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Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks

In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have igno...

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Autores principales: Park, Heewon, Yamaguchi, Rui, Imoto, Seiya, Miyano, Satoru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9116684/
https://www.ncbi.nlm.nih.gov/pubmed/35584089
http://dx.doi.org/10.1371/journal.pone.0261630
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author Park, Heewon
Yamaguchi, Rui
Imoto, Seiya
Miyano, Satoru
author_facet Park, Heewon
Yamaguchi, Rui
Imoto, Seiya
Miyano, Satoru
author_sort Park, Heewon
collection PubMed
description In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells.
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spelling pubmed-91166842022-05-19 Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks Park, Heewon Yamaguchi, Rui Imoto, Seiya Miyano, Satoru PLoS One Research Article In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells. Public Library of Science 2022-05-18 /pmc/articles/PMC9116684/ /pubmed/35584089 http://dx.doi.org/10.1371/journal.pone.0261630 Text en © 2022 Park et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Park, Heewon
Yamaguchi, Rui
Imoto, Seiya
Miyano, Satoru
Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks
title Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks
title_full Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks
title_fullStr Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks
title_full_unstemmed Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks
title_short Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks
title_sort xprediction: explainable egfr-tkis response prediction based on drug sensitivity specific gene networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9116684/
https://www.ncbi.nlm.nih.gov/pubmed/35584089
http://dx.doi.org/10.1371/journal.pone.0261630
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AT imotoseiya xpredictionexplainableegfrtkisresponsepredictionbasedondrugsensitivityspecificgenenetworks
AT miyanosatoru xpredictionexplainableegfrtkisresponsepredictionbasedondrugsensitivityspecificgenenetworks