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Annual Report 2018

Department of Bioinformatics

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

Introduction

 The objectives of the Department of Bioinformatics are as follows: 1) researching bioinformatics and its application in cancer genome medicine, 2) formulating new theories on cancer biology through big-data analysis, such as OMICS-data and computational methods, and 3) providing bioinformatics support to experimental groups at the National Cancer Center (NCC) and other research institutions.

The Team and What We Do

 We are responsible for handling the bioinformatics-related technologies of the cancer genomic medicine project being conducted at the NCC. We create new programs and obtain knowledge on cancer mechanisms through big-data analysis for cancer genomic medicine. We also provide bioinformatics analysis support.

Research activities

1) While in charge of the bioinformatics aspect of the cancer genomic medicine project, we undertook a number of activities this year, which are listed below:

A) We had previously developed computer programs, optimized for FFPE samples used in cancer genomic medicine, to detect SNVs, indels, gene fusions, and copy number alterations from a large amount of data produced by the next generation sequencer (NGS). Using these programs, we performed the following activities this year:

I. We fixed bugs, refactored source codes, and improved functions and the fusion-calling algorithm, while also implementing the detection algorithm SV (Structural Variant).

II. We customized cisCall and performed data analysis for IVD (In Vitro Diagnostics) applications

III. We improved the public version of cisCall and updated the existing version of the software.

B) Our clinical sequence was used as a part of the advanced medical care programs for public insurance coverage; accordingly, we provided technical support to Sysmex and RIKEN GENESIS for setting up the mutation call environment and manually removing false positives.

C) We organized the mutation and clinical data obtained from the clinical sequence and registered them on the public clinical database MGeND. We also organized the data registered to AGD.

2) We investigated how the NCC Oncopanel could be applied to cfDNA sequencing. We initially estimated the appropriate DNA input and lead depth, analyzed mutations detected from TOP-GEAR tumor tissue and cfDNA. Next, we confirmed that customized cisCall parameters could detect some common mutations in tumor tissue and cfDNA.

3) We performed a preliminary experiment on a mouse colorectal cancer model with cell tracking barcode DNA to understand the mechanisms underlying the very early stages of cancer. This year, we extracted DNA from APC knock out mouse samples and the complexity of the cell population was quantified.

4) We developed a deep learning-based computational tool that detects the chromosomal positions of DNA adducts from Nanopore sequencer data, and as the first test case, we succeeded in detecting the chromosomal positions of DNA adducts in PCR products of the Salmonella genome with an accuracy of >90%.

5) We developed the tumor gene-Hallmark (tugHall) program to simulate cancer cell evolution and intra-tumor heterogeneity. In tugHall, the known hallmarks of cancer were set as variables which stochastically changed the behavior of the tumor cell. By simulating individual tumor cell behavior, we can better understand cancer-cell evolution and intra-tumor heterogeneity.

6) Using multi-omics bileduct cancer data, we were able to identify biomarkers for use in cancer genomic medicine. During the course of this year, we assessed the significance of biomarkers on selecting patients who may benefit from immune checkpoint therapy.

7) To investigate the relationship between carcinogens and mutational signature, and the potential application of mouse data on humans, we obtained WGS data from rats that were fed different carcinogens to develop different cancer types. This year, we called variants and extracted mutational signatures from the WGS data of rats, and then compared the extracted rat signatures with those of humans in the COSMIC database.

8) We provided bioinformatics analysis support for the following projects:

  • Genome-wide analysis and classification of methylation data in digestive refractory cancer.
  • Support for the annotation of mutational data of colorectal cancer from JCOG.
  • Copy number analysis on 127 datasets of esophageal cancer from JCOG.
  • Data analysis to investigate the relationship between copy number analysis and poor prognosis in lung squamous cell carcinoma
  • Omics data and comparative analyses of PCAWG and TCGA.
  • PTI score calculations, which could be an index of new gene codes for various species.
  • Analysis of bacterial mutations and mutational signatures of bacterial colonies, exposed to different carcinogens.
  • Analysis of mesothelioma mutational signatures in rats.
  • Setting the clinical molecular diagnostic criteria on dividing into subgroups of ependymoma

Education

 We give guidance on bioinformatics to experimental groups at NCC and other research institutions through bioinformatics support.

Future prospects

 We will continue to develop bioinformatics technologies for cancer genomic medicine, including cfDNA sequencing, with the goal of clinical implementation. Collaborating with C-CAT, we will analyze medical big data to identify novel molecular tumor markers and subtypes that are applicable to cancer genomic medicine. We will utilize machine-learning, AI technologies, and high computational complexity analysis using cloud computing platforms in our studies and will continue to provide bioinformatics support for research groups at the NCC and other institutions.

List of papers published in 2018

Journal

 1. 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

 2. Sekimizu M, Yoshida A, Mitani S, Asano N, Hirata M, Kubo T, Yamazaki F, Sakamoto H, Kato M, Makise N, Mori T, Yamazaki N, Sekine S, Oda I, Watanabe SI, Hiraga H, Yonemoto T, Kawamoto T, Naka N, Funauchi Y, Nishida Y, Honoki K, Kawano H, Tsuchiya H, Kunisada T, Matsuda K, Inagaki K, Kawai A, Ichikawa H. Frequent mutations of genes encoding vacuolar H+ -ATPase components in granular cell tumors. Genes Chromosomes Cancer, 58:373-380, 2019

 3. Fukuoka K, Kanemura Y, Shofuda T, Fukushima S, Yamashita S, Narushima D, Kato M, Honda-Kitahara M, Ichikawa H, Kohno T, Sasaki A, Hirato J, Hirose T, Komori T, Satomi K, Yoshida A, Yamasaki K, Nakano Y, Takada A, Nakamura T, Takami H, Matsushita Y, Suzuki T, Nakamura H, Makino K, Sonoda Y, Saito R, Tominaga T, Matsusaka Y, Kobayashi K, Nagane M, Furuta T, Nakada M, Narita Y, Hirose Y, Ohba S, Wada A, Shimizu K, Kurozumi K, Date I, Fukai J, Miyairi Y, Kagawa N, Kawamura A, Yoshida M, Nishida N, Wataya T, Yamaoka M, Tsuyuguchi N, Uda T, Takahashi M, Nakano Y, Akai T, Izumoto S, Nonaka M, Yoshifuji K, Kodama Y, Mano M, Ozawa T, Ramaswamy V, Taylor MD, Ushijima T, Shibui S, Yamasaki M, Arai H, Sakamoto H, Nishikawa R, Ichimura K. Significance of molecular classification of ependymomas: C11orf95-RELA fusion-negative supratentorial ependymomas are a heterogeneous group of tumors. Acta Neuropathol Commun, 6:134, 2018

 4. Itahashi K, Kondo S, Kubo T, Fujiwara Y, Kato M, Ichikawa H, Koyama T, Tokumasu R, Xu J, Huettner CS, Michelini VV, Parida L, Kohno T, Yamamoto N. Evaluating Clinical Genome Sequence Analysis by Watson for Genomics. Front Med, 5:305, 2018

 5. Xia E, Kanematsu S, Suenaga Y, Elzawahry A, Kondo H, Otsuka N, Moriya Y, Iizasa T, Kato M, Yoshino I, Yokoi S. MicroRNA induction by copy number gain is associated with poor outcome in squamous cell carcinoma of the lung. Sci Rep, 8:15363, 2018

 6. Saito T, Niida A, Uchi R, Hirata H, Komatsu H, Sakimura S, Hayashi S, Nambara S, Kuroda Y, Ito S, Eguchi H, Masuda T, Sugimachi K, Tobo T, Nishida H, Daa T, Chiba K, Shiraishi Y, Yoshizato T, Kodama M, Okimoto T, Mizukami K, Ogawa R, Okamoto K, Shuto M, Fukuda K, Matsui Y, Shimamura T, Hasegawa T, Doki Y, Nagayama S, Yamada K, Kato M, Shibata T, Mori M, Aburatani H, Murakami K, Suzuki Y, Ogawa S, Miyano S, Mimori K. A temporal shift of the evolutionary principle shaping intratumor heterogeneity in colorectal cancer. Nat Commun, 9:2884, 2018

 7. Arai E, Miura F, Totoki Y, Yamashita S, Tian Y, Gotoh M, Ojima H, Nakagawa H, Takahashi Y, Nakamura H, Hama N, Kato M, Kimura H, Suzuki Y, Ito T, Shibata T, Kanai Y. Epigenome mapping of human normal purified hepatocytes: personal epigenome variation and genome-epigenome correlation. Epigenomics, 10:955- 979, 2018

 8. Kato M, Nakamura H, Nagai M, Kubo T, Elzawahry A, Totoki Y, Tanabe Y, Furukawa E, Miyamoto J, Sakamoto H, Matsumoto S, Sunami K, Arai Y, Suzuki Y, Yoshida T, Tsuchihara K, Tamura K, Yamamoto N, Ichikawa H, Kohno T, Shibata T. A computational tool to detect DNA alterations tailored to formalin-fixed paraffin-embedded samples in cancer clinical sequencing. Genome Med, 10:44, 2018

 9. Nishino J, Kochi Y, Shigemizu D, Kato M, Ikari K, Ochi H, Noma H, Matsui K, Morizono T, Boroevich KA, Tsunoda T, Matsui S. Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures. Front Genet, 9:115, 2018

10. Ogura K, Hosoda F, Arai Y, Nakamura H, Hama N, Totoki Y, Yoshida A, Nagai M, Kato M, Arakawa E, Mukai W, Rokutan H, Kawai A, Tanaka S, Shibata T. Integrated genetic and epigenetic analysis of myxofibrosarcoma. Nat Commun, 9:2765, 2018