Leveraging a pharmacogenomics knowledgebase to formulate a drug response phenotype terminology for genomic medicine

Author:

Zhao Yiqing1ORCID,Brush Matthew2,Wang Chen3ORCID,Wagner Alex H456ORCID,Liu Hongfang1,Freimuth Robert R1ORCID

Affiliation:

1. Department of Artificial Intelligence and Informatics, Mayo Clinic , Rochester, MN 55905, USA

2. Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus , Aurora, CO 80045, USA

3. Department of Quantitative Health Sciences, Mayo Clinic , Rochester, MN 55905, USA

4. The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital , Columbus, OH 43205, USA

5. Department of Pediatrics, The Ohio State University College of Medicine , Columbus, OH 43210, USA

6. Department of Biomedical Informatics, The Ohio State University College of Medicine , Columbus, OH 43210, USA

Abstract

Abstract Motivation Despite the increasing evidence of utility of genomic medicine in clinical practice, systematically integrating genomic medicine information and knowledge into clinical systems with a high-level of consistency, scalability and computability remains challenging. A comprehensive terminology is required for relevant concepts and the associated knowledge model for representing relationships. In this study, we leveraged PharmGKB, a comprehensive pharmacogenomics (PGx) knowledgebase, to formulate a terminology for drug response phenotypes that can represent relationships between genetic variants and treatments. We evaluated coverage of the terminology through manual review of a randomly selected subset of 200 sentences extracted from genetic reports that contained concepts for ‘Genes and Gene Products’ and ‘Treatments’. Results Results showed that our proposed drug response phenotype terminology could cover 96% of the drug response phenotypes in genetic reports. Among 18 653 sentences that contained both ‘Genes and Gene Products’ and ‘Treatments’, 3011 sentences were able to be mapped to a drug response phenotype in our proposed terminology, among which the most discussed drug response phenotypes were response (994), sensitivity (829) and survival (332). In addition, we were able to re-analyze genetic report context incorporating the proposed terminology and enrich our previously proposed PGx knowledge model to reveal relationships between genetic variants and treatments. In conclusion, we proposed a drug response phenotype terminology that enhanced structured knowledge representation of genomic medicine. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Mayo Clinic Center for Individualized Medicine, Mayo Clinic Division of Digital Health Sciences

National Human Genome Research Institute

National Institutes of Health

Departments of Pediatrics and Biomedical Informatics of the Ohio State University College of Medicine

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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