Annual Report 2022

Division of Genome Analysis Platform Development

Yuichi Shiraishi, Naoko Iida, Ai Okada, Kenichi Chiba, Raúl Nicolás Mateos

Introduction

 We are constructing various analysis workflows to efficiently analyze cancer genome and transcriptome data from short- and long-read sequencing data. This includes building software for comprehensive detection of somatic structural variants in cancer genomes, identifying translocations in centromeric regions associated with cancer, creating workflows for adaptive sampling data, and developing tools for predicting hotspot genomic mutations from transcriptome data of cancer-related genes. We also perform integrated analysis of genome and transcriptome data. In the rapidly evolving landscape of high-throughput sequencing technologies and the accompanying development of numerous methodologies and software worldwide, we continue to make improvements in terms of speed and precision. Our goal is to develop platforms that assist in understanding the progression, causes, and states of cancer while keeping pace with these advancements.

Research Activities

(1) Development of structural variant detection software, Nanomonsv

 We have developed software to detect somatic structural variants using long-read sequencing data from tumor and normal tissue samples. In addition to the conventional approach of detecting two breakpoints in structural variants by comparing tumor and normal data, we have incorporated methods into the software for detecting structural variants where breakpoints are not accurately identified.

(2) Identification of translocations associated with cancer

 Some cancers exhibit translocations occurring at centromeric regions, which have been challenging to identify using conventional methods. We have focused on leveraging long-read sequencing technologies to develop and establish methodologies for identifying these breakpoints.

(3) Construction of adaptive sampling data analysis workflow

 We have constructed a data analysis workflow for adaptive sampling, a technique that amplifies specific regions for long-read sequencing. Adaptive sampling is highly valuable for detecting rare mutations and proves to be exceptionally useful in the analysis of cancer genomes.

(4) Estimation of hotspot genomic mutations from transcriptome data

 Using transcriptome data from The Cancer Genome Atlas (TCGA) and genomic data, we have developed a tool for estimating hotspot genomic mutations by learning the aberrant structures of transcripts from cancer-related genes and their associated hotspot genomic mutations.

Education

 We supported the many researchers using our analysis pipeline by answering their bioinformatics questions. We hired postdocs and supported their research.

Future Prospects

 We have established the technological basis for cancer genome and transcriptome analysis. To develop the application of whole-genome analysis to genomic medicine, we will construct various genome analysis flows. In addition, we will apply the established analysis flow to large-scale data and conduct knowledge discovery from the obtained information.

List of papers published in 2022

Journal

1. Totoki Y, Saito-Adachi M, Shiraishi Y, Komura D, Nakamura H, Suzuki A, Tatsuno K, Rokutan H, Hama N, Yamamoto S, Ono H, Arai Y, Hosoda F, Katoh H, Chiba K, Iida N, Nagae G, Ueda H, Shihang C, Sekine S, Abe H, Nomura S, Matsuura T, Sakai E, Ohshima T, Rino Y, Yeoh KG, So J, Sanghvi K, Soong R, Fukagawa A, Yachida S, Kato M, Seto Y, Ushiku T, Nakajima A, Katai H, Tan P, Ishikawa S, Aburatani H, Shibata T. Multiancestry genomic and transcriptomic analysis of gastric cancer. Nature genetics, 55:581-594, 2023

2. Makishima H, Saiki R, Nannya Y, Korotev S, Gurnari C, Takeda J, Momozawa Y, Best S, Krishnamurthy P, Yoshizato T, Atsuta Y, Shiozawa Y, Iijima-Yamashita Y, Yoshida K, Shiraishi Y, Nagata Y, Kakiuchi N, Onizuka M, Chiba K, Tanaka H, Kon A, Ochi Y, Nakagawa MM, Okuda R, Mori T, Yoda A, Itonaga H, Miyazaki Y, Sanada M, Ishikawa T, Chiba S, Tsurumi H, Kasahara S, Müller-Tidow C, Takaori-Kondo A, Ohyashiki K, Kiguchi T, Matsuda F, Jansen JH, Polprasert C, Blombery P, Kamatani Y, Miyano S, Malcovati L, Haferlach T, Kubo M, Cazzola M, Kulasekararaj AG, Godley LA, Maciejewski JP, Ogawa S. Germ line DDX41 mutations define a unique subtype of myeloid neoplasms. Blood, 141:534-549, 2023

3. Isobe T, Takagi M, Sato-Otsubo A, Nishimura A, Nagae G, Yamagishi C, Tamura M, Tanaka Y, Asada S, Takeda R, Tsuchiya A, Wang X, Yoshida K, Nannya Y, Ueno H, Akazawa R, Kato I, Mikami T, Watanabe K, Sekiguchi M, Seki M, Kimura S, Hiwatari M, Kato M, Fukuda S, Tatsuno K, Tsutsumi S, Kanai A, Inaba T, Shiozawa Y, Shiraishi Y, Chiba K, Tanaka H, Kotecha RS, Cruickshank MN, Ishikawa F, Morio T, Eguchi M, Deguchi T, Kiyokawa N, Arakawa Y, Koh K, Aoki Y, Ishihara T, Tomizawa D, Miyamura T, Ishii E, Mizutani S, Wilson NK, Göttgens B, Miyano S, Kitamura T, Goyama S, Yokoyama A, Aburatani H, Ogawa S, Takita J. Multi-omics analysis defines highly refractory RAS burdened immature subgroup of infant acute lymphoblastic leukemia. Nature communications, 13:4501, 2022

4. Fukuhara S, Oshikawa-Kumade Y, Kogure Y, Shingaki S, Kariyazono H, Kikukawa Y, Koya J, Saito Y, Tabata M, Yoshifuji K, Mizuno K, Miyagi-Maeshima A, Matsushita H, Sugiyama M, Ogawa C, Inamoto Y, Fukuda T, Sugano M, Yamauchi N, Minami Y, Hirata M, Yoshida T, Kohno T, Kohsaka S, Mano H, Shiraishi Y, Ogawa S, Izutsu K, Kataoka K. Feasibility and clinical utility of comprehensive genomic profiling of hematological malignancies. Cancer science, 113:2763-2777, 2022

5. Takeuchi Y, Yoshida K, Halik A, Kunitz A, Suzuki H, Kakiuchi N, Shiozawa Y, Yokoyama A, Inoue Y, Hirano T, Yoshizato T, Aoki K, Fujii Y, Nannya Y, Makishima H, Pfitzner BM, Bullinger L, Hirata M, Jinnouchi K, Shiraishi Y, Chiba K, Tanaka H, Miyano S, Okamoto T, Haga H, Ogawa S, Damm F. The landscape of genetic aberrations in myxofibrosarcoma. International journal of cancer, 151:565-577, 2022

6. Shiraishi Y, Okada A, Chiba K, Kawachi A, Omori I, Mateos RN, Iida N, Yamauchi H, Kosaki K, Yoshimi A. Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data. Nature communications, 13:5357, 2022

7. Sakamoto Y, Miyake S, Oka M, Kanai A, Kawai Y, Nagasawa S, Shiraishi Y, Tokunaga K, Kohno T, Seki M, Suzuki Y, Suzuki A. Phasing analysis of lung cancer genomes using a long read sequencer. Nature communications, 13:3464, 2022

8. Ogasawara T, Fujii Y, Kakiuchi N, Shiozawa Y, Sakamoto R, Ogawa Y, Ootani K, Ito E, Tanaka T, Watanabe K, Yoshida Y, Kimura N, Shiraishi Y, Chiba K, Tanaka H, Miyano S, Ogawa S. Genetic Analysis of Pheochromocytoma and Paraganglioma Complicating Cyanotic Congenital Heart Disease. The Journal of clinical endocrinology and metabolism, 107:2545-2555, 2022

9. Takeda J, Yoshida K, Nakagawa MM, Nannya Y, Yoda A, Saiki R, Ochi Y, Zhao L, Okuda R, Qi X, Mori T, Kon A, Chiba K, Tanaka H, Shiraishi Y, Kuo MC, Kerr CM, Nagata Y, Morishita D, Hiramoto N, Hangaishi A, Nakazawa H, Ishiyama K, Miyano S, Chiba S, Miyazaki Y, Kitano T, Usuki K, Sezaki N, Tsurumi H, Miyawaki S, Maciejewski JP, Ishikawa T, Ohyashiki K, Ganser A, Heuser M, Thol F, Shih LY, Takaori-Kondo A, Makishima H, Ogawa S. Amplified EPOR/JAK2 Genes Define a Unique Subtype of Acute Erythroid Leukemia. Blood cancer discovery, 3:410-427, 2022

10. Yamato G, Kawai T, Shiba N, Ikeda J, Hara Y, Ohki K, Tsujimoto SI, Kaburagi T, Yoshida K, Shiraishi Y, Miyano S, Kiyokawa N, Tomizawa D, Shimada A, Sotomatsu M, Arakawa H, Adachi S, Taga T, Horibe K, Ogawa S, Hata K, Hayashi Y. Genome-wide DNA methylation analysis in pediatric acute myeloid leukemia. Blood advances, 6:3207-3219, 2022