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Statistical Modeling and Prediction for Therapy-induced Cancer Drug Resistance and Prediction

Research Project

Project/Area Number 15H06008
Research Category

Grant-in-Aid for Research Activity Start-up

Allocation TypeSingle-year Grants
Research Field Statistical science
Research InstitutionThe University of Tokyo

Principal Investigator

Hasagawa Takanori  東京大学, 医科学研究所, 助教 (80753756)

Project Period (FY) 2015-08-28 – 2017-03-31
Project Status Completed (Fiscal Year 2016)
Budget Amount *help
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Keywordsデータ同化 / 統計科学 / がん解析 / シミュレーション / 統計数学 / 生体生命情報学
Outline of Final Research Achievements

In recent years, high tumor heterogeneity has been confirmed in many cancer tumors. When an anticancer agent is administered to such a cancer tumor, although the target predominant sensitive clone decreases, the inferior resistant clone, which are suppressed by such predominant clone, becomes dominant and begins to proliferate as drug resistant cancer. In this study, we developed a statistical method that integrates genomic information and blood marker information of tumor cells obtained by the next generation sequencing technology and recurrence simulation model of tolerable cancer by data assimilation framework. This makes it possible to predict and suggest an effective drug administration schedule for cancer tumor with intratumoral heterogeneity.

Report

(3 results)
  • 2016 Annual Research Report   Final Research Report ( PDF )
  • 2015 Annual Research Report
  • Research Products

    (2 results)

All 2016 2015

All Journal Article (1 results) (of which Peer Reviewed: 1 results) Presentation (1 results) (of which Int'l Joint Research: 1 results)

  • [Journal Article] A likelihood-free filtering method via approximate Bayesian computation in evaluating biological simulation models2015

    • Author(s)
      Hasegawa T, Niida A, Mori T, Shimamura T, Yamaguchi R, Miyano S, Akutsu T, Imoto S
    • Journal Title

      Computational Statistics & Data Analysis

      Volume: 94 Pages: 63-74

    • DOI

      10.1016/j.csda.2015.08.003

    • Related Report
      2015 Annual Research Report
    • Peer Reviewed
  • [Presentation] Time-series filtering for replicated observations via a kernel approximate Bayesian computation2016

    • Author(s)
      Takanori Hasegawa
    • Organizer
      9th International Conference of the ERCIM WG on Computational and Methodological Statistics 10th International Conference on Computational and Financial Econometrics
    • Place of Presentation
      Higher Technical School of Engineering, University of Seville, Spain
    • Year and Date
      2016-12-09
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research

URL: 

Published: 2015-08-26   Modified: 2018-03-22  

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