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Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs

Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge i...

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Autores principales: Zolotovskaia, Marianna A., Sorokin, Maxim I., Emelianova, Anna A., Borisov, Nikolay M., Kuzmin, Denis V., Borger, Pieter, Garazha, Andrew V., Buzdin, Anton A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6351482/
https://www.ncbi.nlm.nih.gov/pubmed/30728774
http://dx.doi.org/10.3389/fphar.2019.00001
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author Zolotovskaia, Marianna A.
Sorokin, Maxim I.
Emelianova, Anna A.
Borisov, Nikolay M.
Kuzmin, Denis V.
Borger, Pieter
Garazha, Andrew V.
Buzdin, Anton A.
author_facet Zolotovskaia, Marianna A.
Sorokin, Maxim I.
Emelianova, Anna A.
Borisov, Nikolay M.
Kuzmin, Denis V.
Borger, Pieter
Garazha, Andrew V.
Buzdin, Anton A.
author_sort Zolotovskaia, Marianna A.
collection PubMed
description Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets.
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spelling pubmed-63514822019-02-06 Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs Zolotovskaia, Marianna A. Sorokin, Maxim I. Emelianova, Anna A. Borisov, Nikolay M. Kuzmin, Denis V. Borger, Pieter Garazha, Andrew V. Buzdin, Anton A. Front Pharmacol Pharmacology Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets. Frontiers Media S.A. 2019-01-23 /pmc/articles/PMC6351482/ /pubmed/30728774 http://dx.doi.org/10.3389/fphar.2019.00001 Text en Copyright © 2019 Zolotovskaia, Sorokin, Emelianova, Borisov, Kuzmin, Borger, Garazha and Buzdin. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Zolotovskaia, Marianna A.
Sorokin, Maxim I.
Emelianova, Anna A.
Borisov, Nikolay M.
Kuzmin, Denis V.
Borger, Pieter
Garazha, Andrew V.
Buzdin, Anton A.
Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs
title Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs
title_full Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs
title_fullStr Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs
title_full_unstemmed Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs
title_short Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs
title_sort pathway based analysis of mutation data is efficient for scoring target cancer drugs
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6351482/
https://www.ncbi.nlm.nih.gov/pubmed/30728774
http://dx.doi.org/10.3389/fphar.2019.00001
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