A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients
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- Minjae Joo
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21999, Korea
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- Aron Park
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21999, Korea
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- Kyungdoc Kim
- VUNO Inc., Seoul 06536, Korea
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- Won-Joon Son
- Samsung Advanced Institute of Technology, Suwon, Gyeonggi-do 16678, Korea
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- Hyo Sug Lee
- Samsung Advanced Institute of Technology, Suwon, Gyeonggi-do 16678, Korea
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- GyuTae Lim
- Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea
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- Jinhyuk Lee
- Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea
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- Dae Ho Lee
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21999, Korea
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- Jungsuk An
- Department of Pathology, Gachon University Gil Medical Center, Gachon University School of Medicine, Incheon 21565, Korea
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- Jung Ho Kim
- Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University School of Medicine, Incheon 21565, Korea
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- TaeJin Ahn
- Department of Life Sciences, Handong Global University, Pohang 37554, Korea
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- Seungyoon Nam
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21999, Korea
説明
<jats:p>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.</jats:p>
収録刊行物
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- International Journal of Molecular Sciences
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International Journal of Molecular Sciences 20 (24), 6276-, 2019-12-12
MDPI AG