TY - JOUR
T1 - Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection
AU - Ge, Qiyang
AU - Huang, Xuelin
AU - Fang, Shenying
AU - Guo, Shicheng
AU - Liu, Yuanyuan
AU - Lin, Wei
AU - Xiong, Momiao
N1 - Funding Information:
WL was supported by the National Key R&D Program of China (Grant No. 2018YFC0116600), the National Natural Science Foundation of China (Grant No. 11925103), and by the STCSM (Grant Nos. 18DZ1201000, 19511101404).
Publisher Copyright:
© Copyright © 2020 Ge, Huang, Fang, Guo, Liu, Lin and Xiong.
PY - 2020/12/11
Y1 - 2020/12/11
N2 - 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.
AB - 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.
KW - causal inference
KW - counterfactuals
KW - generative adversarial networks
KW - precision medicine
KW - treatment estimation
UR - http://www.scopus.com/inward/record.url?scp=85098170711&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098170711&partnerID=8YFLogxK
U2 - 10.3389/fgene.2020.585804
DO - 10.3389/fgene.2020.585804
M3 - Article
C2 - 33362849
AN - SCOPUS:85098170711
SN - 1664-8021
VL - 11
JO - Frontiers in Genetics
JF - Frontiers in Genetics
M1 - 585804
ER -