J, van Herpen C, Lolkema MP, Steeghs N, Getz G, Morris Q, Stein LD. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nat Commun, 11:728, 2020

Book

1. Iurii N, Nishino J, Kato M. tugHall: a tool to reproduce darwinian evolution of cancer cells for simulation-based personalized medicine. In: Bebis G, Alekseyev M, Cho H, Gevertz J, Rodriguez Martinez M (ed), Mathematical and Computational Oncology, Switzerland, Springer, pp 71-76, 2020