業績

Publication

  1. Kobayashi Y, Niida A, Nagayama S, Saeki K, Haeno H, Takahashi KK, Hayashi S, Ozato Y, Saito H, Hasegawa T, Nakamura H, Tobo T, Kitagawa A, Sato K, Shimizu D, Hirata H, Hisamatsu Y, Toshima T, Yonemura Y, Masuda T, Mizuno S, Kawazu M, Kohsaka S, Ueno T, Mano H, Ishihara S, Uemura M, Mori M, Doki Y, Eguchi H, Oshima M, Suzuki Y, Shibata T, Mimori K. Subclonal accumulation of immune escape mechanisms in microsatellite instability-high colorectal cancers. Br. J Cancer. Accepted. (doi: 10.1038/s41416-023-02395-8. Online ahead of print. PMID: 37596408)
  2. Abubakar SD, Takaki M, Haeno H. Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles. Front Oncol. 13:1116210. 2023. (doi: 10.3389/fonc.2023.1116210.)
  3. Kawazu M, Ueno T, Saeki K, Sax N, Togashi Y, Kaneseki T, Chida K, Kishigami F, Sato K, Kojima S, Otsuka M, Kawazoe A, Nishinakamura H, Maeda Y, Yamamoto Y, Yamashita K, Inoue S, Tanegashima T, Matsubara D, Tane K, Tanaka Y, Iinuma H, Hashiguchi Y, Hazama S, Khor SS, Tokunaga K, Tsuboi M, Niki T, Eto M, Shitara K, Torigoe T, Ishihara S, Aburatani H, Haeno H, Nishikawa H, Mano H. HLA Class I analysis provides insight into the genetic and epigenetic background of immune evasion in colorectal cancer with high microsatellite instability. Gastroenterology. 2021 S0016-5085(21)03644-1. 2022 Mar;162(3):799-812.(doi: 10.1053/j.gastro.2021.10.010. Epub 2021 Oct 21.) 日経電子版(https://www.nikkei.com/article/DGXLRSP619870_Z11C21A0000000/
  4. Shimizu D, Taniue K, Matsui Y, Haeno H, Araki H, Miura F, Fukunaga M, Shiraishi K, Miyamoto Y, Tsukamoto S, Komine A, Kobayashi Y, Kitagawa A, Yoshikawa Y, Sato K, Saito T, Ito S, Masuda T, Niida A, Suzuki M, Baba H, Ito T, Akimitsu N, Kodera Y, Mimori K. Pan-cancer Methylome Analysis for Cancer Diagnosis and Classification of Cancer Cell of Origin. Cancer Gene Ther. 2022 May;29(5):428-436.(doi: 10.1038/s41417-021-00401-w. Epub 2021 Nov 8.)
  5. Takaki M, Haeno H. Mathematical modeling of locoregional recurrence caused by premalignant lesions formed before initial treatment. Front Oncol.2021 Oct 13;11:743328.(doi: 10.3389/fonc.2021.743328. eCollection 2021.)
  6. Aoki K, Suzuki H, Yamamoto T, Yamamoto KN, Maeda S, Okuno Y, Ranjit M, Motomura K, Ohka F, Tanahashi K, Hirano M, Nishikawa T, Shimizu H, Kitano Y, Yamaguchi J, Yamazaki S, Nakamura H, Takahashi M, Narita Y, Nakada M, Deguchi S, Mizoguchi M, Momii Y, Muragaki Y, Abe T, Akimoto J, Wakabayashi T, Saito R, Ogawa S, Haeno H, Natsume A. Mathematical modeling and mutational analysis reveal optimal therapy to prevent malignant transformation in grade II IDH-mutant gliomas. Cancer Res. Online ahead of print. 2021. (doi: 10.1158/0008-5472.CAN-21-0985.) 日経電子版(https://www.nikkei.com/article/DGXLRSP615489_Z20C21A7000000/
  7. Iwai K, Nambu T, Kashima Y, Yu J, Eng K, Miyamoto K, Kakoi K, Gotou M, Takeuchi T, Kogame A, Sappal J, Murai S, Haeno H, Kageyama SI, Kurasawa O, Niu H, Kannan K, Ohashi A. A CDC7 inhibitor sensitizes DNA-damaging chemotherapies by suppressing homologous recombination repair to delay DNA damage recovery. Sci Adv. 7: eabf0197. 2021. (doi: 10.1126/sciadv.abf0197.)
  8. Sakai K, Yamada Y, Yoshida K, Yoshinaga S, Sato K, Ogata H, Iwasaki T, Kudo S, Asada Y, Kawaguchi I, Haeno H, Sasaki M. Conclusions and suggestions on low-dose and low-dose rate radiation risk estimation methodology. J Radiat Prot Res. 46: 14-23. 2021. (doi: 10.14407/JRPR.2020.00262).
  9. Fujimoto Y, Morita TY, Ohashi A, Haeno H, Hakozaki Y, Fujii M, Kashima Y, Kobayashi SS, Mukohara T. Combination treatment with a PI3K/Akt/mTOR pathway inhibitor overcomes resistance to anti-HER2 therapy in PIK3CA-mutant HER2-positive breast cancer cells. Sci Rep. 10: 21762. 2020. (doi: 10.1038/s41598-020-78646-y.)
  10. Hirahara N, Nakamura HM, Sasaki S, Matsushita A, Ohba K, Kuroda G, Sakai Y, Shinkai S, Haeno H, Nishio T, Yoshida S, Oki Y, Suda T. Liganded T3 receptor β2 inhibits the positive feedback autoregulation of the gene for GATA2, a transcription factor critical for thyrotropin production. PLoS One 15: e02276462020. 2020. (doi: 10.1371/journal.pone.0227646.)
  11. Mizukoshi K, Okazawa Y, Haeno H, Koyama Y, Sulidan K, Komiyama H, Saeki H, Ohtsuji N, Ito Y, Kojima Y, Goto M, Habu S, Hino O, Sakamoto K, Orimo A. Metastatic seeding of human colon cancer cell clusters expressing the hybrid epithelial/mesenchymal state. Int J Cancer. 146:2547-2562. 2020. (doi: 10.1002/ijc.32672.) がんプラス(https://cancer.qlife.jp/news/article11468.html
  12. Mimaki S, Watanabe M, Kinoshita M, Yamashita R, Haeno H, Takemura S, Tanaka S, Marubashi S, Totsuka Y, Shibata T, Nakagama H, Ochiai A, Nakamori S, Kubo S, Tsuchihara K. Multifocal origin of occupational cholangiocarcinoma revealed by comparison of multilesion mutational profiles. Carcinogenesis 41: 368-376. 2020. (doi: 10.1093/carcin/bgz120.)
  13. Yamamoto KN, Nakamura A, Liu LL, Stein S, Tramontano AC, Kartoun U, Shimizu T, Inoue Y, Asakuma M, Haeno H, Kong CY, Uchiyama K, Gonen M, Hur C, Michor F. Computational modeling of pancreatic cancer patients receiving FOLFIRINOX and gemcitabine-based therapies identifies optimum intervention strategies. PLoS One 14: e0215409. 2019. (doi: 10.1371/journal.pone.0215409.)
  14. Yamamoto KN, Liu LL, Nakamura A, Haeno H, Michor F. Stochastic Evolution of Pancreatic Cancer Metastases During Logistic Clonal Expansion. JCO Clin Cancer Inform. 3:1-11. 2019. (doi: 10.1200/CCI.18.00079.)
  15. Stein S, Zhao R, Haeno H, Vivanco I, Michor F. Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients. PLoS Comput Biol. 14: e1005924 2018. (doi: 10.1371/journal.pcbi.1005924.)
  16. Yamamoto KN, Yachida S, Nakamura A, Niida A, Oshima M, De S, Rosati LM, Herman JM, Iacobuzio-Donahue CA, Haeno H. Personalized Management of Pancreatic Ductal Adenocarcinoma Patients through Computational Modeling. Cancer Res. 77:3325-3335 2017. (doi: 10.1158/0008-5472.CAN-16-1208.)
  17. Yamamoto KN, Ishii M, Inoue Y, Hirokawa F, MacArthur BD, Nakamura A, Haeno H, Uchiyama K. Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model. Sci Rep. 6:34214 2016. (doi: 10.1038/srep34214.)
  18. Uchi R, Takahashi Y, Niida A, Shimamura T, Hirata H, Sugimachi K, Sawada G, Iwaya T, Kurashige J, Shinden Y, Iguchi T, Eguchi H, Chiba K, Shiraishi Y, Nagae G, Yoshida K, Nagata Y, Haeno H, Yamamoto H, Ishii H, Doki Y, Iinuma H, Sasaki S, Nagayama S, Yamada K, Yachida S, Kato M, Shibata T, Oki E, Saeki H, Shirabe K, Oda Y, Maehara Y, Komune S, Mori M, Suzuki Y, Yamamoto K, Aburatani H, Ogawa S, Miyano S, Mimori K. Integrated Multiregional analysis proposing a new model of colorectal cancer evolution. PLoS Genet. 12:e1005778, 2016. (doi: 10.1371/journal.pgen.1005778.)
  19. Yamamoto KN, Nakamura A, Haeno H. The evolution of tumor metastasis during clonal expansion with alterations in metastasis driver genes. Sci Rep. 5:15886, 2015. (doi: 10.1038/srep15886.)
  20. Kobayashi H, Kobayashi CI, Nakamura-Ishizu A, Karigane D, Haeno H, Yamamoto KN, Sato T, Ohteki T, Hayakawa Y, Barber GN, Kurokawa M, Suda T, Takubo K. Bacterial c-di-GMP affects hematopoietic stem/progenitors and their niches through STING. Cell Rep. 11:71-84. 2015. (doi: 10.1016/j.celrep.2015.02.066.)
  21. Yamamoto KN, Hirota K, Takeda S, Haeno H. Evolution of pre-existing versus acquired resistance to platinum drugs and PARP inhibitors in BRCA-associated cancers. PLoS One. 9:e105724, 2014. (doi: 10.1371/journal.pone.0105724.)
  22. Haeno H, Maruvka YE, Iwasa Y, Michor F. Stochastic Tunneling of Two Mutations in a Population of Cancer Cells. PLoS One. 8:e65724, 2013. (doi: 10.1371/journal.pone.0065724.)
  23. Iwami S*, Haeno H*, Michor F. (*co-first authors) A race between tumor immunoescape and genome maintenance selects for optimum levels of (epi)genetic instability. PLoS Comput Biol 8(2):e1002370. 2012. (doi: 10.1371/journal.pcbi.1002370.)
  24. Haeno H*, Gonen M*, Davis MB, Herman JM, Iacobuzio-Donahue CA, Michor F. (*co-first authors) Computational modeling of pancreatic cancer reveals kinetics of metastasis suggesting optimum treatment strategies. Cell 148(1-2):362-75. 2012. (doi: 10.1016/j.cell.2011.11.060.)
  25. Hambardzumyan D*, Cheng YK*, Haeno H*, Holland EC, Michor F. (*co-first authors) The Probable Cell of Origin of NF1-and PDGF-Driven Glioblastomas. PLoS One 6(9):e24454. 2011. (doi: 10.1371/journal.pone.0024454.)
  26. Klinakis A, Lobry C, Abdel-Wahab O, Oh P, Haeno H, Buonamici S, van De Walle I, Cathelin S, Trimarchi T, Araldi E, Liu C, Ibrahim S, Beran M, Zavadil J, Efstratiadis A, Taghon T, Michor F, Levine RL, Aifantis I. A novel tumour-suppressor function for the Notch pathway in myeloid leukaemia. Nature 473, 230-3. 2011. (doi: 10.1038/nature09999.)
  27. Haeno H, Michor F. The evolution of tumor metastases during clonal expansion. J Theor Biol 263, 30-44. 2009. (doi: 10.1016/j.jtbi.2009.11.005.)
  28. Haeno H, Levine RL, Gilliland DG, Michor F. A progenitor cell origin of myeloid malignancies. Proc Natl Acad Sci USA 106, 16616-16621. 2009. (doi: 10.1073/pnas.0908107106.)
  29. Haeno H, Iwasa Y, Michor F. The evolution of two mutations during clonal expansion. Genetics 177, 2209-2221. 2007. (PMID: 18073428)
  30. Haeno H, Iwasa Y. Probability of resistance evolution for exponentially growing virus in the host. J Theor Biol 246, 323-331. 2007. (PMID: 17306832)

日本語総説

  1. 波江野 洋「コンピュータ科学と数学を使ったがん研究」2023年2月 理大 科学フォーラム Vol.433 p12-15.
  2. 岩波 翔也, 山本 玲, 岩見 真吾, 波江野 洋 「骨髄球バイパスを含む造血システムの数理モデルを用いた1細胞移植実験のデータ解析 (第14回生物数学の理論とその応用 : 構造化個体群ダイナミクスとその応用)」 2018年8月 数理解析研究所講究録 (2087) 77-85.
  3. 高木 舜晟, 波江野 洋 「2種の確率過程を用いたがん再発の数理モデルの構築 (第14回生物数学の理論とその応用 : 構造化個体群ダイナミクスとその応用)」2018年8月 数理解析研究所講究録 (2087) 93-102.
  4. 岩波 翔也, 山本 玲, 岩見 真吾, 波江野 洋 「骨髄球バイパスを含む造血システムの数理モデル (第13回生物数学の理論とその応用 : 連続および離散モデルのモデリングと解析)」 2017年9月 数理解析研究所講究録 (2043) 102-108.
  5. 巌佐 庸、波江野 洋「進化プロセスとしての発がん」 2017年3月 実験医学増刊号Vol.35 No.5, p227-230.
  6. 波江野 洋、山本 君代 「がん進展過程における薬剤耐性獲得の数理モデル」2014年9月 がん分子標的治療 Vol. 12, No. 3, p324-p329.
  7. 波江野 洋、山本 君代 「がんにおける突然変異蓄積過程の数理モデル」2014年8月 実験医学増刊号 Vol. 32, No. 12, p208-p212.
  8. 波江野 洋「突然変異の蓄積を含むがんの動態の数理モデリング」2013年10月 実験医学 Vol. 31, No. 18, p2939-p2944.
  9. 波江野 洋, 巌佐 庸, Michor Franziska「増殖する細胞集団中における薬剤耐性獲得と発がんの数理モデル (第4回生物数学の理論とその応用 RIMS研究集会報告集)」2008年5月 数理解析研究所講究録 (1597) 118-122.
  10. 福井 義高, 小嶋 雄太, 波江野 洋, 成尾 佳美「癌因子制御の数理モデル--力学系の視点から (新しい生物数学の研究交流プロジェクト--RIMS共同研究報告集)」2008年5月 数理解析研究所講究録 (1598) 29-35.
  11. 波江野 洋, 川口 喬, 李 聖林「細胞分化モデルの探索 (新しい生物数学の研究交流プロジェクト--RIMS共同研究報告集)」2007年5月 数理解析研究所講究録 (1556) 138-147.