Abstract
We demonstrated a new method for the prediction of in vivo drug sensitivity using before-treatment baseline tumor gene expression data. First, we fitted ridge regression models for differential gene expression against drug sensitivity in a large panel of cell lines. Following data homogenization and filtering, drug response was predicted based on baseline expression levels from primary tumor biopsies. We validated this approach on two clinical trial datasets, and obtained predictions better than those from whole-genome gene expression. The findings may point out new directions for the prediction of anticancer drug sensitivity and the development of personalized medicine.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (31301101), the Anhui Provincial Natural Science Foundation (1408085QF106), the Specialized Research Fund for the Doctoral Program of Higher Education (20133401120011), and the Technology Foundation for Selected Overseas Chinese Scholars from Department of Human Resources and Social Security of Anhui Province (No. [2014]-243).
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Yue, Z., Chen, Y., Xia, J. (2015). Prediction of Clinical Drug Response Based on Differential Gene Expression Levels. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_48
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DOI: https://doi.org/10.1007/978-3-319-22186-1_48
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