Optimal drug prediction from personal genomics profiles

IEEE Journal of Biomedical and Health Informatics
Jianting ShengStephen T C Wong

Abstract

Cancer patients often show heterogeneous drug responses such that only a small subset of patients is sensitive to a given anticancer drug. With the availability of large-scale genomic profiling via next-generation sequencing, it is now economically feasible to profile the whole transcriptome and genome of individual patients in order to identify their unique genetic mutations and differentially expressed genes, which are believed to be responsible for heterogeneous drug responses. Although subtyping analysis has identified patient subgroups sharing common biomarkers, there is no effective method to predict the drug response of individual patients precisely and reliably. Herein, we propose a novel computational algorithm to predict the drug response of individual patients based on personal genomic profiles, as well as pharmacogenomic and drug sensitivity data. Specifically, more than 600 cancer cell lines (viewed as individual patients) across over 50 types of cancers and their responses to 75 drugs were obtained from the genomics of drug sensitivity in cancer database. The drug-specific sensitivity signatures were determined from the changes in genomic profiles of individual cell lines in response to a specific drug. The optima...Continue Reading

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Citations

Jul 28, 2016·Drug Resistance Updates : Reviews and Commentaries in Antimicrobial and Anticancer Chemotherapy·Wen LiZhe-Sheng Chen
Jun 6, 2018·Bioinformatics·Chayaporn SuphavilaiNiranjan Nagarajan
Jul 25, 2021·International Journal of Molecular Sciences·Yeeun Lee, Seungyoon Nam
Oct 8, 2021·Briefings in Bioinformatics·Farzaneh FiroozbakhtBenno Schwikowski

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