1
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Bae J, Jeon H, Kim T. Full-Combinatorial Concentration Gradient Array with 3D Micro/Nanofluidics for Antibiotic Susceptibility Testing. Anal Chem 2024; 96:5462-5470. [PMID: 38511829 DOI: 10.1021/acs.analchem.3c05501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
Recent advancements in micro/nanofluidics have facilitated on-chip microscopy of cellular responses in a high-throughput and controlled microenvironment with the desired physicochemical properties. Despite its potential benefits to combination drug discovery, generating a complete combinatorial set of concentration gradients for multiple reagents in an array format remains challenging. The main reason is limited layouts of conventional micro/nanofluidic systems based on two-dimensional channel networks. In this paper, we present a device with three-dimensional (3D) interconnection of micro/nanochannels capable of generating a complete combinatorial set of concentration gradients for two reagents. The device was readily fabricated by laminating a pair of multilayered monolithic films containing a Christmas tree-like mixer, a cell culture chamber array, and through-holes, all within each single film. We assessed the reliable generation of a full-combinatorial concentration gradient array and validated it by using numerical analysis. We applied the proposed device to test the antibiotic susceptibility of bacterial cells in a convenient one-step manner. Furthermore, we explored the potential of the device to accommodate the arrayed complete combinatorial set for two or more drugs, while extending the capabilities of our laminated object manufacturing method for realizing 3D micro/nanofluidic systems.
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Affiliation(s)
- Juyeol Bae
- Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-Gil, Ulsan 44919, Republic of Korea
| | - Hwisu Jeon
- Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-Gil, Ulsan 44919, Republic of Korea
- TK Medical Solution Inc., 50 UNIST-Gil, Ulsan 44919, Republic of Korea
| | - Taesung Kim
- Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-Gil, Ulsan 44919, Republic of Korea
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-Gil, Ulsan 44919, Republic of Korea
- TK Medical Solution Inc., 50 UNIST-Gil, Ulsan 44919, Republic of Korea
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2
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Alam W, Tayara H, Chong KT. Unlocking the therapeutic potential of drug combinations through synergy prediction using graph transformer networks. Comput Biol Med 2024; 170:108007. [PMID: 38242015 DOI: 10.1016/j.compbiomed.2024.108007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/03/2024] [Accepted: 01/13/2024] [Indexed: 01/21/2024]
Abstract
Drug combinations are frequently used to treat cancer to reduce side effects and increase efficacy. The experimental discovery of drug combination synergy is time-consuming and expensive for large datasets. Therefore, an efficient and reliable computational approach is required to investigate these drug combinations. Advancements in deep learning can handle large datasets with various biological problems. In this study, we developed a SynergyGTN model based on the Graph Transformer Network to predict the synergistic drug combinations against an untreated cancer cell line expression profile. We represent the drug via a graph, with each node and edge of the graph containing nine types of atomic feature vectors and four bonds features, respectively. The cell lines represent based on their gene expression profiles. The drug graph was passed through the GTN layers to extract a generalized feature map for each drug pairs. The drug pair extracted features and cell-line gene expression profiles were concatenated and subsequently subjected to processing through multiple densely connected layers. SynergyGTN outperformed the state-of-the-art methods, with a receiver operating characteristic area under the curve improvement of 5% on the 5-fold cross-validation. The accuracy of SynergyGTN was further verified through three types of cross-validation tests strategies namely leave-drug-out, leave-combination-out, and leave-tissue-out, resulting in improvement in accuracy of 8%, 1%, and 2%, respectively. The Astrazeneca Dream dataset was utilized as an independent dataset to validate and assess the generalizability of the proposed method, resulting in an improvement in balanced accuracy of 13%. In conclusion, SynergyGTN is a reliable and efficient computational approach for predicting drug combination synergy in cancer treatment. Finally, we developed a web server tool to facilitate the pharmaceutical industry and researchers, as available at: http://nsclbio.jbnu.ac.kr/tools/SynergyGTN/.
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Affiliation(s)
- Waleed Alam
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju, 54896, South Korea.
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3
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Chadwick W, Angulo-Herrera I, Cogram P, Deacon RJM, Mason DJ, Brown D, Roberts I, O’Donovan DJ, Tranfaglia MR, Guilliams T, Thompson NT. A novel combination treatment for fragile X syndrome predicted using computational methods. Brain Commun 2024; 6:fcad353. [PMID: 38226317 PMCID: PMC10789243 DOI: 10.1093/braincomms/fcad353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 11/07/2023] [Accepted: 12/21/2023] [Indexed: 01/17/2024] Open
Abstract
Fragile X syndrome is a neurodevelopmental disorder caused by silencing of the fragile X messenger ribonucleotide gene. Patients display a wide spectrum of symptoms ranging from intellectual and learning disabilities to behavioural challenges including autism spectrum disorder. In addition to this, patients also display a diversity of symptoms due to mosaicism. These factors make fragile X syndrome a difficult syndrome to manage and suggest that a single targeted therapeutic approach cannot address all the symptoms. To this end, we utilized Healx's data-driven drug discovery platform to identify a treatment strategy to address the wide range of diverse symptoms among patients. Computational methods identified the combination of ibudilast and gaboxadol as a treatment for several pathophysiological targets that could potentially reverse multiple symptoms associated with fragile X syndrome. Ibudilast is an approved broad-spectrum phosphodiesterase inhibitor, selective against both phosphodiesterase 4 and phosphodiesterase 10, and has demonstrated to have several beneficial effects in the brain. Gaboxadol is a GABAA receptor agonist, selective against the delta subunit, which has previously displayed encouraging results in a fragile X syndrome clinical trial. Alterations in GABA and cyclic adenosine monophosphate metabolism have long since been associated with the pathophysiology of fragile X syndrome; however, targeting both pathways simultaneously has never been investigated. Both drugs have a good safety and tolerability profile in the clinic making them attractive candidates for repurposing. We set out to explore whether the combination of ibudilast and gaboxadol could demonstrate therapeutic efficacy in a fragile X syndrome mouse model. We found that daily treatment with ibudilast significantly enhanced the ability of fragile X syndrome mice to perform a number of different cognitive assays while gaboxadol treatment improved behaviours such as hyperactivity, aggression, stereotypy and anxiety. Importantly, when ibudilast and gaboxadol were co-administered, the cognitive deficits as well as the aforementioned behaviours were rescued. Moreover, this combination treatment showed no evidence of tolerance, and no adverse effects were reported following chronic dosing. This work demonstrates for the first time that by targeting multiple pathways, with a combination treatment, we were able to rescue more phenotypes in a fragile X syndrome mouse model than either ibudilast or gaboxadol could achieve as monotherapies. This combination treatment approach holds promise for addressing the wide spectrum of diverse symptoms in this heterogeneous patient population and may have therapeutic potential for idiopathic autism.
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Affiliation(s)
| | | | - Patricia Cogram
- Department of Genetics, Faculty of Science, Institute of Ecology and Biodiversity (IEB), University of Chile, Santiago 7800024, Chile
- Center for Neural Circuit Mapping, UCI, School of Medicine, University of California, Irvine, CA 92617, USA
| | - Robert J M Deacon
- Department of Genetics, Faculty of Science, Institute of Ecology and Biodiversity (IEB), University of Chile, Santiago 7800024, Chile
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4
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Chen D, Wang X, Zhu H, Jiang Y, Li Y, Liu Q, Liu Q. Predicting anticancer synergistic drug combinations based on multi-task learning. BMC Bioinformatics 2023; 24:448. [PMID: 38012551 PMCID: PMC10680313 DOI: 10.1186/s12859-023-05524-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 10/09/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND The discovery of anticancer drug combinations is a crucial work of anticancer treatment. In recent years, pre-screening drug combinations with synergistic effects in a large-scale search space adopting computational methods, especially deep learning methods, is increasingly popular with researchers. Although achievements have been made to predict anticancer synergistic drug combinations based on deep learning, the application of multi-task learning in this field is relatively rare. The successful practice of multi-task learning in various fields shows that it can effectively learn multiple tasks jointly and improve the performance of all the tasks. METHODS In this paper, we propose MTLSynergy which is based on multi-task learning and deep neural networks to predict synergistic anticancer drug combinations. It simultaneously learns two crucial prediction tasks in anticancer treatment, which are synergy prediction of drug combinations and sensitivity prediction of monotherapy. And MTLSynergy integrates the classification and regression of prediction tasks into the same model. Moreover, autoencoders are employed to reduce the dimensions of input features. RESULTS Compared with the previous methods listed in this paper, MTLSynergy achieves the lowest mean square error of 216.47 and the highest Pearson correlation coefficient of 0.76 on the drug synergy prediction task. On the corresponding classification task, the area under the receiver operator characteristics curve and the area under the precision-recall curve are 0.90 and 0.62, respectively, which are equivalent to the comparison methods. Through the ablation study, we verify that multi-task learning and autoencoder both have a positive effect on prediction performance. In addition, the prediction results of MTLSynergy in many cases are also consistent with previous studies. CONCLUSION Our study suggests that multi-task learning is significantly beneficial for both drug synergy prediction and monotherapy sensitivity prediction when combining these two tasks into one model. The ability of MTLSynergy to discover new anticancer synergistic drug combinations noteworthily outperforms other state-of-the-art methods. MTLSynergy promises to be a powerful tool to pre-screen anticancer synergistic drug combinations.
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Affiliation(s)
- Danyi Chen
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Xiaowen Wang
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Hongming Zhu
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Yizhi Jiang
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Yulong Li
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Qi Liu
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Qin Liu
- School of Software Engineering, Tongji University, Shanghai, 201804, China.
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5
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Rafiei F, Zeraati H, Abbasi K, Ghasemi JB, Parsaeian M, Masoudi-Nejad A. DeepTraSynergy: drug combinations using multimodal deep learning with transformers. Bioinformatics 2023; 39:btad438. [PMID: 37467066 PMCID: PMC10397534 DOI: 10.1093/bioinformatics/btad438] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 06/27/2023] [Accepted: 07/17/2023] [Indexed: 07/21/2023] Open
Abstract
MOTIVATION Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. RESULTS Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug-target interaction, protein-protein interaction, and cell-target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug-target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synergy loss, toxic loss, and drug-protein interaction loss. The last two loss functions are designed as auxiliary losses to help learn a better solution. DeepTraSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the two latest drug combination datasets. The DeepTraSynergy algorithm achieves accuracy values of 0.7715 and 0.8052 (an improvement over other approaches) on the DrugCombDB and Oncology-Screen datasets, respectively. Also, we evaluate the contribution of each component of DeepTraSynergy to show its effectiveness in the proposed method. The introduction of the relation between proteins (PPI networks) and drug-protein interaction significantly improves the prediction of synergistic drug combinations. AVAILABILITY AND IMPLEMENTATION The source code and data are available at https://github.com/fatemeh-rafiei/DeepTraSynergy.
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Affiliation(s)
- Fatemeh Rafiei
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1417613151, Iran
| | - Hojjat Zeraati
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1417613151, Iran
| | - Karim Abbasi
- Laboratory of System Biology, Bioinformatics & Artificial Intelligent in Medicine (LBB&AI), Faculty of Mathematics and Computer Science, Kharazmi University, Tehran 1571914911, Iran
| | - Jahan B Ghasemi
- Chemistry Department, Faculty of Chemistry, School of Sciences, University of Tehran, Tehran 1417614411, Iran
| | - Mahboubeh Parsaeian
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1417613151, Iran
- Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London W21PG, United Kingdom
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran 1417614411, Iran
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6
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Zhang G, Gao Z, Yan C, Wang J, Liang W, Luo J, Luo H. KGANSynergy: knowledge graph attention network for drug synergy prediction. Brief Bioinform 2023; 24:7147878. [PMID: 37130580 DOI: 10.1093/bib/bbad167] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 03/10/2023] [Accepted: 04/03/2023] [Indexed: 05/04/2023] Open
Abstract
Combination therapy is widely used to treat complex diseases, particularly in patients who respond poorly to monotherapy. For example, compared with the use of a single drug, drug combinations can reduce drug resistance and improve the efficacy of cancer treatment. Thus, it is vital for researchers and society to help develop effective combination therapies through clinical trials. However, high-throughput synergistic drug combination screening remains challenging and expensive in the large combinational space, where an array of compounds are used. To solve this problem, various computational approaches have been proposed to effectively identify drug combinations by utilizing drug-related biomedical information. In this study, considering the implications of various types of neighbor information of drug entities, we propose a novel end-to-end Knowledge Graph Attention Network to predict drug synergy (KGANSynergy), which utilizes neighbor information of known drugs/cell lines effectively. KGANSynergy uses knowledge graph (KG) hierarchical propagation to find multi-source neighbor nodes for drugs and cell lines. The knowledge graph attention network is designed to distinguish the importance of neighbors in a KG through a multi-attention mechanism and then aggregate the entity's neighbor node information to enrich the entity. Finally, the learned drug and cell line embeddings can be utilized to predict the synergy of drug combinations. Experiments demonstrated that our method outperformed several other competing methods, indicating that our method is effective in identifying drug combinations.
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Affiliation(s)
- Ge Zhang
- School of Computer and Information Engineering, Henan University, Jinming Street, 475004 Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Jinming Street, 475004 Kaifeng, China
| | - Zhijie Gao
- School of Computer and Information Engineering, Henan University, Jinming Street, 475004 Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Jinming Street, 475004 Kaifeng, China
| | - Chaokun Yan
- School of Computer and Information Engineering, Henan University, Jinming Street, 475004 Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Jinming Street, 475004 Kaifeng, China
| | - Jianlin Wang
- School of Computer and Information Engineering, Henan University, Jinming Street, 475004 Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Jinming Street, 475004 Kaifeng, China
| | - Wenjuan Liang
- School of Computer and Information Engineering, Henan University, Jinming Street, 475004 Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Jinming Street, 475004 Kaifeng, China
| | - Junwei Luo
- College of Computer Science and Technology, Henan Polytechnic University, Shiji Street, 454003 Jiaozuo, China
| | - Huimin Luo
- School of Computer and Information Engineering, Henan University, Jinming Street, 475004 Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Jinming Street, 475004 Kaifeng, China
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7
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Zhang P, Tu S. MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders. PLoS Comput Biol 2023; 19:e1010951. [PMID: 36867661 PMCID: PMC10027223 DOI: 10.1371/journal.pcbi.1010951] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 03/20/2023] [Accepted: 02/14/2023] [Indexed: 03/04/2023] Open
Abstract
Accurate prediction of synergistic effects of drug combinations can reduce the experimental costs for drug development and facilitate the discovery of novel efficacious combination therapies for clinical studies. The drug combinations with high synergy scores are regarded as synergistic ones, while those with moderate or low synergy scores are additive or antagonistic ones. The existing methods usually exploit the synergy data from the aspect of synergistic drug combinations, paying little attention to the additive or antagonistic ones. Also, they usually do not leverage the common patterns of drug combinations across different cell lines. In this paper, we propose a multi-channel graph autoencoder (MGAE)-based method for predicting the synergistic effects of drug combinations (DC), and shortly denote it as MGAE-DC. A MGAE model is built to learn the drug embeddings by considering not only synergistic combinations but also additive and antagonistic ones as three input channels. The later two channels guide the model to explicitly characterize the features of non-synergistic combinations through an encoder-decoder learning process, and thus the drug embeddings become more discriminative between synergistic and non-synergistic combinations. In addition, an attention mechanism is incorporated to fuse each cell-line's drug embeddings across various cell lines, and a common drug embedding is extracted to capture the invariant patterns by developing a set of cell-line shared decoders. The generalization performance of our model is further improved with the invariant patterns. With the cell-line specific and common drug embeddings, our method is extended to predict the synergy scores of drug combinations by a neural network module. Experiments on four benchmark datasets demonstrate that MGAE-DC consistently outperforms the state-of-the-art methods. In-depth literature survey is conducted to find that many drug combinations predicted by MGAE-DC are supported by previous experimental studies. The source code and data are available at https://github.com/yushenshashen/MGAE-DC.
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Affiliation(s)
- Peng Zhang
- Department of Computer Science and Engineering, Center for Cognitive Machines and Computational Health (CMaCH), Shanghai Jiao Tong University, Shanghai, China
| | - Shikui Tu
- Department of Computer Science and Engineering, Center for Cognitive Machines and Computational Health (CMaCH), Shanghai Jiao Tong University, Shanghai, China
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8
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Zhang P, Tu S, Zhang W, Xu L. Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism. Brief Bioinform 2022; 23:6711412. [PMID: 36136353 DOI: 10.1093/bib/bbac403] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 08/04/2022] [Accepted: 08/20/2022] [Indexed: 12/14/2022] Open
Abstract
Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific. The existing computational methods either did not consider the cell line specificity of SDC, or did not perform well by building model for each cell line independently. In this paper, we present a novel encoder-decoder network named SDCNet for predicting cell line-specific SDCs. SDCNet learns common patterns across different cell lines as well as cell line-specific features in one model for drug combinations. This is realized by considering the SDC graphs of different cell lines as a relational graph, and constructing a relational graph convolutional network (R-GCN) as the encoder to learn and fuse the deep representations of drugs for different cell lines. An attention mechanism is devised to integrate the drug features from different layers of the R-GCN according to their relative importance so that representation learning is further enhanced. The common patterns are exploited through partial parameter sharing in cell line-specific decoders, which not only reconstruct the known SDCs but also predict new ones for each cell line. Experiments on various datasets demonstrate that SDCNet is superior to state-of-the-art methods and is also robust when generalized to new cell lines that are different from the training ones. Finally, the case study again confirms the effectiveness of our method in predicting novel reliable cell line-specific SDCs.
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Affiliation(s)
- Peng Zhang
- Department of Computer Science and Engineering, Center for Cognitive Machines and Computational Health (CMaCH), Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shikui Tu
- Department of Computer Science and Engineering, Center for Cognitive Machines and Computational Health (CMaCH), Shanghai Jiao Tong University, Shanghai 200240, China
| | - Wen Zhang
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, College of informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Lei Xu
- Department of Computer Science and Engineering, Center for Cognitive Machines and Computational Health (CMaCH), Shanghai Jiao Tong University, Shanghai 200240, China
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9
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Alharbi WS, Rashid M. A review of deep learning applications in human genomics using next-generation sequencing data. Hum Genomics 2022; 16:26. [PMID: 35879805 PMCID: PMC9317091 DOI: 10.1186/s40246-022-00396-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 07/12/2022] [Indexed: 12/02/2022] Open
Abstract
Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.
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Affiliation(s)
- Wardah S Alharbi
- Department of AI and Bioinformatics, King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdulaziz Medical City, Ministry of National Guard Health Affairs, P.O. Box 22490, Riyadh, 11426, Saudi Arabia
| | - Mamoon Rashid
- Department of AI and Bioinformatics, King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdulaziz Medical City, Ministry of National Guard Health Affairs, P.O. Box 22490, Riyadh, 11426, Saudi Arabia.
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10
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Yan J, Hu Z, Li ZW, Sun S, Guo WF. Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer. Front Oncol 2022; 12:891676. [PMID: 35712516 PMCID: PMC9195174 DOI: 10.3389/fonc.2022.891676] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/12/2022] [Indexed: 11/25/2022] Open
Abstract
Due to rapid development of high-throughput sequencing and biotechnology, it has brought new opportunities and challenges in developing efficient computational methods for exploring personalized genomics data of cancer patients. Because of the high-dimension and small sample size characteristics of these personalized genomics data, it is difficult for excavating effective information by using traditional statistical methods. In the past few years, network control methods have been proposed to solve networked system with high-dimension and small sample size. Researchers have made progress in the design and optimization of network control principles. However, there are few studies comprehensively surveying network control methods to analyze the biomolecular network data of individual patients. To address this problem, here we comprehensively surveyed complex network control methods on personalized omics data for understanding tumor heterogeneity in precision medicine of individual patients with cancer.
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Affiliation(s)
- Jipeng Yan
- Department of Nephrology, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Zhuo Hu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Zong-Wei Li
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Shiren Sun
- Department of Nephrology, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
- *Correspondence: Wei-Feng Guo, ; Shiren Sun,
| | - Wei-Feng Guo
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- *Correspondence: Wei-Feng Guo, ; Shiren Sun,
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11
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The landscape of receptor-mediated precision cancer combination therapy via a single-cell perspective. Nat Commun 2022; 13:1613. [PMID: 35338126 PMCID: PMC8956718 DOI: 10.1038/s41467-022-29154-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 02/22/2022] [Indexed: 02/08/2023] Open
Abstract
Mining a large cohort of single-cell transcriptomics data, here we employ combinatorial optimization techniques to chart the landscape of optimal combination therapies in cancer. We assume that each individual therapy can target any one of 1269 genes encoding cell surface receptors, which may be targets of CAR-T, conjugated antibodies or coated nanoparticle therapies. We find that in most cancer types, personalized combinations composed of at most four targets are then sufficient for killing at least 80% of tumor cells while sparing at least 90% of nontumor cells in the tumor microenvironment. However, as more stringent and selective killing is required, the number of targets needed rises rapidly. Emerging individual targets include PTPRZ1 for brain and head and neck cancers and EGFR in multiple tumor types. In sum, this study provides a computational estimate of the identity and number of targets needed in combination to target cancers selectively and precisely. Intra-tumor heterogeneity is often associated with resistance to targeted therapy, requiring the design of combinatorial therapies. Here, based on tumor single-cell transcriptomic datasets, the authors develop a computational approach to identify optimal combinatorial treatments targeting membrane receptors for cancer therapy.
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12
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Tang YC, Gottlieb A. SynPathy: Predicting Drug Synergy through Drug-Associated Pathways Using Deep Learning. Mol Cancer Res 2022; 20:762-769. [PMID: 35046110 DOI: 10.1158/1541-7786.mcr-21-0735] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/01/2021] [Accepted: 01/12/2022] [Indexed: 11/16/2022]
Abstract
Drug combination therapy has become a promising therapeutic strategy for cancer treatment. While high-throughput drug combination screening is effective for identifying synergistic drug combinations, measuring all possible combinations is impractical due to the vast space of therapeutic agents and cell lines. In this study, we propose a biologically-motivated deep learning approach to identify pathway-level features from drug and cell lines' molecular data for predicting drug synergy and quantifying the interactions in synergistic drug pairs. This method obtained an MSE of 70.6{plus minus}6.4, significantly surpassing previous approaches while providing potential candidate pathways to explain the prediction. We further demonstrate that drug combinations tend to be more synergistic when their top contributing pathways are closer to each other on a protein interaction network, suggesting a potential strategy for combination therapy with topologically interacting pathways. Our computational approach can thus be utilized both for pre-screening of potential drug combinations and for designing new combinations based on proximity of pathways associated with drug targets and cell lines. Implications: Our computational framework may be translated in the future to clinical scenarios where synergistic drugs are tailored to the patient and additionally, drug development could benefit from designing drugs that target topologically close pathways.
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Affiliation(s)
- Yi-Ching Tang
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston
| | - Assaf Gottlieb
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston
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13
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Kuru HI, Cicek AE, Tastan O. From cell lines to cancer patients: personalized drug synergy prediction. Bioinformatics 2022; 40:btae134. [PMID: 38718189 PMCID: PMC11215552 DOI: 10.1093/bioinformatics/btae134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 12/18/2023] [Indexed: 05/12/2024] Open
Abstract
MOTIVATION Combination drug therapies are effective treatments for cancer. However, the genetic heterogeneity of the patients and exponentially large space of drug pairings pose significant challenges for finding the right combination for a specific patient. Current in silico prediction methods can be instrumental in reducing the vast number of candidate drug combinations. However, existing powerful methods are trained with cancer cell line gene expression data, which limits their applicability in clinical settings. While synergy measurements on cell line models are available at large scale, patient-derived samples are too few to train a complex model. On the other hand, patient-specific single-drug response data are relatively more available. RESULTS In this work, we propose a deep learning framework, Personalized Deep Synergy Predictor (PDSP), that enables us to use the patient-specific single drug response data for customizing patient drug synergy predictions. PDSP is first trained to learn synergy scores of drug pairs and their single drug responses for a given cell line using drug structures and large scale cell line gene expression data. Then, the model is fine-tuned for patients with their patient gene expression data and associated single drug response measured on the patient ex vivo samples. In this study, we evaluate PDSP on data from three leukemia patients and observe that it improves the prediction accuracy by 27% compared to models trained on cancer cell line data. AVAILABILITY AND IMPLEMENTATION PDSP is available at https://github.com/hikuru/PDSP.
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Affiliation(s)
- Halil Ibrahim Kuru
- Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey
| | - A Ercument Cicek
- Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey
- Computational Biology Department, Carnegie Mellon University, Pittsburgh 15213, United States
| | - Oznur Tastan
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
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14
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Network Biology and Artificial Intelligence Drive the Understanding of the Multidrug Resistance Phenotype in Cancer. Drug Resist Updat 2022; 60:100811. [DOI: 10.1016/j.drup.2022.100811] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/22/2022] [Accepted: 01/24/2022] [Indexed: 02/07/2023]
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15
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Giri AK, Ianevski A. High-throughput screening for drug discovery targeting the cancer cell-microenvironment interactions in hematological cancers. Expert Opin Drug Discov 2021; 17:181-190. [PMID: 34743621 DOI: 10.1080/17460441.2022.1991306] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
INTRODUCTION The interactions between leukemic blasts and cells within the bone marrow environment affect oncogenesis, cancer stem cell survival, as well as drug resistance in hematological cancers. The importance of this interaction is increasingly being recognized as a potentially important target for future drug discoveries and developments. Recent innovations in the high throughput drug screening-related technologies, novel ex-vivo disease-models, and freely available machine-learning algorithms are advancing the drug discovery process by targeting earlier undruggable proteins, complex pathways, as well as physical interactions (e.g. leukemic cell-bone microenvironment interaction). AREA COVERED In this review, the authors discuss the recent methodological advancements and existing challenges to target specialized hematopoietic niches within the bone marrow during leukemia and suggest how such methods can be used to identify drugs targeting leukemic cell-bone microenvironment interactions. EXPERT OPINION The recent development in cell-cell communication scoring technology and culture conditions can speed up the drug discovery by targeting the cell-microenvironment interaction. However, to accelerate this process, collecting clinical-relevant patient tissues, developing culture model systems, and implementing computational algorithms, especially trained to predict drugs and their combination targeting the cancer cell-bone microenvironment interaction are needed.
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Affiliation(s)
- Anil K Giri
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Aleksander Ianevski
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
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16
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Ma J, Motsinger-Reif A. Prediction of synergistic drug combinations using PCA-initialized deep learning. BioData Min 2021; 14:46. [PMID: 34670583 PMCID: PMC8527604 DOI: 10.1186/s13040-021-00278-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 09/07/2021] [Indexed: 01/16/2023] Open
Abstract
Background Cancer is one of the main causes of death worldwide. Combination drug therapy has been a mainstay of cancer treatment for decades and has been shown to reduce host toxicity and prevent the development of acquired drug resistance. However, the immense number of possible drug combinations and large synergistic space makes it infeasible to screen all effective drug pairs experimentally. Therefore, it is crucial to develop computational approaches to predict drug synergy and guide experimental design for the discovery of rational combinations for therapy. Results We present a new deep learning approach to predict synergistic drug combinations by integrating gene expression profiles from cell lines and chemical structure data. Specifically, we use principal component analysis (PCA) to reduce the dimensionality of the chemical descriptor data and gene expression data. We then propagate the low-dimensional data through a neural network to predict drug synergy values. We apply our method to O’Neil’s high-throughput drug combination screening data as well as a dataset from the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. We compare the neural network approach with and without dimension reduction. Additionally, we demonstrate the effectiveness of our deep learning approach and compare its performance with three state-of-the-art machine learning methods: Random Forests, XGBoost, and elastic net, with and without PCA-based dimensionality reduction. Conclusions Our developed approach outperforms other machine learning methods, and the use of dimension reduction dramatically decreases the computation time without sacrificing accuracy.
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Affiliation(s)
- Jun Ma
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.,Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Drive, Durham, NC, 27709, USA
| | - Alison Motsinger-Reif
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Drive, Durham, NC, 27709, USA.
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17
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Wu L, Wen Y, Leng D, Zhang Q, Dai C, Wang Z, Liu Z, Yan B, Zhang Y, Wang J, He S, Bo X. Machine learning methods, databases and tools for drug combination prediction. Brief Bioinform 2021; 23:6363058. [PMID: 34477201 PMCID: PMC8769702 DOI: 10.1093/bib/bbab355] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/09/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.
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Affiliation(s)
- Lianlian Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Yuqi Wen
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Dongjin Leng
- Beijing Institute of Radiation Medicine, Beijing, China
| | | | - Chong Dai
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Zhongming Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Ziqi Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, AMMS, Beijing, China
| | - Bowei Yan
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Yixin Zhang
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Jing Wang
- School of Medicine, Tsinghua University, Beijing, China
| | - Song He
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Xiaochen Bo
- Beijing Institute of Radiation Medicine, Beijing, China
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18
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Wang B, Warden AR, Ding X. The optimization of combinatorial drug therapies: Strategies and laboratorial platforms. Drug Discov Today 2021; 26:2646-2659. [PMID: 34332097 DOI: 10.1016/j.drudis.2021.07.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 06/19/2021] [Accepted: 07/14/2021] [Indexed: 12/26/2022]
Abstract
Designing optimal combinatorial drug therapies is challenging, because the drug interactions depend not only on the drugs involved, but also on their doses. With recent advances, combinatorial drug therapy is closer than ever to clinical application. Herein, we summarize approaches and advances over the past decade for identifying and optimizing drug combination therapies, with innovations across research fields, covering physical laboratory platforms for combination screening to computational models and algorithms designed for synergism prediction and optimization. By comparing different types of approach, we detail a three-step workflow that could maximize the overall optimization efficiency, thus enabling the application of personalized optimization of combinatorial drug therapy.
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Affiliation(s)
- Boqian Wang
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, PR China
| | - Antony R Warden
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, PR China
| | - Xianting Ding
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, PR China.
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19
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Karimi M, Hasanzadeh A, Shen Y. Network-principled deep generative models for designing drug combinations as graph sets. Bioinformatics 2021; 36:i445-i454. [PMID: 32657357 PMCID: PMC7355302 DOI: 10.1093/bioinformatics/btaa317] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Motivation Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery. Results We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed hierarchical variational graph auto-encoders trained end-to-end to jointly embed gene–gene, gene–disease and disease–disease networks. Novel attentional pooling is introduced here for learning disease representations from associated genes’ representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for disease-specific drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, hierarchical variational graph auto-encoder learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space. Availability and implementation https://github.com/Shen-Lab/Drug-Combo-Generator. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mostafa Karimi
- Department of Electrical and Computer Engineering.,TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA
| | | | - Yang Shen
- Department of Electrical and Computer Engineering.,TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA
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20
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Pang K, Wang L, Wang W, Zhou J, Cheng C, Han K, Zoghbi HY, Liu Z. Coexpression enrichment analysis at the single-cell level reveals convergent defects in neural progenitor cells and their cell-type transitions in neurodevelopmental disorders. Genome Res 2020; 30:835-848. [PMID: 32554779 PMCID: PMC7370880 DOI: 10.1101/gr.254987.119] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 06/11/2020] [Indexed: 12/31/2022]
Abstract
A large number of genes have been implicated in neurodevelopmental disorders (NDDs), but their contributions to NDD pathology are difficult to decipher without understanding their diverse roles in different brain cell types. Here, we integrated NDD genetics with single-cell RNA sequencing data to assess coexpression enrichment patterns of various NDD gene sets. We identified midfetal cortical neural progenitor cell development—more specifically, the ventricular radial glia-to-intermediate progenitor cell transition at gestational week 10—as a key point of convergence in autism spectrum disorder (ASD) and epilepsy. Integrated Gene Ontology–based analysis further revealed that ASD genes activate neural differentiation and inhibit cell cycle during the transition, whereas epilepsy genes function as downstream effectors in the same processes, offering one possible explanation for the high comorbidity rate of the two disorders. This approach provides a framework for investigating the cell-type-specific pathophysiology of NDDs.
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Affiliation(s)
- Kaifang Pang
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, Texas 77030, USA.,Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas 77030, USA.,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Li Wang
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas 77030, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA.,Department of Neurology, University of California, San Francisco, San Francisco, California 94143, USA.,The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, California 94143, USA
| | - Wei Wang
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas 77030, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Jian Zhou
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas 77030, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Chao Cheng
- Department of Medicine, Baylor College of Medicine, Houston, Texas 77030, USA.,Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Kihoon Han
- Department of Neuroscience, College of Medicine, Korea University, Seoul 02841, South Korea
| | - Huda Y Zoghbi
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, Texas 77030, USA.,Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas 77030, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA.,Howard Hughes Medical Institute, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Zhandong Liu
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, Texas 77030, USA.,Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas 77030, USA.,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas 77030, USA
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21
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Liu H, Zhang W, Zou B, Wang J, Deng Y, Deng L. DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy. Nucleic Acids Res 2020; 48:D871-D881. [PMID: 31665429 PMCID: PMC7145671 DOI: 10.1093/nar/gkz1007] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 10/14/2019] [Accepted: 10/17/2019] [Indexed: 01/09/2023] Open
Abstract
Drug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administration in cancer therapies and thus have drawn intensive attention from researchers and pharmaceutical enterprises. Due to the rapid development of high-throughput screening (HTS), the number of drug combination datasets available has increased tremendously in recent years. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of synergistic drug combinations. In this paper, we present DrugCombDB, a comprehensive database devoted to the curation of drug combinations from various data sources: (i) HTS assays of drug combinations; (ii) manual curations from the literature; and (iii) FDA Orange Book and external databases. Specifically, DrugCombDB includes 448 555 drug combinations derived from HTS assays, covering 2887 unique drugs and 124 human cancer cell lines. In particular, DrugCombDB has more than 6000 000 quantitative dose responses from which we computed multiple synergy scores to determine the overall synergistic or antagonistic effects of drug combinations. In addition to the combinations extracted from existing databases, we manually curated 457 drug combinations from thousands of PubMed publications. To benefit the further experimental validation and development of computational models, multiple datasets that are ready to train prediction models for classification and regression analysis were constructed and other significant related data were gathered. A website with a user-friendly graphical visualization has been developed for users to access the wealth of data and download prebuilt datasets. Our database is available at http://drugcombdb.denglab.org/.
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Affiliation(s)
- Hui Liu
- Lab of Information Management, Changzhou University, Changzhou 213164, China
| | - Wenhao Zhang
- Lab of Information Management, Changzhou University, Changzhou 213164, China
| | - Bo Zou
- School of Computer Science and Engineering, Central South University, Changsha 410075, China
| | - Jinxian Wang
- School of Computer Science and Engineering, Central South University, Changsha 410075, China
| | - Yuanyuan Deng
- School of Computer Science and Engineering, Central South University, Changsha 410075, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha 410075, China.,School of Software, Xinjiang University, Urumqi 830008, China
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22
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Jiang P, Huang S, Fu Z, Sun Z, Lakowski TM, Hu P. Deep graph embedding for prioritizing synergistic anticancer drug combinations. Comput Struct Biotechnol J 2020; 18:427-438. [PMID: 32153729 PMCID: PMC7052513 DOI: 10.1016/j.csbj.2020.02.006] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 02/07/2020] [Accepted: 02/09/2020] [Indexed: 12/11/2022] Open
Abstract
Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein-protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers in vitro or in vivo. Taken together, the results indicate that our study provides a promising way to better predict and optimize synergistic drug pairs in silico.
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Key Words
- ACC, accuracy
- AUC, area under the curve
- CNN, convolutional neural network
- Cancer
- Cell line
- DDS, drug-drug synergy
- DNN, deep neural network
- DTI, drug-target interaction
- ER, estrogen receptor
- FPR, false positive rate
- GBM, glioblastoma multiforme
- GCN, graph convolutional network
- Graph convolutional network
- HTS, high throughput screening
- Heterogenous network
- PPI, protein–protein interaction
- RF, random forest
- ROC, receiver operating characteristic
- SD, standard deviation
- SVM, support vector machine
- Synergistic drug combination
- TNBC, triple negative breast cancer
- TPR, true positive rate
- XGBoost, extreme gradient boosting
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Affiliation(s)
- Peiran Jiang
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba R3E 0J9, Canada
- Department of Bioinformatics & Systems Biology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shujun Huang
- College of Pharmacy, University of Manitoba, Winnipeg, Manitoba R3E 0T5, Canada
| | - Zhenyuan Fu
- Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zexuan Sun
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba R3E 0J9, Canada
- School of Mathematics and Statistic, Wuhan University, Wuhan 430072, China
| | - Ted M. Lakowski
- College of Pharmacy, University of Manitoba, Winnipeg, Manitoba R3E 0T5, Canada
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba R3E 0J9, Canada
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg R3E 0V9, Canada
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23
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Liu H, Zhang W, Nie L, Ding X, Luo J, Zou L. Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network. BMC Bioinformatics 2019; 20:645. [PMID: 31818267 PMCID: PMC6902475 DOI: 10.1186/s12859-019-3288-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 11/21/2019] [Indexed: 01/30/2023] Open
Abstract
Background Although targeted drugs have contributed to impressive advances in the treatment of cancer patients, their clinical benefits on tumor therapies are greatly limited due to intrinsic and acquired resistance of cancer cells against such drugs. Drug combinations synergistically interfere with protein networks to inhibit the activity level of carcinogenic genes more effectively, and therefore play an increasingly important role in the treatment of complex disease. Results In this paper, we combined the drug similarity network, protein similarity network and known drug-protein associations into a drug-protein heterogenous network. Next, we ran random walk with restart (RWR) on the heterogenous network using the combinatorial drug targets as the initial probability, and obtained the converged probability distribution as the feature vector of each drug combination. Taking these feature vectors as input, we trained a gradient tree boosting (GTB) classifier to predict new drug combinations. We conducted performance evaluation on the widely used drug combination data set derived from the DCDB database. The experimental results show that our method outperforms seven typical classifiers and traditional boosting algorithms. Conclusions The heterogeneous network-derived features introduced in our method are more informative and enriching compared to the primary ontology features, which results in better performance. In addition, from the perspective of network pharmacology, our method effectively exploits the topological attributes and interactions of drug targets in the overall biological network, which proves to be a systematic and reliable approach for drug discovery.
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Affiliation(s)
- Hui Liu
- Lab of Information Management, Changzhou University, Jiangsu, China
| | - Wenhao Zhang
- Lab of Information Management, Changzhou University, Jiangsu, China
| | - Lixia Nie
- School of Information Science and Engineering, Changzhou University, Jiangsu, China
| | - Xiancheng Ding
- Information Center, Changzhou University, Jiangsu, 213164, China
| | - Judong Luo
- Department of Radiation Oncology, the Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China.
| | - Ling Zou
- School of Information Science and Engineering, Changzhou University, Jiangsu, China.
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24
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Shi JY, Li JX, Mao KT, Cao JB, Lei P, Lu HM, Yiu SM. Predicting combinative drug pairs via multiple classifier system with positive samples only. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 168:1-10. [PMID: 30527128 DOI: 10.1016/j.cmpb.2018.11.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 10/24/2018] [Accepted: 11/12/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Due to the synergistic effects of drugs, drug combination is one of the effective approaches for treating complex diseases. However, the identification of drug combinations by dose-response methods is still costly. It is promising to develop supervised learning-based approaches to predict potential drug combinations on a large scale. Nevertheless, these approaches have the inadequate utilization of heterogeneous features, which causes the loss of information useful to classification. Moreover, they have an intrinsic bias, because they assume unknown drug pairs as non-combinations, of which some could be real drug combinations in practice. METHODS To address above issues, this work first designs a two-layer multiple classifier system (TLMCS) to effectively integrate heterogeneous features involving anatomical therapeutic chemical codes of drugs, drug-drug interactions, drug-target interactions, gene ontology of drug targets, and side effects. To avoid the bias caused by labelling unknown samples as negative, it then utilizes the one-class support vector machines, (which requires no negative instance and only labels approved drug combinations as positive instances), as the member classifiers in TLMCS. Last, both a 10-fold cross validation (10-CV) and a novel prediction are performed to validate the performance of TLMCS. RESULTS The comparison with three state-of-the-art approaches under 10-CV exhibits the superiority of TLMCS, which achieves the area under the receiver operating characteristic curve = 0.824 and the area under the precision-recall curve = 0.372. Moreover, the experiment under the novel prediction demonstrates its ability, where 9 out of the top-20 predicted combinative drug pairs are validated by checking the published literature. Furthermore, for each of the newly-validated drug combinations, this work analyses the combining mode of the member drugs and investigates their relationship in terms of drug targeting pathways. CONCLUSIONS The proposed TLMCS provides an effective framework to integrate those heterogeneous features and is trained by only positive samples such that the bias of taking unknown drug pairs as negative samples can be avoided. Furthermore, its results in the novel prediction reveal five types of drug combinations and three types of drug relationships in terms of pathways.
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Affiliation(s)
- Jian-Yu Shi
- School of Life Science, Northwestern Polytechnical University, China.
| | - Jia-Xin Li
- School of Life Science, Northwestern Polytechnical University, China.
| | - Kui-Tao Mao
- School of Computer Science, Northwestern Polytechnical University, China.
| | - Jiang-Bo Cao
- School of Life Science, Northwestern Polytechnical University, China.
| | - Peng Lei
- Department of Chinese Medicine, Shaanxi Provincial People's Hospital, China.
| | - Hui-Meng Lu
- School of Life Science, Northwestern Polytechnical University, China.
| | - Siu-Ming Yiu
- Department of Computer Science, The University of Hong Kong, Hong Kong, China.
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25
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Al-Ramahi I, Lu B, Di Paola S, Pang K, de Haro M, Peluso I, Gallego-Flores T, Malik NT, Erikson K, Bleiberg BA, Avalos M, Fan G, Rivers LE, Laitman AM, Diaz-García JR, Hild M, Palacino J, Liu Z, Medina DL, Botas J. High-Throughput Functional Analysis Distinguishes Pathogenic, Nonpathogenic, and Compensatory Transcriptional Changes in Neurodegeneration. Cell Syst 2018; 7:28-40.e4. [PMID: 29936182 PMCID: PMC6082401 DOI: 10.1016/j.cels.2018.05.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 02/14/2018] [Accepted: 05/16/2018] [Indexed: 01/17/2023]
Abstract
Discriminating transcriptional changes that drive disease pathogenesis from nonpathogenic and compensatory responses is a daunting challenge. This is particularly true for neurodegenerative diseases, which affect the expression of thousands of genes in different brain regions at different disease stages. Here we integrate functional testing and network approaches to analyze previously reported transcriptional alterations in the brains of Huntington disease (HD) patients. We selected 312 genes whose expression is dysregulated both in HD patients and in HD mice and then replicated and/or antagonized each alteration in a Drosophila HD model. High-throughput behavioral testing in this model and controls revealed that transcriptional changes in synaptic biology and calcium signaling are compensatory, whereas alterations involving the actin cytoskeleton and inflammation drive disease. Knockdown of disease-driving genes in HD patient-derived cells lowered mutant Huntingtin levels and activated macroautophagy, suggesting a mechanism for mitigating pathogenesis. Our multilayered approach can thus untangle the wealth of information generated by transcriptomics and identify early therapeutic intervention points.
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Affiliation(s)
- Ismael Al-Ramahi
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Boxun Lu
- State Key Laboratory of Medical Neurobiology, Collaborative Innovation Center for Brain Science, School of Life Sciences, Fudan University, Shanghai, China
| | - Simone Di Paola
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, Italy
| | - Kaifang Pang
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Maria de Haro
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Ivana Peluso
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, Italy
| | - Tatiana Gallego-Flores
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Nazish T Malik
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Kelly Erikson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Benjamin A Bleiberg
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Matthew Avalos
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - George Fan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Laura Elizabeth Rivers
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Andrew M Laitman
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Javier R Diaz-García
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Marc Hild
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - James Palacino
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Diego L Medina
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, Italy
| | - Juan Botas
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA.
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Preuer K, Lewis RPI, Hochreiter S, Bender A, Bulusu KC, Klambauer G. DeepSynergy: predicting anti-cancer drug synergy with Deep Learning. Bioinformatics 2018; 34:1538-1546. [PMID: 29253077 PMCID: PMC5925774 DOI: 10.1093/bioinformatics/btx806] [Citation(s) in RCA: 235] [Impact Index Per Article: 39.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 12/06/2017] [Accepted: 12/14/2017] [Indexed: 12/29/2022] Open
Abstract
Motivation While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies. Results DeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations. Availability and implementation DeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy. Contact klambauer@bioinf.jku.at. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kristina Preuer
- Institute of Bioinformatics, Johannes Kepler University, Linz, Austria
| | - Richard P I Lewis
- Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Cambridge, UK
| | - Sepp Hochreiter
- Institute of Bioinformatics, Johannes Kepler University, Linz, Austria
| | - Andreas Bender
- Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Cambridge, UK
| | - Krishna C Bulusu
- Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Cambridge, UK
- Oncology Innovative Medicines and Early Development, AstraZeneca, Hodgkin Building, Chesterford Research Campus, Saffron Walden, Cambs, UK
| | - Günter Klambauer
- Institute of Bioinformatics, Johannes Kepler University, Linz, Austria
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Yoo S, Noh K, Shin M, Park J, Lee KH, Nam H, Lee D. In silico profiling of systemic effects of drugs to predict unexpected interactions. Sci Rep 2018; 8:1612. [PMID: 29371651 PMCID: PMC5785495 DOI: 10.1038/s41598-018-19614-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 01/03/2018] [Indexed: 12/16/2022] Open
Abstract
Identifying unexpected drug interactions is an essential step in drug development. Most studies focus on predicting whether a drug pair interacts or is effective on a certain disease without considering the mechanism of action (MoA). Here, we introduce a novel method to infer effects and interactions of drug pairs with MoA based on the profiling of systemic effects of drugs. By investigating propagated drug effects from the molecular and phenotypic networks, we constructed profiles of 5,441 approved and investigational drugs for 3,833 phenotypes. Our analysis indicates that highly connected phenotypes between drug profiles represent the potential effects of drug pairs and the drug pairs with strong potential effects are more likely to interact. When applied to drug interactions with verified effects, both therapeutic and adverse effects have been successfully identified with high specificity and sensitivity. Finally, tracing drug interactions in molecular and phenotypic networks allows us to understand the MoA.
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Affiliation(s)
- Sunyong Yoo
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Kyungrin Noh
- Bio-Synergy Research Center, Daejeon, 34141 Republic of Korea
| | - Moonshik Shin
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Junseok Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Kwang-Hyung Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Republic of Korea
| | - Doheon Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
- Bio-Synergy Research Center, Daejeon, 34141 Republic of Korea
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28
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Process System Engineering Methodologies Applied to Tissue Development and Regenerative Medicine. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1078:445-463. [PMID: 30357637 DOI: 10.1007/978-981-13-0950-2_23] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Tissue engineering and the manufacturing of regenerative medicine products demand strict control over the production process and product quality monitoring. In this chapter, the application of process systems engineering (PSE) approaches in the production of cell-based products has been discussed. Mechanistic, empirical, continuum and discrete models are compared and their use in describing cellular phenomena is reviewed. In addition, model-based optimization strategies employed in the field of tissue engineering and regenerative medicine are discussed. An introduction to process control theory is given and the main applications of classical and advanced methods in cellular production processes are described. Finally, new nondestructive and noninvasive monitoring techniques have been reviewed, focusing on large-scale manufacturing systems for cell-based constructs and therapeutic products. The application of the PSE methodologies presented here offers a promising alternative to overcome the main challenges in manufacturing engineered tissue and regeneration products.
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Shi JY, Li JX, Gao K, Lei P, Yiu SM. Predicting combinative drug pairs towards realistic screening via integrating heterogeneous features. BMC Bioinformatics 2017; 18:409. [PMID: 29072137 PMCID: PMC5657064 DOI: 10.1186/s12859-017-1818-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Abstract
Background Drug Combination is one of the effective approaches for treating complex diseases. However, determining combinative drug pairs in clinical trials is still costly. Thus, computational approaches are used to identify potential drug pairs in advance. Existing computational approaches have the following shortcomings: (i) the lack of an effective integration of heterogeneous features leads to a time-consuming training and even results in an over-fitted classifier; and (ii) the narrow consideration of predicting potential drug combinations only among known drugs having known combinations cannot meet the demand of realistic screenings, which pay more attention to potential combinative pairs among newly-coming drugs that have no approved combination with other drugs at all. Results In this paper, to tackle the above two problems, we propose a novel drug-driven approach for predicting potential combinative pairs on a large scale. We define four new features based on heterogeneous data and design an efficient fusion scheme to integrate these feature. Moreover importantly, we elaborate appropriate cross-validations towards realistic screening scenarios of drug combinations involving both known drugs and new drugs. In addition, we perform an extra investigation to show how each kind of heterogeneous features is related to combinative drug pairs. The investigation inspires the design of our approach. Experiments on real data demonstrate the effectiveness of our fusion scheme for integrating heterogeneous features and its predicting power in three scenarios of realistic screening. In terms of both AUC and AUPR, the prediction among known drugs achieves 0.954 and 0.821, that between known drugs and new drugs achieves 0.909 and 0.635, and that among new drugs achieves 0.809 and 0.592 respectively. Conclusions Our approach provides not only an effective tool to integrate heterogeneous features but also the first tool to predict potential combinative pairs among new drugs.
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Affiliation(s)
- Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Jia-Xin Li
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Ke Gao
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Peng Lei
- Department of Chinese Medicine, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Siu-Ming Yiu
- Department of Computer Science, the University of Hong Kong, Hong Kong, China.
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30
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Liu H, Guo M, Xue T, Guan J, Luo L, Zhuang Z. Screening lifespan-extending drugs in Caenorhabditis elegans via label propagation on drug-protein networks. BMC SYSTEMS BIOLOGY 2016; 10:131. [PMID: 28155715 PMCID: PMC5260106 DOI: 10.1186/s12918-016-0362-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Background One of the most challenging tasks in the exploration of anti-aging is to discover drugs that can promote longevity and delay the incidence of age-associated diseases of human. Up to date, a number of drugs, including some antioxidants, metabolites and synthetic compounds, have been found to effectively delay the aging of nematodes and insects. Results We proposed a label propagation algorithm on drug-protein network to infer drugs that can extend the lifespan of C. elegans. We collected a set of drugs of which functions on lifespan extension of C. elegans have been reliably determined, and then built a large-scale drug-protein network by collecting a set of high-confidence drugprotein interactions. A label propagation algorithm was run on the drug-protein bipartite network to predict new drugs with lifespan-extending effect on C. elegans. We calibrated the performance of the proposed method by conducting performance comparison with two classical models, kNN and SVM. We also showed that the screened drugs significantly mediate in the aging-related pathways, and have higher chemical similarities to the effective drugs than ineffective drugs in promoting longevity of C. elegans. Moreover, we carried out wet-lab experiments to verify a screened drugs, 2- Bromo-4’-nitroacetophenone, and found that it can effectively extend the lifespan of C. elegans. These results showed that our method is effective in screening lifespanextending drugs in C. elegans. Conclusions In this paper, we proposed a semi-supervised algorithm to predict drugs with lifespan-extending effects on C. elegans. In silico empirical evaluations and in vivo experiments in C. elegans have demonstrated that our method can effectively narrow down the scope of candidate drugs needed to be verified by wet lab experiments. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0362-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hui Liu
- Changzhou University, Jiangsu, 213164, China.,Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China
| | | | - Ting Xue
- Changzhou University, Jiangsu, 213164, China
| | - Jihong Guan
- Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China
| | - Libo Luo
- Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China. @fudan.edu.cn
| | - Ziheng Zhuang
- Changzhou University, Jiangsu, 213164, China. .,Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China.
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Liu Y, Zhao H. Predicting synergistic effects between compounds through their structural similarity and effects on transcriptomes. Bioinformatics 2016; 32:3782-3789. [PMID: 27540269 DOI: 10.1093/bioinformatics/btw509] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2016] [Revised: 07/16/2016] [Accepted: 07/28/2016] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Combinatorial therapies have been under intensive research for cancer treatment. However, due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Hence, it is important to develop computational tools that can predict compound combination effects, prioritize combinations and limit the search space to facilitate and accelerate the development of combinatorial therapies. RESULTS In this manuscript we consider the NCI-DREAM Drug Synergy Prediction Challenge dataset to identify features informative about combination effects. Through systematic exploration of differential expression profiles after single compound treatments and comparison of molecular structures of compounds, we found that synergistic levels of combinations are statistically significantly associated with compounds' dissimilarity in structure and similarity in induced gene expression changes. These two types of features offer complementary information in predicting experimentally measured combination effects of compound pairs. Our findings offer insights on the mechanisms underlying different combination effects and may help prioritize promising combinations in the very large search space. AVAILABILITY AND IMPLEMENTATION The R code for the analysis is available on https://github.com/YiyiLiu1/DrugCombination CONTACT: hongyu.zhao@yale.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yiyi Liu
- Department of Biostatistics, School of Public Health, Yale University New Haven, CT, 06520, USA
| | - Hongyu Zhao
- Department of Biostatistics, School of Public Health, Yale University New Haven, CT, 06520, USA.,Program of Computational Biology and Bioinformatics, CT0610, Yale University, New Haven, CT, 06511, USA
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Liu Q, Zhang C, Ding X, Deng H, Zhang D, Cui W, Xu H, Wang Y, Xu W, Lv L, Zhang H, He Y, Wu Q, Szyf M, Ho CM, Zhu J. Preclinical optimization of a broad-spectrum anti-bladder cancer tri-drug regimen via the Feedback System Control (FSC) platform. Sci Rep 2015; 5:11464. [PMID: 26088171 PMCID: PMC5155572 DOI: 10.1038/srep11464] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Accepted: 05/22/2015] [Indexed: 12/18/2022] Open
Abstract
Therapeutic outcomes of combination chemotherapy have not significantly advanced during the past decades. This has been attributed to the formidable challenges of optimizing drug combinations. Testing a matrix of all possible combinations of doses and agents in a single cell line is unfeasible due to the virtually infinite number of possibilities. We utilized the Feedback System Control (FSC) platform, a phenotype oriented approach to test 100 options among 15,625 possible combinations in four rounds of assaying to identify an optimal tri-drug combination in eight distinct chemoresistant bladder cancer cell lines. This combination killed between 82.86% and 99.52% of BCa cells, but only 47.47% of the immortalized benign bladder epithelial cells. Preclinical in vivo verification revealed its markedly enhanced anti-tumor efficacy as compared to its bi- or mono-drug components in cell line-derived tumor xenografts. The collective response of these pathways to component drugs was both cell type- and drug type specific. However, the entire spectrum of pathways triggered by the tri-drug regimen was similar in all four cancer cell lines, explaining its broad spectrum killing of BCa lines, which did not occur with its component drugs. Our findings here suggest that the FSC platform holdspromise for optimization of anti-cancer combination chemotherapy.
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Affiliation(s)
- Qi Liu
- School of Life Science and Technology, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China, and Department of Anatomy and Cell Biology, University of Iowa, Carver College of Medicine, Iowa City, IA 52242, USA
| | - Cheng Zhang
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Xianting Ding
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Hui Deng
- Cancer Epigenetics Program, Anhui Cancer Hospital, Hefei, Anhui 230031, China
| | - Daming Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Wei Cui
- School of Life Science and Technology, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China, and Department of Anatomy and Cell Biology, University of Iowa, Carver College of Medicine, Iowa City, IA 52242, USA
| | - Hongwei Xu
- Department of Pathology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Yingwei Wang
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Wanhai Xu
- Department of Urology, The Forth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Lei Lv
- Cancer Epigenetics Program, Anhui Cancer Hospital, Hefei, Anhui 230031, China
| | - Hongyu Zhang
- Cancer Epigenetics Program, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University, Shanghai 200032, China
| | - Yinghua He
- Cancer Epigenetics Program, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University, Shanghai 200032, China
| | - Qiong Wu
- School of Life Science and Technology, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China, and Department of Anatomy and Cell Biology, University of Iowa, Carver College of Medicine, Iowa City, IA 52242, USA
| | - Moshe Szyf
- Department of Pharmacology and Therapeutics McGill University Medical School 3655 Sir William Osler Promenade #1309, Montreal, Quebec Canada
| | - Chih-Ming Ho
- Mechanical and Aerospace Engineering Department, Biomedical Engineering Department, University of California, Los Angeles, CA 90095-1597, USA
| | - Jingde Zhu
- 1] Cancer Epigenetics Program, Anhui Cancer Hospital, Hefei, Anhui 230031, China [2] Cancer Epigenetics Program, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University, Shanghai 200032, China
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Ryall KA, Tan AC. Systems biology approaches for advancing the discovery of effective drug combinations. J Cheminform 2015; 7:7. [PMID: 25741385 PMCID: PMC4348553 DOI: 10.1186/s13321-015-0055-9] [Citation(s) in RCA: 92] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2014] [Accepted: 02/02/2015] [Indexed: 01/23/2023] Open
Abstract
Complex diseases like cancer are regulated by large, interconnected networks with many pathways affecting cell proliferation, invasion, and drug resistance. However, current cancer therapy predominantly relies on the reductionist approach of one gene-one disease. Combinations of drugs may overcome drug resistance by limiting mutations and induction of escape pathways, but given the enormous number of possible drug combinations, strategies to reduce the search space and prioritize experiments are needed. In this review, we focus on the use of computational modeling, bioinformatics and high-throughput experimental methods for discovery of drug combinations. We highlight cutting-edge systems approaches, including large-scale modeling of cell signaling networks, network motif analysis, statistical association-based models, identifying correlations in gene signatures, functional genomics, and high-throughput combination screens. We also present a list of publicly available data and resources to aid in discovery of drug combinations. Integration of these systems approaches will enable faster discovery and translation of clinically relevant drug combinations. Spectrum of Systems Biology Approaches for Drug Combinations. ![]()
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Affiliation(s)
- Karen A Ryall
- Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, 12801 E.17th Ave., L18-8116, Aurora, CO 80045 USA
| | - Aik Choon Tan
- Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, 12801 E.17th Ave., L18-8116, Aurora, CO 80045 USA ; Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO USA ; Department of Computer Science and Engineering, Korea University, Seoul, South Korea
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