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A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients

Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs’ molecular “fingerprints”, along with mutation statuses,...

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Autores principales: Joo, Minjae, Park, Aron, Kim, Kyungdoc, Son, Won-Joon, Lee, Hyo Sug, Lim, GyuTae, Lee, Jinhyuk, Lee, Dae Ho, An, Jungsuk, Kim, Jung Ho, Ahn, TaeJin, Nam, Seungyoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6941066/
https://www.ncbi.nlm.nih.gov/pubmed/31842404
http://dx.doi.org/10.3390/ijms20246276
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author Joo, Minjae
Park, Aron
Kim, Kyungdoc
Son, Won-Joon
Lee, Hyo Sug
Lim, GyuTae
Lee, Jinhyuk
Lee, Dae Ho
An, Jungsuk
Kim, Jung Ho
Ahn, TaeJin
Nam, Seungyoon
author_facet Joo, Minjae
Park, Aron
Kim, Kyungdoc
Son, Won-Joon
Lee, Hyo Sug
Lim, GyuTae
Lee, Jinhyuk
Lee, Dae Ho
An, Jungsuk
Kim, Jung Ho
Ahn, TaeJin
Nam, Seungyoon
author_sort Joo, Minjae
collection PubMed
description Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs’ molecular “fingerprints”, along with mutation statuses, have not been considered. Here, we constructed a 1-dimensional convolution neural network model, DeepIC50, to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular fingerprints. As a result, DeepIC50 showed better cell viability IC50 prediction accuracy in pan-cancer cell lines over two independent cancer cell line datasets. Gastric cancer (GC) is not only one of the lethal cancer types in East Asia, but also a heterogeneous cancer type. Currently approved targeted therapies in GC are only trastuzumab and ramucirumab. Responsive GC patients for the drugs are limited, and more drugs should be developed in GC. Due to the importance of GC, we applied DeepIC50 to a real GC patient dataset. Drug responsiveness prediction in the patient dataset by DeepIC50, when compared to the other models, were comparable to responsiveness observed in GC cell lines. DeepIC50 could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process.
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spelling pubmed-69410662020-01-09 A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients Joo, Minjae Park, Aron Kim, Kyungdoc Son, Won-Joon Lee, Hyo Sug Lim, GyuTae Lee, Jinhyuk Lee, Dae Ho An, Jungsuk Kim, Jung Ho Ahn, TaeJin Nam, Seungyoon Int J Mol Sci Article Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs’ molecular “fingerprints”, along with mutation statuses, have not been considered. Here, we constructed a 1-dimensional convolution neural network model, DeepIC50, to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular fingerprints. As a result, DeepIC50 showed better cell viability IC50 prediction accuracy in pan-cancer cell lines over two independent cancer cell line datasets. Gastric cancer (GC) is not only one of the lethal cancer types in East Asia, but also a heterogeneous cancer type. Currently approved targeted therapies in GC are only trastuzumab and ramucirumab. Responsive GC patients for the drugs are limited, and more drugs should be developed in GC. Due to the importance of GC, we applied DeepIC50 to a real GC patient dataset. Drug responsiveness prediction in the patient dataset by DeepIC50, when compared to the other models, were comparable to responsiveness observed in GC cell lines. DeepIC50 could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process. MDPI 2019-12-12 /pmc/articles/PMC6941066/ /pubmed/31842404 http://dx.doi.org/10.3390/ijms20246276 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Joo, Minjae
Park, Aron
Kim, Kyungdoc
Son, Won-Joon
Lee, Hyo Sug
Lim, GyuTae
Lee, Jinhyuk
Lee, Dae Ho
An, Jungsuk
Kim, Jung Ho
Ahn, TaeJin
Nam, Seungyoon
A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients
title A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients
title_full A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients
title_fullStr A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients
title_full_unstemmed A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients
title_short A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients
title_sort deep learning model for cell growth inhibition ic50 prediction and its application for gastric cancer patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6941066/
https://www.ncbi.nlm.nih.gov/pubmed/31842404
http://dx.doi.org/10.3390/ijms20246276
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