Journal
CELL CYCLE
Volume 17, Issue 4, Pages 486-491Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/15384101.2017.1417706
Keywords
Bioinformatics; cancer; personalized medicine; machine learning; drug scoring; support vector machines; cell lines; gene expression profiling; pathway activation scoring
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Funding
- National Research Center Kurchatov Institute [2017-1025]
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Personalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained from, e.g., gene expression profile. The majority of machine learning methods suffer from the deficiency of preceding cases, i.e. the gene expression data on patients combined with the confirmed outcome of known treatment methods. At the same time, there exist thousands of various cell lines that were treated with hundreds of anti-cancer drugs in order to check the ability of these drugs to stop the cell proliferation, and all these cell line cultures were profiled in terms of their gene expression. Here we present a new approach in machine learning, which can predict clinical efficiency of anti-cancer drugs for individual patients by transferring features obtained from the expression-based data from cell lines. The method was validated on three datasets for cancer-like diseases (chronic myeloid leukemia, as well as lung adenocarcinoma and renal carcinoma) treated with targeted drugs - kinase inhibitors, such as imatinib or sorafenib.
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