Abstract
The ability to predict the drug response for cancer disease based on genomics information is an essential problem in modern oncology, leading to personalized treatment. By predicting accurate anticancer responses, oncologists achieve a complete understanding of the effective treatment for each patient. In this paper, we present DSPLMF (Drug Sensitivity Prediction using Logistic Matrix Factorization) approach based on Recommender Systems. DSPLMF focuses on discovering effective features of cell lines and drugs for computing the probability of the cell lines are sensitive to drugs by logistic matrix factorization approach. Since similar cell lines and similar drugs may have similar drug responses and incorporating similarities between cell lines and drugs can potentially improve the drug response prediction, gene expression profile, copy number alteration, and single-nucleotide mutation information are used for cell line similarity and chemical structures of drugs are used for drug similarity. Evaluation of the proposed method on CCLE and GDSC datasets and comparison with some of the state-of-the-art methods indicates that the result of DSPLMF is significantly more accurate and more efficient than these methods. To demonstrate the ability of the proposed method, the obtained latent vectors are used to identify subtypes of cancer of the cell line and the predicted IC50 values are used to depict drug-pathway associations. The source code of DSPLMF method is available in https://github.com/emdadi/DSPLMF.
Highlights
Cancer is a genetic disease that results when cellular changes and accumulation of different types of mutations cause the uncontrolled growth and division of cells
We modeled the cancer drug sensitivity problem based on “Recommender Systems” approach
By applying the proposed model to Genomics of Drug Sensitivity in Cancer (GDSC) and Cell Line Encyclopedia (CCLE) datasets, we proved that DSPLMF is of excellent prediction accuracy
Summary
Cancer is a genetic disease that results when cellular changes and accumulation of different types of mutations cause the uncontrolled growth and division of cells. There are more than 200 different types of cancer, having a significant global impact on public health. Since cancer is a disease of genetic complexity and diversity, the drug response for different patients can be different. The main reason for this occurrence is the difference in the molecular and genetic information of individuals, such as gene expression data, the type of mutation in the genome and copy number alteration. These findings and achievements have recently made a significant challenge in the prediction of drug response for an individual patient in the research of precision medicine. The IC50 measure (minimal concentration of drug that induced 50% cell line death) is usually used as a sensitivity measure. To facilitate and speed up drug discovery and prediction process, many methods have been developed in these fields by researches from numerous domains such as computational biology, machine learning, and data mining approaches
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