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Data-driven approach to identify prognostic determinants and therapeutic targets for lung cancer

Research Project

Project/Area Number 21K17856
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 62010:Life, health and medical informatics-related
Research InstitutionTokyo Medical and Dental University

Principal Investigator

Shimizu Hideyuki  東京医科歯科大学, M&Dデータ科学センター, 教授 (70826263)

Project Period (FY) 2021-03-01 – 2023-03-31
Project Status Completed (Fiscal Year 2022)
Budget Amount *help
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2022: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2021: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Keywords人工知能 / 肺がん / 層別化 / 個別化医療 / 免疫チェックポイント阻害薬 / データ科学 / 治療法
Outline of Research at the Start

日本人の死因のトップはがんであり、がんの中でも肺がんは最も多いがんの一つである。従来は病理学的探索からいくつかの組織型に分けられていたが、遺伝子検査が進んだ結果、さまざまな分子標的治療薬が開発され今日に至っている。本研究では、人工知能を含むデータ科学を使って肺がん研究にさらに切り込み、診断・治療法の開発をさらに加速させていく。

Outline of Final Research Achievements

Lung cancer, a prominent cause of cancer-related deaths in Japan, necessitates enhanced control measures. This study identified a comprehensive set of factors influencing the treatment response and prognosis of non-small cell lung cancer, accounting for over 80% of all lung cancer cases, via sophisticated mathematical informatics analysis. A distinct form of artificial intelligence was established, capable of predicting responses to immunotherapy for lung cancer prior to the actual treatment. Utilizing a Bayesian approach and integrating prediction uncertainty, this investigation provides a solid foundation for future research endeavors.

Academic Significance and Societal Importance of the Research Achievements

がんの中でも肺がんが最も多く、その制圧は急務である。特に、がん免疫療法はおよそ2割の肺がん患者にしか効かないが、治療前に成否を予測することは非常に難しく、効果がなかった場合の高額な治療コストや副作用が社会問題になっている。理想的には、効果がある患者さんを事前に層別化してがん免疫療法を行うのが望ましい。本研究課題はまさにそれを具現化し、治療前の遺伝子発現情報や年齢性別等のプロファイルからがん免疫療法の応答性を予測したという点で学術的のみならず社会的意義が大きい。

Report

(3 results)
  • 2022 Annual Research Report   Final Research Report ( PDF )
  • 2021 Research-status Report
  • Research Products

    (5 results)

All 2023 2022 2021

All Journal Article (4 results) (of which Peer Reviewed: 4 results,  Open Access: 4 results) Presentation (1 results) (of which Invited: 1 results)

  • [Journal Article] Bayesian network enables interpretable and state-of-the-art prediction of immunotherapy responses in cancer patients2023

    • Author(s)
      Hozumi Hideki、Shimizu Hideyuki
    • Journal Title

      PNAS Nexus

      Volume: 2 Issue: 5

    • DOI

      10.1093/pnasnexus/pgad133

    • Related Report
      2022 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] LIGHTHOUSE illuminates therapeutics for a variety of diseases including COVID-192022

    • Author(s)
      Shimizu Hideyuki、Kodama Manabu、Matsumoto Masaki、Orba Yasuko、Sasaki Michihito、Sato Akihiko、Sawa Hirofumi、Nakayama Keiichi I.
    • Journal Title

      iScience

      Volume: 25 Issue: 11 Pages: 105314-105314

    • DOI

      10.1016/j.isci.2022.105314

    • Related Report
      2022 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Kastor and Polluks polypeptides encoded by a single gene locus cooperatively regulate VDAC and spermatogenesis.2022

    • Author(s)
      Mise S#, Matsumoto A#*, Shimada K, Hosaka T, Takahashi M, Ichihara K, Shimizu H, Shiraishi C, Saito D, Suyama M, Yasuda T, Ide T, Izumi Y, Bamba T, Kimura-Someya T, Shirouzu M, Miyata H, Ikawa M, Nakayama KI*.
    • Journal Title

      Nat. Commun.

      Volume: 13(1) Issue: 1 Pages: 1071-1071

    • DOI

      10.1038/s41467-022-28677-y

    • Related Report
      2021 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] Combinatorial analysis of translation dynamics reveals eIF2 dependence of translation initiation at near-cognate codons2021

    • Author(s)
      Ichihara Kazuya、Matsumoto Akinobu、Nishida Hiroshi、Kito Yuki、Shimizu Hideyuki、Shichino Yuichi、Iwasaki Shintaro、Imami Koshi、Ishihama Yasushi、Nakayama Keiichi I
    • Journal Title

      Nucleic Acids Research

      Volume: 49 Issue: 13 Pages: 7298-7317

    • DOI

      10.1093/nar/gkab549

    • Related Report
      2021 Research-status Report
    • Peer Reviewed / Open Access
  • [Presentation] Machine learning-guided drug discovery: Beyond protein structures2022

    • Author(s)
      清水秀幸
    • Organizer
      第45回日本分子生物学会年会
    • Related Report
      2022 Annual Research Report
    • Invited

URL: 

Published: 2021-04-28   Modified: 2024-01-30  

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