Research Projects

List of Projects

●Bioinformatics for cancer genome medicine
●Numerical simulation for predictive cancer genome medicine
●Application of quantum computers to next-generation cancer genome medicine
●Search for biomarkers and development of bioinformatics search methods
●Bioinformatics analysis for other laboratories

Bioinformatics for cancer genome medicine

We research a variety of bioinformatics technologies for cancer genome medicine.

Cancer genome medicine by comprehensive genomic profiling utilizes massive data generated by next-generation sequencer to examine several types (SNVs/indels, copy number alterations, and fusions) of aberrations in more than 100 genes in a single test. Then, moleculary targeted drugs and immume checkpoint inhibitors are prescribed to target detected gene alterations for cancer cure. 

Bioinfomatics is needed to process the massive data to detect gene alterations. We developed cisCall, a bioinformatics tool that accurately detects gene alterations for clinical use. We also developed cisInter, which automatically suggests molecularly targeted drugs appropriate to detected gene aberrations and generates a report document listing detected aberrations and drugs. A product version of cisCall and cisInter was implemented into the OncoGuide™ NCC Oncopanel System and this system was approved by the government for the first time as a Medical Device in cancer genome medicine in Japan (December, 2018). Further, the system was approved for the first time to be usable under the National Health Insurance in cancer genome medicine in Japan (June, 2019). cisCall/cisInter is the first bioinformatics tool clinically applied under the National Health Insurance in Japan for cancer genome medicine.

Building on these achievements, we now research the followings.

• Program to detect DNA alterations for FFPE whole-genome sequencing
     Current cancer genome medicine detects hundreds of gene alterations, but the ultimate expansion is to all genes―and beyond, to whole genome including noncoding regions. We are developing a whole-genomic alteration-calling program that processes whole-genome sequencing data to detect DNA alterations in FFPE-derived DNA used in routine clinical practice.

• Deep learning filter for data noise
     In cisCall, filters to remove data noise specific to FFPE samples were carefully hand‑designed. But, to automate their design and improve accuracy, we are developing deep learning–based noise filters.

References:

• Mamoru Kato, Hiromi Nakamura, Momoko Nagai, Takashi Kubo, Asmaa Elzawahry, Yasushi Totoki, Yuko Tanabe, Eisaku Furukawa, Joe Miyamoto, Hiromi Sakamoto, Shingo Matsumoto, Kuniko Sunami, Yasuhito Arai, Yutaka Suzuki, Teruhiko Yoshida, Katsuya Tsuchihara, Kenji Tamura, Noboru Yamamoto, Hitoshi Ichikawa, Takashi Kohno, and Tatsuhiro Shibata
  A computational tool to detect DNA alterations tailored to formalin-fixed paraffin-embedded samples in cancer clinical sequencing
  Genome Medicine, 2018, 10, 44.1-44.11
     This is the paper on cisCall.
     cisCall is equipped with algorithms that utilize robust methods (non-parametric statistics, computer-intensive statistics, use of internal controls based on random sampling of targeted data) enabling precise detection without disturbance by noise in next-generation sequencing data on DNA derived from FFPE samples, which are daily used in hospitals. It detects all the aberration types necessary for cancer genome medicine―not only short variants (SNVs/indels), but also copy-number alterations and gene fusions. 
  
  
• Takashi Kubo, Kuniko Sunami, Takafumi Koyama, Mayuko Kitami, Yasuhiro Fujiwara, Shunsuke Kondo, Kan Yonemori, Emi Noguchi, Chigusa Morizane, Yasushi Goto, Aiko Maejima, Satoru Iwasa, Tetsuya Hamaguchi, Akira Kawai, Kenjiro Namikawa, Ayumu Arakawa, Masanaka Sugiyama, Makoto Ohno, Teruhiko Yoshida, Nobuyoshi Hiraoka, Akihiko Yoshida, Masayuki Yoshida, Takahiro Nishino, Eiji Furukawa, Daichi Narushima, Momoko Nagai, Mamoru Kato, Hiroshi Ichikawa, Yasuhiro Fujiwara, Takashi Kohno, Noboru Yamamoto
  The impact of rare cancer and early-line treatments on the benefit of comprehensive genome profiling-based precision oncology
  ESMO Open,2024 9(4), 102981
• Kuniko Sunami, Hitoshi Ichikawa, Takashi Kubo, Mamoru Kato, Yutaka Fujiwara, Akihiko Shimomura, Takafumi Koyama, Hiroki Kakishima, Mayuko Kitami, Hiromichi Matsushita, Eisaku Furukawa, Daichi Narushima, Momoko Nagai, Hirokazu Taniguchi, Noriko Motoi, Shigeaki Sekine, Akiko Maeshima, Taisuke Mori, Reiko Watanabe, Masayuki Yoshida, Akihiko Yoshida, Hiroshi Yoshida, Kaishi Satomi, Aoi Sukeda, Taiki Hashimoto, Toshio Shimizu, Satoru Iwasa, Kan Yonemori, Ken Kato, Chigusa Morizane, Chitose Ogawa, Noriko Tanabe, Kokichi Sugano, Nobuyoshi Hiraoka, Kenji Tamura, Teruhiko Yoshida, Yasuhiro Fujiwara, Atsushi Ochiai, Noboru Yamamoto, Takashi Kohno
  Feasibility and utility of a panel testing for 114 cancer-associated genes in a clinical setting: A hospital-based study
  Cancer Science, 2019, 110, 1480.1-11
• Yuko Tanabe, Hitoshi Ichikawa, Takashi Kohno, Hiroshi Yoshida, Takashi Kubo, Mamoru Kato, Satoru Iwasa, Atsushi Ochiai, Noboru Yamamoto, Yasuhiro Fujiwara, and Kenji Tamura
  Comprehensive screening of target molecules by next-generation sequencing in patients with malignant solid tumors: guiding entry into phase I clinical trials
  Molecular Cancer, 2016, 15, 73-77
     A pioneering research project called TOP-GEAR was launched at National Cancer Center Japan in 2012 to realize cancer genome medicine. These three papers reported its major scientific results. In these papers, cisCall was used for actual clinical samples to evaluate clinical application.
  
  
• Kota Itahashi, Shunsuke Kondo, Takashi Kubo, Yutaka Fujiwara, Mamoru Kato, Hitoshi Ichikawa, Takahiko Koyama, Reitaro Tokumasu, Jia Xu, Claudia S. Huettner, Vanessa V. Michelinim, Laxmi Parida, Takashi Kohno, and Noboru Yamamoto
  Evaluating clinical genome sequence analysis by Watson for Genomics
  Frontiers in Medicine, 2018, 5, 305.1-10
    This is a study that applied IBM Watson, the hotly discussed AI at the time, to TOP-GEAR. This paper is considered the first full-fledged academic evaluation of Watson in Japan.
  
  

Practical bioinformatics for cancer genome medicine in C-CAT

Around the same time that cancer genome medicine started in Japan with the medical device into which the product version of cisCall/cisInter was implemented, our center established the Center for Cancer Genomics and Advanced Therapeutics, abbreviated in English as C-CAT. The data of cancer genomic tests performed under the National Health Insurance in Japan are all sent to C-CAT, which documents the nationwide clinical trials of matched drug therapies individually for every patient (in “C-CAT Findings”) and which sends these documents back to hospitals that initially send the data. The PI is concurrently positioned as the Chief of the Section of Genomic Data Management in C-CAT; some members of the Division of Bioinformatics are also members of the Section of Genomic Data Management. The C-CAT system is a large-scale system across Japan and a partial system related to genomic data is being developed and managed by the Section of Genomic Data Management.

For the clinical implementation of cancer genome medicine, we conduct the following research and development.

• Development of the CATS format
     C-CAT receives cancer genome data generated under the National Health Insurance from across Japan. If the data arrive in disparate formats, significant effort is wasted on ad hoc handling. To address this, we develop the CATS (CAncer genomic Test Standardized) format―a unified, convenient data format that can store, in a single file, short variants, copy-number alterations, structural variants, TMB/MSI, and other data-quality metrics and metadata. We currently update it roughly once a year.
     CATS format has been used for cancer genome data received by C-CAT in more than 110,000 cases to date (as of September 2025).

• Development of catstools
     We develop a bioinformatics tool that can manipulate cancer genome data expressed in the CATS format with simple commands. The tool performs operations such as the validation and version-upgrade of the CATS format, the aggregation of mutations in onco-plots and Circos plots, conversion to the HL7 FHIR format, variant annotation, and interconversion with the VCF format via simple subcommands. 
     Note: An older version is currently public, but we are actively developing a new version to replace it.
  

References:

• CATS format
  
  
• Takashi Kohno, Mamoru Kato, Shinji Kohsaka, Tomohisa Sudo, Ikuo Tamai, Yuichi Shiraishi, Yusuke Okuma, Daisuke Ogasawara, Tatsuya Suzuki, Teruhiko Yoshida, Hiroyuki Mano
  C-CAT: The National Datacenter for Cancer Genomic Medicine in Japan
  Cancer Discov, 2022, 12, 2509-2515
  　This paper describes an overview of C-CAT and the CATS format.

AMED Clinical Translation Research on Whole-Genome Analysis, and MHLW Whole-Genome Analysis and Related Initiatives

We participate in these whole-genome studies and initiatives, producing "standard" cancer whole-genome reports as a clinical study, and applying the insights gained to the initiatives.

Numerical simulation for predictive cancer genome medicine

Molecularly targeted drugs are prescribed to address gene alterations detected by comprehensive genomic profiling, but they are not always effective. To evaluate effective drug administration for individual patients, we are developing a computer system of numerical simulation that models cancer cells to predict the dynamics of tumor cell counts under specific dosing regimens―analogous to numerical simulation for weather prediction models the Earth’s atmosphere to forecast typhoon trajectories. 

Cancer cells are cultured in the computer with virtual genomes, reflecting both intertumoral and intratumoral heterogeneity. Inspired by the PDX (Patient-Derived Xenograft) mouse model, we refer to this as the PDV (Patient-Derived Virtual tumor) model. We aim to realize numerical simulation–based personalized cancer genome medicine and, leveraging this system, ultimately eradicate cancer cells in the body. 

References:

• Iurii Nagornov, Eisaku Furukawa, Momoko Nagai, Shigehiro Yagishita, Tatsuhiro Shibata, Mamoru Kato.
  tugMedi: simulator of cancer-cell evolution for personalized medicine based on the genomic data of patients.
  bioRxiv, 2025, doi: 10.1101/2025.06.27.661855
     We implemented the basic functions of PDV and the methods for virtual drug intervention.
     Specifically, we incorporated copy-number alterations, homologous chromosomes with dominant/recessive models, and the exon–intron structure of the human reference genome. We also developed methods for converting simulation time to real time; handling driver and passenger mutations along with an associated acceleration algorithm; ensemble prediction; mapping to drug dosing; generation of input value sets by generators; and parameter estimation using approximate Bayesian computation and Bayesian optimization. From dynamics predicted by simulations under concrete dosing regimens, it is deduced whether a patient will achieve complete response or experience relapse. In cases of relapse, the tumor size is predicted at any specified time. In addition, the dynamics of TMB/VAF―likely not previously observed―are predicted.
  
  
• Iurii S. Nagornov, Jo Nishino, Mamoru Kato.
  tugHall: A Tool to Reproduce Darwinian Evolution of Cancer Cells for Simulation-Based Personalized Medicine.  
  ISMCO 2020: Mathematical and Computational Oncology.
  Lecture Notes in Computer Science, vol 12508, 71-76.
     We implemented a clone-based agent model.
     In the first version of our simulator, cells served as agents in the agent-based model. However, when cells were too many, the computation became intractable. We solved this problem by switching from a cell-based to a clone-based model.
  
  
• Iurii S. Nagornov and Mamoru Kato
  tugHall: a simulator of cancer-cell evolution based on the hallmarks of cancer and tumor-related genes
  Bioinformatics, 2020, 36, 3597–3599.
     This was the first version of our simulator and, conceptually, the first to implement virtual drug intervention.
     Originally, we developed an agent-based branching-process simulator that links specific genes to phenotypes (the hallmarks of cancer) to reveal intratumoral heterogeneity. Leveraging this framework, we implemented virtual drug intervention by nullifying particular estimated parameters. When applied to real data from a male colorectal cancer patient in TCGA, the simulation predicted that tumor cell proliferation would be suppressed only when TP53 abnormalities were blocked―not when abnormalities in APC, KRAS, or PIK3CA were blocked. This illustrates the core idea of virtual drug intervention: molecularly targeted therapies against APC, KRAS, or PIK3CA would likely be ineffective for this patient, whereas a therapy targeting TP53 would likely be effective.
  
  
• Hanako Ono, Yasuhito Arai, Eisaku Furukawa, Daichi Narushima, Tetsuya Matsuura, Hiromi Nakamura, Daisuke Shiokawa, Momoko Nagai, Toshio Imai, Koshi Mimori, Koji Okamoto, Yoshitaka Hippo, Tatsuhiro Shibata, Mamoru Kato
  Single-cell DNA and RNA sequencing reveals the dynamics of intra-tumor heterogeneity in a colorectal cancer model.
  BMC Biology, 2021, 19, 207.1-17
     Using single-cell DNA and RNA sequencing together with an allograft mouse model, we experimentally investigated how cancer cells change dynamically when subjected to the dramatic environmental change of subcutaneous transplantation. We observed a decrease in intratumoral diversity at the DNA level but an increase at the RNA level, likely because, after selection, new transcriptional subpopulations emerged. The findings suggest that, during metastasis, a selected subset of cancer cells can colonize secondary sites through the emergence of new transcriptional subpopulations without undergoing genetic changes.
  
  
• Mamoru Kato, Daniel A. Vasco, Ryuichi Sugino, Daichi Narushima, and Alexander Krasnitz
  Sweepstake evolution revealed by population-genetic analysis of copy-number alterations in single genomes of breast cancer
  Royal Society Open Science, 2017, 4, 171060.1-171060.11
     We analyzed data from next-generation sequencing for single-cell DNA (single-cell sequencing) by combining coalescent simulations, as used in population genetics, with approximate Bayesian computation, and demonstrated that, in breast cancer, cancer cells evolve through a high-birth–high-death process akin to those observed in fish populations.
  
  

Application of quantum computers to next-generation cancer genome medicine

We have begun research on applying quantum computers to cancer genome medicine. Quantum computers, if fault-tolerant quantum computers (FTQCs) are realized, have the potential to transform society―and medicine will be no exception. Some predict that FTQCs will be achieved in the 2030s, and we are preparing for that now. At present, quantum computers require a programming paradigm entirely different from that of classical computers, making them a subject of great technical interest to us.

Search for biomarkers and development of bioinformatics search methods

We search for clinically useful biomarkers and develop computational search methods.

References:

• Mamoru Kato, Jo Nishino, Momoko Nagai, Hirofumi Rokutan, Daichi Narushima, Hanako Ono, Takanori Hasegawa, Seiya Imoto, Shigeyuki Matsui, Tatsuhiko Tsunoda, Tatsuhiro Shibata
  Comprehensive analysis of prognosis markers with molecular features derived from pan-cancer whole-genome sequence
  Human Genomics, 2025, 19, 39
    Using pan-cancer whole-genome data from the Pan-Cancer Analysis of Whole Genomes (PCAWG), a joint effort by ICGC and TCGA, we comprehensively searched for prognostic markers associated with overall survival and clarified their characteristics. We found that it is not necessary to asses a large number of molecular markers for prognosis prediction; however, different markers need to be assessed in different cancer types. We also identified DNA markers with predictive performance comparable to RNA markers, and observed that factors not previously emphasized―such as HLA haplotypes, neoantigens, and the number of structural variants (SVs)―were associated with prognosis in several cancer types.
  
  
• Jo Nishino, Fuyuki Miya, Mamoru Kato.
  Gene based Hardy–Weinberg equilibrium test using genotype count data: application to six types of cancers.
  BMC Genomics, 2025, 26, 124.1-10
  
  

Bioinformatics analysis for other laboratories

Our laboratory performs bioinformatics analyses of data generated by experimental labs as collaborative research.

If you would like analysis support, please contact the Office of Research Coordination (e-mail: fioc-renkei and ml.res.ncc.go.jp [replace and]) in FIOC for bioinformatics analysis support managed by Department of Bioinformatics in FIOC (we aim to reply in about one week).

 

Selected papers:

• Takashi Kamatani, et al, 
  Clonal diversity shapes the tumour microenvironment leading to distinct immunotherapy responses in metastatic urothelial carcinoma, 
  Nature Communications, 2025, 16, 7995
• Kana Shimomura, et al, 
  Sleeping Beauty transposon mutagenesis identified genes and pathways involved in inflammation-associated colon tumor development, 
  Nature communications, 2023, 14, 6514.1-16
• Mihoko Saito-Adachi, et al, 
  Oncogenic structural aberration landscape in gastric cancer genomes, 
  Nature Communications, 2023, 14(3688)
• Yasushi Totoki, et al, 
  Multiancestry genomic and transcriptomic analysis of gastric cancer, 
  Nature Genetics, 2023, 55(4):581-594
• Shinichi Yachida, et al, 
  Comprehensive Genomic Profiling of Neuroendocrine Carcinomas of the Gastrointestinal System, 
  Cancer Discov, 2021, 4, 1398–1405
• The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium
  Pan-cancer analysis of whole genomes
  Nature, 2020, 578, 82-93.
• Tomoko Saito, et al, 
  A temporal shift of the evolutionary principle shaping intratumor heterogeneity in colorectal cancer, 
  Nature Communications, 2018, 9, 2884.1-11
• Koichi Ogura, et al, 
  Integrated genetic and epigenetic analysis of myxofibrosarcoma, 
  Nature Communications, 2018, 9, 2765.1-11
• Akihiro Fujimoto, et al,
  Whole genome mutational landscape and characterization of non-coding and structural mutations in liver cancer,
  Nature Genetics, 2016, 48, 500-509.
• Shinichi Yachida, et al,
  Genomic sequencing identifies ELF3 as a driver of ampullary carcinoma,
  Cancer Cell, 2016, 29, 229-240.
• Hiromi Nakamura, et al,
  Genomic spectra of biliary tract cancer,
  Nature Genetics, 2015, 47, 1003-1010.
• Yasushi Totoki, et al,
  Trans-ancestry mutational landscape of hepatocellular carcinoma genomes,
  Nature Genetics, 2014, 46, 1267-1273.