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Evolutionary Diagnosis of non-synonymous variants involved in differential drug response

BACKGROUND: Many pharmaceutical drugs are known to be ineffective or have negative side effects in a substantial proportion of patients. Genomic advances are revealing that some non-synonymous single nucleotide variants (nsSNVs) may cause differences in drug efficacy and side effects. Therefore, it...

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Autores principales: Gerek, Nevin Z, Liu, Li, Gerold, Kristyn, Biparva, Pegah, Thomas, Eric D, Kumar, Sudhir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4315320/
https://www.ncbi.nlm.nih.gov/pubmed/25952014
http://dx.doi.org/10.1186/1755-8794-8-S1-S6
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author Gerek, Nevin Z
Liu, Li
Gerold, Kristyn
Biparva, Pegah
Thomas, Eric D
Kumar, Sudhir
author_facet Gerek, Nevin Z
Liu, Li
Gerold, Kristyn
Biparva, Pegah
Thomas, Eric D
Kumar, Sudhir
author_sort Gerek, Nevin Z
collection PubMed
description BACKGROUND: Many pharmaceutical drugs are known to be ineffective or have negative side effects in a substantial proportion of patients. Genomic advances are revealing that some non-synonymous single nucleotide variants (nsSNVs) may cause differences in drug efficacy and side effects. Therefore, it is desirable to evaluate nsSNVs of interest in their ability to modulate the drug response. RESULTS: We found that the available data on the link between drug response and nsSNV is rather modest. There were only 31 distinct drug response-altering (DR-altering) and 43 distinct drug response-neutral (DR-neutral) nsSNVs in the whole Pharmacogenomics Knowledge Base (PharmGKB). However, even with this modest dataset, it was clear that existing bioinformatics tools have difficulties in correctly predicting the known DR-altering and DR-neutral nsSNVs. They exhibited an overall accuracy of less than 50%, which was not better than random diagnosis. We found that the underlying problem is the markedly different evolutionary properties between positions harboring nsSNVs linked to drug responses and those observed for inherited diseases. To solve this problem, we developed a new diagnosis method, Drug-EvoD, which was trained on the evolutionary properties of nsSNVs associated with drug responses in a sparse learning framework. Drug-EvoD achieves a TPR of 84% and a TNR of 53%, with a balanced accuracy of 69%, which improves upon other methods significantly. CONCLUSIONS: The new tool will enable researchers to computationally identify nsSNVs that may affect drug responses. However, much larger training and testing datasets are needed to develop more reliable and accurate tools.
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spelling pubmed-43153202015-02-12 Evolutionary Diagnosis of non-synonymous variants involved in differential drug response Gerek, Nevin Z Liu, Li Gerold, Kristyn Biparva, Pegah Thomas, Eric D Kumar, Sudhir BMC Med Genomics Research BACKGROUND: Many pharmaceutical drugs are known to be ineffective or have negative side effects in a substantial proportion of patients. Genomic advances are revealing that some non-synonymous single nucleotide variants (nsSNVs) may cause differences in drug efficacy and side effects. Therefore, it is desirable to evaluate nsSNVs of interest in their ability to modulate the drug response. RESULTS: We found that the available data on the link between drug response and nsSNV is rather modest. There were only 31 distinct drug response-altering (DR-altering) and 43 distinct drug response-neutral (DR-neutral) nsSNVs in the whole Pharmacogenomics Knowledge Base (PharmGKB). However, even with this modest dataset, it was clear that existing bioinformatics tools have difficulties in correctly predicting the known DR-altering and DR-neutral nsSNVs. They exhibited an overall accuracy of less than 50%, which was not better than random diagnosis. We found that the underlying problem is the markedly different evolutionary properties between positions harboring nsSNVs linked to drug responses and those observed for inherited diseases. To solve this problem, we developed a new diagnosis method, Drug-EvoD, which was trained on the evolutionary properties of nsSNVs associated with drug responses in a sparse learning framework. Drug-EvoD achieves a TPR of 84% and a TNR of 53%, with a balanced accuracy of 69%, which improves upon other methods significantly. CONCLUSIONS: The new tool will enable researchers to computationally identify nsSNVs that may affect drug responses. However, much larger training and testing datasets are needed to develop more reliable and accurate tools. BioMed Central 2015-01-15 /pmc/articles/PMC4315320/ /pubmed/25952014 http://dx.doi.org/10.1186/1755-8794-8-S1-S6 Text en Copyright © 2015 Gerek et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Gerek, Nevin Z
Liu, Li
Gerold, Kristyn
Biparva, Pegah
Thomas, Eric D
Kumar, Sudhir
Evolutionary Diagnosis of non-synonymous variants involved in differential drug response
title Evolutionary Diagnosis of non-synonymous variants involved in differential drug response
title_full Evolutionary Diagnosis of non-synonymous variants involved in differential drug response
title_fullStr Evolutionary Diagnosis of non-synonymous variants involved in differential drug response
title_full_unstemmed Evolutionary Diagnosis of non-synonymous variants involved in differential drug response
title_short Evolutionary Diagnosis of non-synonymous variants involved in differential drug response
title_sort evolutionary diagnosis of non-synonymous variants involved in differential drug response
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4315320/
https://www.ncbi.nlm.nih.gov/pubmed/25952014
http://dx.doi.org/10.1186/1755-8794-8-S1-S6
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