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Cell Cycle. 2018;17(4):486-491. doi: 10.1080/15384101.2017.1417706. Epub 2018 Jan 17.

A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency.

Author information

1
a National Research Centre "Kurchatov Institute" , Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow , Russia.
2
b Department of R&D , First Oncology Research and Advisory Center, Moscow , Russia.
3
c Department of R&D , OmicsWay Corporation, Walnut , CA , USA.
4
d Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry , Moscow , Russia.
5
e Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology , Oncology and Immunology, Moscow , 117198 , Russia.
6
f Department of Biological Sciences , University of Lethbridge , Lethbridge , AB , Canada.
7
g Canada Cancer and Aging Research Laboratories , Lethbridge , AB , Canada.
8
h Insilico Medicine, Inc, ETC, Johns Hopkins University , Baltimore , MD , USA.
9
i Rutgers University , Hill Center, Busch Campus, Piscataway , NJ , USA.

Abstract

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.

KEYWORDS:

Bioinformatics; cancer; cell lines; drug scoring; gene expression profiling; machine learning; pathway activation scoring; personalized medicine; support vector machines

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