2016 Fiscal Year Final Research Report
Statistical Modeling and Prediction for Therapy-induced Cancer Drug Resistance and Prediction
| Project/Area Number |
15H06008
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Single-year Grants |
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| Research Field |
Statistical science
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| Research Institution | The University of Tokyo |
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Principal Investigator |
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| Project Period (FY) |
2015-08-28 – 2017-03-31
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| Keywords | データ同化 / 統計科学 / がん解析 / シミュレーション |
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| 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.
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| Free Research Field |
統計科学
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