Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection
Treatment response is heterogeneous. However, the classical methods treat the treatment response as homogeneous and estimate the average treatment effects. The traditional methods are difficult to apply to precision oncology. Artificial intelligence (AI) is a powerful tool for precision oncology. It...
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| Format: | Article |
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Frontiers Media S.A.
2020-12-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2020.585804/full |
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| author | Qiyang Ge Qiyang Ge Xuelin Huang Shenying Fang Shicheng Guo Yuanyuan Liu Wei Lin Momiao Xiong |
| author_facet | Qiyang Ge Qiyang Ge Xuelin Huang Shenying Fang Shicheng Guo Yuanyuan Liu Wei Lin Momiao Xiong |
| author_sort | Qiyang Ge |
| collection | DOAJ |
| description | Treatment response is heterogeneous. However, the classical methods treat the treatment response as homogeneous and estimate the average treatment effects. The traditional methods are difficult to apply to precision oncology. Artificial intelligence (AI) is a powerful tool for precision oncology. It can accurately estimate the individualized treatment effects and learn optimal treatment choices. Therefore, the AI approach can substantially improve progress and treatment outcomes of patients. One AI approach, conditional generative adversarial nets for inference of individualized treatment effects (GANITE) has been developed. However, GANITE can only deal with binary treatment and does not provide a tool for optimal treatment selection. To overcome these limitations, we modify conditional generative adversarial networks (MCGANs) to allow estimation of individualized effects of any types of treatments including binary, categorical and continuous treatments. We propose to use sparse techniques for selection of biomarkers that predict the best treatment for each patient. Simulations show that MCGANs outperform seven other state-of-the-art methods: linear regression (LR), Bayesian linear ridge regression (BLR), k-Nearest Neighbor (KNN), random forest classification [RF (C)], random forest regression [RF (R)], logistic regression (LogR), and support vector machine (SVM). To illustrate their applications, the proposed MCGANs were applied to 256 patients with newly diagnosed acute myeloid leukemia (AML) who were treated with high dose ara-C (HDAC), Idarubicin (IDA) and both of these two treatments (HDAC+IDA) at M. D. Anderson Cancer Center. Our results showed that MCGAN can more accurately and robustly estimate the individualized treatment effects than other state-of-the art methods. Several biomarkers such as GSK3, BILIRUBIN, SMAC are identified and a total of 30 biomarkers can explain 36.8% of treatment effect variation. |
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| language | English |
| last_indexed | 2024-12-16T16:51:14Z |
| publishDate | 2020-12-01 |
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| series | Frontiers in Genetics |
| spelling | doaj.art-23c7edd78a4b4d01a6716bd7d7e2ac992022-12-21T22:24:01ZengFrontiers Media S.A.Frontiers in Genetics1664-80212020-12-011110.3389/fgene.2020.585804585804Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment SelectionQiyang Ge0Qiyang Ge1Xuelin Huang2Shenying Fang3Shicheng Guo4Yuanyuan Liu5Wei Lin6Momiao Xiong7Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United StatesSchool of Mathematical Sciences, Fudan University, Shanghai, ChinaDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Medical Genetics, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United StatesSchool of Mathematical Sciences, Fudan University, Shanghai, ChinaDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United StatesTreatment response is heterogeneous. However, the classical methods treat the treatment response as homogeneous and estimate the average treatment effects. The traditional methods are difficult to apply to precision oncology. Artificial intelligence (AI) is a powerful tool for precision oncology. It can accurately estimate the individualized treatment effects and learn optimal treatment choices. Therefore, the AI approach can substantially improve progress and treatment outcomes of patients. One AI approach, conditional generative adversarial nets for inference of individualized treatment effects (GANITE) has been developed. However, GANITE can only deal with binary treatment and does not provide a tool for optimal treatment selection. To overcome these limitations, we modify conditional generative adversarial networks (MCGANs) to allow estimation of individualized effects of any types of treatments including binary, categorical and continuous treatments. We propose to use sparse techniques for selection of biomarkers that predict the best treatment for each patient. Simulations show that MCGANs outperform seven other state-of-the-art methods: linear regression (LR), Bayesian linear ridge regression (BLR), k-Nearest Neighbor (KNN), random forest classification [RF (C)], random forest regression [RF (R)], logistic regression (LogR), and support vector machine (SVM). To illustrate their applications, the proposed MCGANs were applied to 256 patients with newly diagnosed acute myeloid leukemia (AML) who were treated with high dose ara-C (HDAC), Idarubicin (IDA) and both of these two treatments (HDAC+IDA) at M. D. Anderson Cancer Center. Our results showed that MCGAN can more accurately and robustly estimate the individualized treatment effects than other state-of-the art methods. Several biomarkers such as GSK3, BILIRUBIN, SMAC are identified and a total of 30 biomarkers can explain 36.8% of treatment effect variation.https://www.frontiersin.org/articles/10.3389/fgene.2020.585804/fullcausal inferencegenerative adversarial networkscounterfactualstreatment estimationprecision medicine |
| spellingShingle | Qiyang Ge Qiyang Ge Xuelin Huang Shenying Fang Shicheng Guo Yuanyuan Liu Wei Lin Momiao Xiong Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection Frontiers in Genetics causal inference generative adversarial networks counterfactuals treatment estimation precision medicine |
| title | Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection |
| title_full | Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection |
| title_fullStr | Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection |
| title_full_unstemmed | Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection |
| title_short | Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection |
| title_sort | conditional generative adversarial networks for individualized treatment effect estimation and treatment selection |
| topic | causal inference generative adversarial networks counterfactuals treatment estimation precision medicine |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2020.585804/full |
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